A review on structure-function mechanism and signaling pathway of serine/threonine protein PIM kinases as a therapeutic target

International Journal of Biological Macromolecules, Год журнала: 2024, Номер 270, С. 132030 - 132030

Опубликована: Май 3, 2024

Язык: Английский

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Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

Proceedings of the National Academy of Sciences, Год журнала: 2024, Номер 121(35)

Опубликована: Авг. 20, 2024

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures proteins has proved feasible using deep-learning-based methods, predicting conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such because available laboratory data on are still limited. In this work, we develop method generating protein allosteric by integrating physical energy landscape information into methods. We show that local energetic frustration, which represents quantification features governing dynamics, can be utilized to empower AlphaFold2 (AF2) predict motions. Starting from ground state structures, integrative generates alternative as well pathways motions, progressive enhancement frustration in input multiple sequence alignment sequences. For model adenylate kinase, generated consistent with experimental and molecular dynamics simulation data. Applying another two KaiB ribose-binding protein, involve large-amplitude changes, also successfully generate conformations. how extract overall AF2 topography, been considered many black box. Incorporating knowledge structure algorithms provides useful strategy address challenges dynamic proteins.

Язык: Английский

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Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(18), С. 10139 - 10139

Опубликована: Сен. 21, 2024

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in field computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough structural biology been established by developing artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 provides state-of-the-art protein structures from nearly all known sequences with high accuracy. This study examined reliability models compared to experimental drug discovery, focusing on common drug-targeted classes as G protein-coupled receptors (GPCRs) class A. total 32 representative targets were selected, including X-ray crystallographic and Cryo-EM their corresponding models. quality was assessed using different validation tools, pLDDT score, RMSD value, MolProbity percentage Ramachandran favored, QMEAN Z-score, QMEANDisCo Global. molecular docking performed Genetic Optimization Ligand Docking (GOLD) software. models’ virtual screening determined ability predict ligand binding poses closest native pose assessing Root Mean Square Deviation (RMSD) metric scoring function. function evaluated enrichment factor (EF). Furthermore, capability identify hits key protein–ligand interactions analyzed. posing power results showed that successfully predicted (RMSD < 2 Å). However, they exhibited lower power, average EF values 2.24, 2.42, 1.82 X-ray, Cryo-EM, structures, respectively. Moreover, our revealed can competitive inhibitors. In conclusion, this found provided comparable particularly certain GPCR targets, could potentially significantly impact discovery.

Язык: Английский

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Dynamics-based drug discovery by time-resolved cryo-EM

Current Opinion in Structural Biology, Год журнала: 2025, Номер 91, С. 103001 - 103001

Опубликована: Фев. 21, 2025

Язык: Английский

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A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

Frontiers in Pharmacology, Год журнала: 2024, Номер 14

Опубликована: Янв. 24, 2024

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) trigeminal innervating the body face, respectively, as well other tissues organs including central nervous system. a versatile that detects harmful heat, pain, various internal external ligands. Hence, it operates polymodal sensory channel. Many pathological conditions neuroinflammation, cancer, psychiatric disorders, are linked to abnormal functioning peripheral tissues. Intense biomedical research underway discover compounds can modulate provide pain relief. molecular mechanisms underlying temperature sensing remain largely unknown, although they closely transduction. Prolonged exposure generates analgesia, hence numerous analogs have been developed efficient analgesics for emergence silico tools offered significant techniques modeling machine learning algorithms indentify druggable sites repositioning current drugs aimed at TRPV1. Here we recapitulate physiological pathophysiological functions channel, structural models obtained through cryo-EM, pharmacological tested on TRPV1, drug discovery repositioning.

Язык: Английский

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From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on “Allosteric Intersection” of Biochemical and Big Data Approaches

International Journal of Molecular Sciences, Год журнала: 2023, Номер 24(9), С. 7747 - 7747

Опубликована: Апрель 24, 2023

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems automated workflows that are able to model complex chemical biological phenomena. In years, approaches been developed actively deployed facilitate computational experimental studies protein dynamics allosteric mechanisms. this review, we discuss detail developments along two major directions research through lens data-intensive biochemical AI-based methods. Despite considerable progress applications AI methods for structure studies, intersection between regulation, emerging structural biology technologies remains largely unexplored, calling development AI-augmented integrative biology. focus on latest remarkable deep high-throughput mining comprehensive mapping landscapes regulatory mechanisms as well prediction characterization binding sites proteome level. We also expand our knowledge universe allostery. conclude with an outlook highlight importance developing open science infrastructure regulation validation using community-accessible tools uniquely leverage existing simulation knowledgebase enable interrogation functions can provide a much-needed boost further innovation integration empowered by booming field.

