1
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Zamanos A, Ioannakis G, Emiris IZ. HydraProt: A New Deep Learning Tool for Fast and Accurate Prediction of Water Molecule Positions for Protein Structures. J Chem Inf Model 2024; 64:2594-2611. [PMID: 38552195 PMCID: PMC11005053 DOI: 10.1021/acs.jcim.3c01559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/13/2024] [Accepted: 02/13/2024] [Indexed: 04/09/2024]
Abstract
Water molecules are integral to the structural stability of proteins and vital for facilitating molecular interactions. However, accurately predicting their precise position around protein structures remains a significant challenge, making it a vibrant research area. In this paper, we introduce HydraProt (deep Hydration of Proteins), a novel methodology for predicting precise positions of water molecule oxygen atoms around protein structures, leveraging two interconnected deep learning architectures: a 3D U-net and a Multi-Layer Perceptron (MLP). Our approach starts by introducing a coarse voxel-based representation of the protein, which allows for rapid sampling of candidate water positions via the 3D U-net. These water positions are then assessed by embedding the water-protein relationship in the Euclidean space by means of an MLP. Finally, a postprocessing step is applied to further refine the MLP predictions. HydraProt surpasses existing state-of-the-art approaches in terms of precision and recall and has been validated on large data sets of protein structures. Notably, our method offers rapid inference runtime and should constitute the method of choice for protein structure studies and drug discovery applications. Our pretrained models, data, and the source code required to reproduce these results are accessible at https://github.com/azamanos/HydraProt.
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Affiliation(s)
- Andreas Zamanos
- Archimedes, Athena Research Center, Marousi 15125, Greece
- Department
of Informatics and Telecommunications, National
and Kapodistrian University of Athens, Athens 16122, Greece
| | - George Ioannakis
- Institute
for Language and Speech Processing, Athena
Research Center, Xanthi 67100, Greece
| | - Ioannis Z. Emiris
- Department
of Informatics and Telecommunications, National
and Kapodistrian University of Athens, Athens 16122, Greece
- Athena
Research Center, Marousi 15125, Greece
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2
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Amini SK, Bashirbanaem P. Evidences for reaction mechanism of 9DB1 DNA catalyst. Int J Biol Macromol 2023; 253:126710. [PMID: 37690649 DOI: 10.1016/j.ijbiomac.2023.126710] [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: 06/03/2023] [Revised: 08/29/2023] [Accepted: 09/03/2023] [Indexed: 09/12/2023]
Abstract
The first reported reaction mechanism of a DNAzyme, i.e. 9DB1, by using molecular dynamics (MD) simulations includes some ambiguities. We try to overcome some of these ambiguous aspects such as the role of mono and divalent metal ions and observed metal rescue effects by surveying the role of functional groups of original 9DB1 and a variety of its rate conserving and rate decreasing mutations via MD simulations. Conformational differences of these two distinct groups are responsible for their opposite rate trends. Blocking of the OH3' of acceptor nucleotide from effective attack by its hydrogen bond to O4' of donor nucleotide is observed in rate decreasing mutations. Our simulations manifest the role of Na+ and Mg2+ ions in bringing close to each other the ligated atoms. These findings along with observed conformational changes explain carefully the reported metal rescue effects for some phosphate groups.
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Affiliation(s)
- Saeed K Amini
- Chemistry and Chemical Engineering Research Centre of Iran, Tehran, Iran.
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3
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Guntamadugu R, Ramakrishnan R, Darala G, Kothandan S. Molecular docking, simulations of animal peptides against the envelope protein of Dengue virus. J Biomol Struct Dyn 2023:1-15. [PMID: 37929876 DOI: 10.1080/07391102.2023.2275183] [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: 06/23/2023] [Accepted: 10/20/2023] [Indexed: 11/07/2023]
Abstract
Peptides are biologically active, small molecules with high specificity in its mode of action that can be effective at nanomolar concentrations. Peptide-based antiviral medicines have already been licensed and used to treat human immunodeficiency virus (HIV), influenza virus and hepatitis C virus. So far, no peptide drug has been approved for antiviral treatment against Dengue virus, and many are under clinical trials. Therefore, developing a reasonable peptide against the Dengue virus Envelope protein structure will be a successful strategy for treating Dengue. Hence, we investigated protein-protein docking interactions between 215 peptides retrieved from the AVP database against the envelope protein using Cluspro and HADDOCK followed by the evaluation of their allegenicity, toxicity and physicochemical characteristics investigation. Further validation of the protein-peptide complexes was performed with Molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann surface area (MMPBSA) analysis on the hit inhibitors. This study revealed that Indolicidin (-75.026 ± 1.54 KJ/mol) and Human Neutrophil peptide-1 (-71.6551 ± 2.112 KJ/mol) shows higher negative ΔG binding implicating their relative stabilization in the protein-peptide interactions during 100 ns of dynamic simulations. Also, both the peptides exhibited desirable physicochemical properties and were nonallergenic. Hence, we further aim to test these peptides by in vitro and in vivo studies to confirm their efficacy against Dengue virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Reena Guntamadugu
- Department of Biotechnology, Saveetha School of Engineering, SIMATS, Thandalam, Tamil Nadu, India
| | - Ranjani Ramakrishnan
- Department of Virology, Sri Venkateswara University, Tirupathi, Andhra Pradesh, India
| | - Gowtham Darala
- Department of Computer Science, College of Engineering, Sri Venkateswara University, Tirupathi, Andhra Pradesh, India
| | - Sangeetha Kothandan
- Department of Biotechnology, Saveetha School of Engineering, SIMATS, Thandalam, Tamil Nadu, India
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4
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Li Y, Zhang Z, Wang R. HydraMap v.2: Prediction of Hydration Sites and Desolvation Energy with Refined Statistical Potentials. J Chem Inf Model 2023; 63:4749-4761. [PMID: 37433022 DOI: 10.1021/acs.jcim.3c00408] [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: 07/13/2023]
Abstract
The complex network of water molecules within the binding pocket of a target protein undergoes alterations upon ligand binding, presenting a significant challenge for conventional molecular modeling methods to accurately characterize and compute the associated energy changes. We have previously developed an empirical method, HydraMap (J. Chem. Inf. Model. 2020, 60, 4359-4375), which employs statistical potentials to predict hydration sites and compute desolvation energy, achieving a reasonable balance between accuracy and speed. In this work, we present its improved version, namely, HydraMap v.2. We updated the statistical potentials for protein-water interactions through an analysis of 17 042 crystal protein structures. We also introduced a new feature to evaluate ligand-water interactions by incorporating statistical potentials derived from the solvated structures of 9878 small organic molecules produced by molecular dynamics simulations. By combining these potentials, HydraMap v.2 can predict and compare the hydration sites in a binding pocket before and after ligand binding, identifying key water molecules involved in the binding process, such as those forming bridging hydrogen bonds and unstable ones that can be replaced. We demonstrated the application of HydraMap v.2 in explaining the structure-activity relationship of a panel of MCL-1 inhibitors. The desolvation energies calculated by summing the energy change of each hydration site before and after ligand binding showed good correlation with known ligand binding affinities on six target proteins. In conclusion, HydraMap v.2 offers a cost-effective solution for estimating the desolvation energy during protein-ligand binding and also is practical in guiding lead optimization in structure-based drug discovery.
