1
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Kazmirchuk TDD, Bradbury-Jost C, Withey TA, Gessese T, Azad T, Samanfar B, Dehne F, Golshani A. Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing. Genes (Basel) 2023; 14:1194. [PMID: 37372372 DOI: 10.3390/genes14061194] [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: 03/30/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.
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Affiliation(s)
- Thomas David Daniel Kazmirchuk
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Calvin Bradbury-Jost
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Taylor Ann Withey
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Tadesse Gessese
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Taha Azad
- Department of Microbiology and Infectious Diseases, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC J1H 5N4, Canada
| | - Bahram Samanfar
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC), Ottawa, ON K1A 0C6, Canada
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Ashkan Golshani
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
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2
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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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3
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Wang S, Mao C, Liu S. Peptides encoded by noncoding genes: challenges and perspectives. Signal Transduct Target Ther 2019; 4:57. [PMID: 31871775 PMCID: PMC6908703 DOI: 10.1038/s41392-019-0092-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 10/17/2019] [Accepted: 10/27/2019] [Indexed: 01/01/2023] Open
Abstract
In recent years, noncoding gene (NCG) translation events have been frequently discovered. The resultant peptides, as novel findings in the life sciences, perform unexpected functions of increasingly recognized importance in many fundamental biological and pathological processes. The emergence of these novel peptides, in turn, has advanced the field of genomics while indispensably aiding living organisms. The peptides from NCGs serve as important links between extracellular stimuli and intracellular adjustment mechanisms. These peptides are also important entry points for further exploration of the mysteries of life that may trigger a new round of revolutionary biotechnological discoveries. Insights into NCG-derived peptides will assist in understanding the secrets of life and the causes of diseases, and will also open up new paths to the treatment of diseases such as cancer. Here, a critical review is presented on the action modes and biological functions of the peptides encoded by NCGs. The challenges and future trends in searching for and studying NCG peptides are also critically discussed.
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Affiliation(s)
- Shuo Wang
- Changhai Hospital, Shanghai, 200433 China
| | - Chuanbin Mao
- Department of Chemistry and Biochemistry, Stephenson Life Sciences Research Center, Institute for Biomedical Engineering, Science and Technology, University of Oklahoma, 101 Stephenson Parkway, Norman, OK 73019-5300 USA
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4
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Janowska-Sejda EI, Lysenko A, Urban M, Rawlings C, Tsoka S, Hammond-Kosack KE. PHI-Nets: A Network Resource for Ascomycete Fungal Pathogens to Annotate and Identify Putative Virulence Interacting Proteins and siRNA Targets. Front Microbiol 2019; 10:2721. [PMID: 31866958 PMCID: PMC6908471 DOI: 10.3389/fmicb.2019.02721] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 11/08/2019] [Indexed: 12/28/2022] Open
Abstract
Interactions between proteins underlie all aspects of complex biological mechanisms. Therefore, methodologies based on complex network analyses can facilitate identification of promising candidate genes involved in phenotypes of interest and put this information into appropriate contexts. To facilitate discovery and gain additional insights into globally important pathogenic fungi, we have reconstructed computationally inferred interactomes using an interolog and domain-based approach for 15 diverse Ascomycete fungal species, across nine orders, specifically Aspergillus fumigatus, Bipolaris sorokiniana, Blumeria graminis f. sp. hordei, Botrytis cinerea, Colletotrichum gloeosporioides, Colletotrichum graminicola, Fusarium graminearum, Fusarium oxysporum f. sp. lycopersici, Fusarium verticillioides, Leptosphaeria maculans, Magnaporthe oryzae, Saccharomyces cerevisiae, Sclerotinia sclerotiorum, Verticillium dahliae, and Zymoseptoria tritici. Network cartography analysis was associated with functional patterns of annotated genes linked to the disease-causing ability of each pathogen. In addition, for the best annotated organism, namely F. graminearum, the distribution of annotated genes with respect to network structure was profiled using a random walk with restart algorithm, which suggested possible co-location of virulence-related genes in the protein–protein interaction network. In a second ‘use case’ study involving two networks, namely B. cinerea and F. graminearum, previously identified small silencing plant RNAs were mapped to their targets. The F. graminearum phenotypic network analysis implicates eight B. cinerea targets and 35 F. graminearum predicted interacting proteins as prime candidate virulence genes for further testing. All 15 networks have been made accessible for download at www.phi-base.org providing a rich resource for major crop plant pathogens.
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Affiliation(s)
- Elzbieta I Janowska-Sejda
- Department of Biointeractions and Crop Protection, Rothamsted Research, Harpenden, United Kingdom.,Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, United Kingdom.,Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London, United Kingdom
| | - Artem Lysenko
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, United Kingdom
| | - Martin Urban
- Department of Biointeractions and Crop Protection, Rothamsted Research, Harpenden, United Kingdom
| | - Chris Rawlings
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, United Kingdom
| | - Sophia Tsoka
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London, United Kingdom
| | - Kim E Hammond-Kosack
- Department of Biointeractions and Crop Protection, Rothamsted Research, Harpenden, United Kingdom
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5
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3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns. Int J Mol Sci 2019; 20:ijms20133174. [PMID: 31261733 PMCID: PMC6651053 DOI: 10.3390/ijms20133174] [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: 05/11/2019] [Revised: 06/19/2019] [Accepted: 06/20/2019] [Indexed: 01/25/2023] Open
Abstract
Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.
