In-silico Binding Site Prediction in Proteins

CASTp : http://sts.bioengr.uic.edu/castp/  Computed Atlas of Surface Topography of proteins (CASTp) provides an online resource for locating, delineating and measuring concave surface regions on three-dimensional structures of proteins. These include pockets located on protein surfaces and voids buried in the interior of proteins. The measurement includes the area and volume of pocket or void by solvent accessible surface model (Richards' surface) and by molecular surface model (Connolly's surface), all calculated analytically. CASTp can be used to study surface features and functional regions of proteins. CASTp includes a graphical user interface, flexible interactive visualization, as well as on-the-fly calculation for user uploaded structures. CASTp is updated daily and can be accessed at http://cast.engr.uic.edu.

LigASite: http://www.bigre.ulb.ac.be/Users/benoit/LigASite/index.php?home is a gold-standard dataset of biologically relevant binding sites in protein structures. It consists of proteins with one unbound structure and at least one structure of the protein-ligand complex. Both a redundant and a non-redundant (sequence identity lower than 25%) version is available. Quaternary structures proposed by PISA (3) are used for all structures in the dataset.

PDBeMotif: http://www.ebi.ac.uk/pdbe-site/pdbemotif/ is an extremely fast and powerful search tool that facilitates exploration of the Protein Data Bank (PDB) by combining protein sequence, chemical structure and 3D data in a single search. Currently it is the only tool that offers this kind of integration at this speed. PDBeMotif can be used to examine the characteristics of the binding sites of single proteins or classes of proteins such as Kinases and the conserved structural features of their immediate environments either within the same specie or across different species. For example, it can highlight a conserved activation loop common to protein kinases, which is important in regulating activity and is marked by conserved DFG and APE motifs at the start and end of the loop, respectively. The prediction of the effect of modifications to small molecules that bind to the active and/or regulatory sites of proteins on their efficacy can be based on the outcome of analytic work done using PDBeMotif.

fPOP: http://pocket.uchicago.edu/fpop/ (footprinting Pockets Of Proteins, http://pocket.uchicago.edu/fpop/) is a database of the protein functional surfaces identified by shape analysis. In this relational database, we collected the spatial patterns of protein binding sites including both holo and apo forms from more than 40,000 structures. To identify protein binding sites, we model the shape of a split pocket induced by a binding ligand(s). Essentially, we use a purely geometric method to extract site-specific spatial patterns of split pockets as templates to match those from unbound structures. To perform an effective shape comparison, we utilize the Smith-Waterman algorithm to footprint an unbound pocket fragment with those selected from the canonical functional surfaces of >19,000 structures in the SplitPocket (http://pocket.uchicago.edu/). The pairwise alignment of the unbound and split-pocket fragments is superimposed to evaluate the local structural similarity for detecting the unbound split characteristic through the RMSD measurement. Furthermore, we conduct a large-scale computation to systematically identify binding sites of proteins. In addition to the geometric measurements, we extensively measure the propensity of surface conservation encapsulated in the evolutionary history.(more)

metaPocket: http://metapocket.eml.org/  is a meta server to identify pockets on protein surface to predict ligand-binding sites. The identification of ligand-binding sites is often the starting point for protein function annotation and structure-based drug design. Many computational methods for the prediction of ligand-binding sites have been developed in recent decades. Here we present a consensus method metaPocket, in which the predicted sites from four methods: LIGSITE^cs, PASS, Q-SiteFinder, and SURFNET are combined together to improve the prediction success rate. All these methods are evaluated on two datasets of 48 unbound/bound structures and 210 bound structures. The comparison results show that metaPocket improves the success rate from 70 to 75% at the top 1 prediction. MetaPocket is available at http://metapocket.eml.org.

PocketQuery: http://pocketquery.csb.pitt.edu/  is a web service for interactively exploring not only hot spot and anchor residues, but hot regions, defined by clusters of residues, at the interface of protein-protein interactions. An assortment of metrics, including changes in solvent accessible surface area, energy-based scores, and sequence conservation, are available to screen and sort clusters of residues. PocketQuery was developed by David Koes from the Camacho Lab in the Department of Computational and System Biology at the University of Pittsburgh.

IBIS: http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi is the NCBI Inferred Biomolecular Interactions Server. For a given protein sequence or structure query, IBIS reports physical interactions observed in experimentally-determined structures for this protein. IBIS also infers/predicts interacting partners and binding sites by homology, by inspecting the protein complexes formed by close homologs of a given query. To ensure biological relevance of inferred binding sites, the IBIS algorithm clusters binding sites formed by homologs based on binding site sequence and structure conservation.

3DLigandStie: http://www.sbg.bio.ic.ac.uk/~3dligandsite/ is an automated method for the prediction of ligand binding sites. Users can either submit a sequence or a protein structure. If a sequence is submitted then Phyre is run to predict the structure. The structure is then ussed to search a structural library to identify homologous structures with bound ligands. These ligands are superimposed onto the protein structure to predict a ligand binding site.

SitesBase: http://www.modelling.leeds.ac.uk/sb/ is a database of known ligand binding sites within the PDB which is navigable by PDB identifier or ligand 3 letter code e.g. NAD. Each binding site has a frequently updated register of structurally similar binding sites sharing atomic similarity detected by geometric hashing (Brakoulias and Jackson 2004). Multiple alignments, structural superpositions and links to other structural databases are also available enabling further analysis.

PROSURFER: http://163.43.140.95/top contains information about structural similarities with respect to the query surfaces. A pocket search algorithm detected 48,347 potential ligand binding sites from the 9,708 non-redundant protein entries in the PDB database. All-against-all structural comparison was performed for the predicted sites, and the similar sites with the Z-score ≥ 2.5 were selected. These results can be accessed by the PDB code or ligand name.

KBDOCK: http://kbdock.loria.fr/index.php is a 3D database system that defines and spatially clusters protein binding sites for knowledge-based protein docking. KBDOCK integrates protein domain-domain interaction information from 3DID and sequence alignments from PFAM together with structural information from the PDB in order to analyse the spatial arrangements of DDIs by Pfam family, and to propose structural templates for protein docking. [More]

Pocketome: http://www.pocketome.org/ The Pocketome is an encyclopedia of conformational ensembles of all druggable binding sites that can be identified experimentally from co-crystal structures in the Protein Data Bank.

sc-PDB: http://cheminfo.u-strasbg.fr:8080/scPDB/2011/db_search/about_scpdb.html  To assist structure-based approaches in drug design, we have processed the PDB to identify binding sites suitable for the docking of a drug-like ligand and we have so created a database called sc-PDB. The sc-PDB database provides separated MOL2 files for the ligand, its binding site and the corresponding protein chain(s). Ions and cofactors at the vicinity of the ligand are included in the protein. More details about the sc-PDB scope, its content and its evolution during the 2004-2009 period are provided in a pdf document.

