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Ali Eltayb W, Abdalla M, Ahmed EL-Arabey A, Boufissiou A, Azam M, Al-Resayes SI, Alam M. Exploring particulate methane monooxygenase (pMMO) proteins using experimentation and computational molecular docking. JOURNAL OF KING SAUD UNIVERSITY - SCIENCE 2023; 35:102634. [DOI: https:/doi.org/10.1016/j.jksus.2023.102634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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2
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Shirai T, Akagawa M, Makino M, Ishii M, Arai A, Nagasawa N, Sada M, Kimura R, Okayama K, Ishioka T, Ishii H, Hirai S, Ryo A, Tomita H, Kimura H. Molecular Evolutionary Analyses of the Pseudomonas-Derived Cephalosporinase Gene. Microorganisms 2023; 11:635. [PMID: 36985209 PMCID: PMC10057138 DOI: 10.3390/microorganisms11030635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Despite the increasing evidence of the clinical impact of Pseudomonas-derived cephalosporinase (PDC) sequence polymorphisms, the molecular evolution of its encoding gene, blaPDC, remains elusive. To elucidate this, we performed a comprehensive evolutionary analysis of blaPDC. A Bayesian Markov Chain Monte Carlo phylogenetic tree revealed that a common ancestor of blaPDC diverged approximately 4660 years ago, leading to the formation of eight clonal variants (clusters A-H). The phylogenetic distances within clusters A to G were short, whereas those within cluster H were relatively long. Two positive selection sites and many negative selection sites were estimated. Two PDC active sites overlapped with negative selection sites. In docking simulation models based on samples selected from clusters A and H, piperacillin was bound to the serine and the threonine residues of the PDC active sites, with the same binding mode for both models. These results suggest that, in P. aeruginosa, blaPDC is highly conserved, and PDC exhibits similar antibiotic resistance functionality regardless of its genotype.
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Affiliation(s)
- Tatsuya Shirai
- Advanced Medical Science Research Center, Gunma Paz University Research Institute, Shibukawa 377-0008, Gunma, Japan
- Department of Respiratory Medicine, Kyorin University School of Medicine, Mitaka 181-8611, Tokyo, Japan
| | - Mao Akagawa
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki 370-0006, Gunma, Japan
| | - Miho Makino
- Department of Medical Technology, Gunma Paz University School of Medical Science and Technology, Takasaki 370-0006, Gunma, Japan
| | - Manami Ishii
- Department of Medical Technology, Gunma Paz University School of Medical Science and Technology, Takasaki 370-0006, Gunma, Japan
| | - Ayaka Arai
- Department of Medical Technology, Gunma Paz University School of Medical Science and Technology, Takasaki 370-0006, Gunma, Japan
| | - Norika Nagasawa
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki 370-0006, Gunma, Japan
| | - Mitsuru Sada
- Department of Respiratory Medicine, Kyorin University School of Medicine, Mitaka 181-8611, Tokyo, Japan
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki 370-0006, Gunma, Japan
| | - Ryusuke Kimura
- Advanced Medical Science Research Center, Gunma Paz University Research Institute, Shibukawa 377-0008, Gunma, Japan
- Department of Bacteriology, Gunma University Graduate School of Medicine, Maebashi 371-8514, Gunma, Japan
| | - Kaori Okayama
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki 370-0006, Gunma, Japan
| | - Taisei Ishioka
- Department of Agriculture, Takasaki University of Health Welfare, Takasaki 370-0033, Gunma, Japan
| | - Haruyuki Ishii
- Department of Respiratory Medicine, Kyorin University School of Medicine, Mitaka 181-8611, Tokyo, Japan
| | - Shinichiro Hirai
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Musashimurayama 162-8640, Tokyo, Japan
| | - Akihide Ryo
- Department of Microbiology, Yokohama City University School of Medicine, Yokohama 236-0004, Kanagawa, Japan
| | - Haruyoshi Tomita
- Department of Bacteriology, Gunma University Graduate School of Medicine, Maebashi 371-8514, Gunma, Japan
| | - Hirokazu Kimura
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki 370-0006, Gunma, Japan
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Yusof NAM, Razali SA, Mohd Padzil A, Lau BYC, Baharum SN, Nor Muhammad NA, Raston NHA, Chong CM, Ikhsan NFM, Situmorang ML, Fei LC. Computationally Designed Anti-LuxP DNA Aptamer Suppressed Flagellar Assembly- and Quorum Sensing-Related Gene Expression in Vibrio parahaemolyticus. BIOLOGY 2022; 11:1600. [PMID: 36358301 PMCID: PMC9687752 DOI: 10.3390/biology11111600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 10/22/2022] [Accepted: 10/26/2022] [Indexed: 11/29/2023]
Abstract
(1) Background: Quorum sensing (QS) is the chemical communication between bacteria that sense chemical signals in the bacterial population to control phenotypic changes through the regulation of gene expression. The inhibition of QS has various potential applications, particularly in the prevention of bacterial infection. QS can be inhibited by targeting the LuxP, a periplasmic receptor protein that is involved in the sensing of the QS signaling molecule known as the autoinducer 2 (AI-2). The sensing of AI-2 by LuxP transduces the chemical information through the inner membrane sensor kinase LuxQ protein and activates the QS cascade. (2) Methods: An in silico approach was applied to design DNA aptamers against LuxP in this study. A method combining molecular docking and molecular dynamics simulations was used to select the oligonucleotides that bind to LuxP, which were then further characterized using isothermal titration calorimetry. Subsequently, the bioactivity of the selected aptamer was examined through comparative transcriptome analysis. (3) Results: Two aptamer candidates were identified from the ITC, which have the lowest dissociation constants (Kd) of 0.2 and 0.5 micromolar. The aptamer with the lowest Kd demonstrated QS suppression and down-regulated the flagellar-assembly-related gene expression. (4) Conclusions: This study developed an in silico approach to design an aptamer that possesses anti-QS properties.
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Affiliation(s)
- Nur Afiqah Md Yusof
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Siti Aisyah Razali
- Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Azyyati Mohd Padzil
- Malaysia Genome and Vaccine Institute (MGVI), National Institute of Biotechnology Malaysia (NIBM), Jalan Bangi, Kajang 43000, Selangor, Malaysia
| | - Benjamin Yii Chung Lau
- Malaysian Palm Oil Board, Persiaran Institusi, Bandar Baru Bangi, Kajang 43000, Selangor, Malaysia
| | - Syarul Nataqain Baharum
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Nurul Hanun Ahmad Raston
- Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Chou Min Chong
- Department of Aquaculture, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Natrah Fatin Mohd Ikhsan
- Department of Aquaculture, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | | | - Low Chen Fei
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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Rajpoot S, Srivastava G, Siddiqi MI, Saqib U, Parihar SP, Hirani N, Baig MS. Identification of novel inhibitors targeting TIRAP interactions with BTK and PKCδ in inflammation through an in silico approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:141-166. [PMID: 35174746 DOI: 10.1080/1062936x.2022.2035817] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Advanced computational tools focusing on protein-protein interaction (PPI) based drug development is a powerful platform to accelerate the therapeutic development of small lead molecules and repurposed drugs. Toll/interleukin-1 receptor (TIR) domain-containing adapter protein (TIRAP) and its interactions with other proteins in macrophages signalling are crucial components of severe or persistent inflammation. TIRAP activation through Bruton's tyrosine kinase (BTK) and Protein Kinase C delta (PKCδ) is essential for downstream inflammatory signalling. We created homology-based structural models of BTK and PKCδ in MODELLER 9.24. TIRAP interactions with BTK and PKCδ in its non-phosphorylated and phosphorylated states were determined by multiple docking tools including HADDOCK 2.4, pyDockWEB and ClusPro 2.0. Food and Drug Administration (FDA)-approved drugs were virtually screened through Discovery Studio LibDock and Autodock Vina tools to target the common TIR domain residues of TIRAP, which interact with both BTK and PKC at the identified interfacial sites of the complexes. Four FDA-approved drugs were identified and found to have stable interactions over a range of 100 ns MD simulation timescales. These drugs block the interactions of both kinases with TIRAP in silico. Hence, these drugs have the potential to dampen downstream inflammatory signalling and inflammation-mediated disease.
