BOL: Related items

Tools for Protein-Protein Docking !

Poonam Mahapatra — Wed, 25 Apr 2018 05:15:53 -0500

Predicting the structure of protein–protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Following are the tools to predict the structure of protein–protein complexes:

3D-Dock Suite

Global rigid search: FFTShape complementarity and electrostatics

Re-scoring and clustering. Refinement of interface side-chains

3D-Garden

Global rigid search in ensamble

Shape complementarity and Lennard–Jones potential

Side chain and backbone dihedral refinement

DOT

Global rigid search: FFTShape complementarity, electrostatics and VDWNone

Escher NG

Global rigid searchShape complementarity, hydrogen bonds and electrostatic

Integrated in VEGA

GRAMM

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potential

Clustering of conformations

GRAMM-X

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potentialminimization and re-scoring with multiple filters

HEX

Global rigid search: Fourier correlation of spherical harmonics

Shape complementarity

HADDOCK

Global rigid searchElectrostatic ,VDW and desolvation energy termsMD simulated annealing refinement . Filtering based on external data.

ICM

Global rigid search: Monte CarloEmpirical scoring function

Clustering and selection of conformations. Refinement of interface side-chains and re-scoring

MolFit

Global rigid search: FFTShape complementarity

Clustering of good solutions, filtering using a priori information and small, local rigid rotations around selected conformations

PatchDock

Global rigid searchShape complementarity and atomic desolvation energy

Clustering of conformations

PyDock

Global rigid search:FFTShape complementarity

rescoring by binding electrostatics and desolvation energy

RosettaDock

Local rigid search: Monte Carlo with low and high resolution structure representation levels

Different scoring parameters for the different resolutions

ZDOCK

Global rigid search: FFTShape complementarity, desolvation energy, and electrostatics.

Energy minimization and re-scoringFree for academics

Point to note:

The proper treatment of flexibility in protein–protein docking is still an active field of research. You first should analyzed your proteins in order to define their conformational space and then choose the most suitable method for your docking problem.

JPred4: A Protein Secondary Structure Prediction Server

Poonam Mahapatra — Fri, 29 Dec 2017 16:14:28 -0600

JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction.

Address of the bookmark: http://www.compbio.dundee.ac.uk/jpred4/

Protein Subcellular Localization Prediction

Poonam Mahapatra — Thu, 01 Mar 2018 06:20:47 -0600

Assigning subcellular localization to a protein is an important step towards elucidating its interaction partners, function, and potential role(s) in the cellular machinery. Computational tools offer an attractive complement to time-consuming and laborious experimental methods.

http://abi.inf.uni-tuebingen.de/Services/YLoc/webloc.cgi

Address of the bookmark: https://abi.inf.uni-tuebingen.de/Research/Systems%20Biology/protein-subcellular-localization

Binding Site Prediction in Protein !

Poonam Mahapatra — Wed, 25 Apr 2018 04:35:57 -0500

The interaction between proteins and other molecules is fundamental to all biological functions. In this section we include tools that can assist in prediction of interaction sites on protein surface and tools for predicting the structure of the intermolecular complex formed between two or more molecules (docking).

Pockets Identification

CASTp

Automatic Identification of pockets and cavities in proteins structure, and quantitation of their volumes using Delaunay triangulation. Available also as PyMOL plugin

Pocket-Finder

Automatic identification of pockets and cavities in proteins structure, and quantitation of their volumes.

PocketPicker

Grid-based technique for the analysis of protein pockets. PocketPicker available as a plugin for PyMOL

Binding Site Prediction

ConSurf

Identification of functional regions in proteins by surface-mapping of phylogenetic information

CRESCENDO

Identification protein interaction sites. It uses sequence conservation patterns in homologous proteins to distinguish between residues that are conserved due to structural restraints from those due to functional restraints.

Ligand Binding Sites

3DLigandSite

The server utilizes protein-structure prediction to provide structural models of the binding site. Ligands bound to structures are superimposed onto the model and use to predict the binding site.

FINDSITE

A threading-based method for ligand-binding site prediction and functional annotation based on binding-site similarity across superimposed groups of threading templates.