The FunFOLD Binding Site Residue Prediction Server: BACKGROUND: The accurate prediction of ligand binding residues from amino acid sequences is important for the automated functional annotation of novel proteins. In the previous two CASP experiments, the most successful methods in the function prediction category were those which used structural superpositions of 3D models and related templates with bound ligands in order to identify putative contacting residues. However, whilst most of this prediction process can be automated, visual inspection and manual adjustments of parameters, such as the distance thresholds used for each target, have often been required to prevent over prediction. Here we describe a novel method FunFOLD, which uses an automatic approach for cluster identification and residue selection. The software provided can easily be integrated into existing fold recognition servers, requiring only a 3D model and list of templates as inputs. A simple web interface is also provided allowing access to non-expert users. The method has been benchmarked against the top servers and manual prediction groups tested at both CASP8 and CASP9.RESULTS: The FunFOLD method shows a significant improvement over the best available servers and is shown to be competitive with the top manual prediction groups that were tested at CASP8. The FunFOLD method is also competitive with both the top server and manual methods tested at CASP9. When tested using common subsets of targets, the predictions from FunFOLD are shown to achieve a significantly higher mean Matthews Correlation Coefficient (MCC) scores and Binding-site Distance Test (BDT) scores than all server methods that were tested at CASP8. Testing on the CASP9 set showed no statistically significant separation in performance between FunFOLD and the other top server groups tested. CONCLUSIONS: The FunFOLD software is freely available as both a standalone package and a prediction server, providing competitive ligand binding site residue predictions for expert and non-expert users alike. The software provides a new fully automated approach for structure based function prediction using 3D models of proteins.

ProBiS: http://probis.cmm.ki.si/index.php  algorithm for detection of structurally similar protein binding sites by local structural alignment. Motivation: Exploitation of locally similar 3D patterns of physicochemical properties on the surface of a protein for detection of binding sites that may lack sequence and global structural conservation. Results: An algorithm, ProBiS is described that detects structurally similar sites on protein surfaces by local surface structure alignment. It compares the query protein to members of a database of protein 3D structures and detects with sub-residue precision, structurally similar sites as patterns of physicochemical properties on the protein surface. Using an efficient maximum clique algorithm, the program identifies proteins that share local structural similarities with the query protein and generates structure-based alignments of these proteins with the query. Structural similarity scores are calculated for the query protein's surface residues, and are expressed as different colors on the query protein surface. The algorithm has been used successfully for the detection of protein–protein, protein–small ligand and protein–DNA binding sites. Availability: The software is available, as a web tool, free of charge for academic users at http://probis.cmm.ki.si

Active Site prediction: http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp Active Site Prediction of Protein server computes the cavities in a given protein.

DEPTH: http://mspc.bii.a-star.edu.sg/tankp/run_depth.html Depth measures the closest distance of a residue/atom to bulk solvent. Accessible surface area is a parameter that is widely used in analyses of protein structure and stability. However accessible surface area does not distinguish between atoms just below the protein surface and those in the core of the protein. In order to differentiate between such buried residues, we describe a computational procedure for calculating the depth of a residue from the protein surface. A detailed description of the computation of depth can be found here.

FINDSITE: http://cssb.biology.gatech.edu/findsite  FINDSITE is a threading-based binding site prediction/protein functional inference/ligand screening algorithm that detects common ligand binding sites in a set of evolutionarily related proteins. Crystal structures as well as protein models can be used as the target structures.

PocketDepth: http://proline.physics.iisc.ernet.in/pocketdepth/  A new depth based algortihm for identification of ligand binding sites. Abstract: Computational methods for identifying and predicting functional sites in protein structures are increasingly becoming important in structural biology and bioinformatics not only for understanding the function of the molecule in detail but also for structure-based design of possible ligands and potential drugs as well as modified protein molecules. While there are a few structure based prediction methods already available, given the complexity and diversity of protein structural types, there is still a great need to explore newer methods and concepts to develop accurate, versatile and efficient binding site prediction algorithms. We have developed a new method PocketDepth, for identification of binding sites in proteins. The method is purely geometry-based and proceeds in two stages, labeling of grid cells with depth factors followed by a depth based clustering that uses neighbourhood information. Depth is an important parameter considered during protein structure visualization and analysis but has been used more often intuitively than systematically. Our current implementation of depth reflects how central a given sub-space is to a putative pocket rather than reflecting merely how far away it is situated from the nearest external surface of the protein. We have tested the algorithm against PDBbind, a large curated set of 1091 proteins obtained from PDB. A prediction was considered a true-positive if the predicted pocket had at-least 10% overlap with the actual ligand. The prediction accuracy using this set was about 96%. Moreover, 87% of the true-positives were identified within the first five ranks for each protein, of which 55% are in the first rank itself. 77% of the predictions had at least 50% overlap with the experimentally observed ligand. High prediction rates were again observed, when the method was tested against a data-set of apo-proteins and compared with their respective ligand complexes. A comparison of our method with four other widely used methods for a chosen representative set is also presented.

GHECOM 1.0 : http://strcomp.protein.osaka-u.ac.jp/ghecom/  Grid-based HECOMi finder. A program for finding multi-scale pockets on protein surfaces using mathematical morphology

Pocket-Finder: http://www.modelling.leeds.ac.uk/pocketfinder/ is based on the Ligsite algorithm written by Hendlich et al. (1997). Pocket-Finder was written to compare pocket detection with our new ligand binding site detction algorithm Q-SiteFinder.

Screen2: http://luna.bioc.columbia.edu/honiglab/screen2/cgi-bin/screen2.cgi  is a tool for identifying protein cavities and computing cavity attributes that can be applied for classification and analysis. The original Screen, written by Murad Nayal, was dependent on the obsolete Irix platform and is no longer available. Screen2 was reengineered by Brian Y. Chen for efficiency and compatibility, and made accessible as a web service by Raquel Norel.

ConCavity: http://compbio.cs.princeton.edu/concavity/ Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavit​y/).

MultiBind and MAPPIS: http://bioinfo3d.cs.tau.ac.il/MultiBind/index.html Web servers for multiple alignment of protein 3D binding sites and their interactions. Analysis of protein–ligand complexes and recognition of spatially conserved physico-chemical properties is important for the prediction of binding and function. Here, we present two webservers for multiple alignment and recognition of binding patterns shared by a set of protein structures. The first webserver, MultiBind (http://bioinfo3d.cs.tau.ac.il/MultiBind), performs multiple alignment of protein binding sites. It recognizes the common spatial chemical binding patterns even in the absence of similarity of the sequences or the folds of the compared proteins. The input to the MultiBind server is a set of protein-binding sites defined by interactions with small molecules. The output is a detailed list of the shared physico-chemical binding site properties. The second webserver, MAPPIS (http://bioinfo3d.cs.tau.ac.il/MAPPIS), aims to analyze protein–protein interactions. It performs multiple alignment of protein–protein interfaces (PPIs), which are regions of interaction between two protein molecules. MAPPIS recognizes the spatially conserved physico-chemical interactions, which often involve energetically important hot-spot residues that are crucial for protein–protein associations. The input to the MAPPIS server is a set of protein-protein complexes. The output is a detailed list of the shared interaction properties of the interfaces.

