1
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Bernard C, Postic G, Ghannay S, Tahi F. RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality. Brief Bioinform 2024; 25:bbae064. [PMID: 38436560 PMCID: PMC10939302 DOI: 10.1093/bib/bbae064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/30/2024] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.
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Affiliation(s)
- Clement Bernard
- Université Paris Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France
| | - Guillaume Postic
- Université Paris Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France
| | - Sahar Ghannay
- LISN - CNRS/Université Paris-Saclay, France, 91400 Orsay, France
| | - Fariza Tahi
- Université Paris Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France
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2
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Conti S, Ovchinnikov V, Karplus M. ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning. J Comput Chem 2022; 43:1747-1757. [PMID: 35930347 DOI: 10.1002/jcc.26974] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 07/01/2022] [Accepted: 07/13/2022] [Indexed: 11/07/2022]
Abstract
This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.
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Affiliation(s)
- Simone Conti
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Victor Ovchinnikov
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Martin Karplus
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.,Laboratoire de Chimie Biophysique, Institut de Science et d'Ingénierie Supramoléculaires, Université de Strasbourg, Strasbourg, France
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3
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On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations. Antibodies (Basel) 2022; 11:antib11030051. [PMID: 35997345 PMCID: PMC9397028 DOI: 10.3390/antib11030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/23/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.
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4
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Ye L, Wu P, Peng Z, Gao J, Liu J, Yang J. Improved estimation of model quality using predicted inter-residue distance. Bioinformatics 2021; 37:3752-3759. [PMID: 34473228 DOI: 10.1093/bioinformatics/btab632] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. RESULTS Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single-model and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. AVAILABILITY AND IMPLEMENTATION http://yanglab.nankai.edu.cn/QDistance. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lisha Ye
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Peikun Wu
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Zhenling Peng
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
| | - Jianzhao Gao
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Jian Liu
- College of Computer Science, Nankai University, Tianjin, 300071, China
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
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5
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Liu J, Wu T, Guo Z, Hou J, Cheng J. Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14. Proteins 2021; 90:58-72. [PMID: 34291486 PMCID: PMC8671168 DOI: 10.1002/prot.26186] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/21/2021] [Accepted: 07/12/2021] [Indexed: 12/15/2022]
Abstract
Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0.
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Affiliation(s)
- Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Tianqi Wu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jie Hou
- Department of Computer Science, Saint Louis University, St. Louis, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
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6
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Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14. Sci Rep 2021; 11:10943. [PMID: 34035363 PMCID: PMC8149836 DOI: 10.1038/s41598-021-90303-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/10/2021] [Indexed: 11/28/2022] Open
Abstract
The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.
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7
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Cheng J, Choe MH, Elofsson A, Han KS, Hou J, Maghrabi AHA, McGuffin LJ, Menéndez-Hurtado D, Olechnovič K, Schwede T, Studer G, Uziela K, Venclovas Č, Wallner B. Estimation of model accuracy in CASP13. Proteins 2019; 87:1361-1377. [PMID: 31265154 DOI: 10.1002/prot.25767] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 06/04/2019] [Accepted: 06/15/2019] [Indexed: 12/28/2022]
Abstract
Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue-residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus-based methods.
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Affiliation(s)
- Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri
| | - Myong-Ho Choe
- Department of Life Science, University of Science, Pyongyang, DPR Korea
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Kun-Sop Han
- Department of Life Science, University of Science, Pyongyang, DPR Korea
| | - Jie Hou
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri
| | - Ali H A Maghrabi
- School of Biological Sciences, University of Reading, Reading, UK
| | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Reading, UK
| | - David Menéndez-Hurtado
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Basel, Switzerland
| | - Gabriel Studer
- Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Basel, Switzerland
| | - Karolis Uziela
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Björn Wallner
- Department of Physics, Chemistry, and Biology, Bioinformatics Division, Linköping University, Linköping, Sweden
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8
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Tan YL, Feng CJ, Jin L, Shi YZ, Zhang W, Tan ZJ. What is the best reference state for building statistical potentials in RNA 3D structure evaluation? RNA (NEW YORK, N.Y.) 2019; 25:793-812. [PMID: 30996105 PMCID: PMC6573789 DOI: 10.1261/rna.069872.118] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 04/06/2019] [Indexed: 05/14/2023]
Abstract
Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walk-chain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.
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Affiliation(s)
- Ya-Lan Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Chen-Jie Feng
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Lei Jin
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430073, China
| | - Wenbing Zhang
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Zhi-Jie Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
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9
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Hou J, Wu T, Cao R, Cheng J. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins 2019; 87:1165-1178. [PMID: 30985027 PMCID: PMC6800999 DOI: 10.1002/prot.25697] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 04/04/2019] [Accepted: 04/12/2019] [Indexed: 12/28/2022]
Abstract
Predicting residue‐residue distance relationships (eg, contacts) has become the key direction to advance protein structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, distance‐driven template‐free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template‐free and template‐based structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue‐residue features such as coevolution scores to substantially improve contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template‐based modeling targets. Deep learning also successfully integrated one‐dimensional structural features, two‐dimensional contact information, and three‐dimensional structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system clearly shows that protein contact distance prediction and model selection driven by deep learning holds the key of solving protein structure prediction problem. However, there are still challenges in accurately predicting protein contact distance when there are few homologous sequences, folding proteins from noisy contact distances, and ranking models of hard targets.
