1
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Shiota K, Suma A, Ogawa H, Yamaguchi T, Iida A, Hata T, Matsushita M, Akutsu T, Tateno M. AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation. ACS OMEGA 2023; 8:23925-23935. [PMID: 37426216 PMCID: PMC10324054 DOI: 10.1021/acsomega.3c02411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 05/31/2023] [Indexed: 07/11/2023]
Abstract
We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model.
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Affiliation(s)
- Koji Shiota
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Akira Suma
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Hiroyuki Ogawa
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Takuya Yamaguchi
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Akio Iida
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Takahiro Hata
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Mutsuyoshi Matsushita
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Tatsuya Akutsu
- Bioinformatics
Center, Institute for Chemical Research,
Kyoto University, Uji, Kyoto 611-0011, Japan
| | - Masaru Tateno
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
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2
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Řezáč J, Stewart JJP. How well do semiempirical QM methods describe the structure of proteins? J Chem Phys 2023; 158:044118. [PMID: 36725526 DOI: 10.1063/5.0135091] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Semiempirical quantum-mechanical (QM) computational methods are an increasingly popular tool for the study of biomolecular systems. They were, however, developed and tested mostly on small model molecules. In this work, we explore one topic fundamental to these applications: the ability of the methods to describe the structure of proteins. In a set of 19 proteins for which a crystal structure with very high resolution is available, we analyze the properties of the protein geometries optimized using several semiempirical QM methods including PM6-D3H4, PM7, and GFN2-xTB. Some of the methods provide a very good description of the general structural features of the protein, yielding results better than or comparable to the AMBER ff03 force field. However, PM7 and PM6-D3H4 optimizations introduce artificial close contacts in the structure, which is partially remediated by reparameterization.
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Affiliation(s)
- J Řezáč
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 16000 Prague, Czech Republic
| | - J J P Stewart
- Stewart Computational Chemistry, 15210 Paddington Circle, Colorado Springs, Colorado 80921, USA
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3
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Li Y, Zhou D, Zheng G, Li X, Wu D, Yuan Y. DyScore: A Boosting Scoring Method with Dynamic Properties for Identifying True Binders and Nonbinders in Structure-Based Drug Discovery. J Chem Inf Model 2022; 62:5550-5567. [PMID: 36327102 PMCID: PMC9983328 DOI: 10.1021/acs.jcim.2c00926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The accurate prediction of protein-ligand binding affinity is critical for the success of computer-aided drug discovery. However, the accuracy of current scoring functions is usually unsatisfactory due to their rough approximation or sometimes even omittance of many factors involved in protein-ligand binding. For instance, the intrinsic dynamics of the protein-ligand binding state is usually disregarded in scoring function because these rapid binding affinity prediction approaches are only based on a representative complex structure of the protein and ligand in the binding state. That is, the dynamic protein-ligand binding complex ensembles are simplified as a static snapshot in calculation. In this study, two novel features were proposed for characterizing the dynamic properties of protein-ligand binding based on the static structure of the complex, which is expected to be a valuable complement to the current scoring functions. The two features demonstrate the geometry-shape matching between a protein and a ligand as well as the dynamic stability of protein-ligand binding. We further combined these two novel features with several classical scoring functions to develop a binary classification model called DyScore that uses the Extreme Gradient Boosting algorithm to classify compound poses as binders or non-binders. We have found that DyScore achieves state-of-the-art performance in distinguishing active and decoy ligands on both enhanced DUD data set and external test sets with both proposed novel features showing significant contributions to the improved performance. Especially, DyScore exhibits superior performance on early recognition, a crucial requirement for success in virtual screening and de novo drug design. The standalone version of DyScore and Dyscore-MF are freely available to all at: https://github.com/YanjunLi-CS/dyscore.
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Affiliation(s)
- Yanjun Li
- NSF Center for Big Learning, University of Florida, Gainesville, Florida 32611, United States; Baidu Research USA, Sunnyvale, California 94089, United States
| | - Daohong Zhou
- Department of Pharmacodynamics, Univerity of Florida, Gainesville, Florida 32611, United States
| | - Guangrong Zheng
- Department of Medicinal Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Xiaolin Li
- Cognization Lab, Palo Alto, California 94306, United States
| | - Dapeng Wu
- NSF Center for Big Learning, University of Florida, Gainesville, Florida 32611, United States
| | - Yaxia Yuan
- Department of Pharmacodynamics, Univerity of Florida, Gainesville, Florida 32611, United States
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4
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Synthesis, Anticancer Activity and Molecular Docking Studies of Novel N-Mannich Bases of 1,3,4-Oxadiazole Based on 4,6-Dimethylpyridine Scaffold. Int J Mol Sci 2022; 23:ijms231911173. [PMID: 36232475 PMCID: PMC9570134 DOI: 10.3390/ijms231911173] [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: 08/18/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/17/2022] Open
Abstract
Cancer is one of the greatest challenges in modern medicine today. Difficult and long-term treatment, the many side effects of the drugs used and the growing resistance to treatment of neoplastic cells necessitate new approaches to therapy. A very promising targeted therapy is based on direct impact only on cancer cells. As a continuation of our research on new biologically active molecules, we report herein the design, synthesis and anticancer evaluation of a new series of N-Mannich-base-type hybrid compounds containing morfoline or different substituted piperazines moieties, a 1,3,4-oxadiazole ring and a 4,6-dimethylpyridine core. All compounds were tested for their potential cytotoxicity against five human cancer cell lines, A375, C32, SNB-19, MCF-7/WT and MCF-7/DX. Two of the active N-Mannich bases (compounds 5 and 6) were further evaluated for growth inhibition effects in melanoma (A375 and C32), and normal (HaCaT) cell lines using clonogenic assay and a population doubling time test. The apoptosis was determined with the neutral version of comet assay. The confocal microscopy method enabled the visualization of F-actin reorganization. The obtained results demonstrated that compounds 5 and 6 have cytotoxic and proapoptotic effects on melanoma cells and are capable of inducing F-actin depolarization in a dose-dependent manner. Moreover, computational chemistry approaches, molecular docking and electrostatic potential were employed to study non-covalent interactions of the investigated compounds with four receptors. It was found that all the examined molecules exhibit a similar binding affinity with respect to the chosen reference drugs.
