1
|
Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024; 29:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [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: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
Collapse
Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| |
Collapse
|
2
|
Venanzi NE, Basciu A, Vargiu AV, Kiparissides A, Dalby PA, Dikicioglu D. Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants. J Chem Inf Model 2024; 64:2681-2694. [PMID: 38386417 PMCID: PMC11005043 DOI: 10.1021/acs.jcim.3c00999] [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: 07/03/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 02/24/2024]
Abstract
Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.
Collapse
Affiliation(s)
| | - Andrea Basciu
- Department
of Physics, University of Cagliari, Cittadella
Universitaria, I-09042 Monserrato, Cagliari, Italy
| | - Attilio Vittorio Vargiu
- Department
of Physics, University of Cagliari, Cittadella
Universitaria, I-09042 Monserrato, Cagliari, Italy
| | - Alexandros Kiparissides
- Department
of Biochemical Engineering, University College
London, Gower Street, WC1E 6BT London, U.K.
- Department
of Chemical Engineering, Aristotle University
of Thessaloniki, 54 124 Thessaloniki, Greece
| | - Paul A. Dalby
- Department
of Biochemical Engineering, University College
London, Gower Street, WC1E 6BT London, U.K.
| | - Duygu Dikicioglu
- Department
of Biochemical Engineering, University College
London, Gower Street, WC1E 6BT London, U.K.
| |
Collapse
|
3
|
Azevedo PHRDA, Peçanha BRDB, Flores-Junior LAP, Alves TF, Dias LRS, Muri EMF, Lima CHDS. In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor. J Biomol Struct Dyn 2024; 42:1417-1428. [PMID: 37054524 DOI: 10.1080/07391102.2023.2199868] [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: 10/20/2022] [Accepted: 04/01/2023] [Indexed: 04/15/2023]
Abstract
O-linked N-acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.
Collapse
Affiliation(s)
| | | | | | - Tatiana Fialho Alves
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Luiza Rosaria Sousa Dias
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Estela Maris Freitas Muri
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | | |
Collapse
|
4
|
Samy MVG, Perumal S. Systems pharmacology and multi-scale mechanism of Enicostema axillare bioactives in treating Alzheimer disease. Inflammopharmacology 2024; 32:575-593. [PMID: 37845599 DOI: 10.1007/s10787-023-01348-0] [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: 08/15/2023] [Accepted: 09/19/2023] [Indexed: 10/18/2023]
Abstract
As a progressive neurological disease with increased morbidity and mortality, Alzheimer Disease (AD) is characterized by neuron damage that controls memory and mental functions. Enicostema axillare (EA), an herb with a history of combativeness and effectiveness in treating Rheumatoid Arthritis, Cancer, and Diabetes, is used in Indian folk medicine from a holistic point of view. Though the herb is used for many illnesses, the molecular mechanism of its bioactive on AD has not been deciphered by intricate research. A unique pharmacology approach based on ADME drug screening and targeting, pathway enrichment (GO and KEGG), and network pharmacology, was established to explore the molecular mechanisms of E. axillare (EA) bioactive compounds for the treatment of AD. In brief, we bring to light the three active compounds of EA and seven potential molecular targets of AD, which are mainly implicated in four signaling pathways, i.e., MAPK, Apoptosis, neurodegeneration, and the TNF pathway. Moreover, the network analysis of the active compounds, molecular targets, and their pathways reveals the pharmacological nature of the compounds. Further, molecular docking studies were carried out to explore the interactions between the EA bioactive compounds and the targets and examine the binding affinity. The outcome of the work reflects the potential therapeutic effects of the compounds for treating AD through the modulation of the key proteins, which further corroborates the reliability of our network pharmacology analysis. This study not only helps in understanding the molecular mechanism of the drugs but also helps in finding and sorting new drugs for the treatment of AD, and other complex diseases through modern medicine.
Collapse
Affiliation(s)
| | - Sasidharan Perumal
- Cell and Molecular Biology Division, Biome Live Analytical Center, Karaikudi, Tamil Nadu, India.
| |
Collapse
|
5
|
Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [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: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
Collapse
Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Plau J, Morgan CE, Fedorov Y, Banerjee S, Adams DJ, Blaner WS, Yu EW, Golczak M. Discovery of Nonretinoid Inhibitors of CRBP1: Structural and Dynamic Insights for Ligand-Binding Mechanisms. ACS Chem Biol 2023; 18:2309-2323. [PMID: 37713257 PMCID: PMC10591915 DOI: 10.1021/acschembio.3c00402] [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: 07/11/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023]
Abstract
The dysregulation of retinoid metabolism has been linked to prevalent ocular diseases including age-related macular degeneration and Stargardt disease. Modulating retinoid metabolism through pharmacological approaches holds promise for the treatment of these eye diseases. Cellular retinol-binding protein 1 (CRBP1) is the primary transporter of all-trans-retinol (atROL) in the eye, and its inhibition has recently been shown to protect mouse retinas from light-induced retinal damage. In this report, we employed high-throughput screening to identify new chemical scaffolds for competitive, nonretinoid inhibitors of CRBP1. To understand the mechanisms of interaction between CRBP1 and these inhibitors, we solved high-resolution X-ray crystal structures of the protein in complex with six selected compounds. By combining protein crystallography with hydrogen/deuterium exchange mass spectrometry, we quantified the conformational changes in CRBP1 caused by different inhibitors and correlated their magnitude with apparent binding affinities. Furthermore, using molecular dynamic simulations, we provided evidence for the functional significance of the "closed" conformation of CRBP1 in retaining ligands within the binding pocket. Collectively, our study outlines the molecular foundations for understanding the mechanism of high-affinity interactions between small molecules and CRBPs, offering a framework for the rational design of improved inhibitors for this class of lipid-binding proteins.
