Comprehensive fitness landscape of SARS-CoV-2 Mpro reveals insights into viral resistance mechanisms

eLife, Год журнала: 2022, Номер 11

Опубликована: Июнь 20, 2022

With the continual evolution of new strains severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that are more virulent, transmissible, and able to evade current vaccines, there is an urgent need for effective anti-viral drugs. The SARS-CoV-2 main protease (M

Язык: Английский

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PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions

Communications Biology, Год журнала: 2021, Номер 4(1)

Опубликована: Ноя. 19, 2021

Abstract Resistance to small-molecule drugs is the main cause of failure therapeutic in clinical practice. Missense mutations altering binding ligands proteins are one critical mechanisms that result genetic disease and drug resistance. Computational methods have made a lot progress for predicting affinity changes identifying resistance mutations, but their prediction accuracy speed still not satisfied need be further improved. To address these issues, we introduce structure-based machine learning method quantitatively estimating effects single on ligand (named as PremPLI). A comprehensive comparison predictive performance PremPLI with other available two benchmark datasets confirms our approach performs robustly presents similar or even higher than approaches relying first-principle statistical mechanics mixed physics- knowledge-based potentials while requires much less computational resources. can used guiding design ligand-binding proteins, understanding driver finding potential different drugs. freely at https://lilab.jysw.suda.edu.cn/research/PremPLI/ allows do large-scale mutational scanning.

Язык: Английский

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Protein–protein interaction and non-interaction predictions using gene sequence natural vector

Communications Biology, Год журнала: 2022, Номер 5(1)

Опубликована: Июль 2, 2022

Abstract Predicting protein–protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide Triplet nucleotide), for the prediction non-interaction. For non-interactions (PPNIs), method obtained accuracies 86.23% Homo sapiens 85.34% Mus musculus , it performed well on three types networks. protein-protein interactions (PPIs), we 99.20, 94.94, 98.56, 95.41, 94.83% Saccharomyces cerevisiae Drosophila melanogaster Helicobacter pylori sapiens, respectively. Furthermore, outperformed established demonstrated high results cross-species interactions. is expected be an effective approach predicting PPIs PPNIs.

Язык: Английский

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Beyond sequence: Structure-based machine learning

Computational and Structural Biotechnology Journal, Год журнала: 2022, Номер 21, С. 630 - 643

Опубликована: Дек. 29, 2022

Recent breakthroughs in protein structure prediction demarcate the start of a new era structural bioinformatics. Combined with various advances experimental determination and uninterrupted pace at which structures are published, this promises an age information is as prevalent ubiquitous sequence. Machine learning bioinformatics has been dominated by sequence-based methods, but now changing to make use deluge rich input. methods making scattered across literature cover number different applications scopes; while some try address questions tasks within single family, others aim capture characteristics all available proteins. In review, we look variety structure-based machine approaches, how can be used input, typical these approaches biology. We also discuss current challenges opportunities all-important increasingly popular field.

Язык: Английский

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Quantum mechanics insights into melatonin and analogs binding to melatonin MT1 and MT2 receptors

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Май 13, 2024

Abstract Melatonin receptors MT 1 and 2 are G protein-coupled that mediate the effects of melatonin, a hormone involved in circadian rhythms other physiological functions. Understanding molecular interactions between these their ligands is crucial for developing novel therapeutic agents. In this study, we used docking, dynamics simulations, quantum mechanics calculation to investigate binding modes affinities three ligands: melatonin (MLT), ramelteon (RMT), 2-phenylmelatonin (2-PMT) with both receptors. Based on results, identified key amino acids contributed receptor-ligand interactions, such as Gln181/194, Phe179/192, Asn162/175, which conserved Additionally, described new meaningful Gly108/Gly121, Val111/Val124, Val191/Val204. Our results provide insights into recognition’s structural energetic determinants suggest potential strategies designing more optimized molecules. This study enhances our understanding offers implications future drug development.

Язык: Английский

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Machine learning approaches for predicting protein-ligand binding sites from sequence data

Frontiers in Bioinformatics, Год журнала: 2025, Номер 5

Опубликована: Фев. 3, 2025

Proteins, composed of amino acids, are crucial for a wide range biological functions. Proteins have various interaction sites, one which is the protein-ligand binding site, essential molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key Accurate prediction these pivotal in computational drug discovery, helping identify therapeutic targets facilitate treatment development. Machine learning has made significant contributions this field by improving interactions. This paper reviews studies that use machine predict from sequence data, focusing on recent advancements. The review examines embedding methods architectures, addressing current challenges ongoing debates field. Additionally, research gaps existing literature highlighted, potential future directions advancing discussed. study provides thorough overview sequence-based approaches predicting offering insights into state possibilities.

