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Ginex T, Vázquez J, Estarellas C, Luque FJ. Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design. Curr Opin Struct Biol 2024; 87:102870. [PMID: 38914031 DOI: 10.1016/j.sbi.2024.102870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 06/02/2024] [Accepted: 06/04/2024] [Indexed: 06/26/2024]
Abstract
The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This directly hits quantum mechanics (QM) methods, which provide chemically accurate properties, but subject to small-sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing performance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formidable, but we will undoubtedly see impressive advances that will define a new era.
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Affiliation(s)
- Tiziana Ginex
- Pharmacelera, Parc Científic de Barcelona (PCB), Baldiri Reixac 4-8, 08028 Barcelona, Spain
| | - Javier Vázquez
- Pharmacelera, Parc Científic de Barcelona (PCB), Baldiri Reixac 4-8, 08028 Barcelona, Spain; Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain
| | - Carolina Estarellas
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Química Teòrica i Computacional (IQTCUB), 08921 Santa Coloma de Gramenet, Spain
| | - F Javier Luque
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Química Teòrica i Computacional (IQTCUB), 08921 Santa Coloma de Gramenet, Spain.
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2
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Toni E, Ayatollahi H, Abbaszadeh R, Fotuhi Siahpirani A. Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review. Pharmaceuticals (Basel) 2024; 17:795. [PMID: 38931462 PMCID: PMC11206653 DOI: 10.3390/ph17060795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. METHODS This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. RESULTS The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. CONCLUSIONS This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.
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Affiliation(s)
- Esmaeel Toni
- Medical Informatics, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran 14496-14535;
| | - Haleh Ayatollahi
- Medical Informatics, Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran 1996-713883
| | - Reza Abbaszadeh
- Pediatric Cardiology, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran 19956-14331;
| | - Alireza Fotuhi Siahpirani
- Systems Biology and Bioinformatics, Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran 14176-14411;
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3
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Mondal R, Vaissier Welborn V. Dynamics accelerate the kinetics of ion diffusion through channels: Continuous-time random walk models beyond the mean field approximation. J Chem Phys 2024; 160:144109. [PMID: 38597306 DOI: 10.1063/5.0188469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
Ion channels are proteins that play a significant role in physiological processes, including neuronal excitability and signal transduction. However, the precise mechanisms by which these proteins facilitate ion diffusion through cell membranes are not well understood. This is because experimental techniques to characterize ion channel activity operate on a time scale too large to understand the role of the various protein conformations on diffusion. Meanwhile, computational approaches operate on a time scale too short to rationalize the observed behavior at the microscopic scale. In this paper, we present a continuous-time random walk model that aims to bridge the scales between the atomistic models of ion channels and the experimental measurement of their conductance. We show how diffusion slows down in complex systems by using 3D lattices that map out the pore geometry of two channels: Nav1.7 and gramicidin. We also introduce spatial and dynamic site disorder to account for system heterogeneity beyond the mean field approximation. Computed diffusion coefficients show that an increase in spatial disorder slows down diffusion kinetics, while dynamic disorder has the opposite effect. Our results imply that microscopic or phenomenological models based on the potential of mean force data overlook the functional importance of protein dynamics on ion diffusion through channels.
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Affiliation(s)
- Ronnie Mondal
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, USA
- Macromolecules Innovation Institute, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Valerie Vaissier Welborn
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, USA
- Macromolecules Innovation Institute, Virginia Tech, Blacksburg, Virginia 24061, USA
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4
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Zhang Y, Gu S, Du J, Huang G, Shi J, Lu X, Wang J, Yang W, Guo X, Zhao C. Plant microphenotype: from innovative imaging to computational analysis. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:802-818. [PMID: 38217351 PMCID: PMC10955502 DOI: 10.1111/pbi.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 01/15/2024]
Abstract
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shenghao Gu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianjun Du
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guanmin Huang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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5
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Nam K, Shao Y, Major DT, Wolf-Watz M. Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development. ACS OMEGA 2024; 9:7393-7412. [PMID: 38405524 PMCID: PMC10883025 DOI: 10.1021/acsomega.3c09084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 02/27/2024]
Abstract
Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.
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Affiliation(s)
- Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Yihan Shao
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019-5251, United States
| | - Dan T. Major
- Department
of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
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6
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Pecina A, Fanfrlík J, Lepšík M, Řezáč J. SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes. Nat Commun 2024; 15:1127. [PMID: 38321025 PMCID: PMC10847445 DOI: 10.1038/s41467-024-45431-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 01/24/2024] [Indexed: 02/08/2024] Open
Abstract
Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization.
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Affiliation(s)
- Adam Pecina
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jindřich Fanfrlík
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Martin Lepšík
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
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7
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McCafferty CL, Klumpe S, Amaro RE, Kukulski W, Collinson L, Engel BD. Integrating cellular electron microscopy with multimodal data to explore biology across space and time. Cell 2024; 187:563-584. [PMID: 38306982 DOI: 10.1016/j.cell.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.
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Affiliation(s)
| | - Sven Klumpe
- Research Group CryoEM Technology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Rommie E Amaro
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Wanda Kukulski
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bühlstrasse 28, 3012 Bern, Switzerland.
| | - Lucy Collinson
- Electron Microscopy Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
| | - Benjamin D Engel
- Biozentrum, University of Basel, Spitalstrasse 41, 4056 Basel, Switzerland.
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8
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Behera S, Balasubramanian S. Lipase A from Bacillus subtilis: Substrate Binding, Conformational Dynamics, and Signatures of a Lid. J Chem Inf Model 2023; 63:7545-7556. [PMID: 37989487 DOI: 10.1021/acs.jcim.3c01681] [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: 11/23/2023]
Abstract
Protein-ligand binding studies are crucial for understanding the molecular basis of biological processes and for further advancing industrial biocatalysis and drug discovery. Using computational modeling and molecular dynamics simulations, we investigated the binding of a butyrate ester substrate to the lipase A (LipA) enzyme of Bacillus subtilis. Besides obtaining a close agreement of the binding free energy with the experimental value, the study reveals a remarkable reorganization of the catalytic triad upon substrate binding, leading to increased essential hydrogen bond populations. The investigation shows the distortion of the oxyanion hole in both the substrate-bound and unbound states of LipA and highlights the strengthening of the same in the tetrahedral intermediate complex. Principal component analysis of the unbound ensemble reveals the dominant motion in LipA to be the movement of Loop-1 (Tyr129-Arg142) between two states that cover and uncover the active site, mirroring that of a lid prevalent in several lipases. This lid-like motion of Loop-1 is also supported by its tendency to spontaneously open up at an oil-water interface. Overall, this study provides valuable insights into the impact of substrate binding on the structure, flexibility, and conformational dynamics of the LipA enzyme.
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Affiliation(s)
- Sudarshan Behera
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560 064, India
| | - Sundaram Balasubramanian
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560 064, India
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9
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Crossley-Lewis J, Dunn J, Buda C, Sunley GJ, Elena AM, Todorov IT, Yong CW, Glowacki DR, Mulholland AJ, Allan NL. Interactive molecular dynamics in virtual reality for modelling materials and catalysts. J Mol Graph Model 2023; 125:108606. [PMID: 37660615 DOI: 10.1016/j.jmgm.2023.108606] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 08/17/2023] [Accepted: 08/17/2023] [Indexed: 09/05/2023]
Abstract
Interactive molecular dynamics simulation in virtual reality (iMD-VR) is emerging as a promising technique in molecular science. Here, we demonstrate its use in a range of fifteen applications in materials science and heterogeneous catalysis. In this work, the iMD-VR package Narupa is used with the MD package, DL_POLY [1]. We show how iMD-VR can be used to: (i) investigate the mechanism of lithium fast ion conduction by directing the formation of defects showing that vacancy transport is favoured over interstitialcy mechanisms, and (ii) guide a molecule through a zeolite pore to explore diffusion within zeolites, examining in detail the motion of methyl n-hexanoate in H-ZSM-5 zeolite and identifying bottlenecks restricting diffusion. iMD-VR allows users to manipulate these systems intuitively, to drive changes in them and observe the resulting changes in structure and dynamics. We make these simulations available, as a resource for both teaching and research. All simulation files, with videos, can be found online (https://doi.org/10.5281/zenodo.8252314) and are provided as open-source material.
