The Need of New Drugs for TB- It's Time for Action

Research resources for tuberculosis

Tuberculist: http://tuberculist.epfl.ch/ The TubercuList knowledge base integrates genome details, protein information, drug and transcriptome data, mutant and operon annotation, bibliography, structural views and comparative genomics, in a structured manner required for the rational development of new diagnostic, therapeutic and prophylactic measures against tuberculosis. With the means of expert curation and continuous updates, we deliver a broad view of the Mycobacterium tuberculosis genome.

TB Database: http://www.tbdb.org/ Tuberculosis (TB) is a public health challenge of paramount importance. Control of TB will require a multifaceted approach integrating efficient public health interventions with the discovery and use of new vaccines and drugs. TBDatabase (TBDB) makes available the tools and resources available at the Stanford Microarray Database and the Broad Institute. Anyone is welcome to access the published data available on the TBDB site without signing in. Some data in TBDB are unpublished and can therefore only be accessed by the authors and their collaborators after they sign in. The "Access Polices" page provides more information about TBDB accounts. A grant from the Bill & Melinda Gates Foundation has enabled us to create an integrated software platform for tuberculosis drug discovery and research. Learn more >

webTB.org:  http://www.webtb.org/ a resource for Mycobacterium tuberculosis researchers is brought to you by the TB Structural Genomics Consortium with lots of tools: Gene expression correlation display: This server presents the pair wise gene expression correlation for two or more genes. For a set of genes you can get all the pair wise correlations and express it as a matrix, graphically display it.Operon Search: Search for operons and directions in the TB genome. Known operons are listed by name. These data will be expanded as more sources are found. BLAST the TB genome: BLAST a sequence against the TB genome and other NCBI databases. Target Explorer: Target Explorer is intended to be an interactive web site that allows researchers in the Tuberculosis research community to experiment with different target selection criteria and explore alternative ways of prioritizing gene targets for experimentation (crystallization structure solution, high-throughput screening for inhibitor discovery, etc.) Mycobacterial Genome DataBase: The site is a database of genomes of mycobacterial strains sequenced in the lab of James C. Sacchettini at Texas A&M University. It provides access to sequence data (including coverage statistics) and comparison of polymorphisms among various strains of tuberculosis, with a focus on drug-resistance. The sequencing is done on an Illumina GenomeAnalyzer II (short reads). The data was analyzed using customized sequence-assembly methods written by Tom Ioerger and his group at Texas A&M. The Genome browser: Graphically scan the entire TB genome for information on each ORF with indications of any predicted operons and includes links back to the quick search page for more gene information ORF Progress search tool: Search the status and progress of MTb ORFs that are targeted and pursued by consortium members. Structure Gallery: See the protein structures determined by members of the consortium.Structure Summary pagesThis page is a front page portal to the structure information on known TB proteins. All other WebTB servers can be accessed from this page, including the Gallery and JMOL viewer. MTBreg Database: A database of proteins up- and down-regulated in Mycobacterium tuberculosis grown under conditions mimicking infection as well as information on proteins that are regulated by selected transcription factors or other regulatory proteins. TBDB Legacy Tools: Legacy tools from the former TBSGC site to search and browse the TB Genome

TBDreamDB : http://www.tbdreamdb.com/index.html Exciting news at TBDreamDB! The Database is currently undergoing complete redesign and data evaluation. The database will be converted to a fully searchable relational Database with a new look front end website as well. We hope to have the beta design up and running towards the end of 2011 early 2012. Any feedback would be great, simply contact us at the curator email below. Also please keep sending in any errors you find in the dataset. The data is being evaluated and corrected as it is being inserted into the new database format. -Your Emails are much appreciated!

Sanger Institute Welcome Trust: The Wellcome Trust and DEFRA has funded the Sanger Instiute to sequence reference genomes for Mycobacterium africanum http://www.sanger.ac.uk/resources/downloads/bacteria/mycobacterium.html Mycobacterium is a genus within the order Actinomycetales that comprises a large number of well characterised species, several of which are associated with human and animal disease such as tuberculosis and leprosy.

