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Contact map detection software tools | Protein structure data analysis

Protein residue–residue contact prediction is the problem of predicting whether any two residues in a protein sequence are spatially close to each other in the folded 3D structure. Contacts occurring between sequentially distant residues, i.e. long-range contacts, impose strong constraints on the 3D structure of a protein and are particularly important for structural analyses, understanding the folding process and predicting the 3D structure.

Source text:
(Di Lena et al., 2012) Deep architectures for protein contact map prediction. Bioinformatics.

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GREMLIN / Generative REgularized ModeLs of proteINs
A method to learn an undirected probabilistic graphical model of the amino acid composition within the multiple sequence alignments. GREMLIN employs regularization to penalize complex models and thus reduce the tendency to over-fit the data. The strength of measured co-evolution is strongly predictive of residue-residue contacts in the 3D structure of the protein. GREMLIN has also been referred to as a maximum-entropy model or a global statistical model.
SMS / STING Millennium Suite
Provides a variety of algorithms and validated data, wrapped-up in a user friendly web interface. STING Millennium Suite (SMS) is a new web-based suite of programs and databases providing visualization and a complex analysis of molecular sequence and structure for the data deposited at the Protein Data Bank (PDB). It is described in terms of a solution that brings together a number of protein analysis tools at a single web server. SMS is a very powerful tool which enables a quick estimate of the level of engagement for each amino acid within its own protein chain and functionally more importantly, in the mechanism of binding to substrate and/or inhibitor.
CMWeb / Contact Map Web Viewer
An online tool for studying basic properties of residue-residue contact formation and contact clusters. CMWeb can be used for inspecting proteins only with 3D structure, for visualizing contact maps, for linking contacts and displaying them in 3D structures and in multiple sequence alignments, for predicting residue contacts using various contact prediction methods (currently five prediction methods are implemented) and for calculating various statistics on contacts.
RBO Aleph
A protein structure prediction web server for template-based modeling, protein contact prediction and ab initio structure prediction. The server has a strong emphasis on modeling difficult protein targets for which templates cannot be detected. RBO Aleph's unique features are (i) the use of combined evolutionary and physicochemical information to perform residue-residue contact prediction and (ii) leveraging this contact information effectively in conformational space search.
A web server for ab initio protein contact and tertiary structure prediction without using any templates. In particular, CoinFold predicts contacts by joint evolutionary coupling (EC) analysis via group graphical lasso (GGL) of multiple (distantly) related protein families which may have divergent sequences but similar folds (i.e. co-evolution patterns). By enforcing co-evolution pattern consistency among a set of related families, we can significantly improve contact prediction accuracy. CoinFold further improves prediction accuracy by integrating supervised learning with this joint EC analysis. Since EC analysis and supervised learning use different types of information, their combination leads to much better prediction. Finally, CoinFold predicts secondary structure using a new in-house tool DeepCNF and then tertiary structure by feeding predicted contacts and secondary structure to the Crystallography & NMR System (CNS) software package, but without using any templates. It significantly outperforms other servers of similar category in both contact prediction and 3D model prediction, especially for those proteins without very good templates.
Predicts inter-protein residue-residue contacts without using any structural templates. ComplexContact uses a deep learning (DL) model for intra-protein contact prediction and utilizes sequential features and contact occurrence patterns to decrease the requirement of sequence homologs and to ameliorate accuracy. The DL model has been trained from single-chain proteins to predict contacts for pair proteins. It also applies a phylogeny-based method to pinpoint interlogs and build better multiple sequence alignments (MSA).
An algorithm for protein residue-residue contact prediction. SVM-SEQ generates the predictions only based on sequence information, where secondary structures, solvent accessibility, sequence profile and sequence separations derived from the sequences are trained on contact maps by the support vector machine (SVM) technique. Based on the same number of predictions, the accuracy of the contact prediction by SVM-SEQ is comparable to the top sequence-based machine-learning methods published in the literature and in recently CASP7 experiments.
Combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred.
A set of Random Forest algorithm based models, for predicting residue–residue contact maps. ProC_S3 is based on a collection of 1490 non–redundant, high-resolution protein structures using >1280 sequence-based features. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L/5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation ≥24). It helps in better understanding the biophysics behind the problem.
STING Contacts
