1
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Tarafder S, Bhattacharya D. lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.04.565599. [PMID: 37961488 PMCID: PMC10635153 DOI: 10.1101/2023.11.04.565599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.
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Affiliation(s)
- Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, 24061, USA
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2
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Chen X, Liu J, Park N, Cheng J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules 2024; 14:574. [PMID: 38785981 PMCID: PMC11117562 DOI: 10.3390/biom14050574] [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: 03/01/2024] [Revised: 04/07/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.
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Affiliation(s)
- Xiao Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Nolan Park
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
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3
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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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4
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Wang X, Yu S, Lou E, Tan YL, Tan ZJ. RNA 3D Structure Prediction: Progress and Perspective. Molecules 2023; 28:5532. [PMID: 37513407 PMCID: PMC10386116 DOI: 10.3390/molecules28145532] [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: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.
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Affiliation(s)
- Xunxun Wang
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Shixiong Yu
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - En Lou
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Lan Tan
- School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, China
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, China
| | - Zhi-Jie Tan
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
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5
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rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation. Biophys J 2022; 121:142-156. [PMID: 34798137 PMCID: PMC8758408 DOI: 10.1016/j.bpj.2021.11.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/23/2021] [Accepted: 11/10/2021] [Indexed: 01/07/2023] Open
Abstract
Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.
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6
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Yadav NS, Kumar P, Singh I. Structural and functional analysis of protein. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00026-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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7
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Alam FF, Shehu A. Unsupervised multi-instance learning for protein structure determination. J Bioinform Comput Biol 2021; 19:2140002. [PMID: 33568002 DOI: 10.1142/s0219720021400023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Many regions of the protein universe remain inaccessible by wet-laboratory or computational structure determination methods. A significant challenge in elucidating these dark regions in silico relates to the ability to discriminate relevant structure(s) among many structures/decoys computed for a protein of interest, a problem known as decoy selection. Clustering decoys based on geometric similarity remains popular. However, it is unclear how exactly to exploit the groups of decoys revealed via clustering to select individual structures for prediction. In this paper, we provide an intuitive formulation of the decoy selection problem as an instance of unsupervised multi-instance learning. We address the problem in three stages, first organizing given decoys of a protein molecule into bags, then identifying relevant bags, and finally drawing individual instances from these bags to offer as prediction. We propose both non-parametric and parametric algorithms for drawing individual instances. Our evaluation utilizes two datasets, one benchmark dataset of ensembles of decoys for a varied list of protein molecules, and a dataset of decoy ensembles for targets drawn from recent CASP competitions. A comparative analysis with state-of-the-art methods reveals that the proposed approach outperforms existing methods, thus warranting further investigation of multi-instance learning to advance our treatment of decoy selection.
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Affiliation(s)
- Fardina Fathmiul Alam
- Department of Computer Science, George Mason University, Fairfax, Virginia 22030, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia 22030, USA
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8
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Hurst T, Chen SJ. Sieving RNA 3D Structures with SHAPE and Evaluating Mechanisms Driving Sequence-Dependent Reactivity Bias. J Phys Chem B 2021; 125:1156-1166. [PMID: 33497570 DOI: 10.1021/acs.jpcb.0c11365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing provides local RNA flexibility information at single-nucleotide resolution. In general, SHAPE is thought of as a secondary structure (2D) technology, but we find evidence that robust tertiary structure (3D) information is contained in SHAPE data. Here, we report a new model that achieves a higher correlation between SHAPE data and native RNA 3D structures than the previous 3D structure-SHAPE relationship model. Furthermore, we demonstrate that the new model improves our ability to discern between SHAPE-compatible and -incompatible structures on model decoys. After identifying sequence-dependent bias in SHAPE experiments, we propose a mechanism driving sequence-dependent bias in SHAPE experiments, using replica-exchange umbrella sampling simulations to confirm that the SHAPE sequence bias is largely explained by the stability of the unreacted SHAPE reagent in the binding pocket. Taken together, this work represents multiple practical advances in our mechanistic and predictive understanding of SHAPE technology.
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Affiliation(s)
- Travis Hurst
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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9
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Abstract
Atom pairwise potential functions make up an essential part of many scoring functions for protein decoy detection. With the development of machine learning (ML) tools, there are multiple ways to combine potential functions to create novel ML models and methods. Potential function parameters can be easily extracted; however, it is usually hard to directly obtain the calculated atom pairwise energies from scoring functions. Amber, as one of the most popular suites of modeling programs, has an extensive history and library of force field potential functions. In this work, we directly used the force field parameters in ff94 and ff14SB from Amber and encoded them to calculate atom pairwise energies for different interactions. Two sets of structures (single amino acid set and a dipeptide set) were used to evaluate the performance of our encoded Amber potentials. From the comparison results between energy terms obtained from our encoding and Amber, we find energy difference within ±0.06 kcal/mol for all tested structures. Previously we have shown that the Random Forest (RF) model can help to emphasize more important atom pairwise interactions and ignore insignificant ones [Pei, J.; Zheng, Z.; Merz, K. M. J. Chem. Inf. Model. 2019, 59, 1919-1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. To determine the performance of our RF models with force field potential functions, 224 different protein native-decoy systems were used as our training and testing sets We find that the RF models with ff94 and ff14SB force field parameters outperformed all other scoring functions (RF models with KECSA2, RWplus, DFIRE, dDFIRE, and GOAP) considered in this work for native structure detection, and they performed similarly in detecting the best decoy. Through inclusion of best decoy to decoy comparisons in building our RF models, we were able to generate models that outperformed the score functions tested herein both on accuracy and best decoy detection, again showing the performance and flexibility of our RF models to tackle this problem. Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. All code and data used in this work are available at https://github.com/JunPei000/FFENCODER_for_Protein_Folding_Pose_Selection.
