1
|
Opuni KFM, Ruß M, Geens R, Vocht LD, Wielendaele PV, Debuy C, Sterckx YGJ, Glocker MO. Mass spectrometry-complemented molecular modeling predicts the interaction interface for a camelid single-domain antibody targeting the Plasmodium falciparum circumsporozoite protein's C-terminal domain. Comput Struct Biotechnol J 2024; 23:3300-3314. [PMID: 39296809 PMCID: PMC11409006 DOI: 10.1016/j.csbj.2024.08.023] [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: 06/17/2024] [Revised: 08/26/2024] [Accepted: 08/26/2024] [Indexed: 09/21/2024] Open
Abstract
Background Bioanalytical methods that enable rapid and high-detail characterization of binding specificities and strengths of protein complexes with low sample consumption are highly desired. The interaction between a camelid single domain antibody (sdAbCSP1) and its target antigen (PfCSP-Cext) was selected as a model system to provide proof-of-principle for the here described methodology. Research design and methods The structure of the sdAbCSP1 - PfCSP-Cext complex was modeled using AlphaFold2. The recombinantly expressed proteins, sdAbCSP1, PfCSP-Cext, and the sdAbCSP1 - PfCSP-Cext complex, were subjected to limited proteolysis and mass spectrometric peptide analysis. ITEM MS (Intact Transition Epitope Mapping Mass Spectrometry) and ITC (Isothermal Titration Calorimetry) were applied to determine stoichiometry and binding strength. Results The paratope of sdAbCSP1 mainly consists of its CDR3 (aa100-118). PfCSP-Cext's epitope is assembled from its α-helix (aa40-52) and opposing loop (aa83-90). PfCSP-Cext's GluC cleavage sites E46 and E58 were shielded by complex formation, confirming the predicted epitope. Likewise, sdAbCSP1's tryptic cleavage sites R105 and R108 were shielded by complex formation, confirming the predicted paratope. ITEM MS determined the 1:1 stoichiometry and the high complex binding strength, exemplified by the gas phase dissociation reaction enthalpy of 50.2 kJ/mol. The in-solution complex dissociation constant is 5 × 10-10 M. Conclusions Combining AlphaFold2 modeling with mass spectrometry/limited proteolysis generated a trustworthy model for the sdAbCSP1 - PfCSP-Cext complex interaction interface.
Collapse
Affiliation(s)
- Kwabena F M Opuni
- Department of Pharmaceutical Chemistry, School of Pharmacy, College of Health Science, University of Ghana, P.O. Box LG43, Legon, Ghana
| | - Manuela Ruß
- Proteome Center Rostock, University Medicine Rostock and University of Rostock, Schillingallee 69, 18057 Rostock, Germany
| | - Rob Geens
- Laboratory of Medical Biochemistry, Faculty of Pharmaceutical, Biomedical, and Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610 Antwerp, Belgium
| | - Line De Vocht
- Laboratory of Medical Biochemistry, Faculty of Pharmaceutical, Biomedical, and Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610 Antwerp, Belgium
| | - Pieter Van Wielendaele
- Laboratory of Medical Biochemistry, Faculty of Pharmaceutical, Biomedical, and Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610 Antwerp, Belgium
| | - Christophe Debuy
- Laboratory of Medical Biochemistry, Faculty of Pharmaceutical, Biomedical, and Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610 Antwerp, Belgium
| | - Yann G-J Sterckx
- Laboratory of Medical Biochemistry, Faculty of Pharmaceutical, Biomedical, and Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610 Antwerp, Belgium
| | - Michael O Glocker
- Proteome Center Rostock, University Medicine Rostock and University of Rostock, Schillingallee 69, 18057 Rostock, Germany
| |
Collapse
|
2
|
Lu H, Zhu Z, Fields L, Zhang H, Li L. Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking. MASS SPECTROMETRY REVIEWS 2024. [PMID: 39300771 DOI: 10.1002/mas.21908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/31/2024] [Accepted: 09/02/2024] [Indexed: 09/22/2024]
Abstract
The exploration of protein structure and function stands at the forefront of life science and represents an ever-expanding focus in the development of proteomics. As mass spectrometry (MS) offers readout of protein conformational changes at both the protein and peptide levels, MS-based structural proteomics is making significant strides in the realms of structural and molecular biology, complementing traditional structural biology techniques. This review focuses on two powerful MS-based techniques for peptide-level readout, namely limited proteolysis-mass spectrometry (LiP-MS) and cross-linking mass spectrometry (XL-MS). First, we discuss the principles, features, and different workflows of these two methods. Subsequently, we delve into the bioinformatics strategies and software tools used for interpreting data associated with these protein conformation readouts and how the data can be integrated with other computational tools. Furthermore, we provide a comprehensive summary of the noteworthy applications of LiP-MS and XL-MS in diverse areas including neurodegenerative diseases, interactome studies, membrane proteins, and artificial intelligence-based structural analysis. Finally, we discuss the factors that modulate protein conformational changes. We also highlight the remaining challenges in understanding the intricacies of protein conformational changes by LiP-MS and XL-MS technologies.