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Affiliation(s)
- Jie Hou
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri
| | - Tianqi Wu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, Washington
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri
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10
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Conti S, Karplus M. Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning. PLoS Comput Biol 2019; 15:e1006954. [PMID: 30970017 PMCID: PMC6457539 DOI: 10.1371/journal.pcbi.1006954] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/18/2019] [Indexed: 11/21/2022] Open
Abstract
HIV is a highly mutable virus for which all attempts to develop a vaccine have been unsuccessful. Nevertheless, few long-infected patients develop antibodies, called broadly neutralizing antibodies (bnAbs), that have a high breadth and can neutralize multiple variants of the virus. This suggests that a universal HIV vaccine should be possible. A measure of the efficacy of a HIV vaccine is the neutralization breadth of the antibodies it generates. The breadth is defined as the fraction of viruses in the Seaman panel that are neutralized by the antibody. Experimentally the neutralization ability is measured as the half maximal inhibitory concentration of the antibody (IC50). To avoid such time-consuming experimental measurements, we developed a computational approach to estimate the IC50 and use it to determine the antibody breadth. Given that no direct method exists for calculating IC50 values, we resort to a combination of atomistic modeling and machine learning. For each antibody/virus complex, an all-atoms model is built using the amino acid sequence and a known structure of a related complex. Then a series of descriptors are derived from the atomistic models, and these are used to train a Multi-Layer Perceptron (an Artificial Neural Network) to predict the value of the IC50 (by regression), or if the antibody binds or not to the virus (by classification). The neural networks are trained by use of experimental IC50 values collected in the CATNAP database. The computed breadths obtained by regression and classification are reported and the importance of having some related information in the data set for obtaining accurate predictions is analyzed. This approach is expected to prove useful for the design of HIV bnAbs, where the computation of the potency must be accompanied by a computation of the breadth, and for evaluating the efficiency of potential vaccination schemes developed through modeling and simulation.
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Affiliation(s)
- Simone Conti
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Martin Karplus
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Laboratoire de Chimie Biophysique, ISIS, Université de Strasbourg, Strasbourg, France
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11
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Role of solvent accessibility for aggregation-prone patches in protein folding. Sci Rep 2018; 8:12896. [PMID: 30150761 PMCID: PMC6110721 DOI: 10.1038/s41598-018-31289-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 08/15/2018] [Indexed: 11/21/2022] Open
Abstract
The arrangement of amino acids in a protein sequence encodes its native folding. However, the same arrangement in aggregation-prone regions may cause misfolding as a result of local environmental stress. Under normal physiological conditions, such regions congregate in the protein’s interior to avoid aggregation and attain the native fold. We have used solvent accessibility of aggregation patches (SAAPp) to determine the packing of aggregation-prone residues. Our results showed that SAAPp has low values for native crystal structures, consistent with protein folding as a mechanism to minimize the solvent accessibility of aggregation-prone residues. SAAPp also shows an average correlation of 0.76 with the global distance test (GDT) score on CASP12 template-based protein models. Using SAAPp scores and five structural features, a random forest machine learning quality assessment tool, SAAP-QA, showed 2.32 average GDT loss between best model predicted and actual best based on GDT score on independent CASP test data, with the ability to discriminate native-like folds having an AUC of 0.94. Overall, the Pearson correlation coefficient (PCC) between true and predicted GDT scores on independent CASP data was 0.86 while on the external CAMEO dataset, comprising high quality protein structures, PCC and average GDT loss were 0.71 and 4.46 respectively. SAAP-QA can be used to detect the quality of models and iteratively improve them to native or near-native structures.
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12
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Anishchenko I, Kundrotas PJ, Vakser IA. Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. Biophys J 2018; 115:809-821. [PMID: 30122295 DOI: 10.1016/j.bpj.2018.07.035] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 07/16/2018] [Accepted: 07/31/2018] [Indexed: 12/18/2022] Open
Abstract
The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.
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Affiliation(s)
- Ivan Anishchenko
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas
| | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
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13
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Yao Y, Gui R, Liu Q, Yi M, Deng H. Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction. BMC Bioinformatics 2017; 18:542. [PMID: 29221443 PMCID: PMC5723101 DOI: 10.1186/s12859-017-1983-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 12/04/2017] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND As one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated. RESULTS Based on different distance cutoffs (from 5 to 22 Å) and residue intervals (from 0 to 15) as well as six different reference states, we constructed a series of distance-dependent atom-pair potentials and tested them on several groups of structural decoy sets collected from diverse sources. A comprehensive investigation has been performed to clarify the effects of distance cutoff and residue interval on the potential's performance. Our results provide a new perspective as well as a practical guidance for optimizing distance-dependent statistical potentials. CONCLUSIONS The optimal distance cutoff and residue interval are highly related with the reference state that the potential is based on, the measurements of the potential's performance, and the decoy sets that the potential is applied to. The performance of distance-dependent statistical potential can be significantly improved when the best statistical parameters for the specific application environment are adopted.
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Affiliation(s)
- Yuangen Yao
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Rong Gui
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Quan Liu
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Ming Yi
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Haiyou Deng
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
- Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070 China
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14
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Cao R, Adhikari B, Bhattacharya D, Sun M, Hou J, Cheng J. QAcon: single model quality assessment using protein structural and contact information with machine learning techniques. Bioinformatics 2017; 33:586-588. [PMID: 28035027 DOI: 10.1093/bioinformatics/btw694] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2016] [Accepted: 11/01/2016] [Indexed: 11/14/2022] Open
Abstract
Motivation Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool. Results In this paper, we develop a novel single-model quality assessment method QAcon utilizing structural features, physicochemical properties, and residue contact predictions. We apply residue-residue contact information predicted by two protein contact prediction methods PSICOV and DNcon to generate a new score as feature for quality assessment. This novel feature and other 11 features are used as input to train a two-layer neural network on CASP9 datasets to predict the quality of a single protein model. We blindly benchmarked our method QAcon on CASP11 dataset as the MULTICOM-CLUSTER server. Based on the evaluation, our method is ranked as one of the top single model QA methods. The good performance of the features based on contact prediction illustrates the value of using contact information in protein quality assessment. Availability and Implementation The web server and the source code of QAcon are freely available at: http://cactus.rnet.missouri.edu/QAcon. Contact chengji@missouri.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, WA 98447, USA
| | - Badri Adhikari
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Debswapna Bhattacharya
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260-0083, USA
| | - Miao Sun
- Department of Electrical and Computer Engineering
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.,Informatics Institute, University of Missouri, Columbia, MO 65211, USA
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15
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All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5760612. [PMID: 29119109 PMCID: PMC5651141 DOI: 10.1155/2017/5760612] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 08/13/2017] [Accepted: 08/23/2017] [Indexed: 02/05/2023]
Abstract
Recent advances in understanding protein folding have benefitted from coarse-grained representations of protein structures. Empirical energy functions derived from these techniques occasionally succeed in distinguishing native structures from their corresponding ensembles of nonnative folds or decoys which display varying degrees of structural dissimilarity to the native proteins. Here we utilized atomic coordinates of single protein chains, comprising a large diverse training set, to develop and evaluate twelve all-atom four-body statistical potentials obtained by exploring alternative values for a pair of inherent parameters. Delaunay tessellation was performed on the atomic coordinates of each protein to objectively identify all quadruplets of interacting atoms, and atomic potentials were generated via statistical analysis of the data and implementation of the inverted Boltzmann principle. Our potentials were evaluated using benchmarking datasets from Decoys-‘R'-Us, and comparisons were made with twelve other physics- and knowledge-based potentials. Ranking 3rd, our best potential tied CHARMM19 and surpassed AMBER force field potentials. We illustrate how a generalized version of our potential can be used to empirically calculate binding energies for target-ligand complexes, using HIV-1 protease-inhibitor complexes for a practical application. The combined results suggest an accurate and efficient atomic four-body statistical potential for protein structure prediction and assessment.
