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Dai L, Yu P, Fan H, Xia W, Zhao Y, Zhang P, Zhang JZH, Zhang H, Chen Y. Identification and Validation of New DNA-PKcs Inhibitors through High-Throughput Virtual Screening and Experimental Verification. Int J Mol Sci 2024; 25:7982. [PMID: 39063224 PMCID: PMC11277333 DOI: 10.3390/ijms25147982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 07/12/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
DNA-PKcs is a crucial protein target involved in DNA repair and response pathways, with its abnormal activity closely associated with the occurrence and progression of various cancers. In this study, we employed a deep learning-based screening and molecular dynamics (MD) simulation-based pipeline, identifying eight candidates for DNA-PKcs targets. Subsequent experiments revealed the effective inhibition of DNA-PKcs-mediated cell proliferation by three small molecules (5025-0002, M769-1095, and V008-1080). These molecules exhibited anticancer activity with IC50 (inhibitory concentration at 50%) values of 152.6 μM, 30.71 μM, and 74.84 μM, respectively. Notably, V008-1080 enhanced homology-directed repair (HDR) mediated by CRISPR/Cas9 while inhibiting non-homologous end joining (NHEJ) efficiency. Further investigations into the structure-activity relationships unveiled the binding sites and critical interactions between these small molecules and DNA-PKcs. This is the first application of DeepBindGCN_RG in a real drug screening task, and the successful discovery of a novel DNA-PKcs inhibitor demonstrates its efficiency as a core component in the screening pipeline. Moreover, this study provides important insights for exploring novel anticancer therapeutics and advancing the development of gene editing techniques by targeting DNA-PKcs.
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Affiliation(s)
- Liujiang Dai
- Department of Physiology, Guangxi University of Chinese Medicine, Nanning 530200, China
- Guangdong Immune Cell Therapy Engineering and Technology Research Center, Center for Protein and Cell-Based Drugs, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Pengfei Yu
- Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Hongjie Fan
- Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
| | - Wei Xia
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yaopeng Zhao
- Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Pengfei Zhang
- Guangdong Key Laboratory of Nanomedicine, CAS-HK Joint Lab of Biomaterials, CAS Key Laboratory of Biomedical Imaging Science and System, Shenzhen Engineering Laboratory of Nanomedicine and Nanoformulations, CAS Key Lab for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - John Z. H. Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Haiping Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yang Chen
- Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
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2
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Pecina A, Fanfrlík J, Lepšík M, Řezáč J. SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes. Nat Commun 2024; 15:1127. [PMID: 38321025 PMCID: PMC10847445 DOI: 10.1038/s41467-024-45431-8] [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: 07/20/2023] [Accepted: 01/24/2024] [Indexed: 02/08/2024] Open
Abstract
Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization.
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Affiliation(s)
- Adam Pecina
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jindřich Fanfrlík
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Martin Lepšík
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
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3
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Guendouzi A, Belkhiri L, Guendouzi A, Derouiche TMT, Djekoun A. A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives. J Biomol Struct Dyn 2024; 42:119-133. [PMID: 36995063 DOI: 10.1080/07391102.2023.2192801] [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: 01/20/2023] [Accepted: 03/10/2023] [Indexed: 03/31/2023]
Abstract
Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.
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Affiliation(s)
| | - Lotfi Belkhiri
- Centre de Recherche en Sciences Pharmaceutiques CRSP, Constantine, Algeria
- Laboratoire de Physique Mathématique et Subatomique LPMS, Département de Chimie, Université des Frères Mentouri, Constantine, Algeria
| | - Abdelkrim Guendouzi
- Laboratoire de Chimie, Synthèse, Propriétés et Applications LCSPA, Département de Chimie, Faculté des Sciences, Université Dr Moulay Tahar de Saida, Saïda, Algeria
| | - Tahar Mohamed Taha Derouiche
- Centre de Recherche en Sciences Pharmaceutiques CRSP, Constantine, Algeria
- Laboratoire Innovation Développement des Actifs Pharmaceutiques LIDAP, Faculté de Médecine, Département Pharmacie, Université Salah Boubnider Constantine 3, El Khroub, Algeria
| | - Abdelhamid Djekoun
- Centre de Recherche en Sciences Pharmaceutiques CRSP, Constantine, Algeria
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4
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Raghavan B, Paulikat M, Ahmad K, Callea L, Rizzi A, Ippoliti E, Mandelli D, Bonati L, De Vivo M, Carloni P. Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations. J Chem Inf Model 2023. [PMID: 37319347 DOI: 10.1021/acs.jcim.3c00557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.
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Affiliation(s)
- Bharath Raghavan
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
| | - Mirko Paulikat
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Katya Ahmad
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Lara Callea
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
| | - Andrea Rizzi
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Atomistic Simulations, Italian Institute of Technology, Genova 16163, Italy
| | - Emiliano Ippoliti
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Davide Mandelli
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Laura Bonati
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
| | - Marco De Vivo
- Molecular Modelling and Drug Discovery, Italian Institute of Technology, Genova 16163, Italy
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Department of Physics and Universitätsklinikum, RWTH Aachen University, Aachen 52074, Germany
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5
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Zhang H, Saravanan KM, Zhang JZH. DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction. Molecules 2023; 28:4691. [PMID: 37375246 DOI: 10.3390/molecules28124691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.
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Affiliation(s)
- Haiping Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India
| | - John Z H Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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6
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Wolf S. Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges. J Chem Inf Model 2023; 63:2902-2910. [PMID: 37133392 DOI: 10.1021/acs.jcim.3c00151] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.
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Affiliation(s)
- Steffen Wolf
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
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7
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Miñarro-Lleonar M, Bertran-Mostazo A, Duro J, Barril X, Juárez-Jiménez J. Lenalidomide Stabilizes Protein-Protein Complexes by Turning Labile Intermolecular H-Bonds into Robust Interactions. J Med Chem 2023; 66:6037-6046. [PMID: 37083375 PMCID: PMC10184122 DOI: 10.1021/acs.jmedchem.2c01692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Targeted protein degradation is a promising therapeutic strategy, spearheaded by the anti-myeloma drugs lenalidomide and pomalidomide. These drugs stabilize very efficiently the complex between the E3 ligase Cereblon (CRBN) and several non-native client proteins (neo-substrates), including the transcription factors Ikaros and Aiolos and the enzyme Caseine Kinase 1α (CK1α,), resulting in their degradation. Although the structures for these complexes have been determined, there are no evident interactions that can account for the high efficiency of formation of the ternary complex. We show that lenalidomide's stabilization of the CRBN-CK1α complex is largely due to hydrophobic shielding of intermolecular hydrogen bonds. We also find a quantitative relationship between hydrogen bond robustness and binding affinities of the ternary complexes. These results pave the way to further understand cooperativity effects in drug-induced protein-protein complexes and could help in the design of improved molecular glues and more efficient protein degraders.
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Affiliation(s)
- Marina Miñarro-Lleonar
- Unitat de Fisicoquímica, Departament de Farmàcia i Tecnologia Farmacèutica, i Fisicoquímica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona (UB), Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
- Institut de Química Teòrica i Computacional (IQTC), Facultat de Química i Física, Universitat de Barcelona (UB), C. Martí i Franquès, 1, 08028 Barcelona, Spain
- Institut de Biomedicina, Facultat de Biologia, Universitat de Barcelona (UB), Av. Diagonal, 643, 08028 Barcelona, Spain
| | - Andrea Bertran-Mostazo
- Unitat de Fisicoquímica, Departament de Farmàcia i Tecnologia Farmacèutica, i Fisicoquímica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona (UB), Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
- Institut de Biomedicina, Facultat de Biologia, Universitat de Barcelona (UB), Av. Diagonal, 643, 08028 Barcelona, Spain
| | - Jorge Duro
- Unitat de Fisicoquímica, Departament de Farmàcia i Tecnologia Farmacèutica, i Fisicoquímica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona (UB), Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
- Institut de Química Teòrica i Computacional (IQTC), Facultat de Química i Física, Universitat de Barcelona (UB), C. Martí i Franquès, 1, 08028 Barcelona, Spain
| | - Xavier Barril
- Unitat de Fisicoquímica, Departament de Farmàcia i Tecnologia Farmacèutica, i Fisicoquímica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona (UB), Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
- Institut de Química Teòrica i Computacional (IQTC), Facultat de Química i Física, Universitat de Barcelona (UB), C. Martí i Franquès, 1, 08028 Barcelona, Spain
- Institut de Biomedicina, Facultat de Biologia, Universitat de Barcelona (UB), Av. Diagonal, 643, 08028 Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Pg. Lluís Companys, 23 08010 Barcelona, Spain
| | - Jordi Juárez-Jiménez
- Unitat de Fisicoquímica, Departament de Farmàcia i Tecnologia Farmacèutica, i Fisicoquímica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona (UB), Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
- Institut de Química Teòrica i Computacional (IQTC), Facultat de Química i Física, Universitat de Barcelona (UB), C. Martí i Franquès, 1, 08028 Barcelona, Spain
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8
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Wang ZZ, Shi XX, Huang GY, Hao GF, Yang GF. Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions. Trends Biochem Sci 2023; 48:539-552. [PMID: 36841635 DOI: 10.1016/j.tibs.2023.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/05/2023] [Accepted: 01/31/2023] [Indexed: 02/26/2023]
Abstract
Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.
