1
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Jiao Z, He Y, Fu X, Zhang X, Geng Z, Ding W. A predicted model-aided reconstruction algorithm for X-ray free-electron laser single-particle imaging. IUCRJ 2024; 11:602-619. [PMID: 38904548 PMCID: PMC11220885 DOI: 10.1107/s2052252524004858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.
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Affiliation(s)
- Zhichao Jiao
- Laboratory of Soft Matter PhysicsInstitute of Physics, Chinese Academy of SciencesBeijing100190People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
| | - Yao He
- Research Instrument ScientistNew York University Abu DhabiAbu DhabiUnited Arab Emirates
| | - Xingke Fu
- Laboratory of Soft Matter PhysicsInstitute of Physics, Chinese Academy of SciencesBeijing100190People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
| | - Xin Zhang
- The University of Hong KongHong Kong SARPeople’s Republic of China
| | - Zhi Geng
- Beijing Synchrotron Radiation FacilityInstitute of High Energy Physics, Chinese Academy of SciencesBeijing100049People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
| | - Wei Ding
- Laboratory of Soft Matter PhysicsInstitute of Physics, Chinese Academy of SciencesBeijing100190People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
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2
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Agarwal V, McShan AC. The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nat Chem Biol 2024:10.1038/s41589-024-01638-w. [PMID: 38907110 DOI: 10.1038/s41589-024-01638-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 04/29/2024] [Indexed: 06/23/2024]
Abstract
Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.
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Affiliation(s)
- Vinayak Agarwal
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Andrew C McShan
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
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3
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Galaz-Montoya JG. The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography. Front Mol Biosci 2024; 11:1390858. [PMID: 38868297 PMCID: PMC11167099 DOI: 10.3389/fmolb.2024.1390858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 05/08/2024] [Indexed: 06/14/2024] Open
Abstract
Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.
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Affiliation(s)
- Jesús G. Galaz-Montoya
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, United States
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4
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Corum MR, Venkannagari H, Hryc CF, Baker ML. Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure. Biophys J 2024; 123:435-450. [PMID: 38268190 PMCID: PMC10912932 DOI: 10.1016/j.bpj.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/09/2024] [Accepted: 01/18/2024] [Indexed: 01/26/2024] Open
Abstract
Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achieving near-atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools, which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. Although they offer new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment, and limitations when integrating predictive modeling with cryo-EM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.
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Affiliation(s)
- Michael R Corum
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas
| | - Harikanth Venkannagari
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas
| | - Corey F Hryc
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas
| | - Matthew L Baker
- Department of Biochemistry and Molecular Biology, McGovern Medical School at the University of Texas Health Science Center, Houston, Texas.
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5
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Xia C, Xing X, Zhang W, Wang Y, Jin X, Wang Y, Tian M, Ba X, Hao F. Cysteine and homocysteine can be exploited by GPX4 in ferroptosis inhibition independent of GSH synthesis. Redox Biol 2024; 69:102999. [PMID: 38150992 PMCID: PMC10829872 DOI: 10.1016/j.redox.2023.102999] [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: 11/22/2023] [Revised: 12/14/2023] [Accepted: 12/14/2023] [Indexed: 12/29/2023] Open
Abstract
Ferroptosis is inhibited by glutathione peroxidase 4 (GPX4), an antioxidant enzyme that uses reduced glutathione (GSH) as a cofactor to detoxify lipid hydroperoxides. As a selenoprotein, the core function of GPX4 is the thiol-dependent redox reaction. In addition to GSH, other small molecules such as cysteine and homocysteine also contain thiols; yet, whether GPX4 can exploit cysteine and homocysteine to directly detoxify lipid hydroperoxides and inhibit ferroptosis has not been addressed. In this study, we found that cysteine and homocysteine inhibit ferroptosis in a GPX4-dependent manner. However, cysteine inhibits ferroptosis independent of GSH synthesis, and homocysteine inhibits ferroptosis through non-cysteine and non-GSH pathway. Furthermore, we used molecular docking and GPX4 activity analysis to study the binding patterns and affinity between GPX4 and GSH, cysteine, and homocysteine. We found that besides GSH, cysteine and homocysteine are also able to serve as substrates for GPX4 though the affinities of GPX4 with cysteine and homocysteine are lower than that with GSH. Importantly, GPX family and the GSH synthetase pathway might be asynchronously evolved. When GSH synthetase is absent, for example in Flexibacter, the fGPX exhibits higher affinity with cysteine and homocysteine than GSH. Taken together, the present study provided the understanding of the role of thiol-dependent redox systems in protecting cells from ferroptosis and propose that GSH might be a substitute for cysteine or homocysteine to be used as a cofactor for GPX4 during the evolution of aerobic metabolism.
