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Liang F, Sun M, Xie L, Zhao X, Liu D, Zhao K, Zhang G. Recent advances and challenges in protein complex model accuracy estimation. Comput Struct Biotechnol J 2024; 23:1824-1832. [PMID: 38707538 PMCID: PMC11066466 DOI: 10.1016/j.csbj.2024.04.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.
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Affiliation(s)
| | | | - Lei Xie
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xuanfeng Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Dong Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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2
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Stapor K, Kotowski K, Smolarczyk T, Roterman I. Lightweight ProteinUnet2 network for protein secondary structure prediction: a step towards proper evaluation. BMC Bioinformatics 2022; 23:100. [PMID: 35317722 PMCID: PMC8939211 DOI: 10.1186/s12859-022-04623-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 02/28/2022] [Indexed: 11/10/2022] Open
Abstract
Background The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings—the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman–Pearson approach is not appropriate. Results We present a lightweight deep network ProteinUnet2 for SS prediction which is based on U-Net convolutional architecture and evolutionary-based input features (from PSSM and HHblits) as well as SPOT-Contact features. Through an extensive evaluation study, we report the performance of ProteinUnet2 in comparison with top SS prediction methods based on evolutionary information (SAINT and SPOT-1D). We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher–Pitman permutation tests accompanied by practical significance measured by Cohen’s effect size. Conclusions Our results suggest that ProteinUnet2 architecture has much shorter training and inference times while maintaining results similar to SAINT and SPOT-1D predictors. Taking into account the relatively long times of calculating evolutionary-based features (from PSSM in particular), it would be worth conducting the predictive ability tests on embeddings as input features in the future. We strongly believe that our proposed here statistical methodology for the evaluation of SS prediction results will be adopted and used (and even expanded) by the research community. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04623-z.
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Affiliation(s)
- Katarzyna Stapor
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland.
| | - Krzysztof Kotowski
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Tomasz Smolarczyk
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Medyczna 7, 30-688, Kraków, Poland
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3
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Antibody structure prediction using interpretable deep learning. PATTERNS (NEW YORK, N.Y.) 2022; 3:100406. [PMID: 35199061 PMCID: PMC8848015 DOI: 10.1016/j.patter.2021.100406] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/03/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022]
Abstract
Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. Here, we present DeepAb, a deep learning method for predicting accurate antibody FV structures from sequence. We evaluate DeepAb on a set of structurally diverse, therapeutically relevant antibodies and find that our method consistently outperforms the leading alternatives. Previous deep learning methods have operated as “black boxes” and offered few insights into their predictions. By introducing a directly interpretable attention mechanism, we show our network attends to physically important residue pairs (e.g., proximal aromatics and key hydrogen bonding interactions). Finally, we present a novel mutant scoring metric derived from network confidence and show that for a particular antibody, all eight of the top-ranked mutations improve binding affinity. This model will be useful for a broad range of antibody prediction and design tasks. DeepAb, a deep learning method for antibody structure, is presented Structures from DeepAb are more accurate than alternatives Outputs of DeepAb provide interpretable insights into structure predictions DeepAb predictions should facilitate design of novel antibody therapeutics
Accurate structure models are critical for understanding the properties of potential therapeutic antibodies. Conventional methods for protein structure determination require significant investments of time and resources and may fail. Although greatly improved, methods for general protein structure prediction still cannot consistently provide the accuracy necessary to understand or design antibodies. We present a deep learning method for antibody structure prediction and demonstrate improvement over alternatives on diverse, therapeutically relevant benchmarks. In addition to its improved accuracy, our method reveals interpretable outputs about specific amino acids and residue interactions that should facilitate design of novel therapeutic antibodies.
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4
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Mulligan VK. Current directions in combining simulation-based macromolecular modeling approaches with deep learning. Expert Opin Drug Discov 2021; 16:1025-1044. [PMID: 33993816 DOI: 10.1080/17460441.2021.1918097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Introduction: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as drugs. Success rates are limited for any of these tasks, however. Recently, deep neural network-based methods have greatly enhanced the accuracy of predictions of protein structure from sequence, generating excitement about the potential impact of deep learning.Areas covered: This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design. Particular focus is given to the interplay between simulation-based and neural network-based approaches.Expert opinion: As deep learning grows integral to macromolecular modeling, simulation- and neural network-based approaches must grow more tightly interconnected. Modular software architecture must emerge allowing both types of tools to be combined with maximal versatility. Open sharing of code under permissive licenses will be essential. Although experiments will remain the gold standard for reliable information to guide drug discovery, we may soon see successful drug development projects based on high-accuracy predictions from algorithms that combine simulation with deep learning - the ultimate validation of this combination's power.
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5
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Calcott MJ, Owen JG, Ackerley DF. Efficient rational modification of non-ribosomal peptides by adenylation domain substitution. Nat Commun 2020; 11:4554. [PMID: 32917865 PMCID: PMC7486941 DOI: 10.1038/s41467-020-18365-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 08/19/2020] [Indexed: 12/22/2022] Open
Abstract
Non-ribosomal peptide synthetase (NRPS) enzymes form modular assembly-lines, wherein each module governs the incorporation of a specific monomer into a short peptide product. Modules are comprised of one or more key domains, including adenylation (A) domains, which recognise and activate the monomer substrate; condensation (C) domains, which catalyse amide bond formation; and thiolation (T) domains, which shuttle reaction intermediates between catalytic domains. This arrangement offers prospects for rational peptide modification via substitution of substrate-specifying domains. For over 20 years, it has been considered that C domains play key roles in proof-reading the substrate; a presumption that has greatly complicated rational NRPS redesign. Here we present evidence from both directed and natural evolution studies that any substrate-specifying role for C domains is likely to be the exception rather than the rule, and that novel non-ribosomal peptides can be generated by substitution of A domains alone. We identify permissive A domain recombination boundaries and show that these allow us to efficiently generate modified pyoverdine peptides at high yields. We further demonstrate the transferability of our approach in the PheATE-ProCAT model system originally used to infer C domain substrate specificity, generating modified dipeptide products at yields that are inconsistent with the prevailing dogma.
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Affiliation(s)
- Mark J Calcott
- School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
- Centre for Biodiscovery and Maurice Wilkins Centre for Molecular Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
| | - Jeremy G Owen
- School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
- Centre for Biodiscovery and Maurice Wilkins Centre for Molecular Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
| | - David F Ackerley
- School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand.
- Centre for Biodiscovery and Maurice Wilkins Centre for Molecular Biodiscovery, Victoria University of Wellington, Wellington, New Zealand.
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6
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Wang W, Wang J, Xu D, Shang Y. Two New Heuristic Methods for Protein Model Quality Assessment. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1430-1439. [PMID: 30418914 PMCID: PMC8988942 DOI: 10.1109/tcbb.2018.2880202] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Protein tertiary structure prediction is an important open challenge in bioinformatics and requires effective methods to accurately evaluate the quality of protein 3-D models generated computationally. Many quality assessment (QA) methods have been proposed over the past three decades. However, the accuracy or robustness is unsatisfactory for practical applications. In this paper, two new heuristic QA methods are proposed: MUfoldQA_S and MUfoldQA_C. The MUfoldQA_S is a quasi-single-model QA method that assesses the model quality based on the known protein structures with similar sequences. This algorithm can be directly applied to protein fragments without the necessity of building a full structural model. A BLOSUM-based heuristic is also introduced to help differentiate accurate templates from poor ones. In MUfoldQA_C, the ideas from MUfoldQA_S were combined with the consensus approach to create a multi-model QA method that could also utilize information from existing reference models and have demonstrated improved performance. Extensive experimental results of these two methods have shown significant improvement over existing methods. In addition, both methods have been blindly tested in the CASP12 world-wide competition in the protein structure prediction field and ranked as top performers in their respective categories.