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Affiliation(s)
- Joe Crossley-Lewis
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol, BS8 1TS, UK
| | - Josh Dunn
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol, BS8 1TS, UK
| | - Corneliu Buda
- Applied Sciences, bp Innovation and Engineering, BP plc, 150 West Warrenville Road, Naperville, IL, 60563, USA
| | - Glenn J Sunley
- Applied Sciences, bp Innovation and Engineering, BP plc, Saltend, Hull, HU12 8DS, UK
| | - Alin M Elena
- Scientific Computing Department, STFC Daresbury Laboratory, Daresbury, UK
| | - Ilian T Todorov
- Scientific Computing Department, STFC Daresbury Laboratory, Daresbury, UK
| | - Chin W Yong
- Scientific Computing Department, STFC Daresbury Laboratory, Daresbury, UK
| | - David R Glowacki
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol, BS8 1TS, UK
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol, BS8 1TS, UK
| | - Neil L Allan
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol, BS8 1TS, UK.
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10
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Colombo G. Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design. Curr Opin Struct Biol 2023; 83:102702. [PMID: 37716095 DOI: 10.1016/j.sbi.2023.102702] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/22/2023] [Accepted: 08/22/2023] [Indexed: 09/18/2023]
Abstract
The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.
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Affiliation(s)
- Giorgio Colombo
- Department of Chemistry, University of Pavia, Via Taramelli 12, 27100 Pavia, Italy.
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11
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Zheng M, Li Y, Zhang Q, Wang W. Impacts of QM region sizes and conformation numbers on modelling enzyme reactions: a case study of polyethylene terephthalate hydrolase. Phys Chem Chem Phys 2023; 25:31596-31603. [PMID: 37917137 DOI: 10.1039/d3cp04519f] [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: 11/03/2023]
Abstract
A quantum mechanics/molecular mechanics (QM/MM) approach is a broadly used tool in computational enzymology. Treating the QM region with a high-level DFT method is one of the important branches. Here, taking leaf-branch compost cutinase-catalyzed polyethylene terephthalate depolymerization as an example, the convergence behavior of energy barriers as well as key structural and charge features with respect to the size of the QM region (up to 1000 atoms) is systematically investigated. BP86/6-31G(d)//CHARMM and M06-2X/6-311G(d,p)//CHARMM level of theories were applied for geometry optimizations and single-point energy calculations, respectively. Six independent enzyme conformations for all the four catalytic steps (steps (i)-(iv)) were considered. Most of the twenty-four cases show that at least 500 QM atoms are needed while only two rare cases show that ∼100 QM atoms are sufficient for convergence when only a single conformation was considered. This explains why most previous studies showed that 500 or more QM atoms are required while a few others showed that ∼100 QM atoms are sufficient for DFT/MM calculations. More importantly, average energy barriers and key structural/charge features from six conformations show an accelerated convergence than that in a single conformation. For instance, to reach energy barrier convergence (within 2.0 kcal mol-1) for step (ii), only ∼100 QM atoms are required if six conformations are considered while 500 or more QM atoms are needed with a single conformation. The convergence is accelerated to be more rapid if hundreds and thousands of conformations were considered, which aligns with previous findings that only several dozens of QM atoms are required for convergence with semi-empirical QM/MM MD simulations.
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Affiliation(s)
- Mingna Zheng
- Environment Research Institute, Shandong University, Qingdao, 266237, PR China.
| | - Yanwei Li
- Environment Research Institute, Shandong University, Qingdao, 266237, PR China.
| | - Qingzhu Zhang
- Environment Research Institute, Shandong University, Qingdao, 266237, PR China.
| | - Wenxing Wang
- Environment Research Institute, Shandong University, Qingdao, 266237, PR China.
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12
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Wei C, Xu Y, Shen Q, Li R, Xiao X, Saw PE, Xu X. Role of long non-coding RNAs in cancer: From subcellular localization to nanoparticle-mediated targeted regulation. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 33:774-793. [PMID: 37655045 PMCID: PMC10466435 DOI: 10.1016/j.omtn.2023.07.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Long non-coding RNAs (lncRNAs) are a class of RNA transcripts more than 200 nucleotides in length that play crucial roles in cancer development and progression. With the rapid development of high-throughput sequencing technology, a considerable number of lncRNAs have been identified as novel biomarkers for predicting the prognosis of cancer patients and/or therapeutic targets for cancer therapy. In recent years, increasing evidence has shown that the biological functions and regulatory mechanisms of lncRNAs are closely associated with their subcellular localization. More importantly, based on the important roles of lncRNAs in regulating cancer progression (e.g., growth, therapeutic resistance, and metastasis) and the specific ability of nucleic acids (e.g., siRNA, mRNA, and DNA) to regulate the expression of any target genes, much effort has been exerted recently to develop nanoparticle (NP)-based nucleic acid delivery systems for in vivo regulation of lncRNA expression and cancer therapy. In this review, we introduce the subcellular localization and regulatory mechanisms of various functional lncRNAs in cancer and systemically summarize the recent development of NP-mediated nucleic acid delivery for targeted regulation of lncRNA expression and effective cancer therapy.
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Affiliation(s)
- Chunfang Wei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan 528200, China
| | - Ya Xu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan 528200, China
| | - Qian Shen
- The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China
| | - Rong Li
- The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China
| | - Xiaoyun Xiao
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Phei Er Saw
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan 528200, China
| | - Xiaoding Xu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan 528200, China
- The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China
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13
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Karabulut S, Kaur H, Gauld JW. Applications and Potential of In Silico Approaches for Psychedelic Chemistry. Molecules 2023; 28:5966. [PMID: 37630218 PMCID: PMC10459288 DOI: 10.3390/molecules28165966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).
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Affiliation(s)
- Sedat Karabulut
- Department of Chemistry and Biochemistry, University of Windsor, Windsor, ON N9B 3P4, Canada;
| | - Harpreet Kaur
- Pharmala Biotech, 82 Richmond Street E, Toronto, ON M5C 1P1, Canada;
| | - James W. Gauld
- Department of Chemistry and Biochemistry, University of Windsor, Windsor, ON N9B 3P4, Canada;
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14
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Van Speybroeck V. Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220239. [PMID: 37211031 DOI: 10.1098/rsta.2022.0239] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/23/2023] [Indexed: 05/23/2023]
Abstract
The question is addressed in how far current modelling strategies are capable of modelling dynamic phenomena in realistic nanostructured materials at operating conditions. Nanostructured materials used in applications are far from perfect; they possess a broad range of heterogeneities in space and time extending over several orders of magnitude. Spatial heterogeneities from the subnanometre to the micrometre scale in crystal particles with a finite size and specific morphology, impact the material's dynamics. Furthermore, the material's functional behaviour is largely determined by the operating conditions. Currently, there exists a huge length-time scale gap between attainable theoretical length-time scales and experimentally relevant scales. Within this perspective, three key challenges are highlighted within the molecular modelling chain to bridge this length-time scale gap. Methods are needed that enable (i) building structural models for realistic crystal particles having mesoscale dimensions with isolated defects, correlated nanoregions, mesoporosity, internal and external surfaces; (ii) the evaluation of interatomic forces with quantum mechanical accuracy albeit at much lower computational cost than the currently used density functional theory methods and (iii) derivation of the kinetics of phenomena taking place in a multi-length-time scale window to obtain an overall view of the dynamics of the process. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.
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15
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Raghavan B, Paulikat M, Ahmad K, Callea L, Rizzi A, Ippoliti E, Mandelli D, Bonati L, De Vivo M, Carloni P. Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations. J Chem Inf Model 2023. [PMID: 37319347 DOI: 10.1021/acs.jcim.3c00557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.
