1
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Lopez-Balastegui M, Stepniewski TM, Kogut-Günthel MM, Di Pizio A, Rosenkilde MM, Mao J, Selent J. Relevance of G protein-coupled receptor (GPCR) dynamics for receptor activation, signalling bias and allosteric modulation. Br J Pharmacol 2024. [PMID: 38978399 DOI: 10.1111/bph.16495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 07/10/2024] Open
Abstract
G protein-coupled receptors (GPCRs) are one of the major drug targets. In recent years, computational drug design for GPCRs has mainly focused on static structures obtained through X-ray crystallography, cryogenic electron microscopy (cryo-EM) or in silico modelling as a starting point for virtual screening campaigns. However, GPCRs are highly flexible entities with the ability to adopt different conformational states that elicit different physiological responses. Including this knowledge in the drug discovery pipeline can help to tailor novel conformation-specific drugs with an improved therapeutic profile. In this review, we outline our current knowledge about GPCR dynamics that is relevant for receptor activation, signalling bias and allosteric modulation. Ultimately, we highlight new technological implementations such as time-resolved X-ray crystallography and cryo-EM as well as computational algorithms that can contribute to a more comprehensive understanding of receptor dynamics and its relevance for GPCR functionality.
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Affiliation(s)
- Marta Lopez-Balastegui
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute & Pompeu Fabra University, Barcelona, Spain
| | - Tomasz Maciej Stepniewski
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute & Pompeu Fabra University, Barcelona, Spain
- InterAx Biotech AG, Villigen, Switzerland
| | | | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
- Chair for Chemoinformatics and Protein Modelling, Department of Molecular Life Science, School of Science, Technical University of Munich, Freising, Germany
| | - Mette Marie Rosenkilde
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences University of Copenhagen, København N, Denmark
| | - Jiafei Mao
- Huairou Research Center, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute & Pompeu Fabra University, Barcelona, Spain
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2
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Nguyen ATN, Nguyen DTN, Koh HY, Toskov J, MacLean W, Xu A, Zhang D, Webb GI, May LT, Halls ML. The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery. Br J Pharmacol 2024; 181:2371-2384. [PMID: 37161878 DOI: 10.1111/bph.16140] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/14/2023] [Accepted: 04/27/2023] [Indexed: 05/11/2023] Open
Abstract
The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.
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Affiliation(s)
- Anh T N Nguyen
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Diep T N Nguyen
- Department of Information Technology, Faculty of Engineering and Technology, Vietnam National University, Cau Giay, Hanoi, Vietnam
| | - Huan Yee Koh
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Jason Toskov
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - William MacLean
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - Andrew Xu
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - Daokun Zhang
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Geoffrey I Webb
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Lauren T May
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Michelle L Halls
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
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3
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Oh M, Rosa M, Xie H, Khelashvili G. Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations. Biophys J 2024:S0006-3495(24)00431-4. [PMID: 38932456 DOI: 10.1016/j.bpj.2024.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/03/2024] [Accepted: 06/24/2024] [Indexed: 06/28/2024] Open
Abstract
Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine-learning algorithms, such as principal component analysis (PCA), support vector machine, and linear discriminant analysis (LDA) based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.
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Affiliation(s)
- Myongin Oh
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Margarida Rosa
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Hengyi Xie
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - George Khelashvili
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.
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4
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Bhattacharjee A, Kar S, Ojha PK. Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation. Int J Biol Macromol 2024; 269:131784. [PMID: 38697440 DOI: 10.1016/j.ijbiomac.2024.131784] [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: 01/24/2024] [Revised: 04/02/2024] [Accepted: 04/21/2024] [Indexed: 05/05/2024]
Abstract
GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies.
