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Liu JX, Zhang X, Huang YQ, Hao GF, Yang GF. Multi-level bioinformatics resources support drug target discovery of protein-protein interactions. Drug Discov Today 2024; 29:103979. [PMID: 38608830 DOI: 10.1016/j.drudis.2024.103979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/14/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.
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Affiliation(s)
- Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China
| | - Xiao Zhang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Yuan-Qin Huang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China; State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China.
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2
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Jermusek FA, Webb LJ. Electrostatic Impact of Brefeldin A on Thiocyanate Probes Surrounding the Interface of Arf1-BFA-ARNO4M, a Protein-Drug-Protein Complex. Biochemistry 2024; 63:27-41. [PMID: 38078826 DOI: 10.1021/acs.biochem.3c00366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Protein-protein interactions regulate many cellular processes, making them ideal drug candidates. Design of such drugs, however, is hindered by a lack of understanding of the factors that contribute to the interaction specificity. Specific protein-protein complexes possess both structural and electrostatic complementarity, and while structural complementarity of protein complexes has been extensively investigated, fundamental understanding of the complicated networks of electrostatic interactions at these interfaces is lacking, thus hindering the rational design of orthosterically binding small molecules. To better understand the electrostatic interactions at protein interfaces and how a small molecule could contribute to and fit within that environment, we used a model protein-drug-protein system, Arf1-BFA-ARNO4M, to investigate how small molecule brefeldin A (BFA) perturbs the Arf1-ARNO4M interface. By using nitrile probe labeled Arf1 sites and measuring vibrational Stark effects as well as temperature dependent infrared shifts, we measured changes in the electric field and hydrogen bonding at this interface upon BFA binding. At all five probe locations of Arf1, we found that the vibrational shifts resulting from BFA binding corroborate trends found in Poisson-Boltzmann calculations of surface potentials of Arf1-ARNO4M and Arf1-BFA-ARNO4M, where BFA contributes negative electrostatic potential to the protein interface. The data also corroborate previous hypotheses about the mechanism of interfacial binding and confirm that alternating patches of hydrophobic and polar interactions lead to BFA binding specificity. These findings demonstrate the impact of BFA on this protein-protein interface and have implications for the design of other interfacial drug candidates.
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Affiliation(s)
- Frank A Jermusek
- Interdisciplinary Life Sciences Graduate Program, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Lauren J Webb
- Department of Chemistry and Interdisciplinary Life Sciences Graduate Program, The University of Texas at Austin, Austin, Texas 78712, United States
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3
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Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis. Cells 2022; 11:cells11233739. [PMID: 36496998 PMCID: PMC9737320 DOI: 10.3390/cells11233739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/16/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein-protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3-10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches.
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4
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Inhibition of FOXP3 by stapled alpha-helical peptides dampens regulatory T cell function. Proc Natl Acad Sci U S A 2022; 119:e2209044119. [PMID: 36227917 PMCID: PMC9586281 DOI: 10.1073/pnas.2209044119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Therapies and preclinical probes designed to drug and better understand the specific functions of intracellular protein–protein interactions (PPIs) remain an area of unmet need. This study describes the development of prototype therapeutics against the FOXP3 homodimer, a PPI essential for regulatory T cell suppressive capacity. We demonstrate that hydrocarbon stapled peptides designed to block this interaction can dampen regulatory T cell (Treg cell) suppressive function and lead to genetic signatures of immune reactivation. This work provides strong scientific justification for continued development of FOXP3-specific peptide-based inhibitors and provides mechanistic insights into the design and delivery of specific inhibitors of the coiled-coil region of FOXP3. These studies ultimately could lead to new immunotherapeutic strategies to amplify immune responsiveness in a number of settings. Despite continuing advances in the development of novel cellular-, antibody-, and chemotherapeutic-based strategies to enhance immune reactivity, the presence of regulatory T cells (Treg cells) remains a complicating factor for their clinical efficacy. To overcome dosing limitations and off-target effects from antibody-based Treg cell deletional strategies or small molecule drugging, we investigated the ability of hydrocarbon stapled alpha-helical (SAH) peptides to target FOXP3, the master transcription factor regulator of Treg cell development, maintenance, and suppressive function. Using the crystal structure of the FOXP3 homodimer as a guide, we developed SAHs in the likeness of a portion of the native FOXP3 antiparallel coiled-coil homodimerization domain (SAH-FOXP3) to block this key FOXP3 protein-protein interaction (PPI) through molecular mimicry. We describe the design, synthesis, and biochemical evaluation of single- and double-stapled SAHs covering the entire coiled-coil expanse. We show that lead SAH-FOXP3s bind FOXP3, are cell permeable and nontoxic to T cells, induce dose-dependent transcript and protein level alterations of FOXP3 target genes, impede Treg cell function, and lead to Treg cell gene expression changes in vivo consistent with FOXP3 dysfunction. These results demonstrate a proof of concept for rationally designed FOXP3-directed peptide therapeutics that could be used as approaches to amplify endogenous immune responsiveness.
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5
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In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex. Pharmaceuticals (Basel) 2022; 15:ph15060691. [PMID: 35745611 PMCID: PMC9230678 DOI: 10.3390/ph15060691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 02/06/2023] Open
Abstract
Malaria causes about half a million deaths per year, mainly in children below 5 years of age. Cytoadherence of Plasmodium falciparum infected erythrocytes in brain and placenta has been linked to severe malaria and malarial related deaths. Cytoadherence is mediated by binding of human receptor gC1qR to the DBLβ12 domain of a P. falciparum erythrocyte membrane protein family 1 (PfEMP1) protein. In the present work, molecular dynamic simulation was extensively studied for the gC1qR-DBLβ12 complex. The stabilized protein complex was used to study the protein–protein interface interactions and mapping of interactive amino acid residues as hotspot were performed. Prediction of inhibitors were performed by using virtual protein–protein inhibitor database Timbal screening of about 15,000 compounds. In silico mutagenesis studies, binding profile and protein ligand interaction fingerprinting were used to strengthen the screening of the potential inhibitors of gC1qR-DBLβ12 interface. Six compounds were selected and were further subjected to the MAIP analysis and ADMET studies. From these six compounds, the compounds 3, 5, and 6 were found to outperform on all screening criteria from the rest selected compounds. These compounds may provide novel drugs to treat and manage severe falciparum malaria. Additionally. the identified hotspots can be used in future for designing novel interventions for disruption of interface interactions, such as through peptides or vaccines. Futher in vitro and in vivo studies are required for the confirmation of these compounds as potential inhibitors of gC1qR-DBLβ12 interaction.
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6
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Structure-based assessment and druggability classification of protein-protein interaction sites. Sci Rep 2022; 12:7975. [PMID: 35562538 PMCID: PMC9106675 DOI: 10.1038/s41598-022-12105-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/20/2022] [Indexed: 11/08/2022] Open
Abstract
The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.
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7
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Zabolotna Y, Volochnyuk DM, Ryabukhin SV, Horvath D, Gavrilenko KS, Marcou G, Moroz YS, Oksiuta O, Varnek A. A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry. J Chem Inf Model 2021; 62:2171-2185. [PMID: 34928600 DOI: 10.1021/acs.jcim.1c00811] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BBs). In this work, we present a detailed analysis of the chemical space of 400 000 purchasable BBs. The chemical space was defined by corresponding synthons─fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BBs were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space that enables visualization of both libraries and analysis of their overlapped and library-specific regions.