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5
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Dutkiewicz Z. Computational methods for calculation of protein-ligand binding affinities in structure-based drug design. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2020-0034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Abstract
Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.
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Affiliation(s)
- Zbigniew Dutkiewicz
- Department of Chemical Technology of Drugs , Poznan University of Medical Sciences , ul. Grunwaldzka 6 , 60-780 Poznań , Poznan , 60-780, Poland
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6
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Rallapalli KL, Ranzau BL, Ganapathy KR, Paesani F, Komor AC. Combined Theoretical, Bioinformatic, and Biochemical Analyses of RNA Editing by Adenine Base Editors. CRISPR J 2022; 5:294-310. [PMID: 35353638 DOI: 10.1089/crispr.2021.0131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Adenine base editors (ABEs) have been subjected to multiple rounds of mutagenesis with the goal of optimizing their function as efficient and precise genome editing agents. Despite an ever-expanding data set of ABE mutants and their corresponding DNA or RNA-editing activity, the molecular mechanisms defining these changes remain to be elucidated. In this study, we provide a systematic interpretation of the nature of these mutations using an entropy-based classification model that relies on evolutionary data from extant protein sequences. Using this model in conjunction with experimental analyses, we identify two previously reported mutations that form an epistatic pair in the RNA-editing functional landscape of ABEs. Molecular dynamics simulations reveal the atomistic details of how these two mutations affect substrate-binding and catalytic activity, via both individual and cooperative effects, hence providing insights into the mechanisms through which these two mutations are epistatically coupled.
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Affiliation(s)
- Kartik L Rallapalli
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, USA; University of California San Diego, La Jolla, California, USA
| | - Brodie L Ranzau
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, USA; University of California San Diego, La Jolla, California, USA
| | - Kaushik R Ganapathy
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California, USA; University of California San Diego, La Jolla, California, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, USA; University of California San Diego, La Jolla, California, USA.,Materials Science and Engineering, University of California San Diego, La Jolla, California, USA; and University of California San Diego, La Jolla, California, USA.,San Diego Supercomputer Center, University of California San Diego, La Jolla, California, USA
| | - Alexis C Komor
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, USA; University of California San Diego, La Jolla, California, USA
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7
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Suárez D, Díaz N. Amphiphilic cyclodextrins: Dimerization and diazepam binding explored by molecular dynamics simulations. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Qu X, Dong L, Si Y, Zhao Y, Wang Q, Su P, Wang B. Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase. J Chem Theory Comput 2022; 18:1692-1700. [PMID: 35107298 DOI: 10.1021/acs.jctc.1c01005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.
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Affiliation(s)
- Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Yuan Zhao
- The Key Laboratory of Natural Medicine and Immuno-Engineering, Henan University, Kaifeng 475004, P. R. China
| | - Qiantao Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, P. R. China
| | - Peifeng Su
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
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9
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Gervasoni S, Spencer J, Hinchliffe P, Pedretti A, Vairoletti F, Mahler G, Mulholland AJ. A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors. Proteins 2022; 90:372-384. [PMID: 34455628 PMCID: PMC8944931 DOI: 10.1002/prot.26227] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/09/2021] [Accepted: 08/18/2021] [Indexed: 02/03/2023]
Abstract
Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.
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Affiliation(s)
- Silvia Gervasoni
- Department of Pharmaceutical Sciences, University of Milan, Milan, Italy
| | - James Spencer
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Philip Hinchliffe
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | | | - Franco Vairoletti
- Laboratorio de Química Farmacéutica, Departamento de Química Orgánica, Facultad de Química, Universidad de la República (UdelaR), Avda. General Flores 2124, Montevideo, Uruguay
| | - Graciela Mahler
- Laboratorio de Química Farmacéutica, Departamento de Química Orgánica, Facultad de Química, Universidad de la República (UdelaR), Avda. General Flores 2124, Montevideo, Uruguay
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10
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Sulimov VB, Kutov DC, Taschilova AS, Ilin IS, Tyrtyshnikov EE, Sulimov AV. Docking Paradigm in Drug Design. Curr Top Med Chem 2021; 21:507-546. [PMID: 33292135 DOI: 10.2174/1568026620666201207095626] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 09/28/2020] [Accepted: 10/16/2020] [Indexed: 11/22/2022]
Abstract
Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.
