1
|
Li C, Luo Y, Xie Y, Zhang Z, Liu Y, Zou L, Xiao F. Structural and functional prediction, evaluation, and validation in the post-sequencing era. Comput Struct Biotechnol J 2024; 23:446-451. [PMID: 38223342 PMCID: PMC10787220 DOI: 10.1016/j.csbj.2023.12.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 01/16/2024] Open
Abstract
The surge of genome sequencing data has underlined substantial genetic variants of uncertain significance (VUS). The decryption of VUS discovered by sequencing poses a major challenge in the post-sequencing era. Although experimental assays have progressed in classifying VUS, only a tiny fraction of the human genes have been explored experimentally. Thus, it is urgently needed to generate state-of-the-art functional predictors of VUS in silico. Artificial intelligence (AI) is an invaluable tool to assist in the identification of VUS with high efficiency and accuracy. An increasing number of studies indicate that AI has brought an exciting acceleration in the interpretation of VUS, and our group has already used AI to develop protein structure-based prediction models. In this review, we provide an overview of the previous research on AI-based prediction of missense variants, and elucidate the challenges and opportunities for protein structure-based variant prediction in the post-sequencing era.
Collapse
Affiliation(s)
- Chang Li
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yixuan Luo
- Beijing Normal University, Beijing, China
| | - Yibo Xie
- Information Center, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Zaifeng Zhang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ye Liu
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Lihui Zou
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Fei Xiao
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Beijing Normal University, Beijing, China
| |
Collapse
|
2
|
Gao J, Tong M, Lee C, Gaertig J, Legal T, Bui KH. DomainFit: Identification of protein domains in cryo-EM maps at intermediate resolution using AlphaFold2-predicted models. Structure 2024:S0969-2126(24)00143-6. [PMID: 38754431 DOI: 10.1016/j.str.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/18/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024]
Abstract
Cryoelectron microscopy (cryo-EM) has revolutionized the structural determination of macromolecular complexes. With the paradigm shift to structure determination of highly complex endogenous macromolecular complexes ex vivo and in situ structural biology, there are an increasing number of structures of native complexes. These complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because side chains cannot be visualized reliably. Here, we present DomainFit, a program for semi-automated domain-level protein identification from cryo-EM maps, particularly at resolutions lower than 4 Å. By fitting domains from AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the domains and protein candidates forming the density. Using DomainFit, we identified two microtubule inner proteins, one of which contains a CCDC81 domain and is exclusively localized in the proximal region of the doublet microtubule in Tetrahymena thermophila.
Collapse
Affiliation(s)
- Jerry Gao
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada
| | - Maxwell Tong
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada
| | - Chinkyu Lee
- Department of Cellular Biology, University of Georgia, Athens 30602-2607, GA, USA
| | - Jacek Gaertig
- Department of Cellular Biology, University of Georgia, Athens 30602-2607, GA, USA
| | - Thibault Legal
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada.
| | - Khanh Huy Bui
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada.
| |
Collapse
|
3
|
Neira JL, López-Redondo ML, Cámara-Artigas A, Marina A, Contreras A. Structure and dynamics of the cyanobacterial regulator SipA. Arch Biochem Biophys 2024; 754:109943. [PMID: 38395125 DOI: 10.1016/j.abb.2024.109943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/09/2024] [Accepted: 02/20/2024] [Indexed: 02/25/2024]
Abstract
The small, 78-residue long, regulator SipA interacts with the non-bleaching sensor histidine kinase (NblS). We have solved the solution structure of SipA on the basis of 990 nuclear Overhauser effect- (NOE-) derived distance constraints. The average pairwise root-mean-square deviation (RMSD) for the twenty best structures for the backbone residues, obtained by CYANA, was 1.35 ± 0.21 Å, and 1.90 ± 0.16 Å when all heavy atoms were considered (the target function of CYANA was 0.540 ± 0.08). The structure is that of a β-II class protein, basically formed by a five-stranded β-sheet composed of antiparallel strands following the arrangement: Gly6-Leu11 (β-strand 1), which packs against Leu66-Val69 (β-strand 5) on one side, and against Gly36-Thr42 (β-strand 2) on the other side; Trp50-Phe54 (β-strand 3); and Gly57-Leu60 (β-strand 4). The protein is highly mobile, as shown by measurements of R1, R2, NOE and ηxy relaxation parameters, with an average order parameter () of 0.70; this mobility encompasses movements in different time scales. We hypothesize that this high flexibility allows the interaction with other proteins (among them NblS), and it explains the large conformational stability of SipA.