MolAxis: http://bioinfo3d.cs.tau.ac.il/MolAxis/  is a tool for the identification of high clearance pathways or corridors which represent molecular channels in the complement space of proteins. It is extremely efficient because it samples the medial axis of the complement of the molecule, reducing the problem dimension to two, since the medial axis is composed of surface patches. It is designed to analyze proteins channels, calculate pore dimensions and analyze atom accessibility. MolAxis reads files in the standard Protein Data Bank format (PDB) containing a single frame or multiple frames generated by molecular dynamics (MD) simulations. MolAxis handles two distinct scenarios: It computes channels that connect a single point (like an inner chamber) to the bulk solvent, and it also computes transmembrane (TM) channels. MolAxis has a friendly web interface (see the Web Server tab). It also has a stand-alone version, statically compiled for linux, which can be downloaded from the Download tab.

fpocket: http://fpocket.sourceforge.net/ fpocket is a very fast open source protein pocket (cavity) detection algorithm based on Voronoi tessellation. It was developed in the C programming language and is currently available as command line driven program. A GUI is in development and mdpocket (fpocket on md trajectories) is out now. fpocket includes two other programs (dpocket & tpocket) that allow you to extract pocket descriptors and test own scoring functions respectively. Furthermore a nifty druggability prediction score has been added to fpocket recently. As the algorithm is very fast it can be used on a large scale level (PDB size for instance). If you use fpocket for publication, please cite : Vincent Le Guilloux, Peter Schmidtke and Pierre Tuffery, "Fpocket: An open source platform for ligand pocket detection", BMC Bioinformatics, 2009, 10:168

SuMo: http://sumo-pbil.ibcp.fr/cgi-bin/sumo-welcome allows you to screen the Protein Data Bank (PDB) for finding ligand binding sites matching your protein structure or inversely, for finding protein structures matching a given site in your protein. This method is neither based on aminoacid sequence nor on fold comparisons. Priority is given to biological relevance. SuMo uses its own heuristics for defining ligand binding sites. Automatically selected ligand binding sites are extracted from PDB structure files and stored into SuMo's own database.

CAVER: http://www.caver.cz/ CAVER is a software tool for analysis and visualization of tunnels and channels in protein structures. Tunnels are void pathways leading from a cavity buried in a protein core to the surrounding solvent. Unlike tunnels, channels lead through the protein structure and their both endings are opened to the surrounding solvent. Studying of these pathways is highly important for drug design and molecular enzymology.

SiteHound: http://scbx.mssm.edu/sitehound/sitehound-download/download.html SiteHound identifies protein regions that are likely to interact with ligands. The only input files required by SITEHOUND are the PDB file of the protein and the Molecular Interaction Field (MIFs) or Affinity Map for that protein structure structure. EasyMIFs is provided as a tool to calculate MIFs, alternatively AutoGrid (part of the AutoDock suite developed by Arthur Olson’s group at The Scripps Research Insitute) or the SiteHound-web server can be used to produce Affinity maps or MIFs. A python script named 'auto.py' is provided in the package and can be used to perform binding site identification in a fully automated fashion. The script will prepare the protein PDB file, compute a Molecular Interaction Fields map with EasyMIFs and carry out binding site identification using SiteHound. It is also possible to use EasyMIFs and SiteHound separately.

SURFNET: http://www.biochem.ucl.ac.uk/~roman/surfnet/surfnet.html The SURFNET program generates surfaces and void regions between surfaces from coordinate data supplied in a PDB file.

MSPocket: http://appserver.biotec.tu-dresden.de/MSPocket/ is an orientation independent program for the detection and graphical analysis of protein surface pockets [Zhu2011]. The approach is based on the solvent excluded surfaces generated by MSMS [Sanner1996].

Pfinder : http://pdbfun.uniroma2.it/pfinder/index.html  Pfinder is a bioinformatic method for the prediction of phosphate-binding sites in protein structures. Given a protein structure, Pfinder compares it with a set of 215 highly conserved structural motifs known to bind the phosphate moiety of phosphorylated ligands.

VOIDOO: http://xray.bmc.uu.se/usf/voidoo.html is a program for detection of cavities in macromolecular structures. It uses an algorithm that makes it possible to detect even certain types of cavities that are connected to "the outside world". Three different types of cavity can be handled by VOIDOO: Vanderwaals cavities (the complement of the molecular Vanderwaals surface), probe-accessible cavities (the cavity volume that can be occupied by the centres of probe atoms) and MS-like probe-occupied cavities (the volume that can be occupied by probe atoms, i.e. including their radii).

PocketPicker: http://gecco.org.chemie.uni-frankfurt.de/pocketpicker/index.html Background: Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results: We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITE^cs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITE^cs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusion: The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections.

McVol: http://www.bisb.uni-bayreuth.de/index.php?page=data/mcvol/mcvol  This program was developed to integrate the molecular volume, solven accessible volume an Van der Waals volume of proteins using a Monte carlo algorithm. Based on this calculations, McVol is also able to identify internal cavities as well as surface clefts und fill these cavities with water molecules. Additionally, a membrane of dummy atoms can be placed as a disc atound the protein. The program is available under the Gnu Public Licence. A precompiled binary (X86) can be downloaded free of charge from here (when the associated paper is published).

Pharmacological compound databases

Zinc: http://zinc.docking.org/ Welcome to ZINC, a free database of commercially-available compounds for virtual screening. ZINC contains over 14 million purchasable compounds in ready-to-dock, 3D formats. ZINC is provided by the Shoichet Laboratory in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSF). To cite ZINC, please reference: Irwin and Shoichet, J. Chem. Inf. Model. 2005;45(1):177-82 PDF, DOI. We thank NIGMS for financial support (GM71896).

PubChem: http://pubchem.ncbi.nlm.nih.gov/ PubChem, released in 2004, provides information on the biological activities of small molecules. PubChem is organized as three linked databases within the NCBI's Entrez information retrieval system. These are PubChem Substance, PubChem Compound, and PubChem BioAssay. PubChem also provides a fast chemical structure similarity search tool. More information about using each component database may be found using the links in the homepage. Links from PubChem's chemical structure records to other Entrez databases provide information on biological properties. These include links to PubMed scientific literature and NCBI's protein 3D structure resource. Links to PubChem's bioassay database present the results of biological screening. Links to depositor web sites provide further information. A PubChem FTP site, Download Facility, Power User Gateway(PUG), Standardization Service, Score Matrix Service, Structure Clustering, and Deposition Gateway are also available. PubChem provides tips and example code to allow users to add PubChem search tool (free) in their sites. A PubChem publication site provides links to published articles. 

The DrugBank database: http://www.drugbank.ca/ is a unique bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. The database contains 6712 drug entries including 1441 FDA-approved small molecule drugs, 134 FDA-approved biotech (protein/peptide) drugs, 83 nutraceuticals and 5086 experimental drugs. Additionally, 4231 non-redundant protein (i.e. drug target/enzyme/transporter/carrier) sequences are linked to these drug entries. Each DrugCard entry contains more than 150 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. DrugBank is supported by David Wishart, Departments of Computing Science & Biological Sciences, University of Alberta. DrugBank is also supported by The Metabolomics Innovation Centre, a Genome Canada-funded core facility serving the scientific community and industry with world-class expertise and cutting-edge technologies in metabolomics. 

ChemDB: http://cdb.ics.uci.edu/index.htm ChemicalSearch: Find Chemicals by Various Criteria Find a chemical by basic criteria like molecular weight and predicted logP, or by the more abstract notion of structural similarity. Virtual Chemical Space: Retro-Synthesis and Combinatorial Library Design Interactively deconstruct target compounds into component precursors and reconstruct similar building-blocks into combinatorial libraries representing the "virtual chemical space" near the target compound. Reaction Explorer: Synthesis Explorer and Mechanism Explorer Interactive system for learning and practicing reactions, syntheses and mechanisms in organic chemistry, with advanced support for the automatic generation of random problems, curved-arrow mechanism diagrams, and inquiry-based learning. Datasets: For Machine Learning and Searching Experiments Various available chemical datasets annotated with interesting properties to train and test machine-learning prediction and searching methods. Supplements: Articles and Support Material Online articles relating to the system with supplementary data and figures referenced in them.

