1
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Koukos PI, Dehghani-Ghahnaviyeh S, Velez-Vega C, Manchester J, Tieleman DP, Duca JS, Souza PCT, Cournia Z. Martini 3 Force Field Parameters for Protein Lipidation Post-Translational Modifications. J Chem Theory Comput 2023; 19:8901-8918. [PMID: 38019969 DOI: 10.1021/acs.jctc.3c00604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Protein lipidations are vital co/post-translational modifications that tether lipid tails to specific protein amino acids, allowing them to anchor to biological membranes, switch their subcellular localization, and modulate association with other proteins. Such lipidations are thus crucial for multiple biological processes including signal transduction, protein trafficking, and membrane localization and are implicated in various diseases as well. Examples of lipid-anchored proteins include the Ras family of proteins that undergo farnesylation; actin and gelsolin that are myristoylated; phospholipase D that is palmitoylated; glycosylphosphatidylinositol-anchored proteins; and others. Here, we develop parameters for cysteine-targeting farnesylation, geranylgeranylation, and palmitoylation, as well as glycine-targeting myristoylation for the latest version of the Martini 3 coarse-grained force field. The parameters are developed using the CHARMM36m all-atom force field parameters as reference. The behavior of the coarse-grained models is consistent with that of the all-atom force field for all lipidations and reproduces key dynamical and structural features of lipid-anchored peptides, such as the solvent-accessible surface area, bilayer penetration depth, and representative conformations of the anchors. The parameters are also validated in simulations of the lipid-anchored peripheral membrane proteins Rheb and Arf1, after comparison with independent all-atom simulations. The parameters, along with mapping schemes for the popular martinize2 tool, are available for download at 10.5281/zenodo.7849262 and also as supporting information.
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Affiliation(s)
- Panagiotis I Koukos
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
| | - Sepehr Dehghani-Ghahnaviyeh
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Camilo Velez-Vega
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - John Manchester
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - D Peter Tieleman
- Department of Biological Sciences, University of Calgary, Calgary T2N 1N4 Alberta, Canada
- Centre for Molecular Simulation, University of Calgary, Calgary T2N 1N4 Alberta, Canada
| | - José S Duca
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Paulo C T Souza
- Molecular Microbiology and Structural Biochemistry, (MMSB, UMR 5086), CNRS & University of Lyon, 69367 Lyon, France
- Laboratory of Biology and Modeling of the Cell, École Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5239 and Inserm U1293, 46 Allée d'Italie, 69364 Lyon, France
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
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2
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van der Krift F, Zijlmans DW, Shukla R, Javed A, Koukos PI, Schwarz LLE, Timmermans-Sprang EP, Maas PE, Gahtory D, van den Nieuwboer M, Mol JA, Strous GJ, Bonvin AM, van der Stelt M, Veldhuizen EJ, Weingarth M, Vermeulen M, Klumperman J, Maurice MM. A novel antifolate suppresses growth of FPGS-deficient cells and overcomes methotrexate resistance. Life Sci Alliance 2023; 6:e202302058. [PMID: 37591722 PMCID: PMC10435995 DOI: 10.26508/lsa.202302058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 08/19/2023] Open
Abstract
Cancer cells make extensive use of the folate cycle to sustain increased anabolic metabolism. Multiple chemotherapeutic drugs interfere with the folate cycle, including methotrexate and 5-fluorouracil that are commonly applied for the treatment of leukemia and colorectal cancer (CRC), respectively. Despite high success rates, therapy-induced resistance causes relapse at later disease stages. Depletion of folylpolyglutamate synthetase (FPGS), which normally promotes intracellular accumulation and activity of natural folates and methotrexate, is linked to methotrexate and 5-fluorouracil resistance and its association with relapse illustrates the need for improved intervention strategies. Here, we describe a novel antifolate (C1) that, like methotrexate, potently inhibits dihydrofolate reductase and downstream one-carbon metabolism. Contrary to methotrexate, C1 displays optimal efficacy in FPGS-deficient contexts, due to decreased competition with intracellular folates for interaction with dihydrofolate reductase. We show that FPGS-deficient patient-derived CRC organoids display enhanced sensitivity to C1, whereas FPGS-high CRC organoids are more sensitive to methotrexate. Our results argue that polyglutamylation-independent antifolates can be applied to exert selective pressure on FPGS-deficient cells during chemotherapy, using a vulnerability created by polyglutamylation deficiency.
