Tricking the Guard: Exploiting Plant Defense for Disease Susceptibility

From www.sciencemag.org - October 28, 4:06 PM 

Typically, pathogens deploy virulence effectors to disable defense. Plants defeat effectors with resistance proteins that guard effector targets. Here, we show that a pathogen exploits a resistance protein by activating it to confer susceptibility. Interactions of victorin, an effector produced by the necrotrophic fungus Cochliobolus victoriae, TRX-h5, a defense-associated thioredoxin, and LOV1, an Arabidopsis susceptibility protein, recapitulate the guard mechanism of plant defense. In LOV1's absence, victorin inhibits TRX-h5 resulting in compromised defense but not disease by C. victoriae. In LOV1's presence, victorin binding to TRX-h5 activates LOV1 and elicits a resistance-like response that confers disease susceptibility. We propose that victorin is or mimics a conventional pathogen virulence effector that was defeated by LOV1 and confers virulence to C. victoriae solely because it incites defense.

http://www.sciencemag.org/content/early/2012/10/18/science.1226743.long

批量Blast

批量Blast就是指多个序列的Blast。


blastall -p blastn -d BlastDB -i in_file.fasta  >blast_output

当in_file.fasta里面只有一个序列时，就是单个Blast啊。in_file.fasta也可以放多个Fasta格式的序列，这样子就是批量Blast了。

当然了，麻烦的是批量Blast之后的结果，一个的话我们可以看得了，当批量上千个时，我们不可能一个个看到的。这种小事情Blast早就想到了。这就引进了-m8参数。-b5参数是指显示匹配的前5个结果
blastall -p blastn -d BlastDB -i in_file.fasta  -m8 -b5 >blast_output

 推荐的命令行如下：
blastall -p blastn -d BlastDB -i in_file.fasta  -m8 -b5 -b1 -a2 -FF >blast_output
-a2参数是用二个CPU，加速。-FF是不过滤简单的重复序列和低复杂度的序列（默认是过滤的）。
本文详细出处参考：http://liucheng.name/1221/

How to Blast sequences against a genome

 How to Blast sequences against a genome
1. Get to a DOS window (e.g. by RUN command)

2. Type the following command to run Blast:

blastp -db databaseName -query contigFile -out filename -evalue e-value

For example:

blastp -db octdata -query maydata.fna -out myResults.txt -evalue .00001

• blastp invokes the program of comparing individual protein sequences to a database of protein sequences
• Other blast programs to consider:
  • blastn to compare nucleotide sequence(s) against a database of nucleotide sequences
  • blastp to compare protein sequence(s) against a database of protein sequences
  • blastx to compare nucleotide sequence(s) translated in all six reading frames against a database of protein sequences
  • tblastn to compare protein sequence(s) against a database of nucleotide sequences translated in all six reading frames
  • tblastx to compare nucleotide sequence(s) translated in all six reading frames against a database of nucleotide sequences translated in all six reading frames

• • .
• -db databaseName tells the program to use the databaseName you identified when you set up the database.
• -query contigFile tells the program to use the specified file as the query (input) to Blast. Give the full path if the file isn't in the same directory as Blast.
• -out filename tells the program to use the specified file as the output file.
• -evalue e-value tells the program to ignore matches that would occur by chance with an e-value(probability) greater than the decimal number given

3. Be prepared to wait a while. With only a few contigs, you shouldn't have to wait more than some 10's of seconds, but with the number of sequences we are using, the output may be hours in coming. The program gives no indication of its progress; it simply brings you back to a DOS prompt (>) when it's done.
4. Output could be modest when comparing two small sequences, but with lots of sequences, you can fill your disk drive with LOTS of output (dozens of megabytes).

5. How do you know whether the program worked? If you have a large output file (i.e. dozens of megabytes), don't try to read it into something like Word (you risk choking it). I don't think that Microsoft has any solution for us, but there is an ancient freeware program from the pre-Windows era that will do the job. Click here to download DR (standing for DiRectory). Put it in the Blast directory. Type DR at a DOS prompt to run.

6. To run DR, type DR at a DOS prompt to get a list of files in \Blast, then press the F10 key to sort the files by date of creation, then press the End key to go to the end of the list. You should see the file you just made. Press the Enter key to see the contents of the file (you can scroll through the file using the usual keys).

7. However you look at the output file, you should see something like: BLASTP 2.2.9 [May-01-2004]
Reference: Altschul, Stephen F., Thomas L. Madden, Alejandro A.
Schaffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J.
Lipman (1997),
"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res. 25:3389-3402. Query= Contig240-R (500 letters) Database: octdata.fna
1 sequences; 2,160,837 total letters

If so, you win!


From http://www.vcu.edu/csbc/bbsi/inst/archives/bioinf/RunLocalBlast.html

How to run a sequence through BLAST at TIGR

How to run a sequence through BLAST at TIGR Go to the TIGR Comprehensive Microbial Resource

Click on CMR Blast on the blue bar near the top
Click in the down arrow next to the Program window and choose the appropriate program. Click in the down arrow next to the Database window and choose the appropriate database. Paste your sequence into the window supplied for that purpose. Click the Submit BLAST job button. from internet

How to set up a local Blast database

Get to directory where you put Blast files
Type in the following:
makeblastdb -in file -out name -dbtype prot
-hash_index
(for a database of proteins)
OR
makeblastdb -in file -out name -dbtype nucl
-hash_index
(for a database of DNA or RNA)


What it means:

• makeblastdb invokes the Blast accessory program to create the database
• -in tells the program that the path that follows leads to the input file.
• -out tells the program that the characters that follow should be used as the name of the database (you can name it anything you want, so long as you use 8 or fewer legal characters).
• -dbtype prot Tells the program "the file does consist of protein sequences".
  -dbtype nucl tells the program "the file consists of nucleotide sequences"
• -hash_index tells the program "you should make an index of the identification numbers for the sequences" Frankly, I don't know what good the index does, but it's cheap.

WARNING #1: Upper/Lower case matters for the commands following -!

WARNING #2: Windows XP and NT users may experience trouble cutting and pasting the command line makeblastdb. Evidently the system does something strange to the hyphens. Type the command in instead.