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16
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Broom A, Jacobi Z, Trainor K, Meiering EM. Computational tools help improve protein stability but with a solubility tradeoff. J Biol Chem 2017; 292:14349-14361. [PMID: 28710274 DOI: 10.1074/jbc.m117.784165] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 07/11/2017] [Indexed: 01/18/2023] Open
Abstract
Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.
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Affiliation(s)
- Aron Broom
- From the Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Zachary Jacobi
- From the Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Kyle Trainor
- From the Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
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17
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Cao R, Bhattacharya D, Hou J, Cheng J. DeepQA: improving the estimation of single protein model quality with deep belief networks. BMC Bioinformatics 2016; 17:495. [PMID: 27919220 PMCID: PMC5139030 DOI: 10.1186/s12859-016-1405-y] [Citation(s) in RCA: 112] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 12/01/2016] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. RESULTS We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. CONCLUSION DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .
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Affiliation(s)
- Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, 98447, USA
| | - Debswapna Bhattacharya
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, 67260, USA
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA. .,Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.
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18
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Dybas JM, Fiser A. Development of a motif-based topology-independent structure comparison method to identify evolutionarily related folds. Proteins 2016; 84:1859-1874. [PMID: 27671894 PMCID: PMC5118133 DOI: 10.1002/prot.25169] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 08/17/2016] [Accepted: 08/25/2016] [Indexed: 11/09/2022]
Abstract
Structure conservation, functional similarities, and homologous relationships that exist across diverse protein topologies suggest that some regions of the protein fold universe are continuous. However, the current structure classification systems are based on hierarchical organizations, which cannot accommodate structural relationships that span fold definitions. Here, we describe a novel, super-secondary-structure motif-based, topology-independent structure comparison method (SmotifCOMP) that is able to quantitatively identify structural relationships between disparate topologies. The basis of SmotifCOMP is a systematically defined super-secondary-structure motif library whose representative geometries are shown to be saturated in the Protein Data Bank and exhibit a unique distribution within the known folds. SmotifCOMP offers a robust and quantitative technique to compare domains that adopt different topologies since the method does not rely on a global superposition. SmotifCOMP is used to perform an exhaustive comparison of the known folds and the identified relationships are used to produce a nonhierarchical representation of the fold space that reflects the notion of a continuous and connected fold universe. The current work offers insight into previously hypothesized evolutionary relationships between disparate folds and provides a resource for exploring novel ones. Proteins 2016; 84:1859-1874. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Joseph M. Dybas
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue Bronx, NY 10461, USA
- Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue Bronx, NY 10461, USA
| | - Andras Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue Bronx, NY 10461, USA
- Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue Bronx, NY 10461, USA
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19
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Protein single-model quality assessment by feature-based probability density functions. Sci Rep 2016; 6:23990. [PMID: 27041353 PMCID: PMC4819172 DOI: 10.1038/srep23990] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 03/17/2016] [Indexed: 11/11/2022] Open
Abstract
Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method–Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.
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Cao R, Bhattacharya D, Adhikari B, Li J, Cheng J. Large-scale model quality assessment for improving protein tertiary structure prediction. Bioinformatics 2015; 31:i116-23. [PMID: 26072473 PMCID: PMC4553833 DOI: 10.1093/bioinformatics/btv235] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation: Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Results: Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM’s outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. Availability and implementation: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. Contact: chengji@missouri.edu
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Affiliation(s)
- Renzhi Cao
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Debswapna Bhattacharya
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Badri Adhikari
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Jilong Li
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Jianlin Cheng
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
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21
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A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11. BMC Bioinformatics 2015; 16:337. [PMID: 26493701 PMCID: PMC4619059 DOI: 10.1186/s12859-015-0775-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 10/14/2015] [Indexed: 11/10/2022] Open
Abstract
Background With more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research. Traditional template-based protein structure prediction methods tend to focus on identifying the best templates, generating the best alignments, and applying the best energy function to rank models, which often cannot achieve the best performance because of the difficulty of obtaining best templates, alignments, and models. Methods We developed a large-scale conformation sampling and evaluation method and its servers to improve the reliability and robustness of protein structure prediction. In the first step, our method used a variety of alignment methods to sample relevant and complementary templates and to generate alternative and diverse target-template alignments, used a template and alignment combination protocol to combine alignments, and used template-based and template-free modeling methods to generate a pool of conformations for a target protein. In the second step, it used a large number of protein model quality assessment methods to evaluate and rank the models in the protein model pool, in conjunction with an exception handling strategy to deal with any additional failure in model ranking. Results The method was implemented as two protein structure prediction servers: MULTICOM-CONSTRUCT and MULTICOM-CLUSTER that participated in the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014. The two servers were ranked among the best 10 server predictors. Conclusions The good performance of our servers in CASP11 demonstrates the effectiveness and robustness of the large-scale conformation sampling and evaluation. The MULTICOM server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0775-x) contains supplementary material, which is available to authorized users.