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Affiliation(s)
- Zhi-Zheng Wang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China
| | - Xing-Xing Shi
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China
| | - Guang-Yi Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China; National 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, Central China Normal University, Wuhan, 430079, PR China.
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9
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Development of new 1, 3-dihydroxyacridone derivatives as Akt pathway inhibitors in skeletal muscle cells. Bioorg Chem 2023; 130:106222. [DOI: 10.1016/j.bioorg.2022.106222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022]
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10
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Lukauskis D, Samways ML, Aureli S, Cossins BP, Taylor RD, Gervasio FL. Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses. J Chem Inf Model 2022; 62:6209-6216. [PMID: 36401553 DOI: 10.1021/acs.jcim.2c01142] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.
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Affiliation(s)
- Dominykas Lukauskis
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom
| | | | - Simone Aureli
- Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland
| | - Benjamin P Cossins
- UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom.,Exscientia Ltd., The Schrödinger Building, Oxford Science Park, OxfordOX4 4GE, United Kingdom
| | | | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom.,Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland.,UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom
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11
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Screening assays for tyrosine kinase inhibitors:A review. J Pharm Biomed Anal 2022; 223:115166. [DOI: 10.1016/j.jpba.2022.115166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/13/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022]
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12
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Warsi MS, Habib S, Talha M, Khan S, Singh P, Mir AR, Abidi M, Ali A. 4-Chloro-1,2-phenylenediamine induced structural perturbation and genotoxic aggregation in human serum albumin. Front Chem 2022; 10:1016354. [PMID: 36199663 PMCID: PMC9527296 DOI: 10.3389/fchem.2022.1016354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/05/2022] [Indexed: 11/24/2022] Open
Abstract
4-Chloro-1,2-phenylenediamine (4-Cl-OPD) is a halogenated aromatic diamine used as a precursor in permanent hair color production. Despite its well-documented mutagenic and carcinogenic effects in various in vitro and in vivo models, its role in fibrillar aggregate formation and their genotoxic effect in therapeutic proteins has received less attention. The significance of human serum albumin (HSA) arises from its involvement in bio-regulatory and transport processes. HSA misfolding and aggregation are responsible for some of the most frequent neurodegenerative disorders. We used various complementary approaches to track the formation of amyloid fibrils and their genotoxic effect. Molecular dynamics study demonstrated the complex stability. The impact of 4-Cl-OPD on the structural dynamics of HSA was confirmed by Raman spectroscopy, X-ray diffraction, HPLC and SDS-PAGE. Fibrilllar aggregates were investigated using Congo red assay, DLS, and SEM. The genotoxic nature of 4-Cl-OPD was confirmed using plasmid nicking assay and DAPI staining, which revealed DNA damage and cell apoptosis. 4-Cl-OPD provides a model system for studying fibrillar aggregation and their genotoxic potential in the current investigation. Future studies should investigate the inhibition of the aggregation/fibrillation process, which may yield valuable clinical insights.
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Affiliation(s)
- Mohd Sharib Warsi
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
| | - Safia Habib
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
| | - Mohd Talha
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
| | - Shifa Khan
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
| | - Priyam Singh
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
| | - Abdul Rouf Mir
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
| | - Minhal Abidi
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
| | - Asif Ali
- Department of Biochemistry, Faculty of Medicine, Jawaharlal Nehru Medical College, Aligarh Muslim University, Aligarh, India
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13
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Zhang H, Gong X, Peng Y, Saravanan KM, Bian H, Zhang JZH, Wei Y, Pan Y, Yang Y. An Efficient Modern Strategy to Screen Drug Candidates Targeting RdRp of SARS-CoV-2 With Potentially High Selectivity and Specificity. Front Chem 2022; 10:933102. [PMID: 35903186 PMCID: PMC9315156 DOI: 10.3389/fchem.2022.933102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/06/2022] [Indexed: 01/18/2023] Open
Abstract
Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity. Here, we developed a reverse DFCNN (Dense Fully Connected Neural Network) and a reverse docking protocol to check a given compound’s ability to bind diversified targets and estimate its specificity with homemade formulas. We used the RNA-dependent RNA polymerase (RdRp) target as a proof-of-concept example to identify drug candidates with high selectivity and high specificity. We first used a previously developed hybrid screening method to find drug candidates from an 8888-size compound database. The hybrid screening method takes advantage of the deep learning-based method, traditional molecular docking, molecular dynamics simulation, and binding free energy calculated by metadynamics, which should be powerful in selecting high binding affinity candidates. Also, we integrated the reverse DFCNN and reversed docking against a diversified 102 proteins to the pipeline for assessing the specificity of those selected candidates, and finally got compounds that have both predicted selectivity and specificity. Among the eight selected candidates, Platycodin D and Tubeimoside III were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 619.5 and 265.5 nM, respectively. Our study discovered that Tubeimoside III could inhibit SARS-CoV-2 replication potently for the first time. Furthermore, the underlying mechanisms of Platycodin D and Tubeimoside III inhibiting SARS-CoV-2 are highly possible by blocking the RdRp cavity according to our screening procedure. In addition, the careful analysis predicted common critical residues involved in the binding with active inhibitors Platycodin D and Tubeimoside III, Azithromycin, and Pralatrexate, which hopefully promote the development of non-covalent binding inhibitors against RdRp.
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Affiliation(s)
- Haiping Zhang
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Yang Yang, ; Haiping Zhang,
| | - Xiaohua Gong
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Yun Peng
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai, , India
| | - Hengwei Bian
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China
| | - John Z. H. Zhang
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yi Pan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yang Yang
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
- *Correspondence: Yang Yang, ; Haiping Zhang,
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14
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Burastero O, Defelipe LA, Gola G, Tateosian NL, Lopez ED, Martinena CB, Arcon JP, Traian MD, Wetzler DE, Bento I, Barril X, Ramirez J, Marti MA, Garcia-Alai MM, Turjanski AG. Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors. J Med Chem 2022; 65:9691-9705. [PMID: 35737472 PMCID: PMC9344462 DOI: 10.1021/acs.jmedchem.1c02012] [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] [Indexed: 02/08/2023]
Abstract
![]()
Computer-aided
drug discovery methods play a major role in the
development of therapeutically important small molecules, but their
performance needs to be improved. Molecular dynamics simulations in
mixed solvents are useful in understanding protein–ligand recognition
and improving molecular docking predictions. In this work, we used
ethanol as a cosolvent to find relevant interactions for ligands toward
protein kinase G, an essential protein of Mycobacterium
tuberculosis (Mtb).
We validated the hot spots by screening a database of fragment-like
compounds and another one of known kinase inhibitors. Next, we performed
a pharmacophore-guided docking simulation and found three low micromolar
inhibitors, including one with a novel chemical scaffold that we expanded
to four derivative compounds. Binding affinities were characterized
by intrinsic fluorescence quenching assays, isothermal titration calorimetry,
and the analysis of melting curves. The predicted binding mode was
confirmed by X-ray crystallography. Finally, the compounds significantly
inhibited the viability of Mtb in infected
THP-1 macrophages.
