1
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Zeng Q, Meng G, Zhao B, Lin H, Guan Y, Qin X, Yuan Y, Li Y, Wang Q. Peptide Drug Design Using Alchemical Free Energy Calculation: An Application and Validation on Agonists of Ghrelin Receptor. J Chem Inf Model 2024; 64:4863-4876. [PMID: 38836743 DOI: 10.1021/acs.jcim.4c00414] [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: 06/06/2024]
Abstract
With recent large-scale applications and validations, the relative binding free energy (RBFE) calculated using alchemical free energy methods has been proven to be an accurate measure to probe the binding of small-molecule drug candidates. On the other hand, given the flexibility of peptides, it is of great interest to find out whether sufficient sampling could be achieved within the typical time scale of such calculation, and a similar level of accuracy could be reached for peptide drugs. However, the systematic evaluation of such calculations on protein-peptide systems has been less reported. Most reported studies of peptides were restricted to a limited number of data points or lacking experimental support. To demonstrate the applicability of the alchemical free energy method for protein-peptide systems in a typical real-world drug discovery project, we report an application of the thermodynamic integration (TI) method to the RBFE calculation of ghrelin receptor and its peptide agonists. Along with the calculation, the synthesis and in vitro EC50 activity of relamorelin and 17 new peptide derivatives were also reported. A cost-effective criterion to determine the data collection time was proposed for peptides in the TI simulation. The average of three TI repeats yielded a mean absolute error of 0.98 kcal/mol and Pearson's correlation coefficient (R) of 0.77 against the experimental free energy derived from the in vitro EC50 activity, showing good repeatability of the proposed method and a slightly better agreement than the results obtained from the arbitrary time frames up to 20 ns. Although it is limited by having one target and a deduced binding pose, we hope that this study can add some insights into alchemical free energy calculation of protein-peptide systems, providing theoretical assistance to the development of peptide drugs.
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Affiliation(s)
- Qin Zeng
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Guangpeng Meng
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610000, China
| | - Bingyu Zhao
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Haodian Lin
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Yuqing Guan
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610000, China
| | - Xiaobin Qin
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610000, China
| | - Yu Yuan
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610000, China
| | - Yuanbo Li
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610000, China
| | - Qiantao Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
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2
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Tu G, Gong Y, Yao X, Liu Q, Xue W, Zhang R. Pathways and mechanism of MRTX1133 binding to KRAS G12D elucidated by molecular dynamics simulations and Markov state models. Int J Biol Macromol 2024; 274:133374. [PMID: 38925182 DOI: 10.1016/j.ijbiomac.2024.133374] [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: 04/12/2024] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024]
Abstract
KRAS G12D is the most common oncogenic mutation identified in several types of cancer. Therefore, design of inhibitors targeting KRAS G12D represents a promising strategy for anticancer therapy. MRTX1133 is a highly potent inhibitor (approximate experiment Kd ≈ 0.0002 nM) of KRAS G12D and is currently in Phase 1/2 study, however, pathways of the compound binding to KRAS G12D has remained unknown, and the mechanism underlying the complicated dynamic process are challenging to capture experimentally, which hinder the structure-based anti-cancer drug design. Here, using MRTX1133 as a probe, unbiased molecular dynamics (MD) was used to simulate the process of MRTX1133 spontaneously binding to KRAS G12D. In six of 42 independent MD simulation (a total of 99 μs), MRTX1133 was observed to successfully associate with KRAS G12D. The kinetically metastable states refer to the potential pathways of MRTX1133 binding to KRAS G12D were revealed by Markov state models (MSM) analysis. Additionally, 8 key residues that are essential for MRTX1133 recognition and tight binding at the preferred low energy states were identified by MM/GBSA analysis. In sum, this study provides a new perspective on understanding the pathways and mechanism of MRTX1133 binding to KRAS G12D.
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Affiliation(s)
- Gao Tu
- Department of Pharmacy, The Second Affiliated Hospital, Army Medical University, 183 Xinqiao Road, Chongqing 400037, China; Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, 999078, Macau
| | - Yaguo Gong
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, 999078, Macau
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macau.
| | - Qing Liu
- Suzhou Institute for Advance Research, University of Science and Technology of China, Suzhou, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
| | - Rong Zhang
- Department of Pharmacy, The Second Affiliated Hospital, Army Medical University, 183 Xinqiao Road, Chongqing 400037, China.
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3
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Bravo-Moraga F, Bedoya M, Vergara-Jaque A, Alzate-Morales J. Understanding the Differences of Danusertib's Residence Time in Aurora Kinases A/B: Dissociation Paths and Key Residues Identified using Conventional and Enhanced Molecular Dynamics Simulations. J Chem Inf Model 2024; 64:4759-4772. [PMID: 38857305 DOI: 10.1021/acs.jcim.4c00387] [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: 06/12/2024]
Abstract
The accurate experimental estimation of protein-ligand systems' residence time (τ) has become very relevant in drug design projects due to its importance in the last stages of refinement of the drug's pharmacodynamics and pharmacokinetics. It is now well-known that it is not sufficient to estimate the affinity of a protein-drug complex in the thermodynamic equilibrium process in in vitro experiments (closed systems), where the concentrations of the drug and protein remain constant. On the contrary, it is mandatory to consider the conformational dynamics of the system in terms of the binding and unbinding processes between protein and drugs in in vivo experiments (open systems), where their concentrations are in constant flux. This last model has been proven to dictate much of several drugs' pharmacological activities in vivo. At the atomistic level, molecular dynamics simulations can explain why some drugs are more effective than others or unveil the molecular aspects that make some drugs work better in one molecular target. Here, the protein kinases Aurora A/B, complexed with its inhibitor Danusertib, were studied using conventional and enhanced molecular dynamics (MD) simulations to estimate the dissociation paths and, therefore, the computational τ values and their comparison with experimental ones. Using classical molecular dynamics (cMD), three differential residues within the Aurora A/B active site, which seems to play an essential role in the observed experimental Danusertib's residence time against these kinases, were characterized. Then, using WT-MetaD, the relative Danusertib's residence times against Aurora A/B kinases were measured in a nanosecond time scale and were compared to those τ values observed experimentally. In addition, the potential dissociation paths of Danusertib in Aurora A and B were characterized, and differences that might be explained by the differential residues in the enzyme's active sites were found. In perspective, it is expected that this computational protocol can be applied to other protein-ligand complexes to understand, at the molecular level, the differences in residence times and amino acids that may contribute to it.
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Affiliation(s)
- Felipe Bravo-Moraga
- Center for Bioinformatics, Simulation and Modeling (CBSM), Faculty of Engineering, Universidad de Talca, 1 Poniente 1141, 3466706 Talca, Chile
| | - Mauricio Bedoya
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado, Universidad Católica del Maule, Talca 3466706, Chile
- Laboratorio de Bioinformática y Química Computacional (LBQC), Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3466706, Chile
| | - Ariela Vergara-Jaque
- Center for Bioinformatics, Simulation and Modeling (CBSM), Faculty of Engineering, Universidad de Talca, 1 Poniente 1141, 3466706 Talca, Chile
- Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), 8380453 Santiago, Chile
| | - Jans Alzate-Morales
- Center for Bioinformatics, Simulation and Modeling (CBSM), Faculty of Engineering, Universidad de Talca, 1 Poniente 1141, 3466706 Talca, Chile
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4
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Coffman RE, Bidone TC. Application of Funnel Metadynamics to the Platelet Integrin αIIbβ3 in Complex with an RGD Peptide. Int J Mol Sci 2024; 25:6580. [PMID: 38928286 PMCID: PMC11203998 DOI: 10.3390/ijms25126580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Integrin αIIbβ3 mediates platelet aggregation by binding the Arginyl-Glycyl-Aspartic acid (RGD) sequence of fibrinogen. RGD binding occurs at a site topographically proximal to the αIIb and β3 subunits, promoting the conformational activation of the receptor from bent to extended states. While several experimental approaches have characterized RGD binding to αIIbβ3 integrin, applying computational methods has been significantly more challenging due to limited sampling and the need for a priori information regarding the interactions between the RGD peptide and integrin. In this study, we employed all-atom simulations using funnel metadynamics (FM) to evaluate the interactions of an RGD peptide with the αIIb and β3 subunits of integrin. FM incorporates an external history-dependent potential on selected degrees of freedom while applying a funnel-shaped restraint potential to limit RGD exploration of the unbound state. Furthermore, it does not require a priori information about the interactions, enhancing the sampling at a low computational cost. Our FM simulations reveal significant molecular changes in the β3 subunit of integrin upon RGD binding and provide a free-energy landscape with a low-energy binding mode surrounded by higher-energy prebinding states. The strong agreement between previous experimental and computational data and our results highlights the reliability of FM as a method for studying dynamic interactions of complex systems such as integrin.
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Affiliation(s)
- Robert E. Coffman
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA;
| | - Tamara C. Bidone
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA;
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Department of Biochemistry, University of Utah, Salt Lake City, UT 84112, USA
- Department of Molecular Pharmaceutics, University of Utah, Salt Lake City, UT 84112, USA
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5
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Marquardt AV, Farshad M, Whitmer JK. Calculating Binding Free Energies in Model Host-Guest Systems with Unrestrained Advanced Sampling. J Chem Theory Comput 2024; 20:3927-3934. [PMID: 38634733 DOI: 10.1021/acs.jctc.3c01186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Host-guest interactions are important to the design of pharmaceuticals and, more broadly, to soft materials as they can enable targeted, strong, and specific interactions between molecules. The binding process between the host and guest may be classified as a "rare event" when viewing the system at atomic scales, such as those explored in molecular dynamics simulations. To obtain equilibrium binding conformations and dissociation constants from these simulations, it is essential to resolve these rare events. Advanced sampling methods such as the adaptive biasing force (ABF) promote the occurrence of less probable configurations in a system, therefore facilitating the sampling of essential collective variables that characterize the host-guest interactions. Here, we present the application of ABF to a rod-cavitand coarse-grained model of host-guest systems to acquire the potential of mean force. We show that the employment of ABF enables the computation of the configurational and thermodynamic properties of bound and unbound states, including the free energy landscape. Moreover, we identify important dynamic bottlenecks that limit sampling and discuss how these may be addressed in more general systems.
