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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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Computational methods-guided design of modulators targeting protein-protein interactions (PPIs). Eur J Med Chem 2020; 207:112764. [PMID: 32871340 DOI: 10.1016/j.ejmech.2020.112764] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/09/2020] [Accepted: 08/16/2020] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.
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Ngounou Wetie AG, Sokolowska I, Channaveerappa D, Dupree EJ, Jayathirtha M, Woods AG, Darie CC. Proteomics and Non-proteomics Approaches to Study Stable and Transient Protein-Protein Interactions. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:121-142. [DOI: 10.1007/978-3-030-15950-4_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Murakami Y, Tripathi LP, Prathipati P, Mizuguchi K. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery. Curr Opin Struct Biol 2017; 44:134-142. [PMID: 28364585 DOI: 10.1016/j.sbi.2017.02.005] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2016] [Revised: 02/05/2017] [Accepted: 02/23/2017] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.
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Affiliation(s)
- Yoichi Murakami
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
| | - Lokesh P Tripathi
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
| | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan
| | - Kenji Mizuguchi
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
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Sheng C, Dong G, Miao Z, Zhang W, Wang W. State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors. Chem Soc Rev 2015; 44:8238-59. [PMID: 26248294 DOI: 10.1039/c5cs00252d] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.
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Affiliation(s)
- Chunquan Sheng
- Department of Medicinal Chemistry, School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai 200433, P. R. China.
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Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J Mol Recognit 2015; 28:581-604. [PMID: 25808539 DOI: 10.1002/jmr.2471] [Citation(s) in RCA: 159] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 01/16/2015] [Accepted: 02/05/2015] [Indexed: 12/11/2022]
Abstract
Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.
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Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Jessica Holien
- ACRF Rational Drug Discovery Centre and Structural Biology Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Victoria, 3065, Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute, Melbourne, Victoria, 3004, Australia.,Department of Surgery Austin Health, University of Melbourne, Melbourne, Victoria, 3084, Australia.,Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Victoria, 3004, Australia.,School of Biomedical Sciences, CHIRI Biosciences, Curtin University, Perth, Western Australia, 6845, Australia
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [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: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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Perna AM, Reisen F, Schmidt TP, Geppert T, Pillong M, Weisel M, Hoy B, Simister PC, Feller SM, Wessler S, Schneider G. Inhibiting Helicobacter pylori HtrA protease by addressing a computationally predicted allosteric ligand binding site. Chem Sci 2014; 5:3583-3590. [PMID: 26819700 PMCID: PMC4724879 DOI: 10.1039/c4sc01443j] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Helicobacter pylori is associated with inflammatory diseases and can cause gastric cancer and mucosa-associated lymphoma. One of the bacterium's key proteins is high temperature requirement A (HpHtrA) protein, an extracellular serine protease that cleaves E-cadherin of gastric epithelial cells, which leads to loss of cell-cell adhesion. Inhibition of HpHtrA may constitute an intervention strategy against H. pylori infection. Guided by the computational prediction of hypothetical ligand binding sites on the surface of HpHtrA, we performed residue mutation experiments that confirmed the functional relevance of an allosteric region. We virtually screened for potential ligands addressing this surface cleft located between the catalytic and PDZ1 domains. Our receptor-based computational method represents protein surface pockets in terms of graph frameworks and retrieves small molecules that satisfy the constraints given by the pocket framework. A new chemical entity was identified that blocked E-cadherin cleavage in vitro by direct binding to HpHtrA, and efficiently blocked pathogen transmigration across the gastric epithelial barrier. A preliminary crystal structure of HpHtrA confirms the validity of a comparative "homology" model of the enzyme, which we used for the computational study. The results of this study demonstrate that addressing orphan protein surface cavities of target macromolecules can lead to new bioactive ligands.
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Affiliation(s)
- Anna Maria Perna
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, 8093 Zurich, Switzerland
| | - Felix Reisen
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, 8093 Zurich, Switzerland
| | - Thomas P Schmidt
- University of Salzburg, Department of Molecular Biology, 5020 Salzburg, Austria
| | - Tim Geppert
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, 8093 Zurich, Switzerland
| | - Max Pillong
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, 8093 Zurich, Switzerland
| | - Martin Weisel
- Goethe-University, Institute of Organic Chemistry and Chemical Biology, 60322 Frankfurt, Germany
| | - Benjamin Hoy
- University of Salzburg, Department of Molecular Biology, 5020 Salzburg, Austria
| | - Philip C Simister
- University of Oxford, Department of Oncology, Weatherall Institute of Molecular Medicine, OX3 9DS Oxford, UK
| | - Stephan M Feller
- University of Oxford, Department of Oncology, Weatherall Institute of Molecular Medicine, OX3 9DS Oxford, UK ; Martin-Luther-University Halle-Wittenberg, Institute of Molecular Medicine, 06120 Halle, Germany
| | - Silja Wessler
- University of Salzburg, Department of Molecular Biology, 5020 Salzburg, Austria
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, 8093 Zurich, Switzerland
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Abstract
The amount of known protein structures is continuously growing, exhibited in over 95,000 3D structures freely available via the PDB. Over the last decade, pharmaceutical research has sparked interest in computationally extracting information from this large data pool, resulting in a homology-driven knowledge transfer from annotated to new structures. Studying protein structures with respect to understanding and modulating their functional behavior means analyzing their centers of action. Therefore, the detection and description of potential binding sites on the protein surface is a major step towards protein classification and assessment. Subsequently, these representations can be incorporated to compare proteins, and to predict their druggability or function. Especially in the context of target identification and polypharmacology, automated tools for large-scale target comparisons are highly needed. In this article, developments for automated structure-based target assessment are reviewed and remaining challenges as well as future perspectives are discussed.
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Silvian L, Enyedy I, Kumaravel G. Inhibitors of protein-protein interactions: new methodologies to tackle this challenge. DRUG DISCOVERY TODAY. TECHNOLOGIES 2013; 10:e509-e515. [PMID: 24451642 DOI: 10.1016/j.ddtec.2012.10.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Several advances in the fields of crystallography, molecular modeling, biophysical assays and chemistry are converging to making protein-protein interaction targets more amenable to drug design. These include steps towards improving crystallization of protein-protein complexes, identifying the clusters of residues that constitute putative small molecule binding 'hot spots', generating new methods for detecting the binding of small molecules to target proteins, and generating custom libraries via diversity oriented synthesis to enable the identification of natural-product-like hits.
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Fayne D. De-peptidising protein-protein interactions - big jobs for small molecules. DRUG DISCOVERY TODAY. TECHNOLOGIES 2013; 10:e467-e474. [PMID: 24451636 DOI: 10.1016/j.ddtec.2013.08.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Virtually all biological processes rely on protein-protein interactions (PPIs) for signal propagation, therefore representing a vast array of potentially viable therapeutic intervention points. Targeting PPIs is a relatively novel drug development strategy so computational approaches towards analysing the interface between protein partners and predicting the likelihood of developing a small molecule inhibitor are still progressing. This review provides an overview of recent successful examples of computational methodologies used to predict druggable PPIs and small molecules designed to inhibit them.
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Walter P, Metzger J, Thiel C, Helms V. Predicting where small molecules bind at protein-protein interfaces. PLoS One 2013; 8:e58583. [PMID: 23505538 PMCID: PMC3591369 DOI: 10.1371/journal.pone.0058583] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2012] [Accepted: 02/07/2013] [Indexed: 11/25/2022] Open
Abstract
Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP) complexes and protein-ligand (PL) complexes with known three-dimensional structures for which (1) one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2) the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10 000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules.
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Affiliation(s)
- Peter Walter
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Jennifer Metzger
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Christoph Thiel
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Volkhard Helms
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- * E-mail:
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Fry D, Huang KS, Di Lello P, Mohr P, Müller K, So SS, Harada T, Stahl M, Vu B, Mauser H. Design of Libraries Targeting Protein-Protein Interfaces. ChemMedChem 2013; 8:726-32. [DOI: 10.1002/cmdc.201200540] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2012] [Indexed: 11/10/2022]
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Lieder F, Reisen F, Geppert T, Sollberger G, Beer HD, auf dem Keller U, Schäfer M, Detmar M, Schneider G, Werner S. Identification of UV-protective activators of nuclear factor erythroid-derived 2-related factor 2 (Nrf2) by combining a chemical library screen with computer-based virtual screening. J Biol Chem 2012; 287:33001-13. [PMID: 22851183 DOI: 10.1074/jbc.m112.383430] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Nuclear factor erythroid-derived 2-related factor 2 (Nrf2) is a master regulator of cellular antioxidant defense systems, and activation of this transcription factor is a promising strategy for protection of skin and other organs from environmental insults. To identify efficient Nrf2 activators in keratinocytes, we combined a chemical library screen with computer-based virtual screening. Among 14 novel Nrf2 activators, the most potent compound, a nitrophenyl derivative of 2-chloro-5-nitro-N-phenyl-benzamide, was characterized with regard to its molecular mechanism of action. This compound induced the expression of cytoprotective genes in keratinocytes isolated from wild-type but not from Nrf2-deficient mice. Most importantly, it showed low toxicity and protected primary human keratinocytes from UVB-induced cell death. Therefore, it represents a potential lead compound for the development of drugs for skin protection under stress conditions. Our study demonstrates that chemical library screening combined with advanced computational similarity searching is a powerful strategy for identification of bioactive compounds, and it points toward an innovative therapeutic approach against UVB-induced skin damage.
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Affiliation(s)
- Franziska Lieder
- Department of Biology, Swiss Federal Institute of Technology (ETH) Zurich, 8093 Zurich, Switzerland
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