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Affiliation(s)
- Osvaldo Burastero
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,European Molecular Biology Laboratory Hamburg, Notkestrasse 85, Hamburg D-22607, Germany
| | - Lucas A Defelipe
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,European Molecular Biology Laboratory Hamburg, Notkestrasse 85, Hamburg D-22607, Germany
| | - Gabriel Gola
- Departamento de Química Orgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Unidad de Microanálisis y Métodos Físicos Aplicados a Química Orgánica (UMYMFOR), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. CONICET, Buenos Aires C1428EGA, Argentina
| | - Nancy L Tateosian
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
| | - Elias D Lopez
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
| | - Camila Belen Martinena
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
| | - Juan Pablo Arcon
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
| | - Martín Dodes Traian
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
| | - Diana E Wetzler
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
| | - Isabel Bento
- European Molecular Biology Laboratory Hamburg, Notkestrasse 85, Hamburg D-22607, Germany
| | - Xavier Barril
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.,Faculty of Pharmacy and Institute of Biomedicine (IBUB), University of Barcelona, Av.Joan XXIII 27-31, Barcelona 08028, Spain
| | - Javier Ramirez
- Departamento de Química Orgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Unidad de Microanálisis y Métodos Físicos Aplicados a Química Orgánica (UMYMFOR), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. CONICET, Buenos Aires C1428EGA, Argentina
| | - Marcelo A Marti
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
| | - Maria M Garcia-Alai
- European Molecular Biology Laboratory Hamburg, Notkestrasse 85, Hamburg D-22607, Germany
| | - Adrián G Turjanski
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EGA, Argentina
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15
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Miñarro-Lleonar M, Ruiz-Carmona S, Alvarez-Garcia D, Schmidtke P, Barril X. Development of an Automatic Pipeline for Participation in the CELPP Challenge. Int J Mol Sci 2022; 23:ijms23094756. [PMID: 35563148 PMCID: PMC9105952 DOI: 10.3390/ijms23094756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 12/01/2022] Open
Abstract
The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.
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Affiliation(s)
- Marina Miñarro-Lleonar
- Pharmacy Faculty, University of Barcelona, Av. de Joan XXIII 27-31, 08028 Barcelona, Spain;
| | | | - Daniel Alvarez-Garcia
- GAIN Therapeutics, Parc Cientific de Barcelona, Baldiri i Reixac 10, 08029 Barcelona, Spain;
| | - Peter Schmidtke
- Discngine S.A.S., 79 Avenue Ledru Rollin, 75012 Paris, France;
| | - Xavier Barril
- Pharmacy Faculty, University of Barcelona, Av. de Joan XXIII 27-31, 08028 Barcelona, Spain;
- GAIN Therapeutics, Parc Cientific de Barcelona, Baldiri i Reixac 10, 08029 Barcelona, Spain;
- Catalan Institute for Research and Advanced Studies (ICREA), Passeig de Lluis Companys 23, 08010 Barcelona, Spain
- Correspondence:
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16
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Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli FOF1-ATP Synthase. Antibiotics (Basel) 2022; 11:antibiotics11050557. [PMID: 35625201 PMCID: PMC9138118 DOI: 10.3390/antibiotics11050557] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 01/27/2023] Open
Abstract
With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic β subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the β subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase.
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17
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Grosjean H, Işık M, Aimon A, Mobley D, Chodera J, von Delft F, Biggin PC. SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction. J Comput Aided Mol Des 2022; 36:291-311. [PMID: 35426591 PMCID: PMC9010448 DOI: 10.1007/s10822-022-00452-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/22/2022] [Indexed: 11/01/2022]
Abstract
A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.
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Affiliation(s)
- Harold Grosjean
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, South Parks Road, OX1 3QU, Oxford, UK
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, OX11 0QX, Didcot, UK
| | - Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 10065, New York, NY, USA
| | - Anthony Aimon
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, OX11 0QX, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA, Didcot, UK
| | - David Mobley
- Department of Pharmaceutical Sciences, Department of Chemistry, University of California, 92617, Irvine, California, USA
| | - John Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 10065, New York, NY, USA
| | - Frank von Delft
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, OX11 0QX, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA, Didcot, UK
- Centre for Medicines Discovery, University of Oxford, Old Road Campus, Roosevelt Drive, OX3 7DQ, Headington, UK
- Structural Genomics Consortium, University of Oxford, Old Road Campus, Roosevelt Drive, OX3 7DQ, Headington, UK
| | - Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, South Parks Road, OX1 3QU, Oxford, UK.
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18
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Ferenczy GG, Kellermayer M. Contribution of Hydrophobic Interactions to Protein Mechanical Stability. Comput Struct Biotechnol J 2022; 20:1946-1956. [PMID: 35521554 PMCID: PMC9062142 DOI: 10.1016/j.csbj.2022.04.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 04/07/2022] [Accepted: 04/17/2022] [Indexed: 11/26/2022] Open
Abstract
The role of hydrophobic and polar interactions in providing thermodynamic stability to folded proteins has been intensively studied, but the relative contribution of these interactions to the mechanical stability is less explored. We used steered molecular dynamics simulations with constant-velocity pulling to generate force-extension curves of selected protein domains and monitor hydrophobic surface unravelling upon extension. Hydrophobic contribution was found to vary between one fifth and one third of the total force while the rest of the contribution is attributed primarily to hydrogen bonds. Moreover, hydrophobic force peaks were shifted towards larger protein extensions with respect to the force peaks attributed to hydrogen bonds. The higher importance of hydrogen bonds compared to hydrophobic interactions in providing mechanical resistance is in contrast with the relative importance of the hydrophobic interactions in providing thermodynamic stability of proteins. The different contributions of these interactions to the mechanical stability are explained by the steeper free energy dependence of hydrogen bonds compared to hydrophobic interactions on the relative positions of interacting atoms. Comparative analyses for several protein domains revealed that the variation of hydrophobic forces is modest, while the contribution of hydrogen bonds to the force peaks becomes increasingly important for mechanically resistant protein domains.
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19
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Alvarez-Garcia D, Schmidtke P, Cubero E, Barril X. Extracting Atomic Contributions to Binding Free Energy Using Molecular Dynamics Simulations with Mixed Solvents (MDmix). Curr Drug Discov Technol 2022; 19:62-68. [PMID: 34951392 PMCID: PMC9906626 DOI: 10.2174/1570163819666211223162829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/28/2021] [Accepted: 10/05/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Mixed solvents MD (MDmix) simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery. One of the assumptions behind the methodology is the transferability of free energy values from the simulated cosolvent molecules to larger drug-like molecules. However, the binding free energy maps (ΔGbind) calculated for the different moieties of the cosolvent molecules (e.g. a hydroxyl map for the ethanol) are largely influenced by the rest of the solvent molecule and do not reflect the intrinsic affinity of the moiety in question. As such, they are hardly transferable to different molecules. METHOD To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions. RESULT We demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential. CONCLUSION Atomic decomposition of binding free energies derived from MDmix simulations provides transferable and quantitative binding free energy maps.