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Affiliation(s)
- Vladimir B Sulimov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Danil C Kutov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Anna S Taschilova
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Ivan S Ilin
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Eugene E Tyrtyshnikov
- Institute of Numerical Mathematics of Russian Academy of Sciences, Moscow, Russian Federation
| | - Alexey V Sulimov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
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11
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Lo R, Lamanec M, Wang W, Manna D, Bakandritsos A, Dračínský M, Zbořil R, Nachtigallová D, Hobza P. Structure-directed formation of the dative/covalent bonds in complexes with C 70piperidine. Phys Chem Chem Phys 2021; 23:4365-4375. [PMID: 33589890 DOI: 10.1039/d0cp06280d] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The combined experimental-computational study has been performed to investigate the complexes formed between C70 carbon allotrope and piperidine. The results of FT-IR, H-NMR, and C-NMR measurements, together with the calculations based on the DFT approach and molecular dynamics simulations, prove the existence of dative/covalent bonding in C70piperidine complexes. The dative bond forms not only at the region of five- and six-membered rings, observed previously with C60, but also at the region formed of six-membered rings. The structure, i.e., nonplanarity, explains the observed dative bond formation. New findings on the character of interaction of secondary amines with C70 bring new aspects for the rational design of modified fullerenes and their applications in electrocatalysis, spintronics, and energy storage.
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Affiliation(s)
- Rabindranath Lo
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo Námstí 542/2, 16000 Prague, Czech Republic. and CATRIN, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic
| | - Maximilián Lamanec
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo Námstí 542/2, 16000 Prague, Czech Republic. and Department of Physical Chemistry, Palacký University Olomouc, Tr. 17 listopadu 12, 771 46 Olomouc, Czech Republic
| | - Weizhou Wang
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang 471934, China
| | - Debashree Manna
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo Námstí 542/2, 16000 Prague, Czech Republic. and CATRIN, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic
| | - Aristides Bakandritsos
- CATRIN, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic and Regional Centre of Advanced Technologies and Materials, Palacký University, Olomouc, Šlechtitelů 27, 78371, Czech Republic
| | - Martin Dračínský
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo Námstí 542/2, 16000 Prague, Czech Republic.
| | - Radek Zbořil
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo Námstí 542/2, 16000 Prague, Czech Republic. and CATRIN, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic and Regional Centre of Advanced Technologies and Materials, Palacký University, Olomouc, Šlechtitelů 27, 78371, Czech Republic and Nanotechnology Centre, VŠB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Dana Nachtigallová
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo Námstí 542/2, 16000 Prague, Czech Republic. and CATRIN, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic
| | - Pavel Hobza
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo Námstí 542/2, 16000 Prague, Czech Republic. and CATRIN, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic
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12
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Willow SY, Xie B, Lawrence J, Eisenberg RS, Minh DDL. On the polarization of ligands by proteins. Phys Chem Chem Phys 2020; 22:12044-12057. [PMID: 32421120 DOI: 10.1039/d0cp00376j] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Although ligand-binding sites in many proteins contain a high number density of charged side chains that can polarize small organic molecules and influence binding, the magnitude of this effect has not been studied in many systems. Here, we use a quantum mechanics/molecular mechanics (QM/MM) approach, in which the ligand is the QM region, to compute the ligand polarization energy of 286 protein-ligand complexes from the PDBBind Core Set (release 2016). Calculations were performed both with and without implicit solvent based on the domain decomposition Conductor-like Screening Model. We observe that the ligand polarization energy is linearly correlated with the magnitude of the electric field acting on the ligand, the magnitude of the induced dipole moment, and the classical polarization energy. The influence of protein and cation charges on the ligand polarization diminishes with the distance and is below 2 kcal mol-1 at 9 Å and 1 kcal mol-1 at 12 Å. Compared to these embedding field charges, implicit solvent has a relatively minor effect on ligand polarization. Considering both polarization and solvation appears essential to computing negative binding energies in some crystallographic complexes. Solvation, but not polarization, is essential for achieving moderate correlation with experimental binding free energies.
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Affiliation(s)
- Soohaeng Yoo Willow
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
| | - Bing Xie
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
| | - Jason Lawrence
- Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois 60616, USA
| | - Robert S Eisenberg
- Department of Applied Mathematics, Illinois Institute of Technology, Chicago, Illinois 60616, USA
| | - David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
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13
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Pecina A, Eyrilmez SM, Köprülüoğlu C, Miriyala VM, Lepšík M, Fanfrlík J, Řezáč J, Hobza P. SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design. Chempluschem 2020; 85:2362-2371. [PMID: 32609421 DOI: 10.1002/cplu.202000120] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/27/2020] [Indexed: 12/17/2022]
Abstract
Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.
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Affiliation(s)
- Adam Pecina
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Saltuk M Eyrilmez
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
| | - Cemal Köprülüoğlu
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
| | - Vijay Madhav Miriyala
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Martin Lepšík
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Jindřich Fanfrlík
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Pavel Hobza
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
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14
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Cavasotto CN, Aucar MG. High-Throughput Docking Using Quantum Mechanical Scoring. Front Chem 2020; 8:246. [PMID: 32373579 PMCID: PMC7186494 DOI: 10.3389/fchem.2020.00246] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 03/16/2020] [Indexed: 11/13/2022] Open
Abstract
Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.
