Tuesday, December 27, 2011

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).


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.


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

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


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


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')




x = range(151)

for j in x:


    for i in s:

        infile = open(i,'r')

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


        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)



##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.


 ##Start of the code##

for(i in 1:length(CSV_Files))
    for(j in 21:171)


##End of the code##

Sunday, December 4, 2011

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 pKa 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.