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Affiliation(s)
- Daniel Alvarez-Garcia
- Gain Therapeutics, Parc Cientific de Barcelona, Baldiri Reixac 10, 08029 Barcelona, Spain
| | - Peter Schmidtke
- Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, 08028 Barcelona, Spain;,Current address: Discngine, 79 Avenue Ledru Rollin, 75012 Paris, France;
| | - Elena Cubero
- Gain Therapeutics, Parc Cientific de Barcelona, Baldiri Reixac 10, 08029 Barcelona, Spain
| | - Xavier Barril
- Gain Therapeutics, Parc Cientific de Barcelona, Baldiri Reixac 10, 08029 Barcelona, Spain;,Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, 08028 Barcelona, Spain;,Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain,Address correspondence to this author at the Gain Therapeutics, Parc Cientific de Barcelona, Baldiri Reixac 10, 08029 Barcelona, Spain; E-mail:
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20
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Codony S, Pont C, Griñán-Ferré C, Di Pede-Mattatelli A, Calvó-Tusell C, Feixas F, Osuna S, Jarné-Ferrer J, Naldi M, Bartolini M, Loza MI, Brea J, Pérez B, Bartra C, Sanfeliu C, Juárez-Jiménez J, Morisseau C, Hammock BD, Pallàs M, Vázquez S, Muñoz-Torrero D. Discovery and In Vivo Proof of Concept of a Highly Potent Dual Inhibitor of Soluble Epoxide Hydrolase and Acetylcholinesterase for the Treatment of Alzheimer's Disease. J Med Chem 2022; 65:4909-4925. [PMID: 35271276 PMCID: PMC8958510 DOI: 10.1021/acs.jmedchem.1c02150] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
With innumerable clinical failures of target-specific drug candidates for multifactorial diseases, such as Alzheimer's disease (AD), which remains inefficiently treated, the advent of multitarget drug discovery has brought a new breath of hope. Here, we disclose a class of 6-chlorotacrine (huprine)-TPPU hybrids as dual inhibitors of the enzymes soluble epoxide hydrolase (sEH) and acetylcholinesterase (AChE), a multitarget profile to provide cumulative effects against neuroinflammation and memory impairment. Computational studies confirmed the gorge-wide occupancy of both enzymes, from the main site to a secondary site, including a so far non-described AChE cryptic pocket. The lead compound displayed in vitro dual nanomolar potencies, adequate brain permeability, aqueous solubility, human microsomal stability, lack of neurotoxicity, and it rescued memory, synaptic plasticity, and neuroinflammation in an AD mouse model, after low dose chronic oral administration.
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Affiliation(s)
- Sandra Codony
- Laboratory
of Medicinal Chemistry (CSIC Associated Unit), Faculty of Pharmacy
and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain
| | - Caterina Pont
- Laboratory
of Medicinal Chemistry (CSIC Associated Unit), Faculty of Pharmacy
and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain
| | - Christian Griñán-Ferré
- Pharmacology
Section, Department of Pharmacology, Toxicology and Therapeutic Chemistry,
Faculty of Pharmacy and Food Sciences, and Institute of Neurosciences, University of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain
| | - Ania Di Pede-Mattatelli
- Department
of Pharmacy and Pharmaceutical Technology and Physical Chemistry,
Faculty of Pharmacy and Food Sciences, and Institute of Theoretical
and Computational Chemistry (IQTCUB), University
of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain
| | - Carla Calvó-Tusell
- CompBioLab
Group, Departament de Química and Institut de Química
Computacional i Catàlisi (IQCC), Universitat de Girona, C/ Maria Aurèlia Capmany 69, E-17003 Girona, Spain
| | - Ferran Feixas
- CompBioLab
Group, Departament de Química and Institut de Química
Computacional i Catàlisi (IQCC), Universitat de Girona, C/ Maria Aurèlia Capmany 69, E-17003 Girona, Spain
| | - Sílvia Osuna
- CompBioLab
Group, Departament de Química and Institut de Química
Computacional i Catàlisi (IQCC), Universitat de Girona, C/ Maria Aurèlia Capmany 69, E-17003 Girona, Spain,Institució
Catalana de Recerca i Estudis Avançats (ICREA), E-08010 Barcelona, Spain
| | - Júlia Jarné-Ferrer
- Pharmacology
Section, Department of Pharmacology, Toxicology and Therapeutic Chemistry,
Faculty of Pharmacy and Food Sciences, and Institute of Neurosciences, University of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain
| | - Marina Naldi
- Department
of Pharmacy and Biotechnology, University
of Bologna, Via Belmeloro, 6, I-40126 Bologna, Italy
| | - Manuela Bartolini
- Department
of Pharmacy and Biotechnology, University
of Bologna, Via Belmeloro, 6, I-40126 Bologna, Italy
| | - María Isabel Loza
- BioFarma
Research Group, Centro Singular de Investigación en Medicina
Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Av. de Barcelona s/n, E-15782 Santiago de Compostela, Spain
| | - José Brea
- BioFarma
Research Group, Centro Singular de Investigación en Medicina
Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Av. de Barcelona s/n, E-15782 Santiago de Compostela, Spain
| | - Belén Pérez
- Department
of Pharmacology, Therapeutics and Toxicology, Autonomous University of Barcelona, E-08193 Bellaterra, Spain
| | - Clara Bartra
- Institute
of Biomedical Research of Barcelona, CSIC and Institut d’Investigacions
Biomèdiques August Pi i Sunyer (IDIBAPS), Rosselló, 149, E-08036 Barcelona, Spain
| | - Coral Sanfeliu
- Institute
of Biomedical Research of Barcelona, CSIC and Institut d’Investigacions
Biomèdiques August Pi i Sunyer (IDIBAPS), Rosselló, 149, E-08036 Barcelona, Spain
| | - Jordi Juárez-Jiménez
- Department
of Pharmacy and Pharmaceutical Technology and Physical Chemistry,
Faculty of Pharmacy and Food Sciences, and Institute of Theoretical
and Computational Chemistry (IQTCUB), University
of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain
| | - Christophe Morisseau
- Department
of Entomology and Nematology and Comprehensive Cancer Center, University of California, One Shields Avenue, Davis, California 95616, United States
| | - Bruce D. Hammock
- Department
of Entomology and Nematology and Comprehensive Cancer Center, University of California, One Shields Avenue, Davis, California 95616, United States
| | - Mercè Pallàs
- Pharmacology
Section, Department of Pharmacology, Toxicology and Therapeutic Chemistry,
Faculty of Pharmacy and Food Sciences, and Institute of Neurosciences, University of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain
| | - Santiago Vázquez
- Laboratory
of Medicinal Chemistry (CSIC Associated Unit), Faculty of Pharmacy
and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain,. Phone: (+34) 934024533
| | - Diego Muñoz-Torrero
- Laboratory
of Medicinal Chemistry (CSIC Associated Unit), Faculty of Pharmacy
and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona (UB), Av. Joan XXIII 27-31, E-08028 Barcelona, Spain,. Phone: (+34) 934024533
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21
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Drug Discovery of Plausible Lead Natural Compounds That Target the Insulin Signaling Pathway: Bioinformatics Approaches. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:2832889. [PMID: 35356248 PMCID: PMC8958086 DOI: 10.1155/2022/2832889] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/16/2022] [Accepted: 02/09/2022] [Indexed: 12/11/2022]
Abstract
The growing smooth talk in the field of natural compounds is due to the ancient and current interest in herbal medicine and their potentially positive effects on health. Dozens of antidiabetic natural compounds were reported and tested in vivo, in silico, and in vitro. The role of these natural compounds, their actions on the insulin signaling pathway, and the stimulation of the glucose transporter-4 (GLUT4) insulin-responsive translocation to the plasma membrane (PM) are all crucial in the treatment of diabetes and insulin resistance. In this review, we collected and summarized a group of available in vivo and in vitro studies which targeted isolated phytochemicals with possible antidiabetic activity. Moreover, the in silico docking of natural compounds with some of the insulin signaling cascade key proteins is also summarized based on the current literature. In this review, hundreds of recent studies on pure natural compounds that alleviate type II diabetes mellitus (type II DM) were revised. We focused on natural compounds that could potentially regulate blood glucose and stimulate GLUT4 translocation through the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway. On attempt to point out potential new natural antidiabetic compounds, this review also focuses on natural ingredients that were shown to interact with proteins in the insulin signaling pathway in silico, regardless of their in vitro/in vivo antidiabetic activity. We invite interested researchers to test these compounds as potential novel type II DM drugs and explore their therapeutic mechanisms.
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22
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Smilova MD, Curran PR, Radoux CJ, von Delft F, Cole JC, Bradley AR, Marsden BD. Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins. J Chem Inf Model 2022; 62:284-294. [PMID: 35020376 PMCID: PMC8790751 DOI: 10.1021/acs.jcim.1c00823] [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] [Indexed: 01/18/2023]
Abstract
![]()
Selectivity is a
crucial property in small molecule development.
Binding site comparisons within a protein family are a key piece of
information when aiming to modulate the selectivity profile of a compound.