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Affiliation(s)
- Andrew V Marquardt
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Mohsen Farshad
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Jonathan K Whitmer
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
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6
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Croney K, McCarty J. Exploring Product Release from Yeast Cytosine Deaminase with Metadynamics. J Phys Chem B 2024; 128:3102-3112. [PMID: 38516924 PMCID: PMC11000218 DOI: 10.1021/acs.jpcb.3c07972] [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: 12/05/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/23/2024]
Abstract
The yeast cytosine deaminase (yCD) enzyme/5-fluorocytosine prodrug system is a promising candidate for targeted chemotherapeutics. After conversion of the prodrug into the toxic chemotherapeutic drug, 5-fluorouracil (5-FU), the slow product release from the enzyme limits the overall catalytic efficiency of the enzyme/prodrug system. Here, we present a computational study of the product release of the anticancer drug, 5-FU, from yCD using metadynamics. We present a comparison of the 5-FU drug to the natural enzyme product, uracil. We use volume-based metadynamics to compute the free energy landscape for product release and show a modest binding affinity for the product to the enzyme, consistent with experiments. Next, we use infrequent metadynamics to estimate the unbiased release rate from Kramers time-dependent rate theory and find a favorable comparison to experiment with a slower rate of product release for the 5-FU system. Our work demonstrates how adaptive sampling methods can be used to study the protein-ligand unbinding process for engineering enzyme/prodrug systems and gives insights into the molecular mechanism of product release for the yCD/5-FU system.
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Affiliation(s)
- Kayla
A. Croney
- Department of Chemistry, Western
Washington University, Bellingham, Washington 98225, United States
| | - James McCarty
- Department of Chemistry, Western
Washington University, Bellingham, Washington 98225, United States
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7
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Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
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Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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8
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Yang K, Liu H. Uncovering New Conformational States of the Substrate Binding Pocket of LSD1 Potential for Inhibitor Design via Funnel Metadynamics. J Phys Chem B 2024; 128:137-149. [PMID: 38151469 DOI: 10.1021/acs.jpcb.3c06900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Lysine-specific demethylase 1 (LSD1) is a promising therapeutic target for cancer therapy. So far, over 80 crystal structures of LSD1 in different complex states have been deposited in the Protein Data Bank, which are valuable resources for performing structure-based drug design. However, among all of the crystal structures of LSD1, the substrate binding pocket, which is the most efficient druggable site for designing LSD1 inhibitors at present, is very similar no matter whether LSD1 is in the apo or any holo forms, which is inconsistent with its versatile demethylase functions. To investigate whether the substrate binding pocket is rigid or exhibits other representative conformations different from the crystal conformations that are feasible for designing new LSD1 inhibitors, we performed funnel metadynamics simulations to study the conformation dynamics of LSD1 in the binding process of two effective LSD1 inhibitors (CC-90011 and 6X0, CC-90011 undergoing clinical trials). Our results showed that the entrance of the substrate binding pocket is very flexible. Two representative entrance conformations of LSD1 counting against binding with the substrate of histone H3 were detected, which may be used for structure-based LSD1 inhibitor design. Besides, alternative optimal binding modes and prebinding modes for both inhibitors were also detected, which depicted that the key interactions changed along with the binding process. Our results should provide great help for LSD1 inhibitor design.
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Affiliation(s)
- Kecheng Yang
- National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Hongmin Liu
- Key Lab of Advanced Drug Preparation Technologies, Ministry of Education of China, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Key Laboratory of Henan Province for Drug Quality and Evaluation, Institute of Drug Discovery and Development, School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
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9
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Liu B, Jiang Y, Yang Y, Chen JX. OmeDDG: Improved Protein Mutation Stability Prediction Based on Predicted 3D Structures. J Phys Chem B 2024; 128:67-76. [PMID: 38130113 DOI: 10.1021/acs.jpcb.3c05601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Determining changes in the protein's thermal stability following mutations is critical in protein engineering and understanding pathogenic missense mutations. Despite the development of various computational methods to predict the effects of single-point mutations, their accuracy remains limited. In this study, we propose a new computational method, OmeDDG, that more accurately predicts mutation-induced Gibbs free energy changes in protein folding (ΔΔG). OmeDDG takes the sequences of wild-type and mutant proteins as input, utilizes OmegaFold to obtain the 3D structure, employs a convolutional neural network to extract structural features, and combines them with protein mutation features and pretraining features to predict the stability of single-point mutations in proteins. We performed a comprehensive comparison between OmeDDG and other available prediction methods on four blind test datasets, confirming that OmeDDG can effectively enhance protein mutation prediction performance. Notably, on the antisymmetric dataset Ssym, OmeDDG achieves the best performance, demonstrating favorable antisymmetry with PCC = 0.79 and RMSE = 0.96 for forward mutations and PCC = 0.77 and RMSE = 0.97 for reverse mutant types.
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Affiliation(s)
- Baoying Liu
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
| | - Yongquan Jiang
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
- Artificial Intelligence Research Institute, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
| | - Yan Yang
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
- Artificial Intelligence Research Institute, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
| | - Jim X Chen
- Department of Computer Science, George Mason University, Fairfax, Virginia 22030-4444, United States
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10
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Calderón JC, Plut E, Keller M, Cabrele C, Reiser O, Gervasio FL, Clark T. Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors. J Chem Inf Model 2024; 64:205-218. [PMID: 38150388 DOI: 10.1021/acs.jcim.3c01574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.
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Affiliation(s)
- Jacqueline C Calderón
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
| | - Eva Plut
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Max Keller
- Institute of Pharmacy, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg D-93040, Germany
| | - Chiara Cabrele
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Oliver Reiser
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | | | - Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
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11
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Sun Q, Biswas A, Lyumkis D, Levy R, Deng N. Elucidating the Molecular Determinants of the Binding Modes of a Third-Generation HIV-1 Integrase Strand Transfer Inhibitor: The Importance of Side Chain and Solvent Reorganization. Viruses 2024; 16:76. [PMID: 38257776 PMCID: PMC11154245 DOI: 10.3390/v16010076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/21/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
The first- and second-generation clinically used HIV-1 integrase (IN) strand transfer inhibitors (INSTIs) are key components of antiretroviral therapy (ART), which work by blocking the integration step in the HIV-1 replication cycle that is catalyzed by a nucleoprotein assembly called an intasome. However, resistance to even the latest clinically used INSTIs is beginning to emerge. Developmental third-generation INSTIs, based on naphthyridine scaffolds, are promising candidates to combat drug-resistant viral variants. Among these novel INSTIs, compound 4f exhibits two distinct conformations when binding with intasomes from HIV-1 and the closely related prototype foamy virus (PFV) despite the high structural similarity of their INSTI binding pockets. The molecular mechanism and the key active site residues responsible for these differing binding modes in closely related intasomes remain elusive. To unravel the molecular determinants governing the two distinct binding modes, we applied a novel molecular dynamics-based free energy method that utilizes alchemical pathways to overcome the sampling challenges associated with transitioning between the two bound conformations of ligand 4f within the crowded environments of the INSTI binding pockets in these intasomes. The calculated conformational free energies successfully recapitulate the experimentally observed binding mode preferences in the two viral intasomes. Analysis of the simulated structures suggests that the observed binding mode preferences are caused by amino acid residue differences in both the front and the central catalytic sub-pocket of the INSTI binding site in HIV-1 and PFV. Additional free energy calculations on mutants of HIV-1 and PFV revealed that while both sub-pockets contribute to binding mode selection, the central sub-pocket plays a more important role. These results highlight the importance of both side chain and solvent reorganization, as well as the conformational entropy in determining the ligand binding mode, and will help inform the development of more effective INSTIs for combatting drug-resistant viral variants.
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Affiliation(s)
- Qinfang Sun
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA 19122, USA; (Q.S.); (R.L.)
| | - Avik Biswas
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA; (A.B.); (D.L.)
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
| | - Dmitry Lyumkis
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA; (A.B.); (D.L.)
- Graduate Schools for Biological Sciences, Section of Molecular Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Ronald Levy
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA 19122, USA; (Q.S.); (R.L.)
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, NY 10038, USA
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12
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Liu R, Li W, Yao Y, Wu Y, Luo HB, Li Z. Accelerating and Automating the Free Energy Perturbation Absolute Binding Free Energy Calculation with the RED-E Function. J Chem Inf Model 2023; 63:7755-7767. [PMID: 38048439 DOI: 10.1021/acs.jcim.3c01670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
The accurate prediction of the binding affinities between small molecules and biological macromolecules plays a fundamental role in structure-based drug design, which is still challenging. The free energy perturbation-based absolute binding free energy (FEP-ABFE) approach has shown potential in its reliability. To correctly calculate the energy related to the ligand being restrained by the receptor, additional restraints between the ligand and the receptor are needed. However, determining the restraint parameters for individual ligands empirically is too trivial to be automated, and usually gives rise to numerical instabilities, which set back the applications of FEP-ABFE. To address these issues, we derived the analytical expression for the probability distribution of energy differences, P(ΔU), during the process of restraint addition, which is called the RED-E (restraint energy distribution at equilibrium position) function. Simulations indicated that the RED-E function can accurately describe P(ΔU) when restraints are added at the equilibrium position. Based on the RED-E function, an automatic restraint selection method was proposed to select the best restraint. With this method, there is a high phase-space overlap between the free and restrained states, such that using a 2-λ perturbation can accurately calculate the free energy of the restraint addition, which is a nearly 6 times acceleration compared with current widely used 12-λ perturbation method. The RED-E function gives insight into the non-Gaussian behavior of the sampled P(ΔU) in certain FEP processes in an analytical way. The highly automated and accelerated restraint selection also makes it possible for the large-scale application of FEP-ABFE in real drug discovery practices.