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7
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Investigation of machine learning techniques on proteomics: A comprehensive survey. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2019; 149:54-69. [PMID: 31568792 DOI: 10.1016/j.pbiomolbio.2019.09.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 09/16/2019] [Accepted: 09/23/2019] [Indexed: 11/21/2022]
Abstract
Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete arrangement of proteins that are created or altered by a life form or framework of the organism. Proteome fluctuates with time and unambiguous prerequisites, or stresses, that a cell or organism experiences. Proteomics is an interdisciplinary area that has derived from the hereditary data of different genome ventures. Much proteomics information is gathered with the assistance of high throughput techniques, for example, mass spectrometry and microarray. It would regularly take weeks or months to analyze the information and perform examinations by hand. Therefore, scholars and scientific experts are teaming up with computer science researchers and mathematicians to make projects and pipeline to computationally examine the protein information. Utilizing bioinformatics procedures, scientists are prepared to do quicker investigation and protein information storing. The goal of this paper is to brief about the review of machine learning procedures and its application in the field of proteomics.
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8
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Nguyen SP, Li Z, Xu D, Shang Y. New Deep Learning Methods for Protein Loop Modeling. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:596-606. [PMID: 29990046 PMCID: PMC6580050 DOI: 10.1109/tcbb.2017.2784434] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Computational protein structure prediction is a long-standing challenge in bioinformatics. In the process of predicting protein 3D structures, it is common that parts of an experimental structure are missing or parts of a predicted structure need to be remodeled. The process of predicting local protein structures of particular regions is called loop modeling. In this paper, five new loop modeling methods based on machine learning techniques, called NearLooper, ConLooper, ResLooper, HyLooper1, and HyLooper2 are proposed. NearLooper is based on the nearest neighbor technique. ConLooper applies deep convolutional neural networks to predict ${\mathrm{C}}_{{{\alpha }}}$Cα atoms distance matrix as an orientation-independent representation of protein structure. ResLooper uses residual neural networks instead of deep convolutional neural networks. HyLooper1 combines the results of NearLooper and ConLooper while HyLooper2 combines NearLooper and ResLooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28 and 54 percent of average RMSD on two datasets while being comparable on the other one.
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9
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Perilla JR, Zhao G, Lu M, Ning J, Hou G, Byeon IJL, Gronenborn AM, Polenova T, Zhang P. CryoEM Structure Refinement by Integrating NMR Chemical Shifts with Molecular Dynamics Simulations. J Phys Chem B 2017; 121:3853-3863. [PMID: 28181439 DOI: 10.1021/acs.jpcb.6b13105] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Single particle cryoEM has emerged as a powerful method for structure determination of proteins and complexes, complementing X-ray crystallography and NMR spectroscopy. Yet, for many systems, the resolution of cryoEM density map has been limited to 4-6 Å, which only allows for resolving bulky amino acids side chains, thus hindering accurate model building from the density map. On the other hand, experimental chemical shifts (CS) from solution and solid state MAS NMR spectra provide atomic level data for each amino acid within a molecule or a complex; however, structure determination of large complexes and assemblies based on NMR data alone remains challenging. Here, we present a novel integrated strategy to combine the highly complementary experimental data from cryoEM and NMR computationally by molecular dynamics simulations to derive an atomistic model, which is not attainable by either approach alone. We use the HIV-1 capsid protein (CA) C-terminal domain as well as the large capsid assembly to demonstrate the feasibility of this approach, termed NMR CS-biased cryoEM structure refinement.
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Affiliation(s)
- Juan R Perilla
- Department of Physics and Beckman Institute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - Gongpu Zhao
- Department of Structural Biology, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States
| | - Manman Lu
- Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Department of Chemistry and Biochemistry, University of Delaware , Newark, Delaware 19716, United States
| | - Jiying Ning
- Department of Structural Biology, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States
| | - Guangjin Hou
- Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Department of Chemistry and Biochemistry, University of Delaware , Newark, Delaware 19716, United States
| | - In-Ja L Byeon
- Department of Structural Biology, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States
| | - Angela M Gronenborn
- Department of Structural Biology, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States
| | - Tatyana Polenova
- Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Department of Chemistry and Biochemistry, University of Delaware , Newark, Delaware 19716, United States
| | - Peijun Zhang
- Department of Structural Biology, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Pittsburgh Center for HIV Protein Interactions, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania 15260, United States.,Division of Structural Biology, University of Oxford, The Henry Wellcome Building for Genomic Medicine , Headington, Oxford OX3 7BN, U.K.,Electron Bio-Imaging Centre, Diamond Light Sources, Harwell Science and Innovation Campus , Didcot OX11 0DE, U.K
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10
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Cao R, Bhattacharya D, Hou J, Cheng J. DeepQA: improving the estimation of single protein model quality with deep belief networks. BMC Bioinformatics 2016; 17:495. [PMID: 27919220 PMCID: PMC5139030 DOI: 10.1186/s12859-016-1405-y] [Citation(s) in RCA: 112] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 12/01/2016] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. RESULTS We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. CONCLUSION DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .
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Affiliation(s)
- Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, 98447, USA
| | - Debswapna Bhattacharya
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, 67260, USA
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA. .,Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.
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11
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Differential protein structural disturbances and suppression of assembly partners produced by nonsense GABRG2 epilepsy mutations: implications for disease phenotypic heterogeneity. Sci Rep 2016; 6:35294. [PMID: 27762395 PMCID: PMC5071880 DOI: 10.1038/srep35294] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 09/14/2016] [Indexed: 12/01/2022] Open
Abstract
Mutations in GABAA receptor subunit genes are frequently associated with epilepsy, and nonsense mutations in GABRG2 are associated with several epilepsy syndromes including childhood absence epilepsy, generalized tonic clonic seizures and the epileptic encephalopathy, Dravet syndrome. The molecular basis for the phenotypic heterogeneity of mutations is unclear. Here we focused on three nonsense mutations in GABRG2 (GABRG2(R136*), GABRG2(Q390*) and GABRG2(W429*)) associated with epilepsies of different severities. Structural modeling and structure-based analysis indicated that the surface of the wild-type γ2 subunit was naturally hydrophobic, which is suitable to be buried in the cell membrane. Different mutant γ2 subunits had different stabilities and different interactions with their wild-type subunit binding partners because they adopted different conformations and had different surface hydrophobicities and different tendency to dimerize. We utilized flow cytometry and biochemical approaches in combination with lifted whole cell patch-clamp recordings. We demonstrated that the truncated subunits had no to minimal surface expression and unchanged or reduced surface expression of wild-type partnering subunits. The amplitudes of GABA-evoked currents from the mutant α1β2γ2(R136*), α1β2γ2(Q390*) and α1β2γ2(W429*) receptors were reduced compared to the currents from α1β2γ2 receptors but with differentially reduced levels. This thus suggests differential protein structure disturbances are correlated with disease severity.