Collapse
Affiliation(s)
- Jacqueline Plau
- Department
of Pharmacology, Small Molecule Drug Development Core Facility, Department of Genetics, and Cleveland Center
for Membrane and Structural Biology, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States
| | - Christopher E. Morgan
- Department
of Pharmacology, Small Molecule Drug Development Core Facility, Department of Genetics, and Cleveland Center
for Membrane and Structural Biology, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States
- Department
of Chemistry, Thiel College, Greenville, Pennsylvania 16125, United States
| | - Yuriy Fedorov
- Department
of Pharmacology, Small Molecule Drug Development Core Facility, Department of Genetics, and Cleveland Center
for Membrane and Structural Biology, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States
| | - Surajit Banerjee
- Department
of Chemistry and Chemical Biology, Cornell
University, Ithaca, New York 14850, United States
- Northeastern
Collaborative Access Team, Argonne National
Laboratory, Argonne, Illinois 60439, United States
| | - Drew J. Adams
- Department
of Pharmacology, Small Molecule Drug Development Core Facility, Department of Genetics, and Cleveland Center
for Membrane and Structural Biology, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States
| | - William S. Blaner
- Department
of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York 10032, United States
| | - Edward W. Yu
- Department
of Pharmacology, Small Molecule Drug Development Core Facility, Department of Genetics, and Cleveland Center
for Membrane and Structural Biology, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States
| | - Marcin Golczak
- Department
of Pharmacology, Small Molecule Drug Development Core Facility, Department of Genetics, and Cleveland Center
for Membrane and Structural Biology, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States
| |
Collapse
|
8
|
Ramasamy M, Vetrivel A, Venugopal S, Murugesan R. Identification of inhibitors for Agr quorum sensing system of Staphylococcus aureus by machine learning, pharmacophore modeling, and molecular dynamics approaches. J Mol Model 2023; 29:258. [PMID: 37468720 DOI: 10.1007/s00894-023-05647-9] [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: 03/07/2023] [Accepted: 07/03/2023] [Indexed: 07/21/2023]
Abstract
CONTEXT Staphylococcus aureus is a highly pathogenic organism that is the most common cause of postoperative complications as well as severe infections like bacteremia and infective endocarditis. By mediating the formation of biofilms and the expression of virulent genes, the quorum sensing (QS) mechanism is a major contributor to the development of these diseases. By hindering its QS network, an innovative approach to avoiding this bacterial infection is taken. Targeting the AgrA of the Agr system serves as beneficial in holding the top position in the QS system cascade. METHODS Using known AgrA inhibitors, the machine learning algorithms (artificial neural network, naïve Bayes, random forest, and support vector machine) and pharmacophore model were developed. The potential lead compounds were screened against the Zinc and COCONUT databases using the best pharmacophore hypothesis. The hits were then subjected second screening process using the best machine learning model. The predicted active compounds were then reranked based on the docking score. The stability of AgrA-lead compounds was studied using molecular dynamics approaches, and an ADME profile was also carried out. Five lead compounds, namely, CNP02386963,4,5-trihydroxy-2-[({7,13,14-trihydroxy-3,10-dioxo-2,9-dioxatetracyclo[6.6.2.04,16.011,15]hexadeca-1(14),4,6,8(16),11(15),12-hexaen-6-yl}oxy)methyl]benzoic acid, CNP0129274 4-(dimethylamino)-1,5,6,10,12,12a-hexahydroxy-6-methyl-3,11-dioxo-3,4,4a,5,5a,6,11,12a-octahydrotetracene-2-carboxamide, CNP0242717 3-Hydroxyasebotin, CNP0361624 3,4,5-trihydroxy-6-[(2,4,5,6,7-pentahydroxy-1-oxooctan-3-yl)oxy]oxane-2-carboxylic acid, and CNP0285058 2-{[4,5-dihydroxy-6-(hydroxymethyl)-3-[(3,4,5-trihydroxy-6-methyloxan-2-yl)oxy]oxan-2-yl]oxy}-2-(4-hydroxyphenyl)acetonitrile were obtained using the two-step virtual screening process. The molecular dynamics study revealed that the CNP0238696 was found to be stable in the binding pocket of AgrA. ADME profiles show that this compound has two Lipinski violations and low bioavailability. Further studies should be performed to assess the anti-biofilm activity of the lead compound in vitro.
