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Versini R, Sritharan S, Aykac Fas B, Tubiana T, Aimeur SZ, Henri J, Erard M, Nüsse O, Andreani J, Baaden M, Fuchs P, Galochkina T, Chatzigoulas A, Cournia Z, Santuz H, Sacquin-Mora S, Taly A. A Perspective on the Prospective Use of AI in Protein Structure Prediction. J Chem Inf Model 2024; 64:26-41. [PMID: 38124369 DOI: 10.1021/acs.jcim.3c01361] [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: 12/23/2023]
Abstract
AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.
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Affiliation(s)
- Raphaelle Versini
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sujith Sritharan
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Burcu Aykac Fas
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Thibault Tubiana
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Sana Zineb Aimeur
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Julien Henri
- Sorbonne Université, CNRS, Laboratoire de Biologie, Computationnelle et Quantitative UMR 7238, Institut de Biologie Paris-Seine, 4 Place Jussieu, F-75005 Paris, France
| | - Marie Erard
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Oliver Nüsse
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Patrick Fuchs
- Sorbonne Université, École Normale Supérieure, PSL University, CNRS, Laboratoire des Biomolécules, LBM, 75005 Paris, France
- Université de Paris, UFR Sciences du Vivant, 75013 Paris, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Alexios Chatzigoulas
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Hubert Santuz
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Antoine Taly
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Drug Discov Today 2023; 28:103551. [PMID: 36907321 PMCID: PMC10238671 DOI: 10.1016/j.drudis.2023.103551] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023]
Abstract
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; 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, MD 21702, USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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Gutnik D, Evseev P, Miroshnikov K, Shneider M. Using AlphaFold Predictions in Viral Research. Curr Issues Mol Biol 2023; 45:3705-3732. [PMID: 37185764 PMCID: PMC10136805 DOI: 10.3390/cimb45040240] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/17/2023] Open
Abstract
Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.
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Affiliation(s)
- Daria Gutnik
- Limnological Institute of the Siberian Branch of the Russian Academy of Sciences, 3 Ulan-Batorskaya Str., 664033 Irkutsk, Russia
| | - Peter Evseev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
| | - Konstantin Miroshnikov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
| | - Mikhail Shneider
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
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Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct Target Ther 2023; 8:115. [PMID: 36918529 PMCID: PMC10011802 DOI: 10.1038/s41392-023-01381-z] [Citation(s) in RCA: 60] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/27/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.
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Affiliation(s)
- Zhenyu Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Yi Zhao
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Runsheng Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, 518118, China.
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Zhao H, Zhang H, She Z, Gao Z, Wang Q, Geng Z, Dong Y. Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein. Int J Mol Sci 2023; 24:ijms24032740. [PMID: 36769074 PMCID: PMC9916901 DOI: 10.3390/ijms24032740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 02/04/2023] Open
Abstract
Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2's performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2's performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.
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Affiliation(s)
- Haifan Zhao
- School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Heng Zhang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Zhun She
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Zengqiang Gao
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Wang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhi Geng
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (Z.G.); (Y.D.)
| | - Yuhui Dong
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (Z.G.); (Y.D.)
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Mating M, Zou Y, Sharbati S, Einspanier R. The Active Site of the Enzyme 10-Formyl-THFDH in the Honey Bee Apis mellifera-A Key Player in Formic Acid Detoxification. Int J Mol Sci 2022; 24:ijms24010354. [PMID: 36613799 PMCID: PMC9820478 DOI: 10.3390/ijms24010354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Honey bees are important managed pollinators that fulfill important ecological and economic functions. In recent decades, the obligate ectoparasite Varroa destructor severely affected the survival of honey bees, as it weakened them by different means. A common treatment against V. destructor is formic acid fumigation, which has been used for decades by beekeepers across the world. This treatment is known to be effective, but many beekeepers report adverse effects of formic acid on bees, which include damage to the brood, worker bee mortality, and queen loss. Little is known about the molecular mechanisms of formic acid detoxification in honey bees. Recently, we reported upregulation of the bee enzyme, 10-formyl-THFDH, under formic acid fumigation. Here, the active site of this enzyme is characterized by an interdisciplinary approach combining homology modeling and protein mutagenesis. In addition, the limitations of the 3D protein structure prediction program AlphaFold2 are shown in regard to docking studies. This study provides a more thorough understanding of the molecular detoxification mechanisms of formic acid in Apis mellifera.
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Affiliation(s)
- Moritz Mating
- Institute of Veterinary Biochemistry, Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany
| | - Ye Zou
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66506, USA
| | - Soroush Sharbati
- Institute of Veterinary Biochemistry, Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany
| | - Ralf Einspanier
- Institute of Veterinary Biochemistry, Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany
- Correspondence:
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