Binding site differences can be exploited to confer selectivity for
a specific target, while shared areas can provide insights into polypharmacology.
As the quantity of structural data grows, automated methods are needed
to process, summarize, and present these data to users. We present
a computational method that provides quantitative and data-driven
summaries of the available binding site information from an ensemble
of structures of the same protein. The resulting ensemble maps identify
the key interactions important for ligand binding in the ensemble.
The comparison of ensemble maps of related proteins enables the identification
of selectivity-determining regions within a protein family. We applied
the method to three examples from the well-researched human bromodomain
and kinase families, demonstrating that the method is able to identify
selectivity-determining regions that have been used to introduce selectivity
in past drug discovery campaigns. We then illustrate how the resulting
maps can be used to automate comparisons across a target protein family.
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Affiliation(s)
- Mihaela D Smilova
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K
| | - Peter R Curran
- The Cambridge Crystallographic Data Centre (CCDC), Cambridge CB2 1EZ, U.K.,Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Chris J Radoux
- Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K
| | - Frank von Delft
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K.,Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0DE, U.K.,Research Complex at Harwell. Harwell Science and Innovation Campus, Didcot OX11 0FA, U.K.,Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
| | - Jason C Cole
- The Cambridge Crystallographic Data Centre (CCDC), Cambridge CB2 1EZ, U.K
| | - Anthony R Bradley
- Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K
| | - Brian D Marsden
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K.,Kennedy Institute of Rheumatology, NDORMS, University of Oxford, Oxford OX3 7DQ, U.K
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23
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Varela-Rial A, Maryanow I, Majewski M, Doerr S, Schapin N, Jiménez-Luna J, De Fabritiis G. PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks. J Chem Inf Model 2022; 62:225-231. [PMID: 34978201 PMCID: PMC8790755 DOI: 10.1021/acs.jcim.1c00691] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
![]()
Deep learning has
been successfully applied to structure-based
protein–ligand affinity prediction, yet the black box nature
of these models raises some questions. In a previous study, we presented
KDEEP, a convolutional neural network that predicted the
binding affinity of a given protein–ligand complex while reaching
state-of-the-art performance. However, it was unclear what this model
was learning. In this work, we present a new application to visualize
the contribution of each input atom to the prediction made by the
convolutional neural network, aiding in the interpretability of such
predictions. The results suggest that KDEEP is able to
learn meaningful chemistry signals from the data, but it has also
exposed the inaccuracies of the current model, serving as a guideline
for further optimization of our prediction tools.
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Affiliation(s)
- Alejandro Varela-Rial
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.,Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - Iain Maryanow
- Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - Maciej Majewski
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Stefan Doerr
- Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - Nikolai Schapin
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.,Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain
| | - José Jiménez-Luna
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.,Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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24
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Cadet XF, Gelly JC, van Noord A, Cadet F, Acevedo-Rocha CG. Learning Strategies in Protein Directed Evolution. Methods Mol Biol 2022; 2461:225-275. [PMID: 35727454 DOI: 10.1007/978-1-0716-2152-3_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.
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Affiliation(s)
- Xavier F Cadet
- PEACCEL, Artificial Intelligence Department, Paris, France
| | - Jean Christophe Gelly
- Laboratoire d'Excellence GR-Ex, Paris, France
- BIGR, DSIMB, UMR_S1134, INSERM, University of Paris & University of Reunion, Paris, France
| | | | - Frédéric Cadet
- Laboratoire d'Excellence GR-Ex, Paris, France
- BIGR, DSIMB, UMR_S1134, INSERM, University of Paris & University of Reunion, Paris, France
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25
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Piticchio SG, Martínez-Cartró M, Scaffidi S, Rachman M, Rodriguez-Arevalo S, Sanchez-Arfelis A, Escolano C, Picaud S, Krojer T, Filippakopoulos P, von Delft F, Galdeano C, Barril X. Discovery of Novel BRD4 Ligand Scaffolds by Automated Navigation of the Fragment Chemical Space. J Med Chem 2021; 64:17887-17900. [PMID: 34898210 DOI: 10.1021/acs.jmedchem.1c01108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Fragment-based drug discovery (FBDD) is a very effective hit identification method. However, the evolution of fragment hits into suitable leads remains challenging and largely artisanal. Fragment evolution is often scaffold-centric, meaning that its outcome depends crucially on the chemical structure of the starting fragment. Considering that fragment screening libraries cover only a small proportion of the corresponding chemical space, hits should be seen as probes highlighting privileged areas of the chemical space rather than actual starting points. We have developed an automated computational pipeline to mine the chemical space around any specific fragment hit, rapidly finding analogues that share a common interaction motif but are structurally novel and diverse. On a prospective application on the bromodomain-containing protein 4 (BRD4), starting from a known fragment, the platform yields active molecules with nonobvious scaffold changes. The procedure is fast and inexpensive and has the potential to uncover many hidden opportunities in FBDD.
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Affiliation(s)
- Serena G Piticchio
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Míriam Martínez-Cartró
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Salvatore Scaffidi
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Moira Rachman
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Sergio Rodriguez-Arevalo
- Laboratory of Medicinal Chemistry (Associated Unit to CSIC), Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Ainoa Sanchez-Arfelis
- Laboratory of Medicinal Chemistry (Associated Unit to CSIC), Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Carmen Escolano
- Laboratory of Medicinal Chemistry (Associated Unit to CSIC), Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Sarah Picaud
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom
| | - Tobias Krojer
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom
| | - Panagis Filippakopoulos
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom
| | - Frank von Delft
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom.,Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, United Kingdom.,Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, United Kingdom.,Centre for Medicines Discovery, University of Oxford, Oxford OX1 3QU, United Kingdom.,Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
| | - Carles Galdeano
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Xavier Barril
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona 08010, Spain
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26
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Wang P, Gao X, Zhang K, Pei Q, Xu X, Yan F, Dong J, Jing C. Exploring the binding mechanism of positive allosteric modulators in human metabotropic glutamate receptor 2 using molecular dynamics simulations. Phys Chem Chem Phys 2021; 23:24125-24139. [PMID: 34596645 DOI: 10.1039/d1cp02157e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Positive allosteric modulators (PAMs) of human metabotropic glutamate receptor 2 (hmGlu2) are well-known in the treatment of psychiatric disorders for their higher selectivity and lower tolerance risk. A variety of PAMs have been reported over the last decade and two compounds were in Phase II clinical trials for schizophrenia and anxiety. These trials were discontinued on account of the unsatisfactory therapeutic efficacy, but PAMs were explored as novel treatments for addiction and epilepsy. Thus, it is still important to explore novel hmGlu2 PAMs in the near future. Nowadays, the challenges in optimizing drug potency and improving scaffold diversity for PAMs are the noncomprehensive character analyses of multiple scaffolds; the exploration of the binding modes of PAMs in the allosteric binding site have been proposed to reduce this difficulty. However, there has been no comprehensive research about the binding profiles of PAMs in the hmGlu2 receptor. To address this issue, this work explores the binding characters of eight PAMs representing five chemical series by multiple computational methods. As a result, the shared binding modes of the eight studied PAMs interacting with 15 residues in the allosteric binding site were defined. In addition, the reduced hydrophobicity with low electronegativity of R1, increased hydrophobicity with low negative electron density of R2 and the electronegativity of the linker were identified as indicators that regulate the affinity of PAMs. This finding agrees well with the physicochemical properties of reported multiple series PAMs. This comprehensive work sheds additional light on the binding mechanism and physicochemical regularity underlining PAMs affinity and could be further utilized as a structural and energetic blueprint for discovering and assessing novel PAMs for hmGlu2.
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Affiliation(s)
- Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Xiaonan Gao
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Ke Zhang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Qinglan Pei
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Xiaobo Xu
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Fengmei Yan
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Jianghong Dong
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
| | - Chenxi Jing
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China.