Collapse
Affiliation(s)
- José L Neira
- IDIBE, Universidad Miguel Hernández, 03202, Elche, Alicante, Spain; Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, 50018, Zaragoza, Spain.
| | - María Luisa López-Redondo
- Unidad Regulación de La Síntesis de Proteínas, Instituto de Biomedicina de Valencia (CSIC), 46010, Valencia, Spain
| | - Ana Cámara-Artigas
- Departamento de Química y Física, Research Center CIAIMBITAL, Universidad de Almería- CeiA3, 04120, Almería, Spain
| | - Alberto Marina
- Instituto de Biomedicina de Valencia (CSIC) and Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), 46010, Valencia, Spain
| | | |
Collapse
|
4
|
Jamali K, Käll L, Zhang R, Brown A, Kimanius D, Scheres SHW. Automated model building and protein identification in cryo-EM maps. Nature 2024; 628:450-457. [PMID: 38408488 PMCID: PMC11006616 DOI: 10.1038/s41586-024-07215-4] [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: 05/17/2023] [Accepted: 02/19/2024] [Indexed: 02/28/2024]
Abstract
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs1,2. Here we present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will therefore remove bottlenecks and increase objectivity in cryo-EM structure determination.
Collapse
Affiliation(s)
| | - Lukas Käll
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rui Zhang
- Washington University in St Louis, St Louis, MO, USA
| | - Alan Brown
- Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | | | | |
Collapse
|
5
|
Haynes JR, Whitmore CA, Behof WJ, Landman CA, Ong HH, Feld AP, Suero IC, Greer CB, Gore JC, Wijesinghe P, Matsubara JA, Wadzinski BE, Spiller BW, Pham W. TARGETING SOLUBLE AMYLOID-BETA OLIGOMERS WITH A NOVEL NANOBODY. RESEARCH SQUARE 2024:rs.3.rs-3944211. [PMID: 38559050 PMCID: PMC10980145 DOI: 10.21203/rs.3.rs-3944211/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The classical amyloid cascade hypothesis postulates that the aggregation of amyloid plaques and the accumulation of intracellular hyperphosphorylated Tau tangles, together, lead to profound neuronal death. However, emerging research has demonstrated that soluble amyloid-β oligomers (SAβOs) accumulate early, prior to amyloid plaque formation. SAβOs induce memory impairment and disrupt cognitive function independent of amyloid-β plaques, and even in the absence of plaque formation. This work describes the development and characterization of a novel anti-SAβO (E3) nanobody generated from an alpaca immunized with SAβO. In-vitro assays and in-vivo studies using 5XFAD mice indicate that the fluorescein (FAM)-labeled E3 nanobody recognizes both SAβOs and amyloid-β plaques. The E3 nanobody traverses across the blood-brain barrier and binds to amyloid species in the brain of 5XFAD mice. Imaging of mouse brains reveals that SAβO and amyloid-β plaques are not only different in size, shape, and morphology, but also have a distinct spatial distribution in the brain. SAβOs are associated with neurons, while amyloid plaques reside in the extracellular matrix. The results of this study demonstrate that the SAβO nanobody can serve as a diagnostic agent with potential theragnostic applications in Alzheimer's disease.
Collapse
Affiliation(s)
- Justin R. Haynes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Clayton A. Whitmore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - William J. Behof
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Charlotte A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Henry H. Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Andrew P. Feld
- Department of Pharmacology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Isabelle C. Suero
- Department of Pharmacology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Celeste B. Greer
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Ingram Cancer Center, Nashville, TN 37232, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Printha Wijesinghe
- Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, V5Z3N9, Canada
| | - Joanne A. Matsubara
- Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, V5Z3N9, Canada
| | - Brian E. Wadzinski
- Department of Pharmacology, Vanderbilt University, Nashville, TN, 37232, USA
- Vanderbilt Ingram Cancer Center, Nashville, TN 37232, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Benjamin W. Spiller
- Department of Pharmacology, Vanderbilt University, Nashville, TN, 37232, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA
- Vanderbilt Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Wellington Pham
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Ingram Cancer Center, Nashville, TN 37232, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA
- Vanderbilt Institute of Nanoscale Science and Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| |
Collapse
|
6
|
Moon J, Hu G, Hayashi T. Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics. Biomimetics (Basel) 2024; 9:162. [PMID: 38534847 DOI: 10.3390/biomimetics9030162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/12/2024] [Accepted: 02/29/2024] [Indexed: 03/28/2024] Open
Abstract
Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.