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Affiliation(s)
- Arnab Bhattacharjee
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry and Physics, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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5
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Hoang SH, Tveter KM, Mezhibovsky E, Roopchand DE. Proanthocyanidin B2 derived metabolites may be ligands for bile acid receptors S1PR2, PXR and CAR: an in silico approach. J Biomol Struct Dyn 2024; 42:4249-4262. [PMID: 37340688 PMCID: PMC10730774 DOI: 10.1080/07391102.2023.2224886] [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: 03/14/2023] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
Bile acids (BAs) act as signaling molecules via their interactions with various nuclear (FXR, VDR, PXR and CAR) and G-protein coupled (TGR5, M3R, S1PR2) BA receptors. Stimulation of these BA receptors influences several processes, including inflammatory responses and glucose and xenobiotic metabolism. BA profiles and BA receptor activity are deregulated in cardiometabolic diseases; however, dietary polyphenols were shown to alter BA profile and signaling in association with improved metabolic phenotypes. We previously reported that supplementing mice with a proanthocyanidin (PAC)-rich grape polyphenol (GP) extract attenuated symptoms of glucose intolerance in association with changes to BA profiles, BA receptor gene expression, and/or downstream markers of BA receptor activity. Exact mechanisms by which polyphenols modulate BA signaling are not known, but some hypotheses include modulation of the BA profile via changes to gut bacteria, or alteration of ligand-availability via BA sequestration. Herein, we used an in silico approach to investigate putative binding affinities of proanthocyanidin B2 (PACB2) and PACB2 metabolites to nuclear and G-protein coupled BA receptors. Molecular docking and dynamics simulations revealed that certain PACB2 metabolites had stable binding affinities to S1PR2, PXR and CAR, comparable to that of known natural and synthetic BA ligands. These findings suggest PACB2 metabolites may be novel ligands of S1PR2, CAR, and PXR receptors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Skyler H. Hoang
- Department of Food Science, New Jersey Institute for Food, Nutrition, and Health (Rutgers Center for Lipid Research and Center for Nutrition, Microbiome, and Health), Rutgers University, 61 Dudley Road, New Brunswick, New Jersey, 08901 USA
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, New Jersey, USA
| | - Kevin M. Tveter
- Department of Food Science, New Jersey Institute for Food, Nutrition, and Health (Rutgers Center for Lipid Research and Center for Nutrition, Microbiome, and Health), Rutgers University, 61 Dudley Road, New Brunswick, New Jersey, 08901 USA
| | - Esther Mezhibovsky
- Department of Food Science, New Jersey Institute for Food, Nutrition, and Health (Rutgers Center for Lipid Research and Center for Nutrition, Microbiome, and Health), Rutgers University, 61 Dudley Road, New Brunswick, New Jersey, 08901 USA
- Department of Nutritional Sciences, Rutgers University, New Brunswick, New Jersey, USA
| | - Diana E. Roopchand
- Department of Food Science, New Jersey Institute for Food, Nutrition, and Health (Rutgers Center for Lipid Research and Center for Nutrition, Microbiome, and Health), Rutgers University, 61 Dudley Road, New Brunswick, New Jersey, 08901 USA
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6
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López-Correa JM, König C, Vellido A. GPCR molecular dynamics forecasting using recurrent neural networks. Sci Rep 2023; 13:20995. [PMID: 38017062 PMCID: PMC10684758 DOI: 10.1038/s41598-023-48346-4] [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: 04/13/2023] [Accepted: 11/25/2023] [Indexed: 11/30/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are a large superfamily of cell membrane proteins that play an important physiological role as transmitters of extracellular signals. Signal transmission through the cell membrane depends on conformational changes in the transmembrane region of the receptor, which makes the investigation of the dynamics in these regions particularly relevant. Molecular dynamics (MD) simulations provide a wealth of data about the structure, dynamics, and physiological function of biological macromolecules by modelling the interactions between their atomic constituents. In this study, a Recurrent and Convolutional Neural Network (RNN) model, namely Long Short-Term Memory (LSTM), is used to predict the dynamics of two GPCR states and three specific simulations of each one, through their activation path and focussing on specific receptor regions. Active and inactive states of the GPCRs are analysed in six scenarios involving APO, Full Agonist (BI 167107) and Partial Inverse Agonist (carazolol) of the receptor. Four Machine Learning models with increasing complexity in terms of neural network architecture are evaluated, and their results discussed. The best method achieves an overall RMSD lower than 0.139 Å and the transmembrane helices are the regions showing the minimum prediction errors and minimum relative movements of the protein.