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Affiliation(s)
- Felix van der Krift
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dick W Zijlmans
- Department of Molecular Biology and Oncode Institute, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Rhythm Shukla
- NMR Spectroscopy, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Ali Javed
- NMR Spectroscopy, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Division of Infectious Diseases and Immunology, Department of Biomolecular Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology, Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Laura LE Schwarz
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Peter Em Maas
- Specs Compound Handling B.V., Zoetermeer, The Netherlands
| | | | | | - Jan A Mol
- Department of Clinical Sciences of Companion Animals, Utrecht University, Utrecht, The Netherlands
| | - Ger J Strous
- Center for Molecular Medicine, Cell Biology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alexandre Mjj Bonvin
- Computational Structural Biology, Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Mario van der Stelt
- Department of Molecular Physiology and Oncode Institute, Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands
| | - Edwin Ja Veldhuizen
- Division of Infectious Diseases and Immunology, Department of Biomolecular Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Markus Weingarth
- NMR Spectroscopy, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Michiel Vermeulen
- Department of Molecular Biology and Oncode Institute, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Judith Klumperman
- Center for Molecular Medicine, Cell Biology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Madelon M Maurice
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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3
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Buchanan CJ, Gaunt B, Harrison PJ, Yang Y, Liu J, Khan A, Giltrap AM, Le Bas A, Ward PN, Gupta K, Dumoux M, Tan TK, Schimaski L, Daga S, Picchiotti N, Baldassarri M, Benetti E, Fallerini C, Fava F, Giliberti A, Koukos PI, Davy MJ, Lakshminarayanan A, Xue X, Papadakis G, Deimel LP, Casablancas-Antràs V, Claridge TDW, Bonvin AMJJ, Sattentau QJ, Furini S, Gori M, Huo J, Owens RJ, Schaffitzel C, Berger I, Renieri A, Naismith JH, Baldwin AJ, Davis BG. Pathogen-sugar interactions revealed by universal saturation transfer analysis. Science 2022; 377:eabm3125. [PMID: 35737812 DOI: 10.1126/science.abm3125] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an "end-on" manner. uSTA-guided modeling and a high-resolution cryo-electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis.
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Affiliation(s)
- Charles J Buchanan
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK.,Kavli Institute of Nanoscience Discovery, University of Oxford, Oxford OX1 3QU, UK
| | - Ben Gaunt
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK
| | - Peter J Harrison
- Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK.,Diamond Light Source, Harwell Science and Innovation Campus, Oxfordshire, UK
| | - Yun Yang
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | - Jiwei Liu
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK
| | - Aziz Khan
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK
| | - Andrew M Giltrap
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK
| | - Audrey Le Bas
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | - Philip N Ward
- Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | - Kapil Gupta
- Max Planck Bristol Centre for Minimal Biology, University of Bristol, Bristol, UK
| | - Maud Dumoux
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK
| | - Tiong Kit Tan
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Lisa Schimaski
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Sergio Daga
- Medical Genetics, University of Siena, Siena, Italy.,Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Nicola Picchiotti