Dictionary of Natural Products Online: http://dnp.chemnetbase.com/intro/index.jsp 

 The Chapman & Hall/CRC Chemical Database is a structured database holding information on chemical substances. It includes descriptive and numerical data on chemical, physical and biological properties of compounds; systematic and common names of compounds; literature references; structure diagrams and their associated connection tables. The Dictionary of Natural Products Online is a subset of this database and includes all compounds contained in the Dictionary of Natural Products (Main Work and Supplements). The Dictionary of Natural Products (DNP) is the only comprehensive and fully-edited database on natural products. It arose as a daughter product of the well-known Dictionary of Organic Compounds (DOC) which, since its inception in the 1930s has, through successive editions, always been a leading source of natural product information. In the early 1980s, following the publication of the Fifth Edition of DOC, the first to be founded on database methods, the Editors and contributors for the various classes of natural products embarked on a programme of enlargement, rationalisation and classification of the natural product entries, while at the same time keeping the coverage up-to-date. In 1992 the results of this major project, which had grown to match DOC in size, were separately published in both book (7 volumes) and CD-ROM format, leaving DOC with coverage of only the most widely distributed and/or practically important natural products. DNP compilation has since continued unabated by a combination of an exhaustive survey of current literature and of historical sources such as reviews to pick up minor natural products and items of data previously overlooked. The compilation of DNP is undertaken by a team of academics and freelancers who work closely with the in-house editorial staff at Chapman & Hall. Each contributor specialises in a particular natural product class (e.g. alkaloids) and is able to reorganise and classify the data in the light of new research so as to present it in the most consistent and logical manner possible. Thus the compilation team is able to reconcile errors and inconsistencies. The resulting on-line version represents an extremely well organised dictionary documenting virtually every known natural product. A valuable feature of the design is that closely related natural products (e.g. where one is a glycoside or simple ester of another) are organised into the same entry, thus simplifying and bringing out the underlying structural and biosynthetic relationships of the compounds. Structure diagrams are drawn and numbered in the most consistent way according to best stereochemical and biogenetic relationships. In addition, every natural product is indexed by structural/biogenetic type under one of more than 1000 headings, allowing the rapid location of all compounds in the category, even where they have undergone biogenetic modification and no longer share exactly the same skeleton. There is extensive (but not complete) coverage of natural products of unknown structure, and the coverage of these is currently being enhanced by various retrospective searches. 

ChemSpider: http://www.chemspider.com/ is a free chemical structure database providing fast text and structure search access to over 26 million structures from hundreds of data sources.

ChemBank: http://chembank.broadinstitute.org/ is a public, web-based informatics environment created by the Broad Institute's Chemical Biology Program and funded in large part by the National Cancer Institute's Initiative for Chemical Genetics (ICG). This knowledge environment includes freely available data derived from small molecules and small-molecule screens, and resources for studying the data so that biological and medical insights can be gained. ChemBank is intended to guide chemists synthesizing novel compounds or libraries, to assist biologists searching for small molecules that perturb specific biological pathways, and to catalyze the process by which drug hunters discover new and effective medicines. ChemBank stores an increasingly varied set of cell measurements derived from, among other biological objects, cell lines treated with small molecules. Analysis tools are available and are being developed that allow the relationships between cell states, cell measurements and small molecules to be determined. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the ICG in collaborations involving biomedical researchers worldwide. These scientists have agreed to perform their experiments in an open data-sharing environment.The goals of ChemBank are to provide life scientists unfettered access to biomedically relevant data and tools heretofore available almost exclusively in the private sector. We intend for ChemBank to be a planning and discovery tool for chemists, biologists, and drug hunters anywhere, with the only necessities being a computer, access to the Internet, and a desire to extract knowledge from public experiments whose greatest value is likely to reside in their collective sum.

SuperDrug: http://bioinf.charite.de/superdrug/ Different resources exist for experimentally determined and computed three-dimensional (3D)-structures of low molecular weight structures but for approved drugs, no free, publicly accessible source of 3D-structures and conformers is available. Furthermore, for selection purposes or for correlation of structural similarity with medical application, the assignment of the Anatomical Therapeutic Chemical (ATC) classification codes to each structure according to the WHO-scheme would be desirable.RESULTS: The database contains approximately 2500 3D-structures of active ingredients of essential marketed drugs. To account for structural flexibility they are represented by 10(5) structural conformers. Here we present a web-query system enabling searches for drug name, synonyms, trade name, trivial name, formula, CAS-number, ATC-code etc. 2D-similarity screening (Tanimoto coefficients) and an automatic 3D-superposition procedure based on conformational representation are implemented. Drug structures above a similarity threshold as well as superimposed conformers can be retrieved in the mol- file format via a graphical interface. AVAILABILITY: For academic use the system is accessible at http://bioinf.charite.de/superdrug . The retrieval system requires the free browser-plugin 'chime' from MDL for visualization.

Ligand Expo: http://ligand-expo.rutgers.edu/ Ligand Expo (formerly Ligand Depot) provides chemical and structural information about small molecules within the structure entries of the Protein Data Bank. Tools are provided to search the PDB dictionary for chemical components, to identify structure entries containing particular small molecules, and to download the 3D structures of the small molecule components in the PDB entry. A sketch tool is also provided for building new chemical definitions from reported PDB chemical components.

Glide Ligand Decoys Set: http://www.schrodinger.com/productpage/14/5/74/ 

Schrödinger has made available a set of the ligand decoys used in Glide enrichment studies. 1K Drug-Like Ligand Decoys Set: This collection of ligands was created by selecting 1000 ligands from a one million compound library that were chosen to exhibit "drug-like" properties. Creation and application of the ligand set is presented in the following publications: 

Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shaw, D. E.; Shelley, M.; Perry, J. K.; Francis, P.; Shenkin, P. S, "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy", J. Med. Chem. 2004, 47, 1739-1749.

Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L., "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening", J. Med. Chem. 2004, 47, 1750-1759.

The SuperLigands: http://bioinf-tomcat.charite.de/superligands/ The SuperLigands is an encyclopedia that is dedicated to a ligand oriented view of the protein structural space. The database contains small molecule structures occurring as ligands in the Protein Data Bank. SuperLigands integrates different information about drug-likeness or binding properties. A 3D superpositioning algorithm is implemented that allows screening all ligands for possible scaffold hoppers as well as a 2D similarity screen for compounds based on fingerprints.

ChEBI: http://www.ebi.ac.uk/chebi/ Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on ‘small’ chemical compounds. The term ‘molecular entity’ refers to any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms.ChEBI incorporates an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified.ChEBI uses nomenclature, symbolism and terminology endorsed by the following international scientific bodies: 

International Union of Pure and Applied Chemistry (IUPAC) 

Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB) 

Molecules directly encoded by the genome (e.g. nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. All data in the database is non-proprietary or is derived from a non-proprietary source. It is thus freely accessible and available to anyone. In addition, each data item is fully traceable and explicitly referenced to the original source.