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Affiliation(s)
| | - Caroline König
- Universitat Politècnica de Catalunya, Barcelona, Spain
- IDEAI-UPC - Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Alfredo Vellido
- Universitat Politècnica de Catalunya, Barcelona, Spain.
- IDEAI-UPC - Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain.
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7
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Mustali J, Yasuda I, Hirano Y, Yasuoka K, Gautieri A, Arai N. Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 M pro. RSC Adv 2023; 13:34249-34261. [PMID: 38019981 PMCID: PMC10663885 DOI: 10.1039/d3ra06375e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/06/2023] [Indexed: 12/01/2023] Open
Abstract
Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.
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Affiliation(s)
- Jessica Mustali
- Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy
| | - Ikki Yasuda
- Department of Mechanical Engineering, Keio University Japan
| | | | - Kenji Yasuoka
- Department of Mechanical Engineering, Keio University Japan
| | - Alfonso Gautieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy
| | - Noriyoshi Arai
- Department of Mechanical Engineering, Keio University Japan
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8
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Bergman S, Cater RJ, Plante A, Mancia F, Khelashvili G. Substrate binding-induced conformational transitions in the omega-3 fatty acid transporter MFSD2A. Nat Commun 2023; 14:3391. [PMID: 37296098 PMCID: PMC10250862 DOI: 10.1038/s41467-023-39088-y] [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: 12/28/2022] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Major Facilitator Superfamily Domain containing 2 A (MFSD2A) is a transporter that is highly enriched at the blood-brain and blood-retinal barriers, where it mediates Na+-dependent uptake of ω-3 fatty acids in the form of lysolipids into the brain and eyes, respectively. Despite recent structural insights, it remains unclear how this process is initiated, and driven by Na+. Here, we perform Molecular Dynamics simulations which demonstrate that substrates enter outward facing MFSD2A from the outer leaflet of the membrane via lateral openings between transmembrane helices 5/8 and 2/11. The substrate headgroup enters first and engages in Na+ -bridged interactions with a conserved glutamic acid, while the tail is surrounded by hydrophobic residues. This binding mode is consistent with a "trap-and-flip" mechanism and triggers transition to an occluded conformation. Furthermore, using machine learning analysis, we identify key elements that enable these transitions. These results advance our molecular understanding of the MFSD2A transport cycle.
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Affiliation(s)
- Shana Bergman
- Department of Physiology and Biophysics, Weill Cornell Medical College, Cornell University, New York, NY, 10065, USA
| | - Rosemary J Cater
- Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, 10032, USA
| | - Ambrose Plante
- Department of Physiology and Biophysics, Weill Cornell Medical College, Cornell University, New York, NY, 10065, USA
| | - Filippo Mancia
- Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, 10032, USA
| | - George Khelashvili
- Department of Physiology and Biophysics, Weill Cornell Medical College, Cornell University, New York, NY, 10065, USA.
- Institute for Computational Biomedicine, Weill Cornell Medical College, Cornell University, New York, NY, 10065, USA.