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Deng H, Jia Y, Zhang Y. 3DRobot: automated generation of diverse and well-packed protein structure decoys. Bioinformatics 2015; 32:378-87. [PMID: 26471454 DOI: 10.1093/bioinformatics/btv601] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 10/10/2015] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Computationally generated non-native protein structure conformations (or decoys) are often used for designing protein folding simulation methods and force fields. However, almost all the decoy sets currently used in literature suffer from uneven root mean square deviation (RMSD) distribution with bias to non-protein like hydrogen-bonding and compactness patterns. Meanwhile, most protein decoy sets are pre-calculated and there is a lack of methods for automated generation of high-quality decoys for any target proteins. RESULTS We developed a new algorithm, 3DRobot, to create protein structure decoys by free fragment assembly with enhanced hydrogen-bonding and compactness interactions. The method was benchmarked with three widely used decoy sets from ab initio folding and comparative modeling simulations. The decoys generated by 3DRobot are shown to have significantly enhanced diversity and evenness with a continuous distribution in the RMSD space. The new energy terms introduced in 3DRobot improve the hydrogen-bonding network and compactness of decoys, which eliminates the possibility of native structure recognition by trivial potentials. Algorithms that can automatically create such diverse and well-packed non-native conformations from any protein structure should have a broad impact on the development of advanced protein force field and folding simulation methods. AVAILIABLITY AND IMPLEMENTATION: http://zhanglab.ccmb.med.umich.edu/3DRobot/ CONTACT jiay@phy.ccnu.edu.cn; zhng@umich.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Haiyou Deng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA, Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan 430079, China and
| | - Ya Jia
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 45108, USA
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Cao R, Bhattacharya D, Adhikari B, Li J, Cheng J. Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11. Proteins 2015; 84 Suppl 1:247-59. [PMID: 26369671 DOI: 10.1002/prot.24924] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 08/21/2015] [Accepted: 09/10/2015] [Indexed: 12/28/2022]
Abstract
Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Renzhi Cao
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | | | - Badri Adhikari
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211. .,Informatics Institute, University of Missouri, Columbia, Missouri, 65211.
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Vallat B, Madrid-Aliste C, Fiser A. Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures. PLoS Comput Biol 2015; 11:e1004419. [PMID: 26252221 PMCID: PMC4529212 DOI: 10.1371/journal.pcbi.1004419] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 06/30/2015] [Indexed: 12/25/2022] Open
Abstract
Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling. Each protein folds into a unique three-dimensional structure that enables it to carry out its biological function. Knowledge of the atomic details of protein structures is therefore a key to understanding their function. Advances in high throughput experimental technologies have lead to an exponential increase in the availability of known protein sequences. Although strong progress has been made in experimental protein structure determination, it remains a fact that more than 99% of structural information is provided by computational modeling methods. We describe here a novel structure prediction method, SmotifTF, which uses a unique library of known protein fragments to assemble the three-dimensional structure of a sequence. The fragment library has saturated over time and therefore provides a complete set of building blocks required for model building. The method performs competitively compared to existing methods of structure prediction.
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Affiliation(s)
- Brinda Vallat
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
| | - Carlos Madrid-Aliste
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
| | - Andras Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
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Xu Y, Zhou X, Huang M. StaRProtein, a web server for prediction of the stability of repeat proteins. PLoS One 2015; 10:e0119417. [PMID: 25807112 PMCID: PMC4373711 DOI: 10.1371/journal.pone.0119417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Accepted: 01/13/2015] [Indexed: 11/25/2022] Open
Abstract
Repeat proteins have become increasingly important due to their capability to bind to almost any proteins and the potential as alternative therapy to monoclonal antibodies. In the past decade repeat proteins have been designed to mediate specific protein-protein interactions. The tetratricopeptide and ankyrin repeat proteins are two classes of helical repeat proteins that form different binding pockets to accommodate various partners. It is important to understand the factors that define folding and stability of repeat proteins in order to prioritize the most stable designed repeat proteins to further explore their potential binding affinities. Here we developed distance-dependant statistical potentials using two classes of alpha-helical repeat proteins, tetratricopeptide and ankyrin repeat proteins respectively, and evaluated their efficiency in predicting the stability of repeat proteins. We demonstrated that the repeat-specific statistical potentials based on these two classes of repeat proteins showed paramount accuracy compared with non-specific statistical potentials in: 1) discriminate correct vs. incorrect models 2) rank the stability of designed repeat proteins. In particular, the statistical scores correlate closely with the equilibrium unfolding free energies of repeat proteins and therefore would serve as a novel tool in quickly prioritizing the designed repeat proteins with high stability. StaRProtein web server was developed for predicting the stability of repeat proteins.
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Affiliation(s)
- Yongtao Xu
- School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom
| | - Xu Zhou
- School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom
| | - Meilan Huang
- School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom
- * E-mail:
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Wang J, Zhao Y, Zhu C, Xiao Y. 3dRNAscore: a distance and torsion angle dependent evaluation function of 3D RNA structures. Nucleic Acids Res 2015; 43:e63. [PMID: 25712091 PMCID: PMC4446410 DOI: 10.1093/nar/gkv141] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Accepted: 02/06/2015] [Indexed: 01/02/2023] Open
Abstract
Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.
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Affiliation(s)
- Jian Wang
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yunjie Zhao
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Chunyan Zhu
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Xiao
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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Jayaram B, Dhingra P, Mishra A, Kaushik R, Mukherjee G, Singh A, Shekhar S. Bhageerath-H: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins. BMC Bioinformatics 2014; 15 Suppl 16:S7. [PMID: 25521245 PMCID: PMC4290660 DOI: 10.1186/1471-2105-15-s16-s7] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals. RESULTS Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥ 0.5 in 91% of the cases and Cα RMSDs ≤ 5 Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution. CONCLUSION Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment.