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Affiliation(s)
- Runduo Liu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Wenchao Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yufen Yao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yinuo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Hai-Bin Luo
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
- Song Li' Academician Workstation of Hainan University (School of Pharmaceutical Sciences), Yazhou Bay, Sanya 572000, China
| | - Zhe Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
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13
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Fu H, Chipot C, Shao X, Cai W. Standard Binding Free-Energy Calculations: How Far Are We from Automation? J Phys Chem B 2023; 127:10459-10468. [PMID: 37824848 DOI: 10.1021/acs.jpcb.3c04370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.
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Affiliation(s)
- Haohao Fu
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Christophe Chipot
- Laboratoire International Associé CNRS and University of Illinois at Urbana-Champaign, UMR no. 7019, Université de Lorraine, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
- Department of Chemistry, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Hawai'i at Ma̅noa, 2545 McCarthy Mall, Honolulu, Hawaii 96822, United States
| | - Xueguang Shao
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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14
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Behera S, Balasubramanian S. Lipase A from Bacillus subtilis: Substrate Binding, Conformational Dynamics, and Signatures of a Lid. J Chem Inf Model 2023; 63:7545-7556. [PMID: 37989487 DOI: 10.1021/acs.jcim.3c01681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Protein-ligand binding studies are crucial for understanding the molecular basis of biological processes and for further advancing industrial biocatalysis and drug discovery. Using computational modeling and molecular dynamics simulations, we investigated the binding of a butyrate ester substrate to the lipase A (LipA) enzyme of Bacillus subtilis. Besides obtaining a close agreement of the binding free energy with the experimental value, the study reveals a remarkable reorganization of the catalytic triad upon substrate binding, leading to increased essential hydrogen bond populations. The investigation shows the distortion of the oxyanion hole in both the substrate-bound and unbound states of LipA and highlights the strengthening of the same in the tetrahedral intermediate complex. Principal component analysis of the unbound ensemble reveals the dominant motion in LipA to be the movement of Loop-1 (Tyr129-Arg142) between two states that cover and uncover the active site, mirroring that of a lid prevalent in several lipases. This lid-like motion of Loop-1 is also supported by its tendency to spontaneously open up at an oil-water interface. Overall, this study provides valuable insights into the impact of substrate binding on the structure, flexibility, and conformational dynamics of the LipA enzyme.
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Affiliation(s)
- Sudarshan Behera
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560 064, India
| | - Sundaram Balasubramanian
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560 064, India
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15
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Sun Q, Biswas A, Lyumkis D, Levy R, Deng N. Elucidating the molecular determinants for binding modes of a third-generation HIV-1 integrase strand transfer inhibitor: Importance of side chain and solvent reorganization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569269. [PMID: 38077045 PMCID: PMC10705364 DOI: 10.1101/2023.11.29.569269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
The first and second-generation clinically used HIV-1 integrase (IN) strand transfer inhibitors (INSTIs) are key components of antiretroviral therapy (ART), which work by blocking the integration step in the HIV-1 replication cycle that is catalyzed by a nucleoprotein assembly called an intasome. However, resistance to even the latest clinically used INSTIs is beginning to emerge. Developmental third-generation INSTIs, based on naphthyridine scaffold, are promising candidates to combat drug-resistant viral variants. Among these novel INSTIs, compound 4f exhibits two distinct conformations when binding to intasomes from HIV-1 and the closely related prototype foamy virus (PFV), despite the high structural similarity of their INSTI binding pockets. The molecular mechanism and the key active site residues responsible for these differing binding modes in closely related intasomes remain elusive. To unravel the molecular determinants governing the two distinct binding modes, we employ a novel molecular dynamics-based free energy approach that utilizes alchemical pathways to overcome the sampling challenges associated with transitioning between two ligand conformations within crowded environments along physical pathways. The calculated conformational free energies successfully recapitulate the experimentally observed binding mode preferences in the two viral intasomes. Analysis of the simulated structures suggests that the observed binding mode preferences are caused by amino acid residue differences in both the front and the central catalytic sub-pocket of the INSTI binding site in HIV-1 and PFV. Additional free energy calculations on mutants of HIV-1 and PFV revealed that while both sub-pockets contribute to the binding mode selection, the central sub-pocket plays a more important role. These results highlight the importance of both side chain and solvent reorganization, as well as the conformational entropy in determining the ligand binding mode and will help inform the development of more effective INSTIs for combatting drug-resistant viral variants.
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Affiliation(s)
- Qinfang Sun
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA 19122
| | - Avik Biswas
- The Salk Institute for Biological Studies, Laboratory of Genetics, La Jolla, CA 92037
- Department of Physics, University of California San Diego, La Jolla, CA, 92093
| | - Dmitry Lyumkis
- The Salk Institute for Biological Studies, Laboratory of Genetics, La Jolla, CA 92037
- Graduate schools for Biological Sciences, Section of Molecular Biology, University of California, San Diego, La Jolla, CA, 92093
| | - Ronald Levy
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA 19122
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, NY10038
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16
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Kuzovlev AS, Zybalov MD, Golovin AV, Gureev MA, Kasatkina MA, Biryukov MV, Belik AR, Silonov SA, Yunin MA, Zigangirova NA, Reshetnikov VV, Isakova YE, Porozov YB, Ivanov RA. Naphthyl-Substituted Indole and Pyrrole Carboxylic Acids as Effective Antibiotic Potentiators-Inhibitors of Bacterial Cystathionine γ-Lyase. Int J Mol Sci 2023; 24:16331. [PMID: 38003521 PMCID: PMC10671052 DOI: 10.3390/ijms242216331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/08/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Over the past decades, the problem of bacterial resistance to most antibiotics has become a serious threat to patients' survival. Nevertheless, antibiotics of a novel class have not been approved since the 1980s. The development of antibiotic potentiators is an appealing alternative to the challenging process of searching for new antimicrobials. Production of H2S-one of the leading defense mechanisms crucial for bacterial survival-can be influenced by the inhibition of relevant enzymes: bacterial cystathionine γ-lyase (bCSE), bacterial cystathionine β-synthase (bCBS), or 3-mercaptopyruvate sulfurtransferase (MST). The first one makes the main contribution to H2S generation. Herein, we present data on the synthesis, in silico analyses, and enzymatic and microbiological assays of novel bCSE inhibitors. Combined molecular docking and molecular dynamics analyses revealed a novel binding mode of these ligands to bCSE. Lead compound 2a manifested strong potentiating activity when applied in combination with some commonly used antibiotics against multidrug-resistant Acinetobacter baumannii, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus. The compound was found to have favorable in vitro absorption, distribution, metabolism, excretion, and toxicity parameters. The high effectiveness and safety of compound 2a makes it a promising candidate for enhancing the activity of antibiotics against high-priority pathogens.
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Affiliation(s)
- Andrey S. Kuzovlev
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Mikhail D. Zybalov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Andrey V. Golovin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1/73 Leninskie gori St., 119234 Moscow, Russia;
- Laboratory of Bioinformatics, Center of AI and Information Technologies, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.A.G.); (Y.B.P.)
| | - Maxim A. Gureev
- Laboratory of Bioinformatics, Center of AI and Information Technologies, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.A.G.); (Y.B.P.)
- Laboratory of Bio- and Chemoinformatics, Institute of Biodesign and Modeling of Complex Systems, I.M. Sechenov First Moscow State Medical University, 8/2 Trubetskaya, 119991 Moscow, Russia
| | - Mariia A. Kasatkina
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Mikhail V. Biryukov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
- Faculty of Biology, Lomonosov Moscow State University, 1/12 Leninskie gori St., 119234 Moscow, Russia
| | - Albina R. Belik
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Sergey A. Silonov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, 4 Tikhoretsky Ave., 194064 St. Petersburg, Russia
| | - Maxim A. Yunin
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Nailya A. Zigangirova
- Medical Microbiology Department, Laboratory of Chlamydiosis, National Research Center for Epidemiology and Microbiology Named after N. F. Gamaleya, 18 Gamaleya St., 123098 Moscow, Russia;
| | - Vasiliy V. Reshetnikov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
- Institute of Cytology and Genetics, Siberian Branch of RAS, 10 Akademika Lavrentyeva, 630090 Novosibirsk, Russia
| | - Yulia E. Isakova
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Yuri B. Porozov
- Laboratory of Bioinformatics, Center of AI and Information Technologies, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.A.G.); (Y.B.P.)
- Laboratory of Bio- and Chemoinformatics, Institute of Biodesign and Modeling of Complex Systems, I.M. Sechenov First Moscow State Medical University, 8/2 Trubetskaya, 119991 Moscow, Russia
| | - Roman A. Ivanov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
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17
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Bobrovs R, Drunka L, Kanepe I, Jirgensons A, Caflisch A, Salvalaglio M, Jaudzems K. Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors. J Chem Inf Model 2023; 63:6890-6899. [PMID: 37801405 DOI: 10.1021/acs.jcim.3c00826] [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: 10/08/2023]
Abstract
Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.