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Affiliation(s)
- Bharath Raghavan
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
| | - Mirko Paulikat
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Katya Ahmad
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Lara Callea
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
| | - Andrea Rizzi
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Atomistic Simulations, Italian Institute of Technology, Genova 16163, Italy
| | - Emiliano Ippoliti
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Davide Mandelli
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Laura Bonati
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
| | - Marco De Vivo
- Molecular Modelling and Drug Discovery, Italian Institute of Technology, Genova 16163, Italy
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
- Department of Physics and Universitätsklinikum, RWTH Aachen University, Aachen 52074, Germany
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16
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Hou Z, Li Y, Zheng M, Liu X, Zhang Q, Wang W. Regioselective oxidation of heterocyclic aromatic hydrocarbons catalyzed by cytochrome P450: A case study of carbazole. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 258:114964. [PMID: 37121081 DOI: 10.1016/j.ecoenv.2023.114964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/11/2023] [Accepted: 04/26/2023] [Indexed: 05/22/2023]
Abstract
Recently there are increasing interests in accurately evaluating the health effects of heterocyclic PAHs. However, the activation mechanism and possible metabolites of heterocyclic PAHs catalyzed by human CYP1A1 is still elusive to a great extent. Here, leveraged to high level QM/MM calculations, the corresponding activation pathways of a representative heterocyclic PAHs, carbazole, were systematically explored. The first stage is electrophilic addition or hydrogen abstraction from N-H group. Electrophilic addition was evidenced to be more feasible and regioselectivity at C3 and C4 sites were identified. Correlations between energy barriers and key structural/electrostatic parameters reveal that O-Cα distance and Fe-O-Cα angle are the main origin for the catalytic regioselectivity. Electrophilic addition was determined as the rate-determining step and the subsequent possible reactions include epoxidation, NIH shift (the hydrogen migration from the site of hydroxylation to the adjacent carbon) and proton shuttle. The corresponding products are epoxides, ketones and hydroxylated carbazoles, respectively. The main metabolites (hydroxylated carbazoles) are estimated to be more toxic than carbazole. The regioselectivity of carbazole activated by CYP1A1 is different from the environmental processes (gas and aqueous phase). Collectively, these results will inform the in-depth understanding the metabolic processes of heterocyclic PAHs and aid the accurate evaluation of their health effects.
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Affiliation(s)
- Zexi Hou
- Environment Research Institute, Shandong University, Qingdao 266237, PR China
| | - Yanwei Li
- Environment Research Institute, Shandong University, Qingdao 266237, PR China; Shenzhen Research Institute, Shandong University, Shenzhen 518057, PR China.
| | - Mingna Zheng
- Environment Research Institute, Shandong University, Qingdao 266237, PR China
| | - Xinning Liu
- Environment Research Institute, Shandong University, Qingdao 266237, PR China
| | - Qingzhu Zhang
- Environment Research Institute, Shandong University, Qingdao 266237, PR China
| | - Wenxing Wang
- Environment Research Institute, Shandong University, Qingdao 266237, PR China
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17
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Bandyopadhyay S, Mondal J. A deep encoder-decoder framework for identifying distinct ligand binding pathways. J Chem Phys 2023; 158:2890463. [PMID: 37184003 DOI: 10.1063/5.0145197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The pathway(s) that a ligand would adopt en route to its trajectory to the native pocket of the receptor protein act as a key determinant of its biological activity. While Molecular Dynamics (MD) simulations have emerged as the method of choice for modeling protein-ligand binding events, the high dimensional nature of the MD-derived trajectories often remains a barrier in the statistical elucidation of distinct ligand binding pathways due to the stochasticity inherent in the ligand's fluctuation in the solution and around the receptor. Here, we demonstrate that an autoencoder based deep neural network, trained using an objective input feature of a large matrix of residue-ligand distances, can efficiently produce an optimal low-dimensional latent space that stores necessary information on the ligand-binding event. In particular, for a system of L99A mutant of T4 lysozyme interacting with its native ligand, benzene, this deep encoder-decoder framework automatically identifies multiple distinct recognition pathways, without requiring user intervention. The intermediates involve the spatially discrete location of the ligand in different helices of the protein before its eventual recognition of native pose. The compressed subspace derived from the autoencoder provides a quantitatively accurate measure of the free energy and kinetics of ligand binding to the native pocket. The investigation also recommends that while a linear dimensional reduction technique, such as time-structured independent component analysis, can do a decent job of state-space decomposition in cases where the intermediates are long-lived, autoencoder is the method of choice in systems where transient, low-populated intermediates can lead to multiple ligand-binding pathways.
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Affiliation(s)
- Satyabrata Bandyopadhyay
- Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences, Hyderabad 500046, India
| | - Jagannath Mondal
- Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences, Hyderabad 500046, India
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18
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Arafet K, Royo S, Schirmeister T, Barthels F, González FV, Moliner V. Impact of the Recognition Part of Dipeptidyl Nitroalkene Compounds on the Inhibition Mechanism of Cysteine Proteases Cruzain and Cathepsin L. ACS Catal 2023; 13:6289-6300. [PMID: 37180968 PMCID: PMC10167892 DOI: 10.1021/acscatal.3c01035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/10/2023] [Indexed: 05/16/2023]
Abstract
Cysteine proteases (CPs) are an important class of enzymes, many of which are responsible for several human diseases. For instance, cruzain of protozoan parasite Trypanosoma cruzi is responsible for the Chagas disease, while the role of human cathepsin L is associated with some cancers or is a potential target for the treatment of COVID-19. However, despite paramount work carried out during the past years, the compounds that have been proposed so far show limited inhibitory action against these enzymes. We present a study of proposed covalent inhibitors of these two CPs, cruzain and cathepsin L, based on the design, synthesis, kinetic measurements, and QM/MM computational simulations on dipeptidyl nitroalkene compounds. The experimentally determined inhibition data, together with the analysis and the predicted inhibition constants derived from the free energy landscape of the full inhibition process, allowed describing the impact of the recognition part of these compounds and, in particular, the modifications on the P2 site. The designed compounds and, in particular, the one with a bulky group (Trp) at the P2 site show promising in vitro inhibition activities against cruzain and cathepsin L for use as a starting lead compound in the development of drugs with medical applications for the treatment of human diseases and future designs.
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Affiliation(s)
- Kemel Arafet
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, 43124 Parma, Italy
- BioComp
Group, Institute of Advanced Materials (INAM),
Universitat Jaume I, 12071 Castelló, Spain
| | - Santiago Royo
- Departament
de Química Inorgànica i Orgànica, Universitat Jaume I, 12071 Castelló, Spain
| | - Tanja Schirmeister
- Institute
of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-Universität, 55128 Mainz, Germany
| | - Fabian Barthels
- Institute
of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-Universität, 55128 Mainz, Germany
| | - Florenci V. González
- Departament
de Química Inorgànica i Orgànica, Universitat Jaume I, 12071 Castelló, Spain
| | - Vicent Moliner
- BioComp
Group, Institute of Advanced Materials (INAM),
Universitat Jaume I, 12071 Castelló, Spain
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19
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Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
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Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
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20
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Ahalawat N, Sahil M, Mondal J. Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning. J Chem Theory Comput 2023; 19:2644-2657. [PMID: 37068044 DOI: 10.1021/acs.jctc.2c00932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.
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Affiliation(s)
- Navjeet Ahalawat
- Department of Bioinformatics and Computational Biology, College of Biotechnology, CCS Haryana Agricultural University, Hisar 125 004, Haryana, India
| | - Mohammad Sahil
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Jagannath Mondal
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
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21
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Ojha AA, Srivastava A, Votapka LW, Amaro RE. Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations. J Chem Inf Model 2023; 63:2469-2482. [PMID: 37023323 PMCID: PMC10131228 DOI: 10.1021/acs.jcim.2c01589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.
