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Assessment of global and local model quality in CASP8 using Pcons and ProQ
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.ORCID iD: 0000-0002-7115-9751
2009 (English)In: Proteins: Structure, Function, and Genetics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 77, no 9, 167-172 p.Article in journal (Refereed) Published
Abstract [en]

Model Quality Assessment Programs (MQAPs) are programs developed to rank protein models. These methods can be trained to predict the overall global quality of a model or what local regions in a model that are likely to be incorrect. In CASP8, we participated with two predictors that predict both global and local quality using either consensus information, Pcons, or purely structural information, ProQ. Consistently with results in previous CASPs, the best performance in CASP8 was obtained using the Pcons method. Furthermore, the results show that the modification introduced into Pcons for CASP8 improved the predictions against GDT_TS and now a correlation coefficient above 0.9 is achieved, whereas the correlation for ProQ is about 0.7. The correlation is better for the easier than for the harder targets, but it is not below 0.5 for a single target and below 0.7 only for three targets. The correlation coefficient for the best local quality MQAP is 0.68 showing that there is still clear room for improvement within this area. We also detect that Pcons still is not always able to identify the best model. However, we show that using a linear combination of Pcons and ProQ it is possible to select models that are better than the models from the best single server. In particular, the average quality over the hard targets increases by about 6% compared with using Pcons alone.

Place, publisher, year, edition, pages
2009. Vol. 77, no 9, 167-172 p.
Keyword [en]
quality assessment, MQAP, consensus
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-34572DOI: 10.1002/prot.22476ISI: 000272244700019PubMedID: 19544566OAI: oai:DiVA.org:su-34572DiVA: diva2:285146
Available from: 2010-01-11 Created: 2010-01-11 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Prediction, modeling, and refinement of protein structure
Open this publication in new window or tab >>Prediction, modeling, and refinement of protein structure
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Accurate predictions of protein structure are important for understanding many processes in cells. The interactions that govern protein folding and structure are complex, and still far from completely understood. However, progress is being made in many areas. Here, efforts to improve the overall quality of protein structure models are described. From a pure evolutionary perspective, in which proteins are viewed in the light of gradually accumulated mutations on the sequence level, it is shown how information from multiple sources helps to create more accurate models. A very simple but surprisingly accurate method for assigning confidence measures for protein structures is also tested. In contrast to models based on evolution, physics based methods view protein structures as the result of physical interactions between atoms. Newly implemented methods are described that both increase the time-scales accessible for molecular dynamics simulations almost 10-fold, and that to some extent might be able to refine protein structures. Finally, I compare the efficiency and properties of different techniques for protein structure refinement.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2010. 64 p.
Keyword
Protein structure prediction, Multiple alignments, Quality assessment, Molecular dynamics, Implicit solvent, Refinement
National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology) Theoretical Chemistry
Research subject
Biochemistry
Identifiers
urn:nbn:se:su:diva-38253 (URN)978-91-7447-036-9 (ISBN)
Public defence
2010-05-12, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: In press. Paper 5: Manuscript. Available from: 2010-04-20 Created: 2010-04-06 Last updated: 2010-04-09Bibliographically approved
2. 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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