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Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dmitriy M Volochnyuk
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Sergey V Ryabukhin
- The Institute of High Technologies, Kyiv National Taras Shevchenko University, 64 Volodymyrska Street, Kyiv 01601, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Dragos Horvath
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Konstantin S Gavrilenko
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Gilles Marcou
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Yurii S Moroz
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Oleksandr Oksiuta
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Alexandre Varnek
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France.,Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
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8
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Farooq QUA, Shaukat Z, Aiman S, Li CH. Protein-protein interactions: Methods, databases, and applications in virus-host study. World J Virol 2021; 10:288-300. [PMID: 34909403 PMCID: PMC8641042 DOI: 10.5501/wjv.v10.i6.288] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/19/2021] [Accepted: 07/30/2021] [Indexed: 02/06/2023] Open
Abstract
Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
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Affiliation(s)
- Qurat ul Ain Farooq
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Zeeshan Shaukat
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Sara Aiman
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Chun-Hua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
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9
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Rodrigues CHM, Pires DEV, Ascher DB. pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors. J Chem Inf Model 2021; 61:5438-5445. [PMID: 34719929 DOI: 10.1021/acs.jcim.1c01135] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.
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Affiliation(s)
- Carlos H M Rodrigues
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
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10
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Truong J, George A, Holien JK. Analysis of physicochemical properties of protein-protein interaction modulators suggests stronger alignment with the "rule of five". RSC Med Chem 2021; 12:1731-1749. [PMID: 34778774 DOI: 10.1039/d1md00213a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 07/27/2021] [Indexed: 11/21/2022] Open
Abstract
Despite the important roles played by protein-protein interactions (PPIs) in disease, they have been long considered as 'undruggable'. However, recent advances have suggested that PPIs are druggable but may not follow conventional rules of 'drug ability'. Here we explore which physicochemical parameters are essential for a PPI modulator to be a clinical drug by analysing the physicochemical properties of small-molecule PPI modulators in the market, in clinical trials, and published. Our analysis reveals that those compounds currently on the market have a larger range of values for most of the physicochemical parameters, whereas those in clinical trials fit much more stringently to standard drug-like parameters. This observation was particularly true for molecular weight, clog P and topological polar surface area, where aside from a few outliers, most of the compounds in clinical trials fit within standard drug-like parameters. This implies that the newer PPI modulators are more drug-like than those currently on the market, suggesting that designing new PPI-specific screening libraries should remain within standard drug-like parameters in order to obtain a clinical candidate. Taken together, our analysis has important implications for designing future drug discovery campaigns aimed at targeting PPIs.
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Affiliation(s)
- Jia Truong
- STEM College, RMIT University Vic Australia
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11
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Exploring the chemical space of protein-protein interaction inhibitors through machine learning. Sci Rep 2021; 11:13369. [PMID: 34183730 PMCID: PMC8238997 DOI: 10.1038/s41598-021-92825-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 06/16/2021] [Indexed: 11/08/2022] Open
Abstract
Although protein-protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.
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12
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Wang C, Kurgan L. Survey of Similarity-Based Prediction of Drug-Protein Interactions. Curr Med Chem 2021; 27:5856-5886. [PMID: 31393241 DOI: 10.2174/0929867326666190808154841] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/16/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022]
Abstract
Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.
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Affiliation(s)
- Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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13
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Rosell M, Fernández-Recio J. Docking-based identification of small-molecule binding sites at protein-protein interfaces. Comput Struct Biotechnol J 2020; 18:3750-3761. [PMID: 33250973 PMCID: PMC7679229 DOI: 10.1016/j.csbj.2020.11.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 11/15/2020] [Accepted: 11/16/2020] [Indexed: 12/19/2022] Open
Abstract
Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein–protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein–protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein–ligand docking. The proposed strategy will be useful for many protein–protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.
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Affiliation(s)
- Mireia Rosell
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja, Gobierno de La Rioja, Logroño, Spain
| | - Juan Fernández-Recio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja, Gobierno de La Rioja, Logroño, Spain
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14
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Shin WH, Kumazawa K, Imai K, Hirokawa T, Kihara D. Current Challenges and Opportunities in Designing Protein-Protein Interaction Targeted Drugs. Adv Appl Bioinform Chem 2020; 13:11-25. [PMID: 33209039 PMCID: PMC7669531 DOI: 10.2147/aabc.s235542] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/22/2020] [Indexed: 12/24/2022] Open
Abstract
It has been noticed that the efficiency of drug development has been decreasing in the past few decades. To overcome the situation, protein-protein interactions (PPIs) have been identified as new drug targets as early as 2000. PPIs are more abundant in human cells than single proteins and play numerous important roles in cellular processes including diseases. However, PPIs have very different physicochemical features from the conventional drug targets, which make targeting PPIs challenging. Therefore, as of now, only a small number of PPI inhibitors have been approved or progressed to a stage of clinical trial. In this article, we first overview previous works that analyzed differences between PPIs with PPI targeting ligands and conventional drugs with their binding pockets. Then, we constructed an up-to-date list of PPI targeting drugs that have been approved or are currently under clinical trial and have bound drug-target structures available. Using the dataset, we analyzed the PPIs and their ligands using several scores of druggability. Druggability scores showed that PPI sites and their drugs targeting PPIs are less druggable than conventional binding pockets and drugs, which also indicates that PPI drugs do not follow the conventional rules for drug design, such as Lipinski's rule of five. Our analyses suggest that developing a new rule would be beneficial for guiding PPI-drug discovery.
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemical Science Education, Sunchon National University, Suncheon57922, Republic of Korea
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo191-8512, Japan
| | - Kenichiro Imai
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo135-0064, Japan
| | - Takatsugu Hirokawa
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo135-0064, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN47906, USA
- Department of Computer Science, Purdue University, West Lafayette, IN47906, USA
- Center for Cancer Research, Purdue University, West Lafayette, IN47906, USA
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Care, University of Cincinnati, Cincinnati, OH45229, USA
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15
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Nakadai M, Tomida S. Diameter Is a Key 3D Characteristic for Assessments of Efficient Inhibitors of Protein-Protein Interactions. J Chem Inf Model 2020; 60:4785-4790. [PMID: 32808775 DOI: 10.1021/acs.jcim.0c00607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Three-dimensional (3D) molecular descriptors, including physicochemical and shape properties, for protein-protein interaction (PPI) interface inhibitors have become a topic of discussion. However, the relationships between such properties and binding free energy have not been adequately investigated. In this study, we focused on identifying key 3D molecular descriptors related to the binding free energy and/or the ligand efficiency (LE) of PPI interface inhibitors. A positive correlation was found between the binding free energy and the diameter (D) of cylindrical 3D molecules, in addition to a correlation between LE and D/heavy atom count (HAC). In addition, we showed a correlation between LE and D/HAC for macrocyclic compounds, suggesting that the present findings could be applied during assessments of the potential of macrocyclic PPI interface inhibitors in drug discovery processes.
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Affiliation(s)
- Masakazu Nakadai
- Genome Pharmaceutical Institute Company, Ltd., 1-27-8-1207 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Shuta Tomida
- Center for Comprehensive Genomic Medicine, Okayama University Hospital, 2-5-1 Shikata-cho, Kita-ku, Okayama 700-8558, Japan
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16
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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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17
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Improved Production and Biophysical Analysis of Recombinant Silicatein-α. Biomolecules 2020; 10:biom10091209. [PMID: 32825281 PMCID: PMC7565869 DOI: 10.3390/biom10091209] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/12/2020] [Accepted: 08/17/2020] [Indexed: 11/16/2022] Open
Abstract
Silicatein-α is a hydrolase found in siliceous sea sponges with a unique ability to condense and hydrolyse silicon–oxygen bonds. The enzyme is thus of interest from the perspective of its unusual enzymology, and for potential applications in the sustainable synthesis of siloxane-containing compounds. However, research into this enzyme has previously been hindered by the tendency of silicatein-α towards aggregation and insolubility. Herein, we report the development of an improved method for the production of a trigger factor-silicatein fusion protein by switching the previous hexahistidine tag for a Strep-II tag, resulting in 244-fold improvement in protein yield compared to previous methods. Light scattering and thermal denaturation analyses show that under the best storage conditions, although oligomerisation is never entirely abolished, these nanoscale aggregates of the Strep-tagged protein exhibit improved colloidal stability and solubility. Enzymatic assays show that the Strep-tagged protein retains catalytic competency, but exhibits lower activity compared to the His6-tagged protein. These results suggest that the hexahistidine tag is capable of non-specific catalysis through their imidazole side chains, highlighting the importance of careful consideration when selecting a purification tag. Overall, the Strep-tagged fusion protein reported here can be produced to a higher yield, exhibits greater stability, and allows the native catalytic properties of this protein to be assessed.