Collapse
Affiliation(s)
- Jooa Moon
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, Yokohama 226-8502, Japan
| | - Guanghao Hu
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, Yokohama 226-8502, Japan
| | - Tomohiro Hayashi
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, Yokohama 226-8502, Japan
- The Institute for Solid State Physics, The University of Tokyo, Kashiwa 277-0882, Japan
| |
Collapse
|
7
|
Banayan NE, Loughlin BJ, Singh S, Forouhar F, Lu G, Wong K, Neky M, Hunt HS, Bateman LB, Tamez A, Handelman SK, Price WN, Hunt JF. Systematic enhancement of protein crystallization efficiency by bulk lysine-to-arginine (KR) substitution. Protein Sci 2024; 33:e4898. [PMID: 38358135 PMCID: PMC10868448 DOI: 10.1002/pro.4898] [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: 06/18/2023] [Revised: 01/01/2024] [Accepted: 01/02/2024] [Indexed: 02/16/2024]
Abstract
Structural genomics consortia established that protein crystallization is the primary obstacle to structure determination using x-ray crystallography. We previously demonstrated that crystallization propensity is systematically related to primary sequence, and we subsequently performed computational analyses showing that arginine is the most overrepresented amino acid in crystal-packing interfaces in the Protein Data Bank. Given the similar physicochemical characteristics of arginine and lysine, we hypothesized that multiple lysine-to-arginine (KR) substitutions should improve crystallization. To test this hypothesis, we developed software that ranks lysine sites in a target protein based on the redundancy-corrected KR substitution frequency in homologs. This software can be run interactively on the worldwide web at https://www.pxengineering.org/. We demonstrate that three unrelated single-domain proteins can tolerate 5-11 KR substitutions with at most minor destabilization, and, for two of these three proteins, the construct with the largest number of KR substitutions exhibits significantly enhanced crystallization propensity. This approach rapidly produced a 1.9 Å crystal structure of a human protein domain refractory to crystallization with its native sequence. Structures from Bulk KR-substituted domains show the engineered arginine residues frequently make hydrogen-bonds across crystal-packing interfaces. We thus demonstrate that Bulk KR substitution represents a rational and efficient method for probabilistic engineering of protein surface properties to improve crystallization.
Collapse
Affiliation(s)
- Nooriel E. Banayan
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Blaine J. Loughlin
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Shikha Singh
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Farhad Forouhar
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Guanqi Lu
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Kam‐Ho Wong
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
Vaccine Research and DevelopmentPfizer Inc.Pearl RiverNew YorkUSA
| | - Matthew Neky
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
Columbia UniversityNew YorkNew YorkUSA
| | - Henry S. Hunt
- Department of PhysicsStanford UniversityStanfordCaliforniaUSA
| | | | | | - Samuel K. Handelman
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
Department of Pain & Neuronal HealthEli Lily & Co.893 Delaware StIndianapolisIndianaUSA
| | - W. Nicholson Price
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
University of Michigan Law SchoolAnn ArborMichiganUSA
| | - John F. Hunt
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
McCafferty CL, Klumpe S, Amaro RE, Kukulski W, Collinson L, Engel BD. Integrating cellular electron microscopy with multimodal data to explore biology across space and time. Cell 2024; 187:563-584. [PMID: 38306982 DOI: 10.1016/j.cell.2024.01.005] [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: 12/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.
Collapse
Affiliation(s)
| | - Sven Klumpe
- Research Group CryoEM Technology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Rommie E Amaro
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Wanda Kukulski
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bühlstrasse 28, 3012 Bern, Switzerland.
| | - Lucy Collinson
- Electron Microscopy Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
| | - Benjamin D Engel
- Biozentrum, University of Basel, Spitalstrasse 41, 4056 Basel, Switzerland.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Lv Q, Zhou F, Liu X, Zhi L. Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work? Bioorg Chem 2023; 141:106894. [PMID: 37776682 DOI: 10.1016/j.bioorg.2023.106894] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.
Collapse
Affiliation(s)
- Qi Lv
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China
| | - Feilong Zhou
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China
| | - Xinhua Liu
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China.
| | - Liping Zhi
- School of Health Management, Anhui Medical University Hefei, 230032, PR China.