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9
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Gluten Exorphins Promote Cell Proliferation through the Activation of Mitogenic and Pro-Survival Pathways. Int J Mol Sci 2023; 24:ijms24043912. [PMID: 36835317 PMCID: PMC9966116 DOI: 10.3390/ijms24043912] [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: 01/04/2023] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Celiac disease (CD) is a chronic and systemic autoimmune disorder that affects preferentially the small intestine of individuals with a genetic predisposition. CD is promoted by the ingestion of gluten, a storage protein contained in the endosperm of the seeds of wheat, barley, rye, and related cereals. Once in the gastrointestinal (GI) tract, gluten is enzymatically digested with the consequent release of immunomodulatory and cytotoxic peptides, i.e., 33mer and p31-43. In the late 1970s a new group of biologically active peptides, called gluten exorphins (GEs), was discovered and characterized. In particular, these short peptides showed a morphine-like activity and high affinity for the δ-opioid receptor (DOR). The relevance of GEs in the pathogenesis of CD is still unknown. Recently, it has been proposed that GEs could contribute to asymptomatic CD, which is characterized by the absence of symptoms that are typical of this disorder. In the present work, GEs cellular and molecular effects were in vitro investigated in SUP-T1 and Caco-2 cells, also comparing viability effects with human normal primary lymphocytes. As a result, GEs treatments increased tumor cell proliferation by cell cycle and Cyclins activation as well as by induction of mitogenic and pro-survival pathways. Finally, a computational model of GEs interaction with DOR is provided. Altogether, the results might suggest a possible role of GEs in CD pathogenesis and on its associated cancer comorbidities.
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10
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Bai Q, Liu S, Tian Y, Xu T, Banegas‐Luna AJ, Pérez‐Sánchez H, Huang J, Liu H, Yao X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1581] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University Lanzhou Gansu China
| | - Shuo Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Yanan Tian
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Tingyang Xu
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Antonio Jesús Banegas‐Luna
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Horacio Pérez‐Sánchez
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Junzhou Huang
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Huanxiang Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering Lanzhou University Lanzhou Gansu China
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11
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Baltrukevich H, Podlewska S. From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output. Front Pharmacol 2022; 13:844293. [PMID: 35359865 PMCID: PMC8960308 DOI: 10.3389/fphar.2022.844293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/26/2022] [Indexed: 12/02/2022] Open
Abstract
An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.
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Affiliation(s)
- Hanna Baltrukevich
- Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
- Faculty of Pharmacy, Chair of Technology and Biotechnology of Medical Remedies, Jagiellonian University Medical College in Krakow, Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
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12
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Li C, Liu J, Chen J, Yuan Y, Yu J, Gou Q, Guo Y, Pu X. An Interpretable Convolutional Neural Network Framework for Analyzing Molecular Dynamics Trajectories: a Case Study on Functional States for G-Protein-Coupled Receptors. J Chem Inf Model 2022; 62:1399-1410. [PMID: 35257580 DOI: 10.1021/acs.jcim.2c00085] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular dynamics (MD) simulations have made great contribution to revealing structural and functional mechanisms for many biomolecular systems. However, how to identify functional states and important residues from vast conformation space generated by MD remains challenging; thus an intelligent navigation is highly desired. Despite intelligent advantages of deep learning exhibited in analyzing MD trajectory, its black-box nature limits its application. To address this problem, we explore an interpretable convolutional neural network (CNN)-based deep learning framework to automatically identify diverse active states from the MD trajectory for G-protein-coupled receptors (GPCRs), named the ICNNMD model. To avoid the information loss in representing the conformation structure, the pixel representation is introduced, and then the CNN module is constructed to efficiently extract features followed by a fully connected neural network to realize the classification task. More importantly, we design a local interpretable model-agnostic explanation interpreter for the classification result by local approximation with a linear model, through which important residues underlying distinct active states can be quickly identified. Our model showcases higher than 99% classification accuracy for three important GPCR systems with diverse active states. Notably, some important residues in regulating different biased activities are successfully identified, which are beneficial to elucidating diverse activation mechanisms for GPCRs. Our model can also serve as a general tool to analyze MD trajectory for other biomolecular systems. All source codes are freely available at https://github.com/Jane-Liu97/ICNNMD for aiding MD studies.
