1
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Xu C, Zhang X, Zhao L, Verkhivker GM, Bai F. Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies. JACS AU 2024; 4:1632-1645. [PMID: 38665669 PMCID: PMC11040708 DOI: 10.1021/jacsau.4c00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 04/28/2024]
Abstract
The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure-kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate constant (koff), enabling a direct assessment of the drug-target residence time. These models demonstrated good predictive performance, where hydrophobic and hydrogen bond interactions significantly influence the koff prediction. In subsequent applications, our models were used to assist in the discovery of new inhibitors for the N-terminal domain of HSP90α (N-HSP90α), demonstrating predictive capabilities on an experimental validation set with a new scaffold. In X-ray crystallography experiments, the loop-middle conformation of apo N-HSP90α was observed for the first time (previously, the loop-middle conformation had only been observed in holo-N-HSP90α structures). Interestingly, we observed different conformations of apo N-HSP90α simultaneously in an asymmetric unit, which was also observed in a holo-N-HSP90α structure, suggesting an equilibrium of conformations between different states in solution, which could be one of the determinants affecting the binding kinetics of the ligand. Different ligands can undergo conformational selection or alter the equilibrium of conformations, inducing conformational rearrangements and resulting in different effects on binding kinetics. We then used molecular dynamics simulations to describe conformational changes of apo N-HSP90α in different conformational states. In summary, the study of the binding kinetics and molecular mechanisms of N-HSP90α provides valuable information for the development of more targeted therapeutic approaches.
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Affiliation(s)
- Chao Xu
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Xianglei Zhang
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Lianghao Zhao
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Gennady M. Verkhivker
- Keck
Center for Science and Engineering, Graduate Program in Computational
and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department
of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
| | - Fang Bai
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
- School
of Information Science and Technology, ShanghaiTech
University, 393 Middle Huaxia Road, Shanghai 201210, China
- Shanghai
Clinical Research and Trial Center, Shanghai 201210, China
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2
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Zhou F, Yin S, Xiao Y, Lin Z, Fu W, Zhang YJ. Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation. ACS OMEGA 2023; 8:18312-18322. [PMID: 37251166 PMCID: PMC10210189 DOI: 10.1021/acsomega.3c02294] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (koff) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the koff value. We observed a high correlation among the simulated, predicted, and experimental -log(koff) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our koff predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental koff values and are not in our ML training dataset. The predicted koff values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict koff of other proteins and will enhance the kinetics-based drug design endeavor.
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3
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Liu H, Su M, Lin HX, Wang R, Li Y. Public Data Set of Protein-Ligand Dissociation Kinetic Constants for Quantitative Structure-Kinetics Relationship Studies. ACS OMEGA 2022; 7:18985-18996. [PMID: 35694511 PMCID: PMC9178723 DOI: 10.1021/acsomega.2c02156] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/13/2022] [Indexed: 06/01/2023]
Abstract
Protein-ligand binding affinity reflects the equilibrium thermodynamics of the protein-ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure-kinetics relationship (QSKR) aim at predicting protein-ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein-ligand complexes with experimental dissociation rate constants (k off), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein-ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (k off) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein-ligand atom pair descriptors for predicting k off values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).