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Affiliation(s)
- S Rajpoot
- Department of Biosciences and Biomedical Engineering (BSBE), Indian Institute of Technology Indore (IITI), Simrol, Indore, India
| | - G Srivastava
- Division of Biochemistry and Structural Biology, CSIR-Central Drug Research Institute (CSIR-CDRI), Jankipuram Extension, Sitapur Road, Lucknow, India
| | - M I Siddiqi
- Division of Biochemistry and Structural Biology, CSIR-Central Drug Research Institute (CSIR-CDRI), Jankipuram Extension, Sitapur Road, Lucknow, India
| | - U Saqib
- Department of Chemistry, Indian Institute of Technology Indore (IITI), Simrol, Indore, India
| | - S P Parihar
- Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa) and Institute of Infectious Diseases and Molecular Medicine (IDM), Division of Medical Microbiology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - N Hirani
- MRC Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - M S Baig
- Department of Biosciences and Biomedical Engineering (BSBE), Indian Institute of Technology Indore (IITI), Simrol, Indore, India
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Elhabashy H, Merino F, Alva V, Kohlbacher O, Lupas AN. Exploring protein-protein interactions at the proteome level. Structure 2022; 30:462-475. [DOI: 10.1016/j.str.2022.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/26/2021] [Accepted: 02/02/2022] [Indexed: 02/08/2023]
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6
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Ma MT, Jennings MR, Blazeck J, Lieberman RL. Catalytically active holo Homo sapiens adenosine deaminase I adopts a closed conformation. Acta Crystallogr D Struct Biol 2022; 78:91-103. [PMID: 34981765 PMCID: PMC8725166 DOI: 10.1107/s2059798321011785] [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: 09/12/2021] [Accepted: 11/08/2021] [Indexed: 01/03/2023] Open
Abstract
Homo sapiens adenosine deaminase 1 (HsADA1; UniProt P00813) is an immunologically relevant enzyme with roles in T-cell activation and modulation of adenosine metabolism and signaling. Patients with genetic deficiency in HsADA1 suffer from severe combined immunodeficiency, and HsADA1 is a therapeutic target in hairy cell leukemias. Historically, insights into the catalytic mechanism and the structural attributes of HsADA1 have been derived from studies of its homologs from Bos taurus (BtADA) and Mus musculus (MmADA). Here, the structure of holo HsADA1 is presented, as well as biochemical characterization that confirms its high activity and shows that it is active across a broad pH range. Structurally, holo HsADA1 adopts a closed conformation distinct from the open conformation of holo BtADA. Comparison of holo HsADA1 and MmADA reveals that MmADA also adopts a closed conformation. These findings challenge previous assumptions gleaned from BtADA regarding the conformation of HsADA1 that may be relevant to its immunological interactions, particularly its ability to bind adenosine receptors. From a broader perspective, the structural analysis of HsADA1 presents a cautionary tale for reliance on homologs to make structural inferences relevant to applications such as protein engineering or drug development.
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Affiliation(s)
- Minh Thu Ma
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, GA 30332, USA
| | - Maria Rain Jennings
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA 30332, USA
| | - John Blazeck
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA 30332, USA
| | - Raquel L Lieberman
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, GA 30332, USA
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A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations. Nat Methods 2021; 18:1477-1488. [PMID: 34845387 PMCID: PMC8665054 DOI: 10.1038/s41592-021-01318-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 10/05/2021] [Indexed: 01/08/2023]
Abstract
Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.
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Fischhoff IR, Castellanos AA, Rodrigues JPGLM, Varsani A, Han BA. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2. Proc Biol Sci 2021; 288:20211651. [PMID: 34784766 PMCID: PMC8596006 DOI: 10.1098/rspb.2021.1651] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/25/2021] [Indexed: 12/13/2022] Open
Abstract
Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.
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Affiliation(s)
- Ilya R. Fischhoff
- Cary Institute of Ecosystem Studies, Box AB Millbrook, NY 12545, USA
| | | | | | - Arvind Varsani
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
- Structural Biology Research Unit, Department of Integrative Biomedical Sciences, University of Cape Town, 7700 Cape Town, Rondebosch, South Africa
| | - Barbara A. Han
- Cary Institute of Ecosystem Studies, Box AB Millbrook, NY 12545, USA
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Quignot C, Postic G, Bret H, Rey J, Granger P, Murail S, Chacón P, Andreani J, Tufféry P, Guerois R. InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps. Nucleic Acids Res 2021; 49:W277-W284. [PMID: 33978743 PMCID: PMC8265070 DOI: 10.1093/nar/gkab358] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/09/2021] [Accepted: 04/23/2021] [Indexed: 12/19/2022] Open
Abstract
The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.
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Affiliation(s)
- Chloé Quignot
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Guillaume Postic
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Hélène Bret
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Julien Rey
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Pierre Granger
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Samuel Murail
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Pablo Chacón
- Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Pierre Tufféry
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Raphaël Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
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10
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Fischhoff IR, Castellanos AA, Rodrigues JP, Varsani A, Han BA. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.02.18.431844. [PMID: 33619481 PMCID: PMC7899445 DOI: 10.1101/2021.02.18.431844] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Back and forth transmission of SARS-CoV-2 between humans and animals may lead to wild reservoirs of virus that can endanger efforts toward long-term control of COVID-19 in people, and protecting vulnerable animal populations that are particularly susceptible to lethal disease. Predicting high risk host species is key to targeting field surveillance and lab experiments that validate host zoonotic potential. A major bottleneck to predicting animal hosts is the small number of species with available molecular information about the structure of ACE2, a key cellular receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with 3D modeling of virus and host cell protein interactions using machine learning methods. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals - an order of magnitude more species than previously possible. The high accuracy predictions achieved by this approach are strongly corroborated by in vivo empirical studies. We identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may further enhance the risk of spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. With molecular sequence data available for only a small fraction of potential host species, predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges.
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Affiliation(s)
- Ilya R. Fischhoff
- Cary Institute of Ecosystem Studies. Box AB Millbrook, NY 12545, USA
| | | | - João P.G.L.M. Rodrigues
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Arvind Varsani
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
- Structural Biology Research Unit, Department of Integrative Biomedical Sciences, University of Cape Town, Rondebosch, 7700, Cape Town, South Africa
| | - Barbara A. Han
- Cary Institute of Ecosystem Studies. Box AB Millbrook, NY 12545, USA
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11
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Marrero Diaz de Villegas R, Seki C, Mattion NM, König GA. Functional and in silico Characterization of Neutralizing Interactions Between Antibodies and the Foot-and-Mouth Disease Virus Immunodominant Antigenic Site. Front Vet Sci 2021; 8:554383. [PMID: 34026880 PMCID: PMC8137985 DOI: 10.3389/fvets.2021.554383] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 02/19/2021] [Indexed: 12/04/2022] Open
Abstract
Molecular knowledge of virus–antibody interactions is essential for the development of better vaccines and for a timely assessment of the spread and severity of epidemics. For foot-and-mouth disease virus (FMDV) research, in particular, computational methods for antigen–antibody (Ag–Ab) interaction, and cross-antigenicity characterization and prediction are critical to design engineered vaccines with robust, long-lasting, and wider response against different strains. We integrated existing structural modeling and prediction algorithms to study the surface properties of FMDV Ags and Abs and their interaction. First, we explored four modeling and two Ag–Ab docking methods and implemented a computational pipeline based on a reference Ag–Ab structure for FMDV of serotype C, to be used as a source protocol for the study of unknown interaction pairs of Ag–Ab. Next, we obtained the variable region sequence of two monoclonal IgM and IgG antibodies that recognize and neutralize antigenic site A (AgSA) epitopes from South America serotype A FMDV and developed two peptide ELISAs for their fine epitope mapping. Then, we applied the previous Ag–Ab molecular structure modeling and docking protocol further scored by functional peptide ELISA data. This work highlights a possible different behavior in the immune response of IgG and IgM Ab isotypes. The present method yielded reliable Ab models with differential paratopes and Ag interaction topologies in concordance with their isotype classes. Moreover, it demonstrates the applicability of computational prediction techniques to the interaction phenomena between the FMDV immunodominant AgSA and Abs, and points out their potential utility as a metric for virus-related, massive Ab repertoire analysis or as a starting point for recombinant vaccine design.