From:http://www.vcu.edu/csbc/bbsi/inst/archives/bioinf/SetupLocalBlast.html

pymol and beta sheet

I found pymol have something strange about showing beta sheet. In the Rasmol, i could see the beta turn at least, but in beta sheet only helix and loop structure. are there any parameter difference between pymol and rasmol?

Can you image what will happen after the enzymes swimming overnight in the water?

Can you image what will happen after the enzymes swimming overnight in the water?

Do you have same experiences? How do you feel if you saw it in your lab?

Hypothesis

Just heard one story concerning the research. One professor has done some trangenic experiment, the purpose is to obtain resistant plant. when she got, it showed really high resistant at the beginning, however, after some days, the plant showed highly infected symptoms. When the student told this results to the supervisor, she did not believe it. Because, in her mind the plant should be resistant and infection only possible caused by contamination. The student has done three individual experiments and got same results, the supervisor still does not believe, because she already think the resistance is the truth, not the results of the experiment....
So, I think it could be really important to have some hypothesis before the research, but it should not affect our mind when we obtain results which does not support the hypothesis. The hypothesis could be right, and could be wrong too.

References............positive and negative

References are important especially when we discuss something,we need some references to support our results, hypothesis, speculations. However, sometime it also could kill the new idea which maybe become milestone in the research.

what is the relationship between Autophagy and Programmed cell death

what is the relationship between Autophagy and Programmed cell death?

are they connected or independently?

Autophagy has been studied on animals for quite long time, but it just started on plant since 2002. Until know there are quite a lot of area are unknown.

多基因克隆方法（多DNA片段组装）

To construct the final plasmid from the six starting materials (gene 1–4, replication origin and a selectable marker), SFs are first prepared by linking every two materials together, usually suing overlap-extension PCR (OE-PCR). So as shown in this figure, every SF has its 3′-half overlapped with the 5′-half of the next SF and the 5′-half of the first SF overlaps with the 3′-half of the last SF. A mixture of these SFs was denatured at 100°C to free all single strands. When it cools back down to room temperature, annealing between the overlaps would assemble the single strands one after another into a cycle which can be further repaired into double-stranded, closed circular molecule after transformation into the cells.
http://www.plosone.org/article/i ... ournal.pone.0030267

PCR-after-ligation method for cloning of multiple DNA inserts

PCR-after-ligation method for cloning of multiple DNA inserts
From  Sciencedirect
http://www.sciencedirect.com/sci ... i/S0003269710002198

Outline of the PCR-after-ligation method for efficient multiple DNA insert cloning. The DNA inserts and vector are digested with restriction enzymes to obtain compatible termini, followed by purification and ligation (step 1). Most of the products obtained should be either non-full-length DNA fragments or inverted repeat fragments by self-ligation. Then PCR is performed to amplify ligationproduct using flanking primers, and the DNA fragment with expected size is obtained by gel purification (step 2). The purified DNA fragment is inserted into the linearized vector, followed by transformation into E. coli (step 3).

Agarose gel shows the products obtained from PCR-after-ligation. Lane M: DNA size marker; lanes 1–3: PCRproducts amplified by using the ligationproducts with different molar ratios of vector/inserts as templates. The molar ratios of vector/inserts for lanes 1, 2, and 3 are 1:1:1:1:1, 1:2:3:4:5, and 1:3:1:3:1, respectively.

Gel extraction of DNA fragments running close together on your agarosegel

Gel extraction of DNA fragments running close together on your agarosegel
from fermentas

If the DNA fragment you would like to extract from a Gel is covert or run very close to a second DNA fragment you can perform a restriction digest with your DNA fragments before loading them on the gel. With the FastDigest® Restriction enzymes in a 5 min reaction.
Choose a restriction enzyme which only cuts in the DNA fragment you are not interested in. The fragment will now be much smaller and will not migrate together with your DNA fragment of interest any more. It is much easier to extract.
You can use the REviewer™ tool on the Fermentas homepage to paste in both sequences and analyse easy what FastDigest® enzyme to choose.

Closely running fragments can not be extracted without contamination

Digestion of second fragment leads to clear separation of fragment of interest

General recommendations to avoid RNase contamination

From fermentas

    Maintain a separate area, dedicated pipettors and reagents when working with RNA.
    Wear gloves when handling RNA and reagents to avoid contact with skin, which is a source of RNases. Change gloves frequently.
    Use sterile, RNase-free plastic tubes.
    Treat water and all solutions used for RNA purification and handling with DEPC. Add DEPC to 0.1% (v/v) final concentration; incubate overnight at room temperature and autoclave.
    High quality reagents must be used for buffer solutions. Buffers containing Tris should be prepared by dissolving Tris base in DEPC-treated water. Solutions containing DTT or nucleotides should be prepared using DEPC-treated water and be passed through a 0.2 µm filter for sterilization.
    Keep all kit components sealed when not in use and all tubes tightly closed during the transcription reaction.

PCR product clean-up prior to sequencing

PCR product clean-up prior to sequencing
From fermentas

The clean-up reaction removes unincorporated primers and degrades unincorporated nucleotides. The resulting PCR product is ready to use for sequencing without additional purification, e.g., using column purification kits.


    Prepare the following reaction mixture:
    PCR mixture (directly after completion of PCR)         5 µl
    Exonuclease I (#EN0581)         0.5 µl (10 u)
    FastAP™ Thermosensitive Alkaline Phosphatase (#EF0651) or
    Shrimp Alkaline Phosphatase (#EF0511)         1 µl (1 u)

    Mix well and incubate at 37°C for 15 min.
    Stop the reaction by heating the mixture at 85°C for 15 min.

Note

    Up to 5 µl of purified PCR products can be used directly for DNA sequencing without further purification.
    For reliable sequencing results there should not be non-specific PCR products.
    The protocol may be applied for clean-up of PCR products, generated by any thermophilic DNA polymerase or polymerase mix.
    The procedure is not recommended for downstream cloning applications.