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Affiliation(s)
- Anupam Anand Ojha
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Ambuj Srivastava
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Lane William Votapka
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
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22
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Babaei M, Evers TMJ, Shokri F, Altucci L, de Lange ECM, Mashaghi A. Biochemical reaction network topology defines dose-dependent Drug-Drug interactions. Comput Biol Med 2023; 155:106584. [PMID: 36805215 DOI: 10.1016/j.compbiomed.2023.106584] [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: 09/20/2022] [Revised: 12/28/2022] [Accepted: 01/22/2023] [Indexed: 01/29/2023]
Abstract
Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis-Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.
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Affiliation(s)
- Mehrad Babaei
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands; Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy.
| | - Tom M J Evers
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
| | - Fereshteh Shokri
- Leiden University Medical Center, Leiden, 2333ZA, the Netherlands.
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy; BIOGEM, Molecular Biology and Genetics Research Institute, Ariano Irpino, Italy.
| | - Elizabeth C M de Lange
- Predictive Pharmacology, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
| | - Alireza Mashaghi
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
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23
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Kar RK. Benefits of hybrid QM/MM over traditional classical mechanics in pharmaceutical systems. Drug Discov Today 2023; 28:103374. [PMID: 36174967 DOI: 10.1016/j.drudis.2022.103374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/27/2022] [Accepted: 09/22/2022] [Indexed: 02/02/2023]
Abstract
Hybrid quantum mechanics/molecular mechanics (QM/MM) is one of the most reliable approaches for accurately modeling and studying the complex pharmaceutical discovery system. Classical mechanics has significantly accelerated the drug discovery process in the past decade. However, the current challenge is the large pool of false positives, which require extensive validation. Hybrid QM/MM is an effective solution for accurately studying ligand binding, structural mechanisms, free energy evaluation, and spectroscopic characterization. This article highlights the methodological details relevant to cost-effective hybrid QM/MM methods. This approach, combined with traditional pharmacoinformatics methods, could be a reliable strategy to balance the cost and accuracy of the calculations.
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Affiliation(s)
- Rajiv K Kar
- Jyoti and Bhupat Mehta School of Health Sciences and Technology, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India.
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24
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Chavez T, Roberts EJ, Zwart PH, Hexemer A. A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering. J Appl Crystallogr 2022; 55:1277-1288. [PMID: 36249508 PMCID: PMC9533742 DOI: 10.1107/s1600576722007105] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 07/10/2022] [Indexed: 11/10/2022] Open
Abstract
The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.
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Affiliation(s)
- Tanny Chavez
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Eric J. Roberts
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Petrus H. Zwart
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Berkeley Synchrotron Infrared Structural Biology Program, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Alexander Hexemer
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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25
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Javan Nikkhah S, Vandichel M. Modeling Polyzwitterion-Based Drug Delivery Platforms: A Perspective of the Current State-of-the-Art and Beyond. ACS ENGINEERING AU 2022; 2:274-294. [PMID: 35996394 PMCID: PMC9389590 DOI: 10.1021/acsengineeringau.2c00008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Drug delivery platforms
are anticipated to have biocompatible and
bioinert surfaces. PEGylation of drug carriers is the most approved
method since it improves water solubility and colloid stability and
decreases the drug vehicles’ interactions with blood components.
Although this approach extends their biocompatibility, biorecognition
mechanisms prevent them from biodistribution and thus efficient drug
transfer. Recent studies have shown (poly)zwitterions to be alternatives
for PEG with superior biocompatibility. (Poly)zwitterions are super
hydrophilic, mainly stimuli-responsive, easy to functionalize and
they display an extremely low protein adsorption and long biodistribution
time. These unique characteristics make them already promising candidates
as drug delivery carriers. Furthermore, since they have highly dense
charged groups with opposite signs, (poly)zwitterions are intensely
hydrated under physiological conditions. This exceptional hydration
potential makes them ideal for the design of therapeutic vehicles
with antifouling capability, i.e., preventing undesired
sorption of biologics from the human body in the drug delivery vehicle.
Therefore, (poly)zwitterionic materials have been broadly applied
in stimuli-responsive “intelligent” drug delivery systems
as well as tumor-targeting carriers because of their excellent biocompatibility,
low cytotoxicity, insignificant immunogenicity, high stability, and
long circulation time. To tailor (poly)zwitterionic drug vehicles,
an interpretation of the structural and stimuli-responsive behavior
of this type of polymer is essential. To this end, a direct study
of molecular-level interactions, orientations, configurations, and
physicochemical properties of (poly)zwitterions is required, which
can be achieved via molecular modeling, which has become an influential
tool for discovering new materials and understanding diverse material
phenomena. As the essential bridge between science and engineering,
molecular simulations enable the fundamental understanding of the
encapsulation and release behavior of intelligent drug-loaded (poly)zwitterion
nanoparticles and can help us to systematically design their next
generations. When combined with experiments, modeling can make quantitative
predictions. This perspective article aims to illustrate key recent
developments in (poly)zwitterion-based drug delivery systems. We summarize
how to use predictive multiscale molecular modeling techniques to
successfully boost the development of intelligent multifunctional
(poly)zwitterions-based systems.
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Affiliation(s)
- Sousa Javan Nikkhah
- Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick V94 T9PX, Republic of Ireland
| | - Matthias Vandichel
- Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick V94 T9PX, Republic of Ireland
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26
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Votapka LW, Stokely AM, Ojha AA, Amaro RE. SEEKR2: Versatile Multiscale Milestoning Utilizing the OpenMM Molecular Dynamics Engine. J Chem Inf Model 2022; 62:3253-3262. [PMID: 35759413 PMCID: PMC9277580 DOI: 10.1021/acs.jcim.2c00501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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We present SEEKR2
(simulation-enabled estimation of kinetic rates
version 2)—the latest iteration in the family of SEEKR programs
for using multiscale simulation methods to computationally estimate
the kinetics and thermodynamics of molecular processes, in particular,
ligand-receptor binding. SEEKR2 generates equivalent, or improved,
results compared to the earlier versions of SEEKR but with significant
increases in speed and capabilities. SEEKR2 has also been built with
greater ease of usability and with extensible features to enable future
expansions of the method. Now, in addition to supporting simulations
using NAMD, calculations may be run with the fast and extensible OpenMM
simulation engine. The Brownian dynamics portion of the calculation
has also been upgraded to Browndye 2. Furthermore, this version of
SEEKR supports hydrogen mass repartitioning, which significantly reduces
computational cost, while showing little, if any, loss of accuracy
in the predicted kinetics.
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Affiliation(s)
- Lane W Votapka
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
| | - Andrew M Stokely
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
| | - Anupam A Ojha
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
| | - Rommie E Amaro
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
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27
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Oliveira AL, Viegas MF, da Silva SL, Soares AM, Ramos MJ, Fernandes PA. The chemistry of snake venom and its medicinal potential. Nat Rev Chem 2022; 6:451-469. [PMID: 37117308 PMCID: PMC9185726 DOI: 10.1038/s41570-022-00393-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2022] [Indexed: 12/15/2022]
Abstract
The fascination and fear of snakes dates back to time immemorial, with the first scientific treatise on snakebite envenoming, the Brooklyn Medical Papyrus, dating from ancient Egypt. Owing to their lethality, snakes have often been associated with images of perfidy, treachery and death. However, snakes did not always have such negative connotations. The curative capacity of venom has been known since antiquity, also making the snake a symbol of pharmacy and medicine. Today, there is renewed interest in pursuing snake-venom-based therapies. This Review focuses on the chemistry of snake venom and the potential for venom to be exploited for medicinal purposes in the development of drugs. The mixture of toxins that constitute snake venom is examined, focusing on the molecular structure, chemical reactivity and target recognition of the most bioactive toxins, from which bioactive drugs might be developed. The design and working mechanisms of snake-venom-derived drugs are illustrated, and the strategies by which toxins are transformed into therapeutics are analysed. Finally, the challenges in realizing the immense curative potential of snake venom are discussed, and chemical strategies by which a plethora of new drugs could be derived from snake venom are proposed.