Collapse
Affiliation(s)
- Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Zexin Zhu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| |
Collapse
|
3
|
Kovalevskiy O, Mateos-Garcia J, Tunyasuvunakool K. AlphaFold two years on: Validation and impact. Proc Natl Acad Sci U S A 2024; 121:e2315002121. [PMID: 39133843 PMCID: PMC11348012 DOI: 10.1073/pnas.2315002121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024] Open
Abstract
Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.
Collapse
|
4
|
Agarwal V, McShan AC. The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nat Chem Biol 2024; 20:950-959. [PMID: 38907110 DOI: 10.1038/s41589-024-01638-w] [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: 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.
Collapse
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.
| |
Collapse
|
5
|
Wang L, Wen Z, Liu SW, Zhang L, Finley C, Lee HJ, Fan HJS. Overview of AlphaFold2 and breakthroughs in overcoming its limitations. Comput Biol Med 2024; 176:108620. [PMID: 38761500 DOI: 10.1016/j.compbiomed.2024.108620] [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: 10/29/2023] [Revised: 05/01/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.
Collapse
Affiliation(s)
- Lei Wang
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Zehua Wen
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Shi-Wei Liu
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Lihong Zhang
- Digestive Department, Binhai New Area Hospital of TCM Tianjin, Tianjin, 300451, China
| | - Cierra Finley
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA
| | - Ho-Jin Lee
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA; Division of Natural & Mathematical Sciences, LeMoyne-Own College, Memphis, TN, 38126, USA.
| | - Hua-Jun Shawn Fan
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China.
| |
Collapse
|
6
|
Sawhney A, Li J, Liao L. Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features. Int J Mol Sci 2024; 25:5247. [PMID: 38791287 PMCID: PMC11121315 DOI: 10.3390/ijms25105247] [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: 04/12/2024] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2's predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.
Collapse
Affiliation(s)
- Aman Sawhney
- Department of Computer and Information Sciences, University of Delaware, Smith Hall, 18 Amstel Avenue, Newark, DE 19716, USA;
| | - Jiefu Li
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Road, Shanghai 200093, China;
| | - Li Liao
- Department of Computer and Information Sciences, University of Delaware, Smith Hall, 18 Amstel Avenue, Newark, DE 19716, USA;
| |
Collapse
|
7
|
Park H, Youn B, Park DJ, Puthanveettil SV, Kang C. Functional implication of the homotrimeric multidomain vacuolar sorting receptor 1 (VSR1) from Arabidopsis thaliana. Sci Rep 2024; 14:9622. [PMID: 38671060 PMCID: PMC11052993 DOI: 10.1038/s41598-024-57975-2] [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: 01/13/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
The vacuolar sorting receptors (VSRs) are specific to plants and are responsible for sorting and transporting particular proteins from the trans-Golgi network to the vacuole. This process is critically important for various cellular functions, including storing nutrients during seed development. Despite many years of intense studies on VSRs, a complete relation between function and structure has not yet been revealed. Here, we present the crystal structure of the entire luminal region of glycosylated VSR1 from Arabidopsis thaliana (AtVSR1) for the first time. The structure provides insights into the tertiary and quaternary structures of VSR1, which are composed of an N-terminal protease-associated (PA) domain, a unique central region, and one epidermal growth factor (EGF)-like domain followed by two disordered EGF-like domains. The structure of VSR1 exhibits unique characteristics, the significance of which is yet to be fully understood.