| |
Collapse
|
12
|
Gao J, Tong M, Lee C, Gaertig J, Legal T, Bui KH. DomainFit: Identification of Protein Domains in cryo-EM maps at Intermediate Resolution using AlphaFold2-predicted Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.569001. [PMID: 38077012 PMCID: PMC10705406 DOI: 10.1101/2023.11.28.569001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Cryo-electron microscopy (cryo-EM) has revolutionized our understanding of macromolecular complexes, enabling high-resolution structure determination. With the paradigm shift to in situ structural biology recently driven by the ground-breaking development of cryo-focused ion beam milling and cryo-electron tomography, there are an increasing number of structures at sub-nanometer resolution of complexes solved directly within their cellular environment. These cellular complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because the side chains cannot be visualized reliably. Here, we present DomainFit, a program for automated domain-level protein identification from cryo-EM maps at resolutions lower than 4 Å. By fitting domains from artificial intelligence-predicted models such as AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the proteins forming the density. Using DomainFit, we identified two microtubule inner proteins, one of them, a CCDC81 domain-containing protein, is exclusively localized in the proximal region of the doublet microtubule from the ciliate Tetrahymena thermophila. The flexibility and capability of DomainFit makes it a valuable tool for analyzing in situ structures.
Collapse
Affiliation(s)
- Jerry Gao
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
- Centre de recherche en biologie structurale, McGill University, Montréal, Quebec, Canada
| | - Max Tong
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
- Centre de recherche en biologie structurale, McGill University, Montréal, Quebec, Canada
| | - Chinkyu Lee
- Department of Cellular Biology, University of Georgia, Athens, GA, USA
| | - Jacek Gaertig
- Department of Cellular Biology, University of Georgia, Athens, GA, USA
| | - Thibault Legal
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
- Centre de recherche en biologie structurale, McGill University, Montréal, Quebec, Canada
| | - Khanh Huy Bui
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
- Centre de recherche en biologie structurale, McGill University, Montréal, Quebec, Canada
| |
Collapse
|
13
|
Jamali K, Käll L, Zhang R, Brown A, Kimanius D, Scheres SH. Automated model building and protein identification in cryo-EM maps. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541002. [PMID: 37292681 PMCID: PMC10245678 DOI: 10.1101/2023.05.16.541002] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention. We present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality as those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy as humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will thus remove bottlenecks and increase objectivity in cryo-EM structure determination.
Collapse
Affiliation(s)
| | - Lukas Käll
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rui Zhang
- Washington University in St. Louis, St. Louis, MO, USA
| | - Alan Brown
- Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | | | | |
Collapse
|
14
|
Parker MT, Fica SM, Barton GJ, Simpson GG. Inter-species association mapping links splice site evolution to METTL16 and SNRNP27K. eLife 2023; 12:e91997. [PMID: 37787376 PMCID: PMC10581693 DOI: 10.7554/elife.91997] [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: 08/18/2023] [Accepted: 09/18/2023] [Indexed: 10/04/2023] Open
Abstract
Eukaryotic genes are interrupted by introns that are removed from transcribed RNAs by splicing. Patterns of splicing complexity differ between species, but it is unclear how these differences arise. We used inter-species association mapping with Saccharomycotina species to correlate splicing signal phenotypes with the presence or absence of splicing factors. Here, we show that variation in 5' splice site sequence preferences correlate with the presence of the U6 snRNA N6-methyladenosine methyltransferase METTL16 and the splicing factor SNRNP27K. The greatest variation in 5' splice site sequence occurred at the +4 position and involved a preference switch between adenosine and uridine. Loss of METTL16 and SNRNP27K orthologs, or a single SNRNP27K methionine residue, was associated with a preference for +4 U. These findings are consistent with splicing analyses of mutants defective in either METTL16 or SNRNP27K orthologs and models derived from spliceosome structures, demonstrating that inter-species association mapping is a powerful orthogonal approach to molecular studies. We identified variation between species in the occurrence of two major classes of 5' splice sites, defined by distinct interaction potentials with U5 and U6 snRNAs, that correlates with intron number. We conclude that variation in concerted processes of 5' splice site selection by U6 snRNA is associated with evolutionary changes in splicing signal phenotypes.
Collapse
Affiliation(s)
- Matthew T Parker
- School of Life Sciences, University of DundeeDundeeUnited Kingdom
| | - Sebastian M Fica
- Department of Biochemistry, University of OxfordOxfordUnited Kingdom
| | | | - Gordon G Simpson
- School of Life Sciences, University of DundeeDundeeUnited Kingdom
- Cell & Molecular Sciences, James Hutton InstituteInvergowrieUnited Kingdom
| |
Collapse
|
15
|
Simpkin AJ, Caballero I, McNicholas S, Stevenson K, Jiménez E, Sánchez Rodríguez F, Fando M, Uski V, Ballard C, Chojnowski G, Lebedev A, Krissinel E, Usón I, Rigden DJ, Keegan RM. Predicted models and CCP4. Acta Crystallogr D Struct Biol 2023; 79:806-819. [PMID: 37594303 PMCID: PMC10478639 DOI: 10.1107/s2059798323006289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/19/2023] [Indexed: 08/19/2023] Open
Abstract
In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.