LIGSITE^csc

Prediction of binding site by pocket identification using the Connolly surface and degree of conservation

metaPocketA meta server for ligand-binding site prediction. metaPocket use LIGSITE^csc, PASS, Q-SiteFinder and SURFNET

STRUM: structure-based prediction of protein stability changes upon single-point mutation

BioStar — Mon, 10 Sep 2018 13:21:49 -0500

STRUM is a method for predicting the fold stability change (ΔΔG) of protein molecules upon single-point nsSNP mutations. STRUM adopts a gradient boosting regression approch to train the Gibbs free-energy changes on a variety of features at different levels of sequence and structure properties. The unique characteristics of STRUM is the combination of sequence profiles with low-resolution structure models from protein structure prediction, which helps enhance the robustness and accuracy of the method and make it applicable to various protein seqences, including those without experimental structures

Address of the bookmark: https://zhanglab.ccmb.med.umich.edu/STRUM/

PolyPhen-2: Prediction of functional effects of human nsSNPs

Anjana — Mon, 23 May 2016 02:27:25 -0500

PolyPhen-2 (Polymorphism Phenotyping v2) is a tool which predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations.

Address of the bookmark: http://genetics.bwh.harvard.edu/pph2/

Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm)

Abhimanyu Singh — Thu, 29 Dec 2016 03:26:45 -0600

Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm) is a microbial (bacterial and archaeal) gene finding program developed at Oak Ridge National Laboratory and the University of Tennessee. Key features of Prodigal include:

Speed: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.
Accuracy: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).
Specificity: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.
GC-Content Indifferent: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the Pseudomonas aeruginosa curated annotations.
Metagenomic Version: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.
Ease of Use: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.
Open Source: Prodigal source code is freely available under the General Public License.

Download the latest version of Prodigal at the Prodigal github page.
or
Browse the wiki documenation.

Address of the bookmark: http://prodigal.ornl.gov/

Alien_Hunter : prediction of putative Horizontal Gene Transfer (HGT) events

Jit — Sun, 07 Jan 2018 19:11:18 -0600

Alien_hunter is an application for the prediction of putative Horizontal Gene Transfer (HGT) events with the implementation of Interpolated Variable Order Motifs (IVOMs).

An IVOM approach exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared to fixed-order methods. Optionally the predictions can be parsed into a 2-state 2nd order Hidden Markov Model (HMM), in a change-point detection framework, to optimize the localization of the boundaries of the predicted regions. The predictions (embl format) can be automatically loaded into the freely available Artemis genome viewer.

Address of the bookmark: http://www.sanger.ac.uk/science/tools/alien-hunter

Master Thesis: Trans-membrane topology prediction through Markov based decoders

Rahul Agarwal — Wed, 17 Jul 2013 16:16:17 -0500

Abstract:

Background/Motivation:

The dearth of structural information on alpha helical membrane protein (MPs) has hindered thus far the development of reliable knowledge –based potentials that can be used for automatic prediction of trans-membrane (TM) protein structure. While algorithm for identification of TM segments is available, modelling of the domains of alpha helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains.

Results: In a cross validated test on single sequences, our trans-membrane MM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 86 proteins with supervised topology. These results compare favorably with existing methods.

Source Code: Matlab

Conclusion/Implementation: Here discriminant data mining approach was used to predict the location and orientation of alpha helices in membrane-spanning proteins. It is based on a first order Markov model (MM) with an architecture that corresponds closely to the biological systems. The model is enriched with three types of states for the loop on the cytoplasmic side (outer loop), loop for the non-cytoplasmic side (inner side), and trans-membrane part. The closed association between the biological and Markov states allows us to infer which part of the model architecture are important to capture the information which encodes the membrane topology, and gain a better understanding of the mechanism and constraints involved. Predictor Model was established by various Markov decoder , and assignment of the membrane helix boundaries was apparent.

eQuant : energy-based quality assessment of protein

Shruti Paniwala — Sat, 24 Jun 2017 19:24:24 -0500

Protein structures are of varying quality. Especially, in-silico modeled structures are prone to contain serious errors, which limit the usefulness and reliability of these particular protein structures.

eQuant is a service for structure quality assessment of single proteins, which utilizes a coarse-grained energy model. The overall quality is calculated as well as the reliability of individual residues. You can submit single PDB files or archives containing a set of proteins.

https://biosciences.hs-mittweida.de/equant/

Address of the bookmark: https://biosciences.hs-mittweida.de/equant/