Язык: Английский

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Phytochemical analysis, in-vitro and in-silico study of antiproliferative activity of ethyl acetate fraction of Launaea cornuta (Hochst. ex Oliv. & Hiern) C. Jeffrey against human cervical cancer cell line

Frontiers in Pharmacology, Год журнала: 2024, Номер 15

Опубликована: Июнь 28, 2024

Introduction: Cervical cancer is one of the leading causes death among women globally due to limitation current treatment methods and their associated adverse side effects. Launaea cornuta used as traditional medicine for a variety diseases including cancer. However, there no scientific validation on antiproliferative activity L. against cervical Objective: This study aimed evaluate selective antiproliferative, cytotoxic antimigratory effects explore its therapeutical mechanisms in human cell lines (HeLa-229) through network analysis approach. Materials methods: The effect ethyl acetate fraction proliferation cells was evaluated by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) bioassay assessed wound healing assays. Compounds were analysed using qualitative colour method gas chromatography-mass spectroscopy (GC-MS). Subsequently, bioinformatic analyses, protein-protein interaction (PPI) analysis, Gene Ontology (GO), Kyoto Encyclopaedia Genes Genomes (KEGG) performed screen potential anticervical therapeutic target genes cornuta. Molecular docking (MD) predict understand molecular interactions ligands Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validate results. Results: exhibited remarkable HeLa-229 (IC 50 20.56 ± 2.83 μg/mL) with selectivity index (SI) 2.36 minimal cytotoxicity non-cancerous (Vero-CCL 81 48.83 23.02). preliminary screening revealed presence glycosides, phenols, saponins, terpenoids, quinones, tannins. Thirteen compounds also identified GC-MS analysis. 124 obtained, AKT1, MDM2, CDK2, MCL1 MTOR top hub PI3K/Akt1, Ras/MAPK, FoxO EGFR signalling pathways significantly enriched pathways. results showed that stigmasteryl methyl ether had good binding affinity ATK1, BCL2, Casp9, energy ranging from −7.0 −12.6 kcal/mol. Tremulone TP53 P21 −8.0 kcal/mol, respectively. suggests stable selected Furthermore, RT-qPCR MDM2 BCL2 downregulated, Casp9 upregulated treated compared negative control (DMSO 0.2%). Conclusion: findings indicate phytochemicals modulates various targets exhibit cells. lays foundation further research develop innovative clinical agents.

Язык: Английский

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AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

Molecules, Год журнала: 2024, Номер 29(15), С. 3512 - 3512

Опубликована: Июль 26, 2024

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge the field is efficiently designing desired bio-elements accurately predicting their using vast datasets. advancement artificial intelligence (AI) technology has enabled machine learning deep algorithms to excel uncovering patterns bio-element data performance. This review explores AI design bio-elements, regulation transcription-factor-based biosensor response performance AI-designed elements. We discuss advantages, adaptability, challenges addressed by various applications, highlighting powerful potential analyzing data. Furthermore, we propose innovative solutions faced suggest future directions. By consolidating current demonstrating practical applications biology, this provides valuable insights for advancing both academic biotechnology.

Язык: Английский

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Designing drugs and chemical probes with the dualsteric approach

Chemical Society Reviews, Год журнала: 2023, Номер 52(24), С. 8651 - 8677

Опубликована: Янв. 1, 2023

Dualsteric modulators are praised for a balance of potency and selectivity, overcoming drug resistance, function bias, an easy scheme partial agonist. It could also be used to design fluorescent tracers study protein conformations.

Язык: Английский

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Special Issue “Advances in Drug Discovery and Synthesis”

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(2), С. 584 - 584

Опубликована: Янв. 11, 2025

In modern medicinal chemistry, drug discovery is a long, difficult, highly expensive and risky process for the identification of new compounds [...]

Язык: Английский

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