1
|
Bhowmick S, Jing T, Wang W, Zhang EY, Zhang F, Yang Y. In Silico Protein Folding Prediction of COVID-19 Mutations and Variants. Biomolecules 2022; 12:1665. [PMID: 36359015 PMCID: PMC9688002 DOI: 10.3390/biom12111665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/08/2022] [Accepted: 11/09/2022] [Indexed: 08/27/2023] Open
Abstract
With its fast-paced mutagenesis, the SARS-CoV-2 Omicron variant has threatened many societies worldwide. Strategies for predicting mutagenesis such as the computational prediction of SARS-CoV-2 structural diversity and its interaction with the human receptor will greatly benefit our understanding of the virus and help develop therapeutics against it. We aim to use protein structure prediction algorithms along with molecular docking to study the effects of various mutations in the Receptor Binding Domain (RBD) of the SARS-CoV-2 and its key interactions with the angiotensin-converting enzyme 2 (ACE-2) receptor. The RBD structures of the naturally occurring variants of SARS-CoV-2 were generated from the WUHAN-Hu-1 using the trRosetta algorithm. Docking (HADDOCK) and binding analysis (PRODIGY) between the predicted RBD sequences and ACE-2 highlighted key interactions at the Receptor-Binding Motif (RBM). Further mutagenesis at conserved residues in the Original, Delta, and Omicron variants (P499S and T500R) demonstrated stronger binding and interactions with the ACE-2 receptor. The predicted T500R mutation underwent some preliminary tests in vitro for its binding and transmissibility in cells; the results correlate with the in-silico analysis. In summary, we suggest conserved residues P499 and T500 as potential mutation sites that could increase the binding affinity and yet do not exist in nature. This work demonstrates the use of the trRosetta algorithm to predict protein structure and future mutations at the RBM of SARS-CoV-2, followed by experimental testing for further efficacy verification. It is important to understand the protein structure and folding to help develop potential therapeutics.
Collapse
Affiliation(s)
| | | | | | | | | | - Yanmin Yang
- Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, 1201 Welch Road, MSLS, P259, Stanford, CA 94305, USA
| |
Collapse
|
2
|
Bongirwar V, Mokhade AS. Different methods, techniques and their limitations in protein structure prediction: A review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 173:72-82. [PMID: 35588858 DOI: 10.1016/j.pbiomolbio.2022.05.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 04/16/2022] [Accepted: 05/11/2022] [Indexed: 11/17/2022]
Abstract
Because of the increase in different types of diseases in human habitats, demands for designing various types of drugs are also increasing. Protein and its structure play a very important role in drug design. Therefore researchers from different areas like mathematics, medicines, and computer science are teaming up for getting better solutions in the said field. In this paper, we have discussed different methods of secondary and tertiary protein structure prediction (PSP), along with the limitations of different approaches. Different types of datasets used in PSP are also discussed here. This paper also tells about different performance measures to evaluate the prediction accuracy of PSP methods. Different software's/servers are available for download, which are used to find the protein structures for the input protein sequence. These softwares will also help to compare the performance of any new algorithm with other available methods. Details of those softwares are also mentioned in this paper.
Collapse
Affiliation(s)
| | - A S Mokhade
- Visvesvaraya National Institute of Technology, Nagpur, India
| |
Collapse
|
3
|
Pearce R, Zhang Y. Toward the solution of the protein structure prediction problem. J Biol Chem 2021; 297:100870. [PMID: 34119522 PMCID: PMC8254035 DOI: 10.1016/j.jbc.2021.100870] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/07/2021] [Accepted: 06/09/2021] [Indexed: 11/20/2022] Open
Abstract
Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.
Collapse
Affiliation(s)
- Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, USA.
| |
Collapse
|
4
|
Pearce R, Zhang Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr Opin Struct Biol 2021; 68:194-207. [PMID: 33639355 PMCID: PMC8222070 DOI: 10.1016/j.sbi.2021.01.007] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 01/09/2021] [Accepted: 01/18/2021] [Indexed: 12/26/2022]
Abstract
Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.
Collapse
Affiliation(s)
- Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
| |
Collapse
|
5
|
Hameduh T, Haddad Y, Adam V, Heger Z. Homology modeling in the time of collective and artificial intelligence. Comput Struct Biotechnol J 2020; 18:3494-3506. [PMID: 33304450 PMCID: PMC7695898 DOI: 10.1016/j.csbj.2020.11.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/04/2020] [Accepted: 11/04/2020] [Indexed: 12/12/2022] Open
Abstract
Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.