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12
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Bhattacharya D, Cao R, Cheng J. UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling. Bioinformatics 2016; 32:2791-9. [PMID: 27259540 PMCID: PMC5018369 DOI: 10.1093/bioinformatics/btw316] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Accepted: 05/15/2016] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named 'foldons' through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling. RESULTS Here, we develop a novel generative, probabilistic model that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in a united-residue representation and performs experimentally motivated conditional conformational sampling via stepwise synthesis and assembly of foldon units that minimizes a composite physics and knowledge-based energy function for de novo protein structure prediction. The proposed method, UniCon3D, has been found to (i) sample lower energy conformations with higher accuracy than traditional random sampling in a small benchmark of 6 proteins; (ii) perform comparably with the top five automated methods on 30 difficult target domains from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficult target domains from the 10th CASP experiment; and (iii) outperform two state-of-the-art approaches and a baseline counterpart of UniCon3D that performs traditional random sampling for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th edition of CASP. AVAILABILITY AND IMPLEMENTATION Source code, executable versions, manuals and example data of UniCon3D for Linux and OSX are freely available to non-commercial users at http://sysbio.rnet.missouri.edu/UniCon3D/ CONTACT: chengji@missouri.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Jianlin Cheng
- Department of Computer Science Informatics Institute, University of Missouri, Columbia, MO 65211, USA
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13
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MQAPsingle: A quasi single-model approach for estimation of the quality of individual protein structure models. Proteins 2016; 84:1021-8. [DOI: 10.1002/prot.24787] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 02/11/2015] [Accepted: 02/24/2015] [Indexed: 01/05/2023]
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14
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QwikMD - Integrative Molecular Dynamics Toolkit for Novices and Experts. Sci Rep 2016; 6:26536. [PMID: 27216779 PMCID: PMC4877583 DOI: 10.1038/srep26536] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 05/03/2016] [Indexed: 12/22/2022] Open
Abstract
The proper functioning of biomolecules in living cells requires them to assume particular structures and to undergo conformational changes. Both biomolecular structure and motion can be studied using a wide variety of techniques, but none offers the level of detail as do molecular dynamics (MD) simulations. Integrating two widely used modeling programs, namely NAMD and VMD, we have created a robust, user-friendly software, QwikMD, which enables novices and experts alike to address biomedically relevant questions, where often only molecular dynamics simulations can provide answers. Performing both simple and advanced MD simulations interactively, QwikMD automates as many steps as necessary for preparing, carrying out, and analyzing simulations while checking for common errors and enabling reproducibility. QwikMD meets also the needs of experts in the field, increasing the efficiency and quality of their work by carrying out tedious or repetitive tasks while enabling easy control of every step. Whether carrying out simulations within the live view mode on a small laptop or performing complex and large simulations on supercomputers or Cloud computers, QwikMD uses the same steps and user interface. QwikMD is freely available by download on group and personal computers. It is also available on the cloud at Amazon Web Services.
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McGreevy R, Teo I, Singharoy A, Schulten K. Advances in the molecular dynamics flexible fitting method for cryo-EM modeling. Methods 2016; 100:50-60. [PMID: 26804562 PMCID: PMC4848153 DOI: 10.1016/j.ymeth.2016.01.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 01/16/2016] [Accepted: 01/20/2016] [Indexed: 02/02/2023] Open
Abstract
Molecular Dynamics Flexible Fitting (MDFF) is an established technique for fitting all-atom structures of molecules into corresponding cryo-electron microscopy (cryo-EM) densities. The practical application of MDFF is simple but requires a user to be aware of and take measures against a variety of possible challenges presented by each individual case. Some of these challenges arise from the complexity of a molecular structure or the limited quality of available structural models and densities to be interpreted, while others stem from the intricacies of MDFF itself. The current article serves as an overview of the strategies that have been developed since MDFF's inception to overcome common challenges and successfully perform MDFF simulations.
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Affiliation(s)
- Ryan McGreevy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA
| | - Ivan Teo
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Abhishek Singharoy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA
| | - Klaus Schulten
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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Wang J, Luttrell J, Zhang N, Khan S, Shi N, Wang MX, Kang JQ, Wang Z, Xu D. Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 939:39-61. [PMID: 27807743 PMCID: PMC6829626 DOI: 10.1007/978-981-10-1503-8_3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.
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Affiliation(s)
- Juexin Wang
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Joseph Luttrell
- School of Computing, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS, 39406, USA
| | - Ning Zhang
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Saad Khan
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA
| | - NianQing Shi
- Department of Medicine, Division of Cardiovascular Medicine, University of Wisconsin, Room 8418, 1111 Highland Ave, Madison, WI, 53706, USA
| | - Michael X Wang
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Jing-Qiong Kang
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Zheng Wang
- School of Computing, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS, 39406, USA
| | - Dong Xu
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.
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Cao R, Bhattacharya D, Adhikari B, Li J, Cheng J. Large-scale model quality assessment for improving protein tertiary structure prediction. Bioinformatics 2015; 31:i116-23. [PMID: 26072473 PMCID: PMC4553833 DOI: 10.1093/bioinformatics/btv235] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation: Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Results: Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM’s outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. Availability and implementation: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. Contact: chengji@missouri.edu
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Affiliation(s)
- Renzhi Cao
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Debswapna Bhattacharya
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Badri Adhikari
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Jilong Li
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
| | - Jianlin Cheng
- Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA
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18
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Wang J, Joshi T, Valliyodan B, Shi H, Liang Y, Nguyen HT, Zhang J, Xu D. A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies. BMC Genomics 2015; 16:1011. [PMID: 26607428 PMCID: PMC4660815 DOI: 10.1186/s12864-015-2217-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 11/16/2015] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND A central question for disease studies and crop improvements is how genetics variants drive phenotypes. Genome Wide Association Study (GWAS) provides a powerful tool for characterizing the genotype-phenotype relationships in complex traits and diseases. Epistasis (gene-gene interaction), including high-order interaction among more than two genes, often plays important roles in complex traits and diseases, but current GWAS analysis usually just focuses on additive effects of single nucleotide polymorphisms (SNPs). The lack of effective computational modelling of high-order functional interactions often leads to significant under-utilization of GWAS data. RESULTS We have developed a novel Bayesian computational method with a Markov Chain Monte Carlo (MCMC) search, and implemented the method as a Bayesian High-order Interaction Toolkit (BHIT) for detecting epistatic interactions among SNPs. BHIT first builds a Bayesian model on both continuous data and discrete data, which is capable of detecting high-order interactions in SNPs related to case--control or quantitative phenotypes. We also developed a pipeline that enables users to apply BHIT on different species in different use cases. CONCLUSIONS Using both simulation data and soybean nutritional seed composition studies on oil content and protein content, BHIT effectively detected some high-order interactions associated with phenotypes, and it outperformed a number of other available tools. BHIT is freely available for academic users at http://digbio.missouri.edu/BHIT/.
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Affiliation(s)
- Juexin Wang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.
- Department of Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
| | - Trupti Joshi
- Department of Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
| | - Babu Valliyodan
- Division of Plant Sciences and National Center for Soybean Biotechnology (NCSB), University of Missouri, Columbia, MO, USA.
| | - Haiying Shi
- Division of Plant Sciences and National Center for Soybean Biotechnology (NCSB), University of Missouri, Columbia, MO, USA.
| | - Yanchun Liang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.
- Department of Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
| | - Henry T Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology (NCSB), University of Missouri, Columbia, MO, USA.
| | - Jing Zhang
- Department of Statistics, Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.
| | - Dong Xu
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.
- Department of Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
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Dorden S, Mahadevan P. Functional prediction of hypothetical proteins in human adenoviruses. Bioinformation 2015; 11:466-73. [PMID: 26664031 PMCID: PMC4658645 DOI: 10.6026/97320630011466] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Accepted: 10/15/2015] [Indexed: 02/06/2023] Open
Abstract
Assigning functional information to hypothetical proteins in virus genomes is crucial for gaining insight into their proteomes. Human adenoviruses are medium sized viruses that cause a range of diseases. Their genomes possess proteins with uncharacterized function known as hypothetical proteins. Using a wide range of protein function prediction servers, functional information was obtained about these hypothetical proteins. A comparison of functional information obtained from these servers revealed that some of them produced functional information, while others provided little functional information about these human adenovirus hypothetical proteins. The PFP, ESG, PSIPRED, 3d2GO, and ProtFun servers produced the most functional information regarding these hypothetical proteins.
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Affiliation(s)
- Shane Dorden
- Department of Biology, University of Tampa, 401 W. Kennedy Blvd., Box 3F, Tampa, FL, 33606, USA
| | - Padmanabhan Mahadevan
- Department of Biology, University of Tampa, 401 W. Kennedy Blvd., Box 3F, Tampa, FL, 33606, USA
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20
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Zhang J, Barz B, Zhang J, Xu D, Kosztin I. Selective refinement and selection of near-native models in protein structure prediction. Proteins 2015; 83:1823-35. [PMID: 26214389 PMCID: PMC4700123 DOI: 10.1002/prot.24866] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 06/22/2015] [Accepted: 07/21/2015] [Indexed: 11/07/2022]
Abstract
In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions.