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18
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Moustaqil M, Gambin Y, Sierecki E. Biophysical Techniques for Target Validation and Drug Discovery in Transcription-Targeted Therapy. Int J Mol Sci 2020; 21:E2301. [PMID: 32225120 PMCID: PMC7178067 DOI: 10.3390/ijms21072301] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/13/2020] [Accepted: 03/13/2020] [Indexed: 01/10/2023] Open
Abstract
In the post-genome era, pathologies become associated with specific gene expression profiles and defined molecular lesions can be identified. The traditional therapeutic strategy is to block the identified aberrant biochemical activity. However, an attractive alternative could aim at antagonizing key transcriptional events underlying the pathogenesis, thereby blocking the consequences of a disorder, irrespective of the original biochemical nature. This approach, called transcription therapy, is now rendered possible by major advances in biophysical technologies. In the last two decades, techniques have evolved to become key components of drug discovery platforms, within pharmaceutical companies as well as academic laboratories. This review outlines the current biophysical strategies for transcription manipulation and provides examples of successful applications. It also provides insights into the future development of biophysical methods in drug discovery and personalized medicine.
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Affiliation(s)
- Mehdi Moustaqil
- EMBL Australia Node in Single Molecule Science and School of Medical Sciences, UNSW Sydney, NSW 2052, Australia;
| | | | - Emma Sierecki
- EMBL Australia Node in Single Molecule Science and School of Medical Sciences, UNSW Sydney, NSW 2052, Australia;
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19
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Allen SJ, Lumb KJ. Protein-protein interactions: a structural view of inhibition strategies and the IL-23/IL-17 axis. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 121:253-303. [PMID: 32312425 DOI: 10.1016/bs.apcsb.2019.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Protein-protein interactions are central to biology and provide opportunities to modulate disease with small-molecule or protein therapeutics. Recent developments in the understanding of the tractability of protein-protein interactions are discussed with a focus on the ligandable nature of protein-protein interaction surfaces. General principles of inhibiting protein-protein interactions are illustrated with structural biology examples from six members of the IL-23/IL-17 signaling family (IL-1, IL-6, IL-17, IL-23 RORγT and TNFα). These examples illustrate the different approaches to discover protein-protein interaction inhibitors on a target-specific basis that has proven fruitful in terms of discovering both small molecule and biologic based protein-protein interaction inhibitors.
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Affiliation(s)
- Samantha J Allen
- Lead Discovery & Profiling, Discovery Sciences, Janssen R&D LLC, Spring House, PA, United States
| | - Kevin J Lumb
- Lead Discovery & Profiling, Discovery Sciences, Janssen R&D LLC, Spring House, PA, United States
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20
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Huggins DJ, Hardwick BS, Sharma P, Emery A, Laraia L, Zhang F, Narvaez AJ, Roberts-Thomson M, Crooks AT, Boyle RG, Boyce R, Walker DW, Mateu N, McKenzie GJ, Spring DR, Venkitaraman AR. Development of a Novel Cell-Permeable Protein-Protein Interaction Inhibitor for the Polo-box Domain of Polo-like Kinase 1. ACS OMEGA 2020; 5:822-831. [PMID: 31956833 PMCID: PMC6964520 DOI: 10.1021/acsomega.9b03626] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 12/13/2019] [Indexed: 05/10/2023]
Abstract
Polo-like kinase 1 (PLK1) is a key regulator of mitosis and a recognized drug target for cancer therapy. Inhibiting the polo-box domain of PLK1 offers potential advantages of increased selectivity and subsequently reduced toxicity compared with targeting the kinase domain. However, many if not all existing polo-box domain inhibitors have been shown to be unsuitable for further development. In this paper, we describe a novel compound series, which inhibits the protein-protein interactions of PLK1 via the polo-box domain. We combine high throughput screening with molecular modeling and computer-aided design, synthetic chemistry, and cell biology to address some of the common problems with protein-protein interaction inhibitors, such as solubility and potency. We use molecular modeling to improve the solubility of a hit series with initially poor physicochemical properties, enabling biophysical and biochemical characterization. We isolate and characterize enantiomers to improve potency and demonstrate on-target activity in both cell-free and cell-based assays, entirely consistent with the proposed binding model. The resulting compound series represents a promising starting point for further progression along the drug discovery pipeline and a new tool compound to study kinase-independent PLK functions.
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Affiliation(s)
- David J. Huggins
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
- TCM
Group, Cavendish Laboratory, University
of Cambridge, 19 JJ Thomson
Avenue, Cambridge CB3 0HE, United Kingdom
- Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United
Kingdom
| | - Bryn S. Hardwick
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
| | - Pooja Sharma
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
| | - Amy Emery
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
| | - Luca Laraia
- Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United
Kingdom
| | - Fengzhi Zhang
- Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United
Kingdom
| | - Ana J. Narvaez
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
| | - Meredith Roberts-Thomson
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
| | - Alex T. Crooks
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
| | - Robert G. Boyle
- Sentinel
Oncology Ltd., Cambridge Science Park, Milton Road, Cambridge CB4 0EY, United Kingdom
| | - Richard Boyce
- Sentinel
Oncology Ltd., Cambridge Science Park, Milton Road, Cambridge CB4 0EY, United Kingdom
| | - David W. Walker
- Sentinel
Oncology Ltd., Cambridge Science Park, Milton Road, Cambridge CB4 0EY, United Kingdom
| | - Natalia Mateu
- Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United
Kingdom
| | - Grahame J. McKenzie
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
| | - David R. Spring
- Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United
Kingdom
| | - Ashok R. Venkitaraman
- Medical
Research Council Cancer Cell Unit, Hutchison/MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, United Kingdom
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21
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Barreto CAV, Baptista SJ, Preto AJ, Matos-Filipe P, Mourão J, Melo R, Moreira I. Prediction and targeting of GPCR oligomer interfaces. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 169:105-149. [PMID: 31952684 DOI: 10.1016/bs.pmbts.2019.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.
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Affiliation(s)
- Carlos A V Barreto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Salete J Baptista
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - António José Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Rita Melo
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - Irina Moreira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Science and Technology Faculty, University of Coimbra, Coimbra, Portugal.
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22
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Analysis of solvent-exposed and buried co-crystallized ligands: a case study to support the design of novel protein–protein interaction inhibitors. Drug Discov Today 2019; 24:551-559. [DOI: 10.1016/j.drudis.2018.11.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/03/2018] [Accepted: 11/16/2018] [Indexed: 12/22/2022]
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23
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Zhou G, Chu S, Nemati A, Huang C, Snyder BA, Ptak RG, Gochin M. Investigation of the molecular characteristics of bisindole inhibitors as HIV-1 glycoprotein-41 fusion inhibitors. Eur J Med Chem 2018; 161:533-542. [PMID: 30390441 DOI: 10.1016/j.ejmech.2018.10.048] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 10/18/2018] [Accepted: 10/19/2018] [Indexed: 11/20/2022]
Abstract
In previous work, we described 6-6'-bisindole compounds targeting a hydrophobic pocket on the N-heptad repeat region of viral glycoprotein-41 as effective inhibitors of HIV-1 fusion. Two promising compounds with sub-micromolar IC50's contained a benzoic acid group and a benzoic acid ester attached at the two indole nitrogens. Here we have conducted a thorough structure-activity relationship (SAR) study evaluating the contribution of each of the ring systems and various substituents to compound potency. Hydrophobicity, polarity and charge were varied to produce 35 new compounds that were evaluated in binding, cell-cell fusion and viral infectivity assays. We found that (a) activity based solely on increasing hydrophobic content plateaued at ∼ 200 nM; (b) the bisindole scaffold surpassed other heterocyclic ring systems in efficacy; (c) a polar interaction possibly involving Gln575 in the pocket could supplant less specific hydrophobic interactions; and (d) the benzoic acid ester moiety did not appear to form specific contacts with the pocket. The importance of this hydrophobic group to compound potency suggests a mechanism whereby it might interact with a tertiary component during fusion, such as membrane. A promising small molecule 10b with sub-μM activity was discovered with molecular weight <500 da and reduced logP compared to earlier compounds. The work provides insight into requirements for small molecule inhibition of HIV-1 fusion.