Collapse
Affiliation(s)
- Tareq Hameduh
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
| | - Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| |
Collapse
|
6
|
Zhang GJ, Wang XQ, Ma LF, Wang LJ, Hu J, Zhou XG. Two-Stage Distance Feature-based Optimization Algorithm for De novo Protein Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2119-2130. [PMID: 31107659 DOI: 10.1109/tcbb.2019.2917452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
De novo protein structure prediction can be treated as a conformational space optimization problem under the guidance of an energy function. However, it is a challenge of how to design an accurate energy function which ensures low-energy conformations close to native structures. Fortunately, recent studies have shown that the accuracy of de novo protein structure prediction can be significantly improved by integrating the residue-residue distance information. In this paper, a two-stage distance feature-based optimization algorithm (TDFO) for de novo protein structure prediction is proposed within the framework of evolutionary algorithm. In TDFO, a similarity model is first designed by using feature information which is extracted from distance profiles by bisecting K-means algorithm. The similarity model-based selection strategy is then developed to guide conformation search, and thus improve the quality of the predicted models. Moreover, global and local mutation strategies are designed, and a state estimation strategy is also proposed to strike a trade-off between the exploration and exploitation of the search space. Experimental results of 35 benchmark proteins show that the proposed TDFO can improve prediction accuracy for a large portion of test proteins.
Collapse
|
7
|
Agbleke AA, Amitai A, Buenrostro JD, Chakrabarti A, Chu L, Hansen AS, Koenig KM, Labade AS, Liu S, Nozaki T, Ovchinnikov S, Seeber A, Shaban HA, Spille JH, Stephens AD, Su JH, Wadduwage D. Advances in Chromatin and Chromosome Research: Perspectives from Multiple Fields. Mol Cell 2020; 79:881-901. [PMID: 32768408 PMCID: PMC7888594 DOI: 10.1016/j.molcel.2020.07.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/12/2020] [Accepted: 07/06/2020] [Indexed: 12/12/2022]
Abstract
Nucleosomes package genomic DNA into chromatin. By regulating DNA access for transcription, replication, DNA repair, and epigenetic modification, chromatin forms the nexus of most nuclear processes. In addition, dynamic organization of chromatin underlies both regulation of gene expression and evolution of chromosomes into individualized sister objects, which can segregate cleanly to different daughter cells at anaphase. This collaborative review shines a spotlight on technologies that will be crucial to interrogate key questions in chromatin and chromosome biology including state-of-the-art microscopy techniques, tools to physically manipulate chromatin, single-cell methods to measure chromatin accessibility, computational imaging with neural networks and analytical tools to interpret chromatin structure and dynamics. In addition, this review provides perspectives on how these tools can be applied to specific research fields such as genome stability and developmental biology and to test concepts such as phase separation of chromatin.
Collapse
Affiliation(s)
| | - Assaf Amitai
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Aditi Chakrabarti
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Lingluo Chu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Anders S Hansen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kristen M Koenig
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; JHDSF Program, Harvard University, Cambridge, MA 02138, USA
| | - Ajay S Labade
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sirui Liu
- FAS Division of Science, Harvard University, Cambridge, MA 02138, USA
| | - Tadasu Nozaki
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Sergey Ovchinnikov
- JHDSF Program, Harvard University, Cambridge, MA 02138, USA; FAS Division of Science, Harvard University, Cambridge, MA 02138, USA
| | - Andrew Seeber
- JHDSF Program, Harvard University, Cambridge, MA 02138, USA; Center for Advanced Imaging, Harvard University, Cambridge, MA 02138, USA.
| | - Haitham A Shaban
- Center for Advanced Imaging, Harvard University, Cambridge, MA 02138, USA; Spectroscopy Department, Physics Division, National Research Centre, Dokki, 12622 Cairo, Egypt
| | - Jan-Hendrik Spille
- Department of Physics, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Andrew D Stephens
- Biology Department, University of Massachusetts, Amherst, Amherst, MA 01003, USA
| | - Jun-Han Su
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Dushan Wadduwage
- JHDSF Program, Harvard University, Cambridge, MA 02138, USA; Center for Advanced Imaging, Harvard University, Cambridge, MA 02138, USA
| |
Collapse
|
8
|
Li Y, Hu J, Zhang C, Yu DJ, Zhang Y. ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks. Bioinformatics 2020; 35:4647-4655. [PMID: 31070716 DOI: 10.1093/bioinformatics/btz291] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 03/18/2019] [Accepted: 04/17/2019] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank. RESULTS We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. AVAILABILITY AND IMPLEMENTATION https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yang Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Jun Hu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| |
Collapse
|
9
|
Shrestha R, Fajardo E, Gil N, Fidelis K, Kryshtafovych A, Monastyrskyy B, Fiser A. Assessing the accuracy of contact predictions in CASP13. Proteins 2019; 87:1058-1068. [PMID: 31587357 PMCID: PMC6851495 DOI: 10.1002/prot.25819] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/17/2019] [Accepted: 09/17/2019] [Indexed: 01/07/2023]
Abstract
The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.