Collapse
Affiliation(s)
- Adam J. Simpkin
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Iracema Caballero
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona, Spain
| | - Stuart McNicholas
- York Structural Biology Laboratory, Department of Chemistry, The University of York, York YO10 5DD, United Kingdom
| | - Kyle Stevenson
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Elisabet Jiménez
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona, Spain
| | - Filomeno Sánchez Rodríguez
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
- York Structural Biology Laboratory, Department of Chemistry, The University of York, York YO10 5DD, United Kingdom
| | - Maria Fando
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Ville Uski
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Charles Ballard
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Grzegorz Chojnowski
- European Molecular Biology Laboratory, Hamburg Unit, Notkestrasse 85, 22607 Hamburg, Germany
| | - Andrey Lebedev
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Eugene Krissinel
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Isabel Usón
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona, Spain
- ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08003 Barcelona, Spain
| | - Daniel J. Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Ronan M. Keegan
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| |
Collapse
|
16
|
Brookes E, Rocco M, Vachette P, Trewhella J. AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank. J Appl Crystallogr 2023; 56:910-926. [PMID: 37555230 PMCID: PMC10405597 DOI: 10.1107/s1600576723005344] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/15/2023] [Indexed: 08/10/2023] Open
Abstract
By providing predicted protein structures from nearly all known protein sequences, the artificial intelligence program AlphaFold (AF) is having a major impact on structural biology. While a stunning accuracy has been achieved for many folding units, predicted unstructured regions and the arrangement of potentially flexible linkers connecting structured domains present challenges. Focusing on single-chain structures without prosthetic groups, an earlier comparison of features derived from small-angle X-ray scattering (SAXS) data taken from the Small-Angle Scattering Biological Data Bank (SASBDB) is extended to those calculated using the corresponding AF-predicted structures. Selected SASBDB entries were carefully examined to ensure that they represented data from monodisperse protein solutions and had sufficient statistical precision and q resolution for reliable structural evaluation. Three examples were identified where there is clear evidence that the single AF-predicted structure cannot account for the experimental SAXS data. Instead, excellent agreement is found with ensemble models generated by allowing for flexible linkers between high-confidence predicted structured domains. A pool of representative structures was generated using a Monte Carlo method that adjusts backbone dihedral allowed angles along potentially flexible regions. A fast ensemble modelling method was employed that optimizes the fit of pair distance distribution functions [P(r) versus r] and intensity profiles [I(q) versus q] computed from the pool to their experimental counterparts. These results highlight the complementarity between AF prediction, solution SAXS and molecular dynamics/conformational sampling for structural modelling of proteins having both structured and flexible regions.
Collapse
Affiliation(s)
- Emre Brookes
- Department of Chemistry and Biochemistry, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA
| | - Mattia Rocco
- Proteomica e Spettrometria di Massa, IRCCS Ospedale Policlinico San Martino, Largo R. Benzi 10, Genova 16132, Italy
| | - Patrice Vachette
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette 91198, France
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| |
Collapse
|
17
|
Brookes EH, Rocco M. Beyond the US-SOMO-AF database: a new website for hydrodynamic, structural, and circular dichroism calculations on user-supplied structures. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023; 52:225-232. [PMID: 36853343 PMCID: PMC10460822 DOI: 10.1007/s00249-023-01636-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/07/2023] [Indexed: 06/13/2023]
Abstract
At the 25th International Analytical Ultracentrifugation Workshop and Symposium, we described the recent implementation of the UltraScan SOlution MOdeler AlphaFold (US-SOMO-AF) database, containing hydrodynamic, structural, CD calculations, and other ancillary information, performed on the entire AF v2 database of predicted protein structures, containing more than 1,000,000 entries. The scope of the US-SOMO-AF database was that of providing direct access to pre-calculated physicochemical parameters for rapid assessment against their experimentally determined counterparts to test the compatibility in solution of predicted AlphaFold structures. In the meantime, the AlphaFold consortium has extended its database of predicted structures to an astonishing > 200 million entries, making it quite impractical for their coverage in the US-SOMO-AF database. Therefore, we have created the US-SOMO-Web site, allowing the rapid calculations of all the properties, as present in the US-SOMO-AF database, on user-supplied PDB and mmCIF structures, as well as allowing direct processing of the latest AlphaFold models. Major features on the website are described, along with current limitations and potential future developments.