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Affiliation(s)
- Jun Pei
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
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10
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Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association. Biomolecules 2020; 10:biom10071056. [PMID: 32679892 PMCID: PMC7407674 DOI: 10.3390/biom10071056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/10/2020] [Accepted: 07/13/2020] [Indexed: 12/22/2022] Open
Abstract
The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.
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11
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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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12
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Yu Z, Yao Y, Deng H, Yi M. ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment. BMC Bioinformatics 2019; 20:299. [PMID: 31159742 PMCID: PMC6547486 DOI: 10.1186/s12859-019-2898-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/13/2019] [Indexed: 01/05/2023] Open
Abstract
Background The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials. Results We developed an atomic ANgle- and DIStance-dependent (ANDIS) statistical potential for protein structure quality assessment with distance cutoff being a tunable parameter. When distance cutoff is ≤9.0 Å, “effective atomic interaction” is employed to enhance the ability of native recognition. For a distance cutoff of ≥10 Å, the distance-dependent atom-pair potential with random-walk reference state is combined to strengthen the ability of decoy discrimination. Benchmark tests on 632 structural decoy sets from diverse sources demonstrate that ANDIS outperforms other state-of-the-art potentials in both native recognition and decoy discrimination. Conclusions Distance cutoff is a crucial parameter for distance-dependent statistical potentials. A lower distance cutoff is better for native recognition, while a higher one is favorable for decoy discrimination. The ANDIS potential is freely available as a standalone application at http://qbp.hzau.edu.cn/ANDIS/. Electronic supplementary material The online version of this article (10.1186/s12859-019-2898-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhongwang Yu
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuangen Yao
- 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.
| | - Ming Yi
- 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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13
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Xu G, Ma T, Wang Q, Ma J. OPUS-SSF: A side-chain-inclusive scoring function for ranking protein structural models. Protein Sci 2019; 28:1157-1162. [PMID: 30919509 DOI: 10.1002/pro.3608] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 03/21/2019] [Accepted: 03/27/2019] [Indexed: 12/21/2022]
Abstract
We introduce a side-chain-inclusive scoring function, named OPUS-SSF, for ranking protein structural models. The method builds a scoring function based on the native distributions of the coordinate components of certain anchoring points in a local molecular system for peptide segments of 5, 7, 9, and 11 residues in length. Differing from our previous OPUS-CSF [Xu et al., Protein Sci. 2018; 27: 286-292], which exclusively uses main chain information, OPUS-SSF employs anchoring points on side chains so that the effect of side chains is taken into account. The performance of OPUS-SSF was tested on 15 decoy sets containing totally 603 proteins, and 571 of them had their native structures recognized from their decoys. Similar to OPUS-CSF, OPUS-SSF does not employ the Boltzmann formula in constructing scoring functions. The results indicate that OPUS-SSF has achieved a significant improvement on decoy recognition and it should be a very useful tool for protein structural prediction and modeling.
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Affiliation(s)
- Gang Xu
- School of Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China
| | - Tianqi Ma
- Applied Physics Program, Rice University, Houston, Texas 77005.,Department of Bioengineering, Rice University, Houston, Texas 77005
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030
| | - Jianpeng Ma
- School of Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China.,Applied Physics Program, Rice University, Houston, Texas 77005.,Department of Bioengineering, Rice University, Houston, Texas 77005.,Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030
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14
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Battisti A, Zamuner S, Sarti E, Laio A. Toward a unified scoring function for native state discrimination and drug-binding pocket recognition. Phys Chem Chem Phys 2019; 20:17148-17155. [PMID: 29900428 DOI: 10.1039/c7cp08170g] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Protein folding and receptor-ligand recognition are fundamental processes for any living organism. Although folding and ligand recognition are based on the same chemistry, the existing empirical scoring functions target just one problem: predicting the correct fold or the correct binding pose. We here introduce a statistical potential which considers moieties as fundamental units. The scoring function is able to deal with both folding and ligand pocket recognition problems with a performance comparable to the scoring functions specifically tailored for one of the two tasks. We foresee that the capability of the new scoring function to tackle both problems in a unified framework will be a key to deal with the induced fit phenomena, in which a target protein changes significantly its conformation upon binding. Moreover, the new scoring function might be useful in docking protocols towards intrinsically disordered proteins, whose flexibility cannot be handled with the available docking software.