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Affiliation(s)
- Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Zhe Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
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5
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Abstract
Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.
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Affiliation(s)
- Christoph Gorgulla
- Harvard Medical School and Physics Department, Harvard University, Boston, Massachusetts, USA;
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Current affiliation: Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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6
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Eberhardt J, Forli S. WaterKit: Thermodynamic Profiling of Protein Hydration Sites. J Chem Theory Comput 2023; 19:2535-2556. [PMID: 37094087 PMCID: PMC10732097 DOI: 10.1021/acs.jctc.2c01087] [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] [Indexed: 04/26/2023]
Abstract
Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.
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Affiliation(s)
- Jerome Eberhardt
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
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7
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Chen W, He H, Wang J, Wang J, Chang CEA. Uncovering water effects in protein-ligand recognition: importance in the second hydration shell and binding kinetics. Phys Chem Chem Phys 2023; 25:2098-2109. [PMID: 36562309 PMCID: PMC9970846 DOI: 10.1039/d2cp04584b] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Developing a ligand with high affinity for a specific protein target is essential for drug design, and water molecules are well known to play a key role in protein-drug recognition. However, predicting the role of particularly ordered water molecules in drug binding remains challenging. Furthermore, hydration free energy contributed from the water network, including the second shell of water molecules, is far from being well studied. In this research we focused on these aspects to accurately and efficiently evaluate water effects in protein-ligand binding affinity. We developed a new strategy using a free-energy calculation method, VM2. We successfully predicted the stable ordered water molecules in a number of protein systems: PDE 10a, HSP90, tryptophan synthase (TRPS), CDK2 and Factor Xa. In some of these, the second shell of water molecules appeared to be critical in protein-ligand binding. We also applied the strategy to largely improve binding free energy calculation using the MM/PBSA method. When applying MM/PBSA alone for two systems, CDK2 and Factor Xa, the computed binding free energy resulted in poor to moderate R2 values with experimental data. However, including water free energy correction greatly improved the free energy calculation. Furthermore, our work helped to explain how xk263 is a 1000 times faster binder to HIVp than ritonavir, a potentially useful tool for investigating binding kinetics. Our studies reveal the importance of fully considering water effects in therapeutic developments in pharmaceutical and biotechnology industries and for fundamental research in protein-ligand recognition.
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Affiliation(s)
- Wei Chen
- School of Pharmacy, Fuzhou Medical College of NanChang University, Fuzhou, JiangXi 344000, P. R. China.
| | - Huan He
- School of Pharmacy, Fuzhou Medical College of NanChang University, Fuzhou, JiangXi 344000, P. R. China.
| | - Jing Wang
- School of Pharmacy, Fuzhou Medical College of NanChang University, Fuzhou, JiangXi 344000, P. R. China.
| | - Jiahui Wang
- School of Pharmacy, Fuzhou Medical College of NanChang University, Fuzhou, JiangXi 344000, P. R. China.
| | - Chia-en A. Chang
- Department of Chemistry, University of California at Riverside, Riverside, CA 92521, USA,Corresponding Authors Wei Chen: School of Pharmacy, Fuzhou Medical College of NanChang University, Fuzhou, JiangXi 344000, China. , Chia-en A. Chang: Department of Chemistry, University of California at Riverside, Riverside, CA 92521, USA.
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8
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Samways M, Bruce Macdonald HE, Taylor RD, Essex JW. Water Networks in Complexes between Proteins and FDA-Approved Drugs. J Chem Inf Model 2023; 63:387-396. [PMID: 36469670 PMCID: PMC9832485 DOI: 10.1021/acs.jcim.2c01225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.
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Affiliation(s)
- Marley
L. Samways
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.
| | - Hannah E. Bruce Macdonald
- Computational
and Systems Biology Program, Memorial Sloan
Kettering Cancer Center, New York, New York 10065, United States
| | | | - Jonathan W. Essex
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.,
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9
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Mteremko D, Chilongola J, Paluch AS, Chacha M. Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water. J Mol Graph Model 2023; 118:108348. [PMID: 36257147 DOI: 10.1016/j.jmgm.2022.108348] [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: 06/07/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/29/2022]
Abstract
A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.
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Affiliation(s)
- Denis Mteremko
- The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania.
| | - Jaffu Chilongola
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
| | - Andrew S Paluch
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, 45056, USA
| | - Musa Chacha
- The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania; Arusha Technical College, Arusha, Tanzania
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10
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Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics 2022; 15:pharmaceutics15010049. [PMID: 36678678 PMCID: PMC9867171 DOI: 10.3390/pharmaceutics15010049] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
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Affiliation(s)
- Yiqun Chang
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Bryson A. Hawkins
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Jonathan J. Du
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Paul W. Groundwater
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - David E. Hibbs
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Felcia Lai
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
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11
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Ge Y, Baumann HM, Mobley DL. Absolute Binding Free Energy Calculations for Buried Water Molecules. J Chem Theory Comput 2022; 18:6482-6499. [PMID: 36197451 PMCID: PMC9873352 DOI: 10.1021/acs.jctc.2c00658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Water often plays a key role in mediating protein-ligand interactions. Understanding contributions from active-site water molecules to binding thermodynamics of a ligand is important in predicting binding free energies for ligand optimization. In this work, we tested a non-equilibrium switching method for absolute binding free energy calculations on water molecules in binding sites of 13 systems. We discuss the lessons we learned about identified issues that affected our calculations and ways to address them. This work fits with our larger focus on how to do accurate ligand binding free energy calculations when water rearrangements are very slow, such as rearrangements due to ligand modification (as in relative free energy calculations) or ligand binding (as in absolute free energy calculations). The method studied in this work can potentially be used to account for limited water sampling via providing endpoint corrections to free energy calculations using our calculated binding free energy of water.
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Affiliation(s)
- Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, California92697, United States
| | - Hannah M Baumann
- Department of Pharmaceutical Sciences, University of California, Irvine, California92697, United States
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California92697, United States
- Department of Chemistry, University of California, Irvine, California92697, United States
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12
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Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5104464. [PMID: 36226242 PMCID: PMC9550495 DOI: 10.1155/2022/5104464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022]
Abstract
Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it is difficult to identify the conserved water molecules (CWMs) from free water molecules (FWMs) directly as CWMs are normally deeply embedded in proteins and form strong hydrogen bonds with surrounding polar groups. To circumvent this difficulty, in this work, the abundance of spatial structure information and physicochemical properties of water molecules in proteins inspires us to adopt machine learning methods for identifying the CWMs. Therefore, in this study, a machine learning framework to identify the CWMs in the binding sites of the proteins was presented. First, by analyzing water molecules' physicochemical properties and spatial structure information, six features (i.e., atom density, hydrophilicity, hydrophobicity, solvent-accessible surface area, temperature B-factors, and mobility) were extracted. Those features were further analyzed and combined to reach a higher CWM identification rate. As a result, an optimal feature combination was determined. Based on this optimal combination, seven different machine learning models (including support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), discriminant analysis (DA), naïve Bayes (NB), and ensemble learning (EL)) were evaluated for their abilities in identifying two categories of water molecules, i.e., CWMs and FWMs. It showed that the EL model was the desired prediction model due to its comprehensive advantages. Furthermore, the presented methodology was validated through a case study of crystal 3skh and extensively compared with Dowser++. The prediction performance showed that the optimal feature combination and the desired EL model in our method could achieve satisfactory prediction accuracy in identifying CWMs from FWMs in the proteins' binding sites.