Collapse
Affiliation(s)
- Emre H Brookes
- Department of Chemistry and Biochemistry, University of Montana, Missoula, MT, 59812, USA.
| | - Mattia Rocco
- Retired, Proteomica e Spettrometria di Massa, IRCCS Ospedale Policlinico San Martino, Largo R. Benzi 10, 16132, Genova, Italy
| |
Collapse
|
18
|
Choi YM, Ajjaji D, Fleming KD, Borbat PP, Jenkins ML, Moeller BE, Fernando S, Bhatia SR, Freed JH, Burke JE, Thiam AR, Airola MV. Structural insights into perilipin 3 membrane association in response to diacylglycerol accumulation. Nat Commun 2023; 14:3204. [PMID: 37268630 PMCID: PMC10238389 DOI: 10.1038/s41467-023-38725-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 05/12/2023] [Indexed: 06/04/2023] Open
Abstract
Lipid droplets (LDs) are dynamic organelles that contain an oil core mainly composed of triglycerides (TAG) that is surrounded by a phospholipid monolayer and LD-associated proteins called perilipins (PLINs). During LD biogenesis, perilipin 3 (PLIN3) is recruited to nascent LDs as they emerge from the endoplasmic reticulum. Here, we analyze how lipid composition affects PLIN3 recruitment to membrane bilayers and LDs, and the structural changes that occur upon membrane binding. We find that the TAG precursors phosphatidic acid and diacylglycerol (DAG) recruit PLIN3 to membrane bilayers and define an expanded Perilipin-ADRP-Tip47 (PAT) domain that preferentially binds DAG-enriched membranes. Membrane binding induces a disorder to order transition of alpha helices within the PAT domain and 11-mer repeats, with intramolecular distance measurements consistent with the expanded PAT domain adopting a folded but dynamic structure upon membrane binding. In cells, PLIN3 is recruited to DAG-enriched ER membranes, and this requires both the PAT domain and 11-mer repeats. This provides molecular details of PLIN3 recruitment to nascent LDs and identifies a function of the PAT domain of PLIN3 in DAG binding.
Collapse
Affiliation(s)
- Yong Mi Choi
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Dalila Ajjaji
- Laboratoire de Physique de l'École normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris Cité, F-75005, Paris, France
| | - Kaelin D Fleming
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8N 1A1, Canada
| | - Peter P Borbat
- National Biomedical Resource for Advanced Electron Spin Resonance Technology (ACERT), Cornell University, Ithaca, NY, 14853, USA
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Meredith L Jenkins
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8N 1A1, Canada
| | - Brandon E Moeller
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8N 1A1, Canada
| | - Shaveen Fernando
- Department of Chemistry, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Surita R Bhatia
- Department of Chemistry, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Jack H Freed
- National Biomedical Resource for Advanced Electron Spin Resonance Technology (ACERT), Cornell University, Ithaca, NY, 14853, USA
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, 14853, USA
| | - John E Burke
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, V8N 1A1, Canada.
- Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada.
| | - Abdou Rachid Thiam
- Laboratoire de Physique de l'École normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris Cité, F-75005, Paris, France.
| | - Michael V Airola
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY, 11794, USA.
| |
Collapse
|
19
|
Agirre J, Atanasova M, Bagdonas H, Ballard CB, Baslé A, Beilsten-Edmands J, Borges RJ, Brown DG, Burgos-Mármol JJ, Berrisford JM, Bond PS, Caballero I, Catapano L, Chojnowski G, Cook AG, Cowtan KD, Croll TI, Debreczeni JÉ, Devenish NE, Dodson EJ, Drevon TR, Emsley P, Evans G, Evans PR, Fando M, Foadi J, Fuentes-Montero L, Garman EF, Gerstel M, Gildea RJ, Hatti K, Hekkelman ML, Heuser P, Hoh SW, Hough MA, Jenkins HT, Jiménez E, Joosten RP, Keegan RM, Keep N, Krissinel EB, Kolenko P, Kovalevskiy O, Lamzin VS, Lawson DM, Lebedev AA, Leslie AGW, Lohkamp B, Long F, Malý M, McCoy AJ, McNicholas SJ, Medina A, Millán C, Murray JW, Murshudov GN, Nicholls RA, Noble MEM, Oeffner R, Pannu NS, Parkhurst JM, Pearce N, Pereira J, Perrakis A, Powell HR, Read RJ, Rigden DJ, Rochira W, Sammito M, Sánchez Rodríguez F, Sheldrick GM, Shelley KL, Simkovic F, Simpkin AJ, Skubak P, Sobolev E, Steiner RA, Stevenson K, Tews I, Thomas JMH, Thorn A, Valls JT, Uski V, Usón I, Vagin A, Velankar S, Vollmar M, Walden H, Waterman D, Wilson KS, Winn MD, Winter G, Wojdyr M, Yamashita K. The CCP4 suite: integrative software for macromolecular crystallography. Acta Crystallogr D Struct Biol 2023; 79:449-461. [PMID: 37259835 PMCID: PMC10233625 DOI: 10.1107/s2059798323003595] [Citation(s) in RCA: 100] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/19/2023] [Indexed: 06/02/2023] Open
Abstract
The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.