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.,Department of Mathematics, University of Pavia, Pavia, Italy
| | - Margherita Baldassarri
- Medical Genetics, University of Siena, Siena, Italy.,Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Elisa Benetti
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Chiara Fallerini
- Medical Genetics, University of Siena, Siena, Italy.,Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Francesca Fava
- Medical Genetics, University of Siena, Siena, Italy.,Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy.,Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Annarita Giliberti
- Medical Genetics, University of Siena, Siena, Italy.,Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Panagiotis I Koukos
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | - Matthew J Davy
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK
| | - Abirami Lakshminarayanan
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK
| | - Xiaochao Xue
- Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK.,Sir William Dunn School of Pathology, Oxford, UK
| | | | | | - Virgínia Casablancas-Antràs
- Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK.,Kavli Institute of Nanoscience Discovery, University of Oxford, Oxford OX1 3QU, UK
| | | | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | | | - Simone Furini
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Marco Gori
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.,Maasai, I3S CNRS, Université Côte d'Azur, Nice, France
| | - Jiandong Huo
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | - Raymond J Owens
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | - Christiane Schaffitzel
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | - Imre Berger
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | - Alessandra Renieri
- Medical Genetics, University of Siena, Siena, Italy.,Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy.,Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | | | - James H Naismith
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | - Andrew J Baldwin
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK.,Kavli Institute of Nanoscience Discovery, University of Oxford, Oxford OX1 3QU, UK
| | - Benjamin G Davis
- Rosalind Franklin Institute, Harwell Science and Innovation Campus, Oxford OX11 0FA, UK.,Department of Chemistry, University of Oxford, Oxford OX1 3TA, UK.,Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
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4
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Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RAG, Clarence T, Bates PA, Kong R, Liu B, Yang G, Liu M, Shi H, Lu X, Chang S, Roy RS, Quadir F, Liu J, Cheng J, Antoniak A, Czaplewski C, Giełdoń A, Kogut M, Lipska AG, Liwo A, Lubecka EA, Maszota-Zieleniak M, Sieradzan AK, Ślusarz R, Wesołowski PA, Zięba K, Del Carpio Muñoz CA, Ichiishi E, Harmalkar A, Gray JJ, Bonvin AMJJ, Ambrosetti F, Vargas Honorato R, Jandova Z, Jiménez-García B, Koukos PI, Van Keulen S, Van Noort CW, Réau M, Roel-Touris J, Kotelnikov S, Padhorny D, Porter KA, Alekseenko A, Ignatov M, Desta I, Ashizawa R, Sun Z, Ghani U, Hashemi N, Vajda S, Kozakov D, Rosell M, Rodríguez-Lumbreras LA, Fernandez-Recio J, Karczynska A, Grudinin S, Yan Y, Li H, Lin P, Huang SY, Christoffer C, Terashi G, Verburgt J, Sarkar D, Aderinwale T, Wang X, Kihara D, Nakamura T, Hanazono Y, Gowthaman R, Guest JD, Yin R, Taherzadeh G, Pierce BG, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Sun Y, Zhu S, Shen Y, Park T, Woo H, Yang J, Kwon S, Won J, Seok C, Kiyota Y, Kobayashi S, Harada Y, Takeda-Shitaka M, Kundrotas PJ, Singh A, Vakser IA, Dapkūnas J, Olechnovič K, Venclovas Č, Duan R, Qiu L, Xu X, Zhang S, Zou X, Wodak SJ. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment. Proteins 2021; 89:1800-1823. [PMID: 34453465 PMCID: PMC8616814 DOI: 10.1002/prot.26222] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/24/2021] [Accepted: 08/05/2021] [Indexed: 12/19/2022]
Abstract
We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.