Collapse
Affiliation(s)
- HaJeung Park
- X-Ray Core, UF Scripps Biomedical Research, University of Florida, Jupiter, FL, 33458, USA
| | - BuHyun Youn
- Department of Biological Sciences, Pusan National University, Busan, 46241, Republic of Korea
| | - Daniel J Park
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, 32827, USA
| | | | - ChulHee Kang
- Department of Chemistry, Washington State University, Pullman, WA, 99164, USA.
| |
Collapse
|
8
|
Manalastas-Cantos K, Adoni KR, Pfeifer M, Märtens B, Grünewald K, Thalassinos K, Topf M. Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry. Mol Cell Proteomics 2024; 23:100724. [PMID: 38266916 PMCID: PMC10884514 DOI: 10.1016/j.mcpro.2024.100724] [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: 09/13/2023] [Revised: 12/23/2023] [Accepted: 12/27/2023] [Indexed: 01/26/2024] Open
Abstract
We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.
Collapse
Affiliation(s)
- Karen Manalastas-Cantos
- Center for Data and Computing in Natural Sciences, Universität Hamburg, Hamburg, Germany; Department of Integrative Virology, Leibniz-Institut für Virologie (LIV), Centre for Structural Systems Biology (CSSB), Hamburg, Germany
| | - Kish R Adoni
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK; Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, United Kingdom
| | - Matthias Pfeifer
- Department of Integrative Virology, Leibniz-Institut für Virologie (LIV), Centre for Structural Systems Biology (CSSB), Hamburg, Germany; Universitätsklinikum Hamburg Eppendorf (UKE), Hamburg, Germany
| | - Birgit Märtens
- Department of Integrative Virology, Leibniz-Institut für Virologie (LIV), Centre for Structural Systems Biology (CSSB), Hamburg, Germany; Universitätsklinikum Hamburg Eppendorf (UKE), Hamburg, Germany
| | - Kay Grünewald
- Department of Integrative Virology, Leibniz-Institut für Virologie (LIV), Centre for Structural Systems Biology (CSSB), Hamburg, Germany; Department of Chemistry, Universität Hamburg, Hamburg, Germany
| | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK; Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, United Kingdom
| | - Maya Topf
- Department of Integrative Virology, Leibniz-Institut für Virologie (LIV), Centre for Structural Systems Biology (CSSB), Hamburg, Germany; Universitätsklinikum Hamburg Eppendorf (UKE), Hamburg, Germany.
| |
Collapse
|
9
|
Versini R, Sritharan S, Aykac Fas B, Tubiana T, Aimeur SZ, Henri J, Erard M, Nüsse O, Andreani J, Baaden M, Fuchs P, Galochkina T, Chatzigoulas A, Cournia Z, Santuz H, Sacquin-Mora S, Taly A. A Perspective on the Prospective Use of AI in Protein Structure Prediction. J Chem Inf Model 2024; 64:26-41. [PMID: 38124369 DOI: 10.1021/acs.jcim.3c01361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.