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13
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Jiang H, Wang J, Cong W, Huang Y, Ramezani M, Sarma A, Dokholyan NV, Mahdavi M, Kandemir MT. Predicting Protein-Ligand Docking Structure with Graph Neural Network. J Chem Inf Model 2022; 62:2923-2932. [PMID: 35699430 PMCID: PMC10279412 DOI: 10.1021/acs.jcim.2c00127] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.
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Affiliation(s)
- Huaipan Jiang
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Jian Wang
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey, Pennsylvania 17033, United States
| | - Weilin Cong
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Yihe Huang
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Morteza Ramezani
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Anup Sarma
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Nikolay V Dokholyan
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey, Pennsylvania 17033, United States
- Departments of Chemistry and Biomedical Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Mehrdad Mahdavi
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Mahmut T Kandemir
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
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14
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Ge Y, Wych DC, Samways ML, Wall ME, Essex JW, Mobley DL. Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques. J Chem Theory Comput 2022; 18:1359-1381. [PMID: 35148093 PMCID: PMC9241631 DOI: 10.1021/acs.jctc.1c00590] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation time scales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed the rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.
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Affiliation(s)
- Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
| | - David C Wych
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Marley L Samways
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Michael E Wall
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
- Department of Chemistry, University of California, Irvine, California 92697, United States
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15
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Meyerowitz JG, Robertson MJ, Barros-Álvarez X, Panova O, Nwokonko RM, Gao Y, Skiniotis G. The oxytocin signaling complex reveals a molecular switch for cation dependence. Nat Struct Mol Biol 2022; 29:274-281. [PMID: 35241813 DOI: 10.1038/s41594-022-00728-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022]
Abstract
Oxytocin (OT) and vasopressin (AVP) are conserved peptide signaling hormones that are critical for diverse processes including osmotic homeostasis, reproduction, lactation and social interaction. OT acts through the oxytocin receptor (OTR), a magnesium-dependent G protein-coupled receptor that is a therapeutic target for treatment of postpartum hemorrhage, dysfunctional labor and autism. However, the molecular mechanisms that underlie OTR activation by OT and the dependence on magnesium remain unknown. Here we present the wild-type active-state structure of human OTR bound to OT and miniGq/i determined by cryo-EM. The structure reveals a unique activation mechanism adopted by OTR involving both the formation of a Mg2+ coordination complex between OT and the receptor, and disruption of transmembrane helix 7 (TM7) by OT. Our functional assays demonstrate the role of TM7 disruption and provide the mechanism of full agonism by OT and partial agonism by OT analogs. Furthermore, we find that the identity of a single cation-coordinating residue across vasopressin family receptors determines whether the receptor is cation-dependent. Collectively, these results demonstrate how the Mg2+-dependent OTR is activated by OT, provide essential information for structure-based drug discovery efforts and shed light on the molecular determinants of cation dependence of vasopressin family receptors throughout the animal kingdom.
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Affiliation(s)
- Justin G Meyerowitz
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael J Robertson
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ouliana Panova
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert M Nwokonko
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yang Gao
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. .,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA.
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16
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Robertson MJ, Meyerowitz JG, Panova O, Borrelli K, Skiniotis G. Plasticity in ligand recognition at somatostatin receptors. Nat Struct Mol Biol 2022; 29:210-217. [PMID: 35210615 PMCID: PMC11073612 DOI: 10.1038/s41594-022-00727-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/14/2022] [Indexed: 11/10/2022]
Abstract
Somatostatin is a signaling peptide that plays a pivotal role in physiologic processes relating to metabolism and growth through its actions at somatostatin receptors (SSTRs). Members of the SSTR subfamily, particularly SSTR2, are key drug targets for neuroendocrine neoplasms, with synthetic peptide agonists currently in clinical use. Here, we show the cryogenic-electron microscopy structures of active-state SSTR2 in complex with heterotrimeric Gi3 and either the endogenous ligand SST14 or the FDA-approved drug octreotide. Complemented by biochemical assays and molecular dynamics simulations, these structures reveal key details of ligand recognition and receptor activation at SSTRs. We find that SSTR ligand recognition is highly diverse, as demonstrated by ligand-induced conformational changes in ECL2 and substantial sequence divergence across subtypes in extracellular regions. Despite this complexity, we rationalize several known sources of SSTR subtype selectivity and identify an additional interaction for specific binding. These results provide valuable insights for structure-based drug discovery at SSTRs.
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Affiliation(s)
- Michael J Robertson
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Justin G Meyerowitz
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ouliana Panova
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA.
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17
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Baishya T, Sharma P, Gomila RM, Frontera A, Barceló-Oliver M, Verma AK, Bhattacharyya MK. Fumarato and Phthalato Bridged Dinuclear Metal-Organic Cu(II) and Mn(II) Compounds involving Infinite Fumarate-water Assemblies and Unusual Structure-guiding H-bonded Synthons: Antiproliferative Evaluation and Theoretical Studies. NEW J CHEM 2022. [DOI: 10.1039/d2nj01860h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Two new dinuclear coordination compounds viz. [Cu2(µ-fum)(phen)2(H2O)6](fum)•6H2O (1) and [Mn2(µ-phth)2(phen)4]•2H2O (2) (phen = 1,10-phenanthroline, fum = fumarate and phth = phthalate) have been synthesized and characterized by elemental analysis, single...
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18
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Can docking scoring functions guarantee success in virtual screening? VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Huang P, Xing H, Zou X, Han Q, Liu K, Sun X, Wu J, Fan J. Accurate Prediction of Hydration Sites of Proteins Using Energy Model With Atom Embedding. Front Mol Biosci 2021; 8:756075. [PMID: 34616774 PMCID: PMC8488165 DOI: 10.3389/fmolb.2021.756075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.
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Affiliation(s)
- Pin Huang
- College of Life Sciences, Beijing Normal University, Beijing, China.,Accutar Biotechnology Inc., Brooklyn, NY, United States
| | - Haoming Xing
- Accutar Biotechnology Inc., Brooklyn, NY, United States
| | - Xun Zou
- Accutar Biotechnology Inc., Brooklyn, NY, United States
| | - Qi Han
- Accutar Biotechnology Inc., Brooklyn, NY, United States
| | - Ke Liu
- Accutar Biotechnology Inc., Brooklyn, NY, United States
| | - Xiangyan Sun
- Accutar Biotechnology Inc., Brooklyn, NY, United States
| | - Junqiu Wu
- Accutar Biotechnology Inc., Brooklyn, NY, United States
| | - Jie Fan
- Accutar Biotechnology Inc., Brooklyn, NY, United States
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20
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Samways ML, Taylor RD, Bruce Macdonald HE, Essex JW. Water molecules at protein-drug interfaces: computational prediction and analysis methods. Chem Soc Rev 2021; 50:9104-9120. [PMID: 34184009 DOI: 10.1039/d0cs00151a] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.