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Thompson JJ, Tabatabaei Ghomi H, Lill MA. Application of information theory to a three-body coarse-grained representation of proteins in the PDB: insights into the structural and evolutionary roles of residues in protein structure. Proteins 2014; 82:3450-65. [PMID: 25269778 DOI: 10.1002/prot.24698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 09/09/2014] [Accepted: 09/19/2014] [Indexed: 01/03/2023]
Abstract
Knowledge-based methods for analyzing protein structures, such as statistical potentials, primarily consider the distances between pairs of bodies (atoms or groups of atoms). Considerations of several bodies simultaneously are generally used to characterize bonded structural elements or those in close contact with each other, but historically do not consider atoms that are not in direct contact with each other. In this report, we introduce an information-theoretic method for detecting and quantifying distance-dependent through-space multibody relationships between the sidechains of three residues. The technique introduced is capable of producing convergent and consistent results when applied to a sufficiently large database of randomly chosen, experimentally solved protein structures. The results of our study can be shown to reproduce established physico-chemical properties of residues as well as more recently discovered properties and interactions. These results offer insight into the numerous roles that residues play in protein structure, as well as relationships between residue function, protein structure, and evolution. The techniques and insights presented in this work should be useful in the future development of novel knowledge-based tools for the evaluation of protein structure.
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Affiliation(s)
- Jared J Thompson
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, Indiana
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Ghomi HT, Thompson JJ, Lill MA. Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction? J Bioinform Comput Biol 2014; 12:1450022. [PMID: 25212727 DOI: 10.1142/s021972001450022x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Distance-based statistical potentials have long been used to model condensed matter systems, e.g. as scoring functions in differentiating native-like protein structures from decoys. These scoring functions are based on the assumption that the total free energy of the protein can be calculated as the sum of pairwise free energy contributions derived from a statistical analysis of pair-distribution functions. However, this fundamental assumption has been challenged theoretically. In fact the free energy of a system with N particles is only exactly related to the N-body distribution function. Based on this argument coarse-grained multi-body statistical potentials have been developed to capture higher-order interactions. Having a coarse representation of the protein and using geometric contacts instead of pairwise interaction distances renders these models insufficient in modeling details of multi-body effects. In this study, we investigated if extending distance-dependent pairwise atomistic statistical potentials to corresponding interaction functions that are conditional on a third interacting body, defined as quasi-three-body statistical potentials, could model details of three-body interactions. We also tested if this approach could improve the predictive capabilities of statistical scoring functions for protein structure prediction. We analyzed the statistical dependency between two simultaneous pairwise interactions and showed that there is surprisingly little if any dependency of a third interacting site on pairwise atomistic statistical potentials. Also the protein structure prediction performance of these quasi-three-body potentials is comparable with their corresponding two-body counterparts. The scoring functions developed in this study showed better or comparable performances compared to some widely used scoring functions for protein structure prediction.
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Affiliation(s)
- Hamed Tabatabaei Ghomi
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, IN 47907, USA
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31
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Menon V, Vallat BK, Dybas JM, Fiser A. Modeling proteins using a super-secondary structure library and NMR chemical shift information. Structure 2013; 21:891-9. [PMID: 23685209 DOI: 10.1016/j.str.2013.04.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2012] [Revised: 04/02/2013] [Accepted: 04/13/2013] [Indexed: 11/29/2022]
Abstract
A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.
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Affiliation(s)
- Vilas Menon
- Department of Systems and Computational Biology, Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
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Capturing native/native like structures with a physico-chemical metric (pcSM) in protein folding. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1834:1520-31. [PMID: 23665455 DOI: 10.1016/j.bbapap.2013.04.023] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2013] [Revised: 04/12/2013] [Accepted: 04/15/2013] [Indexed: 12/15/2022]
Abstract
Specification of the three dimensional structure of a protein from its amino acid sequence, also called a "Grand Challenge" problem, has eluded a solution for over six decades. A modestly successful strategy has evolved over the last couple of decades based on development of scoring functions (e.g. mimicking free energy) that can capture native or native-like structures from an ensemble of decoys generated as plausible candidates for the native structure. A scoring function must be fast enough in discriminating the native from unfolded/misfolded structures, and requires validation on a large data set(s) to generate sufficient confidence in the score. Here we develop a scoring function called pcSM that detects true native structure in the top 5 with 93% accuracy from an ensemble of candidate structures. If we eliminate the native from ensemble of decoys then pcSM is able to capture near native structure (RMSD<=5Ǻ) in top 10 with 86% accuracy. The parameters considered in pcSM are a C-alpha Euclidean metric, secondary structural propensity, surface areas and an intramolecular energy function. pcSM has been tested on 415 systems consisting 142,698 decoys (public and CASP-largest reported hitherto in literature). The average rank for the native is 2.38, a significant improvement over that existing in literature. In-silico protein structure prediction requires robust scoring technique(s). Therefore, pcSM is easily amenable to integration into a successful protein structure prediction strategy. The tool is freely available at http://www.scfbio-iitd.res.in/software/pcsm.jsp.
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Pujato M, MacCarthy T, Fiser A, Bergman A. The underlying molecular and network level mechanisms in the evolution of robustness in gene regulatory networks. PLoS Comput Biol 2013; 9:e1002865. [PMID: 23300434 PMCID: PMC3536627 DOI: 10.1371/journal.pcbi.1002865] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Accepted: 11/13/2012] [Indexed: 11/18/2022] Open
Abstract
Gene regulatory networks show robustness to perturbations. Previous works identified robustness as an emergent property of gene network evolution but the underlying molecular mechanisms are poorly understood. We used a multi-tier modeling approach that integrates molecular sequence and structure information with network architecture and population dynamics. Structural models of transcription factor-DNA complexes are used to estimate relative binding specificities. In this model, mutations in the DNA cause changes on two levels: (a) at the sequence level in individual binding sites (modulating binding specificity), and (b) at the network level (creating and destroying binding sites). We used this model to dissect the underlying mechanisms responsible for the evolution of robustness in gene regulatory networks. Results suggest that in sparse architectures (represented by short promoters), a mixture of local-sequence and network-architecture level changes are exploited. At the local-sequence level, robustness evolves by decreasing the probabilities of both the destruction of existent and generation of new binding sites. Meanwhile, in highly interconnected architectures (represented by long promoters), robustness evolves almost entirely via network level changes, deleting and creating binding sites that modify the network architecture.