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Affiliation(s)
- Ruben Marrero Diaz de Villegas
- Instituto de Agrobiotecnología y Biología Molecular, Instituto Nacional de Tecnología Agropecuaria, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Buenos Aires, Argentina
| | - Cristina Seki
- Centro de Virología Animal, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Universidad Abierta Interamericana, Buenos Aires, Argentina
| | - Nora M Mattion
- Centro de Virología Animal, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Universidad Abierta Interamericana, Buenos Aires, Argentina
| | - Guido A König
- Instituto de Agrobiotecnología y Biología Molecular, Instituto Nacional de Tecnología Agropecuaria, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Buenos Aires, Argentina
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12
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Hudson ML, Samudrala R. Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform. Molecules 2021; 26:2581. [PMID: 33925237 PMCID: PMC8125683 DOI: 10.3390/molecules26092581] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 12/02/2022] Open
Abstract
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.
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Affiliation(s)
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA;
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13
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de Araújo RSA, Mendonça FJ, Scotti MT, Scotti L. Protein modeling. PHYSICAL SCIENCES REVIEWS 2021. [DOI: 10.1515/psr-2018-0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Proteins are essential and versatile polymers consisting of sequenced amino acids that often possess an organized three-dimensional arrangement, (a result of their monomeric composition), which determines their biological role in cellular function. Proteins are involved in enzymatic catalysis; they participate in genetic information decoding and transmission processes, in cell recognition, in signaling, and transport of substances, in regulation of intra and extracellular conditions, and other functions.
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Affiliation(s)
- Rodrigo S. A. de Araújo
- Biological Science Department, Laboratory of Synthesis and Drug Delivery , State University of Paraiba , 58070-450 , João Pessoa , PB , Brazil
| | - Francisco J. B. Mendonça
- Biological Science Department, Laboratory of Synthesis and Drug Delivery , State University of Paraiba , 58070-450 , João Pessoa , PB , Brazil
| | - Marcus T. Scotti
- Health Center , Federal University of Paraíba , 50670-910 , João Pessoa , PB , Brazil
| | - Luciana Scotti
- Health Center , Federal University of Paraíba , 50670-910 , João Pessoa , PB , Brazil
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14
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Prediction of Functional Consequences of Missense Mutations in ANO4 Gene. Int J Mol Sci 2021; 22:ijms22052732. [PMID: 33800471 PMCID: PMC7962975 DOI: 10.3390/ijms22052732] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/02/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022] Open
Abstract
The anoctamin (TMEM16) family of transmembrane protein consists of ten members in vertebrates, which act as Ca2+-dependent ion channels and/or Ca2+-dependent scramblases. ANO4 which is primarily expressed in the CNS and certain endocrine glands, has been associated with various neuronal disorders. Therefore, we focused our study on prioritizing missense mutations that are assumed to alter the structure and stability of ANO4 protein. We employed a wide array of evolution and structure based in silico prediction methods to identify potentially deleterious missense mutations in the ANO4 gene. Identified pathogenic mutations were then mapped to the modeled human ANO4 structure and the effects of missense mutations were studied on the atomic level using molecular dynamics simulations. Our data show that the G80A and A500T mutations significantly alter the stability of the mutant proteins, thus providing new perspective on the role of missense mutations in ANO4 gene. Results obtained in this study may help to identify disease associated mutations which affect ANO4 protein structure and function and might facilitate future functional characterization of ANO4.
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15
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Koivunen J, Tu H, Kemppainen A, Anbazhagan P, Finnilä MA, Saarakkala S, Käpylä J, Lu N, Heikkinen A, Juffer AH, Heino J, Gullberg D, Pihlajaniemi T. Integrin α11β1 is a receptor for collagen XIII. Cell Tissue Res 2021; 383:1135-1153. [PMID: 33306155 PMCID: PMC7960628 DOI: 10.1007/s00441-020-03300-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/14/2020] [Indexed: 12/28/2022]
Abstract
Collagen XIII is a conserved transmembrane collagen mainly expressed in mesenchymal tissues. Previously, we have shown that collagen XIII modulates tissue development and homeostasis. Integrins are a family of receptors that mediate signals from the environment into the cells and vice versa. Integrin α11β1 is a collagen receptor known to recognize the GFOGER (O=hydroxyproline) sequence in collagens. Interestingly, collagen XIII and integrin α11β1 both have a role in the regulation of bone homeostasis. To study whether α11β1 is a receptor for collagen XIII, we utilized C2C12 cells transfected to express α11β1 as their only collagen receptor. The interaction between collagen XIII and integrin α11β1 was also confirmed by surface plasmon resonance and pull-down assays. We discovered that integrin α11β1 mediates cell adhesion to two collagenous motifs, namely GPKGER and GF(S)QGEK, that were shown to act as the recognition sites for the integrin α11-I domain. Furthermore, we studied the in vivo significance of the α11β1-collagen XIII interaction by crossbreeding α11 null mice (Itga11-/-) with mice overexpressing Col13a1 (Col13a1oe). When we evaluated the bone morphology by microcomputed tomography, Col13a1oe mice had a drastic bone overgrowth followed by severe osteoporosis, whereas the double mutant mouse line showed a much milder bone phenotype. To conclude, our data identifies integrin α11β1 as a new collagen XIII receptor and demonstrates that this ligand-receptor pair has a role in the maintenance of bone homeostasis.
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Affiliation(s)
- Jarkko Koivunen
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. Box 5400, FIN-90014, Oulu, Finland
| | - Hongmin Tu
- Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. Box 5000, FIN-90014, Oulu, Finland
| | - Antti Kemppainen
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. Box 5400, FIN-90014, Oulu, Finland
| | - Padmanabhan Anbazhagan
- Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. Box 5000, FIN-90014, Oulu, Finland
| | - Mikko A Finnilä
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, P.O. Box 5000, FIN-90014, Oulu, Finland
| | - Simo Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, P.O. Box 5000, FIN-90014, Oulu, Finland
| | - Jarmo Käpylä
- Department of Biochemistry and MediCity Research Laboratory, University of Turku, Tykistökatu 6A, 20520, Turku, Finland
| | - Ning Lu
- Department of Biomedicine and Center of Cancer Biomarkers, University of Bergen, Jonas Lies vei 91, N-5009, Bergen, Norway
| | - Anne Heikkinen
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. Box 5400, FIN-90014, Oulu, Finland
| | - André H Juffer
- Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. Box 5000, FIN-90014, Oulu, Finland
| | - Jyrki Heino
- Department of Biochemistry and MediCity Research Laboratory, University of Turku, Tykistökatu 6A, 20520, Turku, Finland
| | - Donald Gullberg
- Department of Biomedicine and Center of Cancer Biomarkers, University of Bergen, Jonas Lies vei 91, N-5009, Bergen, Norway
| | - Taina Pihlajaniemi
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. Box 5400, FIN-90014, Oulu, Finland.