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27
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Sanchez-Andrada P, Vidal-Vidal A, Prieto T, Elguero J, Alkorta I, Marin-Luna M. Alkylammonium Cation Affinities of Nitrogenated Organobases: The Roles of Hydrogen Bonding and Proton Transfer. Chempluschem 2021; 86:1097-1105. [PMID: 34251758 DOI: 10.1002/cplu.202100235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/29/2021] [Indexed: 11/06/2022]
Abstract
Alkylammonium cation affinities of 64 nitrogen-containing organobases, as well as the respective proton transfer processes from the alkylammonium cations to the base, have been computed in the gas phase by using DFT methods. The guanidine bases show the highest proton transfer values (191.9-233 kJ mol-1 ) whereas the cis-2,2'-biimidazole presents the largest affinity towards the alkylammonium cations (>200 kJ mol-1 ) values. The resulting data have been compared with the experimentally reported proton affinities of the studied nitrogen-containing organobases revealing that the propensity of an organobase for the proton transfer process increases linearly with its proton affinity. This work can provide a tool for designing senors for bioactive compounds containing amino groups that are protonated at physiological pH.
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Affiliation(s)
- Pilar Sanchez-Andrada
- Departamento de Química Orgánica Regional Campus of International Excellence "Campus Mare Nostrum", Universidad de Murcia Facultad de Química, Campus de Espinardo, E-30100, Murcia, Spain
| | - Angel Vidal-Vidal
- Departamento de Química Orgánica, Universidade de Vigo Campus Lagoas-Marcosende, Vigo, Spain
| | - Tania Prieto
- Departamento de Química Orgánica, Universidade de Vigo Campus Lagoas-Marcosende, Vigo, Spain
| | - José Elguero
- Instituto de Química Médica, Centro Superior de Investigaciones Científicas (CSIC), Juan de la Cierva, 3, E-28006, Madrid, Spain
| | - Ibon Alkorta
- Instituto de Química Médica, Centro Superior de Investigaciones Científicas (CSIC), Juan de la Cierva, 3, E-28006, Madrid, Spain
| | - Marta Marin-Luna
- Departamento de Química Orgánica Regional Campus of International Excellence "Campus Mare Nostrum", Universidad de Murcia Facultad de Química, Campus de Espinardo, E-30100, Murcia, Spain
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28
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Varela‐Rial A, Majewski M, De Fabritiis G. Structure based virtual screening: Fast and slow. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Alejandro Varela‐Rial
- Acellera Labs Barcelona Spain
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Maciej Majewski
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
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29
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Wang ZZ, Shi XX, Huang GY, Hao GF, Yang GF. Fragment-based drug design facilitates selective kinase inhibitor discovery. Trends Pharmacol Sci 2021; 42:551-565. [PMID: 33958239 DOI: 10.1016/j.tips.2021.04.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/30/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022]
Abstract
Protein kinases (PKs) are important drug targets, but kinases selectivity poses a challenge to protein kinase inhibitors (PKIs) design. Fragment-based drug discovery (FBDD) has achieved great success in the discovery of highly specific PKIs. It makes full use of kinase-fragment interaction in target kinase subpockets to obtain promising selectivity. However, it's difficult to understand the complicated kinase-fragment interaction space, and systemic discussion of these interactions is still lacking. Herein, we introduce the advantages of the FBDD strategy in PKIs design. Key features of the selectivity of kinase-fragment interactions are summarized and analyzed. Some promising PKIs are introduced as case studies to help understand the fragment-to-lead (F2L) optimization process. Novel strategies and technologies for FBDD in PKIs discovery are also outlooked.
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Affiliation(s)
- Zhi-Zheng Wang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, 430079, China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Xing-Xing Shi
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, 430079, China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Guang-Yi Huang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, 430079, China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Ge-Fei Hao
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, 430079, China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China; State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China.
| | - Guang-Fu Yang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, 430079, China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
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30
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Abdelsattar AS, Mansour Y, Aboul-Ela F. The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding. Chembiochem 2021; 22:1499-1516. [PMID: 33351206 DOI: 10.1002/cbic.202000695] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/19/2020] [Indexed: 12/24/2022]
Abstract
The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.
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Affiliation(s)
- Abdallah S Abdelsattar
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Youssef Mansour
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Fareed Aboul-Ela
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
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31
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Chaves EJF, Gomes da Cruz LE, Padilha IQM, Silveira CH, Araujo DAM, Rocha GB. Discovery of RTA ricin subunit inhibitors: a computational study using PM7 quantum chemical method and steered molecular dynamics. J Biomol Struct Dyn 2021; 40:5427-5445. [PMID: 33526002 DOI: 10.1080/07391102.2021.1878058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Ricin is a potent toxin derived from the castor bean plant and comprises two subunits, RTA and RTB. Because of its cytotoxicity, ricin has alarmed world authorities for its potential use as a chemical weapon. Ricin also affects castor bean agribusiness, given the risk of animal and human poisoning. Over the years, many groups attempted to propose small-molecules that bind to the RTA active site, the catalytic chain. Despite such efforts, there is still no effective countermeasure against ricin poisoning. The computational study carried out in the present work renews the discussion about small-molecules that may inhibit this toxin. Here, a structure-based virtual screening protocol capable of discerning active RTA inhibitors from inactive ones was performed to screen over 2 million compounds from the ZINC database to find novel scaffolds that strongly bind into the active site of the RTA. Besides, a novel score method based on ligand undocking force profiles and semi-empirical quantum chemical calculations provided insights into the rescore of docking poses. Summing up, the filtering steps pointed out seven main compounds, with the SCF00-451 as a promising candidate to inhibit the killing activity of such potent phytotoxin.
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Affiliation(s)
| | | | | | | | | | - Gerd Bruno Rocha
- Department of Chemistry, Federal University of Paraíba, João Pessoa, PB, Brazil
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32
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Zhang H, Yang Y, Li J, Wang M, Saravanan KM, Wei J, Tze-Yang Ng J, Tofazzal Hossain M, Liu M, Zhang H, Ren X, Pan Y, Peng Y, Shi Y, Wan X, Liu Y, Wei Y. A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro. PLoS Comput Biol 2020; 16:e1008489. [PMID: 33382685 PMCID: PMC7774833 DOI: 10.1371/journal.pcbi.1008489] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/03/2020] [Indexed: 01/18/2023] Open
Abstract
The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.
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Affiliation(s)
- Haiping Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yang Yang
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Junxin Li
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Min Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Konda Mani Saravanan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jinli Wei
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Justin Tze-Yang Ng
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Md. Tofazzal Hossain
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Shijingshan District, Beijing, China
| | - Maoxuan Liu
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Huiling Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xiaohu Ren
- Institute of Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Yin Peng
- Department of Pathology, School of Medicine, Shenzhen University, Shenzhen, China
| | - Yi Shi
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Xiaochun Wan
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
- * E-mail: (XW); (YL); (YW)
| | - Yingxia Liu
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
- * E-mail: (XW); (YL); (YW)
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- * E-mail: (XW); (YL); (YW)
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Bianco G, Goodsell DS, Forli S. Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors. Trends Pharmacol Sci 2020; 41:1038-1049. [PMID: 33153778 PMCID: PMC7669701 DOI: 10.1016/j.tips.2020.10.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/28/2022]
Abstract
Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.
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Affiliation(s)
- Giulia Bianco
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Research Collaboratory for Structure Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
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Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020; 25:E4723. [PMID: 33076254 PMCID: PMC7587536 DOI: 10.3390/molecules25204723] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 12/20/2022] Open
Abstract
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| | - Manel López
- AB Science, Parc Scientifique de Luminy, Zone Luminy Enterprise, Case 922, 163 Av. de Luminy, 13288 Marseille, France;
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - F. Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
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Cutrona KJ, Newton AS, Krimmer SG, Tirado-Rives J, Jorgensen WL. Metadynamics as a Postprocessing Method for Virtual Screening with Application to the Pseudokinase Domain of JAK2. J Chem Inf Model 2020; 60:4403-4415. [PMID: 32383599 DOI: 10.1021/acs.jcim.0c00276] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
With standard scoring methods, top-ranked compounds from virtual screening by docking often turn out to be inactive. For this reason, metadynamics, a method used to sample rare events, was studied to further evaluate docking poses with the aim of reducing false positives. Specifically, virtual screening was performed with Glide SP to seek potential molecules to bind to the ATP site in the pseudokinase domain of JAK2 kinase, and promising compounds were selected from the top-ranked 1000 based on visualization. Rescoring with Glide XP, GOLD, and MM/GBSA was unable to differentiate well between active and inactive compounds. Metadynamics was then used to gauge the relative binding affinity from the required time or the potential of mean force needed to dissociate the ligand from the bound complex. With consideration of previously known binders of varying affinities, metadynamics was able to differentiate between the most active compounds and inactive or weakly active ones, and it could identify correctly most of the selected virtual screening compounds as false positives. Thus, metadynamics has the potential to be a viable postprocessing method for virtual screening, minimizing the expense of buying or synthesizing inactive compounds.
