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PconsC: combination of direct information methods and alignments improves contact prediction
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0002-7115-9751
2013 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 29, no 14, 1815-1816 p.Article in journal (Refereed) Published
Abstract [en]

Recently, several new contact prediction methods have been published. They use (i) large sets of multiple aligned sequences and (ii) assume that correlations between columns in these alignments can be the results of indirect interaction. These methods are clearly superior to earlier methods when it comes to predicting contacts in proteins. Here, we demonstrate that combining predictions from two prediction methods, PSICOV and plmDCA, and two alignment methods, HHblits and jackhmmer at four different e-value cut-offs, provides a relative improvement of 20% in comparison with the best single method, exceeding 70% correct predictions for one contact prediction per residue.

Place, publisher, year, edition, pages
2013. Vol. 29, no 14, 1815-1816 p.
National Category
Biological Sciences
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-92624DOI: 10.1093/bioinformatics/btt259ISI: 000321747800017OAI: oai:DiVA.org:su-92624DiVA: diva2:641795
Note

AuthorCount:3;

Available from: 2013-08-19 Created: 2013-08-14 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Ensemble methods for protein structure prediction
Open this publication in new window or tab >>Ensemble methods for protein structure prediction
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Proteins play an essential role in virtually all of life's processes. Their function is tightly coupled to the three-dimensional structure they adopt.

Solving protein structures experimentally is a complicated, time- and resource-consuming endeavor. With the rapid growth of the amount of protein sequences known, it is very likely that only a small fraction of known proteins will ever have their structures solved experimentally. Recently, computational methods for protein structure prediction have become increasingly accurate and offer a promise for bridging this gap.

In this work, we show the ways the rapidly growing amounts of available biological data can be used to improve the accuracy of protein structure prediction. We discuss the use of multiple sources of structural information to improve the quality of predicted models. The methods for assigning the estimated quality scores for predicted models are discussed as well.  In particular we present a novel, successful approach to the clustering-based quality assessment, which runs nearly 50 times faster than other methods of comparable accuracy, allowing to tackle much larger problems.

Additionally, this thesis discusses the impact the recent breakthroughs in sequencing and the consequent rapid growth of sequence data have on the prediction of residue-residue contacts. We propose a novel methodology, which allows for predicting such contacts with astonishing, previously unheard-of accuracy. These contacts in turn can be used to guide protein modeling, allowing for discovering protein structures that have been unattainable by conventional prediction methods.

Finally, a considerable part of this dissertation discusses the community efforts in protein structure prediction, as embodied by CASP (Critical Assessment of protein Structure Prediction) experiments.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2013. 60 p.
Keyword
protein structure prediction, model quality assessment, contact prediction, homology modeling, ab-initio prediction, consensus prediction, structural bioinformatics, bioinformatics, protein structure
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-89366 (URN)978-91-7447-698-9 (ISBN)
Public defence
2013-05-31, Magnéli Hall, Arrhenius Laboratory, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 215524
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Submitted. Paper 4: Submitted.

Available from: 2013-05-09 Created: 2013-04-23 Last updated: 2015-10-27Bibliographically approved

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Skwark, Marcin J.Elofsson, Arne
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