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Affiliation(s)
- Chaoyi Xia
- Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Xiyue Xing
- Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Wenxia Zhang
- Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Yang Wang
- Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Xin Jin
- Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Yang Wang
- Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Meihong Tian
- School of Physical Education, Northeast Normal University, 5268 Renmin Street, Changchun, Jilin, 130024, China.
| | - Xueqing Ba
- Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China.
| | - Fengqi Hao
- School of Physical Education, Northeast Normal University, 5268 Renmin Street, Changchun, Jilin, 130024, China; Key Laboratory of Molecular Epigenetics of Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China.
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6
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Terwilliger TC, Liebschner D, Croll TI, Williams CJ, McCoy AJ, Poon BK, Afonine PV, Oeffner RD, Richardson JS, Read RJ, Adams PD. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat Methods 2024; 21:110-116. [PMID: 38036854 PMCID: PMC10776388 DOI: 10.1038/s41592-023-02087-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 10/11/2023] [Indexed: 12/02/2023]
Abstract
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.
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Affiliation(s)
- Thomas C Terwilliger
- New Mexico Consortium, Los Alamos, NM, USA.
- Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Dorothee Liebschner
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Tristan I Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Airlie J McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Billy K Poon
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Pavel V Afonine
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Robert D Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
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7
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Terashi G, Wang X, Prasad D, Nakamura T, Kihara D. DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. Nat Methods 2024; 21:122-131. [PMID: 38066344 DOI: 10.1038/s41592-023-02099-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/22/2023] [Indexed: 12/19/2023]
Abstract
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, there are still many cases where tracing protein main chains is difficult, even in maps determined at a near-atomic resolution. Here we developed a protein structure modeling method, DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, we integrated AlphaFold2 with the de novo density tracing protocol to combine their complementary strengths and achieved even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign the chain identity to the structure models of homo-multimers, which is not a trivial task for existing methods.
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Affiliation(s)
- Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Devashish Prasad
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Tsukasa Nakamura
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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8
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Park J, Joung I, Joo K, Lee J. Application of conformational space annealing to the protein structure modeling using cryo-EM maps. J Comput Chem 2023; 44:2332-2346. [PMID: 37585026 DOI: 10.1002/jcc.27200] [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: 12/08/2022] [Revised: 04/26/2023] [Accepted: 07/16/2023] [Indexed: 08/17/2023]
Abstract
Conformational space annealing (CSA), a global optimization method, has been applied to various protein structure modeling tasks. In this paper, we applied CSA to the cryo-EM structure modeling task by combining the python subroutine of CSA (PyCSA) and the fast relax (FastRelax) protocol of PyRosetta. Refinement of initial structures generated from two methods, rigid fitting of predicted structures to the Cryo-EM map and de novo protein modeling by tracing the Cryo-EM map, was performed by CSA. In the refinement of the rigid-fitted structures, the final models showed that CSA can generate reliable atomic structures of proteins, even when large movements of protein domains were required. In the de novo modeling case, although the overall structural qualities of the final models were rather dependent on the initial models, the final models generated by CSA showed improved MolProbity scores and cross-correlation coefficients to the maps. These results suggest that CSA can accomplish flexible fitting and refinement together by sampling diverse conformations effectively and thus can be utilized for cryo-EM structure modeling.