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Affiliation(s)
- Jiong Zhang
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211
| | - Bagdan Barz
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211
| | - Jingfen Zhang
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Dong Xu
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Ioan Kosztin
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211
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21
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Cao R, Bhattacharya D, Adhikari B, Li J, Cheng J. Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11. Proteins 2015; 84 Suppl 1:247-59. [PMID: 26369671 DOI: 10.1002/prot.24924] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 08/21/2015] [Accepted: 09/10/2015] [Indexed: 12/28/2022]
Abstract
Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Renzhi Cao
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | | | - Badri Adhikari
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211. .,Informatics Institute, University of Missouri, Columbia, Missouri, 65211.
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22
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Kang JQ, Shen W, Zhou C, Xu D, Macdonald RL. The human epilepsy mutation GABRG2(Q390X) causes chronic subunit accumulation and neurodegeneration. Nat Neurosci 2015; 18:988-96. [PMID: 26005849 PMCID: PMC4482801 DOI: 10.1038/nn.4024] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 04/22/2015] [Indexed: 02/07/2023]
Abstract
Genetic epilepsy and neurodegenerative diseases are two common neurological disorders conventionally viewed as being unrelated. A subset of patients with severe genetic epilepsies with impaired development and often death respond poorly to anticonvulsant drug therapy, suggesting a need for new therapeutic targets. Previously, we reported that multiple GABAA receptor epilepsy mutations caused protein misfolding and abnormal receptor trafficking. Here we establish in a novel model of a severe human genetic epileptic encephalopathy, the Gabrg2+/Q390X knock-in mouse, that in addition to impairing inhibitory neurotransmission, mutant GABAA receptor γ2(Q390X) subunits accumulated and aggregated intracellularly, activated caspase 3 and caused widespread, age-dependent neurodegeneration. These novel findings suggest that the fundamental protein metabolism and cellular consequences of the epilepsy-associated mutant γ2(Q390X) ion channel subunit are not fundamentally different from those associated with neurodegeneration. The study has far-reaching significance for identification of conserved pathological cascades and mechanism-based therapies that overlap genetic epilepsies and neurodegenerative diseases.
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Affiliation(s)
- Jing-Qiong Kang
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wangzhen Shen
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Chengwen Zhou
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Dong Xu
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Robert L Macdonald
- 1] Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA. [2] Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. [3] Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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23
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Abbass J, Nebel JC. Customised fragments libraries for protein structure prediction based on structural class annotations. BMC Bioinformatics 2015; 16:136. [PMID: 25925397 PMCID: PMC4419399 DOI: 10.1186/s12859-015-0576-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 04/17/2015] [Indexed: 12/05/2022] Open
Abstract
Background Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets’ lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process. Results Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (−0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area. Conclusions Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0576-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jad Abbass
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, UK.
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, UK.
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24
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Zuckerman DM, Boucher LE, Xie K, Engelhardt H, Bosch J, Hoiczyk E. The bactofilin cytoskeleton protein BacM of Myxococcus xanthus forms an extended β-sheet structure likely mediated by hydrophobic interactions. PLoS One 2015; 10:e0121074. [PMID: 25803609 PMCID: PMC4372379 DOI: 10.1371/journal.pone.0121074] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Accepted: 01/29/2015] [Indexed: 11/18/2022] Open
Abstract
Bactofilins are novel cytoskeleton proteins that are widespread in Gram-negative bacteria. Myxococcus xanthus, an important predatory soil bacterium, possesses four bactofilins of which one, BacM (Mxan_7475) plays an important role in cell shape maintenance. Electron and fluorescence light microscopy, as well as studies using over-expressed, purified BacM, indicate that this protein polymerizes in vivo and in vitro into ~3 nm wide filaments that further associate into higher ordered fibers of about 10 nm. Here we use a multipronged approach combining secondary structure determination, molecular modeling, biochemistry, and genetics to identify and characterize critical molecular elements that enable BacM to polymerize. Our results indicate that the bactofilin-determining domain DUF583 folds into an extended β-sheet structure, and we hypothesize a left-handed β-helix with polymerization into 3 nm filaments primarily via patches of hydrophobic amino acid residues. These patches form the interface allowing head-to-tail polymerization during filament formation. Biochemical analyses of these processes show that folding and polymerization occur across a wide variety of conditions and even in the presence of chaotropic agents such as one molar urea. Together, these data suggest that bactofilins are comprised of a structure unique to cytoskeleton proteins, which enables robust polymerization.
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Affiliation(s)
- David M. Zuckerman
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Lauren E. Boucher
- Department of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Malaria Research Institute, Baltimore, Maryland, United States of America
| | - Kefang Xie
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Harald Engelhardt
- Department of Structural Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, Martinsried, Germany
| | - Jürgen Bosch
- Department of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Malaria Research Institute, Baltimore, Maryland, United States of America
| | - Egbert Hoiczyk
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- * E-mail:
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25
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Sun HP, Huang Y, Wang XF, Zhang Y, Shen HB. Improving accuracy of protein contact prediction using balanced network deconvolution. Proteins 2015; 83:485-96. [PMID: 25524593 DOI: 10.1002/prot.24744] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2014] [Revised: 11/20/2014] [Accepted: 12/02/2014] [Indexed: 12/28/2022]
Abstract
Residue contact map is essential for protein three-dimensional structure determination. But most of the current contact prediction methods based on residue co-evolution suffer from high false-positives as introduced by indirect and transitive contacts (i.e., residues A-B and B-C are in contact, but A-C are not). Built on the work by Feizi et al. (Nat Biotechnol 2013; 31:726-733), which demonstrated a general network model to distinguish direct dependencies by network deconvolution, this study presents a new balanced network deconvolution (BND) algorithm to identify optimized dependency matrix without limit on the eigenvalue range in the applied network systems. The algorithm was used to filter contact predictions of five widely used co-evolution methods. On the test of proteins from three benchmark datasets of the 9th critical assessment of protein structure prediction (CASP9), CASP10, and PSICOV (precise structural contact prediction using sparse inverse covariance estimation) database experiments, the BND can improve the medium- and long-range contact predictions at the L/5 cutoff by 55.59% and 47.68%, respectively, without additional central processing unit cost. The improvement is statistically significant, with a P-value < 5.93 × 10(-3) in the Student's t-test. A further comparison with the ab initio structure predictions in CASPs showed that the usefulness of the current co-evolution-based contact prediction to the three-dimensional structure modeling relies on the number of homologous sequences existing in the sequence databases. BND can be used as a general contact refinement method, which is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/BND/.
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Affiliation(s)
- Hai-Ping Sun
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
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26
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Bunkóczi G, Wallner B, Read RJ. Local error estimates dramatically improve the utility of homology models for solving crystal structures by molecular replacement. Structure 2015; 23:397-406. [PMID: 25619999 PMCID: PMC4321884 DOI: 10.1016/j.str.2014.11.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Revised: 11/25/2014] [Accepted: 11/25/2014] [Indexed: 12/19/2022]
Abstract
Predicted structures submitted for CASP10 have been evaluated as molecular replacement models against the corresponding sets of structure factor amplitudes. It has been found that the log-likelihood gain score computed for each prediction correlates well with common structure quality indicators but is more sensitive when the accuracy of the models is high. In addition, it was observed that using coordinate error estimates submitted by predictors to weight the model can improve its utility in molecular replacement dramatically, and several groups have been identified who reliably provide accurate error estimates that could be used to extend the application of molecular replacement for low-homology cases. Error estimates increase the value of homology models for molecular replacement Poorer models with good error estimates trump better models without errors A simple protocol creates coordinate error estimates for individual models Local coordinate error estimates enable molecular replacement for more targets
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Affiliation(s)
- Gábor Bunkóczi
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
| | - Björn Wallner
- IFM, Linköping University, S-581 83 Linköping, Sweden
| | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK.
