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Protein Model Quality Assessment: A Machine Learning Approach
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. (Arne Elofsson)ORCID iD: 0000-0003-2232-3006
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). These programs are able to predict model accuracy before the native structure is determined. The accuracy estimation can be divided into two parts: global (the whole model accuracy) and local (the accuracy of each residue). ProQ2 is one of the most successful MQAPs for prediction of both local and global model accuracy and is based on a Machine Learning approach.

In this thesis, I present my own contribution to Model Quality Assessment (MQA) and the newest developments of ProQ program series. Firstly, I describe a new ProQ2 implementation in the protein modelling software package Rosetta. This new implementation allows use of ProQ2 as a scoring function for conformational sampling inside Rosetta, which was not possible before. Moreover, I present two new methods, ProQ3 and ProQ3D that both outperform their predecessor. ProQ3 introduces new training features that are calculated from Rosetta energy functions and ProQ3D introduces a new machine learning approach based on deep learning. ProQ3 program participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) and was one of the best methods in the MQA category. Finally, an important issue in model quality assessment is how to select a target function that the predictor is trying to learn. In the fourth manuscript, I show that MQA results can be improved by selecting a contact-based target function instead of more conventional superposition based functions.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University , 2017. , p. 46
Keywords [en]
Protein Model Quality Assessment, structural bioinformatics, machine learning, deep learning, support vector machine, proq, Artificial Neural Network, protein structure prediction
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-137695ISBN: 978-91-7649-633-6 (print)ISBN: 978-91-7649-634-3 (print)OAI: oai:DiVA.org:su-137695DiVA, id: diva2:1063493
Public defence
2017-02-10, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 14:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, VR-NT 2012-5046
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

Available from: 2017-01-18 Created: 2017-01-10 Last updated: 2017-01-18Bibliographically approved
List of papers
1. ProQ2: estimation of model accuracy implemented in Rosetta
Open this publication in new window or tab >>ProQ2: estimation of model accuracy implemented in Rosetta
2016 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, no 9, p. 1411-1413Article in journal (Refereed) Published
Abstract [en]

Motivation: Model quality assessment programs are used to predict the quality of modeled protein structures. They can be divided into two groups depending on the information they are using: ensemble methods using consensus of many alternative models and methods only using a single model to do its prediction. The consensus methods excel in achieving high correlations between prediction and true quality measures. However, they frequently fail to pick out the best possible model, nor can they be used to generate and score new structures. Single-model methods on the other hand do not have these inherent shortcomings and can be used both to sample new structures and to improve existing consensus methods. Results: Here, we present an implementation of the ProQ2 program to estimate both local and global model accuracy as part of the Rosetta modeling suite. The current implementation does not only make it possible to run large batch runs locally, but it also opens up a whole new arena for conformational sampling using machine learned scoring functions and to incorporate model accuracy estimation in to various existing modeling schemes. ProQ2 participated in CASP11 and results from CASP11 are used to benchmark the current implementation. Based on results from CASP11 and CAMEO-QE, a continuous benchmark of quality estimation methods, it is clear that ProQ2 is the single-model method that performs best in both local and global model accuracy.

National Category
Biological Sciences Environmental Biotechnology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-131551 (URN)10.1093/bioinformatics/btv767 (DOI)000376106100020 ()26733453 (PubMedID)
Available from: 2016-06-30 Created: 2016-06-21 Last updated: 2017-11-28Bibliographically approved
2. ProQ3: Improved model quality assessments using Rosetta energy terms
Open this publication in new window or tab >>ProQ3: Improved model quality assessments using Rosetta energy terms
2016 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, article id 33509Article in journal (Refereed) Published
Abstract [en]

Quality assessment of protein models using no other information than the structure of the model itself has been shown to be useful for structure prediction. Here, we introduce two novel methods, ProQRosFA and ProQRosCen, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model. ProQ2 uses contacts and other features calculated from a model, while the new predictors are based on Rosetta energies: ProQRosFA uses the full-atom energy function that takes into account all atoms, while ProQRosCen uses the coarse-grained centroid energy function. The two new predictors also include residue conservation and terms corresponding to the agreement of a model with predicted secondary structure and surface area, as in ProQ2. We show that the performance of these predictors is on par with ProQ2 and significantly better than all other model quality assessment programs. Furthermore, we show that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods. ProQ3, ProQRosFA and ProQRosCen are freely available both as a webserver and stand-alone programs at http://proq3.bioinfo.se/.

National Category
Biological Sciences
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-135223 (URN)10.1038/srep33509 (DOI)000384595800001 ()
Available from: 2016-11-14 Created: 2016-11-01 Last updated: 2017-11-29Bibliographically approved
3. Improved protein model quality prediction by changing the target function
Open this publication in new window or tab >>Improved protein model quality prediction by changing the target function
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Protein modeling quality is an important part of protein structure prediction. We have for more than a decade developed a set of methods using various types of protein descriptions and machine learning methods. Common to all these methods has been that the target function, i.e. the description of the quality of a residue in a protein model, has been the S-score. However, many other quality estimation functions also exist. These can roughly be divided into superposition, like S-score, and contact-based functions. The contact-based methods have been shown to be better at evaluating the quality of multi-domain proteins.

Here, we examine the effects of retraining ProQ3D using identical inputs but different target functions. We find that using the same target and test function provides the best agreement. However using contact-based methods provide higher correlations and a better ranking of individual models.

Keywords
Model Quality Assessment, Protein Model Quality Assessment, structural bioinformatics, machine learning, deep learning, target function, S-score, TM-score, GDT_TS, LDDT, CAD
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-137694 (URN)
Funder
Swedish Research Council, VR-NT 2012-5046
Available from: 2017-01-10 Created: 2017-01-10 Last updated: 2017-01-16Bibliographically approved
4. ProQ3D: improved model quality assessments using deep learning
Open this publication in new window or tab >>ProQ3D: improved model quality assessments using deep learning
Show others...
2017 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 10, p. 1578-1580Article in journal (Refereed) Published
Abstract [en]

Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features).

Keywords
Model Quality Assessment, Protein Bioinformatics, Machine Learning, Deep Learning, Neural Networks, Multi Layer Perceptron, Deep neural networks
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-137679 (URN)10.1093/bioinformatics/btw819 (DOI)000402130700023 ()
Funder
Swedish Research Council, VR-NT 2012-5046Swedish Research Council, VR-NT 2012-5270Swedish e‐Science Research Center
Available from: 2017-01-09 Created: 2017-01-09 Last updated: 2017-06-26Bibliographically approved

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