1
|
Asediya VS, Anjaria PA, Mathakiya RA, Koringa PG, Nayak JB, Bisht D, Fulmali D, Patel VA, Desai DN. Vaccine development using artificial intelligence and machine learning: A review. Int J Biol Macromol 2024; 282:136643. [PMID: 39426778 DOI: 10.1016/j.ijbiomac.2024.136643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 09/30/2024] [Accepted: 10/15/2024] [Indexed: 10/21/2024]
Abstract
The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.
Collapse
Affiliation(s)
| | | | | | | | | | - Deepanker Bisht
- Indian Veterinary Research Institute, Izatnagar, U.P., India
| | | | | | | |
Collapse
|
2
|
Liu J, Guo Z, You H, Zhang C, Lai L. All-Atom Protein Sequence Design Based on Geometric Deep Learning. Angew Chem Int Ed Engl 2024:e202411461. [PMID: 39295564 DOI: 10.1002/anie.202411461] [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: 06/18/2024] [Revised: 09/09/2024] [Accepted: 09/18/2024] [Indexed: 09/21/2024]
Abstract
Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to produce the complete all-atom structures for designed sequences. GeoSeqBuilder uses spatial geometric features from protein backbones and explicitly includes three-body interactions of neighboring residues. GeoSeqBuilder achieves native residue type recovery rate of 51.6 %, comparable to ProteinMPNN and other leading methods, while accurately predicting side chain conformations. We first used GeoSeqBuilder to design sequences for thioredoxin and a hallucinated three-helical bundle protein. All the 15 tested sequences expressed as soluble monomeric proteins with high thermal stability, and the 2 high-resolution crystal structures solved closely match the designed models. The generated protein sequences exhibit low similarity (minimum 23 %) to the original sequences, with significantly altered hydrophobic cores. We further redesigned the hydrophobic core of glutathione peroxidase 4, and 3 of the 5 designs showed improved enzyme activity. Although further testing is needed, the high experimental success rate in our testing demonstrates that GeoSeqBuilder is a powerful tool for designing novel sequences for predefined protein structures with atomic details. GeoSeqBuilder is available at https://github.com/PKUliujl/GeoSeqBuilder.
Collapse
Affiliation(s)
- Jiale Liu
- Center for Life Sciences Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Zheng Guo
- Center for Life Sciences Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Hantian You
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Changsheng Zhang
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Luhua Lai
- Center for Life Sciences Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Peking University, Chengdu, 510100, Sichuan, China
| |
Collapse
|
3
|
Zhang J, Qian J. Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins. Biomolecules 2024; 14:1083. [PMID: 39334850 PMCID: PMC11430421 DOI: 10.3390/biom14091083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2024] [Accepted: 08/26/2024] [Indexed: 09/30/2024] Open
Abstract
Proteins serve as the building blocks of life and play essential roles in almost every cellular process [...].
Collapse
Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China;
| | | |
Collapse
|
4
|
Kumar H, Kim P. Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective. Clin Transl Med 2024; 14:e1789. [PMID: 39090739 PMCID: PMC11294035 DOI: 10.1002/ctm2.1789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 07/16/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024] Open
Abstract
Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.
Collapse
Affiliation(s)
- Himansu Kumar
- Department of Bioinformatics and Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Pora Kim
- Department of Bioinformatics and Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| |
Collapse
|
5
|
Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [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: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
Collapse
Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| |
Collapse
|
6
|
Abstract
In the past 2 decades, structural biology has transformed from a single technique used on single proteins to a multimodal integrative approach. Recently, protein structure prediction algorithms have opened new avenues to address challenging biological questions.
Collapse
Affiliation(s)
| | - Kristina Djinović-Carugo
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
- European Molecular Biology Laboratory (EMBL), Grenoble, France
- Faculty of Chemistry and Chemical Technology, Department of Biochemistry, University of Ljubljana, Ljubljana, Slovenia
| |
Collapse
|
7
|
Burroughs A, Aravind L. New biochemistry in the Rhodanese-phosphatase superfamily: emerging roles in diverse metabolic processes, nucleic acid modifications, and biological conflicts. NAR Genom Bioinform 2023; 5:lqad029. [PMID: 36968430 PMCID: PMC10034599 DOI: 10.1093/nargab/lqad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/10/2023] [Accepted: 03/09/2023] [Indexed: 03/25/2023] Open
Abstract
The protein-tyrosine/dual-specificity phosphatases and rhodanese domains constitute a sprawling superfamily of Rossmannoid domains that use a conserved active site with a cysteine to catalyze a range of phosphate-transfer, thiotransfer, selenotransfer and redox activities. While these enzymes have been extensively studied in the context of protein/lipid head group dephosphorylation and various thiotransfer reactions, their overall diversity and catalytic potential remain poorly understood. Using comparative genomics and sequence/structure analysis, we comprehensively investigate and develop a natural classification for this superfamily. As a result, we identified several novel clades, both those which retain the catalytic cysteine and those where a distinct active site has emerged in the same location (e.g. diphthine synthase-like methylases and RNA 2' OH ribosyl phosphate transferases). We also present evidence that the superfamily has a wider range of catalytic capabilities than previously known, including a set of parallel activities operating on various sugar/sugar alcohol groups in the context of NAD+-derivatives and RNA termini, and potential phosphate transfer activities involving sugars and nucleotides. We show that such activities are particularly expanded in the RapZ-C-DUF488-DUF4326 clade, defined here for the first time. Some enzymes from this clade are predicted to catalyze novel DNA-end processing activities as part of nucleic-acid-modifying systems that are likely to function in biological conflicts between viruses and their hosts.
