ArticlePloS one2014
Combining physicochemical and evolutionary information for protein contact prediction.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age.Proteins · 2018Article
- EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction.BMC bioinformatics · 2017Article
- A Biologically-validated HCV E1E2 Heterodimer Structural Model.Scientific reports · 2017Article
- Comparing co-evolution methods and their application to template-free protein structure prediction.Bioinformatics (Oxford, England) · 2017Article
- Assessing Predicted Contacts for Building Protein Three-Dimensional Models.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Co-evolution techniques are reshaping the way we do structural bioinformatics.F1000Research · 2017Review
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- New encouraging developments in contact prediction: Assessment of the CASP11 results.Proteins · 2016Article
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- Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology.Molecular & cellular proteomics : MCP · 2016Article
- Protein Residue Contacts and Prediction Methods.Methods in molecular biology (Clifton, N.J.) · 2016Article
- RBO Aleph: leveraging novel information sources for protein structure prediction.Nucleic acids research · 2015Article
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Authors and funding
2 authors.
Funding
Abstract
We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.