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16
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An in silico approach to analyze HCV genotype-specific binding-site variation and its effect on drug-protein interaction. Sci Rep 2020; 10:20885. [PMID: 33257748 PMCID: PMC7705671 DOI: 10.1038/s41598-020-77720-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 09/18/2020] [Indexed: 01/06/2023] Open
Abstract
Genotype variation in viruses can affect the response of antiviral treatment. Several studies have established approaches to determine genotype-specific variations; however, analyses to determine the effect of these variations on drug–protein interactions remain unraveled. We present an in-silico approach to explore genotype-specific variations and their effect on drug–protein interaction. We have used HCV NS3 helicase and fluoroquinolones as a model for drug–protein interaction and have investigated the effect of amino acid variations in HCV NS3 of genotype 1a, 1b, 2b and 3a on NS3-fluoroquinolone interaction. We retrieved 687, 667, 101 and 248 nucleotide sequences of HCV NS3 genotypes 1a, 1b, 2b, and 3a, respectively, and translated these into amino acid sequences and used for genotype variation analysis, and also to construct 3D protein models for 2b and 3a genotypes. For 1a and 1b, crystal structures were used. Drug–protein interactions were determined using molecular docking analyses. Our results revealed that individual genotype-specific HCV NS3 showed substantial sequence heterogeneity that resulted in variations in docking interactions. We believe that our approach can be extrapolated to include other viruses to study the clinical significance of genotype-specific variations in drug–protein interactions.
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17
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Sala D, Cerofolini L, Fragai M, Giachetti A, Luchinat C, Rosato A. A protocol to automatically calculate homo-oligomeric protein structures through the integration of evolutionary constraints and NMR ambiguous contacts. Comput Struct Biotechnol J 2019; 18:114-124. [PMID: 31969972 PMCID: PMC6961069 DOI: 10.1016/j.csbj.2019.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 11/20/2019] [Accepted: 12/06/2019] [Indexed: 12/15/2022] Open
Abstract
Protein assemblies are involved in many important biological processes. Solid-state NMR (SSNMR) spectroscopy is a technique suitable for the structural characterization of samples with high molecular weight and thus can be applied to such assemblies. A significant bottleneck in terms of both effort and time required is the manual identification of unambiguous intermolecular contacts. This is particularly challenging for homo-oligomeric complexes, where simple uniform labeling may not be effective. We tackled this challenge by exploiting coevolution analysis to extract information on homo-oligomeric interfaces from NMR-derived ambiguous contacts. After removing the evolutionary couplings (ECs) that are already satisfied by the 3D structure of the monomer, the predicted ECs are matched with the automatically generated list of experimental contacts. This approach provides a selection of potential interface residues that is used directly in monomer-monomer docking calculations. We validated the protocol on tetrameric L-asparaginase II and dimeric Sod1.
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Affiliation(s)
- Davide Sala
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Linda Cerofolini
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Marco Fragai
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
| | - Andrea Giachetti
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
| | - Antonio Rosato
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
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18
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Perthold JW, Oostenbrink C. GroScore: Accurate Scoring of Protein–Protein Binding Poses Using Explicit-Solvent Free-Energy Calculations. J Chem Inf Model 2019; 59:5074-5085. [DOI: 10.1021/acs.jcim.9b00687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jan Walther Perthold
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Chris Oostenbrink
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
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19
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A Model for the Homotypic Interaction between Na +,K +-ATPase β 1 Subunits Reveals the Role of Extracellular Residues 221-229 in Its Ig-Like Domain. Int J Mol Sci 2019; 20:ijms20184538. [PMID: 31540261 PMCID: PMC6770782 DOI: 10.3390/ijms20184538] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 08/13/2019] [Accepted: 08/16/2019] [Indexed: 12/15/2022] Open
Abstract
The Na+, K+-ATPase transports Na+ and K+ across the membrane of all animal cells. In addition to its ion transporting function, the Na+, K+-ATPase acts as a homotypic epithelial cell adhesion molecule via its β1 subunit. The extracellular region of the Na+, K+-ATPase β1 subunit includes a single globular immunoglobulin-like domain. We performed Molecular Dynamics simulations of the ectodomain of the β1 subunit and a refined protein-protein docking prediction. Our results show that the β1 subunit Ig-like domain maintains an independent structure and dimerizes in an antiparallel fashion. Analysis of the putative interface identified segment Lys221-Tyr229. We generated triple mutations on YFP-β1 subunit fusion proteins to assess the contribution of these residues. CHO fibroblasts transfected with mutant β1 subunits showed a significantly decreased cell-cell adhesion. Association of β1 subunits in vitro was also reduced, as determined by pull-down assays. Altogether, we conclude that two Na+, K+-ATPase molecules recognize each other by a large interface spanning residues 221–229 and 198–207 on their β1 subunits.
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20
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Jespersen MC, Mahajan S, Peters B, Nielsen M, Marcatili P. Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes. Front Immunol 2019; 10:298. [PMID: 30863406 PMCID: PMC6399414 DOI: 10.3389/fimmu.2019.00298] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 02/05/2019] [Indexed: 11/13/2022] Open
Abstract
B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.
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Affiliation(s)
- Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Swapnil Mahajan
- La Jolla Institute for Allergy and Immunology, Center for Infectious Disease, Allergy and Asthma Research, La Jolla, CA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, Center for Infectious Disease, Allergy and Asthma Research, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
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21
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Don CG, Smieško M. Out‐compute drug side effects: Focus on cytochrome P450 2D6 modeling. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1366] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Charleen G. Don
- Department of Pharmaceutical SciencesUniversity of BaselBaselSwitzerland
| | - Martin Smieško
- Department of Pharmaceutical SciencesUniversity of BaselBaselSwitzerland
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22
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Vreven T, Schweppe DK, Chavez JD, Weisbrod CR, Shibata S, Zheng C, Bruce JE, Weng Z. Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking. J Mol Biol 2018; 430:1814-1828. [PMID: 29665372 DOI: 10.1016/j.jmb.2018.04.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 03/19/2018] [Accepted: 04/10/2018] [Indexed: 12/23/2022]
Abstract
Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Devin K Schweppe
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Juan D Chavez
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Chad R Weisbrod
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Sayaka Shibata
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Chunxiang Zheng
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - James E Bruce
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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23
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Simões ICM, Coimbra JTS, Neves RPP, Costa IPD, Ramos MJ, Fernandes PA. Properties that rank protein:protein docking poses with high accuracy. Phys Chem Chem Phys 2018; 20:20927-20942. [DOI: 10.1039/c8cp03888k] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The development of docking algorithms to predict near-native structures of protein:protein complexes from the structure of the isolated monomers is of paramount importance for molecular biology and drug discovery.
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Affiliation(s)
- Inês C. M. Simões
- UCIBIO
- REQUIMTE
- Departamento de Química e Bioquímica
- Faculdade de Ciências
- Universidade do Porto
| | - João T. S. Coimbra
- UCIBIO
- REQUIMTE
- Departamento de Química e Bioquímica
- Faculdade de Ciências
- Universidade do Porto
| | - Rui P. P. Neves
- UCIBIO
- REQUIMTE
- Departamento de Química e Bioquímica
- Faculdade de Ciências
- Universidade do Porto
| | - Inês P. D. Costa
- UCIBIO
- REQUIMTE
- Departamento de Química e Bioquímica
- Faculdade de Ciências
- Universidade do Porto
| | - Maria J. Ramos
- UCIBIO
- REQUIMTE
- Departamento de Química e Bioquímica
- Faculdade de Ciências
- Universidade do Porto
| | - Pedro A. Fernandes
- UCIBIO
- REQUIMTE
- Departamento de Química e Bioquímica
- Faculdade de Ciências
- Universidade do Porto
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24
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Hua CK, Gacerez AT, Sentman CL, Ackerman ME, Choi Y, Bailey-Kellogg C. Computationally-driven identification of antibody epitopes. eLife 2017; 6:29023. [PMID: 29199956 PMCID: PMC5739537 DOI: 10.7554/elife.29023] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 12/02/2017] [Indexed: 12/21/2022] Open
Abstract
Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.