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Affiliation(s)
- Mario Pujato
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Thomas MacCarthy
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Applied Mathematics and Statistics, SUNY, Stony Brook, New York, United States of America
| | - Andras Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Aviv Bergman
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
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Abstract
Functional characterization of proteins being one of the major issues in molecular biology is still unsolved due to several resource and technical limitations of experimental structure determination methods. A suitable methodology for accurate prediction of protein confirmations simply from sequence is therefore emerging as the primary modeling goal of researchers today. Global blind protein structure prediction summit, entitled Critical Assessment of Structure Prediction (CASP), critically assesses the modeling methodologies, to track our algorithmic path development. But our success is still impeded by incompetent modeling methodologies and several key technical lacunas. There is still a great need to focus some key issues for bridging the major though considered trivial gaps, in the upcoming CASP to pave and demarcate our correct way of developing a consistently accurate prediction methodology in the near future. Major problems resulting in divergence of our predicted models from their actual native states are thus highlighted with suggested more stringent and reliable assessment considerations in the CASP test.
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Affiliation(s)
- Ashish Runthala
- Biological Sciences, Faculty Division III, Birla Institute of Technology & Science, Pilani, Rajasthan, India.
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35
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Deng H, Jia Y, Wei Y, Zhang Y. What is the best reference state for designing statistical atomic potentials in protein structure prediction? Proteins 2012; 80:2311-22. [PMID: 22623012 DOI: 10.1002/prot.24121] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Revised: 04/30/2012] [Accepted: 05/21/2012] [Indexed: 01/01/2023]
Abstract
Many statistical potentials were developed in last two decades for protein folding and protein structure recognition. The major difference of these potentials is on the selection of reference states to offset sampling bias. However, since these potentials used different databases and parameter cutoffs, it is difficult to judge what the best reference states are by examining the original programs. In this study, we aim to address this issue and evaluate the reference states by a unified database and programming environment. We constructed distance-specific atomic potentials using six widely-used reference states based on 1022 high-resolution protein structures, which are applied to rank modeling in six sets of structure decoys. The reference state on random-walk chain outperforms others in three decoy sets while those using ideal-gas, quasi-chemical approximation and averaging sample stand out in one set separately. Nevertheless, the performance of the potentials relies on the origin of decoy generations and no reference state can clearly outperform others in all decoy sets. Further analysis reveals that the statistical potentials have a contradiction between the universality and pertinence, and optimal reference states should be extracted based on specific application environments and decoy spaces.
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Affiliation(s)
- Haiyou Deng
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan 430079, China
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Ceres N, Lavery R. Coarse-grain Protein Models. INNOVATIONS IN BIOMOLECULAR MODELING AND SIMULATIONS 2012. [DOI: 10.1039/9781849735049-00219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Coarse-graining is a powerful approach for modeling biomolecules that, over the last few decades, has been extensively applied to proteins. Coarse-grain models offer access to large systems and to slow processes without becoming computationally unmanageable. In addition, they are very versatile, enabling both the protein representation and the energy function to be adapted to the biological problem in hand. This review concentrates on modeling soluble proteins and their assemblies. It presents an overview of the coarse-grain representations, of the associated interaction potentials, and of the optimization procedures used to define them. It then shows how coarse-grain models have been used to understand processes involving proteins, from their initial folding to their functional properties, their binary interactions, and the assembly of large complexes.
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Affiliation(s)
- N. Ceres
- Bases Moléculaires et Structurales des Systèmes Infectieux Université Lyon1/CNRS UMR 5086, IBCP, 7 Passage du Vercors, 69367, Lyon France
| | - R. Lavery
- Bases Moléculaires et Structurales des Systèmes Infectieux Université Lyon1/CNRS UMR 5086, IBCP, 7 Passage du Vercors, 69367, Lyon France
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Shirota M, Ishida T, Kinoshita K. Absolute quality evaluation of protein model structures using statistical potentials with respect to the native and reference states. Proteins 2011; 79:1550-63. [PMID: 21365682 DOI: 10.1002/prot.22982] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2010] [Revised: 11/19/2010] [Accepted: 12/19/2010] [Indexed: 11/06/2022]
Abstract
In protein structure prediction, it is crucial to evaluate the degree of native-likeness of given model structures. Statistical potentials extracted from protein structure data sets are widely used for such quality assessment problems, but they are only applicable for comparing different models of the same protein. Although various other methods, such as machine learning approaches, were developed to predict the absolute similarity of model structures to the native ones, they required a set of decoy structures in addition to the model structures. In this paper, we tried to reformulate the statistical potentials as absolute quality scores, without using the information from decoy structures. For this purpose, we regarded the native state and the reference state, which are necessary components of statistical potentials, as the good and bad standard states, respectively, and first showed that the statistical potentials can be regarded as the state functions, which relate a model structure to the native and reference states. Then, we proposed a standardized measure of protein structure, called native-likeness, by interpolating the score of a model structure between the native and reference state scores defined for each protein. The native-likeness correlated with the similarity to the native structures and discriminated the native structures from the models, with better accuracy than the raw score. Our results show that statistical potentials can quantify the native-like properties of protein structures, if they fully utilize the statistical information obtained from the data set.