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Affiliation(s)
- Raitis Bobrovs
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Laura Drunka
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Iveta Kanepe
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Aigars Jirgensons
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Matteo Salvalaglio
- Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom
| | - Kristaps Jaudzems
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
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18
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Di Marino D, Conflitti P, Motta S, Limongelli V. Structural basis of dimerization of chemokine receptors CCR5 and CXCR4. Nat Commun 2023; 14:6439. [PMID: 37833254 PMCID: PMC10575954 DOI: 10.1038/s41467-023-42082-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are prominent drug targets responsible for extracellular-to-intracellular signal transduction. GPCRs can form functional dimers that have been poorly characterized so far. Here, we show the dimerization mechanism of the chemokine receptors CCR5 and CXCR4 by means of an advanced free-energy technique named coarse-grained metadynamics. Our results reproduce binding events between the GPCRs occurring in the minute timescale, revealing a symmetric and an asymmetric dimeric structure for each of the three investigated systems, CCR5/CCR5, CXCR4/CXCR4, and CCR5/CXCR4. The transmembrane helices TM4-TM5 and TM6-TM7 are the preferred binding interfaces for CCR5 and CXCR4, respectively. The identified dimeric states differ in the access to the binding sites of the ligand and G protein, indicating that dimerization may represent a fine allosteric mechanism to regulate receptor activity. Our study offers structural basis for the design of ligands able to modulate the formation of CCR5 and CXCR4 dimers and in turn their activity, with therapeutic potential against HIV, cancer, and immune-inflammatory diseases.
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Affiliation(s)
- Daniele Di Marino
- Department of Life and Environmental Sciences - New York-Marche Structural Biology Centre (NY-MaSBiC), Polytechnic University of Marche, Via Brecce Bianche, 60131, Ancona, Italy
- Neuronal Death and Neuroprotection Unit, Department of Neuroscience, Mario Negri Institute for Pharmacological Research-IRCCS, Via Mario Negri 2, 20156, Milan, Italy
- National Biodiversity Future Center (NBFC), Palermo, Italy
| | - Paolo Conflitti
- Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Euler Institute, Via G. Buffi 13, CH-6900, Lugano, Switzerland
| | - Stefano Motta
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126, Milan, Italy
| | - Vittorio Limongelli
- Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Euler Institute, Via G. Buffi 13, CH-6900, Lugano, Switzerland.
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19
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Nguyen D, Wu J, Corrigan P, Li Y. Computational investigation on lipid bilayer disruption induced by amphiphilic Janus nanoparticles: combined effect of Janus balance and charged lipid concentration. NANOSCALE 2023; 15:16112-16130. [PMID: 37753922 DOI: 10.1039/d3nr00403a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Janus nanoparticles (NPs) with charged/hydrophobic compartments have garnered attention for their potential antimicrobial activity. These NPs have been shown to disrupt lipid bilayers in experimental studies, yet the underlying mechanisms of this disruption at the particle-membrane interface remain unclear. To address this knowledge gap, the present study conducts a computational investigation to systematically examine the disruption of lipid bilayers induced by amphiphilic Janus NPs. The focus of this study is on the combined effects of the hydrophobicity of the Janus NP, referred to as the Janus balance, defined as the ratio of hydrophilic to hydrophobic surface coverage, and the concentration of charged phospholipids on the interactions between Janus NPs and lipid bilayers. Computational simulations were conducted using a coarse-grained molecular dynamics (MD) approach. The results of these MD simulations reveal that while the area change of the bilayer increases monotonically with the Janus balance, the effect of charged lipid concentration in the membrane is not easy to be predicted. Specifically, it was found that the concentration of negatively charged lipids is directly proportional to the intensity of membrane disruption. Conversely, positively charged lipids have a negligible effect on membrane defects. This study provides molecular insights into the significant role of Janus balance in the disruption of lipid bilayers by Janus NPs and supports the selectivity of Janus NPs for negatively charged lipid membranes. Furthermore, the anisotropic properties of Janus NPs were found to play a crucial role in their ability to disrupt the membrane via the combination of hydrophobic and electrostatic interactions. This finding is validated by testing the current Janus NP design on a bacterial membrane-mimicking model. This computational study may serve as a foundation for further studies aimed at optimizing the properties of Janus NPs for specific antimicrobial applications.
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Affiliation(s)
- Danh Nguyen
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
| | - James Wu
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
| | - Patrick Corrigan
- Department of Chemistry, University of Connecticut, Storrs, CT 06269, USA
| | - Ying Li
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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20
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Zantza I, Pyrris Y, Raniolo S, Papadaki GF, Lambrinidis G, Limongelli V, Diallinas G, Mikros E. Uracil/H + Symport by FurE Refines Aspects of the Rocking-bundle Mechanism of APC-type Transporters. J Mol Biol 2023; 435:168226. [PMID: 37544358 DOI: 10.1016/j.jmb.2023.168226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/22/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Transporters mediate the uptake of solutes, metabolites and drugs across the cell membrane. The eukaryotic FurE nucleobase/H+ symporter of Aspergillus nidulans has been used as a model protein to address structure-function relationships in the APC transporter superfamily, members of which are characterized by the LeuT-fold and seem to operate by the so-called 'rocking-bundle' mechanism. In this study, we reveal the binding mode, translocation and release pathway of uracil/H+ by FurE using path collective variable, funnel metadynamics and rational mutational analysis. Our study reveals a stepwise, induced-fit, mechanism of ordered sequential transport of proton and uracil, which in turn suggests that FurE, functions as a multi-step gated pore, rather than employing 'rocking' of compact domains, as often proposed for APC transporters. Finally, our work supports that specific residues of the cytoplasmic N-tail are involved in substrate translocation, in line with their essentiality for FurE function.
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Affiliation(s)
- Iliana Zantza
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece.
| | - Yiannis Pyrris
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece.
| | - Stefano Raniolo
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), Lugano 6900, Switzerland.
| | - Georgia F Papadaki
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece
| | - George Lambrinidis
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece.
| | - Vittorio Limongelli
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), Lugano 6900, Switzerland; Department of Pharmacy, University of Naples "Federico II", Naples 80131, Italy.
| | - George Diallinas
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Heraklion 70013, Greece.
| | - Emmanuel Mikros
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece; Athena Research and Innovation Center in Information Communication & Knowledge Technologies, Marousi 15125, Greece.
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21
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Conflitti P, Raniolo S, Limongelli V. Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness. J Chem Theory Comput 2023; 19:6047-6061. [PMID: 37656199 PMCID: PMC10536999 DOI: 10.1021/acs.jctc.3c00641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 09/02/2023]
Abstract
Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.
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Affiliation(s)
- Paolo Conflitti
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Stefano Raniolo
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Vittorio Limongelli
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
- Department
of Pharmacy, University of Naples “Federico
II”, 80131 Naples, Italy
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22
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Rizzi V, Aureli S, Ansari N, Gervasio FL. OneOPES, a Combined Enhanced Sampling Method to Rule Them All. J Chem Theory Comput 2023; 19:5731-5742. [PMID: 37603295 PMCID: PMC10500989 DOI: 10.1021/acs.jctc.3c00254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Indexed: 08/22/2023]
Abstract
Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multireplica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.
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Affiliation(s)
- Valerio Rizzi
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Simone Aureli
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Narjes Ansari
- Atomistic
Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Francesco Luigi Gervasio
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
- Department
of Chemistry, University College London, WC1E 6BT London, U.K.
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23
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Mafi A, Kim SK, Goddard WA. The dynamics of agonist-β 2-adrenergic receptor activation induced by binding of GDP-bound Gs protein. Nat Chem 2023:10.1038/s41557-023-01238-6. [PMID: 37349378 DOI: 10.1038/s41557-023-01238-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 05/12/2023] [Indexed: 06/24/2023]
Abstract
There is considerable uncertainty about the mechanism by which the β2-adrenergic receptor (β2AR) is activated. Here we use molecular metadynamics computations to predict the mechanism by which an agonist induces the activation of the β2AR and its cognate Gs protein. We found that binding agonist alone to the inactive β2AR does not break the ionic lock and hence does not drive the β2AR towards the activated conformation. However, we found that attaching the inactive Gs protein to the agonist-bound inactive β2AR (containing the ionic lock) leads to partial insertion of Gαs-α5 into the core of β2AR, which breaks the ionic lock, leading to activation of the Gs protein coupled to β2AR. Upon activation, the Gαs protein undergoes a remarkable opening of the GDP binding pocket, making the GDP available for exchange or release. Concomitantly, Gαs-α5 undergoes a remarkable expansion in the β2AR cytoplasmic region after the ionic lock is broken, inducing TM6 to displace outward by ~5 Å from TM3.
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Affiliation(s)
- Amirhossein Mafi
- Materials and Process Simulation Center, Caltech, Pasadena, CA, USA
- California Institute of Technology, Pasadena, CA, USA
| | - Soo-Kyung Kim
- Materials and Process Simulation Center, Caltech, Pasadena, CA, USA
- California Institute of Technology, Pasadena, CA, USA
| | - William A Goddard
- Materials and Process Simulation Center, Caltech, Pasadena, CA, USA.
- California Institute of Technology, Pasadena, CA, USA.
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24
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Hardie A, Cossins BP, Lovera S, Michel J. Deconstructing allostery by computational assessment of the binding determinants of allosteric PTP1B modulators. Commun Chem 2023; 6:125. [PMID: 37322137 DOI: 10.1038/s42004-023-00926-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/08/2023] [Indexed: 06/17/2023] Open
Abstract
Fragment-based drug discovery is an established methodology for finding hit molecules that can be elaborated into lead compounds. However it is currently challenging to predict whether fragment hits that do not bind to an orthosteric site could be elaborated into allosteric modulators, as in these cases binding does not necessarily translate into a functional effect. We propose a workflow using Markov State Models (MSMs) with steered molecular dynamics (sMD) to assess the allosteric potential of known binders. sMD simulations are employed to sample protein conformational space inaccessible to routine equilibrium MD timescales. Protein conformations sampled by sMD provide starting points for seeded MD simulations, which are combined into MSMs. The methodology is demonstrated on a dataset of protein tyrosine phosphatase 1B ligands. Experimentally confirmed allosteric inhibitors are correctly classified as inhibitors, whereas the deconstructed analogues show reduced inhibitory activity. Analysis of the MSMs provide insights into preferred protein-ligand arrangements that correlate with functional outcomes. The present methodology may find applications for progressing fragments towards lead molecules in FBDD campaigns.