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Affiliation(s)
- Guangyan Zhou
- Department of Basic Sciences, Touro University-California, Vallejo, CA, 94592, USA
| | - Shidong Chu
- Department of Basic Sciences, Touro University-California, Vallejo, CA, 94592, USA
| | - Ariana Nemati
- Department of Basic Sciences, Touro University-California, Vallejo, CA, 94592, USA
| | - Chunsheng Huang
- Southern Research Institute, 431 Aviation Way, Frederick, MD, 21701, USA
| | - Beth A Snyder
- Southern Research Institute, 431 Aviation Way, Frederick, MD, 21701, USA
| | - Roger G Ptak
- Southern Research Institute, 431 Aviation Way, Frederick, MD, 21701, USA
| | - Miriam Gochin
- Department of Basic Sciences, Touro University-California, Vallejo, CA, 94592, USA; Department of Pharmaceutical Chemistry, University of California San Francisco, CA, 94143, USA.
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24
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Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
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25
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Inhibition of protein interactions: co-crystalized protein-protein interfaces are nearly as good as holo proteins in rigid-body ligand docking. J Comput Aided Mol Des 2018; 32:769-779. [PMID: 30003468 DOI: 10.1007/s10822-018-0124-z] [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: 12/03/2017] [Accepted: 05/22/2018] [Indexed: 12/15/2022]
Abstract
Modulating protein interaction pathways may lead to the cure of many diseases. Known protein-protein inhibitors bind to large pockets on the protein-protein interface. Such large pockets are detected also in the protein-protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein-protein complex as a starting point for drug design.
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26
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A competitive cell-permeable peptide impairs Nme-1 (NDPK-A) and Prune-1 interaction: therapeutic applications in cancer. J Transl Med 2018; 98:571-581. [PMID: 29449633 DOI: 10.1038/s41374-017-0011-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 11/15/2017] [Accepted: 11/22/2017] [Indexed: 01/14/2023] Open
Abstract
The understanding of protein-protein interactions is crucial in order to generate a second level of functional genomic analysis in human disease. Within a cellular microenvironment, protein-protein interactions generate new functions that can be defined by single or multiple modes of protein interactions. We outline here the clinical importance of targeting of the Nme-1 (NDPK-A)-Prune-1 protein complex in cancer, where an imbalance in the formation of this protein-protein complex can result in inhibition of tumor progression. We discuss here recent functional data using a small synthetic competitive cell-permeable peptide (CPP) that has shown therapeutic efficacy for impairing formation of the Nme-1-Prune-1 protein complex in mouse preclinical xenograft tumor models (e.g., breast, prostate, colon, and neuroblastoma). We thus believe that further discoveries in the near future related to the identification of new protein-protein interactions will have great impact on the development of new therapeutic strategies against various cancers.
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27
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Kilburg D, Gallicchio E. Assessment of a Single Decoupling Alchemical Approach for the Calculation of the Absolute Binding Free Energies of Protein-Peptide Complexes. Front Mol Biosci 2018; 5:22. [PMID: 29568737 PMCID: PMC5852065 DOI: 10.3389/fmolb.2018.00022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 02/21/2018] [Indexed: 01/24/2023] Open
Abstract
The computational modeling of peptide inhibitors to target protein-protein binding interfaces is growing in interest as these are often too large, too shallow, and too feature-less for conventional small molecule compounds. Here, we present a rare successful application of an alchemical binding free energy method for the calculation of converged absolute binding free energies of a series of protein-peptide complexes. Specifically, we report the binding free energies of a series of cyclic peptides derived from the LEDGF/p75 protein to the integrase receptor of the HIV1 virus. The simulations recapitulate the effect of mutations relative to the wild-type binding motif of LEDGF/p75, providing structural, energetic and dynamical interpretations of the observed trends. The equilibration and convergence of the calculations are carefully analyzed. Convergence is aided by the adoption of a single-decoupling alchemical approach with implicit solvation, which circumvents the convergence difficulties of conventional double-decoupling protocols. We hereby present the single-decoupling methodology and critically evaluate its advantages and limitations. We also discuss some of the challenges and potential pitfalls of binding free energy calculations for complex molecular systems which have generally limited their applicability to the quantitative study of protein-peptide binding equilibria.
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Affiliation(s)
- Denise Kilburg
- Department of Chemistry, Brooklyn College, Brooklyn, NY, United States.,Ph.D. Program in Chemistry, The Graduate Center, City University of New York, New York, NY, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, Brooklyn, NY, United States.,Ph.D. Program in Chemistry, The Graduate Center, City University of New York, New York, NY, United States.,Ph.D. Program in Biochemistry, The Graduate Center, City University of New York, New York, NY, United States
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28
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Wang Z, Kang Y, Li D, Sun H, Dong X, Yao X, Xu L, Chang S, Li Y, Hou T. Benchmark Study Based on 2P2I DB to Gain Insights into the Discovery of Small-Molecule PPI Inhibitors. J Phys Chem B 2018; 122:2544-2555. [PMID: 29420886 DOI: 10.1021/acs.jpcb.7b12658] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein-protein interactions (PPIs) have been regarded as novel and highly promising drug targets in drug discovery. Numerous new experimental techniques and computational approaches have been developed to assist the design of PPI modulators during the past two decades. However, identification and optimization of small-molecule inhibitors targeting PPIs is still a particularly challenging task due to the "undruggable" profiles of PPI interfaces. Nowadays, in silico screening, especially docking-based virtual screening, has emerged as an effective method to complement experimental high-throughput screening in identifying novel and potent small-molecule PPI inhibitors. Here, on the basis of the 2P2IDB database, we explored the structural features of the known small-molecule PPI inhibitors and analyzed the characteristics of the PPI binding pockets. More importantly, we evaluated the sampling power and screening power of six popular docking programs for PPI targets. Our results indicate that the chlorinated conjugate group and amidelike linkage are two types of privileged fragments of PPI inhibitors; the average druggability of the binding sites of the PPI targets in 2P2IDB is slightly worse than that of traditional ones; both academic and commercial docking programs exhibit an acceptable accuracy on pose prediction for PPI inhibitors, but their screening powers for identifying PPI inhibitors are still not satisfactory. It is expected that our work can provide valuable guidance on the construction of PPI-focused library, the determination of druggable PPI binding pocket, and the selection of docking program for the screening of small-molecule PPI inhibitors.