Collapse
Affiliation(s)
- Raphaelle Versini
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sujith Sritharan
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Burcu Aykac Fas
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Thibault Tubiana
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Sana Zineb Aimeur
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Julien Henri
- Sorbonne Université, CNRS, Laboratoire de Biologie, Computationnelle et Quantitative UMR 7238, Institut de Biologie Paris-Seine, 4 Place Jussieu, F-75005 Paris, France
| | - Marie Erard
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Oliver Nüsse
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Patrick Fuchs
- Sorbonne Université, École Normale Supérieure, PSL University, CNRS, Laboratoire des Biomolécules, LBM, 75005 Paris, France
- Université de Paris, UFR Sciences du Vivant, 75013 Paris, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Alexios Chatzigoulas
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Hubert Santuz
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Antoine Taly
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Hevler JF, Heck AJR. Higher-Order Structural Organization of the Mitochondrial Proteome Charted by In Situ Cross-Linking Mass Spectrometry. Mol Cell Proteomics 2023; 22:100657. [PMID: 37805037 PMCID: PMC10651688 DOI: 10.1016/j.mcpro.2023.100657] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023] Open
Abstract
Mitochondria are densely packed with proteins, of which most are involved physically or more transiently in protein-protein interactions (PPIs). Mitochondria host among others all enzymes of the Krebs cycle and the oxidative phosphorylation pathway and are foremost associated with cellular bioenergetics. However, mitochondria are also important contributors to apoptotic cell death and contain their own genome indicating that they play additionally an eminent role in processes beyond bioenergetics. Despite intense efforts in identifying and characterizing mitochondrial protein complexes by structural biology and proteomics techniques, many PPIs have remained elusive. Several of these (membrane embedded) PPIs are less stable in vitro hampering their characterization by most contemporary methods in structural biology. Particularly in these cases, cross-linking mass spectrometry (XL-MS) has proven valuable for the in-depth characterization of mitochondrial protein complexes in situ. Here, we highlight experimental strategies for the analysis of proteome-wide PPIs in mitochondria using XL-MS. We showcase the ability of in situ XL-MS as a tool to map suborganelle interactions and topologies and aid in refining structural models of protein complexes. We describe some of the most recent technological advances in XL-MS that may benefit the in situ characterization of PPIs even further, especially when combined with electron microscopy and structural modeling.
Collapse
Affiliation(s)
- Johannes F Hevler
- Division of Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Albert J R Heck
- Division of Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands.
| |
Collapse
|
12
|
Sawhney A, Li J, Liao L. Improving AlphaFold predicted contacts in alpha-helical transmembrane proteins structures using structural features. RESEARCH SQUARE 2023:rs.3.rs-3475769. [PMID: 37961476 PMCID: PMC10635369 DOI: 10.21203/rs.3.rs-3475769/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Residue contacts maps offer a 2-d reduced representation of 3-d protein structures and constitute a structural constraint and scaffold in structural modeling. In addition, contact maps are also an effective tool in identifying interhelical binding sites and drawing insights about protein function. While most works predict contact maps using features derived from sequences, we believe information from known structures can be leveraged for a prediction improvement in unknown structures where decent approximate structures such as ones predicted by AlphaFold2 are available. Results Alphafold2's predicted structures are found to be quite accurate at inter-helical residue contact prediction task, achieving 83% average precision. We adopt an unconventional approach, using features extracted from atomic structures in the neighborhood of a residue pair and use them to predicting residue contact. We trained on features derived from experimentally determined structures and predicted on features derived from AlphaFold2's predicted structures. Our results demonstrate a remarkable improvement over AlphaFold2 achieving over 91.9% average precision for held-out and over 89.5% average precision in cross validation experiments. Conclusion Training on features generated from experimentally determined structures, we were able to leverage knowledge from known structures to significantly improve the contacts predicted using AlphaFold2 structures. We demonstrated that using coordinates directly (instead of the proposed features) does not lead to an improvement in contact prediction performance.
Collapse
Affiliation(s)
- Aman Sawhney
- Department of Computer and Information Sciences, University of
Delaware, Smith Hall, 18 Amstel Avenue, Newark, DE, 19716,United States
| | - Jiefu Li
- School of Optical-Electrical and Computer Engineering, University
of Shanghai for Science and Technology, 516 Jun Gong Road, Shanghai 200093, P. R.