Язык: Английский

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Decoding the effects of mutation on protein interactions using machine learning

Biophysics Reviews, Год журнала: 2025, Номер 6(1)

Опубликована: Фев. 21, 2025

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect between proteins and other biomolecules, such as proteins, DNA/RNA, ligands, which are vital regulating numerous biological processes. Developing computational approaches with high accuracy efficiency critical elucidating the mechanisms underlying various diseases, identifying potential biomarkers early diagnosis, developing targeted therapies. This review provides a comprehensive overview of recent advancements in impact mutations across different interaction types, central to processes disease mechanisms, including cancer. We summarize progress predictive approaches, physicochemical-based, machine learning, deep learning methods, evaluating strengths limitations each. Additionally, we discuss challenges related mutational data, biases, data quality, dataset size, explore difficulties accurate prediction tools mutation-induced effects interactions. Finally, future directions advancing these tools, highlighting capabilities technologies, artificial intelligence drive significant improvements prediction.

Язык: Английский

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Cell envelope polysaccharide modifications alter the surface properties and interactions of Mycobacterium abscessus with innate immune cells in a morphotype-dependent manner

mBio, Год журнала: 2025, Номер unknown

Опубликована: Март 14, 2025

ABSTRACT Mycobacterium abscessus is one of the leading causes pulmonary infections caused by non-tuberculous mycobacteria. The ability M. to establish a chronic infection in lung relies on series adaptive mutations impacting, part, global regulators and cell envelope biosynthetic enzymes. One genes under strong evolutionary pressure during host adaptation ubiA , which participates elaboration arabinan domains two major polysaccharides: arabinogalactan (AG) lipoarabinomannan (LAM). We here show that patient-derived UbiA not only cause alterations AG, LAM, mycolic acid contents but also tend render bacterium more prone forming biofilms while evading uptake innate immune cells enhancing their pro-inflammatory properties. fact effects physiology pathogenicity were impacted rough or smooth morphotype strain suggests timing selection relative switching may be key promote persistence host. IMPORTANCE Multidrug-resistant subspecies are increasing U.S.A. globally. Little known mechanisms these microorganisms. have identified single-nucleotide polymorphisms (SNPs) gene involved biosynthesis polysaccharides, lipoarabinomannan, lung-adapted isolates from 13 patients. Introduction individual SNPs reference allowed us study impact its interactions with cells. significance our work identifying some used colonize persist human lung, will facilitate early detection potentially virulent clinical lead new therapeutic strategies. Our findings further broader biomedical impacts, as conserved other tuberculous mycobacterial pathogens.

Язык: Английский

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Dr. Kinase: predicting the drug-resistance hotspots of protein kinases

Nucleic Acids Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

Protein kinases (PKs) regulate various cellular functions, and are targeted by small-molecule kinase inhibitors (KIs) in cancers other diseases. However, drug resistance (DR) of KIs occurs through critical mutations four types representative hotspots, including gatekeeper, G-loop, αC-helix, A-loop. KI DR has become a common clinical complication affecting multiple cancers, kinases, drugs. To tackle this challenge, we report an upgraded web server, namely Dr. Kinase, for predicting the loci hotspots assessing effects on PKs our previous studies, utilizing multimodal features deep hybrid learning. The performance Kinase been rigorously evaluated using independent testing, demonstrating excellent accuracy with area under curve values exceeding 0.89 different hotspot predictions. We further conducted silico analyses to evaluate validate epidermal growth factor receptor protein conformation KIs' binding efficacy. is freely available at http://modinfor.com/drkinase, comprehensive annotations visualizations. anticipate that will be highly useful service basic, translational, community unveil molecular mechanisms development next-generation emerging cancer precision medicine.

Язык: Английский

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Computational structure-guided approach to simulate delamanid and pretomanid binding to mycobacterial F420 redox cycling proteins: identification of key determinants of resistance

Journal of Biomolecular Structure and Dynamics, Год журнала: 2025, Номер unknown, С. 1 - 21

Опубликована: Май 6, 2025

The recently approved delamanid (DLM) and pretomanid (PTM) improved the existing options to treat multidrug-resistant tuberculosis (MDR-TB). However, high spontaneous mutation rates in mycobacterial F420 genes ddn, fgd1, fbiA, fbiB, fbiC, fbiD create a bottleneck successful anti-TB treatments. Of known mutations, identifying therapeutically relevant ones is prerequisite for understanding drug resistance mechanism. Here, we applied multistep computational pipeline rank mutations associated with DLM/PTM resistance. DLM-/PTM-resistant protein mutants were built simulated their innate sensitivity towards drugs. molecular dynamics (MD) mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations quantified effect of key on union. dynamic cross-correlated map (DCCM) principal component analysis (PCA) showed substantial link between binding region other sections mutants, hints potential role as an allosteric site. Also, alterations induced conformationally unstable proteins decreased affinity. These investigations highlighted DLM-tolerant G53D Y65S PTM-resilient Y133M (Ddn), L308P (FbiA), C562W (FbiC) candidate loss-of-function progressive research. present results interpretations could supply vital clues engineering development.

Язык: Английский

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