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Affiliation(s)
- Huisi Liu
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shangda Road, Shanghai 200444, People's Republic of China
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, People's Republic of China
| | - Minyi Su
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, People's Republic of China
| | - Hai-Xia Lin
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shangda Road, Shanghai 200444, 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
| | - Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
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4
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Penna E, Niso M, Podlewska S, Volpicelli F, Crispino M, Perrone-Capano C, Bojarski AJ, Lacivita E, Leopoldo M. In Vitro and In Silico Analysis of the Residence Time of Serotonin 5-HT 7 Receptor Ligands with Arylpiperazine Structure: A Structure-Kinetics Relationship Study. ACS Chem Neurosci 2022; 13:497-509. [PMID: 35099177 DOI: 10.1021/acschemneuro.1c00710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
During the last decade, the kinetics of drug-target interaction has received increasing attention as an important pharmacological parameter in the drug development process. Several studies have suggested that the lipophilicity of a molecule can play an important role. To date, this aspect has been studied for several G protein-coupled receptors (GPCRs) ligands but not for the 5-HT7 receptor (5-HT7R), a GPCR proposed as a valid therapeutic target in neurodevelopmental and neuropsychiatric disorders associated with abnormal neuronal connectivity. In this study, we report on structure-kinetics relationships of a set of arylpiperazine-based 5-HT7R ligands. We found that it is not the overall lipophilicity of the molecule that influences drug-target interaction kinetics but rather the position of polar groups within the molecule. Next, we performed a combination of molecular docking studies and molecular dynamics simulations to gain insights into structure-kinetics relationships. These studies did not suggest specific contact patterns between the ligands and the receptor-binding site as determinants for compounds kinetics. Finally, we compared the abilities of two 5-HT7R agonists with similar receptor-binding affinities and different residence times to stimulate the 5-HT7R-mediated neurite outgrowth in mouse neuronal primary cultures and found that the compounds induced the effect with different timing. This study provides the first insights into the binding kinetics of arylpiperazine-based 5-HT7R ligands that can be helpful to design new 5-HT7R ligands with fine-tuning of the kinetic profile.
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Affiliation(s)
- Eduardo Penna
- Department of Biology, University of Naples Federico II, via Cintia 26, 80126 Naples, Italy
- Biofordrug srl, via Dante 99, 70019 Triggiano (Bari), Italy
| | - Mauro Niso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, via Orabona 4, 70125 Bari, Italy
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Floriana Volpicelli
- Department of Pharmacy, School of Medicine and Surgery, University of Naples Federico II, via Domenico Montesano 49, 80131 Naples, Italy
| | - Marianna Crispino
- Department of Biology, University of Naples Federico II, via Cintia 26, 80126 Naples, Italy
| | - Carla Perrone-Capano
- Department of Pharmacy, School of Medicine and Surgery, University of Naples Federico II, via Domenico Montesano 49, 80131 Naples, Italy
- Institute of Genetics and Biophysics “Adriano Buzzati Traverso”, National Research Council (CNR), via Pietro Castellino 111, 80131 Naples, Italy
| | - Andrzej J. Bojarski
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Enza Lacivita
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, via Orabona 4, 70125 Bari, Italy
| | - Marcello Leopoldo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, via Orabona 4, 70125 Bari, Italy
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5
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Ritonavir and xk263 Binding-Unbinding with HIV-1 Protease: Pathways, Energy and Comparison. LIFE (BASEL, SWITZERLAND) 2022; 12:life12010116. [PMID: 35054509 PMCID: PMC8779838 DOI: 10.3390/life12010116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/04/2022] [Accepted: 01/10/2022] [Indexed: 01/22/2023]
Abstract
Understanding non-covalent biomolecular recognition, which includes drug-protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease (HIVp) plays an integral role in the HIV life cycle, so it is a prime target for drug therapy. HIVp has flexible flaps, and the binding pocket can be accessible by a ligand via various pathways. Comparing ligand association and dissociation pathways can help elucidate the ligand-protein interactions such as key residues directly involved in the interaction or specific protein conformations that determine the binding of a ligand under certain pathway(s). Here, we investigated the ligand unbinding process for a slow binder, ritonavir, and a fast binder, xk263, by using unbiased all-atom accelerated molecular dynamics (aMD) simulation with a re-seeding approach and an explicit solvent model. Using ritonavir-HIVp and xk263-HIVp ligand-protein systems as cases, we sampled multiple unbinding pathways for each ligand and observed that the two ligands preferred the same unbinding route. However, ritonavir required a greater HIVp motion to dissociate as compared with xk263, which can leave the binding pocket with little conformational change of HIVp. We also observed that ritonavir unbinding pathways involved residues which are associated with drug resistance and are distal from catalytic site. Analyzing HIVp conformations sampled during both ligand-protein binding and unbinding processes revealed significantly more overlapping HIVp conformations for ritonavir-HIVp rather than xk263-HIVp. However, many HIVp conformations are unique in xk263-HIVp unbinding processes. The findings are consistent with previous findings that xk263 prefers an induced-fit model for binding and unbinding, whereas ritonavir favors a conformation selection model. This study deepens our understanding of the dynamic process of ligand unbinding and provides insights into ligand-protein recognition mechanisms and drug discovery.