MGDD : http://mirna.jnu.ac.in/mgdd/index.html (Mycobacterial Genome Divergence Database) is a repository of genetic differences among different strains and species of organisms belonging to Mycobacterium tuberculosis complex. The differences are based on comparison of user chosen organisms. The query sequences are used to compare against subject sequences. The users can also choose the type of genetic divergence, that is, SNPs (Single Nucleotide Polymorphism), insertions, repeat expansion and divergent sequences that they are interested in. The results from a specific region (based on boundary defined by nucleotide sequence) or a specific gene can be displayed based on user's choice. Presently, the database has precomputed analysis from three different fully sequenced genomes of this complex. These are Mycobacterium tuberculosis H37Rv, Mycobacterium tuberculosis CDC1551 and Mycobacterium bovis AF2122/97. In future it will be updated with more strains species as fully sequenced genomes become available.

MTBreg: http://www.doe-mbi.ucla.edu/Services/MTBreg/ Proteins up- and down- regulated in Mycobacterium tuberculosis grown under conditions mimicking infection are included in this database. It also includes information on proteins that are regulated by selected transcription factors or other regulatory proteins. The literature data provided here is complimentary to the databases provided by Michael Strong that include recent TB computational functional linkages and the Prolinks Database by Peter Bowers.

MycoperonDB:  http://cdfd.org.in/mycoperondb/home.html is a database of computationaly predicted operons and transcriptional units of Mycobacteria. MycoperonDB is setup to provide operon and trancriptional unit information of different mycobacterial species at one place. At present, this database covers five species from mycobacteria and consist of an insilico model of operon organization of 18,053 genes . The operon information provides a basis and a refenece for a comprehensive understanding of how the transcriptional control are encoded in genome. The database has a user friendly web interface which takes simple sequence, gene name or ORF ID as an input and reports the transcription unit and operon associated with the input query.

GenoMycDB Browser: http://157.86.176.108/~catanho/genomycdb/ 

Several databases and computational tools have been created with the aim of organizing, integrating and analyzing the wealth of information generated by large-scale sequencing projects of mycobacterial genomes and those of other organisms. However, with very few exceptions, these databases and tools do not allow for massive and/or dynamic comparison of these data. GenoMycDB (http://www.dbbm.fiocruz.br/GenoMycDB) is a relational database built for large-scale comparative analyses of completely sequenced mycobacterial genomes, based on their predicted protein content. Its central structure is composed of the results obtained after pair-wise sequence alignments among all the predicted proteins coded by the genomes of six mycobacteria: Mycobacterium tuberculosis (strains H37Rv and CDC1551), M. bovis AF2122/97, M. avium subsp. paratuberculosis K10, M. leprae TN, and M. smegmatis MC2 155. The database stores the computed similarity parameters of every aligned pair, providing for each protein sequence the predicted subcellular localization, the assigned cluster of orthologous groups, the features of the corresponding gene, and links to several important databases. Tables containing pairs or groups of potential homologs between selected species/strains can be produced dynamically by user-defined criteria, based on one or multiple sequence similarity parameters. In addition, searches can be restricted according to the predicted subcellular localization of the protein, the DNA strand of the corresponding gene and/or the description of the protein. Massive data search and/or retrieval are available, and different ways of exporting the result are offered. GenoMycDB provides an on-line resource for the functional classification of mycobacterial proteins as well as for the analysis of genome structure, organization, and evolution.

TB Drug Target Database: http://www.bioinformatics.org/tbdtdb/ TB Drug Target Database contains information on the antituberculer drugs and the target proteins for the treatment of TB. Informations are avilable on the drugs and other possible inhibitors including their Structural details, also the analysis made to the target proteins are made available.

MIRU-VNTRplus web application: http://www.miru-vntrplus.org/MIRU/index.faces Molecular typing of bacteria from the Mycobacterium tuberculosis complex (MTBC) is essential for epidemiological purposes such as investigating the spreading of specific genotypes. Recently, mycobacterial interspersed repetitive units (MIRU) typing has become an important method, as it allows high-throughput, discriminatory and reproducible analysis of clinical isolates. MIRU is a MTBC specific name of a multiple locus VNTR [variable number of tandem repeats] analysis (MLVA) bacterial typing scheme. Because of its portable data format, MIRU typing has the potential to be a versatile tool for individual strain identification based on large reference databases. However, specialized bioinformatic web tools to analyze MIRU data and public reference databases are not available. To meet this need, a collection of 186 strains representing the major MTBC lineages was used for implementing a web server, MIRU-VNTRplus (http://www.miru-vntrplus.org/). For each strain species, lineage, and epidemiologic information was stored together with copy numbers of 24 MIRU loci, spoligotyping patterns, regions of difference (RD) profiles, single nucleotide polymorphisms (SNPs), susceptibility data, and IS6110 RFLP fingerprint images. Via the freely accessible MIRU-VNTRplus service users can compare their strain(s) with the reference strains for the assignment of MTBC species, lineages, and genotypes. For easier scientific communication a universal expanding nomenclature (MLVA MtbC15-9) to name different MIRU genotypes is maintained at the server. Comparisons can be based on MIRU-, spoligo-, RD-, SNP-, susceptibility-typing data, or by a combination of different data types. Several distance coefficients are available, including Jaccard's and categorical. Based upon the respective distance matrix, a dendrogram can be calculated using UPGMA or neighbor-joining clustering algorithms. The resulting trees may be exported in various data formats. MIRU-VNTRplus provides also functions for the user to analyze own strains without interrogating the reference database. Extensive documentation (manual and tutorials) of the service is available to make best use of all features.

Web resources for Mycobacterium tuberculosis

Mycobacterium tuberculosis the causative agent of tuberculosis (TB) still the major killer among human population, that has co-evolved with the human civilization so beautifully and so robustly, is really surprising. The war against tuberculosis is on and we are trying here to integrate major resources that are available for the disease and the organism all over the web to a single place. This is one of the open articles that would be upgraded time to time for acknowledging the recent development in the field, so that you as a used need not spend much time in data collection and integration. Hope this helps.   

General information about tuberculosis: 

Wiki page on tuberculosis on Wikipedia: http://en.wikipedia.org/wiki/Tuberculosis Tuberculosis, MTB, or TB (short for tubercle bacillus) is a common, and in many cases lethal, infectious disease caused by various strains of mycobacteria, usually Mycobacterium tuberculosis. Tuberculosis usually attacks the lungs but can also affect other parts of the body. It is spread through the air when people who have an active MTB infection cough, sneeze, or otherwise transmit their saliva through the air. Most infections in humans result in an asymptomatic, latent infection, and about one in ten latent infections eventually progress to active disease, which, if left untreated, kills more than 50% of those infected.

Wiki page on M. tuberculosis:   Mycobacterium tuberculosis (MTB) is a pathogenic bacterial species in the genus Mycobacterium and the causative agent of most cases of tuberculosis (TB). First discovered in 1882 by Robert Koch, M. tuberculosis has an unusual, waxy coating on its cell surface (primarily mycolic acid), which makes the cells impervious to Gram staining, so acid-fast detection techniques are used, instead. The physiology of M. tuberculosis is highly aerobic and requires high levels of oxygen. Primarily a pathogen of the mammalian respiratory system, MTB infects the lungs. The most frequently used diagnostic methods for TB are the tuberculin skin test, acid-fast stain, and chest radiographs. The M. tuberculosis genome was sequenced in 1998. 

Centers for Disease Control and Prevention (CDC) page on tuberculosis: Tuberculosis (TB) is a disease caused by a bacterium called Mycobacterium tuberculosis. The bacteria usually attack the lungs, but TB bacteria can attack any part of the body such as the kidney, spine, and brain. If not treated properly, TB disease can be fatal. TB disease was once the leading cause of death in the United States. Focusing on Data and Statistics, Diseases and Conditions, Emergencies and Disasters, Environmental Health, Healthy Living, Injury, Violence and Safety, Life Stages and Populations, Travelers' Health, Workplace Safety and Health,  Learn More »

Global TB database: http://www.who.int/tb/country/global_tb_database/en/index2.html Access the database to make data queries, interactive maps and country profiles. Country profiles: Country profiles provide key indicators, notification and treatment outcome data, and budget and financing graphs (for a subset of countries, including all high-burden countries). For high-burden countries, there is also a brief overview of TB control and epidemiology in the country, and a summary of achievements, challenges and planned activities related to implementing the first 5 components of the Stop TB Strategy.

U.S. National Library of Medicine Resource on tuberculosis:  Has a resourced on various fields of the disease, from the basics to the advanced including the disease data.

WHO page on tuberculosis: Too many people have undetected TB for too long; late detection of TB increases their risk of transmitting the disease to others, having poor health outcomes, or that they and their family will suffer distress and economic hardship. WHO has produced an overview of approaches, guidelines and tools to improve early detection of TB. It presents a framework to assess barriers for early detection and helps identify appropriate actions.

