Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

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Structural insights into SARS-CoV-2 main protease conformational plasticity

Budheswar Dehury et al.

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Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime

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Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Shukai Gu et al.

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Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics

Zun Wang et al.

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Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19

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Recommendations for the Management of Drug-Drug Interactions Between the COVID-19 Antiviral Nirmatrelvir/Ritonavir (Paxlovid) and Comedications

Catia Marzolini et al.

Summary: The antiviral nirmatrelvir/ritonavir has been authorized for the treatment of high-risk COVID-19 patients. However, due to its potential for drug-drug interactions, caution is needed when prescribing it with other medications metabolized through the same pathway. This review provides recommendations for managing these interactions and presents a list of comedications categorized by their DDI risk.

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Structural basis for replicase polyprotein cleavage and substrate specificity of main protease from SARS-CoV-2

Yao Zhao et al.

Summary: This study determines the high-resolution structures of SARS-CoV-2 M-Pro in different states, revealing the conformational changes that occur during substrate processing. The study also demonstrates the enzyme's specificity for certain sequences and provides a foundation for the development of new anti-SARS-CoV-2 drugs.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2022)

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Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities

Ikki Yasuda et al.

Summary: Differences in ligand-induced protein dynamics extracted as a single feature from a deep learning-based analysis of MD simulations correlate with ligand binding affinity, indicating potential for binding dynamics-based drug discovery.

COMMUNICATIONS BIOLOGY (2022)

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Correlation-Based Feature Selection to Identify Functional Dynamics in Proteins

Georg Diez et al.

Summary: To interpret molecular dynamics simulations of biomolecular systems, dimensionality reduction methods like PCA and TICA are commonly used. The first step is to identify suitable input coordinates and discard uncorrelated motions or noise coordinates. Clustering methods are then used to block-diagonalize the correlation matrix, distinguishing collective motions from uncorrelated ones. The combination of linear correlation and the Leiden community detection algorithm yields excellent results.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2022)

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Article Chemistry, Medicinal

Evolutionary and Structural Insights about Potential SARS-CoV-2 Evasion of Nirmatrelvir

Kai S. Yang et al.

Summary: The approval of the combination therapy PAXLOVID has boosted morale in fighting the COVID-19 pandemic. The analysis in the article discusses possible mutations in the M-Pro that could lead to acquired viral resistance to nirmatrelvir and its implications for fighting future drug-resistant viral variants.

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Hepatitis C virus NS3/4A inhibitors and other drug-like compounds as covalent binders of SARS-CoV-2 main protease

Babak Andi et al.

Summary: This study found that three clinically approved anti-hepatitis C virus drugs and two other drug-like compounds can covalently bind to the main protease of SARS-CoV-2, suggesting their potential as COVID-19 therapeutics. Molecular docking studies also provide insights for the design of new antiviral inhibitors.

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Letter Infectious Diseases

Rapid COVID-19 rebound in a severe COVID-19 patient during 20-day course of Paxlovid

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A universal graph deep learning interatomic potential for the periodic table

Chi Chen et al.

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NATURE COMPUTATIONAL SCIENCE (2022)

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Review Biochemical Research Methods

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions

Debby D. Wang et al.

Summary: This paper reviews two classes of methods for accurately predicting protein-ligand binding affinities: free energy-based simulations and machine learning-based scoring functions. It follows thermodynamic cycles for the former and a feature-representation taxonomy for the latter. Additionally, recent deep learning-based predictions are also discussed, with comparisons of strengths, weaknesses, and future directions for improvements.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Pharmacology & Pharmacy

Screening Malaria-box compounds to identify potential inhibitors against SARS-CoV-2 MPro, using molecular docking and dynamics simulation studies

Shahzaib Ahamad et al.

Summary: The study identified three promising lead compounds from the MMV Malaria Box through virtual screening against M-pro enzyme. These compounds may be evaluated for their inhibitory activities using experimental techniques.

EUROPEAN JOURNAL OF PHARMACOLOGY (2021)

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Article Chemistry, Physical

Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1

Mariarosaria Ferraro et al.

Summary: The study utilized machine learning to uncover the relationship between different levels of allosteric inhibition and local dynamic patterns in the protein TRAP1, providing a new approach to infer the functionality of allosteric ligands. The combination of molecular dynamics and machine learning offers a promising strategy to support in silico mechanistic studies and drug design.

JOURNAL OF PHYSICAL CHEMISTRY B (2021)

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A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening

Christoph Gorgulla et al.

Summary: The global effort to combat the ongoing COVID-19 pandemic has led to promising prophylactic measures, but there is still a need for effective therapeutics. Through a large-scale virtual screening platform, inhibitors targeting SARS-CoV-2 are being searched for, focusing on both viral enzymes' active sites and critical protein-protein interactions.

ISCIENCE (2021)

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An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19

Dafydd R. Owen et al.

Summary: PF-07321332, an orally bioavailable SARS-CoV-2 main protease inhibitor, has been discovered with in vitro pan-human coronavirus antiviral activity and excellent off-target selectivity and in vivo safety profiles. This new drug has shown promise in countering the threat of COVID-19 with its oral activity and safety in clinical trials.

SCIENCE (2021)

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Review Chemistry, Multidisciplinary

Unsupervised Learning Methods for Molecular Simulation Data

Aldo Glielmo et al.

Summary: This review provides a comprehensive overview of the importance of unsupervised learning in analyzing data from atomistic and molecular simulations, discussing state-of-the-art algorithms and methods in feature representation, dimensionality reduction, density estimation, clustering, and kinetic models. The article is well-structured, with detailed discussions in each section on the mathematical and algorithmic foundations, strengths and limitations of each method, and specific applications in analyzing molecular simulation data.

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Elucidation of Cryptic and Allosteric Pockets within the SARS-CoV-2 Main Protease

Terra Sztain et al.

Summary: By using Gaussian accelerated molecular dynamics, this study identified cryptic pockets within the SARS-CoV-2 main protease, revealing druggable conformational states far from the active site. The simulations demonstrated comparable dynamics and pocket volumes to conventional brute force simulations conducted on significantly longer timescales.

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Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics

Filippo Marchetti et al.

Summary: Allosteric drugs have gained increasing interest, with high-throughput screening being a common practice for their discovery. Despite growing biological information and computational power, challenges remain in selecting allosteric ligands and predicting their impact on target protein function. Computational approaches typically involve molecular docking and molecular dynamics to recover information on the long-range regulation typical of allosteric proteins.

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Comprehensive Survey of Recent Drug Discovery Using Deep Learning

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Masitinib is a broad coronavirus 3CL inhibitor that blocks replication of SARS-CoV-2

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Recognition of Divergent Viral Substrates by the SARS-CoV-2 Main Protease

Elizabeth A. MacDonald et al.

Summary: The main protease (Mpro) of SARS-CoV-2 plays a key role in viral replication by cleaving viral polyproteins at specific sites, and its interactions with substrates can have significant effects on catalytic efficiency. The study provides insights into potential targets for antiviral drug design and highlights the importance of finely tuned substrate-dependent catalytic parameters in the coronavirus lifecycle.

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β-Blockers bearing hydroxyethylamine and hydroxyethylene as potential SARS-CoV-2 Mpro inhibitors: rational based design, in silico, in vitro, and SAR studies for lead optimization

Mohammed I. A. Hamed et al.

Summary: Through molecular docking and molecular dynamics simulations, researchers found that beta-adrenergic blockers have inhibitory effects on SARS-CoV-2, with Carvedilol and Nebivolol showing the most promising antiviral activities.

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Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

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