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6
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Potterton A, Heifetz A, Townsend-Nicholson A. Predicting Residence Time of GPCR Ligands with Machine Learning. Methods Mol Biol 2022; 2390:191-205. [PMID: 34731470 DOI: 10.1007/978-1-0716-1787-8_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Drug-target residence time, the duration of binding at a given protein target, has been shown in some protein families to be more significant for conferring efficacy than binding affinity. To carry out efficient optimization of residence time in drug discovery, machine learning models that can predict that value need to be developed. One of the main challenges with predicting residence time is the paucity of data. This chapter outlines all of the currently available ligand kinetic data, providing a repository that contains the largest publicly available source of GPCR-ligand kinetic data to date. To help decipher the features of kinetic data that might be beneficial to include in computational models for the prediction of residence time, the experimental evidence for properties that influence residence time are summarized. Finally, two different workflows for predicting residence time with machine learning are outlined. The first is a single-target model trained on ligand features; the second is a multi-target model trained on features generated from molecular dynamics simulations.
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Affiliation(s)
- Andrew Potterton
- Structural and Molecular Biology, University College London, London, UK
- Evotec (U.K.) Ltd., Abingdon, Oxfordshire, UK
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7
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Nunes-Alves A, Ormersbach F, Wade RC. Prediction of the Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis. J Chem Inf Model 2021; 61:3708-3721. [PMID: 34197096 DOI: 10.1021/acs.jcim.1c00639] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (koff) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of koff values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.
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Affiliation(s)
- Ariane Nunes-Alves
- Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Fabian Ormersbach
- Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
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8
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Amangeldiuly N, Karlov D, Fedorov MV. Baseline Model for Predicting Protein–Ligand Unbinding Kinetics through Machine Learning. J Chem Inf Model 2020; 60:5946-5956. [DOI: 10.1021/acs.jcim.0c00450] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Nurlybek Amangeldiuly
- Center for Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Dmitry Karlov
- Center for Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Maxim V. Fedorov
- Center for Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- Department of Physics, Scottish Universities Physics Alliance (SUPA), University of Strathclyde, Glasgow G4 0NG, U.K
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9
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Huang S, Chen L, Mei H, Zhang D, Shi T, Kuang Z, Heng Y, Xu L, Pan X. In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method. Int J Mol Sci 2020; 21:ijms21072456. [PMID: 32252223 PMCID: PMC7177943 DOI: 10.3390/ijms21072456] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 03/30/2020] [Indexed: 12/26/2022] Open
Abstract
Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the koff values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.
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Affiliation(s)
- Shuheng Huang
- Key Laboratory of Biorheological Science and Technology (Ministry of Education), Chongqing University, Chongqing 400044, China; (S.H.); (L.C.)
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
| | - Linxin Chen
- Key Laboratory of Biorheological Science and Technology (Ministry of Education), Chongqing University, Chongqing 400044, China; (S.H.); (L.C.)
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
| | - Hu Mei
- Key Laboratory of Biorheological Science and Technology (Ministry of Education), Chongqing University, Chongqing 400044, China; (S.H.); (L.C.)
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
- Correspondence: (H.M.); (X.P.); Tel.: +86-23-65112677 (H.M.)
| | - Duo Zhang
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
| | - Tingting Shi
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
| | - Zuyin Kuang
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
| | - Yu Heng
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
| | - Lei Xu
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
| | - Xianchao Pan
- College of Bioengineering, Chongqing University, Chongqing 400044, China; (D.Z.); (T.S.); (Z.K.); (Y.H.); (L.X.)
- Department of Medicinal Chemistry, College of Pharmacy, Southwest Medical University, Luzhou 646000, China
- Correspondence: (H.M.); (X.P.); Tel.: +86-23-65112677 (H.M.)