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Affiliation(s)
- Ana L Oliveira
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal.,LAQV/Requimte, University of Porto, Porto, Portugal
| | - Matilde F Viegas
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal.,LAQV/Requimte, University of Porto, Porto, Portugal
| | - Saulo L da Silva
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal.,LAQV/Requimte, University of Porto, Porto, Portugal
| | - Andreimar M Soares
- Biotechnology Laboratory for Proteins and Bioactive Compounds from the Western Amazon, Oswaldo Cruz Foundation, National Institute of Epidemiology in the Western Amazon (INCT-EpiAmO), Porto Velho, Brazil.,Sao Lucas Universitary Center (UniSL), Porto Velho, Brazil
| | - Maria J Ramos
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal.,LAQV/Requimte, University of Porto, Porto, Portugal
| | - Pedro A Fernandes
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal.,LAQV/Requimte, University of Porto, Porto, Portugal
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28
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Salahub DR. Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond. Phys Chem Chem Phys 2022; 24:9051-9081. [PMID: 35389399 DOI: 10.1039/d1cp05928a] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Important contemporary biological and materials problems often depend on interactions that span orders of magnitude differences in spatial and temporal dimensions. This Tutorial Review attempts to provide an introduction to such fascinating problems through a series of case studies, aimed at beginning researchers, graduate students, postdocs and more senior colleagues who are changing direction to focus on multiscale aspects of their research. The choice of specific examples is highly personal, with examples either chosen from our own work or outstanding multiscale efforts from the literature. I start with various embedding schemes, as exemplified by polarizable continuum models, 3-D RISM, molecular DFT and frozen-density embedding. Next, QM/MM (quantum mechanical/molecular mechanical) techniques are the workhorse of pm-to-nm/ps-to-ns simulations; examples are drawn from enzymes and from nanocatalysis for oil-sands upgrading. Using polarizable force-fields in the QM/MM framework represents a burgeoning subfield; with examples from ion channels and electron dynamics in molecules subject to strong external fields, probing the atto-second dynamics of the electrons with RT-TDDFT (real-time - time-dependent density functional theory) eventually coupled with nuclear motion through the Ehrenfest approximation. This is followed by a section on coarse graining, bridging dimensions from atoms to cells. The penultimate chapter gives a quick overview of multiscale approaches that extend into the meso- and macro-scales, building on atomistic and coarse-grained techniques to enter the world of materials engineering, on the one hand, and cell biology, on the other. A final chapter gives just a glimpse of the burgeoning impact of machine learning on the structure-dynamics front. I aim to capture the excitement of contemporary leading-edge breakthroughs in the description of physico-chemical systems and processes in complex environments, with only enough historical content to provide context and aid the next generation of methodological development. While I aim also for a clear description of the essence of methodological breakthroughs, equations are kept to a minimum and detailed formalism and implementation details are left to the references. My approach is very selective (case studies) rather than exhaustive. I think that these case studies should provide fodder to build as complete a reference tree on multiscale modelling as the reader may wish, through forward and backward citation analysis. I hope that my choices of cases will excite interest in newcomers and help to fuel the growth of multiscale modelling in general.
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Affiliation(s)
- Dennis R Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS-Centre for Molecular Simulation, IQST-Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
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29
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Golub M, Gätcke J, Subramanian S, Kölsch A, Darwish T, Howard JK, Feoktystov A, Matsarskaia O, Martel A, Porcar L, Zouni A, Pieper J. "Invisible" Detergents Enable a Reliable Determination of Solution Structures of Native Photosystems by Small-Angle Neutron Scattering. J Phys Chem B 2022; 126:2824-2833. [PMID: 35384657 DOI: 10.1021/acs.jpcb.2c01591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Photosystems I (PSI) and II (PSII) are pigment-protein complexes capable of performing the light-induced charge separation necessary to convert solar energy into a biochemically storable form, an essential step in photosynthesis. Small-angle neutron scattering (SANS) is unique in providing structural information on PSI and PSII in solution under nearly physiological conditions without the need for crystallization or temperature decrease. We show that the reliability of the solution structure critically depends on proper contrast matching of the detergent belt surrounding the protein. Especially, specifically deuterated ("invisible") detergents are shown to be properly matched out in SANS experiments by a direct, quantitative comparison with conventional matching strategies. In contrast, protonated detergents necessarily exhibit incomplete matching so that related SANS results systematically overestimate the size of the membrane protein under study. While the solution structures obtained are close to corresponding high-resolution structures, we show that temperature and solution state lead to individual structural differences compared with high-resolution structures. We attribute these differences to the presence of a manifold of conformational substates accessible by protein dynamics under physiological conditions.
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Affiliation(s)
- M Golub
- Institute of Physics, University of Tartu, Wilhelm Ostwald str. 1, 50411 Tartu, Estonia
| | - J Gätcke
- Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - S Subramanian
- Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - A Kölsch
- Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - T Darwish
- National Deuteration Facility, Australian Nuclear Science and Technology Organization, New Illawarra Road, Lucas Heights, NSW 2234, Australia
| | - J K Howard
- National Deuteration Facility, Australian Nuclear Science and Technology Organization, New Illawarra Road, Lucas Heights, NSW 2234, Australia
| | - A Feoktystov
- Forschungszentrum Jülich GmbH, Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Lichtenbergstr. 1, 85748 Garching, Germany
| | - O Matsarskaia
- Institut Laue-Langevin, 71 Avenue des Martyrs CS 20156, 38042 Grenoble Cedex 9, France
| | - A Martel
- Institut Laue-Langevin, 71 Avenue des Martyrs CS 20156, 38042 Grenoble Cedex 9, France
| | - L Porcar
- Institut Laue-Langevin, 71 Avenue des Martyrs CS 20156, 38042 Grenoble Cedex 9, France
| | - A Zouni
- Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - J Pieper
- Institute of Physics, University of Tartu, Wilhelm Ostwald str. 1, 50411 Tartu, Estonia
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30
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Maritan M, Autin L, Karr J, Covert MW, Olson AJ, Goodsell DS. Building Structural Models of a Whole Mycoplasma Cell. J Mol Biol 2022; 434:167351. [PMID: 34774566 PMCID: PMC8752489 DOI: 10.1016/j.jmb.2021.167351] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 02/01/2023]
Abstract
Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.
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Affiliation(s)
- Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/MartinaMaritan
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/grinche
| | - Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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31
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Gorai B, Vashisth H. Progress in Simulation Studies of Insulin Structure and Function. Front Endocrinol (Lausanne) 2022; 13:908724. [PMID: 35795141 PMCID: PMC9252437 DOI: 10.3389/fendo.2022.908724] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 04/28/2022] [Indexed: 01/02/2023] Open
Abstract
Insulin is a peptide hormone known for chiefly regulating glucose level in blood among several other metabolic processes. Insulin remains the most effective drug for treating diabetes mellitus. Insulin is synthesized in the pancreatic β-cells where it exists in a compact hexameric architecture although its biologically active form is monomeric. Insulin exhibits a sequence of conformational variations during the transition from the hexamer state to its biologically-active monomer state. The structural transitions and the mechanism of action of insulin have been investigated using several experimental and computational methods. This review primarily highlights the contributions of molecular dynamics (MD) simulations in elucidating the atomic-level details of conformational dynamics in insulin, where the structure of the hormone has been probed as a monomer, dimer, and hexamer. The effect of solvent, pH, temperature, and pressure have been probed at the microscopic scale. Given the focus of this review on the structure of the hormone, simulation studies involving interactions between the hormone and its receptor are only briefly highlighted, and studies on other related peptides (e.g., insulin-like growth factors) are not discussed. However, the review highlights conformational dynamics underlying the activities of reported insulin analogs and mimetics. The future prospects for computational methods in developing promising synthetic insulin analogs are also briefly highlighted.