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6
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Krieger C, Roselli S, Kellner-Thielmann S, Galati G, Schneider B, Grosjean J, Olry A, Ritchie D, Matern U, Bourgaud F, Hehn A. The CYP71AZ P450 Subfamily: A Driving Factor for the Diversification of Coumarin Biosynthesis in Apiaceous Plants. FRONTIERS IN PLANT SCIENCE 2018; 9:820. [PMID: 29971079 PMCID: PMC6018538 DOI: 10.3389/fpls.2018.00820] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 05/28/2018] [Indexed: 05/19/2023]
Abstract
The production of coumarins and furanocoumarins (FCs) in higher plants is widely considered a model illustration of the adaptation of plants to their environment. In this report, we show that the multiplication of cytochrome P450 variants within the CYP71AZ subfamily has contributed to the diversification of these molecules. Multiple copies of genes encoding this enzyme family are found in Apiaceae, and their phylogenetic analysis suggests that they have different functions within these plants. CYP71AZ1 from Ammi majus and CYP71AZ3, 4, and 6 from Pastinaca sativa were functionally characterized. While CYP71AZ3 merely hydroxylated esculetin, the other enzymes accepted both simple coumarins and FCs. Superimposing in silico models of these enzymes led to the identification of different conformations of three regions in the enzyme active site. These sequences were subsequently utilized to mutate CYP71AZ4 to resemble CYP71AZ3. The swapping of these regions lead to significantly modified substrate specificity. Simultaneous mutations of all three regions shifted the specificity of CYP71AZ4 to that of CYP71AZ3, exclusively accepting esculetin. This approach may explain the evolution of this cytochrome P450 family regarding the appearance of FCs in parsnip and possibly in the Apiaceae.
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Affiliation(s)
- Célia Krieger
- Laboratoire Agronomie et Environnement, Institut National de la Recherche Agronomique, Université de Lorraine, Nancy, France
| | - Sandro Roselli
- Laboratoire Agronomie et Environnement, Institut National de la Recherche Agronomique, Université de Lorraine, Nancy, France
| | - Sandra Kellner-Thielmann
- Institut für Pharmazeutische Biologie und Biotechnologie, Philipps-Universität Marburg, Marburg, Germany
| | - Gianni Galati
- Laboratoire Agronomie et Environnement, Institut National de la Recherche Agronomique, Université de Lorraine, Nancy, France
| | | | - Jérémy Grosjean
- Laboratoire Agronomie et Environnement, Institut National de la Recherche Agronomique, Université de Lorraine, Nancy, France
| | - Alexandre Olry
- Laboratoire Agronomie et Environnement, Institut National de la Recherche Agronomique, Université de Lorraine, Nancy, France
| | - David Ritchie
- INRIA Nancy, Grand-Est Research Centre, Laboratoire Lorrain De Recherche En Informatique Et Ses Applications, Nancy, France
| | - Ulrich Matern
- Institut für Pharmazeutische Biologie und Biotechnologie, Philipps-Universität Marburg, Marburg, Germany
| | | | - Alain Hehn
- Laboratoire Agronomie et Environnement, Institut National de la Recherche Agronomique, Université de Lorraine, Nancy, France
- *Correspondence: Alain Hehn,
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7
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Abstract
Comparing and classifying protein domain interactions according to their three-dimensional (3D) structures can help to understand protein structure-function and evolutionary relationships. Additionally, structural knowledge of existing domain-domain interactions can provide a useful way to find structural templates with which to model the 3D structures of unsolved protein complexes. Here we present a straightforward guide to using the "Kbdock" protein domain structure database and its associated web site for exploring and comparing protein domain-domain interactions (DDIs) and domain-peptide interactions (DPIs) at the Pfam domain family level. We also briefly explain how the Kbdock web site works, and we provide some notes and suggestions which should help to avoid some common pitfalls when working with 3D protein domain structures.