Determination of RNA fragment lenghts in Northern Blots without labelled markers

Determination of RNA fragment lenghts in Northern Blots without labelled markers
From Fermentas

1.) Load the RNA marker on the Northern Gel beside your samples

Important: Use the same loading dye for the marker and your samples AND load same volumes. Adjust the volumes with DEPC water

2.) Run the gel and blot the RNA. Crosslink the RNA on the membrane
3.) Cut off the marker lane

Possibility A: Staining with methylene blue

4.) Put the membrane containing the marker lane into a clean, flat bowl. Add methylene blue solution. The membrane must be completely covered (ideally, the gauge should be around 0.8 cm)

5.) Shake the bowl very carefully for about 5-10 min

6.) When the RNA marker bands appear blue, discard the methylene blue solution and wash the membrane with tap water (optionally, 2 x 30 s). The methylene blue solution can be reused several times (store at 4°C)
7.) The marker lane can be a) scanned beside the developed blot or b) mark with a pen the marker bands on the blot (pen writings are mostly visible on fluorescence scanners)
Methylene blue solution: e.g. the Methylene blue solution from MRC (Molecular Research Center), distributed by Fermentas

Possibility B: Visualizing with UV light

4.) Put the membrane containing the marker lane on an UV screen. Mark with a pen the marker bands and the marker lane can be scanned beside the developed blot

Analysis of ligation products by agarose gel electrophoresis

fermentas
Analysis of ligation products by agarose gel electrophoresis

Ligation efficiency can be assessed by agarose gel electrophoresis of ligation reaction products. For sample loading, usage of SDS-supplemented loading dye, e.g., 6X DNA Loading Dye & SDS Solution (#R1151) is recommended to eliminate band shift due to T4 DNA ligase binding to DNA.

    Prepare the loading mixture:
    Ligation reaction product         10 µl
    6X DNA Loading Dye & SDS Solution (#R1151)（可以自己配）         2 µl
    Heat the sample for 10 min at 65°C and load.

Analysis of ligation reaction products on an agarose gel

ligation reaction         400 ng of vector and insert in total were used. Real ligation experiments normally use less DNA, therefore bands on a gel may appear at lower intensity.
M – GeneRuler™ DNA Ladder Mix (#SM0331).
1 – Mixture of DNA insert and vector in T4 DNA Ligase Buffer.
2 – Mixture of DNA insert and vector after the ligation sample loaded with 6X DNA Loading Dye (#R0611).
3 – Mixture of DNA insert and vector after the ligation sample loaded with 6X DNA Loading Dye & SDS Solution (#R1141).

Interpretation of results


    Appearance of higher molecular weight bands and decreased intensity of the vector and insert bands indicate successful ligation.
    Unchanged band pattern after ligation indicates unsuccessful ligation.

Chromatography

Chromatography lecture and my learning diaries

The chromatography is a collective terms for separation of mixtures techniques. The sample (Mixtures) is dissolved in the mobile phase, which carried it through the stationary phase. The separation was achieved by the difference of travel time/speeds. Several chromatography methods have been developed during the past time including paper chromatography, gas chromatography, and high performance liquid chromatography (HPLC). In this lecture, the teacher mainly introduced the principle of HPLC.
HPLC is a technique to separate a mixture of compounds in analytical chemistry and biochemistry with the purpose of identifying, quantifying and purifying the individual component. The instruments consists of PUMP (moves the mobile phase and sample through the column), injector (add samples), column and detector.
Different liquid chromatography including gel filtration, ion exchange, affinity and reversed phase chromatography are utilized according to the protein/peptide properties including size, net surface charge, hydrophobicity, respectively. Different methods can be combined according to the purpose, but the most important is keep it simple because quite a lot of samples are lost during the process.

Introduction to MS

Mass spectrometry (MS) can identify chemical composition of a sample based on the mass-to-charge ratio of charged particles. The instrument mainly contains three parts including ion source, mass analyzer and detector. The samples are ionized in the ion source using chemical or electron modes. Ions from the ion source were separated according to the m/z ratios in the analyzer part.
Two biological mass spectrometries were introduced during the lecture: Matix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI). Both of them belong to soft ionization methods. The irradiated substance is embedded in crystallized matrix in MALDI. In contrast of MALDI, the ionization in ESI is achieved by spraying a solution into an electrical field. MS has been applied in many fields of protein research including protein identification, molecular weight determination, characterisation of post-translation modifications, relative quantification and also protein complex, etc.
It is important to note that the protein needs to be in solution without salts and detergents and should be purified before the MW measurement. For protein identification, the protein needs to be digested into peptide before analyzing by MS. The identification can base on peptide mass finger print (PMF) and MS/MS data from one or more peptides.

Using Perl script to download sequence from database NCBI

最近发现ncbi上可以用perl scripts 下载序列。虽然是perl，但不需要你来输入命令，你只需输入关键词即可，再就是你的电脑安装了perl。
这就是Ebot!!

Ebot is an interactive tool that generates a Perl script that implements an E-utility pipeline. Ebot will guide you step by step in building the pipeline and then will download the Perl script to your computer.

http://www.ncbi.nlm.nih.gov/Class/PowerTools/eutils/ebot/ebot.cgi

Modk-Prototypes for Simultaneous Clustering of Gene Expression Data with Clinical Chemistry and Pathological Evaluations

Overview

The modk-prototypes algorithm, for clustering biological samples based on simultaneously considering microarray gene expression data and classes of known phenotypic variables such as clinical chemistry evaluations and histopathologic observations involves constructing an objective function with the sum of the squared Euclidean distances for numeric microarray and clinical chemistry data and simple matching for histopathology categorical values in order to measure dissimilarity of samples. Separate weighting terms are used for microarray, clinical chemistry and histopathology measurements to control the influence of each data domain on the clustering of the samples. The dynamic validity index for numeric data was modified with a category utility measure for determining the number of clusters in the data sets. A cluster’s prototype, formed from the mean of the values for numeric features and the mode of the categorical values of all the samples in the group, is representative of the phenotype of the cluster members.

Reference for Citing

Bushel PR, Wolfinger RD and Gibson G. Simultaneous Clustering of Gene Expression Data with Clinical Chemistry and Pathological Evaluations Reveals Phenotypic Prototypes. BMC Bioinformatics 2006.