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Affiliation(s)
| | | | - Keehyoung Joo
- Center for Advanced Computations, Korea Institute for Advanced Study, Seoul, South Korea
| | - Jooyoung Lee
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
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9
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Sun Z, Lu Z, Xiao T, Chen Y, Fu P, Lu K, Gui F. Genome-Wide Scanning Loci and Differentially Expressed Gene Analysis Unveils the Molecular Mechanism of Chlorantraniliprole Resistance in Spodoptera frugiperda. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:14092-14107. [PMID: 37699662 DOI: 10.1021/acs.jafc.3c04228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Chlorantraniliprole has been widely used to controlSpodoptera frugiperda, but it has led to the development of chlorantraniliprole resistance. Multiomics analysis of strains with two extreme traits helps to elucidate the complex mechanisms involved. Herein, following genome resequencing and application of the Euclidean distance algorithm, 550 genes within a 16.20-Mb-linked region were identified from chlorantraniliprole-resistant (Ch-R) and chlorantraniliprole-susceptible (Ch-Sus) strains. Using transcriptome sequencing, 2066 differentially expressed genes were identified between Ch-R and Ch-Sus strains. Through association analysis, three glutathione S-transferase family genes and four trehalose transporter genes were selected for functional verification. Notably, SfGSTD1 had the strongest binding ability with chlorantraniliprole and is responsible for chlorantraniliprole tolerance. The Ch-R strain also increased the intracellular trehalose content by upregulating the transcription of SfTret1, thereby contributing to chlorantraniliprole resistance. These findings provide a new perspective to reveal the mechanism of resistance of agricultural pests to insecticides.
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Affiliation(s)
- Zhongxiang Sun
- State Key Laboratory of Conservation and Utilization of Biological Resources of Yunnan, College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China
| | - Zhihui Lu
- State Key Laboratory of Conservation and Utilization of Biological Resources of Yunnan, College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China
| | - Tianxiang Xiao
- Anhui Province Key Laboratory of Crop Integrated Pest Management, Anhui Province Engineering Laboratory for Green Pesticide Development and Application, School of Plant Protection, Anhui Agricultural University, Hefei 230036, China
| | - Yaping Chen
- State Key Laboratory of Conservation and Utilization of Biological Resources of Yunnan, College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China
| | - Pengfei Fu
- State Key Laboratory of Conservation and Utilization of Biological Resources of Yunnan, College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China
| | - Kai Lu
- Anhui Province Key Laboratory of Crop Integrated Pest Management, Anhui Province Engineering Laboratory for Green Pesticide Development and Application, School of Plant Protection, Anhui Agricultural University, Hefei 230036, China
| | - Furong Gui
- State Key Laboratory of Conservation and Utilization of Biological Resources of Yunnan, College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China
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10
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Stephens DC, Crabtree A, Beasley HK, Garza-Lopez E, Mungai M, Vang L, Neikirk K, Vue Z, Vue N, Marshall AG, Turner K, Shao JQ, Sarker B, Murray S, Gaddy JA, Hinton AO, Damo S, Davis J. In the Age of Machine Learning Cryo-EM Research is Still Necessary: A Path toward Precision Medicine. Adv Biol (Weinh) 2023; 7:e2300122. [PMID: 37246245 DOI: 10.1002/adbi.202300122] [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: 03/21/2023] [Revised: 04/29/2023] [Indexed: 05/30/2023]
Abstract
Machine learning has proven useful in analyzing complex biological data and has greatly influenced the course of research in structural biology and precision medicine. Deep neural network models oftentimes fail to predict the structure of complex proteins and are heavily dependent on experimentally determined structures for their training and validation. Single-particle cryogenic electron microscopy (cryoEM) is also advancing the understanding of biology and will be needed to complement these models by continuously supplying high-quality experimentally validated structures for improvements in prediction quality. In this perspective, the significance of structure prediction methods is highlighted, but the authors also ask, what if these programs cannot accurately predict a protein structure important for preventing disease? The role of cryoEM is discussed to help fill the gaps left by artificial intelligence predictive models in resolving targetable proteins and protein complexes that will pave the way for personalized therapeutics.
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Affiliation(s)
- Dominique C Stephens
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37232, USA
| | - Amber Crabtree
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Heather K Beasley
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Edgar Garza-Lopez
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Margaret Mungai
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Larry Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Kit Neikirk
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Zer Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Neng Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Andrea G Marshall
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Kyrin Turner
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37232, USA
| | - Jian-Qiang Shao
- Central Microscopy Research Facility, University of Iowa, Iowa City, IA, 52242, USA
| | - Bishnu Sarker
- School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, 37208, USA
| | - Sandra Murray
- Department of Cell Biology, College of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Jennifer A Gaddy
- Division of Infectious Diseases, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- U.S. Department of Veterans Affairs, Tennessee Valley Healthcare Systems, Nashville, TN, 37212, USA
| | - Antentor O Hinton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Steven Damo
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37232, USA
| | - Jamaine Davis
- Department of Biochemistry and Cancer Biology, Meharry Medical College, Nashville, TN, 37208, USA
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11
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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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12
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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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