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Affiliation(s)
- Adele Hardie
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, EH9 3FJ, UK
| | - Benjamin P Cossins
- UCB Pharma, 216 Bath Road, Slough, UK
- Exscientia, The Schrödinger Building, Oxford Science Park, Oxford, UK
| | - Silvia Lovera
- UCB Pharma, Chemin du Foriest 1, 1420, Braine-l'Alleud, Belgium
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, EH9 3FJ, UK.
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25
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Matos IDA, Goes Pinto AC, Ferraz MVF, Adan WCS, Rodrigues RP, Dos Santos JX, Kitagawa RR, Lins RD, Oliveira TB, Costa Junior NBD. Identification of potential Staphylococcus aureus dihydrofolate reductase inhibitors using QSAR, molecular docking, dynamics simulations and free energy calculation. J Biomol Struct Dyn 2023; 41:3835-3846. [PMID: 35356863 DOI: 10.1080/07391102.2022.2057361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 03/19/2022] [Indexed: 10/18/2022]
Abstract
Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2train, CVLOO and R2test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Isaac de Araujo Matos
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Ana Carolina Goes Pinto
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | | | - Wenny Camilla Santos Adan
- Department of Pharmaceutical Sciences, Postgraduate Program in Pharmaceutical Sciences, Federal University of Espírito Santo-UFES, Vitória-ES, Brazil
| | - Ricardo Pereira Rodrigues
- Department of Pharmacy, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Juliane Xavier Dos Santos
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Rodrigo Rezende Kitagawa
- Department of Pharmaceutical Sciences, Postgraduate Program in Pharmaceutical Sciences, Federal University of Espírito Santo-UFES, Vitória-ES, Brazil
| | | | - Tiago Branquinho Oliveira
- Department of Pharmacy, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
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26
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Chung MKJ, Miller RJ, Novak B, Wang Z, Ponder JW. Accurate Host-Guest Binding Free Energies Using the AMOEBA Polarizable Force Field. J Chem Inf Model 2023; 63:2769-2782. [PMID: 37075788 PMCID: PMC10878370 DOI: 10.1021/acs.jcim.3c00155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
A grand challenge of computational biophysics is accurate prediction of interactions between molecules. Molecular dynamics (MD) simulations have recently gained much interest as a tool to directly compute rigorous intermolecular binding affinities. The choice of a fixed point-charge or polarizable multipole force field used in MD is a topic of ongoing discussion. To compare alternative methods, we participated in the SAMPL7 and SAMPL8 Gibb octaacid host-guest challenges to assess the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) polarizable multipole force field. Advantages of AMOEBA over fixed charge models include improved representation of molecular electrostatic potentials and better description of water occupying the unligated host cavity. Prospective predictions for 26 host-guest systems exhibit a mean unsigned error vs experiment of 0.848 kcal/mol across all absolute binding free energies, demonstrating excellent agreement between computational and experimental results. In addition, we explore two topics related to the inclusion of ions in MD simulations: use of a neutral co-alchemical protocol and the effect of salt concentration on binding affinity. Use of the co-alchemical method minimally affects computed energies, but salt concentration significantly perturbs our binding results. Higher salt concentration strengthens binding through classical charge screening. In particular, added Na+ ions screen negatively charged carboxylate groups near the binding cavity, thereby diminishing repulsive coulomb interactions with negatively charged guests. Overall, the AMOEBA results demonstrate the accuracy available through a force field providing a detailed energetic description of the four octaacid hosts and 13 charged organic guests. Use of the AMOEBA polarizable atomic multipole force field in conjunction with an alchemical free energy protocol can achieve chemical accuracy in application to realistic molecular systems.
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Affiliation(s)
- Moses K. J. Chung
- Medical Scientist Training Program, Washington University School of Medicine, Saint Louis, MO 63110, USA
- Department of Physics, Washington University in St. Louis, Saint Louis, MO 63130, USA
| | - Ryan J. Miller
- Department of Chemistry, Washington University in St. Louis, Saint Louis, MO 63130, USA
| | - Borna Novak
- Medical Scientist Training Program, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Zhi Wang
- Department of Chemistry, Washington University in St. Louis, Saint Louis, MO 63130, USA
| | - Jay W. Ponder
- Department of Chemistry, Washington University in St. Louis, Saint Louis, MO 63130, USA
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, Saint Louis, MO 63110, USA
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27
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Motta S, Siani P, Donadoni E, Frigerio G, Bonati L, Di Valentin C. Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles. NANOSCALE 2023; 15:7909-7919. [PMID: 37066796 DOI: 10.1039/d3nr00397c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO2 nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.
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Affiliation(s)
- Stefano Motta
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Paulo Siani
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Edoardo Donadoni
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Giulia Frigerio
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Laura Bonati
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Cristiana Di Valentin
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
- BioNanoMedicine Center NANOMIB, University of Milano-Bicocca, Italy
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28
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Parui S, Robertson JC, Somani S, Tresadern G, Liu C, Dill KA. MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands. J Chem Inf Model 2023; 63:2857-2865. [PMID: 37093848 DOI: 10.1021/acs.jcim.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.
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Affiliation(s)
- Sridip Parui
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - James C Robertson
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Sandeep Somani
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Gary Tresadern
- Janssen Research and Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Cong Liu
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
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29
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Ligand binding free energy evaluation by Monte Carlo Recursion. Comput Biol Chem 2023; 103:107830. [PMID: 36812825 DOI: 10.1016/j.compbiolchem.2023.107830] [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: 11/22/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023]
Abstract
The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: (i) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; (ii) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called 'end-point' methods; (iii) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and (iv) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W(b,T), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N = 75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa/MCLiBELa project (https://github.com/alessandronascimento/LiBELa).
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30
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Canini G, Lo Cascio E, Della Longa S, Cecconi F, Arcovito A. Human Glucosylceramide Synthase at Work as Provided by " In Silico" Molecular Docking, Molecular Dynamics, and Metadynamics. ACS OMEGA 2023; 8:8755-8765. [PMID: 36910965 PMCID: PMC9996764 DOI: 10.1021/acsomega.2c08219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Glucosylceramide synthase (GCS) is an enzyme that catalyzes the first reaction of ceramide glycosylation in sphingolipid metabolism. It represents a primary target in the pharmacological treatment of some lysosomal storage diseases (LSDs), such as Gaucher and Niemann-Pick syndromes. In this study, starting from the model reported in the AlphaFold Protein Structure Database, the location and conformations of GCS substrates and cofactors have been provided by a step-by-step in silico procedure, by which the functional manganese ion and the substrates have been inserted in the GCS structure through combined molecular docking and full-atomistic molecular dynamics approaches, including metadynamics. A detailed analysis by structural dynamics of the complete model system, i.e., the enzyme anchored to the plasma membrane, containing the manganese ion and the two substrates, has been carried out to identify its complex conformational landscape by means of well-tempered metadynamics. A final structure was selected, in which both substrates were present in the active site of the enzyme at minimum distance, thus giving support to a SNi-type reaction mechanism for catalysis. Asp236, Glu235, and Asp144 are found to interact with the metal cofactor, which is able to trap the phosphates of UDP-glucose, while Gly210, Trp276, and Val208 cooperate to provide its correct orientation. Phe205, Cys207, Tyr237, and Leu284 form a pocket for the polar head of the ceramide, which is transiently placed in position to determine the catalytic event, when His193 interacts with the head of the ceramide, thus anchoring the substrate to the active site.
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Affiliation(s)
- Giorgia Canini
- Dipartimento di
Scienze Biotecnologiche di Base, Cliniche Intensivologiche e Perioperatorie, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Roma, Italy
| | - Ettore Lo Cascio
- Dipartimento di
Scienze Biotecnologiche di Base, Cliniche Intensivologiche e Perioperatorie, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Roma, Italy
| | - Stefano Della Longa
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Francesco Cecconi
- Dipartimento di
Scienze Biotecnologiche di Base, Cliniche Intensivologiche e Perioperatorie, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Roma, Italy
- Fondazione Policlinico Universitario “A. Gemelli”,
IRCCS, Largo A. Gemelli
8, 00168 Roma, Italy
| | - Alessandro Arcovito
- Dipartimento di
Scienze Biotecnologiche di Base, Cliniche Intensivologiche e Perioperatorie, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Roma, Italy
- Fondazione Policlinico Universitario “A. Gemelli”,
IRCCS, Largo A. Gemelli
8, 00168 Roma, Italy
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31
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Sahil M, Sarkar S, Mondal J. Long-time-step molecular dynamics can retard simulation of protein-ligand recognition process. Biophys J 2023; 122:802-816. [PMID: 36726313 PMCID: PMC10027446 DOI: 10.1016/j.bpj.2023.01.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/31/2022] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
Molecular dynamics (MD) simulation of biologically relevant processes at realistic time scale and atomistic precision is generally limited by prohibitively large computational cost, due to its restriction of using an ultrashort integration time step (1-2 fs). A popular numerical recipe to reduce the associated computational burden is adopting schemes that would allow relatively longer-time-step for MD propagation. Here, we explore the perceived potential of one of the most frequently used long-time-step protocols, namely the hydrogen mass repartitioning (HMR) approach, in alleviating the computational overhead associated with simulation of the kinetic process of protein-ligand recognition events. By repartitioning the mass of heavier atoms to their linked hydrogen atoms, HMR leverages around twofold longer time step than regular simulation, holding promise of significant performance boost. However, our probe into direct simulation of the protein-ligand recognition event, one of the computationally most challenging processes, shows that long-time-step HMR MD simulations do not necessarily translate to a computationally affordable solution. Our investigations spanning cumulative 176 μs in three independent proteins (T4 lysozyme, sensor domain of MopR, and galectin-3) show that long-time-step HMR-based MD simulations can catch the ligand in its act of recognizing the native cavity. But, as a major caveat, the ligand is found to require significantly longer time to identify buried native protein cavity in an HMR MD simulation than regular simulation, thereby defeating the purpose of its usage for performance upgrade. A molecular analysis shows that the longer time required by a ligand to recognize the protein in HMR is rooted in faster diffusion of the ligand, which reduces the survival probability of decisive on-pathway metastable intermediates, thereby slowing down the eventual recognition process at the native cavity. Together, the investigation stresses careful assessment of pitfalls of long-time-step algorithms before attempting to utilize them for higher performance for biomolecular recognition simulations.