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32
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Winter SD, Jones HBL, Răsădean DM, Crean RM, Danson MJ, Pantoş GD, Katona G, Prentice E, Arcus VL, van der Kamp MW, Pudney CR. Chemical Mapping Exposes the Importance of Active Site Interactions in Governing the Temperature Dependence of Enzyme Turnover. ACS Catal 2021; 11:14854-14863. [PMID: 34956689 PMCID: PMC8689651 DOI: 10.1021/acscatal.1c04679] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/16/2021] [Indexed: 12/26/2022]
Abstract
Uncovering the role of global protein dynamics in enzyme turnover is needed to fully understand enzyme catalysis. Recently, we have demonstrated that the heat capacity of catalysis, ΔC P ‡, can reveal links between the protein free energy landscape, global protein dynamics, and enzyme turnover, suggesting that subtle changes in molecular interactions at the active site can affect long-range protein dynamics and link to enzyme temperature activity. Here, we use a model promiscuous enzyme (glucose dehydrogenase from Sulfolobus solfataricus) to chemically map how individual substrate interactions affect the temperature dependence of enzyme activity and the network of motions throughout the protein. Utilizing a combination of kinetics, red edge excitation shift (REES) spectroscopy, and computational simulation, we explore the complex relationship between enzyme-substrate interactions and the global dynamics of the protein. We find that changes in ΔC P ‡ and protein dynamics can be mapped to specific substrate-enzyme interactions. Our study reveals how subtle changes in substrate binding affect global changes in motion and flexibility extending throughout the protein.
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Affiliation(s)
- Samuel D. Winter
- Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, U.K
| | - Hannah B. L. Jones
- Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, U.K
| | | | - Rory M. Crean
- Science for Life Laboratory, Department of Chemistry − BMC, Uppsala University, Uppsala 752 37, Sweden
| | - Michael J. Danson
- Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, U.K
| | - G. Dan Pantoş
- Department of Chemistry, University of Bath, Bath BA2 7AY, U.K
| | - Gergely Katona
- Department of Chemistry and Biology, University of Gothenburg, Göteborg 412 96, Sweden
| | - Erica Prentice
- School of Science, University of Waikato, Hamilton 3216, New Zealand
| | - Vickery L. Arcus
- School of Science, University of Waikato, Hamilton 3216, New Zealand
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33
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Yang Z, Twidale RM, Gervasoni S, Suardíaz R, Colenso CK, Lang EJM, Spencer J, Mulholland AJ. Multiscale Workflow for Modeling Ligand Complexes of Zinc Metalloproteins. J Chem Inf Model 2021; 61:5658-5672. [PMID: 34748329 DOI: 10.1021/acs.jcim.1c01109] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Zinc metalloproteins are ubiquitous, with protein zinc centers of structural and functional importance, involved in interactions with ligands and substrates and often of pharmacological interest. Biomolecular simulations are increasingly prominent in investigations of protein structure, dynamics, ligand interactions, and catalysis, but zinc poses a particular challenge, in part because of its versatile, flexible coordination. A computational workflow generating reliable models of ligand complexes of biological zinc centers would find broad application. Here, we evaluate the ability of alternative treatments, using (nonbonded) molecular mechanics (MM) and quantum mechanics/molecular mechanics (QM/MM) at semiempirical (DFTB3) and density functional theory (DFT) levels of theory, to describe the zinc centers of ligand complexes of six metalloenzyme systems differing in coordination geometries, zinc stoichiometries (mono- and dinuclear), and the nature of interacting groups (specifically the presence of zinc-sulfur interactions). MM molecular dynamics (MD) simulations can overfavor octahedral geometries, introducing additional water molecules to the zinc coordination shell, but this can be rectified by subsequent semiempirical (DFTB3) QM/MM MD simulations. B3LYP/MM geometry optimization further improved the accuracy of the description of coordination distances, with the overall effectiveness of the approach depending upon factors, including the presence of zinc-sulfur interactions that are less well described by semiempirical methods. We describe a workflow comprising QM/MM MD using DFTB3 followed by QM/MM geometry optimization using DFT (e.g., B3LYP) that well describes our set of zinc metalloenzyme complexes and is likely to be suitable for creating accurate models of zinc protein complexes when structural information is more limited.
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Affiliation(s)
- Zongfan Yang
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K.,School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, U.K
| | - Rebecca M Twidale
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
| | - Silvia Gervasoni
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K.,Department of Pharmaceutical Sciences, University of Milan, Via Mangiagalli, 25, I-20133 Milano, Italy
| | - Reynier Suardíaz
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
| | - Charlotte K Colenso
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K.,School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, U.K
| | - Eric J M Lang
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
| | - James Spencer
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, U.K
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
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34
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Ozvoldik K, Stockner T, Rammner B, Krieger E. Assembly of Biomolecular Gigastructures and Visualization with the Vulkan Graphics API. J Chem Inf Model 2021; 61:5293-5303. [PMID: 34528431 PMCID: PMC8549067 DOI: 10.1021/acs.jcim.1c00743] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Building and displaying
all-atom models of biomolecular structures
with millions or billions of atoms, like virus particles or cells,
remain a challenge due to the sheer size of the data, the required
levels of automated building, and the visualization limits of today’s
graphics hardware. Based on concepts introduced with the CellPack
program, we report new algorithms to create such large-scale models
using an intermediate coarse-grained “pet representation”
of biomolecules with 1/10th the normal size. Pet atoms are placed
such that they optimally trace the surface of the original molecule
with just ∼1/50th the original atom number and are joined with
covalent bonds. Molecular dynamics simulations of pet molecules allow
for efficient packing optimization, as well as the generation of realistic
DNA/RNA conformations. This pet world can be expanded back to the
all-atom representation to be explored and visualized with full details.
Essential for the efficient interactive visualization of gigastructures
is the use of multiple levels of detail (LODs), where distant molecules
are drawn with a heavily reduced polygon count. We present a grid-based
algorithm to create such LODs for all common molecular graphics styles
(including ball-and-sticks, ribbons, and cartoons) that do not require
monochrome molecules to hide LOD transitions. As a practical application,
we built all-atom models of SARS-CoV-2, HIV, and an entire presynaptic
bouton with 1 μm diameter and 3.6 billion atoms, using modular
building blocks to significantly reduce GPU memory requirements through
instancing. We employ the Vulkan graphics API to maximize performance
on consumer grade hardware and describe how to use the mmCIF format
to efficiently store such giant models. An implementation is available
as part of the YASARA molecular modeling and simulation program from www.YASARA.org. The free YASARA
View program can be used to explore the presented models, which can
be downloaded from www.YASARA.org/petworld, a Creative Commons platform for sharing giant biomolecular structures.
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Affiliation(s)
- Kornel Ozvoldik
- YASARA Biosciences GmbH, Wagramer Str. 25/3/45, 1220 Vienna, Austria.,Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Waehringerstr. 13A, 1090 Vienna, Austria
| | - Thomas Stockner
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Waehringerstr. 13A, 1090 Vienna, Austria
| | | | - Elmar Krieger
- YASARA Biosciences GmbH, Wagramer Str. 25/3/45, 1220 Vienna, Austria
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35
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Róg T, Girych M, Bunker A. Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design. Pharmaceuticals (Basel) 2021; 14:1062. [PMID: 34681286 PMCID: PMC8537670 DOI: 10.3390/ph14101062] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/14/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022] Open
Abstract
We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.