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Affiliation(s)
- Zhe Wang
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Yu Kang
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Dan Li
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Huiyong Sun
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health , Macau University of Science and Technology , Avenida Wai Long , Taipa , Macau (SAR) , China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering , Jiangsu University of Technology , Changzhou 213001 , China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering , Jiangsu University of Technology , Changzhou 213001 , China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM) , Soochow University , Suzhou , Jiangsu 215123 , China
| | - Tingjun Hou
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
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Rosell M, Fernández-Recio J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opin Drug Discov 2018; 13:327-338. [PMID: 29376444 DOI: 10.1080/17460441.2018.1430763] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
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Affiliation(s)
- Mireia Rosell
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain
| | - Juan Fernández-Recio
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain.,b Structural Biology Unit , Institut de Biologia Molecular de Barcelona (IBMB), CSIC , Barcelona , Spain
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Bojadzic D, Buchwald P. Toward Small-Molecule Inhibition of Protein-Protein Interactions: General Aspects and Recent Progress in Targeting Costimulatory and Coinhibitory (Immune Checkpoint) Interactions. Curr Top Med Chem 2018; 18:674-699. [PMID: 29848279 PMCID: PMC6067980 DOI: 10.2174/1568026618666180531092503] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 02/27/2018] [Accepted: 05/11/2018] [Indexed: 02/06/2023]
Abstract
Protein-Protein Interactions (PPIs) that are part of the costimulatory and coinhibitory (immune checkpoint) signaling are critical for adequate T cell response and are important therapeutic targets for immunomodulation. Biologics targeting them have already achieved considerable clinical success in the treatment of autoimmune diseases or transplant recipients (e.g., abatacept, belatacept, and belimumab) as well as cancer (e.g., ipilimumab, nivolumab, pembrolizumab, atezolizumab, durvalumab, and avelumab). In view of such progress, there have been only relatively limited efforts toward developing small-molecule PPI inhibitors (SMPPIIs) targeting these cosignaling interactions, possibly because they, as all other PPIs, are difficult to target by small molecules and were not considered druggable. Nevertheless, substantial progress has been achieved during the last decade. SMPPIIs proving the feasibility of such approaches have been identified through various strategies for a number of cosignaling interactions including CD40-CD40L, OX40-OX40L, BAFFR-BAFF, CD80-CD28, and PD-1-PD-L1s. Here, after an overview of the general aspects and challenges of SMPPII-focused drug discovery, we review them briefly together with relevant structural, immune-signaling, physicochemical, and medicinal chemistry aspects. While so far only a few of these SMPPIIs have shown activity in animal models (DRI-C21045 for CD40-D40L, KR33426 for BAFFR-BAFF) or reached clinical development (RhuDex for CD80-CD28, CA-170 for PD-1-PD-L1), there is proof-of-principle evidence for the feasibility of such approaches in immunomodulation. They can result in products that are easier to develop/ manufacture and are less likely to be immunogenic or encounter postmarket safety events than corresponding biologics, and, contrary to them, can even become orally bioavailable.
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Affiliation(s)
- Damir Bojadzic
- Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Peter Buchwald
- Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, Florida, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, Florida, USA
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Bosc N, Kuenemann MA, Bécot J, Vavrusa M, Cerdan AH, Sperandio O. Privileged Substructures to Modulate Protein-Protein Interactions. J Chem Inf Model 2017; 57:2448-2462. [PMID: 28922596 DOI: 10.1021/acs.jcim.7b00435] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Given the difficulties to identify chemical probes that can modulate protein-protein interactions (PPIs), actors in the field have started to agree on the necessity to use PPI-tailored screening chemical collections. However, which type of scaffolds may promote the binding of compounds to PPI targets remains unclear. In this big data analysis, we have identified a list of privileged chemical substructures that are most often observed within inhibitors of PPIs. Using molecular frameworks as a way to perceive chemical substructures with the combination of an experimental and a machine-learning based predicted data set of iPPI compounds, we propose a list of privileged substructures in the form of scaffolds and chemical moieties that can be substantially chemically functionalized and do not present any toxicophore nor pan-assay interference (PAINS) alerts. We think that such chemical guidance will be valuable for medicinal chemists in their attempt to identify initial quality chemical probes on PPI targets.
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Affiliation(s)
- Nicolas Bosc
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France.,Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France.,CNRS UMR3528, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France
| | - Mélaine A Kuenemann
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Jerome Bécot
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Marek Vavrusa
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Adrien H Cerdan
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Olivier Sperandio
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France.,Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France.,CNRS UMR3528, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France
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32
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Uversky VN. Intrinsic Disorder, Protein-Protein Interactions, and Disease. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2017; 110:85-121. [PMID: 29413001 DOI: 10.1016/bs.apcsb.2017.06.005] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
It is recognized now that biologically active proteins without stable tertiary structure (known as intrinsically disordered proteins, IDPs) and hybrid proteins containing ordered domains and intrinsically disordered protein regions (IDPRs) are important players found in any given proteome. These IDPs/IDPRs possess functions that complement functional repertoire of their ordered counterparts, being commonly related to recognition, as well as control and regulation of various signaling pathways. They are interaction masters, being able to utilize a wide spectrum of interaction mechanisms, ranging from induced folding to formation of fuzzy complexes where significant levels of disorder are preserved, to polyvalent stochastic interactions playing crucial roles in the liquid-liquid phase transitions leading to the formation of proteinaceous membrane-less organelles. IDPs/IDPRs are tightly controlled themselves via various means, including alternative splicing, precisely controlled expression and degradation, binding to specific partners, and posttranslational modifications. Distortions in the regulation and control of IDPs/IDPRs, as well as their aberrant interactivity are commonly associated with various human diseases. This review presents some aspects of the intrinsic disorder-based functionality and dysfunctionality, paying special attention to the normal and pathological protein-protein interactions.
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Affiliation(s)
- Vladimir N Uversky
- USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, United States; Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow Region, Russia.
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Bazzoli A, Karanicolas J. "Solvent hydrogen-bond occlusion": A new model of polar desolvation for biomolecular energetics. J Comput Chem 2017; 38:1321-1331. [PMID: 28318014 PMCID: PMC5407913 DOI: 10.1002/jcc.24740] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 12/31/2016] [Accepted: 01/03/2017] [Indexed: 12/14/2022]
Abstract
Water engages in two important types of interactions near biomolecules: it forms ordered "cages" around exposed hydrophobic regions, and it participates in hydrogen bonds with surface polar groups. Both types of interaction are critical to biomolecular structure and function, but explicitly including an appropriate number of solvent molecules makes many applications computationally intractable. A number of implicit solvent models have been developed to address this problem, many of which treat these two solvation effects separately. Here, we describe a new model to capture polar solvation effects, called SHO ("solvent hydrogen-bond occlusion"); our model aims to directly evaluate the energetic penalty associated with displacing discrete first-shell water molecules near each solute polar group. We have incorporated SHO into the Rosetta energy function, and find that scoring protein structures with SHO provides superior performance in loop modeling, virtual screening, and protein structure prediction benchmarks. These improvements stem from the fact that SHO accurately identifies and penalizes polar groups that do not participate in hydrogen bonds, either with solvent or with other solute atoms ("unsatisfied" polar groups). We expect that in future, SHO will enable higher-resolution predictions for a variety of molecular modeling applications. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Andrea Bazzoli
- Center for Computational Biology, University of Kansas, 2030 Becker Dr., Lawrence, KS 66045-7534
- Computational Chemical Biology Core, University of Kansas, 2030 Becker Dr., Lawrence, KS 66045-7534
| | - John Karanicolas
- Center for Computational Biology, University of Kansas, 2030 Becker Dr., Lawrence, KS 66045-7534
- Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, KS 66045-7534
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497
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Jana T, Ghosh A, Das Mandal S, Banerjee R, Saha S. PPIMpred: a web server for high-throughput screening of small molecules targeting protein-protein interaction. ROYAL SOCIETY OPEN SCIENCE 2017; 4:160501. [PMID: 28484602 PMCID: PMC5414239 DOI: 10.1098/rsos.160501] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 03/20/2017] [Indexed: 05/31/2023]
Abstract
PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein-protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively. About 80% of the predicted SVM scores of training/testing datasets from Mdm2/P53 and Bcl2/Bak have significant IC50 values and docking scores. The proposed models achieved an accuracy of 66-90% with blind sets. The three mentioned (Mdm2/P53, Bcl2/Bak and c-Myc/Max) proposed models were screened in a large dataset of 265 242 small chemicals from National Cancer Institute open database. To further realize the robustness of this approach, hits with high and random SVM scores were used for molecular docking in AutoDock Vina wherein the molecules with high and random predicted SVM scores yielded moderately significant docking scores (p-values < 0.1). In addition to the above-mentioned classification scheme, this web server also allows users to get the structural and chemical similarities with known chemical modulators or drug-like molecules based on Tanimoto coefficient similarity search algorithm. PPIMpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/PPIMpred/.