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Affiliation(s)
- Chuan Li
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Jiangting Liu
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Jianfang Chen
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yuan Yuan
- College of Management, Southwest University for Nationalities, Chengdu 610041, China
| | - Jin Yu
- Department of Physics and Astronomy, University of California, Irvine, California 92697, United States
| | - Qiaolin Gou
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
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13
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Do HN, Wang J, Bhattarai A, Miao Y. GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling. J Chem Theory Comput 2022; 18:1423-1436. [PMID: 35200019 PMCID: PMC9773012 DOI: 10.1021/acs.jctc.1c01055] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A1 receptor (A1AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A1AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.
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Affiliation(s)
- Hung N. Do
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047
| | - Jinan Wang
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047
| | - Apurba Bhattarai
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047
| | - Yinglong Miao
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047,Corresponding author:
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14
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Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, Maojo V, Pazos A, Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021; 19:4538-4558. [PMID: 34471498 PMCID: PMC8387781 DOI: 10.1016/j.csbj.2021.08.011] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 12/30/2022] Open
Abstract
Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
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Key Words
- ADMET, Absorption, distribution, metabolism, elimination and toxicity
- ADR, Adverse Drug Reaction
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APFP, Atom Pairs 2d FingerPrint
- AUC, Area under the Curve
- BBB, Blood–Brain barrier
- CDK, Chemical Development Kit
- CNN, Convolutional Neural Networks
- CNS, Central Nervous System
- CPI, Compound-protein interaction
- CV, Cross Validation
- Cheminformatics
- DL, Deep Learning
- DNA, Deoxyribonucleic acid
- Deep Learning
- Drug Discovery
- ECFP, Extended Connectivity Fingerprints
- FDA, Food and Drug Administration
- FNN, Fully Connected Neural Networks
- FP, Fringerprints
- FS, Feature Selection
- GCN, Graph Convolutional Networks
- GEO, Gene Expression Omnibus
- GNN, Graph Neural Networks
- GO, Gene Ontology
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- MACCS, Molecular ACCess System
- MCC, Matthews correlation coefficient
- MD, Molecular Descriptors
- MKL, Multiple Kernel Learning
- ML, Machine Learning
- Machine Learning
- Molecular Descriptors
- NB, Naive Bayes
- OOB, Out of Bag
- PCA, Principal Component Analyisis
- QSAR
- QSAR, Quantitative structure–activity relationship
- RF, Random Forest
- RNA, Ribonucleic Acid
- SMILES, simplified molecular-input line-entry system
- SVM, Support Vector Machines
- TCGA, The Cancer Genome Atlas
- WHO, World Health Organization
- t-SNE, t-Distributed Stochastic Neighbor Embedding
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Jose Liñares-Blanco
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
| | - Nereida Rodríguez-Fernández
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco Cedrón
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco J. Novoa
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Adrian Carballal
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Victor Maojo
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, Madrid 28660, Spain
| | - Alejandro Pazos
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
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15
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Plante A, Weinstein H. Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol. Molecules 2021; 26:molecules26103059. [PMID: 34065494 PMCID: PMC8161244 DOI: 10.3390/molecules26103059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 01/14/2023] Open
Abstract
Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery.
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Affiliation(s)
- Ambrose Plante
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA;
| | - Harel Weinstein
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA;
- Institute for Computational Biomedicine, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA
- Correspondence: ; Tel.: +1-212-746-6358
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16
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Zhu S, Wu M, Huang Z, An J. Trends in application of advancing computational approaches in GPCR ligand discovery. Exp Biol Med (Maywood) 2021; 246:1011-1024. [PMID: 33641446 PMCID: PMC8113737 DOI: 10.1177/1535370221993422] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.
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Affiliation(s)
- Siyu Zhu
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
- Ciechanover Institute of Precision and Regenerative Medicine, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen 518172, China
| | - Meixian Wu
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
| | - Ziwei Huang
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
- Ciechanover Institute of Precision and Regenerative Medicine, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jing An
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
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17
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The Cream of the Crop of the Medicinal Chemistry Section of Molecules-2019. Molecules 2021; 26:molecules26041053. [PMID: 33671435 PMCID: PMC7922884 DOI: 10.3390/molecules26041053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 02/08/2021] [Indexed: 11/29/2022] Open
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18
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Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1513] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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19
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Rosário-Ferreira N, Marques-Pereira C, Gouveia RP, Mourão J, Moreira IS. Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View. Methods Mol Biol 2021; 2315:3-28. [PMID: 34302667 DOI: 10.1007/978-1-0716-1468-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.