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Affiliation(s)
- Marley L Samways
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
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21
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Seven AB, Barros-Álvarez X, de Lapeyrière M, Papasergi-Scott MM, Robertson MJ, Zhang C, Nwokonko RM, Gao Y, Meyerowitz JG, Rocher JP, Schelshorn D, Kobilka BK, Mathiesen JM, Skiniotis G. G-protein activation by a metabotropic glutamate receptor. Nature 2021; 595:450-454. [PMID: 34194039 DOI: 10.1038/s41586-021-03680-3] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 06/01/2021] [Indexed: 01/14/2023]
Abstract
Family C G-protein-coupled receptors (GPCRs) operate as obligate dimers with extracellular domains that recognize small ligands, leading to G-protein activation on the transmembrane (TM) domains of these receptors by an unknown mechanism1. Here we show structures of homodimers of the family C metabotropic glutamate receptor 2 (mGlu2) in distinct functional states and in complex with heterotrimeric Gi. Upon activation of the extracellular domain, the two transmembrane domains undergo extensive rearrangement in relative orientation to establish an asymmetric TM6-TM6 interface that promotes conformational changes in the cytoplasmic domain of one protomer. Nucleotide-bound Gi can be observed pre-coupled to inactive mGlu2, but its transition to the nucleotide-free form seems to depend on establishing the active-state TM6-TM6 interface. In contrast to family A and B GPCRs, G-protein coupling does not involve the cytoplasmic opening of TM6 but is facilitated through the coordination of intracellular loops 2 and 3, as well as a critical contribution from the C terminus of the receptor. The findings highlight the synergy of global and local conformational transitions to facilitate a new mode of G-protein activation.
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Affiliation(s)
- Alpay B Seven
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Makaía M Papasergi-Scott
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael J Robertson
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chensong Zhang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert M Nwokonko
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yang Gao
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Justin G Meyerowitz
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Brian K Kobilka
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Jesper M Mathiesen
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA. .,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA.
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22
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Abstract
Structure-based drug discovery has become a promising and efficient approach for
identifying novel and potent drug candidates with less time and cost than conventional drug
discovery approaches. It has been widely used in the pharmaceutical industry since it uses the 3D
structure of biological protein targets and thereby allows us to understand the molecular basis of
diseases. For the virtual identification of drug candidates based on structure, there are a few steps for
protein and compound preparations to obtain accurate results. In this review, the software and webtools
for the preparation and structure-based simulation are introduced. In addition, recent
improvements in structure-based virtual screening, target library designing for virtual screening,
docking, scoring, and post-processing of top hits are also introduced.
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Affiliation(s)
- Bilal Shaker
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea
| | - Kha Mong Tran
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea
| | - Chanjin Jung
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea
| | - Dokyun Na
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea
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23
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Varela‐Rial A, Majewski M, De Fabritiis G. Structure based virtual screening: Fast and slow. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Alejandro Varela‐Rial
- Acellera Labs Barcelona Spain
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Maciej Majewski
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
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24
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Hüfner-Wulsdorf T, Klebe G. Mapping Water Thermodynamics on Drug Candidates via Molecular Building Blocks: a Strategy to Improve Ligand Design and Rationalize SAR. J Med Chem 2021; 64:4662-4676. [PMID: 33797902 DOI: 10.1021/acs.jmedchem.0c02115] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The consideration of interactions involving water molecules in protein-ligand binding is widely appreciated in drug discovery nowadays. However, it is not ultimately clear how insights about these interactions translate into molecular design concepts. In this work, we introduce a computational strategy that, trained with high-precision experimental data, allows for the decomposition of water-related thermodynamic properties into chemically relevant building blocks (BBs) of a given ligand scaffold. For each of these BBs, a score based on solvation energy and entropy is computed, thus enabling the analysis of solvent-related affinity contributions for individual BBs. We find the nonvariable BB in a congeneric ligand pair to have a larger impact on the binding affinity than the variable part thus suggesting strong cooperative effects. Furthermore, we find enhanced solute-solvent interactions for a BB due to the presence of a C-F bond. Our investigation may be used to design drug molecules with tailored solvent thermodynamic properties.
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Affiliation(s)
- Tobias Hüfner-Wulsdorf
- Philipps Universität Marburg, Institut für Pharmazeutische Chemie, Marbacher Weg 6, Marburg 35037, Germany
| | - Gerhard Klebe
- Philipps Universität Marburg, Institut für Pharmazeutische Chemie, Marbacher Weg 6, Marburg 35037, Germany
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25
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Sharma P, Sarma P, Frontera A, Barceló-Oliver M, Verma AK, Sarma B, Barthakur T, Bhattacharyya MK. Energetically significant cooperative π-stacked ternary assemblies in Ni(II) phenanthroline compounds involving discrete water clusters: Anticancer activities and theoretical studies. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2020.129486] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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26
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Kim KH. Outliers in SAR and QSAR: 3. Importance of considering the role of water molecules in protein-ligand interactions and quantitative structure-activity relationship studies. J Comput Aided Mol Des 2021; 35:371-396. [PMID: 33712973 DOI: 10.1007/s10822-021-00377-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/05/2021] [Indexed: 11/30/2022]
Abstract
It is frequently mentioned that QSARs have not generally lived up to expectations, especially in cases where high predictability is expected yet failed to deliver satisfactory results. Even though outliers can provide an increased opportunity in drug discovery research, outliers in SAR and QSAR can contort predictions and affect the accuracy if proper attention is not given. The percentages of outliers in QSARs have not changed appreciably over the last decade. In our previous studies, we suggested two possible sources of outliers in SAR and QSAR. In this paper, we suggest an additional possible source of outliers in QSAR. We presented several literature examples that show one or more water molecules that play a critical role in protein-ligand binding interactions as observed in their crystal structures. These examples illustrate that failing to account for the effects of water molecules in protein-ligand interactions could mislead interpretation and possibly yield outliers in SAR and QSAR. Examples include cases where QSAR, considering the role of water molecules in protein-ligand crystal structures, provided deeper insight into the understanding and interpretation of the developed QSAR.