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Affiliation(s)
- Kara J Cutrona
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Ana S Newton
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Stefan G Krimmer
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Julian Tirado-Rives
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - William L Jorgensen
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
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36
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Bissaro M, Sturlese M, Moro S. The rise of molecular simulations in fragment-based drug design (FBDD): an overview. Drug Discov Today 2020; 25:1693-1701. [PMID: 32592867 PMCID: PMC7314695 DOI: 10.1016/j.drudis.2020.06.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/24/2020] [Accepted: 06/19/2020] [Indexed: 12/31/2022]
Abstract
Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.
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Affiliation(s)
- Maicol Bissaro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy.
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37
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Ghahremanpour MM, Tirado-Rives J, Deshmukh M, Ippolito JA, Zhang CH, de Vaca IC, Liosi ME, Anderson KS, Jorgensen WL. Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 32869018 DOI: 10.1101/2020.08.28.271957] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (M pro ) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with M pro , 17 were chosen for evaluation in a kinetic assay for M pro inhibition. Remarkably 14 of the compounds at 100-μM concentration were found to reduce the enzymatic activity and 5 provided IC 50 values below 40 μM: manidipine (4.8 μM), boceprevir (5.4 μM), lercanidipine (16.2 μM), bedaquiline (18.7 μM), and efonidipine (38.5 μM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1', and P2 pockets of M pro . Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.
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38
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Distinct binding of cetirizine enantiomers to human serum albumin and the human histamine receptor H 1. J Comput Aided Mol Des 2020; 34:1045-1062. [PMID: 32572668 DOI: 10.1007/s10822-020-00328-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 06/18/2020] [Indexed: 02/02/2023]
Abstract
Cetirizine, a major metabolite of hydroxyzine, became a marketed second-generation H1 antihistamine that is orally active and has a rapid onset of action, long duration of effects and a very good safety record at recommended doses. The approved drug is a racemic mixture of (S)-cetirizine and (R)-cetirizine, the latter being the levorotary enantiomer that also exists in the market as a third-generation, non-sedating and highly selective antihistamine. Both enantiomers bind tightly to the human histamine H1 receptor (hH1R) and behave as inverse agonists but the affinity and residence time of (R)-cetirizine are greater than those of (S)-cetirizine. In blood plasma, cetirizine exists in the zwitterionic form and more than 90% of the circulating drug is bound to human serum albumin (HSA), which acts as an inactive reservoir. Independent X-ray crystallographic work has solved the structure of the hH1R:doxepin complex and has identified two drug-binding sites for cetirizine on equine serum albumin (ESA). Given this background, we decided to model a membrane-embedded hH1R in complex with either (R)- or (S)-cetirizine and also the complexes of both ESA and HSA with these two enantiomeric drugs to analyze possible differences in binding modes between enantiomers and also among targets. The ensuing molecular dynamics simulations in explicit solvent and additional computational chemistry calculations provided structural and energetic information about all of these complexes that is normally beyond current experimental possibilities. Overall, we found very good agreement between our binding energy estimates and extant biochemical and pharmacological evidence. A much higher degree of solvent exposure in the cetirizine-binding site(s) of HSA and ESA relative to the more occluded orthosteric binding site in hH1R is translated into larger positional fluctuations and considerably lower affinities for these two nonspecific targets. Whereas it is demonstrated that the two known pockets in ESA provide enough stability for cetirizine binding, only one such site does so in HSA due to a number of amino acid replacements. At the histamine-binding site in hH1R, the distinct interactions established between the phenyl and chlorophenyl moieties of the two enantiomers with the amino acids lining up the pocket and between their free carboxylates and Lys179 in the second extracellular loop account for the improved pharmacological profile of (R)-cetirizine.
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Domingos RM, Teixeira RD, Zeida A, Agudelo WA, Alegria TGP, da Silva Neto JF, Vieira PS, Murakami MT, Farah CS, Estrin DA, Netto LES. Substrate and Product-Assisted Catalysis: Molecular Aspects behind Structural Switches along Organic Hydroperoxide Resistance Protein Catalytic Cycle. ACS Catal 2020. [DOI: 10.1021/acscatal.0c01257] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Renato M. Domingos
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, 05508-090 São Paulo, Brazil
| | - Raphael D. Teixeira
- Departamento de Biociências, Instituto de Quı́mica, Universidade de São Paulo, 05508-000 Sao Paulo, Brazil
| | - Ari Zeida
- Departamento de Quı́mica Inorgánica Analı́tica y Quı́mica Fı́sica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EGA, Argentina
| | - William A. Agudelo
- Departamento de Quı́mica Inorgánica Analı́tica y Quı́mica Fı́sica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EGA, Argentina
| | - Thiago G. P. Alegria
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, 05508-090 São Paulo, Brazil
| | - José F. da Silva Neto
- Departamento de Biologia Celular e Molecular e Bioagentes Biociências, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, 14040-900 São Paulo, Brazil
| | - Plínio S. Vieira
- Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-970, Brazil
| | - Mario T. Murakami
- Brazilian Biorenewables National Laboratory (LNBR), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-970, Brazil
| | - Chuck S. Farah
- Departamento de Biociências, Instituto de Quı́mica, Universidade de São Paulo, 05508-000 Sao Paulo, Brazil
| | - Dario A. Estrin
- Departamento de Quı́mica Inorgánica Analı́tica y Quı́mica Fı́sica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EGA, Argentina
| | - Luis E. S. Netto
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, 05508-090 São Paulo, Brazil
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40
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Rachman M, Bajusz D, Hetényi A, Scarpino A, Merő B, Egyed A, Buday L, Barril X, Keserű GM. Discovery of a novel kinase hinge binder fragment by dynamic undocking. RSC Med Chem 2020; 11:552-558. [PMID: 33479656 PMCID: PMC7593776 DOI: 10.1039/c9md00519f] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/11/2020] [Indexed: 12/17/2022] Open
Abstract
A virtual screening workflow for fragment-sized kinase inhibitors is presented, along with a newly identified and validated hinge binder fragment.
One of the key motifs of type I kinase inhibitors is their interactions with the hinge region of ATP binding sites. These interactions contribute significantly to the potency of the inhibitors; however, only a tiny fraction of the available chemical space has been explored with kinase inhibitors reported in the last twenty years. This paper describes a workflow utilizing docking with rDock and dynamic undocking (DUck) for the virtual screening of fragment libraries in order to identify fragments that bind to the kinase hinge region. We have identified 8-amino-2H-isoquinolin-1-one (MR1), a novel and potent hinge binding fragment, which was experimentally tested on a diverse set of kinases, and is hereby suggested for future fragment growing or merging efforts against various kinases, particularly MELK. Direct binding of MR1 to MELK was confirmed by STD-NMR, and its binding to the ATP-pocket was confirmed by a new competitive binding assay based on microscale thermophoresis.