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Affiliation(s)
- Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Argentina.,Facultad de Ciencias Biomédicas and Facultad de Ingeniería, Universidad Austral, Pilar, Argentina.,Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Argentina
| | - M Gabriela Aucar
- Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Argentina
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15
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Optimization of norbornyl‐based carbocyclic nucleoside analogs as cyclin‐dependent kinase 2 inhibitors. J Mol Recognit 2020; 33:e2842. [DOI: 10.1002/jmr.2842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 01/26/2020] [Accepted: 02/17/2020] [Indexed: 02/06/2023]
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16
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Kříž K, Řezáč J. Benchmarking of Semiempirical Quantum-Mechanical Methods on Systems Relevant to Computer-Aided Drug Design. J Chem Inf Model 2020; 60:1453-1460. [PMID: 32062970 DOI: 10.1021/acs.jcim.9b01171] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The semiempirical quantum mechanical (SQM) methods used in drug design are commonly parametrized and tested on data sets of systems that may not be representative models for drug-biomolecule interactions in terms of both size and chemical composition. This is addressed here with a new benchmark data set, PLF547, derived from protein-ligand complexes, consisting of complexes of ligands with protein fragments (such as amino-acid side chains), with interaction energies based on MP2-F12 and DLPNO-CCSD(T) calculations. From these, composite benchmark interaction energies are also built for complexes of the ligand with the complete active site of the protein (PLA15 data set). These data sets are used to test multiple SQM methods with corrections for noncovalent interactions; the role of the solvation model in the calculations is tested as well.
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Affiliation(s)
- Kristian Kříž
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 166 10 Prague, Czech Republic.,Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University, Hlavova 8, 128 40 Praha 2, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 166 10 Prague, Czech Republic
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17
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Sheik Amamuddy O, Veldman W, Manyumwa C, Khairallah A, Agajanian S, Oluyemi O, Verkhivker GM, Tastan Bishop Ö. Integrated Computational Approaches and Tools forAllosteric Drug Discovery. Int J Mol Sci 2020; 21:E847. [PMID: 32013012 PMCID: PMC7036869 DOI: 10.3390/ijms21030847] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 12/16/2022] Open
Abstract
Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.
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Affiliation(s)
- Olivier Sheik Amamuddy
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Wayde Veldman
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Colleen Manyumwa
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Afrah Khairallah
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Steve Agajanian
- Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA; (S.A.); (O.O.)
| | - Odeyemi Oluyemi
- Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA; (S.A.); (O.O.)
| | - Gennady M. Verkhivker
- Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA; (S.A.); (O.O.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
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18
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Abstract
Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.
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Affiliation(s)
- M Gabriela Aucar
- Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina
| | - Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina.
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina.
- Facultad de Ciencias Biomédicas, Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina.
- Facultad de Ingeniería, Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina.
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19
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Ortiz-Mahecha CA, Bohórquez HJ, Agudelo WA, Patarroyo MA, Patarroyo ME, Suárez CF. Assessing Peptide Binding to MHC II: An Accurate Semiempirical Quantum Mechanics Based Proposal. J Chem Inf Model 2019; 59:5148-5160. [DOI: 10.1021/acs.jcim.9b00672] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
| | - Hugo J. Bohórquez
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá D.C., Colombia
- Universidad de Ciencias Aplicadas y Ambientales (UDCA), Bogotá D.C., Colombia
| | - William A. Agudelo
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá D.C., Colombia
| | - Manuel A. Patarroyo
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá D.C., Colombia
- School of Medicine and Health Sciences, Universidad del Rosario, Bogotá D.C., Colombia
| | - Manuel E. Patarroyo
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá D.C., Colombia
- Universidad Nacional de Colombia, Bogotá D.C., Colombia
| | - Carlos F. Suárez
- Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá D.C., Colombia
- School of Medicine and Health Sciences, Universidad del Rosario, Bogotá D.C., Colombia
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20
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Moman E, Grishina MA, Potemkin VA. Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions. J Comput Aided Mol Des 2019; 33:943-953. [PMID: 31728812 DOI: 10.1007/s10822-019-00248-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/04/2019] [Indexed: 12/20/2022]
Abstract
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accuracy and speed, with mathematical devices referred to as scoring functions being the fastest. Among the many shortcomings of scoring functions there is the lack of universal applicability to every molecular system. This is so largely due to their reliance on atom type perception and/or parametrization. This article proposes the use of nonparametric Model of Effective Radii of Atoms descriptors that can be readily computed for the entire Periodic Table and demonstrate that, in combination with machine learning algorithms, they can yield competitive performances and chemically meaningful insights.
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Affiliation(s)
- Edelmiro Moman
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080.
| | - Maria A Grishina
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
| | - Vladimir A Potemkin
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
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21
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Řezáč J. Description of halogen bonding in semiempirical quantum-mechanical and self-consistent charge density-functional tight-binding methods. J Comput Chem 2019; 40:1633-1642. [PMID: 30941801 DOI: 10.1002/jcc.25816] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 02/14/2019] [Accepted: 02/21/2019] [Indexed: 12/20/2022]
Abstract
This article analyzes the ability of semiempirical quantum-mechanical methods (PM6 and PM7) and self-consistent charge density-functional tight-binding (SCC-DFTB) method DFTB3 to describe halogen bonds. Calculations of the electrostatic potential on the surface of molecules containing halogens show that the σ-hole could be described well in modified neglect of diatomic overlap-based methods. The situation is more complex in the case of DFTB3 where a simpler model is used for the electrostatics, but short-ranged effects are covered in the Hamiltonian. All these methods can thus capture the effects that, for example, define the geometry of halogen bonds. The interaction energies are, however, affected by generally underestimated repulsion, which has been addressed earlier by standalone empirical corrections. Another approach to correcting this issue in DFTB3 is presented here-a modification of the energies of d-orbitals on halogens yields better results than the empirical correction in DFTB3-D3X, although it remains difficult to describe halogen and hydrogen bonds simultaneously. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Jan Řezáč
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 166 10, Prague, Czech Republic
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22
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Lukac I, Abdelhakim H, Ward RA, St-Gallay SA, Madden JC, Leach AG. Predicting protein-ligand binding affinity and correcting crystal structures with quantum mechanical calculations: lactate dehydrogenase A. Chem Sci 2019; 10:2218-2227. [PMID: 30881647 PMCID: PMC6388092 DOI: 10.1039/c8sc04564j] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 12/19/2018] [Indexed: 12/22/2022] Open
Abstract
Accurately computing the geometry and energy of host-guest and protein-ligand interactions requires a physically accurate description of the forces in action. Quantum mechanics can provide this accuracy but the calculations can require a prohibitive quantity of computational resources. The size of the calculations can be reduced by including only the atoms of the receptor that are in close proximity to the ligand. We show that when combined with log P values for the ligand (which can be computed easily) this approach can significantly improve the agreement between computed and measured binding energies. When the approach is applied to lactate dehydrogenase A, it can make quantitative predictions about conformational, tautomeric and protonation state preferences as well as stereoselectivity and even identifies potential errors in structures deposited in the Protein Data Bank for this enzyme. By broadening the evidence base for these structures from only the diffraction data, more chemically realistic structures can be proposed.
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Affiliation(s)
- Iva Lukac
- School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Byrom Street , Liverpool , L3 3AF , UK .
| | - Hend Abdelhakim
- School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Byrom Street , Liverpool , L3 3AF , UK .
| | - Richard A Ward
- Chemistry, Oncology, IMED Biotech Unit , AstraZeneca , Cambridge , UK
| | - Stephen A St-Gallay
- Sygnature Discovery Ltd , Bio City, Pennyfoot St , Nottingham , NG1 1GF , UK
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Byrom Street , Liverpool , L3 3AF , UK .
| | - Andrew G Leach
- School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Byrom Street , Liverpool , L3 3AF , UK .
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23
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Suárez D, Díaz N. Affinity Calculations of Cyclodextrin Host-Guest Complexes: Assessment of Strengths and Weaknesses of End-Point Free Energy Methods. J Chem Inf Model 2019; 59:421-440. [PMID: 30566348 DOI: 10.1021/acs.jcim.8b00805] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The end-point methods like MM/PBSA or MM/GBSA estimate the free energy of a biomolecule by combining its molecular mechanics energy with solvation free energy and entropy terms. On the one hand, their performance largely depends on the particular system of interest, and despite numerous attempts to improve their reliability that have resulted in many variants, there is still no clear alternative to improve their accuracy. On the other hand, the relatively small cyclodextrin host-guest complexes, for which high-quality binding calorimetric data are usually available, are becoming reference models for testing the accuracy of free energy methods. In this work, we further assess the performance of various MM/PBSA-like approaches as applied to cyclodextrin complexes. To this end, we select a set of complexes between β-cyclodextrin and 57 small organic molecules that has been previously studied with the binding energy distribution analysis method in combination with an implicit solvent model ( Wickstrom, L.; He, P.; Gallicchio, E.; Levy, R. M. J. Chem. Theory Comput. 2013 , 9 , 3136 - 3150 ). For each complex, a conventional 1.0 μs molecular dynamics simulation in explicit solvent is performed. Then we employ semiempirical quantum chemical calculations, several variants of the MM-PB(GB)SA methods, entropy estimations, etc., to assess the reliability of the end-point affinity calculations. The best end-point protocol in this study, which combines DFTB3 energies with entropy corrections, yields estimations of the binding free energies that still have substantial errors (RMSE = 2.2 kcal/mol), but it exhibits a good prediction capacity in terms of ligand ranking ( R2 = 0.66) that is close to or even better than that of rigorous free energy methodologies. Our results can be helpful to discriminate between the intrinsic limitations of the end-point methods and other sources of error, such as the underlying energy and continuum solvation methods.
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Affiliation(s)
- Dimas Suárez
- Departamento de Química Física y Analítica , Universidad de Oviedo , Avda. Julián Clavería 8 , Oviedo , Asturias 33006 , Spain
| | - Natalia Díaz
- Departamento de Química Física y Analítica , Universidad de Oviedo , Avda. Julián Clavería 8 , Oviedo , Asturias 33006 , Spain
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24
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Kříž K, Řezáč J. Reparametrization of the COSMO Solvent Model for Semiempirical Methods PM6 and PM7. J Chem Inf Model 2019; 59:229-235. [DOI: 10.1021/acs.jcim.8b00681] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Kristian Kříž
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 166 10 Prague, Czech Republic
- Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University, Hlavova 8, 128 40 Prague 2, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 166 10 Prague, Czech Republic
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25
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Sulaiman KO, Kolapo TU, Onawole AT, Islam MA, Adegoke RO, Badmus SO. Molecular dynamics and combined docking studies for the identification of Zaire ebola virus inhibitors. J Biomol Struct Dyn 2018; 37:3029-3040. [PMID: 30058446 DOI: 10.1080/07391102.2018.1506362] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Ebola virus (EBOV) is a lethal human pathogen with a risk of global spread of its zoonotic infections, and Ebolavirus Zaire specifically has the highest fatality rate amongst other species. There is a need for continuous effort towards having therapies, as a single licensed treatment to neutralize the EBOV is yet to come into reality. This present study virtually screened the MCULE database containing almost 36 million compounds against the structure of a Zaire Ebola viral protein (VP) 35 and a consensus scoring of both MCULE and CLCDDW docking programs remarked five compounds as potential hits. These compounds, with binding energies ranging from -7.9 to -8.9 kcal/mol, were assessed for predictions of their physicochemical and bioactivity properties, as well as absorption, distribution, metabolism, excretion, and toxicity (ADMET) criteria. The results of the 50 ns molecular dynamics simulations showed the presence of dynamic stability between ligand and protein complexes, and the structures remained significantly unchanged at the ligand-binding site throughout the simulation period. Both docking analysis and molecular dynamics simulation studies suggested strong binding affinity towards the receptor cavity and these selected compounds as potential inhibitors against the Zaire Ebola VP 35. With respect to inhibition constant values, bioavailability radar and other physicochemical properties, compound A (MCULE-1018045960-0-1) appeared to be the most promising hit compound. However, the ligand efficiency and ligand efficiency scale need improvement during optimization, and also validation via in vitro and in vivo studies are necessary to finally make a lead compound in treating Ebola virus diseases. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Kazeem O Sulaiman
- a Department of Chemistry , University of Saskatchewan , Saskatoon , Saskatchewan , Canada
| | - Temitope U Kolapo
- b Department of Veterinary Parasitology and Entomology , University of Ilorin , Ilorin , Nigeria.,c Department of Veterinary Microbiology , University of Saskatchewan , Saskatchewan , Canada
| | | | - Md Ataul Islam
- e Department of Chemical Pathology Faculty of Health Sciences , University of Pretoria and National Health Laboratory Service Tshwane Academic Division , Pretoria , South Africa.,f School of Health Sciences , University of Kwazulu-Natal Westville Campus , Durban , South Africa
| | - Rukayat O Adegoke
- g Department of Pure and Applied Biology , Ladoke Akintola University of Technology , Ogbomoso , Nigeria
| | - Suaibu O Badmus
- g Department of Pure and Applied Biology , Ladoke Akintola University of Technology , Ogbomoso , Nigeria
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26
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Interface Interactions of the Bowman-Birk Inhibitor BTCI in a Ternary Complex with Trypsin and Chymotrypsin Evaluated by Semiempirical Quantum Mechanical Calculations. European J Org Chem 2018. [DOI: 10.1002/ejoc.201800754] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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27
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Roston D, Lu X, Fang D, Demapan D, Cui Q. Analysis of Phosphoryl-Transfer Enzymes with QM/MM Free Energy Simulations. Methods Enzymol 2018; 607:53-90. [PMID: 30149869 DOI: 10.1016/bs.mie.2018.05.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We discuss the application of quantum mechanics/molecular mechanics (QM/MM) free energy simulations to the analysis of phosphoryl transfers catalyzed by two enzymes: alkaline phosphatase and myosin. We focus on the nature of the transition state and the issue of mechanochemical coupling, respectively, in the two enzymes. The results illustrate unique insights that emerged from the QM/MM simulations, especially concerning the interpretation of experimental data regarding the nature of enzymatic transition states and coupling between global structural transition and catalysis in the active site. We also highlight a number of technical issues worthy of attention when applying QM/MM free energy simulations, and comment on a number of technical and mechanistic issues that require further studies.
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Affiliation(s)
- Daniel Roston
- Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin, Madison, Madison, WI, United States
| | - Xiya Lu
- Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin, Madison, Madison, WI, United States
| | - Dong Fang
- Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin, Madison, Madison, WI, United States
| | - Darren Demapan
- Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin, Madison, Madison, WI, United States
| | - Qiang Cui
- Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin, Madison, Madison, WI, United States.
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28
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Cavasotto CN, Adler NS, Aucar MG. Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization. Front Chem 2018; 6:188. [PMID: 29896472 PMCID: PMC5986912 DOI: 10.3389/fchem.2018.00188] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 05/09/2018] [Indexed: 12/05/2022] Open
Abstract
Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.
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Affiliation(s)
- Claudio N. Cavasotto
- Laboratory of Computational Chemistry and Drug Design, Instituto de Investigación en Biomedicina de Buenos Aires, CONICET, Partner Institute of the Max Planck Society, Buenos Aires, Argentina
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29
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Ajani H, Jansa J, Köprülüoğlu C, Hobza P, Kryštof V, Lyčka A, Lepsik M. Imidazo[1,2-c
]pyrimidin-5(6H
)-one as a novel core of cyclin-dependent kinase 2 inhibitors: Synthesis, activity measurement, docking, and quantum mechanical scoring. J Mol Recognit 2018; 31:e2720. [DOI: 10.1002/jmr.2720] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 02/26/2018] [Accepted: 03/21/2018] [Indexed: 12/17/2022]
Affiliation(s)
- Haresh Ajani
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences; Prague 6 Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry; Palacký University; Olomouc Czech Republic
| | - Josef Jansa
- Research Institute for Organic Syntheses (VUOS); Pardubice-Rybitví Czech Republic
- Department of Organic Chemistry, Faculty of Science; Palacký University; Olomouc Czech Republic
| | - Cemal Köprülüoğlu
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences; Prague 6 Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry; Palacký University; Olomouc Czech Republic
| | - Pavel Hobza
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences; Prague 6 Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry; Palacký University; Olomouc Czech Republic
| | - Vladimír Kryštof
- Laboratory of Growth Regulators, Centre of the Region Haná for Biotechnological and Agricultural Research, Faculty of Science; Palacký University and Institute of Experimental Botany; Olomouc Czech Republic
| | - Antonín Lyčka
- Research Institute for Organic Syntheses (VUOS); Pardubice-Rybitví Czech Republic
- Faculty of Science; University of Hradec Králové; Hradec Králové Czech Republic
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30
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Miriyala VM, Řezáč J. Testing Semiempirical Quantum Mechanical Methods on a Data Set of Interaction Energies Mapping Repulsive Contacts in Organic Molecules. J Phys Chem A 2018; 122:2801-2808. [PMID: 29473742 DOI: 10.1021/acs.jpca.8b00260] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Semiempirical quantum mechanical (QM) methods with corrections for noncovalent interactions provide a favorable combination of accuracy and computational efficiency that makes them a useful tool for a study of large molecular systems. It was, however, noted that the accuracy of these methods deteriorates at intermolecular distances shorter than equilibrium. In this work, we explore this issue systematically using a newly developed data set of benchmark interaction energies named R160×6. This data set maps repulsive contacts in organic molecules, and it consists of 160 model complexes for which six points along the dissociation curve are provided. Testing a wide range of semiempirical QM methods against the CCSD(T)/CBS benchmark revealed that most methods, and all the dispersion-corrected ones, underestimate the repulsion systematically. The worst cases are usually hydrogen-hydrogen contacts. The best results were obtained with PM6-D3H4 and DFTB3-D3H4, as these methods already contain a correction for the H-H repulsion, but the errors are still about twice as large as in equilibrium geometries.
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Affiliation(s)
- V M Miriyala
- Department of Computational Chemistry , Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences , Flemingovo Náměstí 542/2 , 16610 Prague , Czech Republic
| | - J Řezáč
- Department of Computational Chemistry , Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences , Flemingovo Náměstí 542/2 , 16610 Prague , Czech Republic
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31
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Structural, spectroscopic and docking properties of resorcinol, its -OD isotopomer and dianion derivative: a comparative study. Struct Chem 2018. [DOI: 10.1007/s11224-017-1037-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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32
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Pecina A, Brynda J, Vrzal L, Gnanasekaran R, Hořejší M, Eyrilmez SM, Řezáč J, Lepšík M, Řezáčová P, Hobza P, Majer P, Veverka V, Fanfrlík J. Ranking Power of the SQM/COSMO Scoring Function on Carbonic Anhydrase II-Inhibitor Complexes. Chemphyschem 2018; 19:873-879. [PMID: 29316128 DOI: 10.1002/cphc.201701104] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Indexed: 11/11/2022]
Abstract
Accurate prediction of protein-ligand binding affinities is essential for hit-to-lead optimization and virtual screening. The reliability of scoring functions can be improved by including quantum effects. Here, we demonstrate the ranking power of the semiempirical quantum mechanics (SQM)/implicit solvent (COSMO) scoring function by using a challenging set of 10 inhibitors binding to carbonic anhydrase II through Zn2+ in the active site. This new dataset consists of the high-resolution (1.1-1.4 Å) crystal structures and experimentally determined inhibitory constant (Ki ) values. It allows for evaluation of the common approximations, such as representing the solvent implicitly or by using a single target conformation combined with a set of ligand docking poses. SQM/COSMO attained a good correlation of R2 of 0.56-0.77 with the experimental inhibitory activities, benefiting from careful handling of both noncovalent interactions (e.g. charge transfer) and solvation. This proof-of-concept study of SQM/COSMO ranking for metalloprotein-ligand systems demonstrates its potential for hit-to-lead applications.
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Affiliation(s)
- Adam Pecina
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic
| | - Jiří Brynda
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic.,Institute of Molecular Genetics of, Czech Academy of Sciences, Videnska 1083, 14220, Prague 4, Czech Republic
| | - Lukáš Vrzal
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic
| | - Ramachandran Gnanasekaran
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic.,Current address: Department of Chemistry, Pondicherry University, Puducherry, 605014, India
| | - Magdalena Hořejší
- Institute of Molecular Genetics of, Czech Academy of Sciences, Videnska 1083, 14220, Prague 4, Czech Republic
| | - Saltuk M Eyrilmez
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic.,Palacký University, 77146, Olomouc, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic
| | - Martin Lepšík
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic
| | - Pavlína Řezáčová
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic.,Institute of Molecular Genetics of, Czech Academy of Sciences, Videnska 1083, 14220, Prague 4, Czech Republic
| | - Pavel Hobza
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Palacký University, 77146, Olomouc, Czech Republic
| | - Pavel Majer
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic
| | - Václav Veverka
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic
| | - Jindřich Fanfrlík
- Institute of Organic Chemistry and Biochemistry of the, Czech Academy of Sciences, Flemingovo nam. 2, 16610, Prague 6, Czech Republic
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33
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Grimme S, Bannwarth C, Caldeweyher E, Pisarek J, Hansen A. A general intermolecular force field based on tight-binding quantum chemical calculations. J Chem Phys 2017; 147:161708. [DOI: 10.1063/1.4991798] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Stefan Grimme
- Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie der Universität Bonn, Beringstr. 4, D-53115 Bonn,
Germany
| | - Christoph Bannwarth
- Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie der Universität Bonn, Beringstr. 4, D-53115 Bonn,
Germany
| | - Eike Caldeweyher
- Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie der Universität Bonn, Beringstr. 4, D-53115 Bonn,
Germany
| | - Jana Pisarek
- Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie der Universität Bonn, Beringstr. 4, D-53115 Bonn,
Germany
| | - Andreas Hansen
- Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie der Universität Bonn, Beringstr. 4, D-53115 Bonn,
Germany
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34
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Řezáč J. Empirical Self-Consistent Correction for the Description of Hydrogen Bonds in DFTB3. J Chem Theory Comput 2017; 13:4804-4817. [DOI: 10.1021/acs.jctc.7b00629] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Jan Řezáč
- Institute of Organic Chemistry
and Biochemistry, Czech Academy of Sciences, 166 10 Prague, Czech Republic
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35
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Ajani H, Pecina A, Eyrilmez SM, Fanfrlík J, Haldar S, Řezáč J, Hobza P, Lepšík M. Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking. ACS OMEGA 2017; 2:4022-4029. [PMID: 30023710 PMCID: PMC6044937 DOI: 10.1021/acsomega.7b00503] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 07/18/2017] [Indexed: 06/08/2023]
Abstract
General and reliable description of structures and energetics in protein-ligand (PL) binding using the docking/scoring methodology has until now been elusive. We address this urgent deficiency of scoring functions (SFs) by the systematic development of corrected semiempirical quantum mechanical (SQM) methods, which correctly describe all types of noncovalent interactions and are fast enough to treat systems of thousands of atoms. Two most accurate SQM methods, PM6-D3H4X and SCC-DFTB3-D3H4X, are coupled with the conductor-like screening model (COSMO) implicit solvation model in so-called "SQM/COSMO" SFs and have shown unique recognition of native ligand poses in cognate docking in four challenging PL systems, including metalloprotein. Here, we apply the two SQM/COSMO SFs to 17 diverse PL complexes and compare their performance with four widely used classical SFs (Glide XP, AutoDock4, AutoDock Vina, and UCSF Dock). We observe superior performance of the SQM/COSMO SFs and identify challenging systems. This method, due to its generality, comparability across the chemical space, and lack of need for any system-specific parameters, gives promise of becoming, after comprehensive large-scale testing in the near future, a useful computational tool in structure-based drug design and serving as a reference method for the development of other SFs.
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Affiliation(s)
- Haresh Ajani
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
- Department
of Physical Chemistry, Palacký University, tř. 17. listopadu 1192/12, 77146 Olomouc, Czech Republic
| | - Adam Pecina
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
| | - Saltuk M. Eyrilmez
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
- Department
of Physical Chemistry, Palacký University, tř. 17. listopadu 1192/12, 77146 Olomouc, Czech Republic
| | - Jindřich Fanfrlík
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
| | - Susanta Haldar
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
| | - Jan Řezáč
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
| | - Pavel Hobza
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
- Department
of Physical Chemistry, Regional Centre of Advanced Technologies and
Materials, Palacký University, 77146 Olomouc, Czech Republic
| | - Martin Lepšík
- Department
of Computational Chemistry, Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, v.v.i., Flemingovo nam. 2, 16610 Praha 6, Czech Republic
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36
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De Vivo M, Cavalli A. Recent advances in dynamic docking for drug discovery. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1320] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Marco De Vivo
- Laboratory of Molecular Modeling and Drug DiscoveryIstituto Italiano di TecnologiaGenoaItaly
- IAS‐S/INM‐9 Computational BiomedicineForschungszentrum JülichJülichGermany
| | - Andrea Cavalli
- CompunetIstituto Italiano di TecnologiaGenoaItaly
- Department of Pharmacy and Biotechnology, Alma Mater StudiorumUniversity of BolognaBolognaItaly
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