Collapse
Affiliation(s)
- Rojan Shrestha
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Eduardo Fajardo
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Nelson Gil
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Bohdan Monastyrskyy
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Andras Fiser
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| |
Collapse
|
10
|
Zhang H, Zhang Q, Ju F, Zhu J, Gao Y, Xie Z, Deng M, Sun S, Zheng WM, Bu D. Predicting protein inter-residue contacts using composite likelihood maximization and deep learning. BMC Bioinformatics 2019; 20:537. [PMID: 31664895 PMCID: PMC6821021 DOI: 10.1186/s12859-019-3051-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 08/22/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. RESULTS In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite-likelihood, i.e., the product of conditional probability of all residue pairs. Composite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, including PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction accuracy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present a successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. CONCLUSIONS Composite likelihood maximization algorithm can efficiently estimate the parameters of Markov Random Fields and can improve the prediction accuracy of protein inter-residue contacts.
Collapse
Affiliation(s)
- Haicang Zhang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qi Zhang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Fusong Ju
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jianwei Zhu
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yujuan Gao
- Center for Quantitative Biology, School of Mathematical Sciences, Center for Statistical Sciences, Peking University, Beijing, China
| | - Ziwei Xie
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Minghua Deng
- Center for Quantitative Biology, School of Mathematical Sciences, Center for Statistical Sciences, Peking University, Beijing, China
| | - Shiwei Sun
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
| | - Wei-Mou Zheng
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China.
| | - Dongbo Bu
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China.
| |
Collapse
|
11
|
Schaarschmidt J, Monastyrskyy B, Kryshtafovych A, Bonvin AM. Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age. Proteins 2018; 86 Suppl 1:51-66. [PMID: 29071738 PMCID: PMC5820169 DOI: 10.1002/prot.25407] [Citation(s) in RCA: 126] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/06/2017] [Accepted: 10/24/2017] [Indexed: 12/20/2022]
Abstract
Following up on the encouraging results of residue-residue contact prediction in the CASP11 experiment, we present the analysis of predictions submitted for CASP12. The submissions include predictions of 34 groups for 38 domains classified as free modeling targets which are not accessible to homology-based modeling due to a lack of structural templates. CASP11 saw a rise of coevolution-based methods outperforming other approaches. The improvement of these methods coupled to machine learning and sequence database growth are most likely the main driver for a significant improvement in average precision from 27% in CASP11 to 47% in CASP12. In more than half of the targets, especially those with many homologous sequences accessible, precisions above 90% were achieved with the best predictors reaching a precision of 100% in some cases. We furthermore tested the impact of using these contacts as restraints in ab initio modeling of 14 single-domain free modeling targets using Rosetta. Adding contacts to the Rosetta calculations resulted in improvements of up to 26% in GDT_TS within the top five structures.
Collapse
Affiliation(s)
- Joerg Schaarschmidt
- Faculty of Science ‐ ChemistryComputational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht UniversityUtrechtThe Netherlands
| | | | | | - Alexandre M.J.J. Bonvin
- Faculty of Science ‐ ChemistryComputational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht UniversityUtrechtThe Netherlands
| |
Collapse
|
12
|
Lam SD, Das S, Sillitoe I, Orengo C. An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences. Acta Crystallogr D Struct Biol 2017; 73:628-640. [PMID: 28777078 PMCID: PMC5571743 DOI: 10.1107/s2059798317008920] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 06/14/2017] [Indexed: 12/02/2022] Open
Abstract
Computational modelling of proteins has been a major catalyst in structural biology. Bioinformatics groups have exploited the repositories of known structures to predict high-quality structural models with high efficiency at low cost. This article provides an overview of comparative modelling, reviews recent developments and describes resources dedicated to large-scale comparative modelling of genome sequences. The value of subclustering protein domain superfamilies to guide the template-selection process is investigated. Some recent cases in which structural modelling has aided experimental work to determine very large macromolecular complexes are also cited.