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Affiliation(s)
- Anna Battisti
- International School for Advanced Studies (SISSA), Via Bonomea 265, I-34136 Trieste, Italy.
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15
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Pei J, Zheng Z, Merz KM. Random Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection. J Chem Inf Model 2019; 59:1919-1929. [DOI: 10.1021/acs.jcim.8b00734] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jun Pei
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zheng Zheng
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
- Institute for Cyber Enabled Research, Michigan State University, 567 Wilson Road, East Lansing, Michigan 48824, United States
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16
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Khare S, Bhasin M, Sahoo A, Varadarajan R. Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information. Proteins 2019; 87:326-336. [PMID: 30615225 DOI: 10.1002/prot.25654] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 12/22/2018] [Accepted: 01/02/2019] [Indexed: 01/02/2023]
Abstract
Structure prediction methods often generate a large number of models for a target sequence. Even if the correct fold for the target sequence is sampled in this dataset, it is difficult to distinguish it from other decoy structures. An attempt to solve this problem using experimental mutational sensitivity data for the CcdB protein was described previously by exploiting the correlation of residue depth with mutational sensitivity (r ~ 0.6). We now show that such a correlation extends to four other proteins with localized active sites, and for which saturation mutagenesis datasets exist. We also examine whether incorporation of predicted secondary structure information and the DOPE model quality assessment score, in addition to mutational sensitivity, improves the accuracy of model discrimination using a decoy dataset of 163 targets from CASP. Although most CASP models would have been subjected to model quality assessment prior to submission, we find that the DOPE score makes a substantial contribution to the observed improvement. We therefore also applied the approach to CcdB and four other proteins for which reliable experimental mutational data exist and observe that inclusion of experimental mutational data results in a small qualitative improvement in model discrimination relative to that seen with just the DOPE score. This is largely because of our limited ability to quantitatively predict effects of point mutations on in vivo protein activity. Further improvements in the methodology are required to facilitate improved utilization of single mutant data.
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Affiliation(s)
- Shruti Khare
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Munmun Bhasin
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Anusmita Sahoo
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Raghavan Varadarajan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,Chemical Biology Unit, Jawaharlal Nehru Center for Advanced Scientific Research, Bangalore, India
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17
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López-Blanco JR, Chacón P. KORP: knowledge-based 6D potential for fast protein and loop modeling. Bioinformatics 2019; 35:3013-3019. [DOI: 10.1093/bioinformatics/btz026] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 01/03/2019] [Accepted: 01/08/2019] [Indexed: 12/18/2022] Open
Abstract
Abstract
Motivation
Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation.
Results
We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function.
Availability and implementation
http://chaconlab.org/modeling/korp.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- José Ramón López-Blanco
- Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
| | - Pablo Chacón
- Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
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18
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Jiang F, Wu HN, Kang W, Wu YD. Developments and Applications of Coil-Library-Based Residue-Specific Force Fields for Molecular Dynamics Simulations of Peptides and Proteins. J Chem Theory Comput 2019; 15:2761-2773. [DOI: 10.1021/acs.jctc.8b00794] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fan Jiang
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Hao-Nan Wu
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Wei Kang
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yun-Dong Wu
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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19
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Rubenstein AB, Blacklock K, Nguyen H, Case DA, Khare SD. Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation. J Chem Theory Comput 2018; 14:6015-6025. [PMID: 30240210 DOI: 10.1021/acs.jctc.8b00303] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. In decoy discrimination tests, both Rosetta and Amber (ff14SBonlySC) energy functions performed well in scoring the native state as the lowest energy conformation in many cases, but several false minima were found in with both talaris2014 and Amber ff14SBonlySC scoring functions. The current default version of the Rosetta energy function, REF2015, which is parametrized on both small molecule and macromolecular benchmark sets to improve decoy discrimination, performs significantly better than talaris2014, highlighting the improvements made to the Rosetta scoring approach. There are no cases in Rosetta REF2015, and 8/140 cases in Amber, where a false minimum is found that is absent in the alternative landscape. In loop modeling tests, Amber ff14SBonlySC and REF2015 perform equivalently, although false minima are detected in several cases for both. The balance between dihedral, electrostatic, solvation and hydrogen bonding scores contribute to the existence of false minima. To take advantage of the semi-orthogonal nature of the Rosetta and Amber energy functions, we developed a technique that combines Amber and Rosetta conformational rankings to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation and should aid in model selection for structure evaluation and loop modeling tasks.
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20
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Deng H, Jia Y, Zhang Y. Protein structure prediction. INTERNATIONAL JOURNAL OF MODERN PHYSICS. B 2018; 32:1840009. [PMID: 30853739 PMCID: PMC6407873 DOI: 10.1142/s021797921840009x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used prediction methods and the community-wide critical assessment of protein structure prediction (CASP) experiments.