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10
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Kokh DB, Kaufmann T, Kister B, Wade RC. Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times. Front Mol Biosci 2019; 6:36. [PMID: 31179286 PMCID: PMC6543870 DOI: 10.3389/fmolb.2019.00036] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 05/02/2019] [Indexed: 12/25/2022] Open
Abstract
Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.
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Affiliation(s)
- Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Tom Kaufmann
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.,Department of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Bastian Kister
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.,Department of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.,Zentrum für Molekulare Biologie der Universität Heidelberg, DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.,Department of Physics, Heidelberg University, Heidelberg, Germany
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11
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Huang S, Zhang D, Mei H, Kevin M, Qu S, Pan X, Lu L. SMD-Based Interaction-Energy Fingerprints Can Predict Accurately the Dissociation Rate Constants of HIV-1 Protease Inhibitors. J Chem Inf Model 2018; 59:159-169. [DOI: 10.1021/acs.jcim.8b00567] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | | | | | | | | | - Xianchao Pan
- Department of Medicinal Chemistry, College of Pharmacy, Southwest Medical University, Luzhou, Sichuan 646000, China
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12
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Huang S, Mei H, Zhang D, Ren Y, Kevin M, Pan X. The emerging chemical patterns applied in predicting human toll-like receptor 8 agonists. MEDCHEMCOMM 2018; 9:1961-1971. [PMID: 30568763 PMCID: PMC6256730 DOI: 10.1039/c8md00276b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 09/19/2018] [Indexed: 02/06/2023]
Abstract
Toll-like receptors (TLRs) are important pattern recognition receptors to human innate immunity, which can recognize pathogen-associated molecular patterns and initiate innate immune responses. As the receptor of single stranded RNA (ssRNA), toll-like receptor 8 (TLR8) has potential in the treatment of tumors, microbial infection, and inflammatory diseases. Herein, an emerging chemical pattern (ECP) method was utilized to predict the key chemical patterns of TLR8 agonists. Based on the ECPs discovered, a robust and predictive ECP model was derived with prediction accuracies of 83.3%, 81.0%, and 80.0% for 132 training samples, 79 validation samples, and 75 test samples, respectively. When the ECP model was applied with a molecular docking method, the hit rate of TLR8 agonists was greatly enhanced. The results of ECP-based hierarchical cluster analysis and Connolly surface analysis of the TLR8 receptor showed that the H-bonding, hydrophilic and hydrophobic potentials as well as the unbalanced degree of property distributions are very important for distinguishing the TLR8 agonists from non-agonists.
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Affiliation(s)
- Shuheng Huang
- Key Laboratory of Biorheological Science and Technology (Ministry of Education) , Chongqing University , Chongqing 400044 , China . ; Tel: +86 23 65112677
- College of Bioengineering , Chongqing University , Chongqing 400044 , China
| | - Hu Mei
- Key Laboratory of Biorheological Science and Technology (Ministry of Education) , Chongqing University , Chongqing 400044 , China . ; Tel: +86 23 65112677
- College of Bioengineering , Chongqing University , Chongqing 400044 , China
| | - Duo Zhang
- College of Bioengineering , Chongqing University , Chongqing 400044 , China
| | - Yubin Ren
- College of Bioengineering , Chongqing University , Chongqing 400044 , China
| | | | - Xianchao Pan
- College of Bioengineering , Chongqing University , Chongqing 400044 , China
- Department of Medicinal Chemistry , College of Pharmacy , Southwest Medical University , Luzhou , Sichuan 646000 , China .
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De Benedetti PG, Fanelli F. Computational modeling approaches to quantitative structure-binding kinetics relationships in drug discovery. Drug Discov Today 2018; 23:1396-1406. [PMID: 29574212 DOI: 10.1016/j.drudis.2018.03.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 02/22/2018] [Accepted: 03/19/2018] [Indexed: 11/22/2022]
Abstract
Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling.