Data Types and Format

Gene expression data needs to be formatted (short and wide) in a tab delimited text file with array observations as row values and gene, clinical chemistry and histopathology variables as column values. The first row is the column header, the second row is an integer denoting the data type (1 = gene expression, 2 = clinical chemistry measurement, 3 = histopathology observation). The order of the data in the file should be from data type 3 to 2, to 1 and be within individual groups or blocks.

Limitations

Only one categorical feature value per observation is permitted. A feature can exist as only one type of data. The application is optimized for clustering the samples and identifying phenotypic prototypes from the groups of them, not of the genes. The application is not guaranteed to find the optimal solution for the clustering of the samples, just the assignment of the samples to clusters according to the reduction of an objective function close to the global minimum.

Requirements

Modk-Prototypes is a set of Matlab functions and scripts tested in Matlab version 6.5.X.X R13.X for Windows (2000 and XP). You may encounter problems in other operating systems, platforms and/or other Matlab versions. The applications require the Matlab Statistics Toolbox Version 4.0, the Resampling Stats Toolbox Version 1.0 by Daniel T. Kaplan (Department of Mathematics and Computer Science, Macalester College, St. Paul, Minnesota, USA), the adjusted Rand Index function by Tijl De Bie(February 2003), the Matlab loadcell.m function to load mixed type data and the cell2csv.m function to convert cell arrays to comma separate value formatted files, both available at the Matlab Central File Exchange (File ID 1965 and 7601 respectively). Be sure to set the path of the Toolboxes in Matlab before running the application.

Downloads

Download the Matlab files and a stand-alone executable version of the program (http://www.niehs.nih.gov/research/resources/assets/docs/modkprototypesdistributionzip.zip) (101MB) . You will be required to register as a user of the application in order to gauge the distribution and to keep you informed of updates and revisions. A demo script, ReadMe file and sample data are provided in the distribution to help get you started with using the application. Report bugs, corrections and suggestions to Pierre Bushel .

Info:http://www.niehs.nih.gov/research/resources/software/biostatistics/modk/index.cfm

Clustering analysis of expression microarray data with Subio Platform and Basic Plug-in fwd

Subio Platform is a free, technology-independent omics data browser and software platform for sharing analysis results. Its integrated visualization tools greatly help handling complex omics data and revealing biological insight.

Basic Plug-ins adds analytical functions which are widely used for microarray data analysis. This movie shows how to use the hierarchical clustering analysis to over-viewing too many genes into clusters.

You can see it more clearly.
http://www.screencast.com/t/N2NjZjA2Nzkt

For more information.
http://www.subio.jp/products/basicplugin

Microarray Analysis with R

This is a short video introducing R as a language and showing some of its capabilities with microarray data.

protein analysis fwd

Again, a lot of information, but better there are some things you can choose than nothing to choose.

Protein analysis:

• 3MATRIX 1.0 – Motif in 3D
• 3MOTIF 2.0 – Motif in 3D
• Alphabet – Find Groups of Amino Acids that Co-occur in Columns Frequently
• Anchor – Predict Binding Regions in Proteins
• annot8r_physprop 0.1 – Predict Physical Properties of Peptides
• ANTHEPROT 6.0 – Protein Sequence Analysis
• APAT 1.4.1 – Automated Protein Annotation Tool
• APEX 1.1.0 – Quantitative Proteomics Tool
• assp 1.2 – Assess Protein Secondary Structure Prediction Accuracy
• BepiPred 1.0b – Linear B-cell epitopes
• BSpred – Predict Binding Site of Proteins
• CDPred 1.01 – Conserved Domain-based Prediction
• ChloroP 1.1 – Predict Chloroplast Transit Peptides
• CLANS 20101007 – Visualize Protein Families based on Pairwise Similarity
• CLC Protein Workbench 5.5.2 – Workbench for Protein Analysis
• CoBaltDB 1.0 – Complete Bacterial and Archaeal Orfeomes Subcellular Localization Database
• ConTest 1.0.1 – Test Constraints in Proteins
• CRC64 2006 – Improved 64-bit Cyclic Redundancy Check for Protein Sequences
• CS-PSeq-Gen 1.0 – Simulation of Protein Sequences under Constraints
• DASher 1.3.4 – Protein Sequence Client for DAS
• dasty 3.0.0.8 – Visualize Protein Sequence Feature Information using DAS
• DEPTH 2.8.7 – Measure Extent of Atom/Residue Burial within Protein
• DICROPROT 2000 – DICHROism of PROTeins
• DISCO 1.0 – Structure Determination of Protein Homo-oligomers
• DiscoTope 1.1a – Predicts Discontinuous B cell Epitope
• Discriminative HMMs – Find Discriminative Motif to Predict Protein Subcellular Localization
• DisEMBL 1.5 – Protein Disorder Prediction
• DISOPRED 2.43 – Intrinsic Protein Disorder Prediction
• DomainFinder 2.0.4 – Determine Dynamical Domains in Protein
• eBLOCKS – Database of Conserved Protein Regions
• EFICAz2 1.3 – Accurate Sequence based Approach to Enzyme Function Inference
• eSIGNAL 1.0 – Database of Medline MeSh Terms & Protein Motifs that Detect Signal Transduction Proteins
• estzmate – Assess Potential for Protein Coding Region
• FFAS 03 – Pretein Structure Prediction based on Profile-profile Comparison
• Folding@home 6.23 – Understand Protein Folding, Misfolding & Related Diseases
• FoldMiner 200312 – Structural Similarity Searches and Motif Discovery
• GPMAW 9.10 – Mass Spectrometric Analysis of Proteins and Peptides
• greylag 0.2.2 – Tandem Mass Spectrum Peptide Identification and Validation
• HBAT 1.1 – Hydrogen Bond Analysis Tool
• HMMER 3.0 – Protein Sequence Homology Search
• HMMSTR 20091212 – Protein Secondary Structure Prediction
• HMMSTR-CM – Protein Contact Map Prediction
• HMMSUM – Structure-based Substitution Matrices
• HMMTOP 2.9 – Predict Transmembrane Helices and Topology of Proteins
• Hydrophobicity – Display Hydropathic Character of Protein
• I-sites 2 – Predict the Local Structure of a Protein
• I-TASSER 1.1 – Protein Structure & Function Predictions
• InteroPorc 2.0.2 – Automatic Molecular Interaction Predictions
• InterProScan 5 – Protein Domains Identifier
• InterViewer 4.0 – Visualize Large-scale Protein Interaction Networks
• IUPred – Predict Intrinsically Unstructured Regions of proteins
• Jali 1.3 – Remote Homology Detection for Protein
• Kalign 2.03 / Kalignvu 2.1 / Mumsa 1.0 – Multiple Sequence Alignment , Viewer & Quality Assessment
• LipoP 1.0a – Prediction of Lipoproteins & Signal Peptides in Gram Negative Bacteria
• MASKER – Molecular Surface Area Calculator
• MDT 5.1 – Prepares a raw Frequency Table
• MEME 4.7.0 – Discovering Motifs within the Sequences
• MEMPACK – SVM Prediction of Membrane Helix Packing
• MEMSAT 3 – Transmembrane Protein Structure Prediction
• MEMSAT-SVM – SVM Transmembrane Protein Structure Prediction
• MetaTM 1.1 – Predicte Transmembrane Topology
• mkdom/Xdom 2 – Build the ProDom Database
• MODELESTIMATOR 1.1 – Estimate Amino Acid Replacement Rates
• MPEx 3.2 – Tool for Exploring Membrane Proteins
• MPtopoQuerier – Search Database of Membrane Proteins
• MultiLoc2 20091026 – Predict Animal, Plant and Fungal Protein Subcellular Localization
• NAP – Nucleotide Amino Acid Alignment
• NASCA 20110516 – Side-chain Resonance Assignment & NOE Assignment
• NetCGlyc 1.0c – Predict C-mannosylation
• NetChop 3.1b – Neural Network Predictions for Cleavage Sites of Human Proteasome
• NetCTL 1.2a – Predict CTL Epitopes in Protein Sequence
• NetMHC 3.0c – predict Binding of Peptides to MHC Class I Alleles
• NetMHCII 2.2 – Predict Binding of Peptides to MHC class II Alleles
• NetMHCIIpan 2.0a – predict Pan-specific Binding of Peptides to MHC class II HLA-DR Alleles
• NetMHCpan 2.4 – Predicts Binding of Peptides to Known MHC Molecule
• NetNGlyc 1.0a – N-linked glycosylation sites in human proteins
• NetOGlyc 3.1d – Predict Mucin-type O-glycosylation
• NetPhos 3.1 – Generic Phosphorylation Sites in Eukaryotic Proteins
• NetSurfP 1.0 – Protein Surface Accessibility & Secondary Structure Predictions
• NOXclass – Prediction of Protein-protein Interaction Types
• NQ-Flipper 2.7 – Validate Asparagine and Glutamine Side-chain Amide Rotamers in Protein Structures
• NRPSpredictor2 20110911 – Predict NRPS Adenylation Domain Specificity
• NucImport – Nuclear Protein Import and Localisation Signals Predictor
• NucPred 1.1 – Predicting Nuclear Localization of Proteins
• OSPREY 1.0 – Computational Structure-based Protein Design
• Osprey 1.2.0 – Protein-protein Interaction Networks Visualization System
• PairwiseStatSig 20081215 – Pairwise Statistical Significance
• PexSPAM 1.2 – Protein Sequence Feature Extraction
• pfilt – Sequence Filtering for Low-complexity, Coiled-coil and Biased Amino Acid Regions
• pGenTHREADER 8.7 – Protein Fold Recognition by Profile-profile Threading
• Phobius /PolyPhobius 1.05 – Combined Transmembrane Topology & Signal Peptide Predictor
• PISCES 1.0 – Protein Sequence Culling Server
• PIVOT 2.0 – Protein Interactions VisualizatiOn Tool
• PoSSuM / PoSSuMsearch 2.0 – Matching of PPSSMs using Enhanced Suffix Arrays
• Pratt 2.1 – Find Flexible Patterns in Unaligned Protein Sequences
• ProBias/BIAS – Detect Compositional Bias in Biological Sequences
• ProCon 1.1 – Localization & Visualization of Protein Conservation
• ProCope 1.2 – Protein Complex Prediction and Evaluation
• ProP 1.0c – Arginine & Lysine propeptide Cleavage Sites in Eukaryotic Protein
• ProQ 1.2 – Protein Quality Predictor
• ProTag 1.4 – Office Add-In for offering SmartTag of Protein
• PROTEAN – Torsion Space Molecular Simulations
• Protein Coverage Summarizer 1.3.4053 – Determine Percent of Residues in Protein Sequence
• ProteinVis 2.1.6 – Tree viewer for Hierarchical Clusterings of Proteins
• ProteoWizard 2.0 – Proteomics Data Analysis
• PROTMAP2D 1.2.2 – Calculation, Visualization & Comparison of Contact Map
• PSAAM – Protein Sequence Analysis And Modelling
• PSI Protein Classifier 1.0.1.49 – Automation of the PSI-BLAST Results Analysis
• PSIPRED 3.21 – Accurate Protein Secondary Structure Prediction
• PSORTb 3.0.3 – High-precision Localization Prediction for Bacterial Proteins
• PSSpred 1.0 – Multiple Neural Network Training program for Protein Secondary Strucure Prediction
• ps_scan 1.75 – PROSITE scanning program
• PyRosetta 2.0 – Python-based Interface to Rosetta Molecular Modeling Suite
• RDC-PANDA 1.0 – NMR NOE Assignment & Protein Structure Determination
• REPRO – Protein Repeats Analysis
• Rosetta 3.3 – Simulation and Design of Protein
• RW 1.0 – Protein Structure Modeling and Structure Decoy Recognition
• SAPTF 1.7 – Sequence Analysis Plugin Tool Framework
• SCANPS 2.3.11 – Protein Sequence Scanning Package
• SCWRL 4.0 – Prediction of Protein Side-chain Conformation
• SecretomeP 2.0 – Prediction of Non-classical Protein Secretion
• Sfixem – SFS Visualisation Tool in Java
• SherLoc2 20091026 – Predicting Protein Subcellular Localization
• SIFT 4.0.4 – Amino Acid Substitution Affects Protein Function
• SignalP 4.0c – Predict Signal Peptides
• SMAP 2.0 – Comparison & Similarity Search of Protein Three-dimensional Motif
• SPICE 0.9 – Protein Sequences, Structures & Annotations Browser
• SPICKER 20101229 – Cluster Protein Structures for Near-native Model Selection
• STORM 1.01 – Protein Analyses of BLAST, FASTA, Pfam and ProtParam
• SubMito 1.1 – Predict Protein Submitochondria Locations from Sequence
• SVMSEQ 1.0 – Protein Contact Prediction
• TargetP 1.1 – Predict Protein Subcellular Localisation
• TESTLoc – Protein Localization Prediction based on ESTs
• TM-score 20110130 – Calculate Similarity of Topologies of two Protein Structures
• TMHMM 2.0c – Prediction of Transmembrane Helices in Proteins
• Topcons – Consensus Prediction of Membrane Protein Topology
• TOPO2 – Create Transmembrane Proteins Images
• TRUST 1.0 – Repeat Detection Method
• Utopia 1.4.5 – Protein Analysis Suite
• VDJsolver 1.0b – Analysis of Human Immunoglobulin VDJ Recombination
• VEMS 5.18042011 – Analysis of MS-based Proteomics Data
• VHMPT – Viewer & Editor for Helical Membrane Protein Topologies
• WinPep 3.01 – Analysis of Aminoacid Sequences
• Wise 2.2.0 – Compare Protein Sequence to Genomic DNA Sequence
• YinOYang 1.2 – O-(beta)-GlcNAc Glycosylation and Yin-Yang Sites