Collapse
Affiliation(s)
- Jon Agirre
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Mihaela Atanasova
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Haroldas Bagdonas
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Charles B. Ballard
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Arnaud Baslé
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - James Beilsten-Edmands
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
| | - Rafael J. Borges
- The Center of Medicinal Chemistry (CQMED), Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Av. Dr. André Tosello 550, 13083-886 Campinas, Brazil
| | - David G. Brown
- Laboratoires Servier SAS Institut de Recherches, Croissy-sur-Seine, France
| | - J. Javier Burgos-Mármol
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - John M. Berrisford
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Paul S. Bond
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Iracema Caballero
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Lucrezia Catapano
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
- Randall Centre for Cell and Molecular Biophysics, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, United Kingdom
| | - Grzegorz Chojnowski
- European Molecular Biology Laboratory, Hamburg Unit, Notkestrasse 85, 22607 Hamburg, Germany
| | - Atlanta G. Cook
- The Wellcome Centre for Cell Biology, University of Edinburgh, Michael Swann Building, Max Born Crescent, The King’s Buildings, Edinburgh EH9 3BF, United Kingdom
| | - Kevin D. Cowtan
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Tristan I. Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
- Altos Labs, Portway Building, Granta Park, Great Abington, Cambridge CB21 6GP, United Kingdom
| | - Judit É. Debreczeni
- Discovery Sciences, R&D BioPharmaceuticals, AstraZeneca, Darwin Building, Cambridge Science Park, Milton Road, Cambridge CB4 0WG, United Kingdom
| | - Nicholas E. Devenish
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
| | - Eleanor J. Dodson
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Tarik R. Drevon
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Paul Emsley
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Gwyndaf Evans
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot OX11 0QS, United Kingdom
| | - Phil R. Evans
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Maria Fando
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - James Foadi
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Luis Fuentes-Montero
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
| | - Elspeth F. Garman
- Department of Biochemistry, University of Oxford, Dorothy Crowfoot Hodgkin Building, Oxford OX1 3QU, United Kingdom
| | - Markus Gerstel
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
| | - Richard J. Gildea
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
| | - Kaushik Hatti
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Maarten L. Hekkelman
- Oncode Institute and Department of Biochemistry, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Philipp Heuser
- European Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany
| | - Soon Wen Hoh
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Michael A. Hough
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom
| | - Huw T. Jenkins
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Elisabet Jiménez
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Robbie P. Joosten
- Oncode Institute and Department of Biochemistry, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Ronan M. Keegan
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Nicholas Keep
- Department of Biological Sciences, Institute of Structural and Molecular Biology, Birkbeck College, London WC1E 7HX, United Kingdom
| | - Eugene B. Krissinel
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Petr Kolenko
- Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague 1, Czech Republic
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, Průmyslová 55, 252 50 Vestec, Czech Republic
| | - Oleg Kovalevskiy
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Victor S. Lamzin
- European Molecular Biology Laboratory, Hamburg Unit, Notkestrasse 85, 22607 Hamburg, Germany
| | - David M. Lawson
- Department of Biochemistry and Metabolism, John Innes Centre, Norwich NR4 7UH, United Kingdom
| | - Andrey A. Lebedev
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Andrew G. W. Leslie
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Bernhard Lohkamp
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Fei Long
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Martin Malý
- Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague 1, Czech Republic
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, Průmyslová 55, 252 50 Vestec, Czech Republic
- Biological Sciences, Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Stuart J. McNicholas
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Ana Medina
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Claudia Millán
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - James W. Murray
- Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Garib N. Murshudov
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Robert A. Nicholls
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Martin E. M. Noble
- Translational and Clinical Research Institute, Newcastle University, Paul O’Gorman Building, Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Robert Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Navraj S. Pannu
- Department of Infectious Diseases, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - James M. Parkhurst
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot OX11 0QS, United Kingdom
| | - Nicholas Pearce
- Department of Physics, Chemistry and Biology (IFM), Linköping University, SE-581 83 Linköping, Sweden
| | - Joana Pereira
- Biozentrum and SIB Swiss Institute of Bioinformatics, University of Basel, 4056 Basel, Switzerland
| | - Anastassis Perrakis