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Affiliation(s)
- Haiyou Deng
- College of Science, Huazhong Agricultural University, Wuhan 4R0070, P. R. China
| | - Ya Jia
- College of Physical Science and Technology, Central China Normal University, Wuhan 430079, P. R. China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA
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21
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An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction. COMPUTATION 2018. [DOI: 10.3390/computation6020039] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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22
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Hurst T, Xu X, Zhao P, Chen SJ. Quantitative Understanding of SHAPE Mechanism from RNA Structure and Dynamics Analysis. J Phys Chem B 2018; 122:4771-4783. [PMID: 29659274 DOI: 10.1021/acs.jpcb.8b00575] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) method probes RNA local structural and dynamic information at single nucleotide resolution. To gain quantitative insights into the relationship between nucleotide flexibility, RNA 3D structure, and SHAPE reactivity, we develop a 3D Structure-SHAPE Relationship model (3DSSR) to rebuild SHAPE profiles from 3D structures. The model starts from RNA structures and combines nucleotide interaction strength and conformational propensity, ligand (SHAPE reagent) accessibility, and base-pairing pattern through a composite function to quantify the correlation between SHAPE reactivity and nucleotide conformational stability. The 3DSSR model shows the relationship between SHAPE reactivity and RNA structure and energetics. Comparisons between the 3DSSR-predicted SHAPE profile and the experimental SHAPE data show correlation, suggesting that the extracted analytical function may have captured the key factors that determine the SHAPE reactivity profile. Furthermore, the theory offers an effective method to sieve RNA 3D models and exclude models that are incompatible with experimental SHAPE data.
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Affiliation(s)
- Travis Hurst
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Xiaojun Xu
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Peinan Zhao
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
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23
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Chu H, Liu H. TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones. J Chem Inf Model 2018; 58:430-442. [PMID: 29314837 DOI: 10.1021/acs.jcim.7b00677] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
To construct backbone structures of high designability is a primary aspect of computational protein design. We report here a side chain-independent statistical energy that aims at realistic modeling of through-space packing of polypeptide backbones. To mitigate the lack of explicit amino acid side chains, the model treats the interbackbone site packing as being dependent on peptide local conformation. In addition, new variables suitable for statistical analysis, one for relative orientation and another for distance, have been introduced to represent the intersite geometry based on the asymmetrical tetrahedron organization of distinct chemical groups surrounding the Cα-carbon atoms. The resulting tetrahedron-based backbone statistical energy (tetraBASE) model has been used to optimize the tertiary organizations of secondary structure elements (SSEs) of designated types with Monte Caro simulated annealing, starting from artificial initial configurations. The tetraBASE minimum energy structures can reproduce SSE packing frequently observed in native proteins with atomic root-mean-square deviations of 1-2 Å. The model has also been tested by examining the stability of native SSE arrangements under tetraBASE. The results suggest that tetraBASE model can be used to effectively represent interbackbone packing when designing backbone structures without explicitly knowing side chain types.
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Affiliation(s)
- Huanyu Chu
- School of Life Sciences, University of Science and Technology of China , 230027 Hefei, Anhui China.,Hefei National Laboratory for Physical Sciences at the Microscales , 230027 Hefei, Anhui China
| | - Haiyan Liu
- School of Life Sciences, University of Science and Technology of China , 230027 Hefei, Anhui China.,Hefei National Laboratory for Physical Sciences at the Microscales , 230027 Hefei, Anhui China.,Collaborative Innovation Center of Chemistry for Life Sciences , 230027 Hefei, Anhui China
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24
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Mirzaie M. Hydrophobic residues can identify native protein structures. Proteins 2018; 86:467-474. [DOI: 10.1002/prot.25466] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 12/28/2017] [Accepted: 01/23/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences; Tarbiat Modares University, Jalal Ale Ahmad Highway; Tehran Iran
- School of Biological Sciences; Institute for Research in Fundamental Sciences (IPM); Tehran Iran
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25
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Li B, Fooksa M, Heinze S, Meiler J. Finding the needle in the haystack: towards solving the protein-folding problem computationally. Crit Rev Biochem Mol Biol 2018; 53:1-28. [PMID: 28976219 PMCID: PMC6790072 DOI: 10.1080/10409238.2017.1380596] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/22/2022]
Abstract
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
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Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michaela Fooksa
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
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26
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Zhou HX, Pang X. Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation. Chem Rev 2018; 118:1691-1741. [PMID: 29319301 DOI: 10.1021/acs.chemrev.7b00305] [Citation(s) in RCA: 454] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.
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Affiliation(s)
- Huan-Xiang Zhou
- Department of Chemistry and Department of Physics, University of Illinois at Chicago , Chicago, Illinois 60607, United States.,Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
| | - Xiaodong Pang
- Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
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27
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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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28
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Hovan L, Oleinikovas V, Yalinca H, Kryshtafovych A, Saladino G, Gervasio FL. Assessment of the model refinement category in CASP12. Proteins 2017; 86 Suppl 1:152-167. [PMID: 29071750 DOI: 10.1002/prot.25409] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/03/2017] [Accepted: 10/24/2017] [Indexed: 01/07/2023]
Abstract
We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (nonrefinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z-scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified eight groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field.