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Affiliation(s)
- Mohammad Sahil
- Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Susmita Sarkar
- Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Jagannath Mondal
- Tata Institute of Fundamental Research, Hyderabad 500046, India.
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32
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Zou Y, Wang R, Du M, Wang X, Xu D. Identifying Protein-Ligand Interactions via a Novel Distance Self-Feedback Biomolecular Interaction Network. J Phys Chem B 2023; 127:899-911. [PMID: 36657025 DOI: 10.1021/acs.jpcb.2c07592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Efficient and accurate characterizations of protein-ligand interactions are key to understanding biology at the molecular level. They are particularly useful in pharmaceutical industry applications. They are usually computationally demanding for those widely applied dynamics-based methods in identifying important residues or calculating ligand binding free energy. In this work, we proposed a graph deep learning (DL) framework, namely, the distance self-feedback biomolecular interaction network (DSBIN), in which the relationship between the complex structure and binding affinity can be established by means of a carefully designed distance self-feedback module and interaction layer. Our model can directly provide a quantitative evaluation of inhibitor binding affinities (pKd). More importantly, the DSBIN model efficiently identifies key interactions for inhibitor binding and thus intrinsically bears the interpretability. Its generalization performance was further verified using 1405 unseen structures. The predicted binding free energies' deviations were calculated to be less than 1.37 kcal/mol for more than 55% structures. Moreover, we also compared the DSBIN model with a commonly used theoretical method in calculating the substrate binding free energy, MM/GBSA. Our results show that the current DL model has generally better performance in predicting the binding free energy. For a specific complex system, mannopentaose/TmCBM27, the DSBIN predicted binding free energy is -8.21 kcal/mol, which is very close to experimentally measured -7.76 kcal/mol and MM/GBSA calculated -7.16 kcal/mol. Meanwhile, all important aromatic residues around the binding pocket can be identified by our DL model. Considering the accuracy and efficiency of the newly developed DL model, it may be very helpful in the field of drug design and molecular recognition.
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Affiliation(s)
- Yurong Zou
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Ruihan Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Meng Du
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Xin Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Dingguo Xu
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China.,Research Center for Materials Genome Engineering, Sichuan University, Chengdu, Sichuan610065, PR China
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33
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Wu L, Wang G, Zhou L, Mo M, Shi Y, Li B, Yin L, Zhao Q, Yang Y, Wu C, Xu Z, Zhu W. Molecular dynamics study on the behavior and binding mechanism of target protein Transgelin-2 with its agonist TSG12 for anti-asthma drug discovery. Comput Biol Med 2023; 153:106515. [PMID: 36610217 DOI: 10.1016/j.compbiomed.2022.106515] [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: 12/06/2022] [Revised: 12/19/2022] [Accepted: 12/31/2022] [Indexed: 01/02/2023]
Abstract
Transgelin-2 (TG2) is a novel promising therapeutic target for the treatment of asthma as it plays an important role in relaxing airway smooth muscles and reducing pulmonary resistance in asthma. The compound TSG12 is the only reported TG2 agonist with in vivo anti-asthma activity. However, the dynamic behavior and ligand binding sites of TG2 and its binding mechanism with TSG12 remain unclear. In this study, we performed 12.6 μs molecular dynamics (MD) simulations for apo-TG2 and TG2-TSG12 complex, respectively. The results suggested that the apo-TG2 has 4 most populated conformations, and that its binding of the agonist could expand the conformation distribution space of the protein. The simulations revealed 3 potential binding sites in 3 most populated conformations, one of which is induced by the agonist binding. Free energy decomposition uncovered 8 important residues with contributions stronger than -1 kcal/mol. Computational alanine scanning for the important residues by 100 ns conventional MD simulation for each mutated TG2-TSG12 complexes demonstrated that E27, R49 and F52 are essential residues for the agonist binding. These results should be helpful to understand the dynamic behavior of TG2 and its binding mechanism with the agonist TSG12, which could provide some structural insights into the novel mechanism for anti-asthma drug development.
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Affiliation(s)
- Leyun Wu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guangpu Wang
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, China
| | - Liping Zhou
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mengxia Mo
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, China
| | - Yulong Shi
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bo Li
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Leimiao Yin
- Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200030, China
| | - Qiang Zhao
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Yongqing Yang
- Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200030, China
| | - Chengkun Wu
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, China.
| | - Zhijian Xu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Weiliang Zhu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China.
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34
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Heydari S, Raniolo S, Livi L, Limongelli V. Transferring chemical and energetic knowledge between molecular systems with machine learning. Commun Chem 2023; 6:13. [PMID: 36697971 PMCID: PMC9839695 DOI: 10.1038/s42004-022-00790-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/07/2022] [Indexed: 01/15/2023] Open
Abstract
Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing multi-atom interactions for a given conformation, and (ii) novel message passing and pooling layers for processing and making free-energy predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable Area Under the Curve of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the same transfer learning approach can also be used in an unsupervised way to group chemically related secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed, paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.
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Affiliation(s)
- Sajjad Heydari
- grid.21613.370000 0004 1936 9609Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2 Canada
| | - Stefano Raniolo
- grid.29078.340000 0001 2203 2861Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), via G. Buffi 13, CH-6900 Lugano, Switzerland
| | - Lorenzo Livi
- grid.21613.370000 0004 1936 9609Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2 Canada ,grid.8391.30000 0004 1936 8024Department of Computer Science, University of Exeter, Exeter, EX4 4QF UK
| | - Vittorio Limongelli
- grid.29078.340000 0001 2203 2861Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), via G. Buffi 13, CH-6900 Lugano, Switzerland ,grid.4691.a0000 0001 0790 385XDepartment of Pharmacy, University of Naples “Federico II”, via D. Montesano 49, I-80131 Naples, Italy
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35
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Sora V, Laspiur AO, Degn K, Arnaudi M, Utichi M, Beltrame L, De Menezes D, Orlandi M, Stoltze UK, Rigina O, Sackett PW, Wadt K, Schmiegelow K, Tiberti M, Papaleo E. RosettaDDGPrediction for high-throughput mutational scans: From stability to binding. Protein Sci 2023; 32:e4527. [PMID: 36461907 PMCID: PMC9795540 DOI: 10.1002/pro.4527] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022]
Abstract
Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.
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Affiliation(s)
- Valentina Sora
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Adrian Otamendi Laspiur
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Matteo Arnaudi
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Mattia Utichi
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Ludovica Beltrame
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Dayana De Menezes
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Matteo Orlandi
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Ulrik Kristoffer Stoltze
- Department of Clinical GeneticsCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Department of Pediatrics and Adolescent MedicineUniversity Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Olga Rigina
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Peter Wad Sackett
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Karin Wadt
- Department of Clinical GeneticsCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Kjeld Schmiegelow
- Department of Pediatrics and Adolescent MedicineUniversity Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
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36
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Lukauskis D, Samways ML, Aureli S, Cossins BP, Taylor RD, Gervasio FL. Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses. J Chem Inf Model 2022; 62:6209-6216. [PMID: 36401553 DOI: 10.1021/acs.jcim.2c01142] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.
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Affiliation(s)
- Dominykas Lukauskis
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom
| | | | - Simone Aureli
- Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland
| | - Benjamin P Cossins
- UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom.,Exscientia Ltd., The Schrödinger Building, Oxford Science Park, OxfordOX4 4GE, United Kingdom
| | | | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom.,Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland.,UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom
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37
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Funnel metadynamics and behavioral studies reveal complex effect of D2AAK1 ligand on anxiety-like processes. Sci Rep 2022; 12:21192. [PMID: 36476619 PMCID: PMC9729218 DOI: 10.1038/s41598-022-25478-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
Anxiety is a troublesome symptom for many patients, especially those suffering from schizophrenia. Its regulation involves serotonin receptors, targeted e.g. by antipsychotics or psychedelics such as LSD. 5-HT2A receptors are known for an extremely long LSD residence time, enabling minute doses to exert a long-lasting effect. In this work, we explore the changes in anxiety-like processes induced by the previously reported antipsychotic, D2AAK1. In vivo studies revealed that the effect of D2AAK1 on the anxiety is mediated through serotonin 5-HT1A and 5-HT2A receptors, and that it is time-dependent (anxiogenic after 30 min, anxiolytic after 60 min) and dose-dependent. The funnel metadynamics simulations suggest complicated ligand-5HT2AR interactions, involving an allosteric site located under the third extracellular loop, which is a possible explanation of the time-dependency. The binding of D2AAK1 at the allosteric site results in a broader opening of the extracellular receptor entry, possibly altering the binding kinetics of orthosteric ligands.
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38
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Tu H, Han Y, Wang Z, Li J. Clustered tree regression to learn protein energy change with mutated amino acid. Brief Bioinform 2022; 23:6702668. [PMID: 36124753 DOI: 10.1093/bib/bbac374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/14/2022] Open
Abstract
Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information.