Collapse
Affiliation(s)
- Su Datt Lam
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
- School of Biosciences and Biotechnology, Faculty of Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Sayoni Das
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
| | - Christine Orengo
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
| |
Collapse
|
13
|
Yang J, Jin QY, Zhang B, Shen HB. R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter. ACTA ACUST UNITED AC 2016; 32:2435-43. [PMID: 27153618 DOI: 10.1093/bioinformatics/btw181] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 04/03/2016] [Indexed: 11/12/2022]
Abstract
MOTIVATION Inter-residue contacts in proteins dictate the topology of protein structures. They are crucial for protein folding and structural stability. Accurate prediction of residue contacts especially for long-range contacts is important to the quality of ab inito structure modeling since they can enforce strong restraints to structure assembly. RESULTS In this paper, we present a new Residue-Residue Contact predictor called R2C that combines machine learning-based and correlated mutation analysis-based methods, together with a two-dimensional Gaussian noise filter to enhance the long-range residue contact prediction. Our results show that the outputs from the machine learning-based method are concentrated with better performance on short-range contacts; while for correlated mutation analysis-based approach, the predictions are widespread with higher accuracy on long-range contacts. An effective query-driven dynamic fusion strategy proposed here takes full advantages of the two different methods, resulting in an impressive overall accuracy improvement. We also show that the contact map directly from the prediction model contains the interesting Gaussian noise, which has not been discovered before. Different from recent studies that tried to further enhance the quality of contact map by removing its transitive noise, we designed a new two-dimensional Gaussian noise filter, which was especially helpful for reinforcing the long-range residue contact prediction. Tested on recent CASP10/11 datasets, the overall top L/5 accuracy of our final R2C predictor is 17.6%/15.5% higher than the pure machine learning-based method and 7.8%/8.3% higher than the correlated mutation analysis-based approach for the long-range residue contact prediction. AVAILABILITY AND IMPLEMENTATION http://www.csbio.sjtu.edu.cn/bioinf/R2C/Contact:hbshen@sjtu.edu.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Jing Yang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Qi-Yu Jin
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Biao Zhang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| |
Collapse
|
14
|
Zhang H, Huang Q, Bei Z, Wei Y, Floudas CA. COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming. Proteins 2016; 84:332-48. [PMID: 26756402 DOI: 10.1002/prot.24979] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 11/19/2015] [Accepted: 12/10/2015] [Indexed: 12/28/2022]
Abstract
In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.
Collapse
Affiliation(s)
- Huiling Zhang
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qingsheng Huang
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhendong Bei
- Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yanjie Wei
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Christodoulos A Floudas
- Department of Chemical Engineering, Texas A&M University, College Station, Texas, 77843.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas, 77843
| |
Collapse
|
15
|
Abstract
Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.
Collapse
Affiliation(s)
- H. Pezeshgi Modarres
- Molecular Cell Biomechanics Laboratory
- Departments of Bioengineering and Mechanical Engineering
- University of California Berkeley
- Berkeley
- USA
| | - M. R. Mofrad
- Molecular Cell Biomechanics Laboratory
- Departments of Bioengineering and Mechanical Engineering
- University of California Berkeley
- Berkeley
- USA
| | - A. Sanati-Nezhad
- BioMEMS and Bioinspired Microfluidic Laboratory
- Department of Mechanical and Manufacturing Engineering
- University of Calgary
- Calgary
- Canada
| |
Collapse
|
16
|
Zhang W, Yang J, He B, Walker SE, Zhang H, Govindarajoo B, Virtanen J, Xue Z, Shen HB, Zhang Y. Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11. Proteins 2015; 84 Suppl 1:76-86. [PMID: 26370505 DOI: 10.1002/prot.24930] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 08/26/2015] [Accepted: 09/10/2015] [Indexed: 11/12/2022]
Abstract
We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Wenxuan Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Jianyi Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Baoji He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Sara Elizabeth Walker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Brandon Govindarajoo
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Jouko Virtanen
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Zhidong Xue
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Hong-Bin Shen
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109. .,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109.
| |
Collapse
|
17
|
Yang J, He BJ, Jang R, Zhang Y, Shen HB. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins. Bioinformatics 2015; 31:3773-81. [PMID: 26254435 DOI: 10.1093/bioinformatics/btv459] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 08/02/2015] [Indexed: 01/19/2023] Open
Abstract
MOTIVATION Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g., >3 bonds, is too low to effectively assist structure assembly simulations. RESULTS We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. AVAILABILITY AND IMPLEMENTATION http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ CONTACT zhng@umich.edu or hbshen@sjtu.edu.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Jing Yang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Bao-Ji He
- State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China, Department of Computational Medicine and Bioinformatics and
| | - Richard Jang
- Department of Computational Medicine and Bioinformatics and
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and
| |
Collapse
|