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Affiliation(s)
- Ladislav Hovan
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | | | - Havva Yalinca
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | | | - Giorgio Saladino
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, United Kingdom
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29
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Xu G, Ma T, Zang T, Wang Q, Ma J. OPUS-CSF: A C-atom-based scoring function for ranking protein structural models. Protein Sci 2017; 27:286-292. [PMID: 29047165 PMCID: PMC5734313 DOI: 10.1002/pro.3327] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 10/14/2017] [Accepted: 10/16/2017] [Indexed: 12/12/2022]
Abstract
We report a C‐atom‐based scoring function, named OPUS‐CSF, for ranking protein structural models. Rather than using traditional Boltzmann formula, we built a scoring function (CSF score) based on the native distributions (derived from the entire PDB) of coordinate components of mainchain C (carbonyl) atoms on selected residues of peptide segments of 5, 7, 9, and 11 residues in length. In testing OPUS‐CSF on decoy recognition, it maximally recognized 257 native structures out of 278 targets in 11 commonly used decoy sets, significantly outperforming other popular all‐atom empirical potentials. The average correlation coefficient with TM‐score was also comparable with those of other potentials. OPUS‐CSF is a highly coarse‐grained scoring function, which only requires input of partial mainchain information, and very fast. Thus, it is suitable for applications at early stage of structural building.
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Affiliation(s)
- Gang Xu
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Tianqi Ma
- Applied Physics Program, Rice University, Houston, Texas.,Department of Bioengineering, Rice University, Houston, Texas
| | - Tianwu Zang
- Applied Physics Program, Rice University, Houston, Texas.,Department of Bioengineering, Rice University, Houston, Texas
| | - Qinghua Wang
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas
| | - Jianpeng Ma
- School of Life Sciences, Tsinghua University, Beijing, China.,Applied Physics Program, Rice University, Houston, Texas.,Department of Bioengineering, Rice University, Houston, Texas.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas
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30
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Xu G, Ma T, Zang T, Sun W, Wang Q, Ma J. OPUS-DOSP: A Distance- and Orientation-Dependent All-Atom Potential Derived from Side-Chain Packing. J Mol Biol 2017; 429:3113-3120. [PMID: 28864201 DOI: 10.1016/j.jmb.2017.08.013] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 07/27/2017] [Accepted: 08/22/2017] [Indexed: 01/18/2023]
Abstract
We report a new distance- and orientation-dependent, all-atom statistical potential derived from side-chain packing, named OPUS-DOSP, for protein structure modeling. The framework of OPUS-DOSP is based on OPUS-PSP, previously developed by us [JMB (2008), 376, 288-301], with refinement and new features. In particular, distance or orientation contribution is considered depending on the range of contact distance. A new auxiliary function in energy function is also introduced, in addition to the traditional Boltzmann term, in order to adjust the contributions of extreme cases. OPUS-DOSP was tested on 11 decoy sets commonly used for statistical potential benchmarking. Among 278 native structures, 239 and 249 native structures were recognized by OPUS-DOSP without and with the auxiliary function, respectively. The results show that OPUS-DOSP has an increased decoy recognition capability comparing with those of other relevant potentials to date.
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Affiliation(s)
- Gang Xu
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Tianqi Ma
- Applied Physics Program, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States
| | - Tianwu Zang
- Applied Physics Program, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States
| | - Weitao Sun
- Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing 100084, China
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Jianpeng Ma
- School of Life Sciences, Tsinghua University, Beijing 100084, China; Applied Physics Program, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States; Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, United States.
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31
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Meric G, Robinson AS, Roberts CJ. Driving Forces for Nonnative Protein Aggregation and Approaches to Predict Aggregation-Prone Regions. Annu Rev Chem Biomol Eng 2017; 8:139-159. [DOI: 10.1146/annurev-chembioeng-060816-101404] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Gulsum Meric
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716
| | - Anne S. Robinson
- Department of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118
| | - Christopher J. Roberts
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716
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32
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Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models. Sci Rep 2017; 7:40629. [PMID: 28074879 PMCID: PMC5225430 DOI: 10.1038/srep40629] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 12/08/2016] [Indexed: 12/31/2022] Open
Abstract
Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP).