Working with multiple urls from text file to tabs and vice versa

Copy or save multiple tabs to a text file

There are various methods; I use this one for Firefox:

1. Install send-tab-urls add-on to Firefox https://addons.mozilla.org/en-US/firefox/addon/send-tab-urls/

2. Open all the urls in different tabs.

3. Go to Files --> Send tab urls --> select your options and send to clipboard

4. Url open in all tabs will be copied to your clipboard, you can just paste them to a text file and save them or you can reuse them for opening in multiple tabs again.

Use: While looking for published articles on Pubmed or on Google, you need to save the relevant article list search wise as a text file, and then reuse them whenever required. At home I did not have accesses to various journals, so I used to save the links in text files and then at institute just open and save all the articles required.

Open multiple urls in different tabs from a text file

1. Copy all urs in a txt file

2. Open http://www.urlopener.com/index.php

3. Paste it in the space provided

4. Click submit and then click open all

Use: Opening all the urls in one go, for faster work. It would be logical to open a new window in Firefox to do this so that you won’t clutter your ongoing work with the multiple urls

Meanwhile I also found a very useful tool for common comparisons or lists and making Venn diagrams

Please cite: Oliveros, J.C. (2007) VENNY. An interactive tool for comparing lists with Venn Diagrams. http://bioinfogp.cnb.csic.es/tools/venny/index.html

Happy surfing.

Homology modeling of proteins

CPHmodels: http://www.cbs.dtu.dk/services/CPHmodels/ CPHmodels-3.0 is a web-server predicting protein 3D-structure by use of single template homology modeling. The server employs a hybrid of the scoring functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A query sequence is first attempted modeled using the fast CPHmodels-2.0 profile-profile scoring function suitable for close homology modeling. The new computational costly remote homology-modeling algorithm is only engaged provided that no suitable PDB template is identified in the initial search. CPHmodels-3.0 was benchmarked in the CASP8 competition and produced models for 94% of the targets (117 out of 128), 74% were predicted as high reliability models (87 out of 117). These achieved an average RMSD of 4.6? When superimposed to the 3D-structure. The remaining 26% low reliably models (30 out of 117) could superimpose to the true 3D-structure with an average RMSD of 9.3?. These performance values place the CPHmodels-3.0 method in the group of high performing 3D-prediction tools. Beside its accuracy, one of the important features of the method is its speed. For most queries, the response time of the server is less than 20 minutes. The web server is available at http://www.cbs.dtu.dk/services/CPHmodels/.

MODELLER: http://www.salilab.org/modeller/ MODELLER is used for homology or comparative modeling of protein three-dimensional structures (1,2). The user provides an alignment of a sequence to be modeled with known related structures and MODELLER automatically calculates a model containing all non-hydrogen atoms. MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints (3,4), and can perform many additional tasks, including de novo modeling of loops in protein structures, optimization of various models of protein structure with respect to a flexibly defined objective function, multiple alignment of protein sequences and/or structures, clustering, searching of sequence databases, comparison of protein structures, etc. MODELLER is available for download for most Unix/Linux systems, Windows, and Mac.

SWISS-MODEL: http://swissmodel.expasy.org/ SWISS-MODEL is a fully automated protein structure homology-modeling server, accessible via the ExPASy web server, or from the program DeepView (Swiss Pdb-Viewer). The purpose of this server is to make Protein Modeling accessible to all biochemists and molecular biologists worldwide.

Phyre2: http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index (Protein Homology/AnalogY Recognition Engine; pronounced as 'fire') are web-based services for protein structure prediction that are free for non-commercial use. Phyre is among the most popular methods for protein structure prediction having been cited over 1000 times. Like other remote homology recognition techniques (see protein threading), it is able to regularly generate reliable protein models when other widely used methods such as PSI-BLAST cannot. Phyre2 has been designed (funded by the BBSRC) to ensure a user-friendly interface for users inexpert in protein structure prediction methods.

HHpred : http://toolkit.tuebingen.mpg.de/hhpred the primary aim in developing HHpred was to provide biologists with a method for sequence database searching and structure prediction that is as easy to use as BLAST or PSI-BLAST and that is at the same time much more sensitive in finding remote homologs. In fact, HHpred's sensitivity is competitive with the most powerful servers for structure prediction currently available. HHpred is the first server that is based on the pair wise comparison of profile hidden Markov models (HMMs). Whereas most conventional sequence search methods search sequence databases such as UniProt or the NR, HHpred searches alignment databases, like Pfam or SMART. This greatly simplifies the list of hits to a number of sequence families instead of a clutter of single sequences. All major publicly available profile and alignment databases are available through HHpred. HHpred accepts a single query sequence or a multiple alignment as input. Within only a few minutes it returns the search results in an easy-to-read format similar to that of PSI-BLAST. Search options include local or global alignment and scoring secondary structure similarity. HHpred can produce pairwise query-template sequence alignments, merged query-template multiple alignments (e.g. for transitive searches), as well as 3D structural models calculated by the MODELLER software from HHpred alignments.

LOMATES: http://zhanglab.ccmb.med.umich.edu/LOMETS/ LOMETS (Local Meta-Threading-Server) is an on-line web service for protein structure prediction. It generates 3D models by collecting high-scoring target-to-template alignments from 8 locally-installed threading programs (FUGUE, HHsearch, MUSTER, PPA, PROSPECT2, SAM-T02, SPARKS, SP3). A detailed description of the server can be seen in the Readme file.

MODBASE: MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence–structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5 152 695 reliable models for domains in 1 593 209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).

Robetta: http://www.robetta.org/ Robetta provides both ab initio and comparative models of protein domains. It uses the ROSETTA fragment insertion method (Simons et al. (1997) J Mol Biol. 268:209-225). Domains without a detectable PDB homolog are modeled with the Rosetta de novo protocol (Bonneau et al. (2002) J Mol Biol. 322:65-78). Comparative models are built from Parent PDBs detected by UW-PDB-BLAST or HHSEARCH and aligned by various methods which include HHSEARCH, Compass, and Promals. Loop regions are assembled from fragments and optimized to fit the aligned template structure (Rohl et al. (2004) Proteins 55:656-677). The procedure is fully automated. Robetta is evaluated in the blind benchmarking experiment CASP. Robetta uses ROSETTA software which is developed and maintained by the Rosetta Commons. 

chunk-TASSER: http://cssb.biology.gatech.edu/skolnick/webservice/chunk-TASSER/index.html A protein structure prediction method that combines threading templates from SP3 and ab initio folded chunk structures (three consecutive segments of regular secondary structures). It is better for extreme hard targets

PSiFR (Protein Structure and Function predicton Resource) http://psifr.cssb.biology.gatech.edu/ provides integrated tools for protein tertiary structure prediction and structure and sequence-based function annotation. The details of various methods used are described below: 

Protein structure prediction methods 

TASSER (Threading/ASSembly/Refinement) is an automated protein structure prediction and modeling method. TASSER employs a hierarchical approach consisting of template identification by threading, followed by tertiary structure assembly by rearranging continuous template fragments (Zhang, Y. and Skolnick, J., 2004, PNAS). 

TASSER-Lite is a comparative protein tertiary structure modeling tool. It is presently optimized for the modeling of single domain (41-200 residues) homologous protein sequences; that is, proteins with a sequence identity greater than 25% with respect to the best threading template (Pandit et. al., 2006, Biophysical Journal). The templates for the modeling of the query sequence are identified using the threading program PROSPECTOR_3 (Skolnick et. al., 2004, Proteins). Subsequently, the structure is refined using TASSER program with optimized parameters. 