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Affiliation(s)
- Tanmoy Jana
- Bioinformatics Centre, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, West Bengal, India
| | - Abhirupa Ghosh
- Department of Bioinformatics, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, West Bengal, India
| | - Sukhen Das Mandal
- Bioinformatics Centre, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, West Bengal, India
| | - Raja Banerjee
- Department of Bioinformatics, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, West Bengal, India
- Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, West Bengal, India
| | - Sudipto Saha
- Bioinformatics Centre, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, West Bengal, India
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Jubb HC, Higueruelo AP, Ochoa-Montaño B, Pitt WR, Ascher DB, Blundell TL. Arpeggio: A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structures. J Mol Biol 2016; 429:365-371. [PMID: 27964945 PMCID: PMC5282402 DOI: 10.1016/j.jmb.2016.12.004] [Citation(s) in RCA: 262] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 11/07/2016] [Accepted: 12/06/2016] [Indexed: 11/30/2022]
Abstract
Interactions between proteins and their ligands, such as small molecules, other proteins, and DNA, depend on specific interatomic interactions that can be classified on the basis of atom type and distance and angle constraints. Visualisation of these interactions provides insights into the nature of molecular recognition events and has practical uses in guiding drug design and understanding the structural and functional impacts of mutations. We present Arpeggio, a web server for calculating interactions within and between proteins and protein, DNA, or small-molecule ligands, including van der Waals', ionic, carbonyl, metal, hydrophobic, and halogen bond contacts, and hydrogen bonds and specific atom–aromatic ring (cation–π, donor–π, halogen–π, and carbon–π) and aromatic ring–aromatic ring (π–π) interactions, within user-submitted macromolecule structures. PyMOL session files can be downloaded, allowing high-quality publication images of the interactions to be generated. Arpeggio is implemented in Python and available as a user-friendly web interface at http://structure.bioc.cam.ac.uk/arpeggio/ and as a downloadable package at https://bitbucket.org/harryjubb/arpeggio. Enumeration and visualisation of molecular interactions can facilitate drug development and provide insights towards understanding the consequences of mutations in genetic diseases and protein engineering. Reliable and comprehensive methods to evaluate and visualise the full range of potential molecular interactions across many atom types present in protein structures are invaluable. Arpeggio calculates all intra- and interatomic interactions in macromolecular structures, including van der Waals', ionic, carbonyl, metal, hydrophobic, and halogen bond contacts, and hydrogen bonds and specific atom–aromatic ring (cation–π, donor–π, halogen–π, and carbon–π) and aromatic ring–aromatic ring (π–π) interactions, within a provided Protein Data Bank file. Calculations can be within or between any combination of protein, DNA, or small organic molecules. The Arpeggio web server (http://bleoberis.bioc.cam.ac.uk/arpeggioweb/) was implemented to provide a freely available, user-friendly web interface for the exploration of molecular interactions within protein structures, including through WebGL-based visualisation of interactions and downloadable interactive PyMOL session files. Arpeggio is written in Python, requires only Open Source dependencies, and is freely available for download at https://bitbucket.org/harryjubb/arpeggio for use in custom analyses.
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Affiliation(s)
- Harry C Jubb
- Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.
| | - Alicia P Higueruelo
- Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK
| | - Bernardo Ochoa-Montaño
- Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK
| | - Will R Pitt
- UCB, 208 Bath Road, Slough, West Berkshire SL1 3WE, UK
| | - David B Ascher
- Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.
| | - Tom L Blundell
- Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.
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Arulmozhiraja S, Matsuo N, Ishitsubo E, Okazaki S, Shimano H, Tokiwa H. Comparative Binding Analysis of Dipeptidyl Peptidase IV (DPP-4) with Antidiabetic Drugs - An Ab Initio Fragment Molecular Orbital Study. PLoS One 2016; 11:e0166275. [PMID: 27832184 PMCID: PMC5104442 DOI: 10.1371/journal.pone.0166275] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 10/25/2016] [Indexed: 02/07/2023] Open
Abstract
Dipeptidyl peptidase IV (DPP-4) enzyme is responsible for the degradation of incretins that stimulates insulin secretion and hence inhibition of DPP-4 becomes an established approach for the treatment of type 2 diabetics. We studied the interaction between DPP-4 and its inhibitor drugs (sitagliptin 1, linagliptin 2, alogliptin 3, and teneligliptin 4) quantitatively by using fragment molecular orbital calculations at the RI-MP2/cc-pVDZ level to analyze the inhibitory activities of the drugs. Apart from having common interactions with key residues, inhibitors encompassing the DPP-4 active site extensively interact widely with the hydrophobic pocket by their hydrophobic inhibitor moieties. The cumulative hydrophobic interaction becomes stronger for these inhibitors and hence linagliptin and teneligliptin have larger interaction energies, and consequently higher inhibitory activities, than their alogliptin and sitagliptin counterparts. Though effective interaction for both 2 and 3 is at [Formula: see text] subsite, 2 has a stronger binding to this subsite interacting with Trp629 and Tyr547 than 3 does. The presence of triazolopiperazine and piperazine moiety in 1 and 4, respectively, provides the interaction to the S2 extensive subsite; however, the latter's superior inhibitory activity is not only due to a relatively tighter binding to the S2 extensive subsite, but also due to the interactions to the S1 subsite. The calculated hydrophobic interfragment interaction energies correlate well with the experimental binding affinities (KD) and inhibitory activities (IC50) of the DPP-4 inhibitors.
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Affiliation(s)
- Sundaram Arulmozhiraja
- Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
- Research Center for Smart molecules, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
| | - Naoya Matsuo
- Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
| | - Erika Ishitsubo
- Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
| | - Seiji Okazaki
- Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
- Research Center for Smart molecules, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
| | - Hitoshi Shimano
- Department of Internal Medicine (Endocrinology and Metabolism), University of Tsukuba, Tsukuba 305–8575, Japan
- AMED-CREST, Tokyo, Japan
| | - Hiroaki Tokiwa
- Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
- Research Center for Smart molecules, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171–8501, Japan
- AMED-CREST, Tokyo, Japan
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Milhas S, Raux B, Betzi S, Derviaux C, Roche P, Restouin A, Basse MJ, Rebuffet E, Lugari A, Badol M, Kashyap R, Lissitzky JC, Eydoux C, Hamon V, Gourdel ME, Combes S, Zimmermann P, Aurrand-Lions M, Roux T, Rogers C, Müller S, Knapp S, Trinquet E, Collette Y, Guillemot JC, Morelli X. Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery. ACS Chem Biol 2016; 11:2140-8. [PMID: 27219844 DOI: 10.1021/acschembio.6b00286] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.