The Stop TB Partnership: The Stop TB Partnership is leading the way to a world without tuberculosis (TB), a disease that is curable but still kills three people every minute. Founded in 2001, the Partnership's mission is to serve every person who is vulnerable to TB and ensure that high-quality treatment is available to all who need it. Together our nearly 1000 partners are a collective force that is transforming the fight against TB in more than 100 countries. They include international and technical organizations, government programmes, research and funding agencies, foundations, NGOs, civil society and community groups and the private sector. We operate through a secretariat hosted by the World Health Organization (WHO) in Geneva, Switzerland and seven working groups whose role is to accelerate progress on access to TB diagnosis and treatment; research and development for new TB diagnostics, drugs and vaccines; and tackling drug resistant- and HIV-associated TB. The secretariat is governed by a coordinating board that sets strategic direction for the global fight against TB.

Binding free energy estimation

X-Score: http://sw16.im.med.umich.edu/software/xtool/  is basically a "scoring function", which computes the binding affinities of the given ligand molecules to their target protein. It can be applied to structure-based drug design studies in combination with molecular docking or de novo structure generation programs. X-Score is developed by Dr. Renxiao Wang in Dr. Shaomeng Wang's group at the Department of Internal Medicine, University of Michigan Medical School. The first paper that reported X-Score was published on Journal of Computer-Aided Molecular Design, 16: 11–26, 2002. Note that X-Score was formerly known as X-CScore for a short while. To learn more about the X-Score program please read the X-Score on-line manual. X-Score is released to the public for free. The latest release is X-Score v1.2. You can download the program by clicking the link below. You will go through a license agreement and fill in some necessary registration information. Once we have received your signed license agreement, we will send you instructions of how to log on our server and download the X-Score package. The X-Score v1.2 package includes the program (executable and source codes), user manual, examples, references and the protein-ligand complex data set originally used for developing X-Score. Click here to get the X-Score v1.2 package now!

eHiTS 2009 Binding Affinity Prediction: eHiTS has a novel scoring function that takes advantage of temperature factor information provided in PDB files to give a more complete picture of interactions.  All atoms in a PDB file have a temperature factor (B) associated with them.  This temperature factor indicates the how much the atom varies from the mean position.  Some atom positions are very precisely defined while others vary greatly, this has a very strong influence on the weight that should be assigned to the position. The novel approach in eHiTS uses the probability of the atom position during the statistic collection to create a statistically derived empirical scoring function. The eHiTS scoring function provides a scoring function that is smooth, and accurately represents a wide variety of problems at hand. One of the most recent studies with eHiTS Score 2009 was done using the PDBBind-2008 dataset. Please see a picture below for correlation of eHiTS Score to the experimental binding affinity.   http://www.simbiosys.ca/ehits/ehits_score.html

DrugScore-Online (DSX): http://pc1664.pharmazie.uni-marburg.de/drugscore/  DSX^ONLINE is a web-based user interface for the knowledge-based scoring function DSX. DSX^ONLINE enables you to score (putative) protein-ligand complexes of your interest, to browse and download the scoring results, and to visualize the per-atom score contributions (see section Visualization). DSX: DSX pair potentials are derived in analogy the the DrugScore formalism developed by Gohlke et al. However, another set of atom types is used and contact types are clustered to circumvent problems with the reference state. Torsion potentials and solvent accessible surface ratio potentials are derived using the same formalism. For more details see the upcoming publication which is currently in preparation. For more consistences, DSX always assigns its own atom types and hydrogens are not regarded. If you have ligand poses from GOLD docking where water molecules were included it is possible to consider the corresponding ON-marked waters in the solutions file. Please note that there are even more options (like considering solutions from a docking with flexible receptor residues) available in the DSX standalone version, which will be freely available after publication. Visualization of the per-atom score contributions: The visualization of the per-atom score contributions is an intuitive way to learn about differences between putative ligand geometries, the effects of scaffold modifications or about the importance of certain binding regions.