Identifies and visualizes amino acid contacts within protein structure and across protein interfaces. STING Contacts calculates atomic contacts among amino acids based on a table of predefined pairs of the atom types and their distances, and then display them using number of different forms. The inventory of currently listed contact includes hydrogen bonds (in nine different flavors), hydrophobic interactions, charge–charge interactions, aromatic stacking and disulfide bonds.
Analyzes contact maps. PROTMAP2D aims to ease comparative analysis of protein structures, mainly in case which the mutual position and interactions of residues have to been take into account. The software allows quantitative and qualitative analysis of contact maps of protein structures such as individual models, different models of the same protein, ensembles of structures and trajectories. In addition, the software can calculate full distance maps for individual models and converted it into contact maps.
Contact maps are a convenient method for the structural biologist to identify structural features through two-dimensional simplification. Binary (yes/no) contact maps with a single cutoff distance can be generalized to show continuous distance ranges. RRDistMaps is a UCSF Chimera tool to compute such generalized maps in order to analyze pairwise variations in intramolecular contacts. An interactive utility, RRDistMaps visualizes conformational changes, both local (e.g., binding-site residues), and global (e.g., hinge motion), between unbound and bound proteins through distance patterns. Users can target residue pairs in RRDistMaps for further navigation in Chimera. The interface contains the unique features of identifying long-range residue motion and aligning sequences to simultaneously compare distance maps.
R2C / Residue-Residue contact
A residue-residue contact predictor that combines machine learning-based and correlated mutation analysis-based methods, together with a two-dimensional Gaussian noise filter to enhance the long-range residue contact prediction. Our results show that the outputs from the machine learning-based method are concentrated with better performance on short-range contacts; while for correlated mutation analysis-based approach, the predictions are widespread with higher accuracy on long-range contacts. An effective query-driven dynamic fusion strategy proposed here takes full advantages of the two different methods, resulting in an impressive overall accuracy improvement.
Uses the Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. NeBcon (Neural-network and Bayes-classifier based contact prediction) is an algorithm for sequence-based protein contact prediction, built on multiple contact prediction programs, which are machine-learning, co-evolution and meta-server based. It first uses the naive Bayes classifier to calculate the posterior probability of multiple contact predictors. Neural Network is then used to train the actual contact maps against the secondary structure, solvent accessibility, Shannon entropy of multiple sequence alignments, in combination with the posterior probability scores calculated from the predictors.
A Bayesian statistical model using knob-socket information that maximizes contact prediction accuracy from a combination of priors and posteriors. The resulting program was then compared over 3 different and difficult test sets to gauge the overall performance on contact predictions against current leading methods. The first is the set of 150 structural families used to comprehensively compare a number of current contact prediction routines that was originally used to characterize PSICOV. The last 2 are the more challenging sets of structures from CASP10.
pyMDmix / python Molecular Dynamics simulations with mixed solvents
Identifies high affinity interaction spots over macromolecular systems by means of molecular dynamics simulations using solvent mixtures as solvation conditions. pyMDMix is a python module and a user interface that aims to ease the application of such technique. It allows an easy set up of several simulations for the same system under different conditions: solvent, temperature, restraining schemes, etc. Moreover, after simulations are done, many analysis tools will help to quality check and extract useful information from these simulations in a aqueous-organic environments.
plmDCA / pseudolikelihood maximization Direct-Coupling Analysis
Separates direct from indirect interactions in the context of protein sequences. plmDCA was applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins. It outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. plmDCA should provide a natural choice for analysts interested in applying state-of-the-art protein structure prediction (PSP) to their protein of interest, as well as for researchers looking to further extend the theory and practical applicability of direct-coupling analysis (DCA).
MemConP / Membrane Contact Prediction
A contact prediction method for α-helical transmembrane proteins, in which evolutionary couplings are combined with a machine learning approach. MemConP achieves a substantially improved accuracy (precision: 56.0%, recall: 17.5%, MCC: 0.288) compared to the use of either machine learning or co-evolution methods alone. The method also achieves 91.4% precision, 42.1% recall and a MCC of 0.490 in predicting helix-helix interactions based on predicted contacts. The approach was trained and rigorously benchmarked by cross-validation and independent testing on up-to-date non-redundant datasets of 90 and 30 experimental three dimensional structures, respectively.
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