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Affiliation(s)
- Casey K Hua
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Albert T Gacerez
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Charles L Sentman
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Yoonjoo Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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25
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Corrada D, Denison MS, Bonati L. Structural modeling of the AhR:ARNT complex in the bHLH-PASA-PASB region elucidates the key determinants of dimerization. MOLECULAR BIOSYSTEMS 2017; 13:981-990. [PMID: 28393157 PMCID: PMC5576476 DOI: 10.1039/c7mb00005g] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Elucidation of the dimerization process of the aryl hydrocarbon receptor (AhR) with the AhR nuclear translocator (ARNT) is crucial for understanding the mechanisms underlying the functional activity of AhR, including mediation of the toxicity of environmental contaminants. In this work, for the first time a structural model of the AhR:ARNT dimer encompassing the entire bHLH-PASA-PASB domain region is proposed. It is developed by using a template-based modeling approach, relying on the recently available crystallographic structures of two dimers of homologous systems in the bHLH-PAS family of proteins: the CLOCK:BMAL1 and the HIF2α:ARNT heterodimers. The structural and energetic characteristics of the modeled AhR:ARNT protein-protein interface are determined by evaluating the variations in solvent accessible surface area, the total binding free energy and the per-residue free energy contributions obtained by the MM-GBSA method and the Energy Decomposition Analysis. The analyses of the intricate network of inter-domain interactions at the dimerization interfaces provide insights into the key determinants of dimerization. These are confirmed by comparison of the computational findings with the available experimental mutagenesis and functional analysis data. The results presented here on the AhR:ARNT dimer structure and interactions provide a framework to start analyzing the mechanism of AhR transformation into its functional DNA binding form.
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Affiliation(s)
- Dario Corrada
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza, 1 - 20126, Milan, Italy.
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26
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Xue LC, Rodrigues JPGLM, Dobbs D, Honavar V, Bonvin AMJJ. Template-based protein-protein docking exploiting pairwise interfacial residue restraints. Brief Bioinform 2017; 18:458-466. [PMID: 27013645 PMCID: PMC5428999 DOI: 10.1093/bib/bbw027] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 02/03/2016] [Indexed: 01/26/2023] Open
Abstract
Although many advanced and sophisticated ab initio approaches for modeling protein-protein complexes have been proposed in past decades, template-based modeling (TBM) remains the most accurate and widely used approach, given a reliable template is available. However, there are many different ways to exploit template information in the modeling process. Here, we systematically evaluate and benchmark a TBM method that uses conserved interfacial residue pairs as docking distance restraints [referred to as alpha carbon-alpha carbon (CA-CA)-guided docking]. We compare it with two other template-based protein-protein modeling approaches, including a conserved non-pairwise interfacial residue restrained docking approach [referred to as the ambiguous interaction restraint (AIR)-guided docking] and a simple superposition-based modeling approach. Our results show that, for most cases, the CA-CA-guided docking method outperforms both superposition with refinement and the AIR-guided docking method. We emphasize the superiority of the CA-CA-guided docking on cases with medium to large conformational changes, and interactions mediated through loops, tails or disordered regions. Our results also underscore the importance of a proper refinement of superimposition models to reduce steric clashes. In summary, we provide a benchmarked TBM protocol that uses conserved pairwise interface distance as restraints in generating realistic 3D protein-protein interaction models, when reliable templates are available. The described CA-CA-guided docking protocol is based on the HADDOCK platform, which allows users to incorporate additional prior knowledge of the target system to further improve the quality of the resulting models.
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Affiliation(s)
- Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, CH Utrecht, The Netherlands
| | - João P G L M Rodrigues
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, CH Utrecht, The Netherlands
| | - Drena Dobbs
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, CH Utrecht, The Netherlands
| | - Vasant Honavar
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, CH Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, CH Utrecht, The Netherlands
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27
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Anishchenko I, Kundrotas PJ, Vakser IA. Modeling complexes of modeled proteins. Proteins 2017; 85:470-478. [PMID: 27701777 PMCID: PMC5313347 DOI: 10.1002/prot.25183] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 09/22/2016] [Accepted: 10/02/2016] [Indexed: 12/21/2022]
Abstract
Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å Cα RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ivan Anishchenko
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas 66047, USA
| | - Petras J. Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas 66047, USA
| | - Ilya A. Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas 66047, USA
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047, USA
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Yan Y, Wen Z, Wang X, Huang SY. Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking. Proteins 2017; 85:497-512. [PMID: 28026062 DOI: 10.1002/prot.25234] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Revised: 12/15/2016] [Accepted: 12/16/2016] [Indexed: 12/23/2022]
Abstract
Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
| | - Zeyu Wen
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
| | - Xinxiang Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
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29
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Bohnuud T, Luo L, Wodak SJ, Vajda S, Bonvin AM, Weng Z, Schueler-Furman O, Kozakov D. A benchmark testing ground for integrating homology modeling and protein docking. Proteins 2017; 85:10-16. [PMID: 27172383 PMCID: PMC5817996 DOI: 10.1002/prot.25063] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 05/08/2016] [Indexed: 12/20/2022]
Abstract
Protein docking procedures carry out the task of predicting the structure of a protein-protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved 'target' complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody-antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark, and is updated on a weekly basis in synchrony with new PDB releases. Proteins 2016; 85:10-16. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Lingqi Luo
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Shoshana J. Wodak
- VIB Structural Biology Research Center, VUB Pleinlaan 2, 1050 Brussels
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Chemistry, Boston University, Boston, MA 02215, USA
| | - Alexandre M.J.J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, the Netherlands
| | - Zhiping Weng
- Biochemistry and Molecular Pharmacology University of Massachusetts Medical School Worcester MA United States
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Hadassah Medical School, Hebrew University, Jerusalem, Israel
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Applied Mathematics and Statistics, Stony Brook University NY, USA
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Conservation of coevolving protein interfaces bridges prokaryote-eukaryote homologies in the twilight zone. Proc Natl Acad Sci U S A 2016; 113:15018-15023. [PMID: 27965389 DOI: 10.1073/pnas.1611861114] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.