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27
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Bergazin TD, Ben-Shalom IY, Lim NM, Gill SC, Gilson MK, Mobley DL. Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo. J Comput Aided Mol Des 2021; 35:167-177. [PMID: 32968887 PMCID: PMC7904576 DOI: 10.1007/s10822-020-00344-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/16/2020] [Indexed: 11/26/2022]
Abstract
Water molecules can be found interacting with the surface and within cavities in proteins. However, water exchange between bulk and buried hydration sites can be slow compared to simulation timescales, thus leading to the inefficient sampling of the locations of water. This can pose problems for free energy calculations for computer-aided drug design. Here, we apply a hybrid method that combines nonequilibrium candidate Monte Carlo (NCMC) simulations and molecular dynamics (MD) to enhance sampling of water in specific areas of a system, such as the binding site of a protein. Our approach uses NCMC to gradually remove interactions between a selected water molecule and its environment, then translates the water to a new region, before turning the interactions back on. This approach of gradual removal of interactions, followed by a move and then reintroduction of interactions, allows the environment to relax in response to the proposed water translation, improving acceptance of moves and thereby accelerating water exchange and sampling. We validate this approach on several test systems including the ligand-bound MUP-1 and HSP90 proteins with buried crystallographic waters removed. We show that our BLUES (NCMC/MD) method enhances water sampling relative to normal MD when applied to these systems. Thus, this approach provides a strategy to improve water sampling in molecular simulations which may be useful in practical applications in drug discovery and biomolecular design.
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Affiliation(s)
| | - Ido Y Ben-Shalom
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Nathan M Lim
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - Sam C Gill
- Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA.
- Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA.
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28
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Chetry S, Sharma P, Frontera A, Saha U, Verma AK, Sarma B, Kalita PJ, Bhattacharyya MK. Biologically relevant and energetically significant cooperative ternary (π–π) 2/(π–π) 1/(π–π) 2 assemblies and fascinating discrete (H 2O) 21 clusters in isostructural 2,5-pyridine dicarboxylato Co( ii) and Zn( ii) phenanthroline compounds: antiproliferative evaluation and theoretical studies. NEW J CHEM 2021. [DOI: 10.1039/d0nj04338a] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Cytotoxicity in cancer cells with structure activity relationship has been explored in isostructural Co(ii) and Zn(ii) compounds involving energetically significant cooperative (π–π)2/(π–π)1/(π–π)2 assemblies and fascinating (H2O)21 clusters.
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Affiliation(s)
- Sanjib Chetry
- Department of Chemistry
- Cotton University
- Guwahati-781001
- India
| | - Pranay Sharma
- Department of Chemistry
- Cotton University
- Guwahati-781001
- India
| | - Antonio Frontera
- Departament de Química
- Universitat de les Illes Balears
- 07122 Palma de Mallorca (Baleares)
- Spain
| | - Utpal Saha
- Department of Chemistry
- Cotton University
- Guwahati-781001
- India
| | - Akalesh K. Verma
- Department of Zoology
- Cell & Biochemical Technology laboratory
- Cotton University
- Guwahati-781001
- India
| | - Bipul Sarma
- Department of Chemical Sciences
- Tezpur University
- India
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29
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Abstract
![]()
We
present a novel web server, named gridSolvate, dedicated to
the prediction of biomolecular hydration properties. Given a solute
in atomic representation, such as a protein or protein–ligand
complex, the server determines positions and excess chemical potential
of buried and first hydration shell water molecules. Calculations
are based on our semiexplicit hydration model that provides computational
efficiency close to implicit solvent approaches, yet captures a number
of physical effects unique to explicit solvent representation. The
model was introduced and validated before in the context of bulk hydration
of drug-like solutes and determination of protein hydration sites.
Current methodological developments merge those two avenues into a
single, easily accessible tool. Here, we focus on the server’s
ability to predict water distribution and affinity within protein–ligand
interfaces. We demonstrate that with possibly minimal user intervention
the server correctly predicts the locations of 77% of interface water
molecules in an external set of test structures. The server is freely
available at https://gsolvate.biomod.cent.uw.edu.pl.
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Affiliation(s)
- Piotr Setny
- Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
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30
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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: 125] [Impact Index Per Article: 31.3] [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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31
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Municoy M, Roda S, Soler D, Soutullo A, Guallar V. aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins. J Chem Theory Comput 2020; 16:7655-7670. [PMID: 33201691 DOI: 10.1021/acs.jctc.0c00925] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.
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Affiliation(s)
- Martí Municoy
- Barcelona Supercomputing Center, Jordi Girona 29, E-08034 Barcelona, Spain
| | - Sergi Roda
- Barcelona Supercomputing Center, Jordi Girona 29, E-08034 Barcelona, Spain
| | - Daniel Soler
- Nostrum Biodiscovery, Jordi Girona 29, Nexus II D128, 08034 Barcelona, Spain
| | - Alberto Soutullo
- Barcelona Supercomputing Center, Jordi Girona 29, E-08034 Barcelona, Spain
| | - Victor Guallar
- Barcelona Supercomputing Center, Jordi Girona 29, E-08034 Barcelona, Spain.,ICREA, Passeig Lluís Companys 23, E-08010 Barcelona, Spain
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32
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Hüfner-Wulsdorf T, Klebe G. Advancing GIST-Based Solvent Functionals through Multiobjective Optimization of Solvent Enthalpy and Entropy Scoring Terms. J Chem Inf Model 2020; 60:6654-6665. [DOI: 10.1021/acs.jcim.0c01133] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Tobias Hüfner-Wulsdorf
- Institut für Pharmazeutische Chemie, Philipps Universität Marburg, Marbacher Weg 6, 35037 Marburg, Germany
| | - Gerhard Klebe
- Institut für Pharmazeutische Chemie, Philipps Universität Marburg, Marbacher Weg 6, 35037 Marburg, Germany
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Lee TS, Allen BK, Giese TJ, Guo Z, Li P, Lin C, McGee TD, Pearlman DA, Radak BK, Tao Y, Tsai HC, Xu H, Sherman W, York DM. Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery. J Chem Inf Model 2020; 60:5595-5623. [PMID: 32936637 PMCID: PMC7686026 DOI: 10.1021/acs.jcim.0c00613] [Citation(s) in RCA: 169] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.
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Affiliation(s)
- Tai-Sung Lee
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Bryce K. Allen
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Timothy J. Giese
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Zhenyu Guo
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Pengfei Li
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Charles Lin
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - T. Dwight McGee
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - David A. Pearlman
- QSimulate Incorporated, Cambridge, Massachusetts 02139, United States
| | - Brian K. Radak
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Yujun Tao
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Hsu-Chun Tsai
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Huafeng Xu
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Darrin M. York
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
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Jiang H, Fan M, Wang J, Sarma A, Mohanty S, Dokholyan NV, Mahdavi M, Kandemir MT. Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks. J Chem Inf Model 2020; 60:4594-4602. [PMID: 33100014 PMCID: PMC10706896 DOI: 10.1021/acs.jcim.0c00542] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most stable structure of the complex, the performance of conventional structure-based molecular docking methods heavily depends on the accuracy of scoring or energy functions (as an approximation of affinity) for each pose of the protein-ligand docking complex to effectively guide the search in an exponentially large solution space. However, due to the heterogeneity of molecular structures, the existing scoring calculation methods are either tailored to a particular data set or fail to exhibit high accuracy. In this paper, we propose a convolutional neural network (CNN)-based model that learns to predict the stability factor of the protein-ligand complex and exhibits the ability of CNNs to improve the existing docking software. Evaluated results on PDBbind data set indicate that our approach reduces the execution time of the traditional docking-based method while improving the accuracy. Our code, experiment scripts, and pretrained models are available at https://github.com/j9650/MedusaNet.