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33
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Saravanan KM, Suvaithenamudhan S, Parthasarathy S, Selvaraj S. Pairwise contact energy statistical potentials can help to find probability of point mutations. Proteins 2016; 85:54-64. [PMID: 27761949 DOI: 10.1002/prot.25191] [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: 03/04/2016] [Revised: 06/16/2016] [Accepted: 10/13/2016] [Indexed: 11/10/2022]
Abstract
To adopt a particular fold, a protein requires several interactions between its amino acid residues. The energetic contribution of these residue-residue interactions can be approximated by extracting statistical potentials from known high resolution structures. Several methods based on statistical potentials extracted from unrelated proteins are found to make a better prediction of probability of point mutations. We postulate that the statistical potentials extracted from known structures of similar folds with varying sequence identity can be a powerful tool to examine probability of point mutation. By keeping this in mind, we have derived pairwise residue and atomic contact energy potentials for the different functional families that adopt the (α/β)8 TIM-Barrel fold. We carried out computational point mutations at various conserved residue positions in yeast Triose phosphate isomerase enzyme for which experimental results are already reported. We have also performed molecular dynamics simulations on a subset of point mutants to make a comparative study. The difference in pairwise residue and atomic contact energy of wildtype and various point mutations reveals probability of mutations at a particular position. Interestingly, we found that our computational prediction agrees with the experimental studies of Silverman et al. (Proc Natl Acad Sci 2001;98:3092-3097) and perform better prediction than iMutant and Cologne University Protein Stability Analysis Tool. The present work thus suggests deriving pairwise contact energy potentials and molecular dynamics simulations of functionally important folds could help us to predict probability of point mutations which may ultimately reduce the time and cost of mutation experiments. Proteins 2016; 85:54-64. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- K M Saravanan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, Tamilnadu, 600 025, India
| | - S Suvaithenamudhan
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tirchirappalli, Tamilnadu, 620 024, India
| | - S Parthasarathy
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tirchirappalli, Tamilnadu, 620 024, India
| | - S Selvaraj
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tirchirappalli, Tamilnadu, 620 024, India
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34
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Zheng Z, Wang T, Li P, Merz KM. KECSA-Movable Type Implicit Solvation Model (KMTISM). J Chem Theory Comput 2016; 11:667-82. [PMID: 25691832 PMCID: PMC4325602 DOI: 10.1021/ct5007828] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Indexed: 11/30/2022]
Abstract
![]()
Computation
of the solvation free energy for chemical and biological
processes has long been of significant interest. The key challenges
to effective solvation modeling center on the choice of potential
function and configurational sampling. Herein, an energy sampling
approach termed the “Movable Type” (MT) method, and
a statistical energy function for solvation modeling, “Knowledge-based
and Empirical Combined Scoring Algorithm” (KECSA) are developed
and utilized to create an implicit solvation model: KECSA-Movable
Type Implicit Solvation Model (KMTISM) suitable for the study of chemical
and biological systems. KMTISM is an implicit solvation model, but
the MT method performs energy sampling at the atom pairwise level.
For a specific molecular system, the MT method collects energies from
prebuilt databases for the requisite atom pairs at all relevant distance
ranges, which by its very construction encodes all possible molecular
configurations simultaneously. Unlike traditional statistical energy
functions, KECSA converts structural statistical information into
categorized atom pairwise interaction energies as a function of the
radial distance instead of a mean force energy function. Within the
implicit solvent model approximation, aqueous solvation free energies
are then obtained from the NVT ensemble partition function generated
by the MT method. Validation is performed against several subsets
selected from the Minnesota Solvation Database v2012. Results are
compared with several solvation free energy calculation methods, including
a one-to-one comparison against two commonly used classical implicit
solvation models: MM-GBSA and MM-PBSA. Comparison against a quantum
mechanics based polarizable continuum model is also discussed (Cramer
and Truhlar’s Solvation Model 12).
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Affiliation(s)
- Zheng Zheng
- Institute for Cyber Enabled Research, Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
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Zhou H, Skolnick J. A knowledge-based approach for predicting gene-disease associations. Bioinformatics 2016; 32:2831-8. [PMID: 27283949 DOI: 10.1093/bioinformatics/btw358] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 05/31/2016] [Indexed: 01/20/2023] Open
Abstract
MOTIVATION Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat deadly diseases and provide possible targets to cure these diseases. Genome-wide association studies (GWAS) have identified many individual genes associated with common diseases. To exploit the large amount of data obtained from GWAS studies and leverage our understanding of common as well as rare diseases, we have developed a knowledge-based approach to predict gene-disease associations. We first derive gene-gene mutual information by utilizing the cooccurrence of genes in known gene-disease association data. Subsequently, the mutual information is combined with known protein-protein interaction networks by a boosted tree regression method. RESULTS The method called Know-GENE is compared with the method of random walking on the heterogeneous network using the same input data. For a set of 960 diseases, using the same training data in testing in 3-fold cross-validation, the average recall rate within the top ranked 100 genes by Know-GENE is 65.0% compared with 37.9% by the state of the art random walking on heterogeneous network. This significant improvement is mostly due to the inclusion of knowledge-based mutual information. AVAILABILITY AND IMPLEMENTATION Predictions for genes associated with the 960 diseases are available at http://cssb2.biology.gatech.edu/knowgene CONTACT : skolnick@gatech.edu.