Collapse
Affiliation(s)
- A Maxwell Burroughs
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - L Aravind
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| |
Collapse
|
8
|
Bhattacharya S, Roche R, Shuvo MH, Moussad B, Bhattacharya D. Contact-Assisted Threading in Low-Homology Protein Modeling. Methods Mol Biol 2023; 2627:41-59. [PMID: 36959441 DOI: 10.1007/978-1-0716-2974-1_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.
Collapse
Affiliation(s)
- Sutanu Bhattacharya
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | | | - Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | | |
Collapse
|
9
|
Wu F, Jing X, Luo X, Xu J. Improving protein structure prediction using templates and sequence embedding. Bioinformatics 2023; 39:6820926. [PMID: 36355462 PMCID: PMC9805584 DOI: 10.1093/bioinformatics/btac723] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and multiple sequence alignment (MSA) embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch, which may not perform very well on some targets. In addition, sequence embedding generated by pre-trained protein language models has not been fully explored for structure prediction. In this article, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction. RESULTS We have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 (13th Critical Assessment of Structure Prediction) targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 (14th Critical Assessment of Structure Prediction) targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore > 0.5) for 16 of 23 CASP14 FM targets and 14 of 18 Continuous Automated Model Evaluation (CAMEO) targets, outperforming RoseTTAFold by 5% and 7%, respectively. AVAILABILITY AND IMPLEMENTATION Available at https://github.com/xluo233/RaptorXFold. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | | | - Xiao Luo
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
| | - Jinbo Xu
- To whom correspondence should be addressed.
| |
Collapse
|
10
|
Avery C, Patterson J, Grear T, Frater T, Jacobs DJ. Protein Function Analysis through Machine Learning. Biomolecules 2022; 12:1246. [PMID: 36139085 PMCID: PMC9496392 DOI: 10.3390/biom12091246] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein-ligand binding, including allosteric effects, protein-protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
Collapse
Affiliation(s)
- Chris Avery
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - John Patterson
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Tyler Grear
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Theodore Frater
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Donald J. Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| |
Collapse
|
11
|
Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, Artificial Intelligence (AI), and Allostery. J Phys Chem B 2022; 126:6372-6383. [PMID: 35976160 PMCID: PMC9442638 DOI: 10.1021/acs.jpcb.2c04346] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/03/2022] [Indexed: 02/08/2023]
Abstract
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
- Department
of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer
Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| |
Collapse
|
12
|
Lee C, Su BH, Tseng YJ. Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors. Brief Bioinform 2022; 23:6658852. [PMID: 35945035 PMCID: PMC9487610 DOI: 10.1093/bib/bbac308] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/22/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is outstanding, their performance for specific protein families has remained unexamined. G-protein-coupled receptor (GPCR) proteins are particularly interesting since they are involved in numerous pathways. This work directly compares the performance of these novel deep learning-based protein modeling methods for GPCRs with the most widely used template-based software—Modeller. We collected the experimentally determined structures of 73 GPCRs from the Protein Data Bank. The official AlphaFold repository and RoseTTAFold web service were used with default settings to predict five structures of each protein sequence. The predicted models were then aligned with the experimentally solved structures and evaluated by the root-mean-square deviation (RMSD) metric. If only looking at each program’s top-scored structure, Modeller had the smallest average modeling RMSD of 2.17 Å, which is better than AlphaFold’s 5.53 Å and RoseTTAFold’s 6.28 Å, probably since Modeller already included many known structures as templates. However, the NN-based methods (AlphaFold and RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model, respectively, where no good templates were available for Modeller. The larger RMSD values generated by the NN-based methods were primarily due to the differences in loop prediction compared to the crystal structures.
Collapse
Affiliation(s)
- Chien Lee
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Bo-Han Su
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yufeng Jane Tseng
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| |
Collapse
|
13
|
Yang P, Ning K. How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives. IMETA 2022; 1:e9. [PMID: 38867727 PMCID: PMC10989767 DOI: 10.1002/imt2.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/23/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2024]
Abstract
It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.