Collapse
Affiliation(s)
- Monica Ramasamy
- Department of Biochemistry, Biotechnology, and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Aishwarya Vetrivel
- Department of Biochemistry, Biotechnology, and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Sharulatha Venugopal
- Department of Chemistry, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Rajeswari Murugesan
- Department of Biochemistry, Biotechnology, and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India.
| |
Collapse
|
9
|
Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
Collapse
Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| |
Collapse
|
10
|
Carmo Bastos ML, Silva-Silva JV, Neves Cruz J, Palheta da Silva AR, Bentaberry-Rosa AA, da Costa Ramos G, de Sousa Siqueira JE, Coelho-Ferreira MR, Percário S, Santana Barbosa Marinho P, Marinho AMDR, de Oliveira Bahia M, Dolabela MF. Alkaloid from Geissospermum sericeum Benth. & Hook.f. ex Miers (Apocynaceae) Induce Apoptosis by Caspase Pathway in Human Gastric Cancer Cells. Pharmaceuticals (Basel) 2023; 16:ph16050765. [PMID: 37242548 DOI: 10.3390/ph16050765] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 04/30/2023] [Accepted: 05/05/2023] [Indexed: 05/28/2023] Open
Abstract
Gastric cancer is among the major causes of death from neoplasia leading causes of death worldwide, with high incidence rates and problems related to its treatment. Here, we outline how Geissospermum sericeum exerts antitumor activity on the ACP02 cell line (human gastric adenocarcinoma) and the mechanism of cell death. The ethanol extract and fractions, neutral fraction and alkaloid fraction, were characterized by thin-layer chromatography and HPLC-DAD, yielding an alkaloid (geissoschizoline N4-methylchlorine) identified by NMR. The cytotoxicity activity of the samples (ethanol extract, neutral fraction, alkaloid fraction, and geissoschizoline N4-methylchlorine) in HepG2 and VERO cells was determined by MTT. The ACP02 cell line was used to assess the anticancer potential. Cell death was quantified with the fluorescent dyes Hoechst 33342, propidium iodide, and fluorescein diacetate. The geissoschizoline N4-methylchlorine was evaluated in silico against caspase 3 and 8. In the antitumor evaluation, there was observed a more significant inhibitory effect of the alkaloid fraction (IC50 18.29 µg/mL) and the geissoschizoline N4-methylchlorine (IC50 12.06 µg/mL). However, geissoschizoline N4-methylchlorine showed lower cytotoxicity in the VERO (CC50 476.0 µg/mL) and HepG2 (CC50 503.5 µg/mL) cell lines, with high selectivity against ACP02 cells (SI 39.47 and 41.75, respectively). The alkaloid fraction showed more significant apoptosis and necrosis in 24 h and 48 h, with increased necrosis in higher concentrations and increased exposure time. For the alkaloid, apoptosis and necrosis were concentration- and time-dependent, with a lower necrosis rate. Molecular modeling studies demonstrated that geissoschizoline N4-methylchlorine could occupy the active site of caspases 3 and 8 energetically favorably. The results showed that fractionation contributed to the activity with pronounced selectivity for ACP02 cells, and geissoschizoline N4-methylchlor is a promising candidate for caspase inhibitors of apoptosis in gastric cancer. Thus, this study provides a scientific basis for the biological functions of Geissospermum sericeum, as well as demonstrates the potential of the geissoschizoline N4-methylchlorine in the treatment of gastric cancer.
Collapse
Affiliation(s)
- Mirian Letícia Carmo Bastos
- Post-Graduate Program in Biodiversity and Biotechnology, Federal University of Pará, Belém 66075-110, PA, Brazil
- Post-Graduate Program in Pharmaceutical Sciences, Federal University of Pará, Belém 66075-110, PA, Brazil
| | - João Victor Silva-Silva
- Laboratory of Medicinal and Computational Chemistry, Institute of Physics of São Carlos, University of São Paulo, São Carlos 13563-120, SP, Brazil
| | - Jorddy Neves Cruz
- Post-Graduate Program in Pharmaceutical Sciences, Federal University of Pará, Belém 66075-110, PA, Brazil
| | | | | | - Gisele da Costa Ramos
- Post-Graduate Program in Chemistry, Federal University of Pará, Belém 66075-110, PA, Brazil
| | | | - Márlia Regina Coelho-Ferreira
- Emílio Goeldi Paraense Museum, Coordination of Botany, Ministry of Science, Technology, Innovation and Communications, Belém 66077-830, PA, Brazil
| | - Sandro Percário
- Post-Graduate Program in Biodiversity and Biotechnology, Federal University of Pará, Belém 66075-110, PA, Brazil
| | | | | | - Marcelo de Oliveira Bahia
- Laboratory of Human Cytogenetic, Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, PA, Brazil
| | - Maria Fâni Dolabela
- Post-Graduate Program in Biodiversity and Biotechnology, Federal University of Pará, Belém 66075-110, PA, Brazil
- Post-Graduate Program in Pharmaceutical Sciences, Federal University of Pará, Belém 66075-110, PA, Brazil
- Faculty of Pharmacy, Federal University of Pará, Belém 66075-110, PA, Brazil
- Post-Graduate Program in Pharmaceutical Innovation, Federal University of Pará, Belém 66075-110, PA, Brazil
| |
Collapse
|
11
|
Gu S, Shen C, Yu J, Zhao H, Liu H, Liu L, Sheng R, Xu L, Wang Z, Hou T, Kang Y. Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning? Brief Bioinform 2023; 24:6995375. [PMID: 36681903 DOI: 10.1093/bib/bbad008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 12/04/2022] [Accepted: 12/30/2023] [Indexed: 01/23/2023] Open
Abstract
Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.
