Automated Protein Structure Classification: A Survey

Classification of proteins based on their structure provides a valuable resource for studying protein structure, function and evolutionary relationships. With the rapidly increasing number of known protein structures, manual and semi-automatic classification is becoming ever more difficult and prohibitively slow. Therefore, there is a growing need for automated, accurate and efficient classification methods to generate classification databases or increase the speed and accuracy of semi-automatic techniques. Recognizing this need, several automated classification methods have been developed. In this survey, we overview recent developments in this area. We classify different methods based on their characteristics and compare their methodology, accuracy and efficiency. We then present a few open problems and explain future directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront

The Phyre2 web portal for protein modeling, prediction and analysis

Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission

SIFTER search: a web server for accurate phylogeny-based protein function prediction.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded

CCTOP: a Consensus Constrained TOPology prediction web server.

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided

Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations

We have developed the following web servers for protein structural modeling and analysis at : THUMBUP, UMDHMM(TMHP) and TUPS, predictors of transmembrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMM(TMHP)) and their combinations (TUPS); SPARKS 2.0 and SP(3), two profile–profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP(3)); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein–protein/peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively

Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations

We have developed the following web servers for protein structural modeling and analysis at http:// theory.med.buffalo.edu: THUMBUP, UMDHMMTMHP and TUPS, predictors of trans-membrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMMTMHP) and their combinations (TUPS); SPARKS 2.0 and SP3, two profile– profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP3); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein–protein/ peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively

Towards a Taxonomically Intelligent Phylogenetic Database

This note outlines some of the key intellectual obstacles that stand in the way of creating a usable phylogenetic database. These challenges include the need to accommodate multiple taxonomic names and classifications, and the need for tools to query trees in biologically meaningful ways. Until these problems are addressed, and a taxonomically intelligent phylogenetic database created, much of our phylogenetic knowledge will languish in the pages of journals