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PconsFold: improved contact predictions improve protein models
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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2014 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 30, no 17, 1482-1488 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used.

Results: In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15-30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved.

Place, publisher, year, edition, pages
2014. Vol. 30, no 17, 1482-1488 p.
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-108840DOI: 10.1093/bioinformatics/btu458ISI: 000342912400020PubMedID: 25161237OAI: oai:DiVA.org:su-108840DiVA: diva2:760838
Available from: 2014-11-04 Created: 2014-11-04 Last updated: 2017-05-08Bibliographically approved
In thesis
1. From Sequence to Structure: Using predicted residue contacts to facilitate template-free protein structure prediction
Open this publication in new window or tab >>From Sequence to Structure: Using predicted residue contacts to facilitate template-free protein structure prediction
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Despite the fundamental role of experimental protein structure determination, computational methods are of essential importance to bridge the ever growing gap between available protein sequence and structure data. Common structure prediction methods rely on experimental data, which is not available for about half of the known protein families.

Recent advancements in amino acid contact prediction have revolutionized the field of protein structure prediction. Contacts can be used to guide template-free structure predictions that do not rely on experimentally solved structures of homologous proteins. Such methods are now able to produce accurate models for a wide range of protein families.

We developed PconsC2, an approach that improved existing contact prediction methods by recognizing intra-molecular contact patterns and noise reduction. An inherent problem of contact prediction based on maximum entropy models is that large alignments with over 1000 effective sequences are needed to infer contacts accurately. These are however not available for more than 80% of all protein families that do not have a representative structure in PDB. With PconsC3, we could extend the applicability of contact prediction to families as small as 100 effective sequences by combining global inference methods with machine learning based on local pairwise measures.

By introducing PconsFold, a pipeline for contact-based structure prediction, we could show that improvements in contact prediction accuracy translate to more accurate models. Finally, we applied a similar technique to Pfam, a comprehensive database of known protein families. In addition to using a faster folding protocol we employed model quality assessment methods, crucial for estimating the confidence in the accuracy of predicted models. We propose models tobe accurate for 558 families that do not have a representative known structure. Out of those, over 75% have not been reported before.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2017
Keyword
protein bioinformatics, protein structure prediction, contact prediction, machine learning
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-141946 (URN)978-91-7649-811-8 (ISBN)978-91-7649-812-5 (ISBN)
Public defence
2017-06-09, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 13:00 (English)
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Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 4: In press.

 

Available from: 2017-05-17 Created: 2017-04-25 Last updated: 2017-05-10Bibliographically approved

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