Collapse
Affiliation(s)
- Shukai Gu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jiahui Yu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hong Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao, SAR, China
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen 518129, Guangdong, China
| | - Rong Sheng
- Health Technology Development Dept, Huawei Device Co., Ltd., Dongguan 523808, Guangdong, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| |
Collapse
|
12
|
Banerjee A, Saha S, Tvedt NC, Yang LW, Bahar I. Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods. Curr Opin Struct Biol 2023; 78:102517. [PMID: 36587424 PMCID: PMC10038760 DOI: 10.1016/j.sbi.2022.102517] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 12/31/2022]
Abstract
Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities.
Collapse
Affiliation(s)
- Anupam Banerjee
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Satyaki Saha
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Nathan C Tvedt
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA; Computational and Applied Mathematics and Statistics, The College of William and Mary, Williamsburg, VA 23185, USA
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, and PhD Program in Biomedical Artificial Intelligence, National Tsing Hua University, Hsinchu 300044, Taiwan; Physics Division, National Center for Theoretical Sciences, Taipei 106319, Taiwan
| | - Ivet Bahar
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA.
| |
Collapse
|
13
|
Nagamani S, Jaiswal L, Sastry GN. Deciphering the importance of MD descriptors in designing Vitamin D Receptor agonists and antagonists using machine learning. J Mol Graph Model 2023; 118:108346. [PMID: 36208593 DOI: 10.1016/j.jmgm.2022.108346] [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: 04/27/2022] [Revised: 09/14/2022] [Accepted: 09/23/2022] [Indexed: 11/16/2022]
Abstract
The Vitamin D Receptor (VDR) ligand-binding domain undergoes conformation change upon the binding of VDR agonists/antagonists. Helix 12 ((H)12) is one of the important helices at VDR ligand binding and its conformational changes are controlled by the binding of agonists and antagonists molecules. Various molecular modeling studies are available to explain the agonistic and antagonistic activity of vitamin D analogs. In this work, for the first time, we attempted to generate a machine learning model with fingerprints, 2D, 3D and MD descriptors that are specific to Vitamin D analogs and VDR. Initially, 2D and 3D descriptors and fingerprints of 1003 vitamin D analogs were calculated using CDK and RDKit. The machine learning model was generated using descriptors and fingerprints. Further, 80 Vitamin D analogs (40 VDR agonists + 40 VDR antagonists) were docked in the VDR active site. 50ns MD simulation was performed for each protein-ligand complex. Different MD descriptors such as Solvent Accessible Surface Area (SASA), radius of gyration, PC1 and PC2 were calculated and considered along with CDK and RDKit descriptors as features for machine learning calculations. A few other descriptors that are related to VDR conformational changes such as conformation of the (H)12, the angle at kink were considered for machine learning model generation. It was observed that the descriptors calculated from VDR conformational changes i) were able to distinguish between agonists and antagonists ii) provide key and comprehensive information about the unique binding characteristics of agonists and antagonists iii) provide a strong basis for the machine learning model generation. Overall, this study attempts the utilization of descriptors that are specific to a protein conformation will be helpful for the generation of an efficient machine learning model.
Collapse
Affiliation(s)
- Selvaraman Nagamani
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, Assam, 785 006, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
| | - Lavi Jaiswal
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, Assam, 785 006, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - G Narahari Sastry
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, Assam, 785 006, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
| |
Collapse
|
14
|
Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
Collapse
|
15
|
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
| |
Collapse
|
16
|
Gervasoni S, Malloci G, Bosin A, Vargiu AV, Zgurskaya HI, Ruggerone P. AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials. Sci Data 2022; 9:148. [PMID: 35365662 PMCID: PMC8976083 DOI: 10.1038/s41597-022-01261-1] [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] [Received: 12/21/2021] [Accepted: 03/11/2022] [Indexed: 12/13/2022] Open
Abstract
Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials. Measurement(s) | molecular physical property analysis objective | Technology Type(s) | Computer Modeling |
Collapse
Affiliation(s)
- Silvia Gervasoni
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| | - Giuliano Malloci
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy.
| | - Andrea Bosin
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| | - Attilio V Vargiu
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| | - Helen I Zgurskaya
- University of Oklahoma, Department of Chemistry and Biochemistry, Norman, OK, 73072, United States
| | - Paolo Ruggerone
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Bhojwani HR, Joshi UJ. Homology Modelling, Docking-based Virtual Screening, ADME Properties, and Molecular Dynamics Simulation for Identification of Probable Type II Inhibitors of AXL Kinase. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180818666211004102043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
AXL kinase is an important member of the TAM family for kinases which is
involved in most cancers. Considering its role in different cancers due to its pro-tumorigenic effects and its
involvement in the resistance, it has gained importance recently. Majority of research carried out is on Type I
inhibitors and limited studies have been carried out for Type II inhibitors. Taking this into consideration, we
have attempted to build Homology models to identify the Type II inhibitors for the AXL kinase.
