Dataset Information

Constructing sequence-dependent protein models using coevolutionary information.

ABSTRACT: Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid sites within the multiple sequence alignment of a protein family. Here, we use the maximum entropy-based approach called mean field Direct Coupling Analysis (mfDCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family. We use the inferred pairwise statistical couplings to generate the sequence-dependent heterogeneous interaction energies of a structure-based model (SBM) where only native contacts are considered. Considering the ribosomal S6 protein and its circular permutants as well as the SH3 protein, we demonstrate that these models quantitatively agree with experimental data on folding mechanisms. This work serves as a new framework for generating coevolutionary data-enriched models that can potentially be used to engineer key functional motions and novel interactions in protein systems.

SUBMITTER: Cheng RR

PROVIDER: S-EPMC4815312 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Constructing sequence-dependent protein models using coevolutionary information.

Cheng Ryan R RR Raghunathan Mohit M Noel Jeffrey K JK Onuchic José N JN

Protein science : a publication of the Protein Society 20150810 1

Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid sites within the multiple sequence alignment of a protein family. Here, we use the maximum entropy-bas ...[more]

PMID: 26223372

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Project description:BACKGROUND: Existing sequence alignment algorithms use heuristic scoring schemes based on biological expertise, which cannot be used as objective distance metrics. As a result one relies on crude measures, like the p- or log-det distances, or makes explicit, and often too simplistic, a priori assumptions about sequence evolution. Information theory provides an alternative, in the form of mutual information (MI). MI is, in principle, an objective and model independent similarity measure, but it is not widely used in this context and no algorithm for extracting MI from a given alignment (without assuming an evolutionary model) is known. MI can be estimated without alignments, by concatenating and zipping sequences, but so far this has only produced estimates with uncontrolled errors, despite the fact that the normalized compression distance based on it has shown promising results. RESULTS: We describe a simple approach to get robust estimates of MI from global pairwise alignments. Our main result uses algorithmic (Kolmogorov) information theory, but we show that similar results can also be obtained from Shannon theory. For animal mitochondrial DNA our approach uses the alignments made by popular global alignment algorithms to produce MI estimates that are strikingly close to estimates obtained from the alignment free methods mentioned above. We point out that, due to the fact that it is not additive, normalized compression distance is not an optimal metric for phylogenetics but we propose a simple modification that overcomes the issue of additivity. We test several versions of our MI based distance measures on a large number of randomly chosen quartets and demonstrate that they all perform better than traditional measures like the Kimura or log-det (resp. paralinear) distances. CONCLUSIONS: Several versions of MI based distances outperform conventional distances in distance-based phylogeny. Even a simplified version based on single letter Shannon entropies, which can be easily incorporated in existing software packages, gave superior results throughout the entire animal kingdom. But we see the main virtue of our approach in a more general way. For example, it can also help to judge the relative merits of different alignment algorithms, by estimating the significance of specific alignments. It strongly suggests that information theory concepts can be exploited further in sequence analysis.

Project description:BackgroundRNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.ResultsWe propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.ConclusionsOur experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.

Project description:BACKGROUND: A central challenge to understanding the ecological and biogeochemical roles of microorganisms in natural and human engineered ecosystems is the reconstruction of metabolic interaction networks from environmental sequence information. The dominant paradigm in metabolic reconstruction is to assign functional annotations using BLAST. Functional annotations are then projected onto symbolic representations of metabolism in the form of KEGG pathways or SEED subsystems. RESULTS: Here we present MetaPathways, an open source pipeline for pathway inference that uses the PathoLogic algorithm to map functional annotations onto the MetaCyc collection of reactions and pathways, and construct environmental Pathway/Genome Databases (ePGDBs) compatible with the editing and navigation features of Pathway Tools. The pipeline accepts assembled or unassembled nucleotide sequences, performs quality assessment and control, predicts and annotates noncoding genes and open reading frames, and produces inputs to PathoLogic. In addition to constructing ePGDBs, MetaPathways uses MLTreeMap to build phylogenetic trees for selected taxonomic anchor and functional gene markers, converts General Feature Format (GFF) files into concatenated GenBank files for ePGDB construction based on third-party annotations, and generates useful file formats including Sequin files for direct GenBank submission and gene feature tables summarizing annotations, MLTreeMap trees, and ePGDB pathway coverage summaries for statistical comparisons. CONCLUSIONS: MetaPathways provides users with a modular annotation and analysis pipeline for predicting metabolic interaction networks from environmental sequence information using an alternative to KEGG pathways and SEED subsystems mapping. It is extensible to genomic and transcriptomic datasets from a wide range of sequencing platforms, and generates useful data products for microbial community structure and function analysis. The MetaPathways software package, installation instructions, and example data can be obtained from http://hallam.microbiology.ubc.ca/MetaPathways.

Dataset Information

Constructing sequence-dependent protein models using coevolutionary information.

Publications

Constructing sequence-dependent protein models using coevolutionary information.

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