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Affiliation(s)
- Pier G De Benedetti
- Department of Life Sciences, University of Modena and Reggio Emilia, via Campi 103, 41125 Modena, Italy.
| | - Francesca Fanelli
- Department of Life Sciences, University of Modena and Reggio Emilia, via Campi 103, 41125 Modena, Italy; Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, via Campi 103, 41125 Modena, Italy
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14
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Ramamurthy M, Sankar S, Kannangai R, Nandagopal B, Sridharan G. Application of viromics: a new approach to the understanding of viral infections in humans. Virusdisease 2017; 28:349-359. [PMID: 29291225 DOI: 10.1007/s13337-017-0415-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 11/17/2017] [Indexed: 12/19/2022] Open
Abstract
This review is focused at exploring the strengths of modern technology driven data compiled in the areas of virus gene sequencing, virus protein structures and their implication to viral diagnosis and therapy. The information for virome analysis (viromics) is generated by the study of viral genomes (entire nucleotide sequence) and viral genes (coding for protein). Presently, the study of viral infectious diseases in terms of etiopathogenesis and development of newer therapeutics is undergoing rapid changes. Currently, viromics relies on deep sequencing, next generation sequencing (NGS) data and public domain databases like GenBank and unique virus specific databases. Two commonly used NGS platforms: Illumina and Ion Torrent, recommend maximum fragment lengths of about 300 and 400 nucleotides for analysis respectively. Direct detection of viruses in clinical samples is now evolving using these methods. Presently, there are a considerable number of good treatment options for HBV/HIV/HCV. These viruses however show development of drug resistance. The drug susceptibility regions of the genomes are sequenced and the prediction of drug resistance is now possible from 3 public domains available on the web. This has been made possible through advances in the technology with the advent of high throughput sequencing and meta-analysis through sophisticated and easy to use software and the use of high speed computers for bioinformatics. More recently NGS technology has been improved with single-molecule real-time sequencing. Here complete long reads can be obtained with less error overcoming a limitation of the NGS which is inherently prone to software anomalies that arise in the hands of personnel without adequate training. The development in understanding the viruses in terms of their genome, pathobiology, transcriptomics and molecular epidemiology constitutes viromics. It could be stated that these developments will bring about radical changes and advancement especially in the field of antiviral therapy and diagnostic virology.
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Affiliation(s)
- Mageshbabu Ramamurthy
- Sri Sakthi Amma Institute of Biomedical Research, Sri Narayani Hospital and Research Centre, Sripuram, Vellore, Tamil Nadu 632 055 India
| | - Sathish Sankar
- Sri Sakthi Amma Institute of Biomedical Research, Sri Narayani Hospital and Research Centre, Sripuram, Vellore, Tamil Nadu 632 055 India
| | - Rajesh Kannangai
- Department of Clinical Virology, Christian Medical College and Hospital, Vellore, Tamil Nadu 632 004 India
| | - Balaji Nandagopal
- Sri Sakthi Amma Institute of Biomedical Research, Sri Narayani Hospital and Research Centre, Sripuram, Vellore, Tamil Nadu 632 055 India
| | - Gopalan Sridharan
- Sri Sakthi Amma Institute of Biomedical Research, Sri Narayani Hospital and Research Centre, Sripuram, Vellore, Tamil Nadu 632 055 India
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15
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Bruce NJ, Ganotra GK, Kokh DB, Sadiq SK, Wade RC. New approaches for computing ligand-receptor binding kinetics. Curr Opin Struct Biol 2017; 49:1-10. [PMID: 29132080 DOI: 10.1016/j.sbi.2017.10.001] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 09/22/2017] [Accepted: 10/01/2017] [Indexed: 02/08/2023]
Abstract
The recent and growing evidence that the efficacy of a drug can be correlated to target binding kinetics has seeded the development of a multitude of novel methods aimed at computing rate constants for receptor-ligand binding processes, as well as gaining an understanding of the binding and unbinding pathways and the determinants of structure-kinetic relationships. These new approaches include various types of enhanced sampling molecular dynamics simulations and the combination of energy-based models with chemometric analysis. We assess these approaches in the light of the varying levels of complexity of protein-ligand binding processes.
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Affiliation(s)
- Neil J Bruce
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Gaurav K Ganotra
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - S Kashif Sadiq
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany.
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