- Oncode Institute and Department of Biochemistry, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Harold R. Powell
- Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Daniel J. Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - William Rochira
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Massimo Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
- Discovery Centre, Biologics Engineering, AstraZeneca, Biomedical Campus, 1 Francis Crick Avenue, Trumpington, Cambridge CB2 0AA, United Kingdom
| | - Filomeno Sánchez Rodríguez
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - George M. Sheldrick
- Department of Structural Chemistry, Georg-August-Universität Göttingen, Tammannstrasse 4, 37077 Göttingen, Germany
| | - Kathryn L. Shelley
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Felix Simkovic
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Adam J. Simpkin
- Laboratoires Servier SAS Institut de Recherches, Croissy-sur-Seine, France
| | - Pavol Skubak
- Department of Infectious Diseases, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Egor Sobolev
- European Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany
| | - Roberto A. Steiner
- Randall Centre for Cell and Molecular Biophysics, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, United Kingdom
- Department of Biomedical Sciences, University of Padova, Italy
| | - Kyle Stevenson
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Ivo Tews
- Biological Sciences, Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Jens M. H. Thomas
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Andrea Thorn
- Institute for Nanostructure and Solid State Physics, Universität Hamburg, 22761 Hamburg, Germany
| | - Josep Triviño Valls
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Ville Uski
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Isabel Usón
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
- ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08003 Barcelona, Spain
| | - Alexei Vagin
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Melanie Vollmar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Helen Walden
- School of Molecular Biosciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - David Waterman
- STFC, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, United Kingdom
| | - Keith S. Wilson
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Martyn D. Winn
- Scientific Computing Department, Science and Technology Facilities Council, Didcot OX11 0FA, United Kingdom
| | - Graeme Winter
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
| | - Marcin Wojdyr
- Global Phasing Limited (United Kingdom), Sheraton House, Castle Park, Cambridge CB3 0AX, United Kingdom
| | - Keitaro Yamashita
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| |
Collapse
|
20
|
Mészáros B, Park E, Malinverni D, Sejdiu BI, Immadisetty K, Sandhu M, Lang B, Babu MM. Recent breakthroughs in computational structural biology harnessing the power of sequences and structures. Curr Opin Struct Biol 2023; 80:102608. [PMID: 37182396 DOI: 10.1016/j.sbi.2023.102608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/16/2023]
Abstract
Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.
Collapse
Affiliation(s)
- Bálint Mészáros
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Electa Park
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Duccio Malinverni
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/DucMalinverni
| | - Besian I Sejdiu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/bisejdiu
| | - Kalyan Immadisetty
- Department of Bone Marrow Transplantation & Cellular Therapy, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/k_immadisetty
| | - Manbir Sandhu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/M5andhu
| | - Benjamin Lang
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/langbnj
| | - M Madan Babu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| |
Collapse
|
21
|
David A, Sternberg MJE. Protein structure-based evaluation of missense variants: Resources, challenges and future directions. Curr Opin Struct Biol 2023; 80:102600. [PMID: 37126977 DOI: 10.1016/j.sbi.2023.102600] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 05/03/2023]
Abstract
We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.
Collapse
Affiliation(s)
- Alessia David
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| |
Collapse
|
22
|
Millán C, McCoy AJ, Terwilliger TC, Read RJ. Likelihood-based docking of models into cryo-EM maps. Acta Crystallogr D Struct Biol 2023; 79:281-289. [PMID: 36920336 PMCID: PMC10071562 DOI: 10.1107/s2059798323001602] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/22/2023] [Indexed: 03/16/2023] Open
Abstract
Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimized strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps.
Collapse
Affiliation(s)
- Claudia Millán
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Thomas C. Terwilliger
- New Mexico Consortium, Los Alamos National Laboratory, 100 Entrada Drive, Los Alamos, NM 87544, USA
| | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| |
Collapse
|
23
|
Terwilliger TC, Afonine PV, Liebschner D, Croll TI, McCoy AJ, Oeffner RD, Williams CJ, Poon BK, Richardson JS, Read RJ, Adams PD. Accelerating crystal structure determination with iterative AlphaFold prediction. Acta Crystallogr D Struct Biol 2023; 79:234-244. [PMID: 36876433 PMCID: PMC9986801 DOI: 10.1107/s205979832300102x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/03/2023] [Indexed: 02/28/2023] Open
Abstract
Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.
Collapse
Affiliation(s)
| | - Pavel V Afonine
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Dorothee Liebschner
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Tristan I Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Airlie J McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Robert D Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | | | - Billy K Poon
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Paul D Adams
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| |
Collapse
|