Methods:
Homology Models for DFG-out C-helix-in/out state were developed using SWISS Model,
PRIMO, and Prime. These models were validated by different methods and further evaluated for stability
by molecular dynamics simulation using Desmond software. Selected models PED1-EB and PEDI1-EB
were used for the docking-based virtual screening of four compound libraries using Glide software. The
hits identified were subjected to interaction analysis and shortlisted compounds were subjected to Prime
MM-GBSA studies for energy calculation. These compounds were also docked in the DFG-in state to
check for binding and elimination of any compounds that may not be Type II inhibitors. The Prime energies
were calculated for these complexes as well and some compounds were eliminated. ADMET studies
were carried out using Qikprop. Some selected compounds were subjected to molecular dynamics simulation
using Desmond for evaluating the stability of the complexes.
Results:
Out of 78 models inclusive of both DFG-out C-helix-in and DFG-out C-helix-out, 5 models were
identified after different types of evaluation as well as validation studies. 1 model representing each type
(PED1-EB and PEDI1-EB) was selected for the screening studies. The screening studies resulted in the
identification of 29 compounds from the screen on PED1-EB and 10 compounds from the screen on
PEDI1-EB. Hydrogen bonding interactions with Pro621, Met623, and Asp690 were observed for these
compounds primarily. In some compounds, hydrogen bonding with Leu542, Glu544, Lys567, and
Asn677 as well as pi-pi stacking interactions with either Phe622 or Phe691 were also seen. 4 compounds
identified from PED1-EB screen were subjected to molecular dynamics simulation and their interactions
were found to be consistent during the simulation. 2 compounds identified from PEDI1-EB screen were
also subjected to the simulation studies, however, their interactions with Asp690 were not observed for a
significant time and in both cases differed from the docked pose.
Conclusion:
Multiple models of DFG-out conformations of AXL kinase were built, validated and used
for virtual screening. Different compounds were identified in the virtual screening, which may possibly
act as Type II inhibitors for AXL kinase. Some more experimental studies can be done to validate these
findings in future. This study will play a guiding role in the further development of the newer Type II
inhibitors of the AXL kinase for the probable treatment of cancer.
Collapse
Affiliation(s)
- Heena R. Bhojwani
- Department of Pharmaceutical Chemistry, Principal K.M. Kundnani College of Pharmacy, Colaba, Cuffe Parade,
Mumbai 400005, India
| | - Urmila J. Joshi
- Department of Pharmaceutical Chemistry, Principal K.M. Kundnani College of Pharmacy, Colaba, Cuffe Parade,
Mumbai 400005, India
| |
Collapse
|
19
|
Pavan M, Bassani D, Bolcato G, Bissaro M, Sturles M, Moro S. Computational strategies to identify new drug candidates against neuroinflammation. Curr Med Chem 2022; 29:4756-4775. [PMID: 35135446 DOI: 10.2174/0929867329666220208095122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022]
Abstract
The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.
Collapse
Affiliation(s)
- Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Mattia Sturles
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| |
Collapse
|
20
|
Hamre J, Jafri MS. Optimizing peptide inhibitors of SARS-Cov-2 nsp10/nsp16 methyltransferase predicted through molecular simulation and machine learning. INFORMATICS IN MEDICINE UNLOCKED 2022; 29:100886. [PMID: 35252541 PMCID: PMC8883729 DOI: 10.1016/j.imu.2022.100886] [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: 12/23/2021] [Revised: 02/04/2022] [Accepted: 02/16/2022] [Indexed: 11/30/2022] Open
Abstract
Coronaviruses, including the recent pandemic strain SARS-Cov-2, use a multifunctional 2′-O-methyltransferase (2′-O-MTase) to restrict the host defense mechanism and to methylate RNA. The nonstructural protein 16 2′-O-MTase (nsp16) becomes active when nonstructural protein 10 (nsp10) and nsp16 interact. Novel peptide drugs have shown promise in the treatment of numerous diseases and new research has established that nsp10 derived peptides can disrupt viral methyltransferase activity via interaction of nsp16. This study had the goal of optimizing new analogous nsp10 peptides that have the ability to bind nsp16 with equal to or higher affinity than those naturally occurring. The following research demonstrates that in silico molecular simulations can shed light on peptide structures and predict the potential of new peptides to interrupt methyltransferase activity via the nsp10/nsp16 interface. The simulations suggest that misalignments at residues F68, H80, I81, D94, and Y96 or rotation at H80 abrogate MTase function. We develop a new set of peptides based on conserved regions of the nsp10 protein in the Coronaviridae species and test these to known MTase variant values. This results in the prediction that the H80R variant is a solid new candidate for potential new testing. We envision that this new lead is the beginning of a reputable foundation of a new computational method that combats coronaviruses and that is beneficial for new peptide drug development.
Collapse
|
21
|
R Hamre J, Klimov DK, McCoy MD, Jafri MS. Machine learning-based prediction of drug and ligand binding in BCL-2 variants through molecular dynamics. Comput Biol Med 2022; 140:105060. [PMID: 34920365 DOI: 10.1016/j.compbiomed.2021.105060] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/13/2021] [Accepted: 11/20/2021] [Indexed: 12/13/2022]
Abstract
Venetoclax is a BH3 (BCL-2 Homology 3) mimetic used to treat leukemia and lymphoma by inhibiting the anti-apoptotic BCL-2 protein thereby promoting apoptosis of cancerous cells. Acquired resistance to Venetoclax via specific variants in BCL-2 is a major problem for the successful treatment of cancer patients. Replica exchange molecular dynamics (REMD) simulations combined with machine learning were used to define the average structure of variants in aqueous solution to predict changes in drug and ligand binding in BCL-2 variants. The variant structures all show shifts in residue positions that occlude the binding groove, and these are the primary contributors to drug resistance. Correspondingly, we established a method that can predict the severity of a variant as measured by the inhibitory constant (Ki) of Venetoclax by measuring the structure deviations to the binding cleft. In addition, we also applied machine learning to the phi and psi angles of the amino acid backbone to the ensemble of conformations that demonstrated a generalizable method for drug resistant predictions of BCL-2 proteins that elucidates changes where detailed understanding of the structure-function relationship is less clear.