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Affiliation(s)
- Matsuyuki Shirota
- Department of Applied Information Sciences, Graduate School of Information Science, Tohoku University, 6-3-09, Aoba, Aramaki, Aoba-Ku, Sendai, Miyagi 980-8579, Japan
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38
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Hamelryck T, Borg M, Paluszewski M, Paulsen J, Frellsen J, Andreetta C, Boomsma W, Bottaro S, Ferkinghoff-Borg J. Potentials of mean force for protein structure prediction vindicated, formalized and generalized. PLoS One 2010; 5:e13714. [PMID: 21103041 PMCID: PMC2978081 DOI: 10.1371/journal.pone.0013714] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2010] [Accepted: 10/04/2010] [Indexed: 11/26/2022] Open
Abstract
Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances – so-called “potentials of mean force” (PMFs) – have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state – a necessary component of these potentials – is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities “reference ratio distributions” deriving from the application of the “reference ratio method.” This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.
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Affiliation(s)
- Thomas Hamelryck
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- * E-mail: (TH); (JFB)
| | - Mikael Borg
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Paluszewski
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jonas Paulsen
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jes Frellsen
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Christian Andreetta
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Wouter Boomsma
- Biomedical Engineering, Technical University of Denmark (DTU) Elektro, Technical University of Denmark, Lyngby, Denmark
- Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Sandro Bottaro
- Biomedical Engineering, Technical University of Denmark (DTU) Elektro, Technical University of Denmark, Lyngby, Denmark
| | - Jesper Ferkinghoff-Borg
- Biomedical Engineering, Technical University of Denmark (DTU) Elektro, Technical University of Denmark, Lyngby, Denmark
- * E-mail: (TH); (JFB)
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Sikic K, Tomic S, Carugo O. Systematic comparison of crystal and NMR protein structures deposited in the protein data bank. Open Biochem J 2010; 4:83-95. [PMID: 21293729 PMCID: PMC3032220 DOI: 10.2174/1874091x01004010083] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2010] [Revised: 05/20/2010] [Accepted: 06/14/2010] [Indexed: 11/22/2022] Open
Abstract
Nearly all the macromolecular three-dimensional structures deposited in Protein Data Bank were determined by either crystallographic (X-ray) or Nuclear Magnetic Resonance (NMR) spectroscopic methods. This paper reports a systematic comparison of the crystallographic and NMR results deposited in the files of the Protein Data Bank, in order to find out to which extent these information can be aggregated in bioinformatics. A non-redundant data set containing 109 NMR – X-ray structure pairs of nearly identical proteins was derived from the Protein Data Bank. A series of comparisons were performed by focusing the attention towards both global features and local details. It was observed that: (1) the RMDS values between NMR and crystal structures range from about 1.5 Å to about 2.5 Å; (2) the correlation between conformational deviations and residue type reveals that hydrophobic amino acids are more similar in crystal and NMR structures than hydrophilic amino acids; (3) the correlation between solvent accessibility of the residues and their conformational variability in solid state and in solution is relatively modest (correlation coefficient = 0.462); (4) beta strands on average match better between NMR and crystal structures than helices and loops; (5) conformational differences between loops are independent of crystal packing interactions in the solid state; (6) very seldom, side chains buried in the protein interior are observed to adopt different orientations in the solid state and in solution.
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Affiliation(s)
- Kresimir Sikic
- Departement of Structural and Computational Biology, Max F. Perutz Laboratories, Vienna University, Austria
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40
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Solis AD, Rackovsky SR. Information-theoretic analysis of the reference state in contact potentials used for protein structure prediction. Proteins 2010; 78:1382-97. [PMID: 20034109 DOI: 10.1002/prot.22652] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Using information-theoretic concepts, we examine the role of the reference state, a crucial component of empirical potential functions, in protein fold recognition. We derive an information-based connection between the probability distribution functions of the reference state and those that characterize the decoy set used in threading. In examining commonly used contact reference states, we find that the quasi-chemical approximation is informatically superior to other variant models designed to include characteristics of real protein chains, such as finite length and variable amino acid composition from protein to protein. We observe that in these variant models, the total divergence, the operative function that quantifies discrimination, decreases along with threading performance. We find that any amount of nativeness encoded in the reference state model does not significantly improve threading performance. A promising avenue for the development of better potentials is suggested by our information-theoretic analysis of the action of contact potentials on individual protein sequences. Our results show that contact potentials perform better when the compositional properties of the data set used to derive the score function probabilities are similar to the properties of the sequence of interest. Results also suggest to use only sequences of similar composition in deriving contact potentials, to tailor the contact potential specifically for a test sequence.
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Affiliation(s)
- Armando D Solis
- Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, New York 10029, USA.
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41
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Rykunov D, Fiser A. New statistical potential for quality assessment of protein models and a survey of energy functions. BMC Bioinformatics 2010; 11:128. [PMID: 20226048 PMCID: PMC2853469 DOI: 10.1186/1471-2105-11-128] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2009] [Accepted: 03/12/2010] [Indexed: 11/30/2022] Open
Abstract
Background Scoring functions, such as molecular mechanic forcefields and statistical potentials are fundamentally important tools in protein structure modeling and quality assessment. Results The performances of a number of publicly available scoring functions are compared with a statistical rigor, with an emphasis on knowledge-based potentials. We explored the effect on accuracy of alternative choices for representing interaction center types and other features of scoring functions, such as using information on solvent accessibility, on torsion angles, accounting for secondary structure preferences and side chain orientation. Partially based on the observations made, we present a novel residue based statistical potential, which employs a shuffled reference state definition and takes into account the mutual orientation of residue side chains. Atom- and residue-level statistical potentials and Linux executables to calculate the energy of a given protein proposed in this work can be downloaded from http://www.fiserlab.org/potentials. Conclusions Among the most influential terms we observed a critical role of a proper reference state definition and the benefits of including information about the microenvironment of interaction centers. Molecular mechanical potentials were also tested and found to be over-sensitive to small local imperfections in a structure, requiring unfeasible long energy relaxation before energy scores started to correlate with model quality.