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Affiliation(s)
- Hongwei Tu
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanqiang Han
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhilong Wang
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinjin Li
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
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39
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Wang D, Cui F, Ren L, Tan X, Li Q, Li J, Li T. Enhancing the Inhibition Potential of AHL Acylase PF2571 against Food Spoilage by Remodeling Its Substrate Scope via a Computationally Driven Protein Design. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:14510-14521. [PMID: 36331356 DOI: 10.1021/acs.jafc.2c05753] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The N-acyl homoserine lactone (AHL) acylases are widely used as quorum sensing (QS) blockers to inhibit bacterial food spoilage. However, their substrate specificity for long-chain substrates weakens their efficiency. In this study, a computer-assisted design of AHL acylase PF2571 was performed to modify its substrate scope. The results showed that the variant PF2571H194Y, L221R could effectively quench N-hexanoyl-l-homoserine lactone and N-octanoyl-l-homoserine lactone without impairing its activity against long-chain AHLs. Kinetic analysis of the enzymatic activities further corroborated the observed substrate expansion. The inhibitory activities of this variant were significantly enhanced against the QS phenotype of Aeromonas veronii BY-8, with inhibition rates of 45.67, 78.25, 54.21, and 54.65% against proteases, motility, biofilms, and extracellular polysaccharides, respectively. Results for molecular dynamics simulation showed that the steric hindrance, induced by residue substitution, could have been responsible for the change in substrate scope. This study dramatically improves the practicability of AHL acylase in controlling food spoilage.
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Affiliation(s)
- Dangfeng Wang
- College of Food Science and Technology, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Bohai University, Liaoning, Jinzhou121013, China
- College of Food Science and Technology, Jiangnan University, Jiangsu, Wuxi214122, China
| | - Fangchao Cui
- College of Food Science and Technology, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Bohai University, Liaoning, Jinzhou121013, China
| | - Likun Ren
- Key Laboratory of Food Science and Engineering of Heilongjiang Province, College of Food Engineering, Harbin University of Commerce, Heilongjiang, Harbin150076, China
| | - Xiqian Tan
- College of Food Science and Technology, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Bohai University, Liaoning, Jinzhou121013, China
| | - Qiuying Li
- College of Food Science and Technology, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Bohai University, Liaoning, Jinzhou121013, China
| | - Jianrong Li
- College of Food Science and Technology, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Bohai University, Liaoning, Jinzhou121013, China
- College of Food Science and Technology, Jiangnan University, Jiangsu, Wuxi214122, China
| | - Tingting Li
- Key Laboratory of Biotechnology and Bioresources Utilization, Ministry of Education, Dalian Minzu University, Liaoning, Dalian116029, China
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40
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Fu H, Zhou Y, Jing X, Shao X, Cai W. Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy. J Med Chem 2022; 65:12970-12978. [PMID: 36179112 DOI: 10.1021/acs.jmedchem.2c00796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.
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Affiliation(s)
- Haohao Fu
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Yan Zhou
- School of Medicine, Nankai University, Tianjin300071, China.,Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xiang Jing
- Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
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41
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Zargari F, Nikfarjam Z, Nakhaei E, Ghorbanipour M, Nowroozi A, Amiri A. Study of tyramine-binding mechanism and insecticidal activity of oil extracted from Eucalyptus against Sitophilus oryzae. Front Chem 2022; 10:964700. [PMID: 36212071 PMCID: PMC9538504 DOI: 10.3389/fchem.2022.964700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/18/2022] [Indexed: 12/02/2022] Open
Abstract
The rice weevil, Sitophilus oryzae (L.), is a major pest of stored grains throughout the world, which causes quantitative and qualitative losses of food commodities. Eucalyptus essential oils (EOs) possess insecticidal and repellent properties, which make them a potential option for insect control in stored grains with environmentally friendly properties. In the current study, the binding mechanism of tyramine (TA) as a control compound has been investigated by funnel metadynamics (FM) simulation toward the homology model of tyramine1 receptor (TyrR) to explore its binding mode and key residues involved in the binding mechanism. EO compounds have been extracted from the leaf and flower part of Eucalyptus camaldulensis and characterized by GC/MS, and their effectiveness has been evaluated by molecular docking and conventional molecular dynamic (CMD) simulation toward the TyrR model. The FM results suggested that Asp114 followed by Asp80, Asn91, and Asn427 are crucial residues in the binding and the functioning of TA toward TyrR in Sitophilus Oryzae. The GC/MS analysis confirmed a total of 54 and 31 constituents in leaf and flower, respectively, where most of the components (29) are common in both groups. This analysis also revealed the significant concentration of Eucalyptus and α-pinene in leaves and flower EOs. The docking followed by CMD was performed to find the most effective compound in Eucalyptus EOs. In this regard, butanoic acid, 3-methyl-, 3-methyl butyl ester (B12) and 2-Octen-1-ol, 3,7-dimethyl- (B23) from leaf and trans- β-Ocimene (G04) from flower showed the maximum dock score and binding free energy, making them the leading candidates to replace tyramine in TyrR. The MM-PB/GBSA and MD analysis proved that the B12 structure is the most effective compound in inhibition of TyrR.
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Affiliation(s)
- Farshid Zargari
- Pharmacology Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
- Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan (USB), Zahedan, Iran
| | - Zahra Nikfarjam
- Department of Physical & Computational Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
- *Correspondence: Zahra Nikfarjam,
| | - Ebrahim Nakhaei
- Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan (USB), Zahedan, Iran
| | - Masoumeh Ghorbanipour
- Department of Physical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
| | - Alireza Nowroozi
- Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan (USB), Zahedan, Iran
| | - Azam Amiri
- College of Geography and Environmental Planning, University of Sistan and Baluchestan, Zahedan, Iran
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42
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Mycobacterium Time-Series Genome Analysis Identifies AAC2′ as a Potential Drug Target with Naloxone Showing Potential Bait Drug Synergism. Molecules 2022; 27:molecules27196150. [PMID: 36234683 PMCID: PMC9571707 DOI: 10.3390/molecules27196150] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/15/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
The World Health Organization has put drug resistance in tuberculosis on its list of significant threats, with a critical emphasis on resolving the genetic differences in Mycobacterium tuberculosis. This provides an opportunity for a better understanding of the evolutionary progression leading to anti-microbial resistance. Anti-microbial resistance has a great impact on the economic stability of the global healthcare sector. We performed a timeline genomic analysis from 2003 to 2021 of 578 mycobacterium genomes to understand the pattern underlying genomic variations. Potential drug targets based on functional annotation was subjected to pharmacophore-based screening of FDA-approved phyto-actives. Reaction search, MD simulations, and metadynamics studies were performed. A total of 4,76,063 mutations with a transition/transversion ratio of 0.448 was observed. The top 10 proteins with the least number of mutations were high-confidence drug targets. Aminoglycoside 2′-N-acetyltransferase protein (AAC2′), conferring resistance to aminoglycosides, was shortlisted as a potential drug target based on its function and role in bait drug synergism. Gentamicin-AAC2′ binding pose was used as a pharmacophore template to screen 10,570 phyto-actives. A total of 66 potential hits were docked to obtain naloxone as a lead—active with a docking score of −6.317. Naloxone is an FDA-approved drug that rapidly reverses opioid overdose. This is a classic case of a repurposed phyto-active. Naloxone consists of an amine group, but the addition of the acetyl group is unfavorable, with a reaction energy of 612.248 kcal/mol. With gentamicin as a positive control, molecular dynamic simulation studies were performed for 200 ns to check the stability of binding. Metadynamics-based studies were carried out to compare unbinding energy with gentamicin. The unbinding energies were found to be −68 and −74 kcal/mol for naloxone and gentamycin, respectively. This study identifies naloxone as a potential drug candidate for a bait drug synergistic approach against Mycobacterium tuberculosis.
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Di Lorenzo G, Iavarone F, Maddaluno M, Plata-Gómez AB, Aureli S, Quezada Meza CP, Cinque L, Palma A, Reggio A, Cirillo C, Sacco F, Stolz A, Napolitano G, Marin O, Pinna LA, Ruzzene M, Limongelli V, Efeyan A, Grumati P, Settembre C. Phosphorylation of FAM134C by CK2 controls starvation-induced ER-phagy. SCIENCE ADVANCES 2022; 8:eabo1215. [PMID: 36044577 PMCID: PMC9432840 DOI: 10.1126/sciadv.abo1215] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 07/20/2022] [Indexed: 05/28/2023]
Abstract
Selective degradation of the endoplasmic reticulum (ER) via autophagy (ER-phagy) is initiated by ER-phagy receptors, which facilitate the incorporation of ER fragments into autophagosomes. FAM134 reticulon family proteins (FAM134A, FAM134B, and FAM134C) are ER-phagy receptors with structural similarities and nonredundant functions. Whether they respond differentially to the stimulation of ER-phagy is unknown. Here, we describe an activation mechanism unique to FAM134C during starvation. In fed conditions, FAM134C is phosphorylated by casein kinase 2 (CK2) at critical residues flanking the LIR domain. Phosphorylation of these residues negatively affects binding affinity to the autophagy proteins LC3. During starvation, mTORC1 inhibition limits FAM134C phosphorylation by CK2, hence promoting receptor activation and ER-phagy. Using a novel tool to study ER-phagy in vivo and FAM134C knockout mice, we demonstrated the physiological relevance of FAM134C phosphorylation during starvation-induced ER-phagy in liver lipid metabolism. These data provide a mechanistic insight into ER-phagy regulation and an example of autophagy selectivity during starvation.