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Affiliation(s)
- Moira Rachman
- Facultat de Farmàcia and Institut de Biomedicina , Universitat de Barcelona , Av. Joan XXIII 27-31 , 08028 Barcelona , Spain.,Medicinal Chemistry Research Group , Research Centre for Natural Sciences , Magyar Tudósok Körútja 2 , Budapest 1117 , Hungary .
| | - Dávid Bajusz
- Medicinal Chemistry Research Group , Research Centre for Natural Sciences , Magyar Tudósok Körútja 2 , Budapest 1117 , Hungary .
| | - Anasztázia Hetényi
- Department of Medical Chemistry , University of Szeged , Dóm tér 8 , H-6720 Szeged , Hungary
| | - Andrea Scarpino
- Medicinal Chemistry Research Group , Research Centre for Natural Sciences , Magyar Tudósok Körútja 2 , Budapest 1117 , Hungary .
| | - Balázs Merő
- Signal Transduction and Functional Genomics Research Group , Research Centre for Natural Sciences , Magyar Tudósok Körútja 2 , Budapest 1117 , Hungary
| | - Attila Egyed
- Medicinal Chemistry Research Group , Research Centre for Natural Sciences , Magyar Tudósok Körútja 2 , Budapest 1117 , Hungary .
| | - László Buday
- Signal Transduction and Functional Genomics Research Group , Research Centre for Natural Sciences , Magyar Tudósok Körútja 2 , Budapest 1117 , Hungary
| | - Xavier Barril
- Facultat de Farmàcia and Institut de Biomedicina , Universitat de Barcelona , Av. Joan XXIII 27-31 , 08028 Barcelona , Spain.,Catalan Institution for Research and Advanced Studies (ICREA) , Passeig Lluís Companys 23 , 08010 Barcelona , Spain
| | - György M Keserű
- Medicinal Chemistry Research Group , Research Centre for Natural Sciences , Magyar Tudósok Körútja 2 , Budapest 1117 , Hungary .
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41
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Majewski M, Barril X. Structural Stability Predicts the Binding Mode of Protein–Ligand Complexes. J Chem Inf Model 2020; 60:1644-1651. [DOI: 10.1021/acs.jcim.9b01062] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Maciej Majewski
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
| | - Xavier Barril
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain
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42
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Wang Z, Sun H, Shen C, Hu X, Gao J, Li D, Cao D, Hou T. Combined strategies in structure-based virtual screening. Phys Chem Chem Phys 2020; 22:3149-3159. [PMID: 31995074 DOI: 10.1039/c9cp06303j] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.
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Affiliation(s)
- Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Xueping Hu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Junbo Gao
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Dan Li
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, P. R. China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
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43
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Majewski M, Ruiz-Carmona S, Barril X. An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder. Commun Chem 2019. [DOI: 10.1038/s42004-019-0205-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Abstract
The predominant view in structure-based drug design is that small-molecule ligands, once bound to their target structures, display a well-defined binding mode. However, structural stability (robustness) is not necessary for thermodynamic stability (binding affinity). In fact, it entails an entropic penalty that counters complex formation. Surprisingly, little is known about the causes, consequences and real degree of robustness of protein-ligand complexes. Since hydrogen bonds have been described as essential for structural stability, here we investigate 469 such interactions across two diverse structure sets, comprising of 79 drug-like and 27 fragment ligands, respectively. Completely constricted protein-ligand complexes are rare and may fulfill a functional role. Most complexes balance order and disorder by combining a single anchoring point with looser regions. 25% do not contain any robust hydrogen bond and may form loose structures. Structural stability analysis reveals a hidden layer of complexity in protein-ligand complexes that should be considered in ligand design.
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44
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Colizzi F, Hospital A, Zivanovic S, Orozco M. Predicting the Limit of Intramolecular Hydrogen Bonding with Classical Molecular Dynamics. Angew Chem Int Ed Engl 2019; 58:3759-3763. [DOI: 10.1002/anie.201810922] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 01/17/2019] [Indexed: 01/23/2023]
Affiliation(s)
- Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
| | - Sanja Zivanovic
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
- Departament de Bioquímica i Biomedicina, Facultat de BiologiaUniversitat de Barcelona Avgda Diagonal 647 Barcelona 08028 Spain
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45
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Rachman M, Scarpino A, Bajusz D, Pálfy G, Vida I, Perczel A, Barril X, Keserű GM. DUckCov: a Dynamic Undocking-Based Virtual Screening Protocol for Covalent Binders. ChemMedChem 2019; 14:1011-1021. [PMID: 30786178 PMCID: PMC6593427 DOI: 10.1002/cmdc.201900078] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Indexed: 12/25/2022]
Abstract
Thanks to recent guidelines, the design of safe and effective covalent drugs has gained significant interest. Other than targeting non‐conserved nucleophilic residues, optimizing the noncovalent binding framework is important to improve potency and selectivity of covalent binders toward the desired target. Significant efforts have been made in extending the computational toolkits to include a covalent mechanism of protein targeting, like in the development of covalent docking methods for binding mode prediction. To highlight the value of the noncovalent complex in the covalent binding process, here we describe a new protocol using tethered and constrained docking in combination with Dynamic Undocking (DUck) as a tool to privilege strong protein binders for the identification of novel covalent inhibitors. At the end of the protocol, dedicated covalent docking methods were used to rank and select the virtual hits based on the predicted binding mode. By validating the method on JAK3 and KRas, we demonstrate how this fast iterative protocol can be applied to explore a wide chemical space and identify potent targeted covalent inhibitors.
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Affiliation(s)
- Moira Rachman
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII 27-31, 08028, Barcelona, Spain.,Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
| | - Andrea Scarpino
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
| | - Gyula Pálfy
- Laboratory of Structural Chemistry and Biology & MTA-ELTE Protein Modelling Research Group, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary
| | - István Vida
- Laboratory of Structural Chemistry and Biology & MTA-ELTE Protein Modelling Research Group, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary
| | - András Perczel
- Laboratory of Structural Chemistry and Biology & MTA-ELTE Protein Modelling Research Group, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary
| | - Xavier Barril
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII 27-31, 08028, Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010, Barcelona, Spain
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
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46
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Colizzi F, Hospital A, Zivanovic S, Orozco M. Predicting the Limit of Intramolecular Hydrogen Bonding with Classical Molecular Dynamics. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201810922] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
| | - Sanja Zivanovic
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute of Science and Technology (BIST) Baldiri Reixac 10 Barcelona 08028 Spain
- Departament de Bioquímica i Biomedicina, Facultat de BiologiaUniversitat de Barcelona Avgda Diagonal 647 Barcelona 08028 Spain
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47
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Zhang L, Bell DR, Luan B, Zhou R. Exploring the binding mechanism between human profilin (PFN1) and polyproline-10 through binding mode screening. J Chem Phys 2019; 150:015102. [PMID: 30621420 DOI: 10.1063/1.5053922] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the time-consuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133, and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average and minimal rupture works calculated from SMD simulations correlate fairly well with the PMFs (R 2 = 0.67), making this a promising PPI screening method.
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Affiliation(s)
- Leili Zhang
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, USA
| | - David R Bell
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, USA
| | - Binquan Luan
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, USA
| | - Ruhong Zhou
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, USA
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48
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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49
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Schuetz DA, Seidel T, Garon A, Martini R, Körbel M, Ecker GF, Langer T. GRAIL: GRids of phArmacophore Interaction fieLds. J Chem Theory Comput 2018; 14:4958-4970. [DOI: 10.1021/acs.jctc.8b00495] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Doris A. Schuetz
- Inte:Ligand GmbH, Mariahilferstrasse 74B/11, A-1070 Vienna, Austria
| | - Thomas Seidel
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Arthur Garon
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Riccardo Martini
- Inte:Ligand GmbH, Mariahilferstrasse 74B/11, A-1070 Vienna, Austria
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Markus Körbel
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Thierry Langer
- Inte:Ligand GmbH, Mariahilferstrasse 74B/11, A-1070 Vienna, Austria
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
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50
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Pantsar T, Poso A. Binding Affinity via Docking: Fact and Fiction. Molecules 2018; 23:molecules23081899. [PMID: 30061498 PMCID: PMC6222344 DOI: 10.3390/molecules23081899] [Citation(s) in RCA: 238] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 07/22/2018] [Accepted: 07/26/2018] [Indexed: 01/03/2023] Open
Abstract
In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking.
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Affiliation(s)
- Tatu Pantsar
- School of Pharmacy, University of Eastern Finland, P.O. BOX 1627, 70211 Kuopio, Finland.
| | - Antti Poso
- School of Pharmacy, University of Eastern Finland, P.O. BOX 1627, 70211 Kuopio, Finland.
- Department of Internal Medicine VIII, University Hospital Tübingen, Otfried-Müller-Strasse 14, 72076 Tübingen, Germany.
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