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31
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Chen H, Sun Y, Shen Y. Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility. Proteins 2016; 85:544-556. [PMID: 27862345 DOI: 10.1002/prot.25212] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 10/17/2016] [Accepted: 11/06/2016] [Indexed: 12/14/2022]
Abstract
Predicting protein conformational changes from unbound structures or even homology models to bound structures remains a critical challenge for protein docking. Here we present a study directly addressing the challenge by reducing the dimensionality and narrowing the range of the corresponding conformational space. The study builds on cNMA-our new framework of partner- and contact-specific normal mode analysis that exploits encounter complexes and considers both intrinsic and induced flexibility. First, we established over a CAPRI (Critical Assessment of PRedicted Interactions) target set that the direction of conformational changes from unbound structures and homology models can be reproduced to a great extent by a small set of cNMA modes. In particular, homology-to-bound interface root-mean-square deviation (iRMSD) can be reduced by 40% on average with the slowest 30 modes. Second, we developed novel and interpretable features from cNMA and used various machine learning approaches to predict the extent of conformational changes. The models learned from a set of unbound-to-bound conformational changes could predict the actual extent of iRMSD with errors around 0.6 Å for unbound proteins in a held-out benchmark subset, around 0.8 Å for unbound proteins in the CAPRI set, and around 1 Å even for homology models in the CAPRI set. Our results shed new insights into origins of conformational differences between homology models and bound structures and provide new support for the low-dimensionality of conformational adjustment during protein associations. The results also provide new tools for ensemble generation and conformational sampling in unbound and homology docking. Proteins 2017; 85:544-556. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Haoran Chen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas, 77843
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32
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Vangone A, Rodrigues JPGLM, Xue LC, van Zundert GCP, Geng C, Kurkcuoglu Z, Nellen M, Narasimhan S, Karaca E, van Dijk M, Melquiond ASJ, Visscher KM, Trellet M, Kastritis PL, Bonvin AMJJ. Sense and simplicity in HADDOCK scoring: Lessons from CASP-CAPRI round 1. Proteins 2016; 85:417-423. [PMID: 27802573 PMCID: PMC5324763 DOI: 10.1002/prot.25198] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 10/14/2016] [Accepted: 10/25/2016] [Indexed: 12/28/2022]
Abstract
Our information-driven docking approach HADDOCK is a consistent top predictor and scorer since the start of its participation in the CAPRI community-wide experiment. This sustained performance is due, in part, to its ability to integrate experimental data and/or bioinformatics information into the modelling process, and also to the overall robustness of the scoring function used to assess and rank the predictions. In the CASP-CAPRI Round 1 scoring experiment we successfully selected acceptable/medium quality models for 18/14 of the 25 targets - a top-ranking performance among all scorers. Considering that for only 20 targets acceptable models were generated by the community, our effective success rate reaches as high as 90% (18/20). This was achieved using the standard HADDOCK scoring function, which, thirteen years after its original publication, still consists of a simple linear combination of intermolecular van der Waals and Coulomb electrostatics energies and an empirically derived desolvation energy term. Despite its simplicity, this scoring function makes sense from a physico-chemical perspective, encoding key aspects of biomolecular recognition. In addition to its success in the scoring experiment, the HADDOCK server takes the first place in the server prediction category, with 16 successful predictions. Much like our scoring protocol, because of the limited time per target, the predictions relied mainly on either an ab initio center-of-mass and symmetry restrained protocol, or on a template-based approach whenever applicable. These results underline the success of our simple but sensible prediction and scoring scheme. Proteins 2017; 85:417-423. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- A Vangone
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - J P G L M Rodrigues
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - L C Xue
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - G C P van Zundert
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - C Geng
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - Z Kurkcuoglu
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - M Nellen
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - S Narasimhan
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - E Karaca
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - M van Dijk
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - A S J Melquiond
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - K M Visscher
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - M Trellet
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - P L Kastritis
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - A M J J Bonvin
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
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33
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Yu J, Guerois R. PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets. Bioinformatics 2016; 32:3760-3767. [PMID: 27551106 DOI: 10.1093/bioinformatics/btw533] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 07/22/2016] [Accepted: 08/10/2016] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Protein-protein docking methods are of great importance for understanding interactomes at the structural level. It has become increasingly appealing to use not only experimental structures but also homology models of unbound subunits as input for docking simulations. So far we are missing a large scale assessment of the success of rigid-body free docking methods on homology models. RESULTS We explored how we could benefit from comparative modelling of unbound subunits to expand docking benchmark datasets. Starting from a collection of 3157 non-redundant, high X-ray resolution heterodimers, we developed the PPI4DOCK benchmark containing 1417 docking targets based on unbound homology models. Rigid-body docking by Zdock showed that for 1208 cases (85.2%), at least one correct decoy was generated, emphasizing the efficiency of rigid-body docking in generating correct assemblies. Overall, the PPI4DOCK benchmark contains a large set of realistic cases and provides new ground for assessing docking and scoring methodologies. AVAILABILITY AND IMPLEMENTATION Benchmark sets can be downloaded from http://biodev.cea.fr/interevol/ppi4dock/ CONTACT: guerois@cea.frSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette, France
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34
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Busato M, Giorgetti A. Structural modeling of G-protein coupled receptors: An overview on automatic web-servers. Int J Biochem Cell Biol 2016; 77:264-74. [PMID: 27102413 DOI: 10.1016/j.biocel.2016.04.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 04/09/2016] [Accepted: 04/15/2016] [Indexed: 12/27/2022]
Abstract
Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.
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Affiliation(s)
- Mirko Busato
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy.
| | - Alejandro Giorgetti
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy; Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Computational Biomedicine, Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Germany.
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35
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de Vries SJ, Chauvot de Beauchêne I, Schindler CEM, Zacharias M. Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling. Biophys J 2016; 110:785-97. [PMID: 26846888 DOI: 10.1016/j.bpj.2015.12.038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 11/18/2015] [Accepted: 12/14/2015] [Indexed: 12/29/2022] Open
Abstract
Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.
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Affiliation(s)
- Sjoerd J de Vries
- Physik-Department T38, Technische Universität München, Garching, Germany.
| | | | - Christina E M Schindler
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
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36
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Schindler C, de Vries S, Zacharias M. Development and Application of a Fully Blind Flexible Peptide-protein Docking Protocol, pepATTRACT. Bio Protoc 2016. [DOI: 10.21769/bioprotoc.1831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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37
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Muratcioglu S, Guven-Maiorov E, Keskin Ö, Gursoy A. Advances in template-based protein docking by utilizing interfaces towards completing structural interactome. Curr Opin Struct Biol 2015; 35:87-92. [PMID: 26539658 DOI: 10.1016/j.sbi.2015.10.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 10/09/2015] [Accepted: 10/13/2015] [Indexed: 11/27/2022]
Abstract
The increase in the number of structurally determined protein complexes strengthens template-based docking (TBD) methods for modelling protein-protein interactions (PPIs). These methods utilize the known structures of protein complexes as templates to predict the quaternary structure of the target proteins. The templates may be partial or complete structures. Interface based (partial) methods have recently gained interest due in part to the observation that the interface regions are reusable. We describe how available template interfaces can be used to obtain the structural models of protein interactions. Despite the agreement that a majority of the protein complexes can be modelled using the available Protein Data Bank (PDB) structures, a handful of studies argue that we need more template proteins to increase the structural coverage of PPIs. We also discuss the performance of the interface TBD methods at large scale, and the significance of capturing multiple conformations for improving accuracy.
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Affiliation(s)
- Serena Muratcioglu
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Emine Guven-Maiorov
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Özlem Keskin
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey.
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38
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van Zundert GCP, Rodrigues JPGLM, Trellet M, Schmitz C, Kastritis PL, Karaca E, Melquiond ASJ, van Dijk M, de Vries SJ, Bonvin AMJJ. The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. J Mol Biol 2015; 428:720-725. [PMID: 26410586 DOI: 10.1016/j.jmb.2015.09.014] [Citation(s) in RCA: 1758] [Impact Index Per Article: 195.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 09/16/2015] [Accepted: 09/17/2015] [Indexed: 11/25/2022]
Abstract
The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2.