Molecular Modeling Software fwd

Maybe too many, select the proper one is also challenging!!

modeling software (collected at http://www.mybiosoftware.com).

3DNA 2.0 – Vsualization of Three-Dimensional Nucleic Acid Structures

ActiveICM 1.1.6 – PowerPoint & Web Browsers Plugin to Display 3D Modules

AlloPathFinder 1.1 – Compute Likely Allosteric Pathways in Proteins

AlphaMol 1.0 – Tools for Biomolecular Geometry

AMBER 11 – Assisted Model Building with Energy Refinement

AmberTools 1.5 – Molecular Dynamics Simulation

ANTHEPROT 3D 1.0.162- Molecule Viewer to look at PDB files

APBS 1.3 – Evaluat Electrostatic Properties of Nanoscale Biomolecular System

ArgusLab 4.0.1 – Molecular Modeling, Graphics & Drug Design Program

Ascalaph 1.7.12 – Molecular Modelling Suite

AtVol 1.2 – Atomic Volume Calculation

AUDocker v1 – GUI for AutoDock Vina

Autobondrot 2.0 – Generate Multiple Molecular Conformation

AutoDock 4.2.3 / AutoDockTools 1.5.6 – Suite of Automated Docking Tools

AutoDock Vina 1.1.2 – Molecular Docking and Virtual Screening Program

Autodock/Vina plugin for PyMOL

AutoGrow 2.0.4 – Use AutoDock Vina in Protein Inhibitor Design

Avogadro 1.0.3 – Molecule Editor & Visualizer

AVP 1.3 – Calculate Protein Void Volumes and Packing Quality

AxPyMOL 1.0r1 – PowerPoint Plug-In for Embedding 3D Molecular Images & Animations

B 1.0alpha – Biomolecular Modeling Package

BALLView 2.0-r1 – Molecular Modeling & Visualization

Benchware® 3D Explorer 2.6 – 3D Chemical Visualization

Bioclipse 2.4 – Life Sciences Workbench

Biodesigner 0.75 – Molecular Modeling & Visualization

BioEditor 1.6.1 – Present Macromolecular Structure & Structural Annotation

BioViewer 1.5.7 – Read only version of BioEditor

Biskit 2.3.1 – Python Platform for Structural Bioinformatics

BndLst 1.6 – List Covalent & H-bonded Neighboring Atoms

C2A 1.0 – Coarse to Atomic

CCOMP 3.70 – Compare Ligand/Receptor Complexes

CHARMM 36 – Macromolecular Dynamics and Mechanics

ChemCraft 1.6 – Graphical Program for working with Quantum Chemistry Computation

Chemis3D 2.89b – Java 3D Molecular Viewer Applet

Chemitorium 3.5 – Molecule Editor & 3D Chemical Structure Viewer

Chime 2.6SP8 – Display 2D / 3D Molecules directly in Web Pages

ClashList 1.1 – Build Lists of van der Waals Clashes from PDB file

ClashScore 1.1 – R Script for VTF Percentile Plot

CLICK – Comparison of Biomolecular 3D Structures

Cluster 1.3 – Build Collections of Interacting Items

CN3D 4.3 – 3D Molecular Structure Viewer

CompuCell3D 3.6.0 – 3D Multiscale Multi-cell Simulations

Concoord 2.1 – Protein Structure Generation from Distance Constraint

CONSCRIPT – Generate Electron Density Isosurfaces in Protein Crystallography

Coot 0.6.2 – Macromolecular Model Building Tool

COSMOS 5.0 / COSMOS Viewer 3.0 – Computer Simulation & Visualisation of Molecular Structures