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27
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McGreevy R, Singharoy A, Li Q, Zhang J, Xu D, Perozo E, Schulten K. xMDFF: molecular dynamics flexible fitting of low-resolution X-ray structures. ACTA CRYSTALLOGRAPHICA. SECTION D, BIOLOGICAL CRYSTALLOGRAPHY 2014; 70:2344-55. [PMID: 25195748 PMCID: PMC4157446 DOI: 10.1107/s1399004714013856] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Accepted: 06/13/2014] [Indexed: 01/07/2023]
Abstract
X-ray crystallography remains the most dominant method for solving atomic structures. However, for relatively large systems, the availability of only medium-to-low-resolution diffraction data often limits the determination of all-atom details. A new molecular dynamics flexible fitting (MDFF)-based approach, xMDFF, for determining structures from such low-resolution crystallographic data is reported. xMDFF employs a real-space refinement scheme that flexibly fits atomic models into an iteratively updating electron-density map. It addresses significant large-scale deformations of the initial model to fit the low-resolution density, as tested with synthetic low-resolution maps of D-ribose-binding protein. xMDFF has been successfully applied to re-refine six low-resolution protein structures of varying sizes that had already been submitted to the Protein Data Bank. Finally, via systematic refinement of a series of data from 3.6 to 7 Å resolution, xMDFF refinements together with electrophysiology experiments were used to validate the first all-atom structure of the voltage-sensing protein Ci-VSP.
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Affiliation(s)
- Ryan McGreevy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Abhishek Singharoy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Qufei Li
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL 60637, USA
| | - Jingfen Zhang
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Eduardo Perozo
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL 60637, USA
| | - Klaus Schulten
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Diverse and divergent protein post-translational modifications in two growth stages of a natural microbial community. Nat Commun 2014; 5:4405. [PMID: 25059763 PMCID: PMC4279252 DOI: 10.1038/ncomms5405] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2013] [Accepted: 06/16/2014] [Indexed: 12/27/2022] Open
Abstract
Detailed characterization of post-translational modifications (PTMs) of proteins in microbial communities remains a significant challenge. Here we directly identify and quantify a broad range of PTMs (hydroxylation, methylation, citrullination, acetylation, phosphorylation, methylthiolation, S-nitrosylation and nitration) in a natural microbial community from an acid mine drainage site. Approximately 29% of the identified proteins of the dominant Leptospirillum group II bacteria are modified, and 43% of modified proteins carry multiple PTM types. Most PTM events, except S-nitrosylations, have low fractional occupancy. Notably, PTM events are detected on Cas proteins involved in antiviral defense, an aspect of Cas biochemistry not considered previously. Further, Cas PTM profiles from Leptospirillum group II differ in early versus mature biofilms. PTM patterns are divergent on orthologues of two closely related, but ecologically differentiated, Leptospirillum group II bacteria. Our results highlight the prevalence and dynamics of PTMs of proteins, with potential significance for ecological adaptation and microbial evolution.
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Nguyen SP, Shang Y, Xu D. DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment. PROCEEDINGS OF ... INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS. INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2014; 2014:2071-2078. [PMID: 25392745 PMCID: PMC4226404 DOI: 10.1109/ijcnn.2014.6889891] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-α atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.
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Affiliation(s)
- Son P. Nguyen
- Department of Computer Science, University of Missouri, Columbia, MO 65211 USA
| | - Yi Shang
- Department of Computer Science, University of Missouri, Columbia, MO 65211 USA
| | - Dong Xu
- Department of Computer Science, University of Missouri, Columbia, MO 65211 USA. Christopher S. Bond Life Science Center, University of Missouri at Columbia
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Guo F, Li SC, Ma W, Wang L. Detecting protein conformational changes in interactions via scaling known structures. J Comput Biol 2014; 20:765-79. [PMID: 24093228 DOI: 10.1089/cmb.2013.0069] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Conformational changes frequently occur when proteins interact with other proteins. How to detect such changes in silico is a major problem. Existing methods for docking with conformational changes remain time-consuming, and they solve only a small portion of protein complexes accurately. This work presents a more accurate method (FlexDoBi) for docking with conformational changes. FlexDoBi generates the possible conformational changes of the interface residues that transform the proteins from their unbound states to bound states. Based on the generated conformational changes, multidimensional scaling is performed to construct candidates for the bound structure. We develop a new energy item for determining the orientation of docking subunits and selecting of plausible conformational changes. Experimental results illustrate that FlexDoBi achieves better results. On 20 complexes, we obtained an average iRMSD of 1.55Å, which compares favorably with the average iRMSD of 1.94Å for FiberDock. Compared to ZDOCK, our results are of 0.27Å less in average iRMSD of the medium difficulty group.
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Affiliation(s)
- Fei Guo
- Department of Computer Science, City University of Hong Kong , Kowloon, Hong Kong
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Cao R, Wang Z, Cheng J. Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment. BMC STRUCTURAL BIOLOGY 2014; 14:13. [PMID: 24731387 PMCID: PMC3996498 DOI: 10.1186/1472-6807-14-13] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Accepted: 04/01/2014] [Indexed: 11/10/2022]
Abstract
BACKGROUND Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. RESULTS MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. CONCLUSIONS Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.
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Affiliation(s)
| | | | - Jianlin Cheng
- Computer Science Department, University of Missouri, Columbia, Missouri 65211, USA.
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Cui X, Li SC, He L, Li M. Fingerprinting protein structures effectively and efficiently. Bioinformatics 2014; 30:949-55. [PMID: 24292940 DOI: 10.1093/bioinformatics/btt659] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION One common task in structural biology is to assess the similarities and differences among protein structures. A variety of structure alignment algorithms and programs has been designed and implemented for this purpose. A major drawback with existing structure alignment programs is that they require a large amount of computational time, rendering them infeasible for pairwise alignments on large collections of structures. To overcome this drawback, a fragment alphabet learned from known structures has been introduced. The method, however, considers local similarity only, and therefore occasionally assigns high scores to structures that are similar only in local fragments. METHOD We propose a novel approach that eliminates false positives, through the comparison of both local and remote similarity, with little compromise in speed. Two kinds of contact libraries (ContactLib) are introduced to fingerprint protein structures effectively and efficiently. Each contact group of the contact library consists of one local or two remote fragments and is represented by a concise vector. These vectors are then indexed and used to calculate a new combined hit-rate score to identify similar protein structures effectively and efficiently. RESULTS We tested our method on the high-quality protein structure subset of SCOP30 containing 3297 protein structures. For each protein structure of the subset, we retrieved its neighbor protein structures from the rest of the subset. The best area under the Receiver-Operating Characteristic curve, archived by ContactLib, is as high as 0.960. This is a significant improvement compared with 0.747, the best result achieved by FragBag. We also demonstrated that incorporating remote contact information is critical to consistently retrieve accurate neighbor protein structures for all- query protein structures. AVAILABILITY AND IMPLEMENTATION https://cs.uwaterloo.ca/∼xfcui/contactlib/.
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Affiliation(s)
- Xuefeng Cui
- David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
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Holtby D, Li SC, Li M. LoopWeaver: loop modeling by the weighted scaling of verified proteins. J Comput Biol 2014; 20:212-23. [PMID: 23461572 DOI: 10.1089/cmb.2012.0078] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Modeling loops is a necessary step in protein structure determination, even with experimental nuclear magnetic resonance (NMR) data, it is widely known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with subangstrom accuracy when compared with ab initio techniques, but the disadvantage of also producing a higher proportion of clashing or highly inaccurate predictions. We introduce LoopWeaver, a database method that uses multidimensional scaling to achieve better, clash-free placement of loops obtained from a database of protein structures. This allows us to maintain the above-mentioned advantage while avoiding the disadvantage. Test results show that we achieve significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper, before refinement. With refinement, our results (LoopWeaver and Loopy consensus) are better than ROSETTA, with 0.42 Å RMSD on average for 206 length 6 loops, 0.64 Å local RMSD for 168 length 7 loops, 0.81Å RMSD for 117 length 8 loops, and 0.98 Å RMSD for length 9 loops, while ROSETTA has 0.55, 0.79, 1.16, 1.42, respectively, at the same average time limit (3 hours). When we allow ROSETTA to run for over a week, it approaches, but does not surpass, our accuracy.