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Affiliation(s)
- Tomasz Róg
- Department of Physics, University of Helsinki, 00014 Helsinki, Finland;
| | - Mykhailo Girych
- Department of Physics, University of Helsinki, 00014 Helsinki, Finland;
| | - Alex Bunker
- Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00014 Helsinki, Finland;
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36
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Hix MA, Leddin EM, Cisneros GA. Combining Evolutionary Conservation and Quantum Topological Analyses To Determine Quantum Mechanics Subsystems for Biomolecular Quantum Mechanics/Molecular Mechanics Simulations. J Chem Theory Comput 2021; 17:4524-4537. [PMID: 34087064 PMCID: PMC8477969 DOI: 10.1021/acs.jctc.1c00313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Selection of residues and other molecular fragments for inclusion in the quantum mechanics (QM) region for QM/molecular mechanics (MM) simulations is an important step for these calculations. Here, we present an approach that combines protein sequence/structure evolution and electron localization function (ELF) analyses. The combination of these two analyses allows the determination of whether a residue needs to be included in the QM subsystem or can be represented by the MM environment. We have applied this approach on two systems previously investigated by QM/MM simulations, 4-oxalocrotonate tautomerase (4OT) and ten-eleven translocation-2 (TET2), that provide examples where fragments may or may not need to be included in the QM subsystem. Subsequently, we present the use of this approach to determine the appropriate QM subsystem to calculate the minimum energy path (MEP) for the reaction catalyzed by human DNA polymerase λ (Polλ) with a third cation in the active site. Our results suggest that the combination of protein evolutionary and ELF analyses provides insights into residue/molecular fragment selection for QM/MM simulations.
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Affiliation(s)
- Mark A Hix
- Department of Chemistry, University of North Texas, Denton, Texas 76201, United States
| | - Emmett M Leddin
- Department of Chemistry, University of North Texas, Denton, Texas 76201, United States
| | - G Andrés Cisneros
- Department of Chemistry, University of North Texas, Denton, Texas 76201, United States
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37
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Macetti G, Genoni A. Three-Layer Multiscale Approach Based on Extremely Localized Molecular Orbitals to Investigate Enzyme Reactions. J Phys Chem A 2021; 125:6013-6027. [PMID: 34190569 DOI: 10.1021/acs.jpca.1c05040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Quantum mechanics/molecular mechanics (QM/MM) calculations are widely used embedding techniques to computationally investigate enzyme reactions. In most QM/MM computations, the quantum mechanical region is treated through density functional theory (DFT), which offers the best compromise between chemical accuracy and computational cost. Nevertheless, to obtain more accurate results, one should resort to wave function-based methods, which however lead to a much larger computational cost already for relatively small QM subsystems. To overcome this drawback, we propose the coupling of our QM/ELMO (quantum mechanics/extremely localized molecular orbital) approach with molecular mechanics, thus introducing the three-layer QM/ELMO/MM technique. The QM/ELMO strategy is an embedding method in which the chemically relevant part of the system is treated at the quantum mechanical level, while the rest is described through frozen ELMOs. Since the QM/ELMO method reproduces results of fully QM computations within chemical accuracy and with a much lower computational effort, it can be considered a suitable strategy to extend the range of applicability and accuracy of the QM/MM scheme. In this paper, other than briefly presenting the theoretical bases of the QM/ELMO/MM technique, we will also discuss its validation on the well-tested deprotonation of acetyl coenzyme A by aspartate in citrate synthase.
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Affiliation(s)
- Giovanni Macetti
- Université de Lorraine & CNRS, Laboratoire de Physique et Chimie Théoriques (LPCT), UMR CNRS 7019, 1 Boulevard Arago, F-57078 Metz, France
| | - Alessandro Genoni
- Université de Lorraine & CNRS, Laboratoire de Physique et Chimie Théoriques (LPCT), UMR CNRS 7019, 1 Boulevard Arago, F-57078 Metz, France
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38
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Giulini M, Rigoli M, Mattiotti G, Menichetti R, Tarenzi T, Fiorentini R, Potestio R. From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules. Front Mol Biosci 2021; 8:676976. [PMID: 34164432 PMCID: PMC8215203 DOI: 10.3389/fmolb.2021.676976] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 05/06/2021] [Indexed: 12/18/2022] Open
Abstract
The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.
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Affiliation(s)
- Marco Giulini
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Marta Rigoli
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Giovanni Mattiotti
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Roberto Menichetti
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Thomas Tarenzi
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Raffaele Fiorentini
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Raffaello Potestio
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
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39
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Abstract
Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be particularly relevant for problems involving both computation and experiments. For one, Bayesian inference is a powerful approach to develop models consistent with knowledge from experiments. Second, ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques, and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, in both research and academic teaching.
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Affiliation(s)
- Markus Meuwly
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, 4056 Basel, Switzerland.,Department of Chemistry, Brown University, Providence, Rhode Island 02912, United States
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40
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Computational methods for exploring protein conformations. Biochem Soc Trans 2021; 48:1707-1724. [PMID: 32756904 PMCID: PMC7458412 DOI: 10.1042/bst20200193] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/07/2020] [Accepted: 07/09/2020] [Indexed: 12/13/2022]
Abstract
Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.
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41
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Zanetti-Polzi L, Smith MD, Chipot C, Gumbart JC, Lynch DL, Pavlova A, Smith JC, Daidone I. Tuning Proton Transfer Thermodynamics in SARS-CoV-2 Main Protease: Implications for Catalysis and Inhibitor Design. J Phys Chem Lett 2021; 12:4195-4202. [PMID: 33900080 PMCID: PMC8097931 DOI: 10.1021/acs.jpclett.1c00425] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/21/2021] [Indexed: 05/03/2023]
Abstract
The catalytic reaction in SARS-CoV-2 main protease is activated by a proton transfer (PT) from Cys145 to His41. The same PT is likely also required for the covalent binding of some inhibitors. Here we use a multiscale computational approach to investigate the PT thermodynamics in the apo enzyme and in complex with two potent inhibitors, N3 and the α-ketoamide 13b. We show that with the inhibitors the free energy cost to reach the charge-separated state of the active-site dyad is lower, with N3 inducing the most significant reduction. We also show that a few key sites (including specific water molecules) significantly enhance or reduce the thermodynamic feasibility of the PT reaction, with selective desolvation of the active site playing a crucial role. The approach presented is a cost-effective procedure to identify the enzyme regions that control the activation of the catalytic reaction and is thus also useful to guide the design of inhibitors.
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Affiliation(s)
- Laura Zanetti-Polzi
- Center
S3, CNR Institute of Nanoscience, Via Campi 213/A, I-41125 Modena, Italy
| | - Micholas Dean Smith
- Department
of Biochemistry, Molecular and Cellular Biology, The University of Tennessee, Knoxville, 309 Ken and Blaire Mossman Bldg., 1311 Cumberland
Avenue, Knoxville, Tennessee 37996, United States
| | - Chris Chipot
- UMR 7019, Université de Lorraine, Laboratoire
International Associé CNRS, 54506 Vandœuvre-lès-Nancy, France
- University
of Illinois at Urbana−Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
| | - James C. Gumbart
- School
of Physics, Georgia Institute of Technology, Atlanta Georgia 30332, United States
| | - Diane L. Lynch
- School
of Physics, Georgia Institute of Technology, Atlanta Georgia 30332, United States
| | - Anna Pavlova
- School
of Physics, Georgia Institute of Technology, Atlanta Georgia 30332, United States
| | - Jeremy C. Smith
- Department
of Biochemistry, Molecular and Cellular Biology, The University of Tennessee, Knoxville, 309 Ken and Blaire Mossman Bldg., 1311 Cumberland
Avenue, Knoxville, Tennessee 37996, United States
- UT/ORNL
Center for Molecular Biophysics, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Isabella Daidone
- Department
of Physical and Chemical Sciences, University
of L’Aquila, Via
Vetoio, I-67010 L’Aquila, Italy
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42
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Wang S, Bianco S. Linking the length scales. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00351-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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43
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Sicard F, Koskin V, Annibale A, Rosta E. Position-Dependent Diffusion from Biased Simulations and Markov State Model Analysis. J Chem Theory Comput 2021; 17:2022-2033. [DOI: 10.1021/acs.jctc.0c01151] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- François Sicard
- Department of Chemistry, King’s College London, SE1 1DB London, U.K
- Department of Physics and Astronomy, University College London, WC1E 6BT London, U.K
| | - Vladimir Koskin
- Department of Chemistry, King’s College London, SE1 1DB London, U.K
- Department of Physics and Astronomy, University College London, WC1E 6BT London, U.K
| | - Alessia Annibale
- Department of Mathematics, King’s College London, SE11 6NJ London, U.K
| | - Edina Rosta
- Department of Chemistry, King’s College London, SE1 1DB London, U.K
- Department of Physics and Astronomy, University College London, WC1E 6BT London, U.K
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44
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Abstract
QM/MM simulations have become an indispensable tool in many chemical and biochemical investigations. Considering the tremendous degree of success, including recognition by a 2013 Nobel Prize in Chemistry, are there still "burning challenges" in QM/MM methods, especially for biomolecular systems? In this short Perspective, we discuss several issues that we believe greatly impact the robustness and quantitative applicability of QM/MM simulations to many, if not all, biomolecules. We highlight these issues with observations and relevant advances from recent studies in our group and others in the field. Despite such limited scope, we hope the discussions are of general interest and will stimulate additional developments that help push the field forward in meaningful directions.