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Affiliation(s)
- Marc F Lensink
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Guillaume Brysbaert
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Théo Mauri
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Nurul Nadzirin
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | - Tereza Clarence
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Bin Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Guangbo Yang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ming Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Anna Antoniak
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Artur Giełdoń
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Mateusz Kogut
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
| | | | | | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Patryk A Wesołowski
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Gdansk, Poland
| | - Karolina Zięba
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Eiichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Nasushiobara City, Japan
| | - Ameya Harmalkar
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo Vargas Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandova
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Siri Van Keulen
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W Van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Manon Réau
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Innopolis University, Russia
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Institute of Computer-Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nasser Hashemi
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Mireia Rosell
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Luis A Rodríguez-Lumbreras
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Juan Fernandez-Recio
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Sergei Grudinin
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tsukasa Nakamura
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - Yuya Hanazono
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Tokai, Ibaraki, Japan
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | | | - Zhen Cao
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yasuomi Kiyota
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Yoshiki Harada
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Shuang Zhang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xiaoqin Zou
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA
- Department of Biochemistry, University of Missouri, Columbia, Missouri, USA
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5
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Abstract
Small-molecule docking remains one of the most valuable computational techniques for the structure prediction of protein-small-molecule complexes. It allows us to study the interactions between compounds and the protein receptors they target at atomic detail in a timely and efficient manner. Here, we present a new protocol in HADDOCK (High Ambiguity Driven DOCKing), our integrative modeling platform, which incorporates homology information for both receptor and compounds. It makes use of HADDOCK's unique ability to integrate information in the simulation to drive it toward conformations, which agree with the provided data. The focal point is the use of shape restraints derived from homologous compounds bound to the target receptors. We have developed two protocols: in the first, the shape is composed of dummy atom beads based on the position of the heavy atoms of the homologous template compound, whereas in the second, the shape is additionally annotated with pharmacophore data for some or all beads. For both protocols, ambiguous distance restraints are subsequently defined between those beads and the heavy atoms of the ligand to be docked. We have benchmarked the performance of these protocols with a fully unbound version of the widely used DUD-E (Database of Useful Decoys-Enhanced) dataset. In this unbound docking scenario, our template/shape-based docking protocol reaches an overall success rate of 81% when a reliable template can be identified (which was the case for 99 out of 102 complexes in the DUD-E dataset), which is close to the best results reported for bound docking on the DUD-E dataset.
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Affiliation(s)
- Panagiotis I Koukos
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht 3584CH, The Netherlands
| | - Manon Réau
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht 3584CH, The Netherlands
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht 3584CH, The Netherlands
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6
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Honorato RV, Koukos PI, Jiménez-García B, Tsaregorodtsev A, Verlato M, Giachetti A, Rosato A, Bonvin AMJJ. Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem. Front Mol Biosci 2021; 8:729513. [PMID: 34395534 PMCID: PMC8356364 DOI: 10.3389/fmolb.2021.729513] [Citation(s) in RCA: 252] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 07/13/2021] [Indexed: 12/05/2022] Open
Abstract
Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is critical for understanding the vast majority of cellular processes, with direct applications in health and food sciences. Since 2010, the WeNMR project (www.wenmr.eu) has implemented numerous web-based services to facilitate the use of advanced computational tools by researchers in the field, using the high throughput computing infrastructure provided by EGI. These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4,000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. The current set of active WeNMR portals are described, together with the complex backend machinery that allows distributed computing resources to be harvested efficiently.
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Affiliation(s)
- Rodrigo V Honorato
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, Netherlands
| | - Panagiotis I Koukos
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, Netherlands
| | - Brian Jiménez-García
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, Netherlands
| | | | | | - Andrea Giachetti
- Department of Chemistry and Magnetic Resonance Center, University of Florence, and C.I.R.M.M.P, Fiorentino, Italy
| | - Antonio Rosato
- Department of Chemistry and Magnetic Resonance Center, University of Florence, and C.I.R.M.M.P, Fiorentino, Italy
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, Netherlands
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7
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Matos-Filipe P, Preto AJ, Koukos PI, Mourão J, Bonvin AMJJ, Moreira IS. MENSAdb: a thorough structural analysis of membrane protein dimers. Database (Oxford) 2021; 2021:baab013. [PMID: 33822911 PMCID: PMC8023553 DOI: 10.1093/database/baab013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 01/19/2021] [Accepted: 03/01/2021] [Indexed: 11/14/2022]
Abstract
Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration-approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π-π stacking, T-stacking and cation-π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.