METATASSER is a protein tertiary prediction method that employs the 3D-Jury approach to select threading templates from SPARKS2 (Zhou H. and Zhou Y., 2004, Proteins), SP3 ( Zhou H. and Zhou Y., 2005, Proteins) and PROSPECTOR_3 (Skolnick et. al., 2004, Proteins), which provides aligned fragments and tertiary restraints as an input to TASSER procedure to generate full-length models. In the CASP7 and CASP8 assessment of server performance, METATASSER is among the top performing servers (Zhou et. al, 2007, Proteins; Zhou et al., 2009, Proteins (in press)). 

ESyPred3D: http://www.fundp.ac.be/sciences/biologie/urbm/bioinfo/esypred/ ESyPred3D is a new automated homology modeling program. The method gets benefit of the increased alignment performances of a new alignment strategy using neural networks. Alignments are obtained by combining, weighting and screening the results of several multiple alignment programs. The final three dimensional structure is built using the modeling package MODELLER.

Protein Model Portal (PMP): http://www.proteinmodelportal.org/ PMP gives access to various models computed by comparative modeling methods provided by different partner sites, and provides access to various interactive services for model building, and quality assessment.

ProModel: http://www.vlifesciences.com/products/VLifeMDS/Protein_Modeller.php ProModel is a complete package for modeling proteins, whose crystal structure is unknown based on the amino acid sequences of a close homologue. ProModel allows homology modeling from either a selected template or a user defined template. Users can perform an automated homology modeling simply by reading in the template file or can perform a knowledge based manual modeling by specific loop insertions or by changing specific amino acid residues. A local BLAST speeds up the process of modeling. ProModel enables an exhaustive analysis of the target protein structure, active site and channels. The user can conveniently view, edit and superimpose proteins with ProModel. Facilities to distribute the secondary structure elements, distribute the Phi-Psi angles of residues in Ramachandran plot, identify and visualize cavities and channels make it a very useful product. ProModel is available for both Linux and Windows® operating systems.

SCWRL4: http://dunbrack.fccc.edu/scwrl4/index.php SCWRL4 is based on a new algorithm and new potential function that results in improved accuracy at reasonable speed. This has been achieved through: 1) a new backbone-dependent rotamer library based on kernel density estimates; 2) averaging over samples of conformations about the positions in the rotamer library; 3) a fast anisotropic hydrogen bonding function; 4) a short-range, soft van der Waals atom-atom interaction potential; 5) fast collision detection using k-discrete oriented polytopes; 6) a tree decomposition algorithm to solve the combinatorial problem; and 7) optimization of all parameters by determining the interaction graph within the crystal environment using symmetry operators of the crystallographic space group. Accuracies as a function of electron density of the side chains demonstrate that side chains with higher electron density are easier to predict than those with low electron density and presumed conformational disorder. For a testing set of 379 proteins, 86% of chi1 angles and 75% of chi1+2 are predicted correctly within 40 degrees of the X-ray positions. Among side chains with higher electron density (25th-100th percentile), these numbers rise to 89% and 80%. The new program maintains its simple command-line interface, designed for homology modeling. To achieve higher accuracy, SCWRL4 is somewhat slower than SCWRL3 when run in the default flexible rotamer model (FRM) by a factor of 3-6, depending on the protein. When run in the rigid rotamer model (RRM), SCWRL4 is about the same speed as SCWRL3. In both cases, SCWRL4 will converge on very large proteins or protein complexes or those with very dense interaction graphs, while SCWRL3 sometimes would not. The SCWRL4 paper has been published in Proteins: Structure, Function, Bioinformatics. A reprint is available. Please cite the paper: G. G. Krivov, M. V. Shapovalov, and R. L. Dunbrack, Jr. Improved prediction of protein side-chain conformations with SCWRL4. Proteins (2009). 

VADAR: http://vadar.wishartlab.com/ VADAR (Volume, Area, Dihedral Angle Reporter) is a compilation of more than 15 different algorithms and programs for analyzing and assessing peptide and protein structures from their PDB coordinate data. The results have been validated through extensive comparison to published data and careful visual inspection. The VADAR web server supports the submission of either PDB formatted files or PDB accession numbers. VADAR produces extensive tables and high quality graphs for quantitatively and qualitatively assessing protein structures determined by X-ray crystallography, NMR spectroscopy, 3D-threading or homology modelling. Please cite the following: Leigh Willard, Anuj Ranjan,Haiyan Zhang,Hassan Monzavi, Robert F. Boyko, Brian D. Sykes, and David S. Wishart "VADAR: a web server for quantitative evaluation of protein structure quality" Nucleic Acids Res. 2003 July 1; 31 (13): 3316.3319

IntFOLD : http://www.reading.ac.uk/bioinf/IntFOLD/ The IntFOLD server provides a unified interface for Tertiary structure prediction/3D modeling, 3D model quality assessment, Intrinsic disorder prediction, Domain prediction, Prediction of protein-ligand binding residues

PEPstr: http://www.imtech.res.in/raghava/pepstr/ The Pepstr server predicts the tertiary structure of small peptides with sequence length varying between 7 to 25 residues. The prediction strategy is based on the realization that β-turn is an important and consistent feature of small peptides in addition to regular structures. Thus, the methods uses both the regular secondary structure information predicted from PSIPRED and β-turns information predicted from BetaTurns. The side-chain abgles are placed using standard backbone-dependent rotamer library. The structure is further refined with energy minimization and molecular dynamic simulations using Amber version6.

BSR: http://cssb.biology.gatech.edu/BSR Binding Site Refinement employs a new template-based method for the local refinement of ligand-binding regions in protein models using closely as well as distantly related templates identified by threading. A Support Vector Regression (SVR) model is used to select likely correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for a specific chemical environment within a binding site and optimize the interactions with putative ligands.

KeyRecep: http://www.immd.co.jp/en/product_2.html KeyRecep is the best-suited solution for rational molecular design when the 3D structure of the target protein is unknown. Users can estimate the characteristics of the binding site of the target protein by superposing multiple active compounds in 3D space so that the physicochemical properties of the compounds match maximally with each other. (Estimation of virtual receptor model) Users can also examine relationship between chemical structures and the activities based on the multiple regression analysis with indices of conformity of each compound to the virtual receptor model and the activity values. (3D-SAR function) For compounds whose activities are unknown, users can estimate the activities based on the indices of conformity to the virtual receptor model and can perform virtual screening. (DB search function) KeyRecep rationally and strategically accelerates the molecular design projects based on hit compounds discovered by high throughput screening (HTS) or based on information on compounds from literature or patents. KeyRecep facilitates the structural expansion of such compounds to obtain lead compounds and further drug candidates.