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8
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El Houasli M, Maigret B, Devignes MD, Ghoorah AW, Grudinin S, Ritchie DW. Modeling and minimizing CAPRI round 30 symmetrical protein complexes from CASP-11 structural models. Proteins 2016; 85:463-469. [PMID: 27701764 DOI: 10.1002/prot.25182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/13/2016] [Accepted: 09/24/2016] [Indexed: 11/06/2022]
Abstract
Many of the modeling targets in the blind CASP-11/CAPRI-30 experiment were protein homo-dimers and homo-tetramers. Here, we perform a retrospective docking-based analysis of the perfectly symmetrical CAPRI Round 30 targets whose crystal structures have been published. Starting from the CASP "stage-2" fold prediction models, we show that using our recently developed "SAM" polar Fourier symmetry docking algorithm combined with NAMD energy minimization often gives acceptable or better 3D models of the target complexes. We also use SAM to analyze the overall quality of all CASP structural models for the selected targets from a docking-based perspective. We demonstrate that docking only CASP "center" structures for the selected targets provides a fruitful and economical docking strategy. Furthermore, our results show that many of the CASP models are dockable in the sense that they can lead to acceptable or better models of symmetrical complexes. Even though SAM is very fast, using docking and NAMD energy minimization to pull out acceptable docking models from a large ensemble of docked CASP models is computationally expensive. Nonetheless, thanks to our SAM docking algorithm, we expect that applying our docking protocol on a modern computer cluster will give us the ability to routinely model 3D structures of symmetrical protein complexes from CASP-quality models. Proteins 2017; 85:463-469. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Marwa El Houasli
- INRIA, Equipe Capsid, Campus Scientique, BP 239, 54506, Vandoeuvre-lès-Nancy, France
| | | | | | - Anisah W Ghoorah
- Department of Computer Science and Engineering, University of Mauritius
| | | | - David W Ritchie
- INRIA, Equipe Capsid, Campus Scientique, BP 239, 54506, Vandoeuvre-lès-Nancy, France
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9
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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10
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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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11
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Dietzen M, Kalinina OV, Taškova K, Kneissl B, Hildebrandt AK, Jaenicke E, Decker H, Lengauer T, Hildebrandt A. Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces. Proteins 2015; 83:1887-99. [PMID: 26248608 PMCID: PMC5049452 DOI: 10.1002/prot.24873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 07/20/2015] [Accepted: 07/29/2015] [Indexed: 11/06/2022]
Abstract
Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.
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Affiliation(s)
- Matthias Dietzen
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Olga V Kalinina
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Katerina Taškova
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Institute for Molecular Biology, Johannes Gutenberg University, Ackermannweg 4, Mainz, 55128, Germany
| | - Benny Kneissl
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Penzberg, Nonnenwald 2, Penzberg, 82377, Germany
| | | | - Elmar Jaenicke
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Heinz Decker
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Thomas Lengauer
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Andreas Hildebrandt
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany
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12
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Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors. Molecules 2015; 20:11569-603. [PMID: 26111183 PMCID: PMC6272567 DOI: 10.3390/molecules200611569] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 06/02/2015] [Accepted: 06/15/2015] [Indexed: 02/06/2023] Open
Abstract
Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.
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13
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A structure-based classification and analysis of protein domain family binding sites and their interactions. BIOLOGY 2015; 4:327-43. [PMID: 25860777 PMCID: PMC4498303 DOI: 10.3390/biology4020327] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Revised: 03/24/2015] [Accepted: 03/31/2015] [Indexed: 11/29/2022]
Abstract
While the number of solved 3D protein structures continues to grow rapidly, the structural rules that distinguish protein-protein interactions between different structural families are still not clear. Here, we classify and analyse the secondary structural features and promiscuity of a comprehensive non-redundant set of domain family binding sites (DFBSs) and hetero domain-domain interactions (DDIs) extracted from our updated KBDOCK resource. We have partitioned 4001 DFBSs into five classes using their propensities for three types of secondary structural elements (“α” for helices, “β” for strands, and “γ” for irregular structure) and we have analysed how frequently these classes occur in DDIs. Our results show that β elements are not highly represented in DFBSs compared to α and γ elements. At the DDI level, all classes of binding sites tend to preferentially bind to the same class of binding sites and α/β contacts are significantly disfavored. Very few DFBSs are promiscuous: 80% of them interact with just one Pfam domain. About 50% of our Pfam domains bear only one single-partner DFBS and are therefore monogamous in their interactions with other domains. Conversely, promiscuous Pfam domains bear several DFBSs among which one or two are promiscuous, thereby multiplying the promiscuity of the concerned protein.
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14
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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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Andreani J, Guerois R. Evolution of protein interactions: From interactomes to interfaces. Arch Biochem Biophys 2014; 554:65-75. [DOI: 10.1016/j.abb.2014.05.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 04/28/2014] [Accepted: 05/12/2014] [Indexed: 12/16/2022]
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Villoutreix BO, Kuenemann MA, Poyet JL, Bruzzoni-Giovanelli H, Labbé C, Lagorce D, Sperandio O, Miteva MA. Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology. Mol Inform 2014; 33:414-437. [PMID: 25254076 PMCID: PMC4160817 DOI: 10.1002/minf.201400040] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 04/21/2014] [Indexed: 12/13/2022]
Abstract
[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.
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Affiliation(s)
- Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Melaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - Jean-Luc Poyet
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- IUH, Hôpital Saint-LouisParis, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Heriberto Bruzzoni-Giovanelli
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CIC, Clinical investigation center, Hôpital Saint-LouisParis, France
| | - Céline Labbé
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
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