Coarse grained modelling highlights the binding differences in the two different allosteric sites of the Human Kinesin EG5 and its implications in inhibitor design DOI

Soundarya Priya Alexandar,

Ragothaman M. Yennamalli, Venkatasubramanian Ulaganathan

et al.

Computational Biology and Chemistry, Journal Year: 2022, Volume and Issue: 99, P. 107708 - 107708

Published: June 9, 2022

Language: Английский

Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials DOI Creative Commons
Xin Xie, Tingting Yu, Xiang Li

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: Sept. 6, 2023

Abstract Undruggable proteins are a class of that often characterized by large, complex structures or functions difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also great opportunity for treatment human diseases and attracted substantial efforts in the field medicine. Therefore, this review, we focus on recent development discovery targeting “undruggable” their application clinic. To make review well organized, discuss strategies proteins, including covalent regulation, allosteric inhibition, protein–protein/DNA interaction targeted nucleic acid-based approach, immunotherapy others.

Language: Английский

Citations

166
Role of Artificial Intelligence in Revolutionizing Drug Discovery DOI Creative Commons
Ashfaq Ur Rehman, Mingyu Li,

Binjian Wu

et al.

Fundamental Research, Journal Year: 2024, Volume and Issue: unknown

Published: May 1, 2024

The application of artificial intelligence (AI) in medicine, particularly through machine learning (ML), marked a significant progression drug discovery. AI acts as powerful catalyst narrowing the gap between disease understanding and identification potential therapeutic agents. This review provides an inclusive summary latest advancements its We examine various stages discovery process, starting from encompassing diagnosis, target identification, screening, lead AI's capability to analyze extensive datasets discern patterns is essential these stages, enhancing predictions efficiencies discovery, clinical trial management. role expediting development emphasized, highlighting vast data volumes, thus reducing time costs associated with new market introduction. importance quality, algorithm training, ethical considerations, especially patient handling during trials, addressed. By considering factors, promises transform development, offering benefits patients society.

Language: Английский

Citations

24
Machine learning approaches in predicting allosteric sites DOI Creative Commons
Francho Nerín-Fonz, Zoe Cournia

Current Opinion in Structural Biology, Journal Year: 2024, Volume and Issue: 85, P. 102774 - 102774

Published: Feb. 13, 2024

Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of modulators over orthosteric ones have sparked the development numerous computational approaches, such as identification sites, facilitate drug discovery. Building on success machine learning (ML) models for solving complex problems in biology and chemistry, several ML predicting sites been developed. In this review, we provide an overview these discuss future perspectives powered by field artificial intelligence language models.

Language: Английский

Citations

19
Targeting cryptic allosteric sites of G protein-coupled receptors as a novel strategy for biased drug discovery DOI Creative Commons

Xin Qiao,

Xiaolong Li, Mingyang Zhang

et al.

Pharmacological Research, Journal Year: 2025, Volume and Issue: 212, P. 107574 - 107574

Published: Jan. 2, 2025

G protein-coupled receptors (GPCRs) represent the largest family of membrane and are highly effective targets for therapeutic drugs. GPCRs couple different downstream effectors, including proteins (such as Gi/o, Gs, G12, Gq) β-arrestins β-arrestin 1 2) to mediate diverse cellular physiological responses. Biased signaling allows specific activation certain pathways from full range receptors' capabilities. Targeting more variable allosteric sites, which spatially conserved orthosteric represents a novel approach in biased GPCR drug discovery, leading innovative strategies targeting GPCRs. Notably, emergence cryptic sites on has expanded repertoire available improved receptor subtype selectivity. Here, we conduct summary recent progress structural determination elucidate mechanisms induced by modulators. Additionally, discuss means identify design modulators based through structure-based design, is an advanced pharmacotherapeutic treating GPCR-associated diseases.

Language: Английский

Citations

3
Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3-d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro DOI Open Access
Hossein Mousavi, Behzad Zeynizadeh, Mehdi Rimaz

et al.