Collapse
Affiliation(s)
- Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Department of Bioinformatics and Systems Biology Center of AI Biology, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan Hubei China
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Department of Bioinformatics and Systems Biology Center of AI Biology, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan Hubei China
| |
Collapse
|
14
|
Bhattacharya S, Roche R, Moussad B, Bhattacharya D. DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins. Proteins 2022; 90:579-588. [PMID: 34599831 PMCID: PMC8738102 DOI: 10.1002/prot.26254] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 02/03/2023]
Abstract
Threading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence-based predicted contact or distance information is used. Contact-assisted or distance-assisted threading methods utilize only the spatial proximity of the interacting residue pairs for template selection and alignment, ignoring their orientation. Moreover, existing threading methods fail to consider the neighborhood effect induced by the query-template alignment. We present a new distance- and orientation-based covariational threading method called DisCovER by effectively integrating information from inter-residue distance and orientation along with the topological network neighborhood of a query-template alignment. Our method first selects a subset of templates using standard profile-based threading coupled with topological network similarity terms to account for the neighborhood effect and subsequently performs distance- and orientation-based query-template alignment using an iterative double dynamic programming framework. Multiple large-scale benchmarking results on query proteins classified as weakly homologous from the continuous automated model evaluation experiment and from the current literature show that our method outperforms several existing state-of-the-art threading approaches, and that the integration of the neighborhood effect with the inter-residue distance and orientation information synergistically contributes to the improved performance of DisCovER. DisCovER is freely available at https://github.com/Bhattacharya-Lab/DisCovER.
Collapse
Affiliation(s)
- Sutanu Bhattacharya
- Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA
| | - Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | | |
Collapse
|
15
|
Tran NH, Xu J, Li M. A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction. Brief Bioinform 2022; 23:bbab493. [PMID: 34891158 PMCID: PMC8769896 DOI: 10.1093/bib/bbab493] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/11/2021] [Accepted: 10/26/2021] [Indexed: 12/30/2022] Open
Abstract
In this article, we review two challenging computational questions in protein science: neoantigen prediction and protein structure prediction. Both topics have seen significant leaps forward by deep learning within the past five years, which immediately unlocked new developments of drugs and immunotherapies. We show that deep learning models offer unique advantages, such as representation learning and multi-layer architecture, which make them an ideal choice to leverage a huge amount of protein sequence and structure data to address those two problems. We also discuss the impact and future possibilities enabled by those two applications, especially how the data-driven approach by deep learning shall accelerate the progress towards personalized biomedicine.
Collapse
Affiliation(s)
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, USA
| | - Ming Li
- University of Waterloo, Canada
| |
Collapse
|
16
|
Su H, Wang W, Du Z, Peng Z, Gao S, Cheng M, Yang J. Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2102592. [PMID: 34719864 PMCID: PMC8693034 DOI: 10.1002/advs.202102592] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 09/12/2021] [Indexed: 06/04/2023]
Abstract
The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi-scale network, i.e., Res2Net, for improved prediction of inter-residue geometries, including distance and orientations. The second is an attention-based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM-score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi-scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.
Collapse
Affiliation(s)
- Hong Su
- School of Mathematical SciencesNankai UniversityTianjin300071China
| | - Wenkai Wang
- School of Mathematical SciencesNankai UniversityTianjin300071China
| | - Zongyang Du
- School of Mathematical SciencesNankai UniversityTianjin300071China
| | - Zhenling Peng
- Research Center for Mathematics and Interdisciplinary SciencesShandong UniversityQingdao266237China
| | - Shang‐Hua Gao
- College of Computer ScienceNankai UniversityTianjin300071China
| | - Ming‐Ming Cheng
- College of Computer ScienceNankai UniversityTianjin300071China
| | - Jianyi Yang
- Research Center for Mathematics and Interdisciplinary SciencesShandong UniversityQingdao266237China
| |
Collapse
|
17
|
Rudnev VR, Kulikova LI, Nikolsky KS, Malsagova KA, Kopylov AT, Kaysheva AL. Current Approaches in Supersecondary Structures Investigation. Int J Mol Sci 2021; 22:11879. [PMID: 34769310 PMCID: PMC8584461 DOI: 10.3390/ijms222111879] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/27/2021] [Accepted: 10/29/2021] [Indexed: 11/16/2022] Open
Abstract
Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.
Collapse
Affiliation(s)
- Vladimir R. Rudnev
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Liudmila I. Kulikova
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
- Institute of Mathematical Problems of Biology RAS—The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Kirill S. Nikolsky
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
| | - Kristina A. Malsagova
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
| | - Arthur T. Kopylov
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
| | - Anna L. Kaysheva
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
| |
Collapse
|