Fold recognition software tools | Protein structure data analysis
Recognizing the correct structural fold among known template protein structures for a target protein (i.e. fold recognition) is essential for template-based protein structure modeling. Since the fold recognition problem can be defined as a binary classification problem of predicting whether or not the unknown fold of a target protein is similar to an already known template protein structure in a library, machine learning methods have been effectively applied to tackle this problem.
Finds evolutionarily related proteins and/or domains, close and remote homologs. HMMER is based on profile hidden Markov models (HMMs) and gathers four algorithms: phmmer, hmmscan, hmmsearch, and jackhmmer. It assists users in the detection of protein sequence conservation, function, and evolution. This tool can be useful for functional annotations. It offers a solution to make research on protein sequence databases.
Detects homology. FFAS includes adding optimized structural features (experimental or predicted), ‘symmetrical’ Z-score calculation and re-ranking the templates with a neural network. It has high success rate at the Structural Classification of Proteins (SCOP) family, superfamily and fold levels. The tool was tested on the Lindahl benchmark set for fold recognition and showed superior success rate on the family and superfamily levels.
A program for recognizing distant homologues by sequence-structure comparison. It utilizes environment-specific substitution tables and structure-dependent gap penalties, where scores for amino acid matching and insertions/deletions are evaluated depending on the local environment of each amino acid residue in a known structure. Given a query sequence (or a sequence alignment), FUGUE scans a database of structural profiles, calculates the sequence-structure compatibility scores and produces a list of potential homologues and alignments.
A web app to predict protein disorder. Metadisorder is a meta method which means that it tries to calculate "consensus" from results returned by other methods, consisting in 4 parts: matedisorder, metadirsorder3d (to fold recognition methods), metadisordermf (optimize components integration using a combination of the first two tools) and metadisordermd2 (metadisordermd with different scoring function). Metadisorder is one of the best predictors of protein disorder, evaluated during independent tests (CASP8 and CASP9).
A protein structure prediction server excelling at predicting 3D structures for protein sequences without close homologs in the Protein Data Bank (PDB). Given an input sequence, RaptorX predicts its secondary and tertiary structures as well as solvent accessibility and disordered regions. RaptorX also assigns the following confidence scores to indicate the quality of a predicted 3D model: P-value for the relative global quality, GDT (global distance test) and uGDT (un-normalized GDT) for the absolute global quality, and RMSD for the absolute local quality of each residue in the model.
A local threading meta-server, for quick and automated predictions of protein tertiary structures and spatial constraints. LOMETS generates 3D models by collecting high-scoring target-to-template alignments from 9 locally-installed threading programs (FFAS-3D, HHsearch, MUSTER, pGenTHREADER, PPAS, PRC, PROSPECT2, SP3, and SPARKS-X).
A computer algorithm for ab initio protein folding and protein structure prediction, which aims to construct the correct protein 3D model from amino acid sequence only. QUARK models are built from small fragments (1-20 residues long) by replica-exchange Monte Carlo simulation under the guide of an atomic-level knowledge-based force field. QUARK was ranked as the No 1 server in Free-modeling (FM) in CASP9 and CASP10 experiments. Since no global template information is used in QUARK simulation, the server is suitable for proteins which are considered without homologous templates.