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Affiliation(s)
- Huaipan Jiang
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Mengran Fan
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Jian Wang
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey 17033, United States
| | - Anup Sarma
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Shruti Mohanty
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Nikolay V Dokholyan
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey 17033, United States
- Departments of Chemistry and Biomedical Engineering, Pennsylvania State University, State College 16802, United States
| | - Mehrdad Mahdavi
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Mahmut T Kandemir
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
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Comparing Fragment Binding Poses Prediction Using HSP90 as a Key Study: When Bound Water Makes the Difference. Molecules 2020; 25:molecules25204651. [PMID: 33053878 PMCID: PMC7587341 DOI: 10.3390/molecules25204651] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/05/2020] [Accepted: 10/08/2020] [Indexed: 12/03/2022] Open
Abstract
Fragment-Based Drug Discovery (FBDD) approaches have gained popularitynot only in industry but also in academic research institutes. However, the computational prediction of the binding mode adopted by fragment-like molecules within a protein binding site is still a very challenging task. One of the most crucial aspects of fragment binding is related to the large amounts of bound waters in the targeted binding pocket. The binding affinity of fragmentsmay not be sufficientto displace the bound water molecules. In the present work, we confirmed the importance of the bound water molecules in the correct prediction of the fragment binding mode. Moreover, we investigate whether the use of methods based on explicit solvent molecular dynamics simulations can improve the accuracy of fragment posing. The protein chosen for this study is HSP-90.
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36
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Ross GA, Russell E, Deng Y, Lu C, Harder ED, Abel R, Wang L. Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo. J Chem Theory Comput 2020; 16:6061-6076. [PMID: 32955877 DOI: 10.1021/acs.jctc.0c00660] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of protein-ligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water.
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Affiliation(s)
- Gregory A Ross
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ellery Russell
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Yuqing Deng
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Chao Lu
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Edward D Harder
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Lingle Wang
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
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37
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Cutrona KJ, Newton AS, Krimmer SG, Tirado-Rives J, Jorgensen WL. Metadynamics as a Postprocessing Method for Virtual Screening with Application to the Pseudokinase Domain of JAK2. J Chem Inf Model 2020; 60:4403-4415. [PMID: 32383599 DOI: 10.1021/acs.jcim.0c00276] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
With standard scoring methods, top-ranked compounds from virtual screening by docking often turn out to be inactive. For this reason, metadynamics, a method used to sample rare events, was studied to further evaluate docking poses with the aim of reducing false positives. Specifically, virtual screening was performed with Glide SP to seek potential molecules to bind to the ATP site in the pseudokinase domain of JAK2 kinase, and promising compounds were selected from the top-ranked 1000 based on visualization. Rescoring with Glide XP, GOLD, and MM/GBSA was unable to differentiate well between active and inactive compounds. Metadynamics was then used to gauge the relative binding affinity from the required time or the potential of mean force needed to dissociate the ligand from the bound complex. With consideration of previously known binders of varying affinities, metadynamics was able to differentiate between the most active compounds and inactive or weakly active ones, and it could identify correctly most of the selected virtual screening compounds as false positives. Thus, metadynamics has the potential to be a viable postprocessing method for virtual screening, minimizing the expense of buying or synthesizing inactive compounds.
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Affiliation(s)
- Kara J Cutrona
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Ana S Newton
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Stefan G Krimmer
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Julian Tirado-Rives
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - William L Jorgensen
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
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38
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Nikolaev DM, Shtyrov AA, Mereshchenko AS, Panov MS, Tveryanovich YS, Ryazantsev MN. An assessment of water placement algorithms in quantum mechanics/molecular mechanics modeling: the case of rhodopsins' first spectral absorption band maxima. Phys Chem Chem Phys 2020; 22:18114-18123. [PMID: 32761024 DOI: 10.1039/d0cp02638g] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Quantum mechanics/molecular mechanics (QM/MM) models are a widely used tool to obtain detailed insight into the properties and functioning of proteins. The outcome of QM/MM studies heavily depends on the quality of the applied QM/MM model. Prediction and right placement of internal water molecules in protein cavities is one of the critical parts of any QM/MM model construction. Herein, we performed a systematic study of four protein hydration algorithms. We tested these algorithms for their ability to predict X-ray-resolved water molecules for a set of membrane photosensitive rhodopsin proteins, as well as the influence of the applied water placement algorithms on the QM/MM calculated absorption maxima (λmax) of these proteins. We used 49 rhodopsins and their intermediates with available X-ray structures as the test set. We found that a proper choice of hydration algorithms and setups is needed to predict functionally important water molecules in the chromophore-binding cavity of rhodopsins, such as the water cluster in the N-H region of bacteriorhodopsin or two water molecules in the binding pocket of bovine visual rhodopsin. The QM/MM calculated λmax of rhodopsins is also quite sensitive to the applied protein hydration protocols. The best methodology allows obtaining an 18.0 nm average value for the absolute deviation of the calculated λmax from the experimental λmax. Although the major effect of water molecules on λmax originates from the water molecules located in the binding pocket, the water molecules outside the binding pocket also affect the calculated λmax mainly by causing a reorganization of the protein structure. The results reported in this study can be used for the evaluation and further development of hydration methodologies, in general, and rhodopsin QM/MM models, in particular.
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Affiliation(s)
- Dmitrii M Nikolaev
- Nanotechnology Research and Education Centre RAS, Saint Petersburg Academic University, 8/3 Khlopina Street, St. Petersburg 194021, Russia.
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39
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Robertson MJ, van Zundert GCP, Borrelli K, Skiniotis G. GemSpot: A Pipeline for Robust Modeling of Ligands into Cryo-EM Maps. Structure 2020; 28:707-716.e3. [PMID: 32413291 DOI: 10.1016/j.str.2020.04.018] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 02/13/2020] [Accepted: 04/22/2020] [Indexed: 12/20/2022]
Abstract
Producing an accurate atomic model of biomolecule-ligand interactions from maps generated by cryoelectron microscopy (cryo-EM) often presents challenges inherent to the methodology and the dynamic nature of ligand binding. Here, we present GemSpot, an automated pipeline of computational chemistry methods that take into account EM map potentials, quantum mechanics energy calculations, and water molecule site prediction to generate candidate poses and provide a measure of the degree of confidence. The pipeline is validated through several published cryo-EM structures of complexes in different resolution ranges and various types of ligands. In all cases, at least one identified pose produced both excellent interactions with the target and agreement with the map. GemSpot will be valuable for the robust identification of ligand poses and drug discovery efforts through cryo-EM.