PROTEUS2: http://wks16338.biology.ualberta.ca/proteus2/ PROTEUS2 is a web server designed to support comprehensive protein structure prediction and structure-based annotation. PROTEUS2 accepts either single sequences (for directed studies) or multiple sequences (for whole proteome annotation) and predicts the secondary and, if possible, tertiary structure of the query protein(s). Unlike most other tools or servers, PROTEUS2 bundles signal peptide identification, transmembrane helix prediction, transmembrane β-strand prediction, secondary structure prediction (for soluble proteins) and homology modeling (i.e. 3D structure generation) into a single prediction pipeline. Using a combination of progressive multi-sequence alignment, structure-based mapping, hidden Markov models, multi-component neural nets and up-to-date databases of known secondary structure assignments, PROTEUS2 is able to achieve among the highest reported levels of predictive accuracy for signal peptides (Q2=94%), membrane spanning helices (Q2=87%) and secondary structure (Q3 score of 81.3% ). PROTEUS2's homology modeling services also provide high quality 3D models that compare favorably with those generated by SWISS-MODEL (within 0.2 Å RMSD). The average PROTEUS2 prediction takes ~2 minutes per query sequence. Source code is also freely available here.

PSIPRED: http://bioinf.cs.ucl.ac.uk/psipred/ is a simple and accurate secondary structure prediction method, incorporating two feed-forward neural networks which perform an analysis on output obtained from PSI-BLAST (Position Specific Iterated - BLAST). Using a very stringent cross validation method to evaluate the method's performance, PSIPRED 2.6 achieves an average Q3 score of 80.7%. Predictions produced by PSIPRED were also submitted to the CASP4 evaluation and assessed during the CASP4 meeting, which took place in December 2000 at Asilomar. PSIPRED 2.0 achieved an average Q3 score of 80.6% across all 40 submitted target domains with no obvious sequence similarity to structures present in PDB, which ranked PSIPRED top out of 20 evaluated methods (an earlier version of PSIPRED was also ranked top in CASP3 held in 1998). It is important to realize, however, that due to the small sample sizes, the results from CASP are not statistically significant, although they do give a rough guide as to the current "state of the art". For a more reliable evaluation, the EVA web site at Columbia University provides a continuous evaluation. Also see the EVA servlet to visualize a breakdown of specific types of errors made by PSIPRED and other secondary structure prediction methods. NOTE that at the time of writing, the EVA site is no longer being updated. The PSIPRED V2.6 software can be downloaded from HERE. Please note that you should read the license terms given in the README file if you wish to incorporate PSIPRED in another program or Web server. Older releases of PSIPRED can be downloaded here HERE.

I-TASSER : http://zhanglab.ccmb.med.umich.edu/I-TASSER/ server is an Internet service for protein structure and function predictions. 3D models are built based on multiple-threading alignments by LOMETS and iterative TASSER assembly simulations; function insights are then derived by matching the predicted models with protein function databases. I-TASSER (as 'Zhang-Server') was ranked as the No 1 server for protein structure prediction in recent CASP7, CASP8 and CASP9 experiments. It was also ranked as the best for function prediction in CASP9. The server is in active development with the goal to provide the most accurate structural and functional predictions using state-of-the-art algorithms.

JPred: http://www.compbio.dundee.ac.uk/www-jpred/ Jpred is a Protein Secondary Structure Prediction server and has been in operation since approximately 1998. Jpred incorporates the Jnet algorithm in order to make more accurate predictions. In addition to protein secondary structure Jpred also makes predictions on Solvent Accessibility and Coiled-coil regions (Lupas method). The current version of Jpred (v3) follows on from previous versions of Jpred developed and maintained by James Cuff and Jonathan Barber

Verifying your modeled protein with online servers: 

Stuctural Analysis and Verification Server (SAVS): http://nihserver.mbi.ucla.edu/SAVES/ SAVS uses following servers to check the quality of the protein structures: Procheck: Checks the stereochemical quality of a protein structure by analyzing residue-by-residue geometry and overall structure geometry. [Reference] What_Check: Derived from a subset of protein verification tools from the WHATIF program (Vriend, 1990), this does extensive checking of many sterochemical parameters of the residues in the model. [Reference] ERRAT: Analyzes the statistics of non-bonded interactions between different atom types and plots the value of the error function versus position of a 9-residue sliding window, calculated by a comparison with statistics from highly refined structures. [Reference] Verify3D: Determines the compatibility of an atomic model (3D) with its own amino acid sequence (1D) by assigned a structural class based on its location and environment (alpha, beta, loop, polar, nonpolar etc) and comparing the results to good structures. [Reference] Prove: Calculates the volumes of atoms in macromolecules using an algorithm which treats the atoms like hard spheres and calculates a statistical Z-score deviation for the model from highly resolved (2.0 Å or better) and refined (R-factor of 0.2 or better) PDB-deposited structures. [Reference]

COLORADO-3D: http://asia2.genesilico.pl/colorado3d/ COLORADO-3D is a www-tool that greatly facilitates the visual analysis of various features in three-dimensional protein structures, directly at the level of the protein structure, with the aid of commonly used viewers such as RASMOL or SWISSPDBVIEWER. Among the features most important for the structural biologist that our server allows to visualize in color are potential errors in protein structure (detected by ANOLEA, PROSA, PROVE,VERIFY3D), regions buried in the protein core and inaccessible to the solvent, and regions of high or low sequence conservation (e.g. detected by RATE4SITE). In particular COLORADO3D may serve to visualize the results of assessment of the protein structure's quality at various stages of the model building and refinement (both in the case of experimental structure determination and homology modeling).

Circular dichroism code to help in data analysis

I was looking for some kind of code for rearranging the data I get for thermal melt from CD (Circular Dichroism). No I could not get a code to convert .jsw files to CSV in batch, neither JASCO’s Spectrum Analysis software helps on that, update me if there's batch conversion option for .jsw files to CSV. You have to convert individual .jsw files to CSV and group them in one folder. What I could get is after converting .jsw files to CSVs you can get data from all the files to one CSV file that assist in data analysis. The code given below will copy the data from all files to one files from 350nm to 200nm with the file name as a header for mdeg and tension (HV).

Steps:

1.Install python (if you do not have already http://www.python.org/getit/)

2.Copy all CSV files to one folder with their names

3. Write the name of CSV in one text file and save it as file_name.txt in the same folder as your data and code

    a.You can do this by Get to the MS-DOS prompt or the Windows command line. Navigate to the directory you wish to print the contents of. If you're new to the command line, familiarize yourself with the cd command and the dir command. Once in the directory you wish to print the contents of, type this command: dir /b > file_name.txt

    b.Open the new file created with name file_name.txt on the same folder and check for the file names and if file_name.txt is also there remove it so that you only have file names listed on the text file.

4.Copy the code given below in notepad and save it as .py file (it’s a python code) in the same folder

5.Right click on the python file and Run this code on python IDLE (press F5)

6.You will get a result file with name final_file.txt. It will be a CSV files with your data for mdeg and HV shorted from 350nm to 200nm, open it with excel. You can make changes in the code to suit your needs like if you are taking data from 200nm to 260 nm, make relevant change in the python code by changing x=range(151) to x=range(61) and then outfile.write(str(350-j)) to outfile.write(str(260-j)) respectively.

7.Hope that helps, thank Rhishikesh Bargaje (he wrote code for me) if it works, write me back if you face some problem, I can try to help.