Collapse
Affiliation(s)
- John R Hamre
- School of Systems Biology, George Mason University, Manassas, VA, USA.
| | - Dmitri K Klimov
- School of Systems Biology, George Mason University, Manassas, VA, USA.
| | - Matthew D McCoy
- Innovation Center for Biomedical Informatics, Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington DC, USA.
| | - M Saleet Jafri
- School of Systems Biology, George Mason University, Fairfax, VA and Center for Biomedical Technology and Engineering, University of Maryland School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
22
|
Bhati AP, Wan S, Alfè D, Clyde AR, Bode M, Tan L, Titov M, Merzky A, Turilli M, Jha S, Highfield RR, Rocchia W, Scafuri N, Succi S, Kranzlmüller D, Mathias G, Wifling D, Donon Y, Di Meglio A, Vallecorsa S, Ma H, Trifan A, Ramanathan A, Brettin T, Partin A, Xia F, Duan X, Stevens R, Coveney PV. Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers. Interface Focus 2021; 11:20210018. [PMID: 34956592 PMCID: PMC8504892 DOI: 10.1098/rsfs.2021.0018] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 12/13/2022] Open
Abstract
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.
Collapse
Affiliation(s)
- Agastya P. Bhati
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
| | - Shunzhou Wan
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
| | - Dario Alfè
- Department of Earth Sciences, London Centre for Nanotechnology and Thomas Young Centre at University College London, University College London, Gower Street, London WC1E 6BT, UK
- Dipartimento di Fisica Ettore Pancini, Università di Napoli Federico II, Monte Sant'Angelo, Napoli 80126, Italy
| | - Austin R. Clyde
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Mathis Bode
- Institute for Combustion Technology, RWTH Aachen University, Aachen 52056, Germany
| | - Li Tan
- Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Mikhail Titov
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Andre Merzky
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Matteo Turilli
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Shantenu Jha
- Brookhaven National Laboratory, Upton, NY 11973, USA
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | | | - Walter Rocchia
- Concept Lab, Italian Institute of Technology, Via Melen, Genova, Italy
| | - Nicola Scafuri
- Concept Lab, Italian Institute of Technology, Via Melen, Genova, Italy
| | - Sauro Succi
- Center for Life Nanosciences at La Sapienza, Italian Institute of Technology, viale Regina Elena, Roma, Italy
| | - Dieter Kranzlmüller
- Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Boltzmannstrasse 1, Garching bei München 85748, Germany
| | - Gerald Mathias
- Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Boltzmannstrasse 1, Garching bei München 85748, Germany
| | - David Wifling
- Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Boltzmannstrasse 1, Garching bei München 85748, Germany
| | | | | | | | - Heng Ma
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Anda Trifan
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Arvind Ramanathan
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Tom Brettin
- Computing, Environment and Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Alexander Partin
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Fangfang Xia
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Xiaotan Duan
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Rick Stevens
- Computing, Environment and Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Peter V. Coveney
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
- Institute for Informatics, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
| |
Collapse
|
23
|
Lagoutte-Renosi J, Allemand F, Ramseyer C, Yesylevskyy S, Davani S. Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives. Drug Discov Today 2021; 27:985-1007. [PMID: 34863931 DOI: 10.1016/j.drudis.2021.11.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/02/2021] [Accepted: 11/25/2021] [Indexed: 01/10/2023]
Abstract
Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.
Collapse
Affiliation(s)
- Jennifer Lagoutte-Renosi
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire de Pharmacologie Clinique et Toxicologie-CHU de Besançon, 25000 Besançon, France
| | - Florentin Allemand
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France
| | - Christophe Ramseyer
- Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France
| | - Semen Yesylevskyy
- Laboratoire Chrono Environnement UMR CNRS 6249, Université de Bourgogne Franche-Comté, 16 route de Gray, 25000 Besançon, France; Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine, Nauky Sve. 46, Kyiv, Ukraine; Receptor.ai inc, 16192 Coastal Highway, Lewes, DE, USA
| | - Siamak Davani
- EA 3920 Université Bourgogne Franche-Comté, 25000 Besançon, France; Laboratoire de Pharmacologie Clinique et Toxicologie-CHU de Besançon, 25000 Besançon, France.
| |
Collapse
|
24
|
Jandova Z, Vargiu AV, Bonvin AMJJ. Native or Non-Native Protein-Protein Docking Models? Molecular Dynamics to the Rescue. J Chem Theory Comput 2021; 17:5944-5954. [PMID: 34342983 PMCID: PMC8444332 DOI: 10.1021/acs.jctc.1c00336] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Indexed: 11/29/2022]
Abstract
Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favorable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow distinguishing native from non-native models to complement scoring functions used in docking. To this end, the first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the Critical Assessment of PRedicted Interaction (CAPRI) competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A random forest classifier was trained, reaching a 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths of the order of 50-100 ns are sufficient to reach this accuracy, which makes this approach applicable in practice.