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Affiliation(s)
- Michael J Robertson
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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40
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Li Y, Gao Y, Holloway MK, Wang R. Prediction of the Favorable Hydration Sites in a Protein Binding Pocket and Its Application to Scoring Function Formulation. J Chem Inf Model 2020; 60:4359-4375. [DOI: 10.1021/acs.jcim.9b00619] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People’s Republic of China
| | - Yingduo Gao
- Merck Research Laboratories, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
- Merck Research Laboratories, 770 Sumneytown Pike, West Point, Pennsylvania 19486, United States
| | | | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People’s Republic of China
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
- Shanxi Key Laboratory of Innovative Drugs for the Treatment of Serious Diseases Basing on Chronic Inflammation, College of Traditional Chinese Medicines, Shanxi University of Chinese Medicine, Taiyuan, Shanxi 030619, People’s Republic of China
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41
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Mitusińska K, Raczyńska A, Bzówka M, Bagrowska W, Góra A. Applications of water molecules for analysis of macromolecule properties. Comput Struct Biotechnol J 2020; 18:355-365. [PMID: 32123557 PMCID: PMC7036622 DOI: 10.1016/j.csbj.2020.02.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/26/2020] [Accepted: 02/01/2020] [Indexed: 01/12/2023] Open
Abstract
Water molecules maintain proteins' structures, functions, stabilities and dynamics. They can occupy certain positions or pass quickly via a protein's interior. Regardless of their behaviour, water molecules can be used for the analysis of proteins' structural features and biochemical properties. Here, we present a list of several software programs that use the information provided by water molecules to: i) analyse protein structures and provide the optimal positions of water molecules for protein hydration, ii) identify high-occupancy water sites in order to analyse ligand binding modes, and iii) detect and describe tunnels and cavities. The analysis of water molecules' distribution and trajectories sheds a light on proteins' interactions with small molecules, on the dynamics of tunnels and cavities, on protein composition and also on the functionality, transportation network and location of functionally relevant residues. Finally, the correct placement of water molecules in protein crystal structures can significantly improve the reliability of molecular dynamics simulations.
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Affiliation(s)
| | | | | | | | - Artur Góra
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, Gliwice, Poland
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42
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Hüfner-Wulsdorf T, Klebe G. Protein–Ligand Complex Solvation Thermodynamics: Development, Parameterization, and Testing of GIST-Based Solvent Functionals. J Chem Inf Model 2020; 60:1409-1423. [DOI: 10.1021/acs.jcim.9b01109] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tobias Hüfner-Wulsdorf
- Institut für Pharmazeutische Chemie, Philipps Universität Marburg, Marbacher Weg 6, 35037 Marburg, Germany
| | - Gerhard Klebe
- Institut für Pharmazeutische Chemie, Philipps Universität Marburg, Marbacher Weg 6, 35037 Marburg, Germany
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43
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Hu X, Maffucci I, Contini A. Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations. Curr Med Chem 2020; 26:7598-7622. [DOI: 10.2174/0929867325666180514110824] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 02/26/2018] [Accepted: 04/18/2018] [Indexed: 12/30/2022]
Abstract
Background:
The inclusion of direct effects mediated by water during the ligandreceptor
recognition is a hot-topic of modern computational chemistry applied to drug discovery
and development. Docking or virtual screening with explicit hydration is still debatable,
despite the successful cases that have been presented in the last years. Indeed, how to select
the water molecules that will be included in the docking process or how the included waters
should be treated remain open questions.
Objective:
In this review, we will discuss some of the most recent methods that can be used in
computational drug discovery and drug development when the effect of a single water, or of a
small network of interacting waters, needs to be explicitly considered.
Results:
Here, we analyse the software to aid the selection, or to predict the position, of water
molecules that are going to be explicitly considered in later docking studies. We also present
software and protocols able to efficiently treat flexible water molecules during docking, including
examples of applications. Finally, we discuss methods based on molecular dynamics
simulations that can be used to integrate docking studies or to reliably and efficiently compute
binding energies of ligands in presence of interfacial or bridging water molecules.
Conclusions:
Software applications aiding the design of new drugs that exploit water molecules,
either as displaceable residues or as bridges to the receptor, are constantly being developed.
Although further validation is needed, workflows that explicitly consider water will
probably become a standard for computational drug discovery soon.
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Affiliation(s)
- Xiao Hu
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
| | - Irene Maffucci
- Pasteur, Département de Chimie, École Normale Supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 75005 Paris, France
| | - Alessandro Contini
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
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Cole DJ, Mones L, Csányi G. A machine learning based intramolecular potential for a flexible organic molecule. Faraday Discuss 2020; 224:247-264. [DOI: 10.1039/d0fd00028k] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Here, we employ the kernel regression machine learning technique to construct an analytical potential that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine.
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Affiliation(s)
- Daniel J. Cole
- School of Natural and Environmental Sciences
- Newcastle University
- Newcastle upon Tyne NE1 7RU
- UK
| | - Letif Mones
- Engineering Laboratory
- University of Cambridge
- Cambridge CB2 1PZ
- UK
| | - Gábor Csányi
- Engineering Laboratory
- University of Cambridge
- Cambridge CB2 1PZ
- UK
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45
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Horton JT, Allen AEA, Cole DJ. Modelling flexible protein–ligand binding in p38α MAP kinase using the QUBE force field. Chem Commun (Camb) 2020; 56:932-935. [DOI: 10.1039/c9cc08574b] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The accuracy of quantum mechanical bespoke (QUBE) force fields for protein–ligand binding free energy calculations are benchmarked against experiment.
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Affiliation(s)
- Joshua T. Horton
- School of Natural and Environmental Sciences
- Newcastle University
- Newcastle upon Tyne NE1 7RU
- UK
| | | | - Daniel J. Cole
- School of Natural and Environmental Sciences
- Newcastle University
- Newcastle upon Tyne NE1 7RU
- UK
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46
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Nath H, Dutta D, Sharma P, Frontera A, Verma AK, Barceló-Oliver M, Devi M, Bhattacharyya MK. Adipato bridged novel hexanuclear Cu(ii) and polymeric Co(ii) coordination compounds involving cooperative supramolecular assemblies and encapsulated guest water clusters in a square grid host: antiproliferative evaluation and theoretical studies. Dalton Trans 2020; 49:9863-9881. [DOI: 10.1039/d0dt01007c] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Novel adipato bridged Cu(ii) and Co(ii) complexes synthesized by considering cytotoxicity, apoptosis, ROS generation, molecular docking and pharmacophore features.
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Affiliation(s)
- Hiren Nath
- Department of Chemistry
- Cotton University
- Guwahati-781001
- India
| | - Debajit Dutta
- Department of Chemistry
- Cotton University
- Guwahati-781001
- India
| | - Pranay Sharma
- Department of Chemistry
- Cotton University
- Guwahati-781001
- India
| | - Antonio Frontera
- Departament de Química
- Universitat de les Illes Balears
- 07122 Palma de Mallorca
- Spain
| | - Akalesh K. Verma
- Department of Zoology
- Cell & Biochemical Technology laboratory
- Cotton University
- Guwahati-781001
- India
| | | | - Mary Devi
- Department of Zoology
- Cell & Biochemical Technology laboratory
- Cotton University
- Guwahati-781001
- India
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47
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Bruno A, Costantino G, Sartori L, Radi M. The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization. Curr Med Chem 2019; 26:3838-3873. [PMID: 29110597 DOI: 10.2174/0929867324666171107101035] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/27/2017] [Accepted: 09/28/2017] [Indexed: 01/04/2023]
Abstract
BACKGROUND Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. METHODS In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. RESULTS A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. CONCLUSION The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.