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Affiliation(s)
- Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
| | - António J Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
- PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research, University of Coimbra, Coimbra, 3030-789, Portugal
| | - Panagiotis I Koukos
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, 3584, CH, Netherlands
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, 3584, CH, Netherlands
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Coimbra, 3000-456, Portugal
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
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8
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Schwarz M, Skrinjar P, Fink MJ, Kronister S, Mechtler T, Koukos PI, Bonvin AMJJ, Kasper DC, Mikula H. A click-flipped enzyme substrate boosts the performance of the diagnostic screening for Hunter syndrome. Chem Sci 2020; 11:12671-12676. [PMID: 34094461 PMCID: PMC8163285 DOI: 10.1039/d0sc04696e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 10/23/2020] [Indexed: 11/23/2022] Open
Abstract
We report on the unexpected finding that click modification of iduronyl azides results in a conformational flip of the pyranose ring, which led to the development of a new strategy for the design of superior enzyme substrates for the diagnostic assaying of iduronate-2-sulfatase (I2S), a lysosomal enzyme related to Hunter syndrome. Synthetic substrates are essential in testing newborns for metabolic disorders to enable early initiation of therapy. Our click-flipped iduronyl triazole showed a remarkably better performance with I2S than commonly used O-iduronates. We found that both O- and triazole-linked substrates are accepted by the enzyme, irrespective of their different conformations, but only the O-linked product inhibits the activity of I2S. Thus, in the long reaction times required for clinical assays, the triazole substrate substantially outperforms the O-iduronate. Applying our click-flipped substrate to assay I2S in dried blood spots sampled from affected patients and random newborns significantly increased the confidence in discriminating between these groups, clearly indicating the potential of the click-flip strategy to control the biomolecular function of carbohydrates.
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Affiliation(s)
- Markus Schwarz
- Institute of Applied Synthetic Chemistry, TU Wien Getreidemarkt 9 1060 Vienna Austria
- ARCHIMED Life Science GmbH Leberstraße 20 1110 Vienna Austria
| | - Philipp Skrinjar
- Institute of Applied Synthetic Chemistry, TU Wien Getreidemarkt 9 1060 Vienna Austria
| | - Michael J Fink
- Department of Chemistry and Chemical Biology, Harvard University 12 Oxford Street Cambridge MA 02138 USA
| | - Stefan Kronister
- Institute of Applied Synthetic Chemistry, TU Wien Getreidemarkt 9 1060 Vienna Austria
| | - Thomas Mechtler
- ARCHIMED Life Science GmbH Leberstraße 20 1110 Vienna Austria
| | - Panagiotis I Koukos
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University Padualaan 8 3584CH Utrecht The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University Padualaan 8 3584CH Utrecht The Netherlands
| | - David C Kasper
- ARCHIMED Life Science GmbH Leberstraße 20 1110 Vienna Austria
| | - Hannes Mikula
- Institute of Applied Synthetic Chemistry, TU Wien Getreidemarkt 9 1060 Vienna Austria
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9
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Jiménez-García B, Elez K, Koukos PI, Bonvin AM, Vangone A. PRODIGY-crystal: a web-tool for classification of biological interfaces in protein complexes. Bioinformatics 2020; 35:4821-4823. [PMID: 31141126 PMCID: PMC9186318 DOI: 10.1093/bioinformatics/btz437] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/06/2019] [Accepted: 05/25/2019] [Indexed: 01/11/2023] Open
Abstract
Summary Distinguishing biologically relevant interfaces from crystallographic ones in
biological complexes is fundamental in order to associate cellular functions to the
correct macromolecular assemblies. Recently, we described a detailed study reporting the
differences in the type of intermolecular residue–residue contacts between biological
and crystallographic interfaces. Our findings allowed us to develop a fast predictor of
biological interfaces reaching an accuracy of 0.92 and competitive to the current state
of the art. Here we present its web-server implementation, PRODIGY-CRYSTAL, aimed at the
classification of biological and crystallographic interfaces. PRODIGY-CRYSTAL has the
advantage of being fast, accurate and simple. This, together with its user-friendly
interface and user support forum, ensures its broad accessibility. Availability and implementation PRODIGY-CRYSTAL is freely available without registration requirements at https://haddock.science.uu.nl/services/PRODIGY-CRYSTAL.