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Affiliation(s)
| | | | | | - Ana Belén Plata-Gómez
- Metabolism and Cell Signaling Laboratory, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Simone Aureli
- Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Euler Institute, Lugano, Switzerland
| | | | - Laura Cinque
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Alessandro Palma
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
| | - Alessio Reggio
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
| | - Carmine Cirillo
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - Alexandra Stolz
- Institute of Biochemistry II, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany
- Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University, Frankfurt am Main, Germany
| | - Gennaro Napolitano
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
- Department of Translational Medicine, Federico II University, Naples, Italy
| | - Oriano Marin
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Lorenzo A. Pinna
- Department of Biomedical Sciences, University of Padova, Padova, Italy
- CNR Neuroscience Institute, Padova, Italy
| | - Maria Ruzzene
- Department of Biomedical Sciences, University of Padova, Padova, Italy
- CNR Neuroscience Institute, Padova, Italy
| | - Vittorio Limongelli
- Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Euler Institute, Lugano, Switzerland
- Department of Pharmacy, Federico II University, Naples, Italy
| | - Alejo Efeyan
- Metabolism and Cell Signaling Laboratory, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Paolo Grumati
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Carmine Settembre
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
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Shah M, Bibi S, Kamal Z, Al-Sabahi JN, Alam T, Ullah O, Murad W, Rehman NU, Al-Harrasi A. Bridging the Chemical Profile and Biomedical Effects of Scutellaria edelbergii Essential Oils. Antioxidants (Basel) 2022; 11:antiox11091723. [PMID: 36139797 PMCID: PMC9496006 DOI: 10.3390/antiox11091723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/21/2022] [Accepted: 08/24/2022] [Indexed: 11/30/2022] Open
Abstract
The present study explored chemical constituents of Scutellaria edelbergii essential oils (SEEO) for the first time, extracted through hydro-distillation, and screened them against the microbes and free radicals scavenging effect, pain-relieving, and anti-inflammatory potential employing standard techniques. The SEEO ingredients were noticed via Gas Chromatography-Mass-Spectrometry (GC-MS) analysis and presented fifty-two bioactive compounds contributed (89.52%) with dominant volatile constituent; 3-oxomanoyl oxide (10.09%), 24-norursa-3,12-diene (8.05%), and methyl 7-abieten-18-oate (7.02%). The MTT assay via 96 well-plate and agar-well diffusion techniques against various microbes was determined for minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), IC50, and zone of inhibitions (ZOIs). The SEEO indicated considerable antimicrobial significance against tested bacterial strains viz. Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Enterococcus faecalis and the fungal strains Fusarium oxysporum and Candida albicans. The free radicals scavenging potential was noticed to be significant in 1,1-Diphenyl-2-picryl-hydrazyl (DPPH) as compared to 2,2′-azino-bis-3-ethylbenzotiazolin-6-sulfonic acid (ABTS) assays with IC50 = 125.0 ± 0.19 µg/mL and IC50 = 153.0 ± 0.31 µg/mL correspondingly; similarly, the antioxidant standard in the DPPH assay was found efficient as compared to ABTS assay. The SEEO also offered an appreciable analgesic significance and presented 54.71% in comparison with standard aspirin, 64.49% reduction in writhes, and an anti-inflammatory potential of 64.13%, as compared to the standard diclofenac sodium inhibition of 71.72%. The SEEO contain bioactive volatile ingredients with antimicrobial, free radical scavenging, pain, and inflammation relieving potentials. Computational analysis validated the anti-inflammatory potential of selected hit “methyl 7-abieten-18-oate” as a COX-2 enzyme inhibitor. Docking results were very good in terms of docked score (−7.8704 kcal/mol) and binding interactions with the functional residues; furthermore, MD simulation for 100 ns has presented a correlation with docking results with minor fluctuations. In silico, ADMET characteristics supported that methyl 7-abieten-18-oate could be recommended for further investigations in clinical tests and could prove its medicinal status as an anti-inflammatory drug.
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Affiliation(s)
- Muddaser Shah
- Department of Botany, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mauz, P.O. Box 33, Nizwa 616, Oman
| | - Shabana Bibi
- Department of Biosciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan
- Yunnan Herbal Laboratory, College of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China
| | - Zul Kamal
- Department of Pharmacy, Shaheed Benazir Bhutto University, Upper Dir 18000, Pakistan
| | - Jamal Nasser Al-Sabahi
- Central Instrument Laboratory, College of Agriculture and Marine Sciences, Sultan Qaboos University, Muscat 123, Oman
| | - Tanveer Alam
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mauz, P.O. Box 33, Nizwa 616, Oman
| | - Obaid Ullah
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mauz, P.O. Box 33, Nizwa 616, Oman
- Department of Chemistry, University of Chakdara, Chakdara 18800, Pakistan
| | - Waheed Murad
- Department of Botany, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan
- Correspondence: (W.M.); (N.U.R.); (A.A.-H.)
| | - Najeeb Ur Rehman
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mauz, P.O. Box 33, Nizwa 616, Oman
- Correspondence: (W.M.); (N.U.R.); (A.A.-H.)
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mauz, P.O. Box 33, Nizwa 616, Oman
- Correspondence: (W.M.); (N.U.R.); (A.A.-H.)
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Structural and dynamic mechanisms of GABA A receptor modulators with opposing activities. Nat Commun 2022; 13:4582. [PMID: 35933426 PMCID: PMC9357065 DOI: 10.1038/s41467-022-32212-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/21/2022] [Indexed: 12/02/2022] Open
Abstract
γ-Aminobutyric acid type A (GABAA) receptors are pentameric ligand-gated ion channels abundant in the central nervous system and are prolific drug targets for treating anxiety, sleep disorders and epilepsy. Diverse small molecules exert a spectrum of effects on γ-aminobutyric acid type A (GABAA) receptors by acting at the classical benzodiazepine site. They can potentiate the response to GABA, attenuate channel activity, or counteract modulation by other ligands. Structural mechanisms underlying the actions of these drugs are not fully understood. Here we present two high-resolution structures of GABAA receptors in complex with zolpidem, a positive allosteric modulator and heavily prescribed hypnotic, and DMCM, a negative allosteric modulator with convulsant and anxiogenic properties. These two drugs share the extracellular benzodiazepine site at the α/γ subunit interface and two transmembrane sites at β/α interfaces. Structural analyses reveal a basis for the subtype selectivity of zolpidem that underlies its clinical success. Molecular dynamics simulations provide insight into how DMCM switches from a negative to a positive modulator as a function of binding site occupancy. Together, these findings expand our understanding of how GABAA receptor allosteric modulators acting through a common site can have diverging activities. GABAA receptors are important targets for anxiety, sedation and anesthesia. Here, the authors present structures bound by zolpidem (Ambien), the most prescribed hypnotic in the US, and DMCM, a negative modulator, providing insights into receptor modulation.
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Votapka LW, Stokely AM, Ojha AA, Amaro RE. SEEKR2: Versatile Multiscale Milestoning Utilizing the OpenMM Molecular Dynamics Engine. J Chem Inf Model 2022; 62:3253-3262. [PMID: 35759413 PMCID: PMC9277580 DOI: 10.1021/acs.jcim.2c00501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
We present SEEKR2
(simulation-enabled estimation of kinetic rates
version 2)—the latest iteration in the family of SEEKR programs
for using multiscale simulation methods to computationally estimate
the kinetics and thermodynamics of molecular processes, in particular,
ligand-receptor binding. SEEKR2 generates equivalent, or improved,
results compared to the earlier versions of SEEKR but with significant
increases in speed and capabilities. SEEKR2 has also been built with
greater ease of usability and with extensible features to enable future
expansions of the method. Now, in addition to supporting simulations
using NAMD, calculations may be run with the fast and extensible OpenMM
simulation engine. The Brownian dynamics portion of the calculation
has also been upgraded to Browndye 2. Furthermore, this version of
SEEKR supports hydrogen mass repartitioning, which significantly reduces
computational cost, while showing little, if any, loss of accuracy
in the predicted kinetics.
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Affiliation(s)
- Lane W Votapka
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
| | - Andrew M Stokely
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
| | - Anupam A Ojha
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
| | - Rommie E Amaro
- University of California, San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
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47
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Schauperl M, Denny RA. AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges. J Chem Inf Model 2022; 62:3142-3156. [PMID: 35727311 DOI: 10.1021/acs.jcim.2c00026] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.
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Affiliation(s)
- Michael Schauperl
- Department of Computational Sciences HotSpot Therapeutics 50 Milk Street, Boston, Massachusetts 02110, United States
| | - Rajiah Aldrin Denny
- Department of Computational Sciences HotSpot Therapeutics 50 Milk Street, Boston, Massachusetts 02110, United States
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Bobrovs R, Basens EE, Drunka L, Kanepe I, Matisone S, Velins KK, Andrianov V, Leitis G, Zelencova-Gopejenko D, Rasina D, Jirgensons A, Jaudzems K. Exploring Aspartic Protease Inhibitor Binding to Design-Selective Antimalarials. J Chem Inf Model 2022; 62:3263-3273. [PMID: 35712895 DOI: 10.1021/acs.jcim.2c00422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Selectivity is a major issue in the development of drugs targeting pathogen aspartic proteases. Here, we explore the selectivity-determining factors by studying specifically designed malaria aspartic protease (plasmepsin) open-flap inhibitors. Metadynamics simulations are used to uncover the complex binding/unbinding pathways of these inhibitors and describe the critical transition states in atomistic resolution. The simulation results are compared with experimentally determined enzymatic activities. Our findings demonstrate that plasmepsin inhibitor selectivity can be achieved by targeting the flap loop with hydrophobic substituents that enable ligand binding under the flap loop, as such a behavior is not observed for several other aspartic proteases. The ability to estimate the selectivity of compounds before they are synthesized is of considerable importance in drug design; therefore, we expect that our approach will be useful in selective inhibitor designs against not only aspartic proteases but also other enzyme classes.
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Affiliation(s)
- Raitis Bobrovs
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | | | - Laura Drunka
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Iveta Kanepe
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Sofija Matisone
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | | | - Victor Andrianov
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Gundars Leitis
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | | | - Dace Rasina
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Aigars Jirgensons
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Kristaps Jaudzems
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
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49
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Molecular mechanism of allosteric modulation for the cannabinoid receptor CB1. Nat Chem Biol 2022; 18:831-840. [DOI: 10.1038/s41589-022-01038-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
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50
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Wang Z, Pan H, Sun H, Kang Y, Liu H, Cao D, Hou T. fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation. Brief Bioinform 2022; 23:6587180. [PMID: 35580866 DOI: 10.1093/bib/bbac201] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/25/2022] [Accepted: 04/28/2022] [Indexed: 01/12/2023] Open
Abstract
Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.
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Affiliation(s)
- Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hong Pan
- Day Surgery Center, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao, SAR, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058, China
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