China
| | - Li Liao
- Department of Computer and Information Sciences, University of
Delaware, Smith Hall, 18 Amstel Avenue, Newark, DE, 19716,United States
| |
Collapse
|
13
|
McCafferty CL, Papoulas O, Lee C, Bui KH, Taylor DW, Marcotte EM, Wallingford JB. An amino acid-resolution interactome for motile cilia illuminates the structure and function of ciliopathy protein complexes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.09.548259. [PMID: 37781579 PMCID: PMC10541116 DOI: 10.1101/2023.07.09.548259] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Motile cilia are ancient, evolutionarily conserved organelles whose dysfunction underlies motile ciliopathies, a broad class of human diseases. Motile cilia contain myriad different proteins that assemble into an array of distinct machines, so understanding the interactions and functional hierarchies among them presents an important challenge. Here, we defined the protein interactome of motile axonemes using cross-linking mass spectrometry (XL/MS) in Tetrahymena thermophila. From over 19,000 XLs, we identified 4,757 unique amino acid interactions among 1,143 distinct proteins, providing both macromolecular and atomic-scale insights into diverse ciliary machines, including the Intraflagellar Transport system, axonemal dynein arms, radial spokes, the 96 nm ruler, and microtubule inner proteins, among others. Guided by this dataset, we used vertebrate multiciliated cells to reveal novel functional interactions among several poorly-defined human ciliopathy proteins. The dataset therefore provides a powerful resource for studying the basic biology of an ancient organelle and the molecular etiology of human genetic disease.
Collapse
Affiliation(s)
- Caitlyn L. McCafferty
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
- Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Ophelia Papoulas
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
| | - Chanjae Lee
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
| | - Khanh Huy Bui
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences McGill University, Québec, Canada
| | - David W. Taylor
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
| | - Edward M. Marcotte
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
| | - John B. Wallingford
- Department of Molecular Biosciences, University of Texas, Austin, TX 78712, USA
| |
Collapse
|
14
|
McCafferty CL, Papoulas O, Jordan MA, Hoogerbrugge G, Nichols C, Pigino G, Taylor DW, Wallingford JB, Marcotte EM. Integrative modeling reveals the molecular architecture of the intraflagellar transport A (IFT-A) complex. eLife 2022; 11:e81977. [PMID: 36346217 PMCID: PMC9674347 DOI: 10.7554/elife.81977] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/07/2022] [Indexed: 11/10/2022] Open
Abstract
Intraflagellar transport (IFT) is a conserved process of cargo transport in cilia that is essential for development and homeostasis in organisms ranging from algae to vertebrates. In humans, variants in genes encoding subunits of the cargo-adapting IFT-A and IFT-B protein complexes are a common cause of genetic diseases known as ciliopathies. While recent progress has been made in determining the atomic structure of IFT-B, little is known of the structural biology of IFT-A. Here, we combined chemical cross-linking mass spectrometry and cryo-electron tomography with AlphaFold2-based prediction of both protein structures and interaction interfaces to model the overall architecture of the monomeric six-subunit IFT-A complex, as well as its polymeric assembly within cilia. We define monomer-monomer contacts and membrane-associated regions available for association with transported cargo, and we also use this model to provide insights into the pleiotropic nature of human ciliopathy-associated genetic variants in genes encoding IFT-A subunits. Our work demonstrates the power of integration of experimental and computational strategies both for multi-protein structure determination and for understanding the etiology of human genetic disease.
Collapse
Affiliation(s)
- Caitlyn L McCafferty
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Ophelia Papoulas
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Mareike A Jordan
- Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany
| | - Gabriel Hoogerbrugge
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Candice Nichols
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | | | - David W Taylor
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - John B Wallingford
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Edward M Marcotte
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| |
Collapse
|