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Affiliation(s)
- Daniel Holtby
- David R. Chariton School of Computer Science, University of Waterloo, Waterloo, Canada.
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Computational Approaches and Resources in Single Amino Acid Substitutions Analysis Toward Clinical Research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 94:365-423. [DOI: 10.1016/b978-0-12-800168-4.00010-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Wang Q, Shang C, Xu D, Shang Y. NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION. INT J ARTIF INTELL T 2013; 22:1360006. [PMID: 24808625 DOI: 10.1142/s0218213013600063] [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: 11/18/2022]
Abstract
In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement.
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Affiliation(s)
- Qingguo Wang
- Bioinformatics and Systems Medicine Laboratory, Vanderbilt University Nashville, TN 37203, USA
| | - Charles Shang
- Computer Science Department, University of Illinois at Urbana-Champaign Urbana, IL 61801, USA
| | - Dong Xu
- Computer Science Department, University of Missouri Columbia, MO 65211, USA
| | - Yi Shang
- Computer Science Department, University of Missouri Columbia, MO 65211, USA
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He Z, Alazmi M, Zhang J, Xu D. Protein structural model selection by combining consensus and single scoring methods. PLoS One 2013; 8:e74006. [PMID: 24023923 PMCID: PMC3759460 DOI: 10.1371/journal.pone.0074006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2013] [Accepted: 07/26/2013] [Indexed: 01/28/2023] Open
Abstract
Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server) applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance.
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Affiliation(s)
- Zhiquan He
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
| | - Meshari Alazmi
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
| | - Jingfen Zhang
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
| | - Dong Xu
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
- * E-mail:
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37
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Truncation of PstS1 antigen of Mycobacterium tuberculosis improves diagnostic efficiency. Tuberculosis (Edinb) 2013; 93:654-9. [PMID: 23978525 DOI: 10.1016/j.tube.2013.07.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Revised: 07/23/2013] [Accepted: 07/30/2013] [Indexed: 11/22/2022]
Abstract
PstS1, also named 38-kDa antigen, is one of the earliest known immune-dominant antigens of Mycobacterium tuberculosis and it has been commonly used in serodiagnostic tests. We constructed a truncated version, tnPstS1, by removing 96 and 14 amino acid residues from the N- and C-terminals, respectively of the native PstS1. The native and the truncated 29.5 kDa proteins were expressed in insoluble forms in Escherichia coli to levels of 15% and 25% of the total cell proteins, respectively. Both the variant molecules reacted equally well with the antisera raised in rabbit against the native protein. PstS1 and tnPstS1 were evaluated through ELISA against plasma samples from 160 culture positive tuberculosis patients and 40 healthy controls. With tnPstS1 43% of the patient samples were detected positive for the antibody as compared to only 36% in the case of the native PstS1. Data for the secondary structures of the native and the truncated variants as obtained by circular dichroism agreed with the known 3-D structure of the native protein and the predicted structure of the truncated version, respectively. The results show that the truncated tnPstS1 is more efficient as compared to the native PstS1 for use as a serodiagnostic agent.
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Wang J, Chen L, Wang Y, Zhang J, Liang Y, Xu D. A computational systems biology study for understanding salt tolerance mechanism in rice. PLoS One 2013; 8:e64929. [PMID: 23762267 PMCID: PMC3676415 DOI: 10.1371/journal.pone.0064929] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Accepted: 04/19/2013] [Indexed: 01/22/2023] Open
Abstract
Salinity is one of the most common abiotic stresses in agriculture production. Salt tolerance of rice (Oryza sativa) is an important trait controlled by various genes. The mechanism of rice salt tolerance, currently with limited understanding, is of great interest to molecular breeding in improving grain yield. In this study, a gene regulatory network of rice salt tolerance is constructed using a systems biology approach with a number of novel computational methods. We developed an improved volcano plot method in conjunction with a new machine-learning method for gene selection based on gene expression data and applied the method to choose genes related to salt tolerance in rice. The results were then assessed by quantitative trait loci (QTL), co-expression and regulatory binding motif analysis. The selected genes were constructed into a number of network modules based on predicted protein interactions including modules of phosphorylation activity, ubiquity activity, and several proteinase activities such as peroxidase, aspartic proteinase, glucosyltransferase, and flavonol synthase. All of these discovered modules are related to the salt tolerance mechanism of signal transduction, ion pump, abscisic acid mediation, reactive oxygen species scavenging and ion sequestration. We also predicted the three-dimensional structures of some crucial proteins related to the salt tolerance QTL for understanding the roles of these proteins in the network. Our computational study sheds some new light on the mechanism of salt tolerance and provides a systems biology pipeline for studying plant traits in general.
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Affiliation(s)
- Juexin Wang
- College of Computer Science and Technology, Jilin University, Changchun, China
- Digital Biology Laboratory, Computer Science Department, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, United States of America
| | - Liang Chen
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Yan Wang
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Jingfen Zhang
- Digital Biology Laboratory, Computer Science Department, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, United States of America
| | - Yanchun Liang
- College of Computer Science and Technology, Jilin University, Changchun, China
- * E-mail: (YL); (DX)
| | - Dong Xu
- College of Computer Science and Technology, Jilin University, Changchun, China
- Digital Biology Laboratory, Computer Science Department, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, United States of America
- * E-mail: (YL); (DX)
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Wang Y, Sun M, Bao H, Zhang Q, Guo D. Effective identification of bacterial type III secretion signals using joint element features. PLoS One 2013; 8:e59754. [PMID: 23593149 PMCID: PMC3617162 DOI: 10.1371/journal.pone.0059754] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2012] [Accepted: 02/18/2013] [Indexed: 11/18/2022] Open
Abstract
Type III secretion system (T3SS) plays important roles in bacteria and host cell interactions by specifically translocating type III effectors into the cytoplasm of the host cells. The N-terminal amino acid sequences of the bacterial type III effectors determine their specific secretion via type III secretion conduits. It is still unclear as to how the N-terminal sequences guide this specificity. In this work, the amino acid composition, secondary structure, and solvent accessibility in the N-termini of type III and non-type III secreted proteins were compared and contrasted. A high-efficacy mathematical model based on these joint features was developed to distinguish the type III proteins from the non-type III ones. The results indicate that secondary structure and solvent accessibility may make important contribution to the specific recognition of type III secretion signals. Analysis also showed that the joint feature of the N-terminal 6th–10th amino acids are especially important for guiding specific type III secretion. Furthermore, a genome-wide screening was performed to predict Salmonella type III secreted proteins, and 8 new candidates were experimentally validated. Interestingly, type III secretion signals were also predicted in gram-positive bacteria and yeasts. Experimental validation showed that two candidates from yeast can indeed be secreted through Salmonella type III secretion conduit. This research provides the first line of direct evidence that secondary structure and solvent accessibility contain important features for guiding specific type III secretion. The new software based on these joint features ensures a high accuracy (general cross-validation sensitivity of ∼96% at a specificity of ∼98%) in silico identification of new type III secreted proteins, which may facilitate our understanding about the specificity of type III secretion and the evolution of type III secreted proteins.