Bioorganic Chemistry, Journal Year: 2023, Volume and Issue: 135, P. 106390 - 106390

Published: Jan. 28, 2023

Language: Английский

Citations

27
AlloReverse: multiscale understanding among hierarchical allosteric regulations DOI Creative Commons
Jinyin Zha, Qian Li, Xinyi Liu

et al.

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 51(W1), P. W33 - W38

Published: April 18, 2023

Abstract Increasing data in allostery are requiring analysis of coupling relationships among different allosteric sites on a single protein. Here, based our previous efforts reversed communication theory, we have developed AlloReverse, web server for multiscale multiple regulations. AlloReverse integrates protein dynamics and machine learning to discover residues, regulation pathways. Especially, could reveal hierarchical between pathways couplings sites, offering whole map allostery. The shows good performance re-emerging known Moreover, applied explore global CDC42 SIRT3. predicted novel residues both systems, the functionality was validated experimentally. It also suggests possible scheme combined therapy or bivalent drugs Taken together, is workflow providing complete believed aid target identification, drug design understanding biological mechanisms. freely available all users at https://mdl.shsmu.edu.cn/AlloReverse/ http://www.allostery.net/AlloReverse/.

Language: Английский

Citations

20
Mapping cryptic binding sites of drug targets to overcome drug resistance DOI
Yangyang Gao, Wei‐Cheng Yang,

Charles R. Ashby

et al.

Drug Resistance Updates, Journal Year: 2023, Volume and Issue: 67, P. 100934 - 100934

Published: Jan. 23, 2023

Language: Английский

Citations

15
Unraveling the Interplay of Extracellular Domain Conformational Changes and Parathyroid Hormone Type 1 Receptor Activation in Class B1 G Protein-Coupled Receptors: Integrating Enhanced Sampling Molecular Dynamics Simulations and Markov State Models DOI
Mengrong Li, Xiaoxiao Zhang, Shu Li

et al.

ACS Chemical Neuroscience, Journal Year: 2024, Volume and Issue: 15(4), P. 844 - 853

Published: Feb. 5, 2024

Parathyroid hormone (PTH) type 1 receptor (PTH1R), as a typical class B1 G protein-coupled (GPCR), is responsible for regulating bone turnover and maintaining calcium homeostasis, its dysregulation has been implicated in the development of several diseases. The extracellular domain (ECD) PTH1R crucial recognition binding ligands, may exhibit an autoinhibited state with closure ECD absence ligands. However, correlation between conformations activation remains unclear. Thus, this study combines enhanced sampling molecular dynamics (MD) simulations Markov models (MSMs) to reveal possible relevance PTH1R. First, 22 intermediate structures are generated from active conducted 10 independent 200 ns each. Then, MSM constructed based on cumulative 44 μs six identified microstates. Finally, potential interplay conformational changes well cryptic allosteric pockets states during revealed. Overall, our findings that specific provide essential insights GPCR biology developing novel modulators targeting sites.

Language: Английский

Citations

6
Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence DOI Creative Commons

Ahrum Son,

Jongham Park, Woojin Kim

et al.

Molecules, Journal Year: 2024, Volume and Issue: 29(19), P. 4626 - 4626

Published: Sept. 29, 2024

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design proteins with unprecedented precision functionality. Computational methods now play a crucial role enhancing stability, activity, specificity for diverse applications biotechnology medicine. Techniques such as deep reinforcement transfer learning have dramatically improved structure prediction, optimization binding affinities, enzyme design. These innovations streamlined process allowing rapid generation targeted libraries, reducing experimental sampling, rational tailored properties. Furthermore, integration approaches high-throughput techniques facilitated development multifunctional novel therapeutics. However, challenges remain bridging gap between predictions validation addressing ethical concerns related to AI-driven This review provides comprehensive overview current state future directions engineering, emphasizing their transformative potential creating next-generation biologics advancing synthetic biology.

Language: Английский

Citations

6
Comparative analysis of structural dynamics and allosteric mechanisms of RecA/Rad51 family proteins: Integrated atomistic MD simulation and network-based analysis DOI

Yue Pan,

Chong Zhao, Wenyu Fu

et al.

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 261, P. 129843 - 129843

Published: Jan. 30, 2024

Language: Английский

Citations

5