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Affiliation(s)
- Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3512 JE, The Netherlands
| | - Katarina Elez
- Present address: University of Bologna, Via Selmi 3 40126, Bologna, Italy
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3512 JE, The Netherlands
| | - Alexandre Mjj Bonvin
- To whom correspondence should be addressed. E-mail: or . Present address: Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, Penzberg 82377, Germany
| | - Anna Vangone
- To whom correspondence should be addressed. E-mail: or . Present address: Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, Penzberg 82377, Germany
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10
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Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue LC, Honorato RV, Moreira I, Kurkcuoglu Z, Vangone A, Bonvin AMJJ. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45. Proteins 2019; 88:1029-1036. [PMID: 31886559 DOI: 10.1002/prot.25869] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 12/17/2019] [Accepted: 12/26/2019] [Indexed: 01/18/2023]
Abstract
Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.
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Affiliation(s)
- Panagiotis I Koukos
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands.,Department of Physics, Sapienza University, Rome, Italy
| | - Cunliang Geng
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands.,Multiscale Materials Modelling and Virtual Design, Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Mikael E Trellet
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Adrien S J Melquiond
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Li C Xue
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V Honorato
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Irina Moreira
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands.,CNC-Center for Neuroscience and Cell Biology, Rua Larga, FMUC, Polo I, 1° andar, Universidade de Coimbra, Coimbra, Portugal
| | - Zeynep Kurkcuoglu
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Anna Vangone
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
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11
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Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu RR, Xu XM, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka E, Lipska A, Krupa P, Mozolewska M, Golon Ł, Samsonov S, Liwo A, Crivelli S, Pagès G, Karasikov M, Kadukova M, Yan Y, Huang SY, Rosell M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L, Fernandez-Recio J, Christoffer C, Terashi G, Shin WH, Aderinwale T, Subraman SRMV, Kihara D, Kozakov D, Vajda S, Porter K, Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie DW, de Beauchêne IC, Maigret B, Devignes MD, Echartea MER, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M, Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D, Dapkūnas J, Olechnovič K, Venclovas Č, Kundrotas PJ, Belkin S, Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T, Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J, Merideth BR, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L, Jiménez-García B, van Noort CW, Honorato RV, Bonvin AMJJ, Wodak SJ. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment. Proteins 2019; 87:1200-1221. [PMID: 31612567 PMCID: PMC7274794 DOI: 10.1002/prot.25838] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 12/28/2022]
Abstract
We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
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Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Guillaume Brysbaert
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Nurul Nadzirin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Tereza Gerguri
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
| | - Alessandra Carbone
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sergei Grudinin
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ran-Ran Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xi-Ming Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Miriam Eisenstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Emilia Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland
| | | | - Paweł Krupa
- Polish Academy of Sciences, Institute of Physics, Warsaw, Poland
| | | | - Łukasz Golon
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | | | - Guillaume Pagès
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | | | - Maria Kadukova
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mireia Rosell
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | - Luis A. Rodríguez-Lumbreras
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | | | | | - Juan Fernandez-Recio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
- Instituto de Biología Molecular de Barcelona (IBMB-CSIC), Barcelona, Spain
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Kathryn Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sergey Kotelnikov
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Iain H. Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | | | | | | | | | - Didier Barradas-Bautista
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhen Cao
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Luigi Cavallo
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University of Naples “Parthenope”, Napoli, Italy
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Merav Braitbard
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lirane Bitton
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Scheidman-Duhovny
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Petras J. Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Saveliy Belkin
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Devlina Chakravarty
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Varsha D. Badal