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Affiliation(s)
- Hongyi Zhou
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jeffrey Skolnick
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Chen L, He J. A distance- and orientation-dependent energy function of amino acid key blocks. Biopolymers 2016; 101:681-92. [PMID: 24222511 DOI: 10.1002/bip.22440] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Revised: 10/31/2013] [Accepted: 11/01/2013] [Indexed: 01/03/2023]
Abstract
Blocks are the selected portions of amino acids. They have been used effectively to represent amino acids in distinguishing the native conformation from the decoys. Although many statistical energy functions exist, most of them rely on the distances between two or more amino acids. In this study, the authors have developed a pairwise energy function "DOKB" that is both distance and orientation dependent, and it is based on the key blocks that bias the distal ends of side chains. The results suggest that both the distance and the orientation are needed to distinguish the fine details of the packing geometry. DOKB appears to perform well in recognizing native conformations when compared with six other energy functions. Highly packed clusters play important roles in stabilizing the structure. The investigation about the highly packed clusters at the residue level suggests that certain residue pairs in a low-energy region have lower probability to appear in the highly packed clusters than in the entire protein. The cluster energy term appears to significantly improve the recognition of the native conformations in ig_structal decoy set, in which more highly packed clusters are contained than in other decoy sets.
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Affiliation(s)
- Lin Chen
- Department of Computer Science, Old Dominion University, Norfolk, Virginia
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37
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Qin W, Zhao G, Carson M, Jia C, Lu H. Knowledge-based three-body potential for transcription factor binding site prediction. IET Syst Biol 2016; 10:23-9. [PMID: 26816396 DOI: 10.1049/iet-syb.2014.0066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
A structure-based statistical potential is developed for transcription factor binding site (TFBS) prediction. Besides the direct contact between amino acids from TFs and DNA bases, the authors also considered the influence of the neighbouring base. This three-body potential showed better discriminate powers than the two-body potential. They validate the performance of the potential in TFBS identification, binding energy prediction and binding mutation prediction.
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Affiliation(s)
- Wenyi Qin
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Guijun Zhao
- Key Laboratory of Molecular Embryology, Ministry of Health & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai 200040, People's Republic of China
| | - Matthew Carson
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Caiyan Jia
- School of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, People's Republic of China
| | - Hui Lu
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.
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38
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39
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Xu Y, Yu S, Zou JW, Hu G, Rahman NABD, Othman RB, Tao X, Huang M. Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine. PLoS One 2015; 10:e0144171. [PMID: 26636321 PMCID: PMC4670226 DOI: 10.1371/journal.pone.0144171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 11/13/2015] [Indexed: 01/12/2023] Open
Abstract
The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew's correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.
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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
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, Henan, China
| | - Shui Yu
- School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom
| | - Jian-Wei Zou
- School of Biotechnology and Chemical Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo, China
| | - Guixiang Hu
- School of Biotechnology and Chemical Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo, China
| | - Noorsaadah A. B. D. Rahman
- Department of Chemistry, Faculty of Sciences, University of Malaya, Kuala Lumpur, Malaysia
- Drug Design & Development Research Group, University of Malaya, Kuala Lumpur, Malaysia
| | - Rozana Binti Othman
- Drug Design & Development Research Group, University of Malaya, Kuala Lumpur, Malaysia
- Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Xia Tao
- State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing, China
| | - 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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40
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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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41
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Popov P, Grudinin S. Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates. J Chem Inf Model 2015; 55:2242-55. [DOI: 10.1021/acs.jcim.5b00372] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Petr Popov
- Université Grenoble Alpes, Laboratoire Jean Kuntzmann (LJK), F-38000 Grenoble, France
- CNRS, LJK, F-38000 Grenoble, France
- Inria, F-38000 Grenoble, France
- Moscow Institute
of Physics and Technology, 141700 Dolgoprudny, Russia
| | - Sergei Grudinin
- Université Grenoble Alpes, Laboratoire Jean Kuntzmann (LJK), F-38000 Grenoble, France
- CNRS, LJK, F-38000 Grenoble, France
- Inria, F-38000 Grenoble, France
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42
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Elhefnawy W, Chen L, Han Y, Li Y. ICOSA: A Distance-Dependent, Orientation-Specific Coarse-Grained Contact Potential for Protein Structure Modeling. J Mol Biol 2015; 427:2562-2576. [DOI: 10.1016/j.jmb.2015.05.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 05/21/2015] [Indexed: 11/16/2022]
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43
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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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44
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Chae MH, Krull F, Knapp EW. Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction. Proteins 2015; 83:881-90. [PMID: 25693513 DOI: 10.1002/prot.24782] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Revised: 02/06/2015] [Accepted: 02/10/2015] [Indexed: 12/20/2022]
Abstract
The DOcking decoy-based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance-dependent atom-pair interactions. To optimize the atom-pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand-receptor systems (or just pairs). Thus, a total of 8609 ligand-receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand-receptor systems, 1000 evenly sampled docking decoys with 0-10 Å interface root-mean-square-deviation (iRMSD) were generated with a method used before for protein-protein docking. A neural network-based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel-like energy landscape for the interaction between these hypothetical ligand-receptor systems. Thus, our method hierarchically models the overall funnel-like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom-pair-based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation-dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand-receptor systems and their decoys are freely available at http://agknapp.chemie.fu-berlin.de/doop/.