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Affiliation(s)
- Dmitry Rykunov
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Ave,, Bronx, NY 10461, USA
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42
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Abstract
Functional characterization of a protein is often facilitated by its 3D structure. However, the fraction of experimentally known 3D models is currently less than 1% due to the inherently time-consuming and complicated nature of structure determination techniques. Computational approaches are employed to bridge the gap between the number of known sequences and that of 3D models. Template-based protein structure modeling techniques rely on the study of principles that dictate the 3D structure of natural proteins from the theory of evolution viewpoint. Strategies for template-based structure modeling will be discussed with a focus on comparative modeling, by reviewing techniques available for all the major steps involved in the comparative modeling pipeline.
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Affiliation(s)
- Andras Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
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43
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Shirota M, Ishida T, Kinoshita K. Analyses on hydrophobicity and attractiveness of all-atom distance-dependent potentials. Protein Sci 2009; 18:1906-15. [PMID: 19588493 DOI: 10.1002/pro.201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Accurate model evaluation is a crucial step in protein structure prediction. For this purpose, statistical potentials, which evaluate a model structure based on the observed atomic distance frequencies in comparison with those in reference states, have been widely used. The reference state is a virtual state where all of the atomic interactions are turned off, and it provides a standard to measure the observed frequencies. In this study, we examined seven all-atom distance-dependent potentials with different reference states. As results, we observed that the variations of atom pair composition and those of distance distributions in the reference states produced systematic changes in the hydrophobic and attractive characteristics of the potentials. The performance evaluations with the CASP7 structures indicated that the preference of hydrophobic interactions improved the correlation between the energy and the GDT-TS score, but decreased the Z-score of the native structure. The attractiveness of potential improved both the correlation and Z-score for template-based modeling targets, but the benefit was smaller in free modeling targets. These results indicated that the performances of the potentials were more strongly influenced by their characteristics than by the accuracy of the definitions of the reference states.
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Affiliation(s)
- Matsuyuki Shirota
- Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan
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44
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Aloy P, Oliva B. Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations. BMC STRUCTURAL BIOLOGY 2009; 9:71. [PMID: 19917096 PMCID: PMC2783033 DOI: 10.1186/1472-6807-9-71] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2009] [Accepted: 11/16/2009] [Indexed: 11/20/2022]
Abstract
Background Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both. Results Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors. Conclusion We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.
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Affiliation(s)
- Patrick Aloy
- Institut de Recerca Biomèdica and Barcelona Supercomputing Center, 10-12 08028 Barcelona, Catalonia, Spain.
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Cohen M, Potapov V, Schreiber G. Four distances between pairs of amino acids provide a precise description of their interaction. PLoS Comput Biol 2009; 5:e1000470. [PMID: 19680437 PMCID: PMC2715887 DOI: 10.1371/journal.pcbi.1000470] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2009] [Accepted: 07/15/2009] [Indexed: 11/18/2022] Open
Abstract
The three-dimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a four-dimensional grid and compared to a random model, from which the preference for specific four-distances was calculated. A clear relation between the quality of the experimental data and the tightness of the distance distribution was observed, with crystal structure data providing far tighter distance distributions than NMR data. Since the four-distance data have higher information content than classical bond descriptions, we were able to identify many unique inter-residue features not found previously in proteins. For example, we found that the side chains of Arg, Glu, Val and Leu are not symmetrical in respect to the interactions of their head groups. The described method may be developed into a function, which computationally models accurately protein structures.
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Affiliation(s)
- Mati Cohen
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel
| | - Vladimir Potapov
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel
| | - Gideon Schreiber
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel
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Improved scoring function for comparative modeling using the M4T method. ACTA ACUST UNITED AC 2008; 10:95-9. [PMID: 18985440 DOI: 10.1007/s10969-008-9044-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2008] [Accepted: 10/16/2008] [Indexed: 10/21/2022]
Abstract
Improvements in comparative protein structure modeling for the remote target-template sequence similarity cases are possible through the optimal combination of multiple template structures and by improving the quality of target-template alignment. Recently developed MMM and M4T methods were designed to address these problems. Here we describe new developments in both the alignment generation and the template selection parts of the modeling algorithms. We set up a new scoring function in MMM to deliver more accurate target-template alignments. This was achieved by developing and incorporating into the composite scoring function a novel statistical pairwise potential that combines local and non-local terms. The non-local term of the statistical potential utilizes a shuffled reference state definition that helped to eliminate most of the false positive signal from the background distribution of pairwise contacts. The accuracy of the scoring function was further increased by using BLOSUM mutation table scores.
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Shirota M, Ishida T, Kinoshita K. Effects of surface-to-volume ratio of proteins on hydrophilic residues: decrease in occurrence and increase in buried fraction. Protein Sci 2008; 17:1596-602. [PMID: 18556475 DOI: 10.1110/ps.035592.108] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The size of a protein is an important factor for understanding the sequence-structure relationship, and it affects both the amino acid composition and the residue burial of proteins. However, it is usually measured as the number of amino acids, although these effects would result from the reduction of surface regions relative to the volume of core regions in larger proteins. In addition, although these two effects are dependent on each other, they have been studied separately. In this study, we investigated them by considering the surface-to-volume ratio (SVR), and observed the correlation between them. We found that the reduction of several hydrophilic residues is more strongly correlated with SVR than with protein size (the number of amino acids) and that SVR directly affects the amino acid composition. The difference as a descriptor between SVR and size is also supported by the observation that the secondary structural elements correlate completely differently with SVR and with size. Furthermore, for the four most hydrophilic residues, glutamine, arginine, glutamic acid, and lysine, balances between the decrease in composition and the increase in core burial were observed. We found that the burial of glutamine and arginine became accelerated at SVR = 0.3 A(-1) (approximately 132 residues) as the protein size increased, but that lysine has an upper limit of 0.9% for its occurrence in the core. The uniqueness of lysine was also elucidated by comparison with the burial environments of the four hydrophilic residues.
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Affiliation(s)
- Matsuyuki Shirota
- Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
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