Collapse
Affiliation(s)
- Zuzana Jandova
- Computational
Structural Biology Group, Bijvoet Centre for Biomolecular Research,
Faculty of Science—Chemistry, Utrecht
University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Attilio Vittorio Vargiu
- Physics
Department, University of Cagliari, Cittadella
Universitaria, S.P. 8 km 0.700, 09042 Monserrato, Italy
| | - Alexandre M. J. J. Bonvin
- Computational
Structural Biology Group, Bijvoet Centre for Biomolecular Research,
Faculty of Science—Chemistry, Utrecht
University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| |
Collapse
|
25
|
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
Collapse
Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
| |
Collapse
|
26
|
Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
Collapse
Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
| |
Collapse
|
27
|
Zanganeh S, Firoozpour L, Sardari S, Afgar A, Cohan RA, Mohajel N. Novel Descriptors Derived from the Aggregation Propensity of Di- and Tripeptides Can Predict the Critical Aggregation Concentration of Longer Peptides. ACS OMEGA 2021; 6:13331-13340. [PMID: 34056481 PMCID: PMC8158804 DOI: 10.1021/acsomega.1c01293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/28/2021] [Indexed: 05/14/2023]
Abstract
Self-assembling amphiphilic peptides have recently received special attention in medicine. Nonetheless, testing the myriad of combinations generated from at least 20 coded and several hundreds of noncoded amino acids to obtain candidate sequences for each application, if possible, is time-consuming and expensive. Therefore, rapid and accurate approaches are needed to select candidates from countless combinations. In the current study, we examined three conventional descriptor sets along with a novel descriptor set derived from the simulated aggregation propensity of di- and tripeptides to model the critical aggregation concentration (CAC) of amphiphilic peptides. In contrast to the conventional descriptors, the radial kernel model derived from the novel descriptor set accurately predicted the critical aggregation concentration of the test set with a residual standard error of 0.10. The importance of aromatic side chains, as well as neighboring amino acids in the self-assembly, was emphasized by analysis of the influential descriptors. The addition of very long peptides (70-100 residues) to the data set decreased the model accuracy and changed the influential descriptors. The developed model can be used to predict the CAC of self-assembling amphiphilic peptides and also to derive rules to apply in designing novel amphiphilic peptides with desired properties.
Collapse
Affiliation(s)
- Saeed Zanganeh
- Department
of Nanobiotechnology, New Technologies Research Group, Pasteur Institute of Iran, Tehran 1316943551, Iran
- Department
of Hematology and Medical Laboratory Sciences, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman 7616911333, Iran
| | - Loghman Firoozpour
- Department
of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 1416753955, Iran
| | - Soroush Sardari
- Drug
Design and Bioinformatics Unit, Medical Biotechnology Department,
Biotechnology Research Center, Pasteur Institute
of Iran, Tehran 1316943551, Iran
| | - Ali Afgar
- Research
Center for Hydatid Disease in Iran, School of Medicine, Kerman University of Medical Sciences, Kerman 7616914115, Iran
| | - Reza Ahangari Cohan
- Department
of Nanobiotechnology, New Technologies Research Group, Pasteur Institute of Iran, Tehran 1316943551, Iran
| | - Nasir Mohajel
- Department
of Molecular Virology, Pasteur Institute
of Iran, Tehran 1316943551, Iran
| |
Collapse
|
28
|
Singh H, Bharadvaja N. Treasuring the computational approach in medicinal plant research. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 164:19-32. [PMID: 34004233 DOI: 10.1016/j.pbiomolbio.2021.05.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 05/11/2021] [Indexed: 01/24/2023]
Abstract
Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.
Collapse
Affiliation(s)
- Harshita Singh
- Plant Biotechnology Laboratory, Delhi Technological University, Delhi, 110042, India
| | - Navneeta Bharadvaja
- Plant Biotechnology Laboratory, Delhi Technological University, Delhi, 110042, India.
| |
Collapse
|
29
|
Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
Collapse
Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
McCoy MD, Hamre J, Klimov DK, Jafri MS. Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations. Biophys J 2020; 120:189-204. [PMID: 33333034 DOI: 10.1016/j.bpj.2020.12.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 11/20/2020] [Accepted: 12/08/2020] [Indexed: 02/08/2023] Open
Abstract
Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.