Code:



infile = open('file_name.txt','r')

s = infile.read().split('\n')

infile.close()



outfile = open('final_file.txt','w')

outfile.write('Wavelength')



for k in s:

    for w in range(2):

        if w == 0:

            outfile.write('\t' + k.replace('.csv','').replace(' ','_') + '_mdeg')

        if w == 1:

            outfile.write('\t' + k.replace('.csv','').replace(' ','_') + '_HV')

      

outfile.write('\n')

      

x = range(151)



for j in x:

    outfile.write(str(350-j))

    for i in s:

        infile = open(i,'r')

        t = infile.read().split('XYDATA\n')

        infile.close()

        data1 = t[1].split('\n\n')[0].split('\n')[j].split(',')[1]

        data2 = t[1].split('\n\n')[0].split('\n')[j].split(',')[2]      

        outfile.write('\t' + data1 + '\t' + data2)

    outfile.write('\n')

outfile.close()


##end of the code##

Alternatively, if you are acquainted with R (Download R if you haven't http://cran.r-project.org/, you can use following script to run it on R for the same result with temperature range for thermal melt from 10 degrees to 70 degrees, edit the code to customize for your use, if needed, remember that you do not have to have directory name printed for this R code and it may not work properly if there are other files in the data folder. Get acquainted with R. Thank Shrikant if you find it useful.

Code:

 ##Start of the code##

CSV_Files=list.files(path=".",pattern="\\.csv",full.names=FALSE);
ResultantMatrix=matrix(nrow=151);
ResultantMatrix[,1]=c(350:200);
for(i in 1:length(CSV_Files))
{
    Current_File=read.table(CSV_Files[[i]],header=FALSE,blank.lines.skip=FALSE);
    tempM=matrix(nrow=151,ncol=2);
    k=1;
    for(j in 21:171)
    {
        temp=strsplit(as.character(Current_File[j,1]),split=",");
        tempM[k,1]=temp[[1]][2];
        tempM[k,2]=temp[[1]][3];
        k=k+1;
       
    }
    t=as.numeric(gsub(".*(\\d+.+?)\\.csv","\\1",CSV_Files[[i]]))+9;
    colnames(tempM)=c(t,t);
    ResultantMatrix=cbind(ResultantMatrix,tempM);
   
}

write.csv(ResultantMatrix,file="Result.csv");

##End of the code##

Protein-Protein Docking Servers

I was looking for protein-protein docking servers to use in my study, here is the list of online servers that are commonly used and are popular. There are other software giving good result for protein-protein docking, I have not listed them here as I am still trying to compile and I would put it here as soon as I am done with the list. Have fun. 

ClusPro: (http://nrc.bu.edu/cluster) represents the first fully automated, web-based program for the computational docking of protein structures. Users may upload the coordinate files of two protein structures through ClusPro's web interface, or enter the PDB codes of the respective structures, which ClusPro will then download from the PDB server (http://www.rcsb.org/pdb/). The docking algorithms evaluate billions of putative complexes, retaining a preset number with favorable surface complementarities. A filtering method is then applied to this set of structures, selecting those with good electrostatic and desolvation free energies for further clustering. The program output is a short list of putative complexes ranked according to their clustering properties, which is automatically sent back to the user via email.

RosettaDock: The RosettaDock protein-protein docking server predicts the structure of protein complexes given the structures of the individual components and an approximate binding orientation. The server uses the Rosetta 2.1 protein structure modeling suite. The RosettaDock server (http://rosettadock.graylab.jhu.edu) identifies low-energy conformations of a protein–protein interaction near a given starting configuration by optimizing rigid-body orientation and side-chain conformations. The server requires two protein structures as inputs and a starting location for the search. RosettaDock generates 1000 independent structures, and the server returns pictures, coordinate files and detailed scoring information for the 10 top-scoring models. A plot of the total energy of each of the 1000 models created shows the presence or absence of an energetic binding funnel. RosettaDock has been validated on the docking benchmark set and through the Critical Assessment of PRedicted Interactions blind prediction challenge.

ZDOCK, RDOCK: ZDOCK uses a fast Fourier transform to search all possible binding modes for the proteins, evaluating based on shape complementarity, desolvation energy, and electrostatics. The top 2000 predictions from ZDOCK are then given to RDOCK where they are minimized by CHARMM to improve the energies and eliminate clashes, and then the electrostatic and desolvation energies are recomputed by RDOCK (in a more detailed fashion than the calculations performed by ZDOCK). We then tested these programs with a benchmark of 49 non-redundant unbound test cases, where we identified a near-native structure (within 2.5 angstrom from the experimental structure) as the top prediction for 37% of the test cases, and within the top 4 predictions for 49% of the test cases. The superior performance of ZDOCK and RDOCK has also been demonstrated in a community-wide protein docking blind test, CAPRI. Check this out for more details. All software, as well as the benchmark is freely available to academic users. For basic information on running ZDOCK, see this site.

 

GPU.proton.DOCK: (Genuine Protein Ultrafast proton equilibria consistent DOCKing) is a state of the art service for in silico prediction of protein–protein interactions via rigorous and ultrafast docking code. It is unique in providing stringent account of electrostatic interactions self-consistency and proton equilibria mutual effects of docking partners. GPU.proton.DOCK is the first server offering such a crucial supplement to protein docking algorithms—a step toward more reliable and high accuracy docking results. The code (especially the Fast Fourier Transform bottleneck and electrostatic fields computation) is parallelized to run on a GPU supercomputer. The high performance will be of use for large-scale structural bioinformatics and systems biology projects, thus bridging physics of the interactions with analysis of molecular networks. We propose workflows for exploring in silico charge mutagenesis effects. Special emphasis is given to the interface-intuitive and user-friendly. The input is comprised of the atomic coordinate files in PDB format. The advanced user is provided with a special input section for addition of non-polypeptide charges, extra ionogenic groups with intrinsic pK_a values or fixed ions. The output is comprised of docked complexes in PDB format as well as interactive visualization in a molecular viewer. GPU.proton.DOCK server can be accessed at http://gpudock.orgchm.bas.bg/.

GRAMM-X: Protein docking software GRAMM-X and its web interface (http://vakser.bioinformatics.ku.edu/resources/gramm/grammx) extend the original GRAMM Fast Fourier Transformation methodology by employing smoothed potentials, refinement stage, and knowledge-based scoring. The web server frees users from complex installation of database-dependent parallel software and maintaining large hardware resources needed for protein docking simulations. Docking problems submitted to GRAMM-X server are processed by a 320 processor Linux cluster. The server was extensively tested by benchmarking, several months of public use, and participation in the CAPRI server track.

HexServer: HexServer (http://hexserver.loria.fr/) is the first Fourier transform (FFT)-based protein docking server to be powered by graphics processors. Using two graphics processors simultaneously, a typical 6D docking run takes ∼15 s, which is up to two orders of magnitude faster than conventional FFT-based docking approaches using comparable resolution and scoring functions. The server requires two protein structures in PDB format to be uploaded, and it produces a ranked list of up to 1000 docking predictions. Knowledge of one or both protein binding sites may be used to focus and shorten the calculation when such information is available. The first 20 predictions may be accessed individually, and a single file of all predicted orientations may be downloaded as a compressed multi-model PDB file. The server is publicly available and does not require any registration or identification by the user.

3D-Garden: a system for modelling protein–protein complexes based on conformational refinement of ensembles generated with the marching cubes algorithm. 3DGarden is an integrated software suite for performing protein-protein and protein-polynucleotide docking. For any pair of biomolecules structures specified by the user, 3DGarden's primary function is to generate an ensemble of putative complexed structures and rank them. The highest-ranking candidates constitute predictions for the structure of the complex. 3DGarden cannot be used to decide whether or not a particular pair of biomolecules interacts. Complexes of protein and nucleic acid chains can also be specified as individual interactors for docking purposes.