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Affiliation(s)
- G C P van Zundert
- Bijvoet Center for Biomolecular Research, Faculty of Science Department of Chemistry, Utrecht University, Domplein 29, 3512 JE Utrecht, the Netherlands
| | - J P G L M Rodrigues
- Bijvoet Center for Biomolecular Research, Faculty of Science Department of Chemistry, Utrecht University, Domplein 29, 3512 JE Utrecht, the Netherlands
| | - M Trellet
- Centre National de la Recherche Scientifique Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur, rue John Von Neumann, 91403 Orsay, France
| | - C Schmitz
- Instaclustr Level 5, 1 Moore Street, Canberra ACT 2600, Australia
| | - P L Kastritis
- European Molecular Biology Laboratory Heidelberg, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - E Karaca
- European Molecular Biology Laboratory Heidelberg, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - A S J Melquiond
- Hubrecht Institute, Uppsalalaan 8, 3584 CT Utrecht, the Netherlands
| | - M van Dijk
- Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, the Netherlands
| | - S J de Vries
- Physik-Department, Technische Universität München, James-Franck-Strasse 1, 85748 Garching, Germany
| | - A M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science Department of Chemistry, Utrecht University, Domplein 29, 3512 JE Utrecht, the Netherlands
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Wang Q, Rosa BA, Nare B, Powell K, Valente S, Rotili D, Mai A, Marshall GR, Mitreva M. Targeting Lysine Deacetylases (KDACs) in Parasites. PLoS Negl Trop Dis 2015; 9:e0004026. [PMID: 26402733 PMCID: PMC4581690 DOI: 10.1371/journal.pntd.0004026] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 08/02/2015] [Indexed: 12/30/2022] Open
Abstract
Due to an increasing problem of drug resistance among almost all parasites species ranging from protists to worms, there is an urgent need to explore new drug targets and their inhibitors to provide new and effective parasitic therapeutics. In this regard, there is growing interest in exploring known drug leads of human epigenetic enzymes as potential starting points to develop novel treatments for parasitic diseases. This approach of repurposing (starting with validated targets and inhibitors) is quite attractive since it has the potential to reduce the expense of drug development and accelerate the process of developing novel drug candidates for parasite control. Lysine deacetylases (KDACs) are among the most studied epigenetic drug targets of humans, and a broad range of small-molecule inhibitors for these enzymes have been reported. In this work, we identify the KDAC protein families in representative species across important classes of parasites, screen a compound library of 23 hydroxamate- or benzamide-based small molecules KDAC inhibitors, and report their activities against a range of parasitic species, including the pathogen of malaria (Plasmodium falciparum), kinetoplastids (Trypanosoma brucei and Leishmania donovani), and nematodes (Brugia malayi, Dirofilaria immitis and Haemonchus contortus). Compound activity against parasites is compared to that observed against the mammalian cell line (L929 mouse fibroblast) in order to determine potential parasite-versus-host selectivity). The compounds showed nanomolar to sub-nanomolar potency against various parasites, and some selectivity was observed within the small panel of compounds tested. The possible binding modes of the active compounds at the different protein target sites within different species were explored by docking to homology models to help guide the discovery of more selective, parasite-specific inhibitors. This current work supports previous studies that explored the use of KDAC inhibitors in targeting Plasmodium to develop new anti-malarial treatments, and also pioneers experiments with these KDAC inhibitors as potential new anthelminthics. The selectivity observed begins to address the challenges of targeting specific parasitic diseases while limiting host toxicity. Due to pandemic drug resistance in the treatment of parasitic infections, there is an urgent need to identify novel drug targets and their associated drug compounds. Although “drug repurposing”, i.e. the application of known drugs and compounds to new indications such as infectious diseases, provides a cost effective approach in the development of novel therapeutics, selectivity is one of the major obstacles to overcome in getting such compounds into clinical trials as anti-parasitic drugs. Using the lysine deacetylases (KDACs) as an example, we explored the activities of a panel of known inhibitors against the KDAC targets in a range of parasitic organisms. The computational study of their binding modes to the targets (by docking the compounds to the homology models within different organisms in comparison with the human proteins) helps to rationalize the different activities observed and provide insight on the optimization of lead compounds to improve selectivity. Our work provides support of “drug repurposing” in the treatment of parasitic diseases, and demonstrates the necessity of optimizing these leads for the ultimate goal of preparing them for clinical use.
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Affiliation(s)
- Qi Wang
- The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Bruce A. Rosa
- The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Bakela Nare
- SCYNEXIS, Inc, Research Triangle Park, North Carolina, United States of America
| | - Kerrie Powell
- SCYNEXIS, Inc, Research Triangle Park, North Carolina, United States of America
| | - Sergio Valente
- Istituto Pasteur-Fondazione Cenci Bolognetti, Dipartimento di Chimica e Tecnologie del Farmaco, Università degli Studi di Roma “La Sapienza”, Roma, Italy
| | - Dante Rotili
- Istituto Pasteur-Fondazione Cenci Bolognetti, Dipartimento di Chimica e Tecnologie del Farmaco, Università degli Studi di Roma “La Sapienza”, Roma, Italy
| | - Antonello Mai
- Istituto Pasteur-Fondazione Cenci Bolognetti, Dipartimento di Chimica e Tecnologie del Farmaco, Università degli Studi di Roma “La Sapienza”, Roma, Italy
| | - Garland R. Marshall
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Makedonka Mitreva
- The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Departments of Genetics and of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America
- * E-mail:
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40
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Vakser IA. Protein-protein docking: from interaction to interactome. Biophys J 2015; 107:1785-1793. [PMID: 25418159 DOI: 10.1016/j.bpj.2014.08.033] [Citation(s) in RCA: 184] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Revised: 08/17/2014] [Accepted: 08/27/2014] [Indexed: 12/29/2022] Open
Abstract
The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.
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Affiliation(s)
- Ilya A Vakser
- Center for Bioinformatics and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
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41
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Petrey D, Chen TS, Deng L, Garzon JI, Hwang H, Lasso G, Lee H, Silkov A, Honig B. Template-based prediction of protein function. Curr Opin Struct Biol 2015; 32:33-8. [PMID: 25678152 DOI: 10.1016/j.sbi.2015.01.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 01/13/2015] [Accepted: 01/19/2015] [Indexed: 12/11/2022]
Abstract
We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that cannot be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.
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Affiliation(s)
- Donald Petrey
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States.
| | - T Scott Chen
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Lei Deng
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Jose Ignacio Garzon
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Howook Hwang
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Gorka Lasso
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Hunjoong Lee
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Antonina Silkov
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Barry Honig
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
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42
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Paudyal S, Alfonso-Prieto M, Carnevale V, Redhu SK, Klein ML, Nicholson AW. Combined computational and experimental analysis of a complex of ribonuclease III and the regulatory macrodomain protein, YmdB. Proteins 2015; 83:459-72. [PMID: 25546632 PMCID: PMC4329070 DOI: 10.1002/prot.24751] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2014] [Revised: 12/04/2014] [Accepted: 12/10/2014] [Indexed: 01/06/2023]
Abstract
Ribonuclease III is a conserved bacterial endonuclease that cleaves double-stranded(ds) structures in diverse coding and noncoding RNAs. RNase III is subject to multiple levels of control that in turn confer global post-transcriptional regulation. The Escherichia coli macrodomain protein YmdB directly interacts with RNase III, and an increase in YmdB amount in vivo correlates with a reduction in RNase III activity. Here, a computational-based structural analysis was performed to identify atomic-level features of the YmdB-RNase III interaction. The docking of monomeric E. coli YmdB with a homology model of the E. coli RNase III homodimer yields a complex that exhibits an interaction of the conserved YmdB residue R40 with specific RNase III residues at the subunit interface. Surface Plasmon Resonance (SPR) analysis provided a KD of 61 nM for the complex, corresponding to a binding free energy (ΔG) of −9.9 kcal/mol. YmdB R40 and RNase III D128 were identified by in silico alanine mutagenesis as thermodynamically important interacting partners. Consistent with the prediction, the YmdB R40A mutation causes a 16-fold increase in KD (ΔΔG = +1.8 kcal/mol), as measured by SPR, and the D128A mutation in both RNase III subunits (D128A/D128′A) causes an 83-fold increase in KD (ΔΔG = +2.7 kcal/mol). The greater effect of the D128A/D128′A mutation may reflect an altered RNase III secondary structure, as revealed by CD spectroscopy, which also may explain the significant reduction in catalytic activity in vitro. The features of the modeled complex relevant to potential RNase III regulatory mechanisms are discussed. Proteins 2015; 83:459–472. © 2014 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Samridhdi Paudyal
- Department of Biology, Temple University, Philadelphia, Pennsylvania, 19122
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43
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Du H, Brender JR, Zhang J, Zhang Y. Protein structure prediction provides comparable performance to crystallographic structures in docking-based virtual screening. Methods 2015; 71:77-84. [PMID: 25220914 PMCID: PMC4431978 DOI: 10.1016/j.ymeth.2014.08.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 08/14/2014] [Accepted: 08/31/2014] [Indexed: 11/26/2022] Open
Abstract
Structure based virtual screening has largely been limited to protein targets for which either an experimental structure is available or a strongly homologous template exists so that a high-resolution model can be constructed. The performance of state of the art protein structure predictions in virtual screening in systems where only weakly homologous templates are available is largely untested. Using the challenging DUD database of structural decoys, we show here that even using templates with only weak sequence homology (<30% sequence identity) structural models can be constructed by I-TASSER which achieve comparable enrichment rates to using the experimental bound crystal structure in the majority of the cases studied. For 65% of the targets, the I-TASSER models, which are constructed essentially in the apo conformations, reached 70% of the virtual screening performance of using the holo-crystal structures. A correlation was observed between the success of I-TASSER in modeling the global fold and local structures in the binding pockets of the proteins versus the relative success in virtual screening. The virtual screening performance can be further improved by the recognition of chemical features of the ligand compounds. These results suggest that the combination of structure-based docking and advanced protein structure modeling methods should be a valuable approach to the large-scale drug screening and discovery studies, especially for the proteins lacking crystallographic structures.