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Affiliation(s)
- Sabine Milhas
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
| | - Brigitt Raux
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Stéphane Betzi
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Carine Derviaux
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Philippe Roche
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Audrey Restouin
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Marie-Jeanne Basse
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Etienne Rebuffet
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Adrien Lugari
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Marion Badol
- Cisbio Bioassays, R&D, Parc Marcel Boiteux, BP 84175, 30200 Codolet, France
| | - Rudra Kashyap
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- Department of Human Genetics, KU Leuven, B-3000 Leuven, Belgium
| | - Jean-Claude Lissitzky
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Cécilia Eydoux
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
| | - Véronique Hamon
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | | | - Sébastien Combes
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Pascale Zimmermann
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- Department of Human Genetics, KU Leuven, B-3000 Leuven, Belgium
| | - Michel Aurrand-Lions
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Thomas Roux
- Cisbio Bioassays, R&D, Parc Marcel Boiteux, BP 84175, 30200 Codolet, France
| | - Catherine Rogers
- Target
Discovery Institute, University of Oxford, NDM Research Building, Roosevelt
Drive, Oxford OX3 7FZ, U.K
- Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, U.K
| | - Susanne Müller
- Target
Discovery Institute, University of Oxford, NDM Research Building, Roosevelt
Drive, Oxford OX3 7FZ, U.K
- Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, U.K
| | - Stefan Knapp
- Target
Discovery Institute, University of Oxford, NDM Research Building, Roosevelt
Drive, Oxford OX3 7FZ, U.K
- Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, U.K
- Goethe-University, Institute
for Pharmaceutical Chemistry and Buchmann
Institute for Life Science, Campus Riedberg, Max-von Laue Str. 9, 60438 Frankfurt am Main, Germany
| | - Eric Trinquet
- Cisbio Bioassays, R&D, Parc Marcel Boiteux, BP 84175, 30200 Codolet, France
| | - Yves Collette
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Jean-Claude Guillemot
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
| | - Xavier Morelli
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
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38
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Kilburg D, Gallicchio E. Recent Advances in Computational Models for the Study of Protein-Peptide Interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2016; 105:27-57. [PMID: 27567483 DOI: 10.1016/bs.apcsb.2016.06.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We review computational models and software tools in current use for the study of protein-peptide interactions. Peptides and peptide derivatives are growing in interest as therapeutic agents to target protein-protein interactions. Protein-protein interactions are pervasive in biological systems and are responsible for the regulation of critical functions within the cell. Mutations or dysregulation of expression can alter the network of interactions among proteins and cause diseases such as cancer. Protein-protein binding interfaces, which are often large, shallow, and relatively feature-less, are difficult to target with small-molecule inhibitors. Peptide derivatives based on the binding motifs present in the target protein complex are increasingly drawing interest as superior alternatives to conventional small-molecule inhibitors. However, the design of peptide-based inhibitors also presents novel challenges. Peptides are more complex and more flexible than standard medicinal compounds. They also tend to form more extended and more complex interactions with their protein targets. Computational modeling is increasingly being employed to supplement synthetic and biochemical work to offer guidance and energetic and structural insights. In this review, we discuss recent in silico structure-based and physics-based approaches currently employed to model protein-peptide interactions with a few examples of their applications.
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Affiliation(s)
- D Kilburg
- Brooklyn College, Brooklyn, NY, United States; The Graduate Center of the City University of New York, New York, NY, United States
| | - E Gallicchio
- Brooklyn College, Brooklyn, NY, United States; The Graduate Center of the City University of New York, New York, NY, United States.
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Cox R, Plemper RK. Structure-guided design of small-molecule therapeutics against RSV disease. Expert Opin Drug Discov 2016; 11:543-556. [PMID: 27046051 PMCID: PMC5074927 DOI: 10.1517/17460441.2016.1174212] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION In the United States, respiratory syncytial virus (RSV) is responsible for the majority of infant hospitalizations resulting from viral infections, as well as a leading source of pneumonia and bronchiolitis in young children and the elderly. In the absence of vaccine prophylaxis or an effective antiviral for improved disease management, the development of novel anti-RSV therapeutics is critical. Several advanced drug development campaigns of the past decade have focused on blocking viral infection. These efforts have returned a chemically distinct panel of small-molecule RSV entry inhibitors, but binding sites and molecular mechanism of action appeared to share a common mechanism, resulting in comprehensive cross-resistance and calling for alternative druggable targets such as viral RNA-dependent RNA-polymerase complex. Areas Covered: In this review, the authors discuss the current status of the mechanism of action of RSV entry inhibitors. They also provide the recent structural insight into the organization of the polymerase complex that have revealed novel drug targets sites, and outline a path towards the discovery of next-generation RSV therapeutics. Expert opinion: Considering the tremendous progress experienced in our structural understanding of RSV biology in recent years and encouraging early results of a nucleoside analog inhibitor in clinical trials, there is high prospect that new generations of much needed effective anti-RSV therapeutics will become available for clinical use in the foreseeable future.
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Affiliation(s)
- Robert Cox
- Institute for Biomedical Sciences, Georgia State University, 100 Piedmont Av, Atlanta, Georgia 30303-3222 USA
| | - Richard K Plemper
- Institute for Biomedical Sciences, Georgia State University, 100 Piedmont Av, Atlanta, Georgia 30303-3222 USA
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40
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Scott DE, Bayly AR, Abell C, Skidmore J. Small molecules, big targets: drug discovery faces the protein–protein interaction challenge. Nat Rev Drug Discov 2016; 15:533-50. [DOI: 10.1038/nrd.2016.29] [Citation(s) in RCA: 625] [Impact Index Per Article: 78.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors. Sci Rep 2016; 6:23815. [PMID: 27034268 PMCID: PMC4817116 DOI: 10.1038/srep23815] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 03/15/2016] [Indexed: 02/03/2023] Open
Abstract
Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach. Using this procedure, we showed that comparable classes of PPI targets can be formed using either the similarity of their ligands or the shared properties of their binding cavities, constituting a proof-of-concept that not only can binding pockets be used to group PPI targets, but that these pockets certainly condition the properties of their corresponding ligands. These results demonstrate that matching regions in both chemical space and target space can be found. Such identified classes of targets could lead to the design of PPI-class-specific chemical libraries and therefore facilitate the development of iPPIs to the stage of drug candidates.
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Kathman SG, Statsyuk AV. Covalent Tethering of Fragments For Covalent Probe Discovery. MEDCHEMCOMM 2016; 7:576-585. [PMID: 27398190 PMCID: PMC4933313 DOI: 10.1039/c5md00518c] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Covalent probes and drugs have found widespread use as research tools and clinical agents. Covalent probes are useful because of their increased intracellular potency and because covalent labeling of cellular proteins can be tracked using click chemistry. Covalent drugs, on the other hand, can overcome drug resistance toward their reversible counterparts. The discovery of covalent probes and drugs usually follows two trajectories: covalent natural products and their analogues are used directly as covalent probes or drugs; or alternatively, a non-covalent probe is equipped with a reactive group and converted into a covalent probe. In both cases, there is a need to either have a natural product or a potent non-covalent scaffold. The alternative approach to discover covalent probes is to start with a drug-like fragment that already has an electrophile, and then grow the fragment into a potent lead compound. In this approach, the electrophilic fragment will react covalently with the target protein, and therefore the initial weak binding of the fragment can be amplified over time and detected using mass spectrometry. With this approach the surface of the protein can be interrogated with a library of covalent fragments to identify covalent drug binding sites. One challenge with this approach is the danger of non-specific covalent labeling of proteins with covalent fragments. The second challenge is the risk of selecting the most reactive fragment rather than the best binder if the covalent fragments are screened in mixtures. This review will highlight how covalent tethering was developed, its current state, and its future.