Collapse
Affiliation(s)
- Matthew D McCoy
- Innovation Center for Biomedical Informatics, Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington DC; School of Systems Biology, George Mason University, Manassas, Virginia.
| | - John Hamre
- School of Systems Biology, George Mason University, Manassas, Virginia
| | - Dmitri K Klimov
- School of Systems Biology, George Mason University, Manassas, Virginia
| | - M Saleet Jafri
- School of Systems Biology, George Mason University, Manassas, Virginia; Krasnow Institute for Advanced Study, Interdisciplinary Program in Neuroscience, School of Systems Biology, George Mason University, Fairfax, Virginia.
| |
Collapse
|
32
|
Taguchi AT, Boyd J, Diehnelt CW, Legutki JB, Zhao ZG, Woodbury NW. Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning. ACS COMBINATORIAL SCIENCE 2020; 22:500-508. [PMID: 32786325 DOI: 10.1021/acscombsci.0c00003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computational prescreening. Here, a different approach is considered that uses sparse measurements of library molecules as the input to a machine learning algorithm which generates a comprehensive, quantitative relationship between covalent molecular structure and function that can then be used to predict the function of any molecule in the possible combinatorial space. To test the feasibility of the approach, a defined combinatorial chemical space consisting of ∼1012 possible linear combinations of 16 different amino acids was used. The binding of a very sparse, but nearly random, sampling of this amino acid sequence space to 9 different protein targets is measured and used to generate a general relationship between peptide sequence and binding for each target. Surprisingly, measuring as little as a few hundred to a few thousand of the ∼1012 possible molecules provides sufficient training to be highly predictive of the binding of the remaining molecules in the combinatorial space. Furthermore, measuring only amino acid sequences that bind weakly to a target allows the accurate prediction of which sequences will bind 10-100 times more strongly. Thus, the molecular recognition information contained in a tiny fraction of molecules in this combinatorial space is sufficient to characterize any set of molecules randomly selected from the entire space, a fact that potentially has significant implications for the design of new chemical function using combinatorial chemical libraries.
Collapse
Affiliation(s)
| | - James Boyd
- HealthTell, Inc., 145 S 79th Street, Chandler, Arizona 85226, United States
| | - Chris W. Diehnelt
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Joseph B. Legutki
- HealthTell, Inc., 145 S 79th Street, Chandler, Arizona 85226, United States
| | - Zhan-Gong Zhao
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Neal W. Woodbury
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| |
Collapse
|
33
|
Ahmad SS, Sinha M, Ahmad K, Khalid M, Choi I. Study of Caspase 8 Inhibition for the Management of Alzheimer's Disease: A Molecular Docking and Dynamics Simulation. Molecules 2020; 25:molecules25092071. [PMID: 32365525 PMCID: PMC7249184 DOI: 10.3390/molecules25092071] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 04/28/2020] [Accepted: 04/28/2020] [Indexed: 01/13/2023] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia and usually manifests as diminished episodic memory and cognitive functions. Caspases are crucial mediators of neuronal death in a number of neurodegenerative diseases, and caspase 8 is considered a major therapeutic target in the context of AD. In the present study, we performed a virtual screening of 200 natural compounds by molecular docking with respect to their abilities to bind with caspase 8. Among them, rutaecarpine was found to have the highest (negative) binding energy (−6.5 kcal/mol) and was further subjected to molecular dynamics (MD) simulation analysis. Caspase 8 was determined to interact with rutaecarpine through five amino acid residues, specifically Thr337, Lys353, Val354, Phe355, and Phe356, and two hydrogen bonds (ligand: H35-A: LYS353:O and A:PHE355: N-ligand: N5). Furthermore, a 50 ns MD simulation was conducted to optimize the interaction, to predict complex flexibility, and to investigate the stability of the caspase 8–rutaecarpine complex, which appeared to be quite stable. The obtained results propose that rutaecarpine could be a lead compound that bears remarkable anti-Alzheimer’s potential against caspase 8.
Collapse
Affiliation(s)
- Syed Sayeed Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Korea; (S.S.A.); (K.A.)
| | - Meetali Sinha
- Department of Bioengineering, Integral University, Lucknow 226026, India;
| | - Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Korea; (S.S.A.); (K.A.)
| | - Mohammad Khalid
- College of Pharmacy, Department of Pharmacognosy, Prince Sattam Bin Abdul Aziz University, Alkharj 16278, Riyadh, Saudi Arabia;
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Korea; (S.S.A.); (K.A.)
- Correspondence: ; Fax: +82-53-810-4769
| |
Collapse
|
34
|
MBLinhibitors.com, a Website Resource Offering Information and Expertise for the Continued Development of Metallo--Lactamase Inhibitors. Biomolecules 2020; 10:biom10030459. [PMID: 32188106 PMCID: PMC7175331 DOI: 10.3390/biom10030459] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/09/2020] [Accepted: 03/12/2020] [Indexed: 12/29/2022] Open
Abstract
In an effort to facilitate the discovery of new, improved inhibitors of the metallo-β-lactamases (MBLs), a new, interactive website called MBLinhibitors.com was developed. Despite considerable efforts from the science community, there are no clinical inhibitors of the MBLs, which are now produced by human pathogens. The website, MBLinhibitors.com, contains a searchable database of known MBL inhibitors, and inhibitors can be searched by chemical name, chemical formula, chemical structure, Simplified Molecular-Input Line-Entry System (SMILES) format, and by the MBL on which studies were conducted. The site will also highlight a “MBL Inhibitor of the Month”, and researchers are invited to submit compounds for this feature. Importantly, MBLinhibitors.com was designed to encourage collaboration, and researchers are invited to submit their new compounds, using the “Submit” function on the site, as well as their expertise using the “Collaboration” function. The intention is for this site to be interactive, and the site will be improved in the future as researchers use the site and suggest improvements. It is hoped that MBLinhibitors.com will serve as the one-stop site for any important information on MBL inhibitors and will aid in the discovery of a clinically useful MBL inhibitor.
Collapse
|