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Feltes BC, Pinto ÉSM, Mangini AT, Dorn M. NIAS-Server 2.0: A versatile complementary tool for structural biology studies. J Comput Chem 2023; 44:1610-1623. [PMID: 37040476 DOI: 10.1002/jcc.27112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/21/2023] [Accepted: 03/24/2023] [Indexed: 04/13/2023]
Abstract
Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X-ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.
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Affiliation(s)
- Bruno César Feltes
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | | | - Márcio Dorn
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
- Center for Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
- National Institute of Forensic Science and Technology, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
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2
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Mufassirin MMM, Newton MAH, Sattar A. Artificial intelligence for template-free protein structure prediction: a comprehensive review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10350-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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3
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Newton MH, Zaman R, Mataeimoghadam F, Rahman J, Sattar A. Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction. Comput Biol Chem 2022; 101:107773. [DOI: 10.1016/j.compbiolchem.2022.107773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022]
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4
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Pan F, Zhao L, Cai S, Tang X, Mehmood A, Alnadari F, Tuersuntuoheti T, Zhou N, Ai X. Prediction and evaluation of the 3D structure of Macadamia integrifolia antimicrobial protein 2 (MiAMP2) and its interaction with palmitoleic acid or oleic acid: An integrated computational approach. Food Chem 2021; 367:130677. [PMID: 34343803 DOI: 10.1016/j.foodchem.2021.130677] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/11/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022]
Abstract
This study investigated the physicochemical properties and 3D structure of Macadamia integrifolia antimicrobial protein 2 (MiAMP2) and its interaction with palmitoleic acid (POA) or oleic acid (OA) in macadamia oil. The 3D structure of MiAMP2 was constructed for the first time by ab initio modelling using the TrRosetta server. The results showed that MiAMP2 was highly hydrophilic and had seven disulfide bonds and higher α-helix and β-sheet/turn contents. Molecular simulation showed that the hydrophobic pocket of MiAMP2 created a favourable environment for the binding of POA and OA. Free energy landscape and independent gradient model (IGM) analyses revealed that hydrogen bonds and van der Waals forces were the major driving forces stabilizing complexes formed by MiAMP2 and POA or OA. The present study provides a theoretical basis and new insight for the future development and utilization of macadamia nut protein in the food industry.
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Affiliation(s)
- Fei Pan
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
| | - Lei Zhao
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China.
| | - Shengbao Cai
- Faculty of Agriculture and Food, Yunnan Institute of Food Safety, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Xiaoning Tang
- Faculty of Chemical Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Arshad Mehmood
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
| | - Fawze Alnadari
- Department of Food Science and Engineering, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
| | - Tuohetisayipu Tuersuntuoheti
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
| | - Na Zhou
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
| | - Xin Ai
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
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5
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Chen X, Song S, Ji J, Tang Z, Todo Y. Incorporating a multiobjective knowledge-based energy function into differential evolution for protein structure prediction. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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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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Wang W, Li Z, Wang J, Xu D, Shang Y. PSICA: a fast and accurate web service for protein model quality analysis. Nucleic Acids Res 2020; 47:W443-W450. [PMID: 31127307 PMCID: PMC6602450 DOI: 10.1093/nar/gkz402] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 04/21/2019] [Accepted: 05/01/2019] [Indexed: 11/17/2022] Open
Abstract
This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.
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Affiliation(s)
- Wenbo Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Zhaoyu Li
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Junlin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.,Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yi Shang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
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Zhang GJ, Ma LF, Wang XQ, Zhou XG. Secondary Structure and Contact Guided Differential Evolution for Protein Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1068-1081. [PMID: 30295627 DOI: 10.1109/tcbb.2018.2873691] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Ab initio protein tertiary structure prediction is one of the long-standing problems in structural bioinformatics. With the help of residue-residue contact and secondary structure prediction information, the accuracy of ab initio structure prediction can be enhanced. In this study, an improved differential evolution with secondary structure and residue-residue contact information referred to as SCDE is proposed for protein structure prediction. In SCDE, two score models based on secondary structure and contact information are proposed, and two selection strategies, namely, secondary structure-based selection strategy and contact-based selection strategy, are designed to guide conformation space search. A probability distribution function is designed to balance these two selection strategies. Experimental results on a benchmark dataset with 28 proteins and four free model targets in CASP12 demonstrate that the proposed SCDE is effective and efficient.
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9
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Li ZW, Sun K, Hao XH, Hu J, Ma LF, Zhou XG, Zhang GJ. Loop Enhanced Conformational Resampling Method for Protein Structure Prediction. IEEE Trans Nanobioscience 2019; 18:567-577. [PMID: 31180866 DOI: 10.1109/tnb.2019.2922101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Protein structure prediction has been a long-standing problem for the past decades. In particular, the loop region structure remains an obstacle in forming an accurate protein tertiary structure because of its flexibility. In this study, Rama torsion angle and secondary structure feature-guided differential evolution named RSDE is proposed to predict three-dimensional structure with the exploitation on the loop region structure. In RSDE, the structure of the loop region is improved by the following: loop-based cross operator, which interchanges configuration of a randomly selected loop region between individuals, and loop-based mutate operator, which considers torsion angle feature into conformational sampling. A stochastic ranking selective strategy is designed to select conformations with low energy and near-native structure. Moreover, the conformational resampling method, which uses previously learned knowledge to guide subsequent sampling, is proposed to improve the sampling efficiency. Experiments on a total of 28 test proteins reveals that the proposed RSDE is effective and can obtain native-like models.
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10
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Al-Sahaf H, Bi Y, Chen Q, Lensen A, Mei Y, Sun Y, Tran B, Xue B, Zhang M. A survey on evolutionary machine learning. J R Soc N Z 2019. [DOI: 10.1080/03036758.2019.1609052] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Harith Al-Sahaf
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Ying Bi
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Qi Chen
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Andrew Lensen
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Yi Mei
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Yanan Sun
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Binh Tran
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Bing Xue
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
| | - Mengjie Zhang
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
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