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Affiliation(s)
- Yejun Wang
- School of Life Sciences and the State Key Lab of Agrobiotechnology, the Chinese University of Hong Kong, Shatin, NT, Hong Kong
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40
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Ahsan N, Huang Y, Tovar-Mendez A, Swatek KN, Zhang J, Miernyk JA, Xu D, Thelen JJ. A versatile mass spectrometry-based method to both identify kinase client-relationships and characterize signaling network topology. J Proteome Res 2013; 12:937-48. [PMID: 23270405 PMCID: PMC3888875 DOI: 10.1021/pr3009995] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
While more than a thousand protein kinases (PK) have been identified in the Arabidopsis thaliana genome, relatively little progress has been made toward identifying their individual client proteins. Herein we describe the use of a mass spectrometry-based in vitro phosphorylation strategy, termed Kinase Client assay (KiC assay), to study a targeted-aspect of signaling. A synthetic peptide library comprising 377 in vivo phosphorylation sequences from developing seed was screened using 71 recombinant A. thaliana PK. Among the initial results, we identified 23 proteins as putative clients of 17 PK. In one instance protein phosphatase inhibitor-2 (AtPPI-2) was phosphorylated at multiple-sites by three distinct PK, casein kinase1-like 10, AME3, and a Ser PK-like protein. To confirm this result, full-length recombinant AtPPI-2 was reconstituted with each of these PK. The results confirmed multiple distinct phosphorylation sites within this protein. Biochemical analyses indicate that AtPPI-2 inhibits type 1 protein phosphatase (TOPP) activity, and that the phosphorylated forms of AtPPI-2 are more potent inhibitors. Structural modeling revealed that phosphorylation of AtPPI-2 induces conformational changes that modulate TOPP binding.
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Affiliation(s)
- Nagib Ahsan
- Department of Biochemistry and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 USA
| | - Yadong Huang
- Department of Biochemistry and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 USA
| | - Alejandro Tovar-Mendez
- Department of Biochemistry and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 USA
| | - Kirby N. Swatek
- Department of Biochemistry and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 USA
| | - Jingfen Zhang
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 USA
- Department of Computer Science and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
| | - Ján A. Miernyk
- Department of Biochemistry and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
- Plant Genetics Research Unit, USDA, Agricultural Research Service, University of Missouri, Columbia, MO 65211 USA
| | - Dong Xu
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 USA
- Department of Computer Science and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
| | - Jay J. Thelen
- Department of Biochemistry and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211 USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211 USA
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Ahsan N, Swatek KN, Zhang J, Miernyk JA, Xu D, Thelen JJ. "Scanning mutagenesis" of the amino acid sequences flanking phosphorylation site 1 of the mitochondrial pyruvate dehydrogenase complex. FRONTIERS IN PLANT SCIENCE 2012; 3:153. [PMID: 22811682 PMCID: PMC3397410 DOI: 10.3389/fpls.2012.00153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Accepted: 06/19/2012] [Indexed: 06/01/2023]
Abstract
The mitochondrial pyruvate dehydrogenase complex (mtPDC) is regulated by reversible seryl-phosphorylation of the E1α subunit by a dedicated, intrinsic kinase. The phospho-complex is reactivated when dephosphorylated by an intrinsic PP2C-type protein phosphatase. Both the position of the phosphorylated Ser-residue and the sequences of the flanking amino acids are highly conserved. We have used the synthetic peptide-based kinase client (KiC) assay plus recombinant pyruvate dehydrogenase E1α and E1α-kinase to perform "scanning mutagenesis" of the residues flanking the site of phosphorylation. Consistent with the results from "phylogenetic analysis" of the flanking sequences, the direct peptide-based kinase assays tolerated very few changes. Even conservative changes such as Leu, Ile, or Val for Met, or Glu for Asp, gave very marked reductions in phosphorylation. Overall the results indicate that regulation of the mtPDC by reversible phosphorylation is an extreme example of multiple, interdependent instances of co-evolution.
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Affiliation(s)
- Nagib Ahsan
- Department of Biochemistry, University of MissouriColumbia, MO, USA
- Interdisciplinary Plant Group, University of MissouriColumbia, MO, USA
| | - Kirby N. Swatek
- Department of Biochemistry, University of MissouriColumbia, MO, USA
- Interdisciplinary Plant Group, University of MissouriColumbia, MO, USA
| | - Jingfen Zhang
- Interdisciplinary Plant Group, University of MissouriColumbia, MO, USA
- Department of Computer Science, University of MissouriColumbia, MO, USA
| | - Ján A. Miernyk
- Department of Biochemistry, University of MissouriColumbia, MO, USA
- Interdisciplinary Plant Group, University of MissouriColumbia, MO, USA
- Plant Genetics Research Unit, USDA, Agricultural Research Service, University of MissouriColumbia, MO, USA
| | - Dong Xu
- Interdisciplinary Plant Group, University of MissouriColumbia, MO, USA
- Department of Computer Science, University of MissouriColumbia, MO, USA
| | - Jay J. Thelen
- Department of Biochemistry, University of MissouriColumbia, MO, USA
- Interdisciplinary Plant Group, University of MissouriColumbia, MO, USA
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Deng H, Jia Y, Wei Y, Zhang Y. What is the best reference state for designing statistical atomic potentials in protein structure prediction? Proteins 2012; 80:2311-22. [PMID: 22623012 DOI: 10.1002/prot.24121] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Revised: 04/30/2012] [Accepted: 05/21/2012] [Indexed: 01/01/2023]
Abstract
Many statistical potentials were developed in last two decades for protein folding and protein structure recognition. The major difference of these potentials is on the selection of reference states to offset sampling bias. However, since these potentials used different databases and parameter cutoffs, it is difficult to judge what the best reference states are by examining the original programs. In this study, we aim to address this issue and evaluate the reference states by a unified database and programming environment. We constructed distance-specific atomic potentials using six widely-used reference states based on 1022 high-resolution protein structures, which are applied to rank modeling in six sets of structure decoys. The reference state on random-walk chain outperforms others in three decoy sets while those using ideal-gas, quasi-chemical approximation and averaging sample stand out in one set separately. Nevertheless, the performance of the potentials relies on the origin of decoy generations and no reference state can clearly outperform others in all decoy sets. Further analysis reveals that the statistical potentials have a contradiction between the universality and pertinence, and optimal reference states should be extracted based on specific application environments and decoy spaces.
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Affiliation(s)
- Haiyou Deng
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan 430079, China
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Fan H, Periole X, Mark AE. Mimicking the action of folding chaperones by Hamiltonian replica-exchange molecular dynamics simulations: application in the refinement of de novo models. Proteins 2012; 80:1744-54. [PMID: 22411697 DOI: 10.1002/prot.24068] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2011] [Revised: 02/11/2012] [Accepted: 03/03/2012] [Indexed: 12/25/2022]
Abstract
The efficiency of using a variant of Hamiltonian replica-exchange molecular dynamics (Chaperone H-replica-exchange molecular dynamics [CH-REMD]) for the refinement of protein structural models generated de novo is investigated. In CH-REMD, the interaction between the protein and its environment, specifically, the electrostatic interaction between the protein and the solvating water, is varied leading to cycles of partial unfolding and refolding mimicking some aspects of folding chaperones. In 10 of the 15 cases examined, the CH-REMD approach sampled structures in which the root-mean-square deviation (RMSD) of secondary structure elements (SSE-RMSD) with respect to the experimental structure was more than 1.0 Å lower than the initial de novo model. In 14 of the 15 cases, the improvement was more than 0.5 Å. The ability of three different statistical potentials to identify near-native conformations was also examined. Little correlation between the SSE-RMSD of the sampled structures with respect to the experimental structure and any of the scoring functions tested was found. The most effective scoring function tested was the DFIRE potential. Using the DFIRE potential, the SSE-RMSD of the best scoring structures was on average 0.3 Å lower than the initial model. Overall the work demonstrates that targeted enhanced-sampling techniques such as CH-REMD can lead to the systematic refinement of protein structural models generated de novo but that improved potentials for the identification of near-native structures are still needed.
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Affiliation(s)
- Hao Fan
- Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California 94158-2330, USA
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44
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Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure 2012; 19:1784-95. [PMID: 22153501 DOI: 10.1016/j.str.2011.09.022] [Citation(s) in RCA: 248] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2011] [Revised: 09/19/2011] [Accepted: 09/24/2011] [Indexed: 11/22/2022]
Abstract
One of critical difficulties of molecular dynamics (MD) simulations in protein structure refinement is that the physics-based energy landscape lacks a middle-range funnel to guide nonnative conformations toward near-native states. We propose to use the target model as a probe to identify fragmental analogs from PDB. The distance maps are then used to reshape the MD energy funnel. The protocol was tested on 181 benchmarking and 26 CASP targets. It was found that structure models of correct folds with TM-score >0.5 can be often pulled closer to native with higher GDT-HA score, but improvement for the models of incorrect folds (TM-score <0.5) are much less pronounced. These data indicate that template-based fragmental distance maps essentially reshaped the MD energy landscape from golf-course-like to funnel-like ones in the successfully refined targets with a radius of TM-score ∼0.5. These results demonstrate a new avenue to improve high-resolution structures by combining knowledge-based template information with physics-based MD simulations.
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45
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Structural modelling and dynamics of proteins for insights into drug interactions. Adv Drug Deliv Rev 2012; 64:323-43. [PMID: 22155026 DOI: 10.1016/j.addr.2011.11.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Revised: 11/17/2011] [Accepted: 11/24/2011] [Indexed: 12/27/2022]
Abstract
Proteins are the workhorses of biomolecules and their function is affected by their structure and their structural rearrangements during ligand entry, ligand binding and protein-protein interactions. Hence, the knowledge of protein structure and, importantly, the dynamic behaviour of the structure are critical for understanding how the protein performs its function. The predictions of the structure and the dynamic behaviour can be performed by combinations of structure modelling and molecular dynamics simulations. The simulations also need to be sensitive to the constraints of the environment in which the protein resides. Standard computational methods now exist in this field to support the experimental effort of solving protein structures. This review presents a comprehensive overview of the basis of the calculations and the well-established computational methods used to generate and understand protein structure and function and the study of their dynamic behaviour with the reference to lung-related targets.
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46
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Wang Q, Shang Y, Xu D. Improving a consensus approach for protein structure selection by removing redundancy. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011; 8:1708-15. [PMID: 21519117 DOI: 10.1109/tcbb.2011.75] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In protein tertiary structure prediction, a crucial step is to select near-native structures from a large number of predicted structural models. Over the years, extensive research has been conducted for the protein structure selection problem with most approaches focusing on developing more accurate energy or scoring functions. Despite significant advances in this area, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a novel consensus-based algorithm for the selection of predicted protein structures. Given a set of predicted models, our method first removes redundant structures to derive a subset of reference models. Then, a structure is ranked based on its average pairwise similarity to the reference models. Using the CASP8 data set containing a large collection of predicted models for 122 targets, we compared our method with the best CASP8 quality assessment (QA) servers, which are all consensus based, and showed that our QA scores correlate better with the GDT-TSs than those of the CASP8 QA servers. We also compared our method with the state-of-the-art scoring functions and showed its improved performance for near-native model selection. The GDT-TSs of the top models picked by our method are on average more than 8 percent better than the ones selected by the best performing scoring function.
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Affiliation(s)
- Qingguo Wang
- Department of Computer Science, University of Missouri, 201 Engineering Building West, Columbia, MO 65211, USA.
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47
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Zhang J, Wang Q, Vantasin K, Zhang J, He Z, Kosztin I, Shang Y, Xu D. A multilayer evaluation approach for protein structure prediction and model quality assessment. Proteins 2011; 79 Suppl 10:172-84. [PMID: 21997706 DOI: 10.1002/prot.23184] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 08/26/2011] [Accepted: 09/05/2011] [Indexed: 01/03/2023]
Abstract
Protein tertiary structures are essential for studying functions of proteins at molecular level. An indispensable approach for protein structure solution is computational prediction. Most protein structure prediction methods generate candidate models first and select the best candidates by model quality assessment (QA). In many cases, good models can be produced, but the QA tools fail to select the best ones from the candidate model pool. Because of incomplete understanding of protein folding, each QA method only reflects partial facets of a structure model and thus has limited discerning power with no one consistently outperforming others. In this article, we developed a set of new QA methods, including two QA methods for evaluating target/template alignments, a molecular dynamics (MD)-based QA method, and three consensus QA methods with selected references to reveal new facets of protein structures complementary to the existing methods. Moreover, the underlying relationship among different QA methods were analyzed and then integrated into a multilayer evaluation approach to guide the model generation and model selection in prediction. All methods are integrated and implemented into an innovative and improved prediction system hereafter referred to as MUFOLD. In CASP8 and CASP9, MUFOLD has demonstrated the proof of the principles in terms of both QA discerning power and structure prediction accuracy.
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Affiliation(s)
- Jingfen Zhang
- Department of Computer Science, University of Missouri, Columbia, MO, USA
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48
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Wang Q, Vantasin K, Xu D, Shang Y. MUFOLD-WQA: A new selective consensus method for quality assessment in protein structure prediction. Proteins 2011; 79 Suppl 10:185-95. [PMID: 21997748 DOI: 10.1002/prot.23185] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2011] [Revised: 08/25/2011] [Accepted: 08/27/2011] [Indexed: 11/07/2022]
Abstract
Assessing the quality of predicted models is essential in protein tertiary structure prediction. In the past critical assessment of techniques for protein structure prediction (CASP) experiments, consensus quality assessment (QA) methods have shown to be very effective, outperforming single-model methods and other competing approaches by a large margin. In the consensus QA approach, the quality score of a model is typically estimated based on pair-wise structure similarity of it to a set of reference models. In CASP8, the differences among the top QA servers were mostly in the selection of the reference models. In this article, we present a new consensus method "SelCon" based on two key ideas: (1) to adaptively select appropriate reference models based on the attributes of the whole set of predicted models and (2) to weigh different reference models differently, and in particular not to use models that are too similar or too different from the candidate model as its references. We have developed several reference selection functions in SelCon and obtained improved QA results over existing QA methods in experiments using CASP7 and CASP8 data. In the recently completed CASP9 in 2010, the new method was implemented in our MUFOLD-WQA server. Both the official CASP9 assessment and our in-house evaluation showed that MUFOLD-WQA performed very well and achieved top performances in both the global structure QA and top-model selection category in CASP9.
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Affiliation(s)
- Qingguo Wang
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
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49
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Shi X, Zhang J, He Z, Shang Y, Xu D. A sampling-based method for ranking protein structural models by integrating multiple scores and features. Curr Protein Pept Sci 2011; 12:540-8. [PMID: 21787308 PMCID: PMC4368063 DOI: 10.2174/138920311796957658] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 04/01/2011] [Accepted: 05/04/2011] [Indexed: 11/22/2022]
Abstract
One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.
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Affiliation(s)
- Xiaohu Shi
- College of Computer Science and Technology, Jilin University, Jilin, Changchun 130012, China
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50
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Xu D, Zhang J, Roy A, Zhang Y. Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement. Proteins 2011; 79 Suppl 10:147-60. [PMID: 22069036 PMCID: PMC3228277 DOI: 10.1002/prot.23111] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2011] [Revised: 06/07/2011] [Accepted: 06/26/2011] [Indexed: 11/09/2022]
Abstract
I-TASSER is an automated pipeline for protein tertiary structure prediction using multiple threading alignments and iterative structure assembly simulations. In CASP9 experiments, two new algorithms, QUARK and fragment-guided molecular dynamics (FG-MD), were added to the I-TASSER pipeline for improving the structural modeling accuracy. QUARK is a de novo structure prediction algorithm used for structure modeling of proteins that lack detectable template structures. For distantly homologous targets, QUARK models are found useful as a reference structure for selecting good threading alignments and guiding the I-TASSER structure assembly simulations. FG-MD is an atomic-level structural refinement program that uses structural fragments collected from the PDB structures to guide molecular dynamics simulation and improve the local structure of predicted model, including hydrogen-bonding networks, torsion angles, and steric clashes. Despite considerable progress in both the template-based and template-free structure modeling, significant improvements on protein target classification, domain parsing, model selection, and ab initio folding of β-proteins are still needed to further improve the I-TASSER pipeline.
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Affiliation(s)
- Dong Xu
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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