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Ilya A. Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Thom Vreven
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Sweta Vangaveti
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Tyler Borrman
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Zhiping Weng
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, Missouri
| | - Benjamin Ryan Merideth
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
- Department of Biochemistry, University of Missouri, Columbia, Missouri
| | - Panagiotis I. Koukos
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Cunliang Geng
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E. Trellet
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Adrien S. J. Melquiond
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Li Xue
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W. van Noort
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V. Honorato
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
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12
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Abstract
We report the performance of HADDOCK in the 2018 iteration of the Grand Challenge organised by the D3R consortium. Building on the findings of our participation in last year's challenge, we significantly improved our pose prediction protocol which resulted in a mean RMSD for the top scoring pose of 3.04 and 2.67 Å for the cross-docking and self-docking experiments respectively, which corresponds to an overall success rate of 63% and 71% when considering the top1 and top5 models respectively. This performance ranks HADDOCK as the 6th and 3rd best performing group (excluding multiple submissions from a same group) out of a total of 44 and 47 submissions respectively. Our ligand-based binding affinity predictor is the 3rd best predictor overall, behind only the two leading structure-based implementations, and the best ligand-based one with a Kendall's Tau correlation of 0.36 for the Cathepsin challenge. It also performed well in the classification part of the Kinase challenges, with Matthews Correlation Coefficients of 0.49 (ranked 1st), 0.39 (ranked 4th) and 0.21 (ranked 4th) for the JAK2, vEGFR2 and p38a targets respectively. Through our participation in last year's competition we came to the conclusion that template selection is of critical importance for the successful outcome of the docking. This year we have made improvements in two additional areas of importance: ligand conformer selection and initial positioning, which have been key to our excellent pose prediction performance this year.
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Affiliation(s)
- Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
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13
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Koukos PI, Faro I, van Noort CW, Bonvin AMJJ. A Membrane Protein Complex Docking Benchmark. J Mol Biol 2018; 430:5246-5256. [PMID: 30414967 DOI: 10.1016/j.jmb.2018.11.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 11/02/2018] [Accepted: 11/05/2018] [Indexed: 01/15/2023]
Abstract
We report the first membrane protein-protein docking benchmark consisting of 37 targets of diverse functions and folds. The structures were chosen based on a set of parameters such as the availability of unbound structures, the modeling difficulty and their uniqueness. They have been cleaned and consistently numbered to facilitate their use in docking. Using this benchmark, we establish the baseline performance of HADDOCK, without any specific optimization for membrane proteins, for two scenarios: true interface-driven docking and ab initio docking. Despite the fact that HADDOCK has been developed for soluble complexes, it shows promising docking performance for membrane systems, but there is clearly room for further optimization. The resulting set of docking decoys, together with analysis scripts, is made freely available. These can serve as a basis for the optimization of membrane complex-specific scoring functions.
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Affiliation(s)
- Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht 3584CH, the Netherlands
| | - Inge Faro
- Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht 3584CH, the Netherlands
| | - Charlotte W van Noort
- Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht 3584CH, the Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht 3584CH, the Netherlands.
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14
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Kurkcuoglu Z, Koukos PI, Citro N, Trellet ME, Rodrigues JPGLM, Moreira IS, Roel-Touris J, Melquiond ASJ, Geng C, Schaarschmidt J, Xue LC, Vangone A, Bonvin AMJJ. Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2. J Comput Aided Mol Des 2018; 32:175-185. [PMID: 28831657 PMCID: PMC5767195 DOI: 10.1007/s10822-017-0049-y] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 08/18/2017] [Indexed: 10/28/2022]
Abstract
We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.
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Affiliation(s)
- Zeynep Kurkcuoglu
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Nevia Citro
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Mikael E Trellet
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - J P G L M Rodrigues
- James H. Clark Center, Stanford University, 318 Campus Drive, S210, Stanford, CA, 94305, USA
| | - Irina S Moreira
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
- CNC - Center for Neuroscience and Cell Biology, FMUC, Universidade de Coimbra, Rua Larga, Polo I, 1ºandar, 3004-517, Coimbra, Portugal
| | - Jorge Roel-Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Adrien S J Melquiond
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - A M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands.
