Dataset Information

ProPhylER: a curated online resource for protein function and structure based on evolutionary constraint analyses.

ABSTRACT: ProPhylER (Protein Phylogeny and Evolutionary Rates) is a next-generation curated proteome resource that uses comparative sequence analysis to predict constraint and mutation impact for eukaryotic proteins. Its purpose is to inform any research program for which protein function and structure are relevant, by the predictive power of evolutionary constraint analyses. ProPhylER currently has nearly 9000 clusters of related proteins, including more than 200,000 sequences. It serves data via two interfaces. The "ProPhylER Interface" displays predictive analyses in sequence space; the "CrystalPainter" maps evolutionary constraints onto solved protein structures. Here we summarize ProPhylER's data content and analysis pipeline, demonstrate the use of ProPhylER's interfaces, and evaluate ProPhylER's unique regional analysis of evolutionary constraint. The high accuracy of ProPhylER's regional analysis complements the high resolution of its single-site analysis to effectively guide and inform structure-function investigations and predict the impact of polymorphisms.

SUBMITTER: Binkley J

PROVIDER: S-EPMC2798826 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ProPhylER: a curated online resource for protein function and structure based on evolutionary constraint analyses.

Binkley Jonathan J Karra Kalpana K Kirby Andrew A Hosobuchi Midori M Stone Eric A EA Sidow Arend A

Genome research 20091021 1

ProPhylER (Protein Phylogeny and Evolutionary Rates) is a next-generation curated proteome resource that uses comparative sequence analysis to predict constraint and mutation impact for eukaryotic proteins. Its purpose is to inform any research program for which protein function and structure are relevant, by the predictive power of evolutionary constraint analyses. ProPhylER currently has nearly 9000 clusters of related proteins, including more than 200,000 sequences. It serves data via two int ...[more]

PMID: 19846609

Similar Datasets

Project description:BACKGROUND:The complexity of metabolic networks can make the origin and impact of changes in central metabolism occurring during diseases difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localisation of reactions and transport steps, incorrect reaction parameters, and confounding of prosthetic groups and free metabolites in reactions. Other common drawbacks are due to their scale, making them difficult to parameterise and simulation results hard to interpret. Therefore, it remains important to develop smaller, manually curated models. RESULTS:We present MitoCore, a manually curated constraint-based computer model of human metabolism that incorporates the complexity of central metabolism and simulates this metabolism successfully under normal and abnormal physiological conditions, including hypoxia and mitochondrial diseases. MitoCore describes 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for easy interpretation of results. Its key innovations include a more accurate partitioning of metabolism between cytosol and mitochondrial matrix; better modelling of connecting transport steps; differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCore's default parameters simulate normal cardiomyocyte metabolism, and to improve usability and allow comparison with other models and types of analysis, its reactions and metabolites have extensive annotation, and cross-reference identifiers from Virtual Metabolic Human database and KEGG. These innovations-including over 100 reactions absent or modified from Recon 2-are necessary to model central metabolism more accurately. CONCLUSION:We anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret their data and test hypotheses, to experienced modellers predicting the consequences of disease or using computationally intensive methods that are infeasible with larger models, as well as a teaching tool for those new to modelling and needing a small, manageable model on which to learn and experiment.

Project description:Accurate protein function annotation is a severe bottleneck when utilizing the deluge of high-throughput, next generation sequencing data. Keeping database annotations up-to-date has become a major scientific challenge that requires the development of reliable automatic predictors of protein function. The CAFA experiment provided a unique opportunity to undertake comprehensive 'blind testing' of many diverse approaches for automated function prediction. We report on the methodology we used for this challenge and on the lessons we learnt.Our method integrates into a single framework a wide variety of biological information sources, encompassing sequence, gene expression and protein-protein interaction data, as well as annotations in UniProt entries. The methodology transfers functional categories based on the results from complementary homology-based and feature-based analyses. We generated the final molecular function and biological process assignments by combining the initial predictions in a probabilistic manner, which takes into account the Gene Ontology hierarchical structure.We propose a novel scoring function called COmbined Graph-Information Content similarity (COGIC) score for the comparison of predicted functional categories and benchmark data. We demonstrate that our integrative approach provides increased scope and accuracy over both the component methods and the naïve predictors. In line with previous studies, we find that molecular function predictions are more accurate than biological process assignments.Overall, the results indicate that there is considerable room for improvement in the field. It still remains for the community to invest a great deal of effort to make automated function prediction a useful and routine component in the toolbox of life scientists. As already witnessed in other areas, community-wide blind testing experiments will be pivotal in establishing standards for the evaluation of prediction accuracy, in fostering advancements and new ideas, and ultimately in recording progress.

Dataset Information

ProPhylER: a curated online resource for protein function and structure based on evolutionary constraint analyses.

Publications

ProPhylER: a curated online resource for protein function and structure based on evolutionary constraint analyses.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets