1
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Bosio S, Bernetti M, Rocchia W, Masetti M. Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin. J Chem Inf Model 2024; 64:4570-4586. [PMID: 38800845 DOI: 10.1021/acs.jcim.4c00420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.
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Affiliation(s)
- Stefano Bosio
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Mattia Bernetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Walter Rocchia
- Computational mOdelling of NanosCalE and bioPhysical sysTems (CONCEPT) Lab, Istituto Italiano di Tecnologia, Via Melen - 83, B Block, 16152 Genova, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
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2
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Karrenbrock M, Rizzi V, Procacci P, Gervasio FL. Addressing Suboptimal Poses in Nonequilibrium Alchemical Calculations. J Phys Chem B 2024; 128:1595-1605. [PMID: 38323915 DOI: 10.1021/acs.jpcb.3c06516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Alchemical transformations can be used to quantitatively estimate absolute binding free energies at a reasonable computational cost. However, most of the approaches currently in use require knowledge of the correct (crystallographic) pose. In this paper, we present a combined Hamiltonian replica exchange nonequilibrium alchemical method that allows us to reliably calculate absolute binding free energies, even when starting from suboptimal initial binding poses. Performing a preliminary Hamiltonian replica exchange enhances the sampling of slow degrees of freedom of the ligand and the target, allowing the system to populate the correct binding pose when starting from an approximate docking pose. We apply the method on 6 ligands of the first bromodomain of the BRD4 bromodomain-containing protein. For each ligand, we start nonequilibrium alchemical transformations from both the crystallographic pose and the top-scoring docked pose that are often significantly different. We show that the method produces statistically equivalent binding free energies, making it a useful tool for computational drug discovery pipelines.
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Affiliation(s)
- Maurice Karrenbrock
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, CH-1206 Geneva, Switzerland
| | - Valerio Rizzi
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, CH-1206 Geneva, Switzerland
| | - Piero Procacci
- Chemistry Department, University of Florence, Via della Lastruccia 3-13, 50019 Sesto Fiorentino, Italy
| | - Francesco Luigi Gervasio
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, CH-1206 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CH-1206 Geneva, Switzerland
- Chemistry Department, University College London (UCL), WC1E 6BT London, U.K
- Swiss Bioinformatics Institute, University of Geneva, CH-1206 Geneva, Switzerland
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3
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Rathee P, Moorkkannur SN, Prabhakar R. Structural studies of catalytic peptides using molecular dynamics simulations. Methods Enzymol 2024; 697:151-180. [PMID: 38816122 DOI: 10.1016/bs.mie.2024.01.019] [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] [Indexed: 06/01/2024]
Abstract
Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.
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Affiliation(s)
- Parth Rathee
- Department of Chemistry, University of Miami, Coral Gables, FL, United States
| | | | - Rajeev Prabhakar
- Department of Chemistry, University of Miami, Coral Gables, FL, United States.
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4
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Mills KR, Torabifard H. Computational approaches to investigate fluoride binding, selectivity and transport across the membrane. Methods Enzymol 2024; 696:109-154. [PMID: 38658077 DOI: 10.1016/bs.mie.2024.01.006] [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] [Indexed: 04/26/2024]
Abstract
The use of molecular dynamics (MD) simulations to study biomolecular systems has proven reliable in elucidating atomic-level details of structure and function. In this chapter, MD simulations were used to uncover new insights into two phylogenetically unrelated bacterial fluoride (F-) exporters: the CLCF F-/H+ antiporter and the Fluc F- channel. The CLCF antiporter, a member of the broader CLC family, has previously revealed unique stoichiometry, anion-coordinating residues, and the absence of an internal glutamate crucial for proton import in the CLCs. Through MD simulations enhanced with umbrella sampling, we provide insights into the energetics and mechanism of the CLCF transport process, including its selectivity for F- over HF. In contrast, the Fluc F- channel presents a novel architecture as a dual topology dimer, featuring two pores for F- export and a central non-transported sodium ion. Using computational electrophysiology, we simulate the electrochemical gradient necessary for F- export in Fluc and reveal details about the coordination and hydration of both F- and the central sodium ion. The procedures described here delineate the specifics of these advanced techniques and can also be adapted to investigate other membrane protein systems.
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Affiliation(s)
- Kira R Mills
- Department of Chemistry & Biochemistry, The University of Texas at Dallas, Richardson, TX, United States
| | - Hedieh Torabifard
- Department of Chemistry & Biochemistry, The University of Texas at Dallas, Richardson, TX, United States.
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5
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Rubina, Moin ST. Attempting Well-Tempered Funnel Metadynamics Simulations for the Evaluation of the Binding Kinetics of Methionine Aminopeptidase-II Inhibitors. J Chem Inf Model 2023; 63:7729-7743. [PMID: 38059911 DOI: 10.1021/acs.jcim.3c01130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Understanding the unbinding kinetics of protein-ligand complexes is considered a significant approach for the design of ligands with desired specificity and safety. In recent years, enhanced sampling methods have emerged as effective tools for studying the unbinding kinetics of protein-ligand complexes at the atomistic level. MetAP-II is a target for the treatment of cancer for which not a single effective drug is available yet. The identification of the dissociation rate of ligands from the complexes often serves as a better predictor for in vivo efficacy than the ligands' binding affinity. Here, funnel-based restraint well-tempered metadynamics simulations were applied to predict the residence time of two ligands bound to MetAP-II, along with the ligand association and dissociation mechanism involving the identification of the binding hotspot during ligand egress. The ligand-egressing route revealed by metadynamics simulations also correlated with the identified pathways from the CAVER analysis and by the enhanced sampling simulation using PLUMED. Ligand 1 formed a strong H-bond interaction with GLU364 estimating a higher residence time of 28.22 ± 5.29 ns in contrast to ligand 2 with a residence time of 19.05 ± 3.58 ns, which easily dissociated from the binding pocket of MetAP-II. The results obtained from the simulations were consistent to reveal ligand 1 being superior to ligand 2; however, the experimental data related to residence time were close for both ligands, and no kinetic data were available for ligand 2. The current study could be considered the first attempt to apply an enhanced sampling method for the evaluation of the binding kinetics and thermodynamics of two different classes of ligands to a binuclear metalloprotein.
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Affiliation(s)
- Rubina
- Third World Center for Science and Technology H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Science University of Karachi, Karachi 75270, Pakistan
| | - Syed Tarique Moin
- Third World Center for Science and Technology H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Science University of Karachi, Karachi 75270, Pakistan
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6
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Siddiqui GA, Stebani JA, Wragg D, Koutsourelakis PS, Casini A, Gagliardi A. Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study. Chemistry 2023; 29:e202302375. [PMID: 37555841 DOI: 10.1002/chem.202302375] [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: 07/28/2023] [Accepted: 08/09/2023] [Indexed: 08/10/2023]
Abstract
In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.
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Affiliation(s)
- Gohar Ali Siddiqui
- Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical and Computer Engineering School of Computation, Information and Technology, Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748, Garching b. München, Germany
| | - Julia A Stebani
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Darren Wragg
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Phaedon-Stelios Koutsourelakis
- Professorship for Data-driven Materials Modeling School of Engineering and Design, Technical University of Munich (TUM), Boltzmannstr. 15, 85748, Garching b. München, Germany
| | - Angela Casini
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Alessio Gagliardi
- Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical and Computer Engineering School of Computation, Information and Technology, Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748, Garching b. München, Germany
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7
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Guo CR, Zhang ZZ, Zhou X, Sun MY, Li TT, Lei YT, Gao YH, Li QQ, Yue CX, Gao Y, Lin YY, Hao CY, Li CZ, Cao P, Zhu MX, Rong MQ, Wang WH, Yu Y. Chronic cough relief by allosteric modulation of P2X3 without taste disturbance. Nat Commun 2023; 14:5844. [PMID: 37730705 PMCID: PMC10511716 DOI: 10.1038/s41467-023-41495-0] [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: 11/02/2022] [Accepted: 09/06/2023] [Indexed: 09/22/2023] Open
Abstract
P2X receptors are cation channels that sense extracellular ATP. Many therapeutic candidates targeting P2X receptors have begun clinical trials or acquired approval for the treatment of refractory chronic cough (RCC) and other disorders. However, the present negative allosteric modulation of P2X receptors is primarily limited to the central pocket or the site below the left flipper domain. Here, we uncover a mechanism of allosteric regulation of P2X3 in the inner pocket of the head domain (IP-HD), and show that the antitussive effects of quercetin and PSFL2915 (our nM-affinity P2X3 inhibitor optimized based on quercetin) on male mice and guinea pigs were achieved by preventing allosteric changes of IP-HD in P2X3. While being therapeutically comparable to the newly licensed P2X3 RCC drug gefapixant, quercetin and PSFL2915 do not have an adverse effect on taste as gefapixant does. Thus, allosteric modulation of P2X3 via IP-HD may be a druggable strategy to alleviate RCC.
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Affiliation(s)
- Chang-Run Guo
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
- School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, China
| | - Zhong-Zhe Zhang
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Xing Zhou
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Meng-Yang Sun
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Tian-Tian Li
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Yun-Tao Lei
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Yu-Hao Gao
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Qing-Quan Li
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Chen-Xi Yue
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Yu Gao
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Yi-Yu Lin
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Cui-Yun Hao
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
| | - Chang-Zhu Li
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha, Hunan, 410004, China
| | - Peng Cao
- Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Michael X Zhu
- Department of Integrative Biology and Pharmacology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA
| | - Ming-Qiang Rong
- The National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China.
| | - Wen-Hui Wang
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
| | - Ye Yu
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211198, China.
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8
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Rizzi V, Aureli S, Ansari N, Gervasio FL. OneOPES, a Combined Enhanced Sampling Method to Rule Them All. J Chem Theory Comput 2023; 19:5731-5742. [PMID: 37603295 PMCID: PMC10500989 DOI: 10.1021/acs.jctc.3c00254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Indexed: 08/22/2023]
Abstract
Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multireplica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.
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Affiliation(s)
- Valerio Rizzi
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Simone Aureli
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Narjes Ansari
- Atomistic
Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Francesco Luigi Gervasio
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
- Department
of Chemistry, University College London, WC1E 6BT London, U.K.
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9
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Matos IDA, Goes Pinto AC, Ferraz MVF, Adan WCS, Rodrigues RP, Dos Santos JX, Kitagawa RR, Lins RD, Oliveira TB, Costa Junior NBD. Identification of potential Staphylococcus aureus dihydrofolate reductase inhibitors using QSAR, molecular docking, dynamics simulations and free energy calculation. J Biomol Struct Dyn 2023; 41:3835-3846. [PMID: 35356863 DOI: 10.1080/07391102.2022.2057361] [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: 08/01/2021] [Accepted: 03/19/2022] [Indexed: 10/18/2022]
Abstract
Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2train, CVLOO and R2test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Isaac de Araujo Matos
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Ana Carolina Goes Pinto
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | | | - Wenny Camilla Santos Adan
- Department of Pharmaceutical Sciences, Postgraduate Program in Pharmaceutical Sciences, Federal University of Espírito Santo-UFES, Vitória-ES, Brazil
| | - Ricardo Pereira Rodrigues
- Department of Pharmacy, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Juliane Xavier Dos Santos
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Rodrigo Rezende Kitagawa
- Department of Pharmaceutical Sciences, Postgraduate Program in Pharmaceutical Sciences, Federal University of Espírito Santo-UFES, Vitória-ES, Brazil
| | | | - Tiago Branquinho Oliveira
- Department of Pharmacy, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
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10
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Zuo K, Kranjc A, Capelli R, Rossetti G, Nechushtai R, Carloni P. Metadynamics simulations of ligands binding to protein surfaces: a novel tool for rational drug design. Phys Chem Chem Phys 2023; 25:13819-13824. [PMID: 37184538 DOI: 10.1039/d3cp01388j] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Structure-based drug design protocols may encounter difficulties to investigate poses when the biomolecular targets do not exhibit typical binding pockets. In this study, by providing two concrete examples from our labs, we suggest that the combination of metadynamics free energy methods (validated against affinity measurements), along with experimental structural information (by X-ray crystallography and NMR), can help to identify the poses of ligands on protein surfaces. The simulation workflow proposed here was implemented in a widely used code, namely GROMACS, and it could straightforwardly be applied to various drug-design campaigns targeting ligands' binding to protein surfaces.
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Affiliation(s)
- Ke Zuo
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
- Department of Physics, Università degli Studi di Ferrara, Ferrara 44121, Italy
| | - Agata Kranjc
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
| | - Riccardo Capelli
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, Milan 20133, Italy
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Jülich Supercomputing Center (JSC), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
- Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen 52074, Germany
| | - Rachel Nechushtai
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- JARA Institute: Molecular Neuroscience and Imaging, Institute of Neuroscience and Medicine INM-11, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
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11
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Motta S, Siani P, Donadoni E, Frigerio G, Bonati L, Di Valentin C. Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles. NANOSCALE 2023; 15:7909-7919. [PMID: 37066796 DOI: 10.1039/d3nr00397c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO2 nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.
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Affiliation(s)
- Stefano Motta
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Paulo Siani
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Edoardo Donadoni
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Giulia Frigerio
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Laura Bonati
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Cristiana Di Valentin
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
- BioNanoMedicine Center NANOMIB, University of Milano-Bicocca, Italy
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12
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Ferraz MVF, Neto JCS, Lins RD, Teixeira ES. An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties. Phys Chem Chem Phys 2023; 25:7257-7267. [PMID: 36810523 DOI: 10.1039/d2cp05644e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol-1 to 2.45 kcal mol-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.
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Affiliation(s)
- Matheus V F Ferraz
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil.,Heidelberg Institute for Theoretical Studies, HITS, Heidelberg, Germany
| | - José C S Neto
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
| | - Roberto D Lins
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil
| | - Erico S Teixeira
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
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13
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Kamal IM, Chakrabarti S. MetaDOCK: A Combinatorial Molecular Docking Approach. ACS OMEGA 2023; 8:5850-5860. [PMID: 36816658 PMCID: PMC9933224 DOI: 10.1021/acsomega.2c07619] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/11/2023] [Indexed: 06/18/2023]
Abstract
Molecular docking plays a major role in academic and industrial drug screening and discovery processes. Despite the availability of numerous docking software packages, there is a lot of scope for improvement for the docking algorithms in terms of becoming more reliable to replicate the experimental binding results. Here, we propose a combinatorial or consensus docking approach where complementary powers of the existing methods are captured. We created a meta-docking protocol by combining the results of AutoDock4.2, LeDock, and rDOCK programs as these are freely available, easy to use, and suitable for large-scale analysis and produced better performance on benchmarking studies. Rigorous benchmarking analyses were undertaken to evaluate the scoring, posing, and screening capability of our approach. Further, the performance measures were compared against one standard state-of-the-art commercial docking software, GOLD, and one freely available software, PLANTS. Performances of MetaDOCK for scoring, posing, and screening the protein-ligand complexes were found to be quite superior compared to the reference programs. Exhaustive molecular dynamics simulation and molecular mechanics Poisson-Boltzmann and surface area-based free energy estimation also suggest better energetic stability of the docking solutions produced by our meta-approach. We believe that the MetaDOCK approach is a useful packaging of the freely available software and provides a better alternative to the scientific community who are unable to afford costly commercial packages.
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Affiliation(s)
- Izaz Monir Kamal
- Division
of Structural Biology & Bioinformatics, CSIR-Indian Institute of Chemical Biology, Salt Lake, Sector V, Kolkata 700032, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Saikat Chakrabarti
- Division
of Structural Biology & Bioinformatics, CSIR-Indian Institute of Chemical Biology, Salt Lake, Sector V, Kolkata 700032, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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14
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Zou Y, Wang R, Du M, Wang X, Xu D. Identifying Protein-Ligand Interactions via a Novel Distance Self-Feedback Biomolecular Interaction Network. J Phys Chem B 2023; 127:899-911. [PMID: 36657025 DOI: 10.1021/acs.jpcb.2c07592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Efficient and accurate characterizations of protein-ligand interactions are key to understanding biology at the molecular level. They are particularly useful in pharmaceutical industry applications. They are usually computationally demanding for those widely applied dynamics-based methods in identifying important residues or calculating ligand binding free energy. In this work, we proposed a graph deep learning (DL) framework, namely, the distance self-feedback biomolecular interaction network (DSBIN), in which the relationship between the complex structure and binding affinity can be established by means of a carefully designed distance self-feedback module and interaction layer. Our model can directly provide a quantitative evaluation of inhibitor binding affinities (pKd). More importantly, the DSBIN model efficiently identifies key interactions for inhibitor binding and thus intrinsically bears the interpretability. Its generalization performance was further verified using 1405 unseen structures. The predicted binding free energies' deviations were calculated to be less than 1.37 kcal/mol for more than 55% structures. Moreover, we also compared the DSBIN model with a commonly used theoretical method in calculating the substrate binding free energy, MM/GBSA. Our results show that the current DL model has generally better performance in predicting the binding free energy. For a specific complex system, mannopentaose/TmCBM27, the DSBIN predicted binding free energy is -8.21 kcal/mol, which is very close to experimentally measured -7.76 kcal/mol and MM/GBSA calculated -7.16 kcal/mol. Meanwhile, all important aromatic residues around the binding pocket can be identified by our DL model. Considering the accuracy and efficiency of the newly developed DL model, it may be very helpful in the field of drug design and molecular recognition.
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Affiliation(s)
- Yurong Zou
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Ruihan Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Meng Du
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Xin Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Dingguo Xu
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China.,Research Center for Materials Genome Engineering, Sichuan University, Chengdu, Sichuan610065, PR China
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15
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Cao W, Xia GJ, Yao Z, Zeng KH, Qiao Y, Wang YG. Aldehyde Hydrogenation by Pt/TiO 2 Catalyst in Aqueous Phase: Synergistic Effect of Oxygen Vacancy and Solvent Water. JACS AU 2023; 3:143-153. [PMID: 36711102 PMCID: PMC9875238 DOI: 10.1021/jacsau.2c00560] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 06/18/2023]
Abstract
The aldehyde hydrogenation for stabilizing and upgrading biomass is typically performed in aqueous phase with supported metal catalysts. By combining density functional theory calculations and ab initio molecular dynamics simulations, the model reaction of formaldehyde hydrogenation with a Pt/TiO2 catalyst is investigated with explicit solvent water molecules. In aqueous phase, both the O vacancy (Ov) on support and solvent molecules could donate charges to a Pt cluster, where the Ov could dominantly reduce the Pt cluster from positive to negative. During the formaldehyde hydrogenation, the water molecules could spontaneously protonate the O in the aldehyde group by acid/base exchange, generating the OH* at the metal-support interface by long-range proton transfer. By comparing the stoichiometric and reduced TiO2 support, it is found that the further hydrogenation of OH* is hard on the positively charged Pt cluster over stoichiometric TiO2. However, with the presence of Ov on reduced support, the OH* hydrogenation could become not only exergonic but also kinetically more facile, which prohibits the catalyst from poisoning. This mechanism suggests that both the proton transfer from solvent water molecules and the easier OH* hydrogenation from Ov could synergistically promote aldehyde hydrogenation. That means, even for such simple hydrogenation in water, the catalytic mechanism could explicitly relate to all of the metal cluster, oxide support, and solvent waters. Considering the ubiquitous Ov defects in reducible oxide supports and the common aqueous environment, this synergistic effect may not be exclusive to Pt/TiO2, which can be crucial for supported metal catalysts in biomass conversion.
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16
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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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17
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Yasuda T, Morita R, Shigeta Y, Harada R. Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace. J Chem Inf Model 2022; 62:6217-6227. [PMID: 36449380 DOI: 10.1021/acs.jcim.2c01173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Since proteins perform biological functions through their dynamic properties, molecular dynamics (MD) simulation is a sophisticated strategy for investigating their functions. Analyses of trajectories provide statistical information about a specific protein as a free-energy landscape (FEL). However, the timescale of normal MD is shorter than that of biological functions, resulting in statistically insufficient conformational sampling, finally leading to unreliable FEL calculation. To search for a broad configurational subspace, an external bias is imposed on a target protein as biased sampling. However, its regulation is challenging because the optimal strength of the perturbation is unknown. Furthermore, a physically irrelevant configurational subspace was searched when imposing an inappropriate external bias. To address this issue, we newly proposed an external biased regulation scheme known as the G-factor external bias limiter (GERBIL). In GERBIL, protein configurations generated by external bias are structurally validated by an indicator (G-factor), enabling the search for a physically relevant subspace. In addition to biased sampling, nonbiased sampling might search for a physically irrelevant configurational subspace because repeating multiple MD simulations from several initial structures tends to search for an overly broad configurational subspace. For this issue, the structural qualities of configurations generated by nonbiased sampling have not been investigated. Therefore, we confirmed whether the G-factor screened the collapsed (low-quality) configurations generated by nonbiased sampling. To address this issue, the outlier flooding method (OFLOOD) was adopted in GERBIL as a nonbiased sampling method, which is referred to as OFLOOD-GERBIL. OFLOOD rapidly expands a configurational subspace by resampling the rarely occurring states of a given protein and tends to search an overly broad subspace. Thus, we considered that GERBIL might improve the excessive conformational search of OFLOOD for a physically irrelevant configurational subspace. As a demonstration, OFLOOD and OFLOOD-GERBIL were applied to a globular protein (T4 lysozyme) and their conformational search qualities were assessed. Based on our assessment, normal OFLOOD without the outlier validation frequently sampled low-quality configurations, whereas OFLOOD-GERBIL with the outlier validation intensively sampled high-quality configurations. In conclusion, OFLOOD-GERBIL derives a smart conformational search in a physically relevant configurational subspace, indicating that protein structure validation works in both nonbiased and biased sampling methods.
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Affiliation(s)
- Takunori Yasuda
- College of Biological Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-0821, Japan
| | - Rikuri Morita
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan
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18
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Water regulates the residence time of Benzamidine in Trypsin. Nat Commun 2022; 13:5438. [PMID: 36114175 PMCID: PMC9481606 DOI: 10.1038/s41467-022-33104-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/01/2022] [Indexed: 12/11/2022] Open
Abstract
The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time. Water is an essential part of any biological system, yet many aspects of its role remain elusive. Here the authors show, in a paradigmatic ligand-protein system, that water modulates the ligand residence time in a complex and non-local way, with possible implications in drug design.
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19
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Zhang X, Sun MY, Zhang X, Guo CR, Lei YT, Wang WH, Fan YZ, Cao P, Li CZ, Wang R, Li XH, Yu Y, Yang XN. Dynamic recognition of naloxone, morphine and endomorphin1 in the same pocket of µ-opioid receptors. Front Mol Biosci 2022; 9:925404. [PMID: 36052166 PMCID: PMC9424762 DOI: 10.3389/fmolb.2022.925404] [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: 04/21/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Morphine, the most widely used analgesic, relieves severe pain by activating the μ-opioid receptor (MOR), whereas naloxone, with only slight structural changes compared to morphine, exhibits inhibitory effect, and is used to treat opioid abuse. The mechanism by which the MOR distinguishes between the two is unclear. Molecular dynamics (MD) simulations on a 1-μs time scale and metadynamics-enhanced conformational sampling are used here to determine the different interactions of these two ligands with MOR: morphine adjusted its pose by continuously flipping deeper into the pocket, whereas naloxone failed to penetrate deeper because its allyl group conflicts with several residues of MOR. The endogenous peptide ligand endomorphin-1 (EM-1) underwent almost no significant conformational changes during the MD simulations. To validate these processes, we employed GIRK4S143T, a MOR-activated Gβγ-protein effector, in combination with mutagenesis and electrophysiological recordings. We verified the role of some key residues in the dynamic recognition of naloxone and morphine and identified the key residue I322, which leads to differential recognition of morphine and naloxone while assisting EM-1 in activating MOR. Reducing the side chain size of I322 (MORI322A) transformed naloxone from an inhibitor directly into an agonist of MOR, and I322A also significantly attenuated the potency of MOR on EM-1, confirming that binding deep in the pocket is critical for the agonistic effect of MOR. This finding reveals a dynamic mechanism for the response of MOR to different ligands and provides a basis for the discovery of new ligands for MOR at the atomic level.
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Affiliation(s)
- Xin Zhang
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Meng-Yang Sun
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xue Zhang
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Chang-Run Guo
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Yun-Tao Lei
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Wen-Hui Wang
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Ying-Zhe Fan
- Putuo Hospital, Shanghai University of Chinese Traditional Medicine, Shanghai, China
| | - Peng Cao
- Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Chang-Zhu Li
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha, China
| | - Rui Wang
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Xing-Hua Li
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
- *Correspondence: Xing-Hua Li, ; Ye Yu, ; Xiao-Na Yang,
| | - Ye Yu
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Xing-Hua Li, ; Ye Yu, ; Xiao-Na Yang,
| | - Xiao-Na Yang
- Department of Basic Medicine and Clinical Pharmacy and State Key laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Xing-Hua Li, ; Ye Yu, ; Xiao-Na Yang,
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20
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Rao P, S A, Masood G, Kusanur R, Niranjan V, Patra SM. Bioinformatics Study of Pioglitazone Analogues as Potential Anti-Diabetic Drugs. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022. [DOI: 10.1134/s106816202205017x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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21
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Yasuda T, Morita R, Shigeta Y, Harada R. Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins. J Chem Inf Model 2022; 62:3442-3452. [PMID: 35786886 DOI: 10.1021/acs.jcim.2c00573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.
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Affiliation(s)
- Takunori Yasuda
- College of Biological Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-0821, Japan
| | - Rikuri Morita
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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22
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Singh H, Raja A, Shekhar N, Chauhan A, Prakash A, Avti P, Medhi B. Computational attributes of protein kinase-C gamma C2-domain & virtual screening for small molecules: elucidation from meta-dynamics simulations & free-energy calculations. J Biomol Struct Dyn 2022:1-12. [DOI: 10.1080/07391102.2022.2077447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Harvinder Singh
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Anupam Raja
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Nishant Shekhar
- Department of Medical Microbiology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Arushi Chauhan
- Department of Biophysics, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Ajay Prakash
- Department of Medical Microbiology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Pramod Avti
- Department of Biophysics, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Bikash Medhi
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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23
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Zhou X, Shi M, Wang X, Xu D. Exploring the Binding Mechanism of a Supramolecular Tweezer CLR01 to 14-3-3σ Protein via Well-Tempered Metadynamics. Front Chem 2022; 10:921695. [PMID: 35646830 PMCID: PMC9133541 DOI: 10.3389/fchem.2022.921695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 04/25/2022] [Indexed: 11/17/2022] Open
Abstract
Using supramolecules for protein function regulation is an effective strategy in chemical biology and drug discovery. However, due to the presence of multiple binding sites on protein surfaces, protein function regulation via selective binding of supramolecules is challenging. Recently, the functions of 14-3-3 proteins, which play an important role in regulating intracellular signaling pathways via protein–protein interactions, have been modulated using a supramolecular tweezer, CLR01. However, the binding mechanisms of the tweezer molecule to 14-3-3 proteins are still unclear, which has hindered the development of novel supramolecules targeting the 14-3-3 proteins. Herein, the binding mechanisms of the tweezer to the lysine residues on 14-3-3σ (an isoform in 14-3-3 protein family) were explored by well-tempered metadynamics. The results indicated that the inclusion complex formed between the protein and supramolecule is affected by both kinetic and thermodynamic factors. In particular, simulations confirmed that K214 could form a strong binding complex with the tweezer; the binding free energy was calculated to be −10.5 kcal·mol−1 with an association barrier height of 3.7 kcal·mol−1. In addition, several other lysine residues on 14-3-3σ were identified as being well-recognized by the tweezer, which agrees with experimental results, although only K214/tweezer was co-crystallized. Additionally, the binding mechanisms of the tweezer to all lysine residues were analyzed by exploring the representative conformations during the formation of the inclusion complex. This could be helpful for the development of new inhibitors based on tweezers with more functions against 14-3-3 proteins via modifications of CLR01. We also believe that the proposed computational strategies can be extended to understand the binding mechanism of multi-binding sites proteins with supramolecules and will, thus, be useful toward drug design.
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Affiliation(s)
- Xin Zhou
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, China
| | - Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Wang
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, China
- *Correspondence: Xin Wang, ; Dingguo Xu,
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, China
- Research Center for Material Genome Engineering, Sichuan University, Chengdu, China
- *Correspondence: Xin Wang, ; Dingguo Xu,
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24
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Wu X, Xu LY, Li EM, Dong G. Application of molecular dynamics simulation in biomedicine. Chem Biol Drug Des 2022; 99:789-800. [PMID: 35293126 DOI: 10.1111/cbdd.14038] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/25/2022] [Accepted: 03/05/2022] [Indexed: 02/05/2023]
Abstract
Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.
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Affiliation(s)
- Xiaodong Wu
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Li-Yan Xu
- Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
- Cancer Research Center, Shantou University Medical College, Shantou, China
| | - En-Min Li
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
| | - Geng Dong
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Medical Informatics Research Center, Shantou University Medical College, Shantou, China
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25
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Mishra L, Bandyopadhyay T. Unbinding of hACE2 and inhibitors from the receptor binding domain of SARS-CoV-2 spike protein. J Biomol Struct Dyn 2022; 41:3245-3264. [PMID: 35293839 DOI: 10.1080/07391102.2022.2046641] [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: 10/18/2022]
Abstract
The first direful biomolecular event leading to COVID-19 disease is the SARS-CoV-2 virus surface spike (S) protein-mediated interaction with the human transmembrane protein, angiotensin-converting enzyme 2 (hACE2). Prevention of this interaction presents an attractive alternative to thwart SARS-CoV-2 replications. The development of monoclonal antibodies (mAbs) in the convalescent plasma treatment, nanobody, and designer peptides, which recognizes epitopes that overlap with hACE2 binding sites in the receptor-binding domain (RBD) of S protein (S/RBD) and thereby blocking the infection has been the center stage of therapeutic research. Here we report atomistic and reliable in silico structure-energetic features of the S/RBD interactions with hACE2 and its two inhibitors (convalescent mAb, B38, and an alpaca nanobody, Ty1). The discovered potential of mean forces exhibits free energy basin and barriers along the interaction pathways, providing sufficient molecular insights to design a B38 mutant and a Ty1-based peptide with higher binding capacity. While the mutated B38 forms a 60-fold deeper free energy minimum, the designer peptide (Ty1-based) constitutes 38 amino acids and is found to form a 100-fold deeper free energy minimum in the first binding basin than their wild-type variants in complex with S/RBD. Our strategy may help to design more efficacious biologics towards therapeutic intervention against the current raging pandemic.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lokpati Mishra
- Radiation Safety Systems Division, Bhabha Atomic Research Centre, Mumbai, India
| | - Tusar Bandyopadhyay
- Theoretical Chemistry Section, Chemistry Division, Bhabha Atomic Research Centre, Mumbai, India
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26
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Motta S, Callea L, Bonati L, Pandini A. PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations. J Chem Theory Comput 2022; 18:1957-1968. [PMID: 35213804 PMCID: PMC8908765 DOI: 10.1021/acs.jctc.1c01163] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Understanding the
process of ligand–protein recognition
is important to unveil biological mechanisms and to guide drug discovery
and design. Enhanced-sampling molecular dynamics is now routinely
used to simulate the ligand binding process, resulting in the need
for suitable tools for the analysis of large data sets of binding
events. Here, we designed, implemented, and tested PathDetect-SOM,
a tool based on self-organizing maps to build concise visual models
of the ligand binding pathways sampled along single simulations or
replicas. The tool performs a geometric clustering of the trajectories
and traces the pathways over an easily interpretable 2D map and, using
an approximate transition matrix, it can build a graph model of concurrent
pathways. The tool was tested on three study cases representing different
types of problems and simulation techniques. A clear reconstruction
of the sampled pathways was derived in all cases, and useful information
on the energetic features of the processes was recovered. The tool
is available at https://github.com/MottaStefano/PathDetect-SOM.
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Affiliation(s)
- Stefano Motta
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan 20126, Italy
| | - Lara Callea
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan 20126, Italy
| | - Laura Bonati
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan 20126, Italy
| | - Alessandro Pandini
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, U.K.,The Thomas Young Centre for Theory and Simulation of Materials, London SW7 2AZ, U.K
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27
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Aida H, Shigeta Y, Harada R. Ligand Binding Path Sampling Based on Parallel Cascade Selection Molecular Dynamics: LB-PaCS-MD. MATERIALS 2022; 15:ma15041490. [PMID: 35208030 PMCID: PMC8878848 DOI: 10.3390/ma15041490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 01/09/2023]
Abstract
Parallel cascade selection molecular dynamics (PaCS-MD) is a rare-event sampling method that generates transition pathways between a reactant and product. To sample the transition pathways, PaCS-MD repeats short-time MD simulations from important configurations as conformational resampling cycles. In this study, PaCS-MD was extended to sample ligand binding pathways toward a target protein, which is referred to as LB-PaCS-MD. In a ligand-concentrated environment, where multiple ligand copies are randomly arranged around the target protein, LB-PaCS-MD allows for the frequent sampling of ligand binding pathways. To select the important configurations, we specified the center of mass (COM) distance between each ligand and the relevant binding site of the target protein, where snapshots generated by the short-time MD simulations were ranked by their COM distance values. From each cycle, snapshots with smaller COM distance values were selected as the important configurations to be resampled using the short-time MD simulations. By repeating conformational resampling cycles, the COM distance values gradually decreased and converged to constants, meaning that a set of ligand binding pathways toward the target protein was sampled by LB-PaCS-MD. To demonstrate relative efficiency, LB-PaCS-MD was applied to several proteins, and their ligand binding pathways were sampled more frequently than conventional MD simulations.
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Affiliation(s)
- Hayato Aida
- Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
- Correspondence:
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28
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Wang J, Ishchenko A, Zhang W, Razavi A, Langley D. A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein-protein and protein-ligand binding potencies. Sci Rep 2022; 12:2024. [PMID: 35132139 PMCID: PMC8821539 DOI: 10.1038/s41598-022-05875-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/17/2022] [Indexed: 12/13/2022] Open
Abstract
Although seeking to develop a general and accurate binding free energy calculation method for protein-protein and protein-ligand interactions has been a continuous effort for decades, only limited successes have been obtained so far. Here, we report the development of a metadynamics-based procedure that calculates Dissociation Free Energy (DFE) and its application to 19 non-congeneric protein-protein complexes and hundreds of protein-ligand complexes covering eight targets. We achieved very high correlations in comparison to experimental binding free energies for these diverse sets of systems, demonstrating the generality and accuracy of the method. Since structures of most proteins are available owing to the recent success of prediction by artificial intelligence, a general free energy method such as DFE, combined with other methods, can make structure-based drug design a widely viable and reliable solution to develop both traditional small molecule drugs and biologic drugs as well as PROTACS.
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Affiliation(s)
- Jing Wang
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA.
| | | | - Wei Zhang
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
| | - Asghar Razavi
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
| | - David Langley
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
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29
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Fukuhara S, Shibuta Y. Free energy surface of initial cap formation in carbon nanotube growth. NANOSCALE ADVANCES 2021; 3:6191-6196. [PMID: 36133938 PMCID: PMC9417703 DOI: 10.1039/d1na00377a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 09/10/2021] [Indexed: 06/16/2023]
Abstract
Initial cap formation is an important process of carbon nanotube (CNT) growth where a hexagonal carbon network is lifted off from the catalyst surface. In this study, free energy surface (FES) of initial cap formation in the CNT growth is investigated by metadynamics simulation. A two-dimensional collective variable (CV) space is newly developed to examine the complicated formation process of the cap structure, which consists of the formation of a hexagonal carbon network and lift-off of the network from the catalyst surface. States before and after the lift-off of the carbon network are clearly distinguished in the two-dimensional FES. Therefore, free energy difference before and after the lift-off can be directly derived from the two-dimensional FES. It was revealed that the cap structure is stable at a high temperature due to the entropy effect, while the carbon network covering the catalyst surface is energetically stable. The new insight in this study is achieved owing to metadynamics simulation in conjunction with a newly developed two-dimensional CV space since it is impossible to explore FES for such complicated processes in the framework of conventional molecular dynamics simulation.
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Affiliation(s)
- Satoru Fukuhara
- Department of Materials Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan +81-3-5841-7118
| | - Yasushi Shibuta
- Department of Materials Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan +81-3-5841-7118
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30
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Abstract
Multiple myeloma is the second most common hematological malignancy in adults, accounting for 2% of all cancer-related deaths in the UK. Current chemotherapy-based regimes are insufficient, as most patients relapse and develop therapy resistance. This review focuses on current novel antibody- and aptamer-based therapies aiming to overcome current therapy limitations, as well as their respective limitations and areas of improvement. The use of computer modeling methods, as a tool to study and improve ligand-receptor alignments for the use of novel therapy development will also be discussed, as it has become a rapid, reliable and comparatively inexpensive method of investigation.
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31
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Castelli M, Serapian SA, Marchetti F, Triveri A, Pirota V, Torielli L, Collina S, Doria F, Freccero M, Colombo G. New perspectives in cancer drug development: computational advances with an eye to design. RSC Med Chem 2021; 12:1491-1502. [PMID: 34671733 PMCID: PMC8459323 DOI: 10.1039/d1md00192b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 07/06/2021] [Indexed: 02/06/2023] Open
Abstract
Computational chemistry has come of age in drug discovery. Indeed, most pharmaceutical development programs rely on computer-based data and results at some point. Herein, we discuss recent applications of advanced simulation techniques to difficult challenges in drug discovery. These entail the characterization of allosteric mechanisms and the identification of allosteric sites or cryptic pockets determined by protein motions, which are not immediately evident in the experimental structure of the target; the study of ligand binding mechanisms and their kinetic profiles; and the evaluation of drug-target affinities. We analyze different approaches to tackle challenging and emerging biological targets. Finally, we discuss the possible perspectives of future application of computation in drug discovery.
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Affiliation(s)
- Matteo Castelli
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Stefano A Serapian
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Filippo Marchetti
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Alice Triveri
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Valentina Pirota
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Luca Torielli
- Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology Section, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Simona Collina
- Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology Section, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Filippo Doria
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Mauro Freccero
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Giorgio Colombo
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
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32
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Yasuda T, Morita R, Shigeta Y, Harada R. Independent Nontargeted Parallel Cascade Selection Molecular Dynamics (Ino-PaCS-MD) to Enhance the Conformational Sampling of Proteins. J Chem Theory Comput 2021; 17:5933-5943. [PMID: 34410106 DOI: 10.1021/acs.jctc.1c00558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Biological functions are related to long-time protein dynamics (rare events) that are induced over microseconds. Such protein dynamics can be investigated using molecular dynamics (MD) simulations. However, the detection of rare events remains challenging using conventional MD (cMD) since the accessible timescales of cMD are shorter than those of the biological functions. Recently, the parallel cascade selection MD (PaCS-MD) has been proposed to detect such rare events, wherein transition paths are generated between a given reactant and product. As an extension, the nontargeted PaCS-MD (nt-PaCS-MD) has been proposed to predict the transition paths without requiring reference to any product. Thus, as a further extension, we herein propose independent nt-PaCS-MD, namely, Ino-PaCS-MD, wherein multiple walkers are launched from a set of different starting configurations. Each walker repeats a cycle of restarting short-time MD simulations from configurations with high potentials for making transitions to neighboring metastable states. To further enhance the sampling ability, Ino-PaCS-MD temporarily stops the conformational search and periodically resets the starting configurations so that they are uniformly distributed in a conformational subspace, thereby preventing a given protein from being trapped in one of the metastable states. As a demonstration, Ino-PaCS-MD successfully detects rare events of a maltose-binding protein as open-close transitions with a nanosecond-order simulation time, although a microsecond-order cMD simulation failed to detect these rare events, showing the high sampling efficiency of Ino-PaCS-MD.
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Affiliation(s)
- Takunori Yasuda
- College of Biological Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-0821, Japan
| | - Rikuri Morita
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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33
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Ali HS, Chakravorty A, Kalayan J, de Visser SP, Henchman RH. Energy-entropy method using multiscale cell correlation to calculate binding free energies in the SAMPL8 host-guest challenge. J Comput Aided Mol Des 2021; 35:911-921. [PMID: 34264476 PMCID: PMC8367938 DOI: 10.1007/s10822-021-00406-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 06/22/2021] [Indexed: 11/29/2022]
Abstract
Free energy drives a wide range of molecular processes such as solvation, binding, chemical reactions and conformational change. Given the central importance of binding, a wide range of methods exist to calculate it, whether based on scoring functions, machine-learning, classical or electronic structure methods, alchemy, or explicit evaluation of energy and entropy. Here we present a new energy-entropy (EE) method to calculate the host-guest binding free energy directly from molecular dynamics (MD) simulation. Entropy is evaluated using Multiscale Cell Correlation (MCC) which uses force and torque covariance and contacts at two different length scales. The method is tested on a series of seven host-guest complexes in the SAMPL8 (Statistical Assessment of the Modeling of Proteins and Ligands) "Drugs of Abuse" Blind Challenge. The EE-MCC binding free energies are found to agree with experiment with an average error of 0.9 kcal mol-1. MCC makes clear the origin of the entropy changes, showing that the large loss of positional, orientational, and to a lesser extent conformational entropy of each binding guest is compensated for by a gain in orientational entropy of water released to bulk, combined with smaller decreases in vibrational entropy of the host, guest and contacting water.
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Affiliation(s)
- Hafiz Saqib Ali
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Arghya Chakravorty
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jas Kalayan
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Samuel P de Visser
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Richard H Henchman
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
- Department of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
- Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
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34
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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35
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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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36
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Miller EB, Murphy RB, Sindhikara D, Borrelli KW, Grisewood MJ, Ranalli F, Dixon SL, Jerome S, Boyles NA, Day T, Ghanakota P, Mondal S, Rafi SB, Troast DM, Abel R, Friesner RA. Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding. J Chem Theory Comput 2021; 17:2630-2639. [PMID: 33779166 DOI: 10.1021/acs.jctc.1c00136] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.
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Affiliation(s)
- Edward B Miller
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Robert B Murphy
- Schrödinger, Inc., 10201 Wateridge Circle, San Diego, California 92121, United States
| | - Daniel Sindhikara
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Kenneth W Borrelli
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Matthew J Grisewood
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Fabio Ranalli
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Steven L Dixon
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Steven Jerome
- Schrödinger, Inc., 10201 Wateridge Circle, San Diego, California 92121, United States
| | - Nicholas A Boyles
- Schrödinger, Inc., 101 SW Main Street, Portland, Oregon 97204, United States
| | - Tyler Day
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Phani Ghanakota
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Sayan Mondal
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Salma B Rafi
- Schrödinger, Inc., 101 SW Main Street, Portland, Oregon 97204, United States
| | - Dawn M Troast
- Morphic Therapeutic, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Robert Abel
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3110, New York, New York 10036, United States
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37
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Namsani S, Pramanik D, Khan MA, Roy S, Singh JK. Metadynamics-based enhanced sampling protocol for virtual screening: case study for 3CLpro protein for SARS-CoV-2. J Biomol Struct Dyn 2021; 40:7002-7017. [PMID: 33663346 DOI: 10.1080/07391102.2021.1892530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
In recent times, computational methods played an important role in the down selection of chemical compounds, which could be a potential drug candidate with a high affinity to target proteins. However, the screening methodologies, including docking, often fails to identify the most effective compound, which could be a ligand for the target protein. To solve that, here we have integrated meta-dynamics, an enhanced sampling molecular simulation method, with all-atom molecular dynamics to determine a specific compound that could target the main protease of novel severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). This combined computational approach uses the enhanced sampling to explore the free energy surface associated with the protein's binding site (including the ligand) in an explicit solvent. We have implemented this method to find new chemical entities that exhibit high specificity of binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the SARS-CoV-2 and segregated to the most strongly bound ligands based on free energy and scoring functions (defined and implemented) from a set of 17 ligands which were prescreened for synthesizability and druggability. Additionally, we have compared these 17 ligands' affinities against controls, N3 and 13b α-ketoamide inhibitors, for which experimental crystal structures are available. Based on our results and analysis from the combined molecular simulation approach, we could identify the best compound which could be further taken as a potential candidate for experimental validation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Debabrata Pramanik
- Department of Chemical Engineering, Indian Institute of Technology, Kanpur, India
| | - Mohd Aamir Khan
- Prescience Insilico Private Limited, Bangalore, India.,Department of Chemical Engineering, Indian Institute of Technology, Kanpur, India
| | - Sudip Roy
- Prescience Insilico Private Limited, Bangalore, India
| | - Jayant Kumar Singh
- Prescience Insilico Private Limited, Bangalore, India.,Department of Chemical Engineering, Indian Institute of Technology, Kanpur, India
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38
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Casalegno M, Raos G, Sello G. Identification of viable TCDD access pathways to human AhR PAS-B ligand binding domain. J Mol Graph Model 2021; 105:107886. [PMID: 33706219 DOI: 10.1016/j.jmgm.2021.107886] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 02/06/2021] [Accepted: 02/22/2021] [Indexed: 12/02/2022]
Abstract
Unintentionally released in the environment as by-products of industrial activities, dioxins, exemplified by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), represent a primary concern for human health. Exposure to these chemicals is known to produce a broad spectrum of adverse effects, including cancer. The main mechanism of action of TCDD in humans involves binding to the Aryl hydrocarbon Receptor (AhR). Although qualitatively established, TCDD capture by the AhR remains poorly characterized at the molecular level. Starting from a recently developed structural model of the human AhR PAS-B domain, in this work we attempt the identification of viable TCDD access pathways to the human AhR ligand binding domain by means of molecular dynamics. Based on the result of metadynamics simulations, we identify two main regions that may potentially serve as access paths for TCDD. For each path, we characterize the residues closely interacting with TCDD, thereby suggesting a possible mechanism for TCDD capture. Our results are reviewed and discussed in the light of the available information about Human AhR structure and functions.
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Affiliation(s)
- Mosè Casalegno
- Dipartimento di Chimica, Materiali e Ingegneria Chimica "G. Natta", Politecnico di Milano, Via L. Mancinelli 7, 20131, Milano, Italy.
| | - Guido Raos
- Dipartimento di Chimica, Materiali e Ingegneria Chimica "G. Natta", Politecnico di Milano, Via L. Mancinelli 7, 20131, Milano, Italy.
| | - Guido Sello
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, I-20133, Milano, Italy.
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39
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Kogut MM, Maszota-Zieleniak M, Marcisz M, Samsonov SA. Computational insights into the role of calcium ions in protein–glycosaminoglycan systems. Phys Chem Chem Phys 2021; 23:3519-3530. [DOI: 10.1039/d0cp05438k] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The prediction power of computational methodologies for studying the role of ions in protein–glycosaminoglycan interactions was critically assessed.
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40
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O'Hagan MP, Haldar S, Morales JC, Mulholland AJ, Galan MC. Enhanced sampling molecular dynamics simulations correctly predict the diverse activities of a series of stiff-stilbene G-quadruplex DNA ligands. Chem Sci 2020; 12:1415-1426. [PMID: 34163904 PMCID: PMC8179204 DOI: 10.1039/d0sc05223j] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Ligands with the capability to bind G-quadruplexes (G4s) specifically, and to control G4 structure and behaviour, offer great potential in the development of novel therapies, technologies and functional materials. Most known ligands bind to a pre-formed topology, but G4s are highly dynamic and a small number of ligands have been discovered that influence these folding equilibria. Such ligands may be useful as probes to understand the dynamic nature of G4 in vivo, or to exploit the polymorphism of G4 in the development of molecular devices. To date, these fascinating molecules have been discovered serendipitously. There is a need for tools to predict such effects to drive ligand design and development, and for molecular-level understanding of ligand binding mechanisms and associated topological perturbation of G4 structures. Here we study the G4 binding mechanisms of a family of stiff-stilbene G4 ligands to human telomeric DNA using molecular dynamics (MD) and enhanced sampling (metadynamics) MD simulations. The simulations predict a variety of binding mechanisms and effects on G4 structure for the different ligands in the series. In parallel, we characterize the binding of the ligands to the G4 target experimentally using NMR and CD spectroscopy. The results show good agreement between the simulated and experimentally observed binding modes, binding affinities and ligand-induced perturbation of the G4 structure. The simulations correctly predict ligands that perturb G4 topology. Metadynamics simulations are shown to be a powerful tool to aid development of molecules to influence G4 structure, both in interpreting experiments and to help in the design of these chemotypes.
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Affiliation(s)
- Michael P O'Hagan
- School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Susanta Haldar
- School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
- Centre for Computational Chemistry, School of Chemistry, University of Bristol Bristol BS8 1TS UK
| | - Juan C Morales
- Instituto de Parasitología y Biomedicina "López Neyra" Consejo Superior de Investigaciones Científicas (CSIC), PTS Granada Avenida del Conocimiento 17, 18016 Armilla Granada Spain
| | - Adrian J Mulholland
- School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
- Centre for Computational Chemistry, School of Chemistry, University of Bristol Bristol BS8 1TS UK
| | - M Carmen Galan
- School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
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41
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Siebenmorgen T, Zacharias M. Efficient Refinement and Free Energy Scoring of Predicted Protein-Protein Complexes Using Replica Exchange with Repulsive Scaling. J Chem Inf Model 2020; 60:5552-5562. [PMID: 33075222 DOI: 10.1021/acs.jcim.0c00853] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Accurate prediction and evaluation of protein-protein complex structures are of major importance to understand the cellular interactome. Typically, putative complexes are predicted based on docking methods, and simple force field or knowledge-based scoring functions are applied to evaluate single complex structures. We have extended a replica-exchange-based scheme employing different levels of a repulsive biasing between partners in each replica simulation (RS-REMD) to simultaneously refine and score protein-protein complexes. The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. The method provides a free energy score that correlates quite well with experimental binding free energies on a set of 36 complexes with correlation coefficients of 0.77 and 0.55 in explicit and implicit solvent simulations, respectively. For a large set of docked decoy complexes, significant improvement of docked complexes was found in many cases with the starting structure in the vicinity (within 20 Å) of the native complex. In the majority of cases (14 out of 20 in explicit solvent), near native docking solutions were identified as the best scoring complexes. The approach is computational demanding but may offer a route for refinement and realistic ranking of predicted protein-protein docking geometries.
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Affiliation(s)
- Till Siebenmorgen
- Physik-Department T38, Technische Universität München, James-Franck-Str. 1, 85748 Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, James-Franck-Str. 1, 85748 Garching, Germany
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42
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Liu C, Brandenburg JG, Valsson O, Kremer K, Bereau T. Free-energy landscape of polymer-crystal polymorphism. SOFT MATTER 2020; 16:9683-9692. [PMID: 33000842 DOI: 10.1039/d0sm01342k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Polymorphism rationalizes how processing can control the final structure of a material. The rugged free-energy landscape and exceedingly slow kinetics in the solid state have so far hampered computational investigations. We report for the first time the free-energy landscape of a polymorphic crystalline polymer, syndiotactic polystyrene. Coarse-grained metadynamics simulations allow us to efficiently sample the landscape at large. The free-energy difference between the two main polymorphs, α and β, is further investigated by quantum-chemical calculations. The results of the two methods are in line with experimental observations: they predict β as the more stable polymorph under standard conditions. Critically, the free-energy landscape suggests how the α polymorph may lead to experimentally observed kinetic traps. The combination of multiscale modeling, enhanced sampling, and quantum-chemical calculations offers an appealing strategy to uncover complex free-energy landscapes with polymorphic behavior.
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Affiliation(s)
- Chan Liu
- Max Planck Institute for Polymer Research, 55128 Mainz, Germany
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43
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Li C, Zhang R, Wang J, Wilson LM, Yan Y. Protein Engineering for Improving and Diversifying Natural Product Biosynthesis. Trends Biotechnol 2020; 38:729-744. [PMID: 31954530 PMCID: PMC7274900 DOI: 10.1016/j.tibtech.2019.12.008] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 11/26/2019] [Accepted: 12/06/2019] [Indexed: 01/26/2023]
Abstract
Proteins found in nature have traditionally been the most frequently used biocatalysts to produce numerous natural products ranging from commodity chemicals to pharmaceuticals. Protein engineering has emerged as a powerful biotechnological toolbox in the development of metabolic engineering, particularly for the biosynthesis of natural products. Recently, protein engineering has become a favored method to improve enzymatic activity, increase enzyme stability, and expand product spectra in natural product biosynthesis. This review summarizes recent advances and typical strategies in protein engineering, highlighting the paramount role of protein engineering in improving and diversifying the biosynthesis of natural products. Future prospects and research directions are also discussed.
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Affiliation(s)
- Chenyi Li
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Ruihua Zhang
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Jian Wang
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Lauren Marie Wilson
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Yajun Yan
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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44
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Abstract
Molecular Docking is used to positioning the computer-generated 3D structure of small
ligands into a receptor structure in a variety of orientations, conformations and positions. This
method is useful in drug discovery and medicinal chemistry providing insights into molecular
recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery
(CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment
of targets and ligand. Over the last decade, advances in the field of computational, proteomics and
genomics have also led to the development of different docking methods which incorporate
protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts
for more accurate binding pose predictions and a more rational depiction of protein binding
interactions with the ligand. Protein flexibility has been included by generating protein ensembles
or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also
fully explores the drug-receptor binding and recognition from both energetic and mechanistic point
of view. Though in the fast-paced drug discovery program, dynamic docking is computationally
expensive but is being progressively used for screening of large compound libraries to identify the
potential drugs. In this review, a quick introduction is presented to the available docking methods
and their application and limitations in drug discovery.
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Affiliation(s)
- Ritu Jakhar
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Mehak Dangi
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Alka Khichi
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
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Evans R, Hovan L, Tribello GA, Cossins BP, Estarellas C, Gervasio FL. Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies. J Chem Theory Comput 2020; 16:4641-4654. [PMID: 32427471 PMCID: PMC7467642 DOI: 10.1021/acs.jctc.0c00075] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on 18 chemically diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calculate, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal pathlike variable obtained using machine learning or with the Hamiltonian replica-exchange algorithm SWISH, this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.
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Affiliation(s)
| | | | - Gareth A Tribello
- Atomistic Simulation Centre, Queen's University, Belfast BT7 1NN, United Kingdom
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46
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Tanida Y, Matsuura A. Alchemical free energy calculations via metadynamics: Application to the theophylline-RNA aptamer complex. J Comput Chem 2020; 41:1804-1819. [PMID: 32449538 DOI: 10.1002/jcc.26221] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 04/03/2020] [Accepted: 04/26/2020] [Indexed: 11/08/2022]
Abstract
We propose a computational workflow for robust and accurate prediction of both binding poses and their affinities at early stage in designing drug candidates. Small, rigid ligands with few intramolecular degrees of freedom, for example, fragment-like molecules, have multiple binding poses, even at a single binding site, and their affinities are often close to each other. We explore various structures of ligand binding to a target through metadynamics using a small number of collective variables, followed by reweighting to obtain the atomic coordinates. After identifying each binding pose by cluster analysis, we perform alchemical free energy calculations on each structure to obtain the overall value. We applied this protocol in computing free energy of binding for the theophylline-RNA aptamer complex. Of the six (meta)stable structures found, the most favorable binding structure is consistent with the structure obtained by NMR. The overall free energy of binding reproduces the experimental values very well.
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47
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Siebenmorgen T, Engelhard M, Zacharias M. Prediction of protein-protein complexes using replica exchange with repulsive scaling. J Comput Chem 2020; 41:1436-1447. [PMID: 32149420 DOI: 10.1002/jcc.26187] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 02/04/2020] [Accepted: 02/22/2020] [Indexed: 12/14/2022]
Abstract
The realistic prediction of protein-protein complex structures is import to ultimately model the interaction of all proteins in a cell and for the design of new protein-protein interactions. In principle, molecular dynamics (MD) simulations allow one to follow the association process under realistic conditions including full partner flexibility and surrounding solvent. However, due to the many local binding energy minima at the surface of protein partners, MD simulations are frequently trapped for long times in transient association states. We have designed a replica-exchange based scheme employing different levels of a repulsive biasing between partners in each replica simulation. The bias acts only on intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. For a set of five protein test cases (out of six) the RS-REMD technique allowed the sampling of near-native complex structures even when starting from the opposide site with respect to the native binding site for one partner. Using the same start structures and same computational demand regular MD simulations sampled near native complex structures only for one case. The method showed also improved results for the refinement of docked structures in the vicinity of the native binding geometry compared to regular MD refinement.
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Affiliation(s)
- Till Siebenmorgen
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Michael Engelhard
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany
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48
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Fusani L, Palmer DS, Somers DO, Wall ID. Exploring Ligand Stability in Protein Crystal Structures Using Binding Pose Metadynamics. J Chem Inf Model 2020; 60:1528-1539. [PMID: 31910338 PMCID: PMC7145342 DOI: 10.1021/acs.jcim.9b00843] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Identification of
correct protein–ligand binding poses is
important in structure-based drug design and crucial for the evaluation
of protein–ligand binding affinity. Protein–ligand coordinates are commonly obtained from
crystallography experiments that provide a static model of an ensemble
of conformations. Binding pose metadynamics (BPMD) is an enhanced
sampling method that allows for an efficient assessment of ligand
stability in solution. Ligand poses that are unstable under the bias
of the metadynamics simulation are expected to be infrequently occupied
in the energy landscape, thus making minimal contributions to the
binding affinity. Here, the robustness of the method is studied using
crystal structures with ligands known to be incorrectly modeled, as
well as 63 structurally diverse crystal structures with ligand fit
to electron density from the Twilight database. Results show that
BPMD can successfully differentiate compounds whose binding pose is
not supported by the electron density from those with well-defined
electron density.
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Affiliation(s)
- Lucia Fusani
- Molecular Design UK, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.,Department of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G11XL, U.K
| | - David S Palmer
- Department of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G11XL, U.K
| | - Don O Somers
- Protein, Cellular and Structural Sciences, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Ian D Wall
- Molecular Design UK, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
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49
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Limongelli V. Ligand binding free energy and kinetics calculation in 2020. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1455] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science – Center for Computational Medicine in Cardiology Università della Svizzera italiana (USI) Lugano Switzerland
- Department of Pharmacy University of Naples “Federico II” Naples Italy
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50
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Lodola A, Callegari D, Scalvini L, Rivara S, Mor M. Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations. Methods Mol Biol 2020; 2114:307-337. [PMID: 32016901 DOI: 10.1007/978-1-0716-0282-9_19] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity.
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Affiliation(s)
- Alessio Lodola
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy.
| | - Donatella Callegari
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
| | - Laura Scalvini
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
| | - Silvia Rivara
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
| | - Marco Mor
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
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