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15
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Moreira IS, Koukos PI, Melo R, Almeida JG, Preto AJ, Schaarschmidt J, Trellet M, Gümüş ZH, Costa J, Bonvin AMJJ. SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots. Sci Rep 2017; 7:8007. [PMID: 28808256 PMCID: PMC5556074 DOI: 10.1038/s41598-017-08321-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 07/07/2017] [Indexed: 12/21/2022] Open
Abstract
We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/.
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Affiliation(s)
- Irina S Moreira
- CNC - Center for Neuroscience and Cell Biology; Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal. .,Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands.
| | - Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands
| | - Rita Melo
- CNC - Center for Neuroscience and Cell Biology; Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal.,Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, Estrada Nacional 10 (ao km 139,7), 2695-066, Bobadela LRS, Portugal
| | - Jose G Almeida
- CNC - Center for Neuroscience and Cell Biology; Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal
| | - Antonio J Preto
- CNC - Center for Neuroscience and Cell Biology; Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal
| | - Joerg Schaarschmidt
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands
| | - Mikael Trellet
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands
| | - Zeynep H Gümüş
- Department of Genetics and Genomics and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joaquim Costa
- CMUP/FCUP, Centro de Matemática da Universidade do Porto, Faculdade de Ciências, Rua do Campo Alegre, 4169-007, Porto, Portugal
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands.
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16
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Koukos PI, Glykos NM. Folding Molecular Dynamics Simulations Accurately Predict the Effect of Mutations on the Stability and Structure of a Vammin-Derived Peptide. J Phys Chem B 2014; 118:10076-84. [DOI: 10.1021/jp5046113] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Panagiotis I. Koukos
- Department of Molecular Biology
and Genetics, Democritus University of Thrace, University campus, 68100 Alexandroupolis, Greece
| | - Nicholas M. Glykos
- Department of Molecular Biology
and Genetics, Democritus University of Thrace, University campus, 68100 Alexandroupolis, Greece
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17
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Abstract
Quantifying convergence and sufficient sampling of macromolecular molecular dynamics simulations is more often than not a source of controversy (and of various ad hoc solutions) in the field. Clearly, the only reasonable, consistent, and satisfying way to infer convergence (or otherwise) of a molecular dynamics trajectory must be based on probability theory. Ideally, the question we would wish to answer is the following: "What is the probability that a molecular configuration important for the analysis in hand has not yet been observed ?". Here we propose a method for answering a variant of this question by using the Good-Turing formalism for frequency estimation of unobserved species in a sample. Although several approaches may be followed in order to deal with the problem of discretizing the configurational space, for this work we use the classical RMSD matrix as a means to answering the following question: "What is the probability that a molecular configuration with an RMSD (from all other already observed configurations) higher than a given threshold has not actually been observed ?". We apply the proposed method to several different trajectories and show that the procedure appears to be both computationally stable and internally consistent. A free, open-source program implementing these ideas is immediately available for download via public repositories.
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Affiliation(s)
- Panagiotis I Koukos
- Department of Molecular Biology and Genetics, Democritus University of Thrace , University campus, 68100 Alexandroupolis, Greece
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18
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Koukos PI, Glykos NM. Grcarma: A fully automated task-oriented interface for the analysis of molecular dynamics trajectories. J Comput Chem 2013; 34:2310-2. [DOI: 10.1002/jcc.23381] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Panagiotis I. Koukos
- Department of Molecular Biology and Genetics; Democritus University of Thrace; University Campus; 68100; Alexandroupolis; Greece
| | - Nicholas M. Glykos
- Department of Molecular Biology and Genetics; Democritus University of Thrace; University Campus; 68100; Alexandroupolis; Greece
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