CueMol 2.0.1.161 – Macromolecular Structure Visualization

Dang 1.8 – Read PDB File & Generate Geometric Measurement Table

Dangle 0.63 – Read PDB File & Generate Geometric Measurement Table

DeepView 4.04 – Analyze Several Proteins 3D Structure at the Same Time

Desmond 2.4 – High-speed Molecular Dynamics Simulation

DINO 0.9.4 – Structural Biology Data 3D Visualization

DireX 0.5 – Low-resolution Structure Refinement

DOCK 6.4 – Docking Molecules to each other

DS Visualizer 3.1 & ActiveX Control 3.1 – Molecular Visualization

DTMM 4.2 – molecular modelling program

EDTSurf – Quick and Accurate Construction of Macromolecular Surfaces

EGO VIII – Molecular Dynamics Simulation

eMovie 1.04 – Make Molecular Movies

Facio 15.1.1 – 3D-Graphics program for Molecular Modeling and Visualization

FEATURE 2.0 – Examine Biological Structures

FiltRest3D – Filtering Protein Models by Fuzzy Restraints

FINDSITE 1.0 – Ligand-binding Site Prediction & Functional Annotation

FINDSITE-LHM 1.0 – Homology Modeling Approach to Flexible Ligand Docking

Flex-EM – Fitting and Refinement of Atomic Structures

FlexS 2.0.0 – Predict Ligand Superpositions

Flipkin 2.4 – Script to Make the Kinemages

FMA 0901 – Protein Functional Mode Analysis

FREEHELIX 98 – Analyze DNA bending

FRETsg 1.0 – Structure Building from Multiple FRET Distances

Friend 2.0 – Multiple Structure Visualization & Multiple Sequence Alignment

FTDock 2.0/ RPScore /MultiDock 1.0 – Protein Molecule 3D-Dock Suite

g0penMol 3.0 – Molecules Visualization & Analysis

Gabedit 2.4.0 – Graphical User Interface to Computational Chemistry Packages

GAP 1.2.14 – Geometric Analysis of Proteins

GDIS 0.90 – Visualization Program for Molecular and Periodic Systems

Ghemical 2.99.2 – Molecular Modeling and Editing Package for GNOME

GPGPUFRAGFOLD 0.1 – CUDA Fragment Assembly Based Protein Structure Prediction

Graphite-MicroMégas – Model in 3D Assemblies of Proteins and DNA

GROMACS 4.5.4 – Molecular Simulation

Gromita 1.06 – GUI for GROMACS

g_correlation 1.02 – Generalized Correlation for Biomolecular Dynamics

g_permute 1.12 – Permutation-Reduced Phase Space Density Compaction

HAAD – Quick and Accurate Hydrogen Atom Addition

Hollow 1.1 – Illustration software for Proteins

ICM-Browser 3.7 2b – Molecules & sequence alignments Visualization

iMol 0.40 – Molecular Visualization Application for Mac OS X

iMolview 1.1 – iPhone & iPad App for Browsing Protein, DNA & Drug Molecules in 3D