BAPPL serve: http://www.scfbio-iitd.res.in/software/drugdesign/bappl.jsp Binding Affinity Prediction of Protein-Ligand (BAPPL) server computes the binding free energy of a non-metallo protein-ligand complex using an all atom energy based empirical scoring function BAPPL server provides two methods as options: Method 1 : Input should be an energy minimized protein-ligand complex with hydrogens added, protonation states, partial atomic charges and van der Waals parameters (R* and ε) assigned for each atom. The server directly computes the binding affinity of the complex using the assigned parameters. For format specifications on the input, please refer to the README file. Method 2 : Input should be an energy minimized protein-ligand complex with hydrogens added and protonation states assigned. The net charge on the ligand should be specified. The server derives the partial atomic charges of the ligand using the AM1-BCC procedure and GAFF force field for van der Waals parameters. Cornell et al. force field is used to assign partial atomic charges and van der Waals parameters for the proteins. For format specifications on the input, please refer to the README file.

PreDDICTA:  http://www.scfbio-iitd.res.in/software/drugdesign/preddicta.jsp employs an all-atom energy based function for computing the binding affinity of a DNA oligomer with a non-covalently bound drug. The function has been validated against experimental binding free energies, ΔG^o bind and change in melting temperature of the DNA oligomer upon drug binding, ΔT_m, for 50 DNA Drug complexes. Click here to access the DNA-drug complex dataset. DNA is an important anticancer/antibiotic target and PreDDICTA can be employed to aid and expedite rational drug design attempts for DNA.Click here to know more about DNA Drug interaction How to use PreDDICTA: 1. Tool 1 incorporates the PreDDICTA energy function which calculates the electrostatics, van der Waals, rotational and translational entropy and hydration free energy change for the DNA-drug complex. These are summed to yield the total calculated binding energy which is converted to the binding free energy and ΔT_m based on the relations reported in. Input for this tool is a PDB file for any DNA-minor groove binder complex, conforming to the standard PDB format, as described in Input format 2. Tool 2 simply converts any number input as ΔT_m to the corresponding expected binding free energy, using the relation between these two quantities reported in. 3. Tool 3 converts any number input as binding free energy to the corresponding expected ΔT_m value, using the relation between these two quantities reported in.

PharmaGist: http://bioinfo3d.cs.tau.ac.il/pharma/about.html Predicting molecular interactions is a major goal in rational drug design. Pharmacophore, which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor, is important for achieving this goal. PharmaGist is a freely available web server for pharmacophore detection. The employed method is ligand based. It does not require the structure of the target receptor. Instead, the input is a set of structures of drug-like molecules that are known to bind to the receptor. We compute candidate pharmacophores by multiple flexible alignments of the input ligands. The main innovation of this approach is that the flexibility of the input ligands is handled explicitly and in deterministic manner within the alignment process. The method is highly efficient, where a typical run with up to 32 drug-like molecules takes seconds to a few minutes on a stardard PC. Another important characteristic of the method is the capability of detecting pharmacophores shared by different subsets of input molecules. This capability is a key advantage when the ligands belong to different binding modes or when the input contains outliers. The download version includes virtual screening capability. The performance of PharmaGist for virtual screening was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening.

IC₅₀-to-K_i converter: http://botdb.abcc.ncifcrf.gov/toxin/kiConverter.jsp The IC₅₀-to-K_i converter computes K_i values from experimentally determined IC₅₀ values for inhibitors of enzymes that obey classic Michaelis-Menten kinetics and of protein-ligand interactions. A new web-server tool estimates K_i values from experimentally determined IC₅₀ values for inhibitors of enzymes and of binding reactions between macromolecules (e.g. proteins, polynucleic acids) and ligands. This converter was developed to enable end users to help gauge the quality of the underlying assumptions used in these calculations which depend on the type of mechanism of inhibitor action and the concentrations of the interacting molecular species. Additional calculations are performed for nonclassical, tightly bound inhibitors of enzyme-substrate or of macromolecule-ligand systems in which free, rather than total concentrations of the reacting species are required. Required user-defined input values include the total enzyme (or another target molecule) and substrate (or ligand) concentrations, the K_m of the enzyme-substrate (or the K_d of the target-ligand) reaction, and the IC₅₀ value. Assumptions and caveats for these calculations are discussed along with examples taken from the literature. The host database for this converter contains kinetic constants and other data for inhibitors of the proteolytic clostridial neurotoxins (http://botdb.abcc.ncifcrf.gov/toxin/kiConverter.jsp).

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).