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Affiliation(s)
- Nícia Rosário-Ferreira
- Coimbra Chemistry Center, Department of Chemistry, University of Coimbra, Coimbra, Portugal.,Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Catarina Marques-Pereira
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal
| | - Raquel P Gouveia
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal.
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20
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Ferraro M, Moroni E, Ippoliti E, Rinaldi S, Sanchez-Martin C, Rasola A, Pavarino LF, Colombo G. Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1. J Phys Chem B 2020; 125:101-114. [PMID: 33369425 PMCID: PMC8016192 DOI: 10.1021/acs.jpcb.0c09742] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
![]()
Allosteric
molecules provide a powerful means to modulate protein
function. However, the effect of such ligands on distal orthosteric
sites cannot be easily described by classical docking methods. Here,
we applied machine learning (ML) approaches to expose the links between
local dynamic patterns and different degrees of allosteric inhibition
of the ATPase function in the molecular chaperone TRAP1. We focused
on 11 novel allosteric modulators with similar affinities to the target
but with inhibitory efficacy between the 26.3 and 76%. Using a set
of experimentally related local descriptors, ML enabled us to connect
the molecular dynamics (MD) accessible to ligand-bound (perturbed)
and unbound (unperturbed) systems to the degree of ATPase allosteric
inhibition. The ML analysis of the comparative perturbed ensembles
revealed a redistribution of dynamic states in the inhibitor-bound
versus inhibitor-free systems following allosteric binding. Linear
regression models were built to quantify the percentage of experimental
variance explained by the predicted inhibitor-bound TRAP1 states.
Our strategy provides a comparative MD–ML framework to infer
allosteric ligand functionality. Alleviating the time scale issues
which prevent the routine use of MD, a combination of MD and ML represents
a promising strategy to support in silico mechanistic
studies and drug design.
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Affiliation(s)
- Mariarosaria Ferraro
- Istituto di Scienze e Tecnologie Chimiche "Giulio Natta"- SCITEC, Via Mario Bianco 9, 20131 Milano, Italy
| | - Elisabetta Moroni
- Istituto di Scienze e Tecnologie Chimiche "Giulio Natta"- SCITEC, Via Mario Bianco 9, 20131 Milano, Italy
| | - Emiliano Ippoliti
- Institute for Advanced Simulation (IAS-5) and Institute of Neuroscience and Medicine (INM-9), Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany.,JARA-HPC, Forschungszentrum Jülich, D-54245 Jülich, Germany
| | - Silvia Rinaldi
- Istituto di Scienze e Tecnologie Chimiche "Giulio Natta"- SCITEC, Via Mario Bianco 9, 20131 Milano, Italy
| | - Carlos Sanchez-Martin
- Dipartimento di Scienze Biomediche, Università di Padova, viale G. Colombo 3, 35131 Padova, Italy
| | - Andrea Rasola
- Dipartimento di Scienze Biomediche, Università di Padova, viale G. Colombo 3, 35131 Padova, Italy
| | - Luca F Pavarino
- Dipartimento di Matematica "F. Casorati", Università di Pavia, Via Ferrata 5, 27100 Pavia Italy
| | - Giorgio Colombo
- Istituto di Scienze e Tecnologie Chimiche "Giulio Natta"- SCITEC, Via Mario Bianco 9, 20131 Milano, Italy.,Dipartimento di Chimica, Università di Pavia, via Taramelli 12, 27100 Pavia, Italy
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21
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Battiti FO, Newman AH, Bonifazi A. Exception That Proves the Rule: Investigation of Privileged Stereochemistry in Designing Dopamine D 3R Bitopic Agonists. ACS Med Chem Lett 2020; 11:1956-1964. [PMID: 33062179 DOI: 10.1021/acsmedchemlett.9b00660] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 02/28/2020] [Indexed: 01/11/2023] Open
Abstract