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Affiliation(s)
- Agostino Bruno
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Gabriele Costantino
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Luca Sartori
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
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Pal RK, Gadhiya S, Ramsey S, Cordone P, Wickstrom L, Harding WW, Kurtzman T, Gallicchio E. Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes. PLoS One 2019; 14:e0222902. [PMID: 31568493 PMCID: PMC6768453 DOI: 10.1371/journal.pone.0222902] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 08/30/2019] [Indexed: 01/04/2023] Open
Abstract
Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.
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Affiliation(s)
- Rajat Kumar Pal
- Department of Chemistry, Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY 11210, United States of America
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
| | - Satishkumar Gadhiya
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Hunter College, 695 Park Avenue, NY 10065, United States of America
| | - Steven Ramsey
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Lehman College, 250 Bedford Park Blvd. West, Bronx, NY 10468, United States of America
| | - Pierpaolo Cordone
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Hunter College, 695 Park Avenue, NY 10065, United States of America
| | - Lauren Wickstrom
- Department of Science, Borough of Manhattan Community College, 199 Chambers Street, New York, NY 10007, United States of America
| | - Wayne W. Harding
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Hunter College, 695 Park Avenue, NY 10065, United States of America
| | - Tom Kurtzman
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Lehman College, 250 Bedford Park Blvd. West, Bronx, NY 10468, United States of America
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY 11210, United States of America
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- * E-mail:
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Discovery of Orexant and Anorexant Agents with Indazole Scaffold Endowed with Peripheral Antiedema Activity. Biomolecules 2019; 9:biom9090492. [PMID: 31527522 PMCID: PMC6770484 DOI: 10.3390/biom9090492] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 09/07/2019] [Accepted: 09/11/2019] [Indexed: 01/16/2023] Open
Abstract
The endocannabinoid system represents an integrated neuronal network involved in the control of several organisms' functions, such as feeding behavior. A series of hybrids of 5-(4-chlorophenyl)-1-(2,4-dichloro-phenyl)-4-methyl-N-(piperidin-1-yl)-1H-pyrazole-3-carboxamide (mimonabant), a well-known inverse agonist of the type-1 cannabinoid receptor (CB1), once used as an antiobesity drug, and the N-(2S)-substitutes of 1-[(4-fluorophenyl)methyl]indazole-3-carboxamide with 1-amino-3-methyl-1-oxobutane (AB-Fubinaca), 1-amino-3,3-dimethyl-1-oxobutane (ADB-Fubinaca), and 3-methylbutanoate (AMB-Fubinaca), endowed with potent agonistic activity towards cannabinoid receptors CB1 and CB2 were in solution as C-terminal amides, acids, methyl esters and N-methyl amides. These compounds have been studied by binding assays to cannabinoid receptors and by functional receptor assays, using rat brain membranes in vitro. The most active among them as an agonist, (S)-1-(2,4-dichlorobenzyl)-N-(3,3-dimethyl-1-(methylamino)-1-oxobutan-2-yl)-1H-indazole-3-carboxamide (LONI11), and an antagonist, (S)-2-(1-(2,4-dichlorobenzyl)-1H-indazole-3-carboxamido)-3-methylbutanoic acid (LONI4), were tested in vivo in mic, to evaluate their ability to stimulate or suppress feeding behavior after intraperitoneal (i.p.) administration. For a LONI11 formalin test and a tail flick test after an administration by the subcutaneous (s.c.) and intracerebroventricular (i.c.v.) routes, respectively, were also carried out in vivo in mice to investigate the antinociceptive property at the central and peripheral levesl. We observed a significant orexant effect for LONI11 and an intense anorexant effect for (S)-methyl 2-(1-(2,4-dichlorobenzyl)-1H-indazole-3-carboxamido)-3,3-dimethylbutanoate (LONI2) and LONI4. In zymosan-induced edema and hyperalgesia, LONI11 reduced the percent of paw volume increase and paw latency after s.c. administration, also suggesting a possible peripheral anti-inflammatory activity.
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Berishvili VP, Perkin VO, Voronkov AE, Radchenko EV, Syed R, Venkata Ramana Reddy C, Pillay V, Kumar P, Choonara YE, Kamal A, Palyulin VA. Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors. J Chem Inf Model 2019; 59:3519-3532. [PMID: 31276400 DOI: 10.1021/acs.jcim.9b00135] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Molecular dynamics simulations provide valuable insights into the behavior of molecular systems. Extending the recent trend of using machine learning techniques to predict physicochemical properties from molecular dynamics data, we propose to consider the trajectories as multidimensional time series represented by 2D tensors containing the ligand-protein interaction descriptor values for each time step. Similar in structure to the time series encountered in modern approaches for signal, speech, and natural language processing, these time series can be directly analyzed using long short-term memory (LSTM) recurrent neural networks or convolutional neural networks (CNNs). The predictive regression models for the ligand-protein affinity were built for a subset of the PDBbind v.2017 database and applied to inhibitors of tankyrase, an enzyme of the poly(ADP-ribose)-polymerase (PARP) family that can be used in the treatment of colorectal cancer. As an additional test set, a subset of the Community Structure-Activity Resource (CSAR) data set was used. For comparison, the random forest and simple neural network models based on the crystal pose or the trajectory-averaged descriptors were used, as well as the commonly employed docking and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) scores. Convolutional neural networks based on the 2D tensors of ligand-protein interaction descriptors for short (2 ns) trajectories provide the best accuracy and predictive power, reaching the Spearman rank correlation coefficient of 0.73 and Pearson correlation coefficient of 0.70 for the tankyrase test set. Taking into account the recent increase in computational power of modern GPUs and relatively low computational complexity of the proposed approach, it can be used as an advanced virtual screening filter for compound prioritization.
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Affiliation(s)
- Vladimir P Berishvili
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia
| | - Valentin O Perkin
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia
| | - Andrew E Voronkov
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Digital BioPharm Ltd. , Hovseterveien 42 A, H0301 , Oslo 0768 , Norway
| | - Eugene V Radchenko
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia
| | - Riyaz Syed
- Department of Chemistry , Jawaharlal Nehru Technological University , Kukatpally, Hyderabad 500 085 , India
| | | | - Viness Pillay
- Wits Advanced Drug Delivery Platform Research Unit, Faculty of Health Sciences, School of Therapeutic Sciences, Department of Pharmacy and Pharmacology , University of the Witwatersrand, Johannesburg , 7 York Road , Parktown 2193 , South Africa
| | - Pradeep Kumar
- Wits Advanced Drug Delivery Platform Research Unit, Faculty of Health Sciences, School of Therapeutic Sciences, Department of Pharmacy and Pharmacology , University of the Witwatersrand, Johannesburg , 7 York Road , Parktown 2193 , South Africa
| | - Yahya E Choonara
- Wits Advanced Drug Delivery Platform Research Unit, Faculty of Health Sciences, School of Therapeutic Sciences, Department of Pharmacy and Pharmacology , University of the Witwatersrand, Johannesburg , 7 York Road , Parktown 2193 , South Africa
| | - Ahmed Kamal
- School of Pharmaceutical Education and Research , Jamia Hamdard , New Delhi 110 062 , India
| | - Vladimir A Palyulin
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia
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