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Affiliation(s)
- Stefan G. Kathman
- Center for Molecular Innovation and Drug Discovery, Chemistry of Life Processes Institute, Department of Chemistry, Northwestern University, Silverman Hall, 2145 Sheridan Road, Evanston, Illinois 60208, United States
| | - Alexander V. Statsyuk
- Center for Molecular Innovation and Drug Discovery, Chemistry of Life Processes Institute, Department of Chemistry, Northwestern University, Silverman Hall, 2145 Sheridan Road, Evanston, Illinois 60208, United States
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Basse MJ, Betzi S, Morelli X, Roche P. 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw007. [PMID: 26980515 PMCID: PMC4792518 DOI: 10.1093/database/baw007] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 01/13/2016] [Indexed: 11/30/2022]
Abstract
2P2Idb is a hand-curated structural database dedicated to protein–protein interactions with known small molecule orthosteric modulators. It compiles the structural information related to orthosteric inhibitors and their target [i.e. related 3D structures available in the RCSB Protein Data Bank (PDB)] and provides links to other useful databases. 2P2Idb includes all interactions for which both the protein–protein and protein–inhibitor complexes have been structurally characterized. Since its first release in 2010, the database has grown constantly and the current version contains 27 protein–protein complexes and 274 protein–inhibitor complexes corresponding to 242 unique small molecule inhibitors which represent almost a 5-fold increase compared to the previous version. A number of new data have been added, including new protein–protein complexes, binding affinities, molecular descriptors, precalculated interface parameters and links to other webservers. A new query tool has been implemented to search for inhibitors within the database using standard molecular descriptors. A novel version of the 2P2I-inspector tool has been implemented to calculate a series of physical and chemical parameters of the protein interfaces. Several geometrical parameters including planarity, eccentricity and circularity have been added as well as customizable distance cutoffs. This tool has also been extended to protein–ligand interfaces. The 2P2I database thus represents a wealth of structural source of information for scientists interested in the properties of protein–protein interactions and the design of protein–protein interaction modulators. Database URL:http://2p2idb.cnrs-mrs.fr
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Affiliation(s)
- Marie-Jeanne Basse
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
| | - Stéphane Betzi
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
| | - Xavier Morelli
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
| | - Philippe Roche
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
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Borkin D, Pollock J, Kempinska K, Purohit T, Li X, Wen B, Zhao T, Miao H, Shukla S, He M, Sun D, Cierpicki T, Grembecka J. Property Focused Structure-Based Optimization of Small Molecule Inhibitors of the Protein-Protein Interaction between Menin and Mixed Lineage Leukemia (MLL). J Med Chem 2016; 59:892-913. [PMID: 26744767 PMCID: PMC5092235 DOI: 10.1021/acs.jmedchem.5b01305] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Development of potent small molecule inhibitors of protein-protein interactions with optimized druglike properties represents a challenging task in lead optimization process. Here, we report synthesis and structure-based optimization of new thienopyrimidine class of compounds, which block the protein-protein interaction between menin and MLL fusion proteins that plays an important role in acute leukemias with MLL translocations. We performed simultaneous optimization of both activity and druglike properties through systematic exploration of substituents introduced to the indole ring of lead compound 1 (MI-136) to identify compounds suitable for in vivo studies in mice. This work resulted in the identification of compound 27 (MI-538), which showed significantly increased activity, selectivity, polarity, and pharmacokinetic profile over 1 and demonstrated a pronounced effect in a mouse model of MLL leukemia. This study, which reports detailed structure-activity and structure-property relationships for the menin-MLL inhibitors, demonstrates challenges in optimizing inhibitors of protein-protein interactions for potential therapeutic applications.
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MESH Headings
- Animals
- Caco-2 Cells
- Cell Proliferation/drug effects
- Cell Survival/drug effects
- Disease Models, Animal
- Dose-Response Relationship, Drug
- Female
- Histone-Lysine N-Methyltransferase/chemistry
- Histone-Lysine N-Methyltransferase/metabolism
- Humans
- Injections, Intraventricular
- Leukemia, Myeloid, Acute/drug therapy
- Leukemia, Myeloid, Acute/metabolism
- Mice
- Mice, Inbred C57BL
- Mice, SCID
- Models, Molecular
- Molecular Structure
- Myeloid-Lymphoid Leukemia Protein/chemistry
- Myeloid-Lymphoid Leukemia Protein/metabolism
- Protein Binding/drug effects
- Proto-Oncogene Proteins/chemistry
- Proto-Oncogene Proteins/metabolism
- Pyrimidines/administration & dosage
- Pyrimidines/chemistry
- Pyrimidines/pharmacology
- Small Molecule Libraries/administration & dosage
- Small Molecule Libraries/chemistry
- Small Molecule Libraries/pharmacology
- Structure-Activity Relationship
- Thiophenes/administration & dosage
- Thiophenes/chemistry
- Thiophenes/pharmacology
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Affiliation(s)
- Dmitry Borkin
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
| | - Jonathan Pollock
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
| | - Katarzyna Kempinska
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
| | - Trupta Purohit
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
| | - Xiaoqin Li
- College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Bo Wen
- College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ting Zhao
- College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hongzhi Miao
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
| | - Shirish Shukla
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
| | - Miao He
- College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Duxin Sun
- College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Tomasz Cierpicki
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
| | - Jolanta Grembecka
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, Michigan 48109, United States
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Impact of germline and somatic missense variations on drug binding sites. THE PHARMACOGENOMICS JOURNAL 2016; 17:128-136. [PMID: 26810135 PMCID: PMC5380835 DOI: 10.1038/tpj.2015.97] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 11/02/2015] [Accepted: 11/13/2015] [Indexed: 11/10/2022]
Abstract
Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein–drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein–drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein–drug binding sites. Using this method we identified 12 993 amino acid–drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid–drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid–drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid–drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein–drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein–drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database.
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Johnson DK, Karanicolas J. Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions. J Chem Inf Model 2016; 56:399-411. [PMID: 26726827 DOI: 10.1021/acs.jcim.5b00572] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.
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Affiliation(s)
- David K Johnson
- Center for Computational Biology, and ‡Department of Molecular Biosciences, University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - John Karanicolas
- Center for Computational Biology, and ‡Department of Molecular Biosciences, University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
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47
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An Intriguing Correlation Based on the Superimposition of Residue Pairs with Inhibitors that Target Protein-Protein Interfaces. Sci Rep 2016; 6:18543. [PMID: 26730437 PMCID: PMC4698585 DOI: 10.1038/srep18543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 11/19/2015] [Indexed: 11/26/2022] Open
Abstract
Druggable sites on protein-protein interfaces are difficult to predict. To survey inhibitor-binding sites onto which residues are superimposed at protein-protein interfaces, we analyzed publicly available information for 39 inhibitors that target the protein-protein interfaces of 8 drug targets. By focusing on the differences between residues that were superimposed with inhibitors and non-superimposed residues, we observed clear differences in the distances and changes in the solvent-accessible surface areas (∆SASA). Based on the observation that two or more residues were superimposed onto inhibitors in 37 (95%) of 39 protein-inhibitor complexes, we focused on the two-residue relationships. Application of a cross-validation procedure confirmed a linear negative correlation between the absolute value of the dihedral angle and the sum of the ∆SASAs of the residues. Finally, we applied the regression equation of this correlation to four inhibitors that bind to new sites not bound by the 39 inhibitors as well as additional inhibitors of different targets. Our results shed light on the two-residue correlation between the absolute value of the dihedral angle and the sum of the ∆SASA, which may be a useful relationship for identifying the key two-residues as potential targets of protein-protein interfaces.
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48
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Bakail M, Ochsenbein F. Targeting protein–protein interactions, a wide open field for drug design. CR CHIM 2016. [DOI: 10.1016/j.crci.2015.12.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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49
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Bazzoli A, Kelow SP, Karanicolas J. Enhancements to the Rosetta Energy Function Enable Improved Identification of Small Molecules that Inhibit Protein-Protein Interactions. PLoS One 2015; 10:e0140359. [PMID: 26484863 PMCID: PMC4617380 DOI: 10.1371/journal.pone.0140359] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 09/24/2015] [Indexed: 11/25/2022] Open
Abstract
Protein-protein interactions are among today’s most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more “traditional” drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of “decoys.” Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new “Talaris” update and the “pwSHO” solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta’s ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta’s ability to identify small-molecule inhibitors of protein-protein interactions.
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Affiliation(s)
- Andrea Bazzoli
- Center for Computational Biology, University of Kansas, 2030 Becker Dr., Lawrence, Kansas, 66045–7534, United States of America
| | - Simon P. Kelow
- Center for Computational Biology, University of Kansas, 2030 Becker Dr., Lawrence, Kansas, 66045–7534, United States of America
| | - John Karanicolas
- Center for Computational Biology, University of Kansas, 2030 Becker Dr., Lawrence, Kansas, 66045–7534, United States of America
- Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, Kansas, 66045–7534, United States of America
- * E-mail:
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50
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Labbé CM, Kuenemann MA, Zarzycka B, Vriend G, Nicolaes GAF, Lagorce D, Miteva MA, Villoutreix BO, Sperandio O. iPPI-DB: an online database of modulators of protein-protein interactions. Nucleic Acids Res 2015; 44:D542-7. [PMID: 26432833 PMCID: PMC4702945 DOI: 10.1093/nar/gkv982] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Accepted: 09/19/2015] [Indexed: 01/13/2023] Open
Abstract
In order to boost the identification of low-molecular-weight drugs on protein–protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.ippidb.cdithem.fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein–protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input. In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.
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Affiliation(s)
- Céline M Labbé
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Mélaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Barbara Zarzycka
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Gerry A F Nicolaes
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
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