IMP 1.0 – Integrative Modeling Platform

ISD 1.1 – Bayesian NMR Structure Calculation

ISIM – Simulation of Ions in the Grand Canonical Ensemble

ISIM Interface 1.3.2 – Graphical Interface for running the program

ISIM

Jamberoo 11 – Cross-Platform Molecular Editor & Builder

Jimp 2 0.091 – Visualize and Manipulate Molecules

Jmol 12.0.50 – Java Viewer for Chemical Structures in 3D

JMVS 4 041122 – Java3D Molecular Visualisation System

jSim for Gromacs 0.63b – Graphical User Interface for Gromacs

JyMOL 1.0 – Java-based Molecular Visualization

Kin2Dcont 1.8 & Kin3Dcont 1.12 – Produce Molecule Contour Map

KiNG 2.20 – Three Dimensional Vector Graphics

KinImmerse 0.5 – Translate Kinemage Files into Software for Virtual Environment

LGscore/LGscore2 2.0 – Measure Quality of Protein Model

LifeExplorer 20100108 – 3D Navigation Tool for Cells

LigandScout 3.02 – Pharmacophore 3D Modeling

LoopTK 2.0.1 – Protein Loop Kinematic Toolkit

lrrr 1.4 beta1 – Determines Ligands on the Surface of Proteins

Mage 6.47 – Kinemage File 3D Display

Maptools 1.0 – Deal with Experimental (X-ray, EM) 3D Maps

MapVol 1.1 – Awk Script to Assign Volume by Atom

MaSK 1.3.0 – Molecular Modeling and Simulation Kit

MD Morphing 1.0 – Perform Molecular Dynamics Morphing Simulations

MDynaMix 5.2 – Molecular Dynamics Program

MetaTASSER – Protein Structure Prediction tool

MGLTools 1.5.6RC2 – Visualization & Analysis of Molecular Structures

MINT 3.2 – User Interface to Modeller

MMB 2.4 – Model the Structure and Dynamics of Macromolecules

mmPDBViewer 2009.3.20.4 – Protein Data Bank Viewer

MMPRO 0.7 – Molecule Visualization & Analysis Program

MMTK 2.7.4 – The Molecular Modelling Toolkit

MMTSB toolset – Multiscale Modeling Tools for Structural Biology

MMV 2.2.0 – Visualization of Molecules

ModeHunter 1.1 – Normal Mode Analysis of Coarse Grained Elastic Networks

MODELLER 9.10 – Comparative Protein Structure Modeling

Models@Home 4.5 – Distributed Computing Software for Protein Modeling

ModeRNA 1.6 – Comparative RNA 3D Modeling

ModPipe 2.2.0 – Calculate Protein Structure Model

ModRefiner 20111024 – High-resolution Protein Structure Refinement

ModView 0.903 – Visualization of Multiple Protein Sequences & Structures

MOIL 12.0.3671 – Molecular Modeling Software

Móilín 2011 – Molecular Modelling Software

Mol2Mol 5.6.3 – Molecule File Manipulation & Conversion

MOLA – System for Virtual Screening using AutoDock4/Vina on Computer Clusters

MolIDE 1.7 – Protein 3D Homology Modeling

MolPOV 2.0.8 – PDB to POV File Converter & Visualizer

MolScript 2.1.2 – Display Molecular 3D Structures

MolTalk 3.0.1 – Computational Environment for Structural Bioinformatics

MoluCAD 1.034 – Molecular Modeling & Visualization Tool

MoSART pr – NMR-based Biomolecular Structure Computation

MSMExplorer 0.02 – Visualization Application for Markov State Models for Folding

MVP/MVP-Fit 2.0 – Macromolecular Visualization and Processing

NAContacts 2.5 – Write Contact Information between Nucleic Acid Bases

NAST 1.0 – Nucleic Acid Simulation Tool

NMFF – Normal Mode Flexible Fitting

NOC 3.01 – Molecular Explorer for Protein Structure Visualization

OB Score 1.0 – Structural Genomics Target Ranking

OpenAstexViewer 3.0 – Software for Molecular Visualisation

OpenMM 3.1.1 – Library for Molecular Modeling Simulation

OpenMM Zephyr 2.0.3 – Molecular Simulation Application

OpenStructure 1.1.0 – Computational Structural Biology Framework

ORTEP-III 1.03 – Crystal Structure Illustration

Oscail 2011 – Crystallography & Molecular Modelling

OVOP 1.0 – View Generation for Protein Structures

PaDEL-ADV 1.6 – Facilitate Virtual Screening with AutoDock Vina

PDB Editor 090203 – PDB (Protein Data Bank) File Editor

PDBCNS 2.0 – Interconvert Atom Names between PDB & CNS formats

PDBlib 2.2 – C++ Macromolecular Class Library

PDBpy – Python Parser for PDB files

PeppeR 0.8.160 – Graphical 3D-EM DAS Client

PHENIX 1.7 – Python-based Hierarchical ENvironment for Integrated Xtallography

PovChem 2.1.1 – Chemical Visualization & Illustration & POV File Converter

Prekin 6.51 – Prepares Kinemages Files from PDB-format Files

PREPI 0.9 – Molecular 3D Representation

Probe 2.12 – Evaluate Atomic Packing & Contact Analysis

ProbeWithO 0.9.0 – Use Small Probe Contact Dots Within O

ProFit 3.1 – Protein Least Squares Fitting

ProSa 2003 – Protein Structure Research Tool

PROTEAND 1.0 – Display Macromolecular Structural Uncertainty

Protein Explorer 2.80 – Visualize 3D Structures of Macromolecules

ProteinGlimpse 1.6 – Visualize Macromolecules Retrieved from PDB

ProteinScope 1.0.5 – 3D Protein Structure Viewer

ProteinShader beta 0.9.4 – Illustrative Rendering of Macromolecules

ProtoMol 3.3 – Molecular Dynamics (MD) Simulation

PULCHRA 3.06 – All-atom Reconstruction & Refinement of Reduced Protein Models

pymacs 0.4 – Python Module for Dealing with Structure files from GROMACS

PyMOL 1.4.1 – Molecular Visualization System

PyOpenMM 3.0 – Python API of OpenMM Library

PyRx 0.8 – Virtual Screening software for Computer-Aided Drug Design

QTree 2.3 – Graphics Rendering using Quad-tree Algorithm

QuickPDB 20021101 – Java Applet for quickly viewing PROTEIN PDB Structure

QuteMol 0.41 – Molecular Visualization System

R.E.D. III.4 – Calculate RESP Charges

Ramachandran Plot Explorer 1.0 – Interactive Cross-platform Protein Viewer

Rasmol 2.7.5.2 – Molecular Graphics Visualisation

Raster3D 3.0-2 – Generate High Quality Raster Images of Proteins or other Molecules

RasTop 2.2 – Molecular Visualization Software Adapted for Rasmol

RDCvis 1.02 – Residual Dipolar Coupling Visualizer

Reduce 3.14 – Add Hydrogens to PDB Molecular Structure File

Remediator 1.60 – Convert PDB Files between PDBv2.3 & PDBv3.2 Formats

REMO 1.0 – Construct Full-atom Protein Models from C-alpha Traces

Ribbons 3.32 – Molecular Graphics Software

RIP 1.0 – Accelerated Molecular Dynamics

RNABC 1.11 – RNA Backbone Correction

Rosetta@home – Grid Software for Protein Folding

rTools 0.7.2 – PyMOL plugins

ScoreDotsAtAtom 1.0 – Bookkeep All-atom Contact Dot

Sculptor 2.0.2 – Docking & Visualization for Atomic Structures

SEQMOL 3.4.6 – Sequence Alignment & PDB Structure Analysis Utility

SHIFTS 4.3 – Predict Nitrogen, Carbon & Proton Chemical Shifts in Proteins

SimTK Core 2.1 – Simbios Biosimulation ToolKit

Situs 2.6 – Integration of Multi-Resolution Structures

Solvate 1.0 – Construct Atomic Solvent Environment Model for Given Atomic Macromolecule Model

StrukEd – Editor for Molecules & 3D Viewer

Suitename 0.3 – RNA Conformer

Superficial 1.2 – Identification of Potential Epitopes or Binding Sites

SuperMimic – Fit Peptide Mimetics into Protein Structures

TASSER-Lite 1.0 – Protein Structure Modeling tool

tCONCOORD 1.0 – Predict Protein Conformational Flexibility

Tessellator 1.0 – Software for Tessellation of 3D Volume in Biological Molecule

Theseus 1.6.1 – Superimpose Macromolecular Structures

THREADER 3.51 – Protein Fold Recognition by Threading

TimeScapes 1.2.2 – Molecular Dynamics Analysis tool

Tinker 5.1.09 – Software Tools for Molecular Design

Torsions – Calculates Backbone Torsion Angles from a PDB file

UCSF Chimera 1.5.3 – Molecular Modeling System

VcPpt – Protein Ligend Docking & in silico High-throughput Screening

VEGA ZZ 2.4.0 – Molecular Modeling Toolkit

VESTA 3.0 – 3D Visualization System for Electronic & Structural Analysis

Viewmol 2.4.1 – Molecule Viewer

ViewMol3D 5.00.alpha.3 – 3D OpenGL Viewer for Molecular Structures

VisProt3DS 3.03 – Stereoscopic Visual Analyzer of Biological Macromolecules

VMD 1.9 – Molecular Graphics Viewer

Voronoia 1.0 – Analyse Packing of Protein Structures

WebMol – JAVA PDB Viewer

WPDB 2.2 – The Protein Data Bank Through Windows

XCrySDen 1.5.24 – Crystalline & Molecular Structure Visualisation

XmMol 3.1 – Macromolecular Visualization and Modeling tool

XtalView 4.0 – Molecular Graphics Program

YAKUSA – Scan Structural database with Query Protein Structure

YASARA 11.9.18 – Molecular Graphics, Modeling & Simulation program

YUP 1.080827 / Yammp 2 – Molecular Simulation

Zodiac 0.6.5 – Molecular Modelling suite for Drug Design

HyperBalls Viewer – Molecular structures and trajectories visualization using GPU rendering