In this study, starting from our selective D3R agonist FOB02-04A (5), we investigated the chemical space around the linker portion of the molecule via insertion of a hydroxyl substituent and ring-expansion of the trans-cyclopropyl moiety into a trans-cyclohexyl scaffold. Moreover, to further elucidate the importance of the primary pharmacophore stereochemistry in the design of bitopic ligands, we investigated the chiral requirements of (+)-PD128907 ((+)-(4a R ,10b R )-2)) by synthesizing and resolving bitopic analogues in all the cis and trans combinations of its 9-methoxy-3,4,4a,10b-tetrahydro-2H,5H-chromeno[4,3-b][1,4] oxazine scaffold. Despite the lack of success in obtaining new analogues with improved biological profiles, in comparison to our current leads, a "negative" result due to a poor or simply not improved biological profile is fundamental toward better understanding chemical space and optimal stereochemistry for target recognition. Herein, we identified essential structural information to understand the differences between orthosteric and bitopic ligand-receptor binding interactions, discriminate D3R active and inactive states, and assist multitarget receptor recognition. Exploring stereochemical complexity and developing extended D3R SAR from this new library complements previously described SAR and inspires future structural and computational biology investigation. Moreover, the expansion of chemical space characterization for D3R agonism may be utilized in machine learning and artificial intelligence (AI)-based drug design, in the future.
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Affiliation(s)
- Francisco O. Battiti
- Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse—Intramural Research Program, National Institutes of Health, 333 Cassell Drive, Baltimore, Maryland 21224, United States
| | - Amy Hauck Newman
- Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse—Intramural Research Program, National Institutes of Health, 333 Cassell Drive, Baltimore, Maryland 21224, United States
| | - Alessandro Bonifazi
- Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse—Intramural Research Program, National Institutes of Health, 333 Cassell Drive, Baltimore, Maryland 21224, United States
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22
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IMPROvER: the Integral Membrane Protein Stability Selector. Sci Rep 2020; 10:15165. [PMID: 32938971 PMCID: PMC7495477 DOI: 10.1038/s41598-020-71744-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 08/04/2020] [Indexed: 01/20/2023] Open
Abstract
Identifying stabilising variants of membrane protein targets is often required for structure determination. Our new computational pipeline, the Integral Membrane Protein Stability Selector (IMPROvER) provides a rational approach to variant selection by employing three independent approaches: deep-sequence, model-based and data-driven. In silico tests using known stability data, and in vitro tests using three membrane protein targets with 7, 11 and 16 transmembrane helices provided measures of success. In vitro, individual approaches alone all identified stabilising variants at a rate better than expected by random selection. Low numbers of overlapping predictions between approaches meant a greater success rate was achieved (fourfold better than random) when approaches were combined and selections restricted to the highest ranked sites. The mix of information IMPROvER uses can be extracted for any helical membrane protein. We have developed the first general-purpose tool for selecting stabilising variants of \documentclass[12pt]{minimal}
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\begin{document}$$\upalpha$$\end{document}α-helical membrane proteins, increasing efficiency and reducing workload. IMPROvER can be accessed at http://improver.ddns.net/IMPROvER/.
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23
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Matsuzaka Y, Hosaka T, Ogaito A, Yoshinari K, Uesawa Y. Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning. Molecules 2020; 25:molecules25061317. [PMID: 32183141 PMCID: PMC7144728 DOI: 10.3390/molecules25061317] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/05/2020] [Accepted: 03/09/2020] [Indexed: 12/31/2022] Open
Abstract
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.