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Affiliation(s)
- Myong-Ho Chae
- Department of Biology, University of Science, Unjong-District, Pyongyang, DPR Korea
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45
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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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46
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Three-dimensional protein structure prediction: Methods and computational strategies. Comput Biol Chem 2014; 53PB:251-276. [DOI: 10.1016/j.compbiolchem.2014.10.001] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 10/03/2014] [Accepted: 10/07/2014] [Indexed: 01/01/2023]
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47
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Kim H, Kihara D. Detecting local residue environment similarity for recognizing near-native structure models. Proteins 2014; 82:3255-72. [PMID: 25132526 PMCID: PMC4237674 DOI: 10.1002/prot.24658] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Revised: 06/10/2014] [Accepted: 07/21/2014] [Indexed: 12/14/2022]
Abstract
We developed a new representation of local amino acid environments in protein structures called the Side-chain Depth Environment (SDE). An SDE defines a local structural environment of a residue considering the coordinates and the depth of amino acids that locate in the vicinity of the side-chain centroid of the residue. SDEs are general enough that similar SDEs are found in protein structures with globally different folds. Using SDEs, we developed a procedure called PRESCO (Protein Residue Environment SCOre) for selecting native or near-native models from a pool of computational models. The procedure searches similar residue environments observed in a query model against a set of representative native protein structures to quantify how native-like SDEs in the model are. When benchmarked on commonly used computational model datasets, our PRESCO compared favorably with the other existing scoring functions in selecting native and near-native models.
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Affiliation(s)
- Hyungrae Kim
- Department of Biological Sciences, Purdue University, West Lafayette IN, 47906, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette IN, 47906, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
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48
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Park J, Saitou K. ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structures. BMC Bioinformatics 2014; 15:307. [PMID: 25236673 PMCID: PMC4262145 DOI: 10.1186/1471-2105-15-307] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2014] [Accepted: 09/09/2014] [Indexed: 12/31/2022] Open
Abstract
Background Multibody potentials accounting for cooperative effects of molecular interactions have shown better accuracy than typical pairwise potentials. The main challenge in the development of such potentials is to find relevant structural features that characterize the tightly folded proteins. Also, the side-chains of residues adopt several specific, staggered conformations, known as rotamers within protein structures. Different molecular conformations result in different dipole moments and induce charge reorientations. However, until now modeling of the rotameric state of residues had not been incorporated into the development of multibody potentials for modeling non-bonded interactions in protein structures. Results In this study, we develop a new multibody statistical potential which can account for the influence of rotameric states on the specificity of atomic interactions. In this potential, named “rotamer-dependent atomic statistical potential” (ROTAS), the interaction between two atoms is specified by not only the distance and relative orientation but also by two state parameters concerning the rotameric state of the residues to which the interacting atoms belong. It was clearly found that the rotameric state is correlated to the specificity of atomic interactions. Such rotamer-dependencies are not limited to specific type or certain range of interactions. The performance of ROTAS was tested using 13 sets of decoys and was compared to those of existing atomic-level statistical potentials which incorporate orientation-dependent energy terms. The results show that ROTAS performs better than other competing potentials not only in native structure recognition, but also in best model selection and correlation coefficients between energy and model quality. Conclusions A new multibody statistical potential, ROTAS accounting for the influence of rotameric states on the specificity of atomic interactions was developed and tested on decoy sets. The results show that ROTAS has improved ability to recognize native structure from decoy models compared to other potentials. The effectiveness of ROTAS may provide insightful information for the development of many applications which require accurate side-chain modeling such as protein design, mutation analysis, and docking simulation. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-307) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Kazuhiro Saitou
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
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49
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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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50
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On simplified global nonlinear function for fitness landscape: a case study of inverse protein folding. PLoS One 2014; 9:e104403. [PMID: 25110986 PMCID: PMC4128808 DOI: 10.1371/journal.pone.0104403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2013] [Accepted: 07/14/2014] [Indexed: 11/19/2022] Open
Abstract
The construction of fitness landscape has broad implication in understanding molecular evolution, cellular epigenetic state, and protein structures. We studied the problem of constructing fitness landscape of inverse protein folding or protein design, with the aim to generate amino acid sequences that would fold into an a priori determined structural fold which would enable engineering novel or enhanced biochemistry. For this task, an effective fitness function should allow identification of correct sequences that would fold into the desired structure. In this study, we showed that nonlinear fitness function for protein design can be constructed using a rectangular kernel with a basis set of proteins and decoys chosen a priori. The full landscape for a large number of protein folds can be captured using only 480 native proteins and 3,200 non-protein decoys via a finite Newton method. A blind test of a simplified version of fitness function for sequence design was carried out to discriminate simultaneously 428 native sequences not homologous to any training proteins from 11 million challenging protein-like decoys. This simplified function correctly classified 408 native sequences (20 misclassifications, 95% correct rate), which outperforms several other statistical linear scoring function and optimized linear function. Our results further suggested that for the task of global sequence design of 428 selected proteins, the search space of protein shape and sequence can be effectively parametrized with just about 3,680 carefully chosen basis set of proteins and decoys, and we showed in addition that the overall landscape is not overly sensitive to the specific choice of this set. Our results can be generalized to construct other types of fitness landscape.
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