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Affiliation(s)
- Hongying Du
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA; Department of Public Health, Lanzhou University, Lanzhou 730000, China
| | - Jeffrey R Brender
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Jian Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
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44
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Don CG, Riniker S. Scents and sense:In silicoperspectives on olfactory receptors. J Comput Chem 2014; 35:2279-87. [DOI: 10.1002/jcc.23757] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 09/23/2014] [Accepted: 09/27/2014] [Indexed: 11/12/2022]
Affiliation(s)
- Charleen G. Don
- Swiss Federal Institute of Technology, Laboratory of Physical Chemistry, ETH Zurich; 8093 Zurich Switzerland
| | - Sereina Riniker
- Swiss Federal Institute of Technology, Laboratory of Physical Chemistry, ETH Zurich; 8093 Zurich Switzerland
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45
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Hopf TA, Schärfe CPI, Rodrigues JPGLM, Green AG, Kohlbacher O, Sander C, Bonvin AMJJ, Marks DS. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 2014; 3. [PMID: 25255213 PMCID: PMC4360534 DOI: 10.7554/elife.03430] [Citation(s) in RCA: 332] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 09/23/2014] [Indexed: 12/24/2022] Open
Abstract
Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.
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Affiliation(s)
- Thomas A Hopf
- Department of Systems Biology, Harvard University, Boston, United States
| | | | - João P G L M Rodrigues
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Anna G Green
- Department of Systems Biology, Harvard University, Boston, United States
| | - Oliver Kohlbacher
- Applied Bioinformatics, Quantitative Biology Center, University of Tübingen, Tübingen, Germany
| | - Chris Sander
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Debora S Marks
- Department of Systems Biology, Harvard University, Boston, United States
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46
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Rey M, Sarpe V, Burns KM, Buse J, Baker CAH, van Dijk M, Wordeman L, Bonvin AMJJ, Schriemer DC. Mass spec studio for integrative structural biology. Structure 2014; 22:1538-48. [PMID: 25242457 DOI: 10.1016/j.str.2014.08.013] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 07/30/2014] [Accepted: 08/06/2014] [Indexed: 02/01/2023]
Abstract
The integration of biophysical data from multiple sources is critical for developing accurate structural models of large multiprotein systems and their regulators. Mass spectrometry (MS) can be used to measure the insertion location for a wide range of topographically sensitive chemical probes, and such insertion data provide a rich, but disparate set of modeling restraints. We have developed a software platform that integrates the analysis of label-based MS and tandem MS (MS(2)) data with protein modeling activities (Mass Spec Studio). Analysis packages can mine any labeling data from any mass spectrometer in a proteomics-grade manner, and link labeling methods with data-directed protein interaction modeling using HADDOCK. Support is provided for hydrogen/deuterium exchange (HX) and covalent labeling chemistries, including novel acquisition strategies such as targeted HX-MS(2) and data-independent HX-MS(2). The latter permits the modeling of highly complex systems, which we demonstrate by the analysis of microtubule interactions.
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Affiliation(s)
- Martial Rey
- Department of Biochemistry and Molecular Biology and Southern Alberta Cancer Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Vladimir Sarpe
- Department of Biochemistry and Molecular Biology and Southern Alberta Cancer Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Kyle M Burns
- Department of Biochemistry and Molecular Biology and Southern Alberta Cancer Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Joshua Buse
- Department of Biochemistry and Molecular Biology and Southern Alberta Cancer Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | | | - Marc van Dijk
- Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht CH 3584, the Netherlands
| | - Linda Wordeman
- Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA 98195-7290, USA
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht CH 3584, the Netherlands
| | - David C Schriemer
- Department of Biochemistry and Molecular Biology and Southern Alberta Cancer Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
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47
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Huang SY. Search strategies and evaluation in protein–protein docking: principles, advances and challenges. Drug Discov Today 2014; 19:1081-96. [DOI: 10.1016/j.drudis.2014.02.005] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Revised: 01/04/2014] [Accepted: 02/24/2014] [Indexed: 01/10/2023]
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48
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Lua RC, Marciano DC, Katsonis P, Adikesavan AK, Wilkins AD, Lichtarge O. Prediction and redesign of protein-protein interactions. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:194-202. [PMID: 24878423 DOI: 10.1016/j.pbiomolbio.2014.05.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 05/02/2014] [Accepted: 05/17/2014] [Indexed: 12/14/2022]
Abstract
Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.
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Affiliation(s)
- Rhonald C Lua
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - David C Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Anbu K Adikesavan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Angela D Wilkins
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA.
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49
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Rodrigues JPGLM, Bonvin AMJJ. Integrative computational modeling of protein interactions. FEBS J 2014; 281:1988-2003. [DOI: 10.1111/febs.12771] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Revised: 01/03/2014] [Accepted: 02/19/2014] [Indexed: 01/09/2023]
Affiliation(s)
- João P. G. L. M. Rodrigues
- Computational Structural Biology Group; Bijvoet Center for Biomolecular Research; Utrecht University; the Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group; Bijvoet Center for Biomolecular Research; Utrecht University; the Netherlands
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50
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Modeling protein-protein complexes using the HADDOCK webserver "modeling protein complexes with HADDOCK". Methods Mol Biol 2014; 1137:163-79. [PMID: 24573481 DOI: 10.1007/978-1-4939-0366-5_12] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Protein-protein interactions lie at the heart of most cellular processes. Determining their high-resolution structures by experimental methods is a nontrivial task, which is why complementary computational approaches have been developed over the years. To gain structural and dynamical insight on an atomic scale in these interactions, computational modeling must often be complemented by low-resolution experimental information. For this purpose, we developed the user-friendly HADDOCK webserver, the interface to our biomolecular docking program, which can make use of a variety of low-resolution data to drive the docking process. In this chapter, we explain the use of the HADDOCK webserver based on the real-life Lys48-linked di-ubiquitin case, which led to the 2BGF PDB model. We demonstrate the use of chemical shift perturbation data in combination with residual dipolar couplings and further highlight a few other cases where our software was successfully used. The HADDOCK webserver is available to the science community for free at haddock.science.uu.nl/services/HADDOCK.
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