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Affiliation(s)
- Qiang Cui
- Departments of Chemistry, Physics, and Biomedical Engineering, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Tanmoy Pal
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Luke Xie
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
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45
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Demir Ö, Barros EP, Offutt TL, Rosenfeld M, Amaro RE. An integrated view of p53 dynamics, function, and reactivation. Curr Opin Struct Biol 2021; 67:187-194. [PMID: 33401096 DOI: 10.1016/j.sbi.2020.11.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 11/16/2020] [Indexed: 12/21/2022]
Abstract
The tumor suppressor p53 plays a vital role in responding to cell stressors such as DNA damage, hypoxia, and tumor formation by inducing cell-cycle arrest, senescence, or apoptosis. Expression level alterations and mutational frequency implicates p53 in most human cancers. In this review, we show how both computational and experimental methods have been used to provide an integrated view of p53 dynamics, function, and reactivation potential. We argue that p53 serves as an exceptional case study for developing methods in modeling intrinsically disordered proteins. We describe how these methods can be leveraged to improve p53 reactivation molecule design and other novel therapeutic modalities, such as PROteolysis TARgeting Chimeras (PROTACs).
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Affiliation(s)
- Özlem Demir
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Emilia P Barros
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Tavina L Offutt
- Dana Farber Cancer Institute, Center for Protein Degradation, Boston, MA, 02215, USA
| | - Mia Rosenfeld
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.
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46
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Juárez-Jiménez J, Tew P, O Connor M, Llabrés S, Sage R, Glowacki D, Michel J. Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes. J Chem Inf Model 2020; 60:6344-6354. [PMID: 33180485 DOI: 10.1021/acs.jcim.0c00221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics, and their biological function. Currently, it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome "collective variable" enhanced sampling protocols. Here, we describe a framework that combines ensemble MD simulations and virtual reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables a priori. We further show that eMD-VR generated pathways can be combined with Markov state models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts.
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Affiliation(s)
- Jordi Juárez-Jiménez
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Philip Tew
- Interactive Scientific, Engine Shed, Station Approach, Bristol BS1 6QH, United Kingdom
| | - Michael O Connor
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom.,Department of Computer Science, University of Bristol, Merchant Venture's Building, Bristol BS8 1UB, United Kingdom.,Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Salomé Llabrés
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Rebecca Sage
- Interactive Scientific, Engine Shed, Station Approach, Bristol BS1 6QH, United Kingdom
| | - David Glowacki
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom.,Department of Computer Science, University of Bristol, Merchant Venture's Building, Bristol BS8 1UB, United Kingdom.,Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
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47
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Mihalovits LM, Ferenczy GG, Keserű GM. Affinity and Selectivity Assessment of Covalent Inhibitors by Free Energy Calculations. J Chem Inf Model 2020; 60:6579-6594. [PMID: 33295760 DOI: 10.1021/acs.jcim.0c00834] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Covalent inhibitors have been gaining increased attention in drug discovery due to their beneficial properties such as long residence time, high biochemical efficiency, and specificity. Optimization of covalent inhibitors is a complex task that involves parallel monitoring of the noncovalent recognition elements and the covalent reactivity of the molecules to avoid potential idiosyncratic side effects. This challenge calls for special design protocols, including a variety of computational chemistry methods. Covalent inhibition proceeds through multiple steps, and calculating free energy changes of the subsequent binding events along the overall binding process would help us to better control the design of drug candidates. Inspired by the recent success of free energy calculations on reversible binders, we developed a complex protocol to compute free energies related to the noncovalent and covalent binding steps with thermodynamic integration and hybrid quantum mechanical/molecular mechanical (QM/MM) potential of mean force (PMF) calculations, respectively. In optimization settings, we examined two therapeutically relevant proteins complexed with congeneric sets of irreversible cysteine targeting covalent inhibitors. In the selectivity paradigm, we studied the irreversible binding of covalent inhibitors to phylogenetically close targets by a mutational approach. The results of the calculations are in good agreement with the experimental free energy values derived from the inhibition and kinetic constants (Ki and kinact) of the enzyme-inhibitor binding. The proposed method might be a powerful tool to predict the potency, selectivity, and binding mechanism of irreversible covalent inhibitors.
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Affiliation(s)
- Levente M Mihalovits
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, Budapest 1117, Hungary
| | - György G Ferenczy
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, Budapest 1117, Hungary
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, Budapest 1117, Hungary
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48
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Decherchi S, Cavalli A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem Rev 2020; 120:12788-12833. [PMID: 33006893 PMCID: PMC8011912 DOI: 10.1021/acs.chemrev.0c00534] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 12/19/2022]
Abstract
Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
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Affiliation(s)
- Sergio Decherchi
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
| | - Andrea Cavalli
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology, University
of Bologna, 40126 Bologna, Italy
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49
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Arcus VL, van der Kamp MW, Pudney CR, Mulholland AJ. Enzyme evolution and the temperature dependence of enzyme catalysis. Curr Opin Struct Biol 2020; 65:96-101. [DOI: 10.1016/j.sbi.2020.06.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/25/2020] [Accepted: 06/04/2020] [Indexed: 10/23/2022]
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50
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Arafet K, Serrano-Aparicio N, Lodola A, Mulholland AJ, González FV, Świderek K, Moliner V. Mechanism of inhibition of SARS-CoV-2 M pro by N3 peptidyl Michael acceptor explained by QM/MM simulations and design of new derivatives with tunable chemical reactivity. Chem Sci 2020; 12:1433-1444. [PMID: 34163906 PMCID: PMC8179034 DOI: 10.1039/d0sc06195f] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The SARS-CoV-2 main protease (Mpro) is essential for replication of the virus responsible for the COVID-19 pandemic, and one of the main targets for drug design. Here, we simulate the inhibition process of SARS-CoV-2 Mpro with a known Michael acceptor (peptidyl) inhibitor, N3. The free energy landscape for the mechanism of the formation of the covalent enzyme-inhibitor product is computed with QM/MM molecular dynamics methods. The simulations show a two-step mechanism, and give structures and calculated barriers in good agreement with experiment. Using these results and information from our previous investigation on the proteolysis reaction of SARS-CoV-2 Mpro, we design two new, synthetically accessible N3-analogues as potential inhibitors, in which the recognition and warhead motifs are modified. QM/MM modelling of the mechanism of inhibition of Mpro by these novel compounds indicates that both may be promising candidates as drug leads against COVID-19, one as an irreversible inhibitor and one as a potential reversible inhibitor.
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Affiliation(s)
- Kemel Arafet
- Departament de Química Física i Analítica, Universitat Jaume I 12071 Castelló Spain
| | | | - Alessio Lodola
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma Italy
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol UK
| | - Florenci V González
- Departament de Química Inorgànica i Orgànica, Universitat Jaume I 12071 Castelló Spain
| | - Katarzyna Świderek
- Departament de Química Física i Analítica, Universitat Jaume I 12071 Castelló Spain
| | - Vicent Moliner
- Departament de Química Física i Analítica, Universitat Jaume I 12071 Castelló Spain
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