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Affiliation(s)
- Yasunari Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 204-8588 Tokyo, Japan;
| | - Takuomi Hosaka
- Laboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8529, Japan; (T.H.); (A.O.); (K.Y.)
| | - Anna Ogaito
- Laboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8529, Japan; (T.H.); (A.O.); (K.Y.)
| | - Kouichi Yoshinari
- Laboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8529, Japan; (T.H.); (A.O.); (K.Y.)
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 204-8588 Tokyo, Japan;
- Correspondence:
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24
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Shiref H, Bergman S, Clivio S, Sahai MA. The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations. Methods 2020; 185:3-14. [PMID: 32081744 PMCID: PMC10062712 DOI: 10.1016/j.ymeth.2020.02.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 02/14/2020] [Accepted: 02/14/2020] [Indexed: 10/25/2022] Open
Abstract
Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.
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Affiliation(s)
- Hana Shiref
- Department of Life Sciences, University of Roehampton, London SW15 4JD, UK
| | - Shana Bergman
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University (WCMC), New York, NY 10065, USA
| | | | - Michelle A Sahai
- Department of Life Sciences, University of Roehampton, London SW15 4JD, UK.
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25
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Babbitt GA, Fokoue EP, Evans JR, Diller KI, Adams LE. DROIDS 3.0-Detecting Genetic and Drug Class Variant Impact on Conserved Protein Binding Dynamics. Biophys J 2019; 118:541-551. [PMID: 31928763 PMCID: PMC7002913 DOI: 10.1016/j.bpj.2019.12.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/09/2019] [Accepted: 12/10/2019] [Indexed: 01/07/2023] Open
Abstract
The application of statistical methods to comparatively framed questions about the molecular dynamics (MD) of proteins can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, the chaotic behavior in single MD trajectories requires statistical inference that is derived from large ensembles of simulations representing the comparative functional states of a protein under investigation. Meaningful interpretation of such complex forms of big data poses serious challenges to users of MD. Here, we announce Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics that includes maxDemon 1.0, a multimethod machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS and deploys learned classifications of these states onto newly generated MD simulations. Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. The subsequent impacts of genetic and/or drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD simulations and quantifying how often these altered protein systems display opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interactions with nucleic acids and small molecules. We demonstrate that our machine learning algorithm can properly identify regions of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP). We quantify the impact of genetic variation in TBP and drug class variation targeting the ATP-binding region of Hsp90 on conserved dynamics. We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. We also demonstrate that dynamic impacts of various Hsp90 inhibitors rank accordingly with how closely they mimic natural ATP binding.
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Affiliation(s)
- Gregory A Babbitt
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York.
| | - Ernest P Fokoue
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York
| | - Joshua R Evans
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Kyle I Diller
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York; Golisano College for Computing and Information Science, Rochester, New York
| | - Lily E Adams
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
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26
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Advancing Drug Discovery via Artificial Intelligence. Trends Pharmacol Sci 2019; 40:592-604. [DOI: 10.1016/j.tips.2019.06.004] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 05/23/2019] [Accepted: 06/11/2019] [Indexed: 01/15/2023]
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27
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Mukherjee K. 40 Years of Trends in Pharmacological Sciences: Blending Man and Machine. Trends Pharmacol Sci 2019; 40:541-542. [PMID: 31303346 DOI: 10.1016/j.tips.2019.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Kusumika Mukherjee
- Trends in Pharmacological Sciences, Cell Press, 50 Hampshire Street, Cambridge, MA 02139, USA.
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Artificial Intelligence: A Novel Approach for Drug Discovery. Trends Pharmacol Sci 2019; 40:550-551. [PMID: 31279568 DOI: 10.1016/j.tips.2019.06.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 06/20/2019] [Indexed: 01/08/2023]
Abstract
Molecular dynamics (MD) simulations can mechanistically explain receptor function. However, the enormous data sets that they may imply can be a hurdle. Plante and colleagues (Molecules, 2019) recently described a machine learning approach to the analysis of MD simulations. The approach successfully classified ligands and identified functional receptor motifs and thus it seems promising for mechanism-based drug discovery.
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