Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials

Signal Transduction and Targeted Therapy, Год журнала: 2023, Номер 8(1)

Опубликована: Сен. 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.

Язык: Английский

---

Role of Artificial Intelligence in Revolutionizing Drug Discovery

Fundamental Research, Год журнала: 2024, Номер unknown

Опубликована: Май 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.

Язык: Английский

---

Machine learning approaches in predicting allosteric sites

Current Opinion in Structural Biology, Год журнала: 2024, Номер 85, С. 102774 - 102774

Опубликована: Фев. 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.

Язык: Английский

---

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

Pharmacological Research, Год журнала: 2025, Номер 212, С. 107574 - 107574

Опубликована: Янв. 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.

Язык: Английский

---

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

Bioorganic Chemistry, Год журнала: 2023, Номер 135, С. 106390 - 106390

Опубликована: Янв. 28, 2023

Язык: Английский

---

AlloReverse: multiscale understanding among hierarchical allosteric regulations

Nucleic Acids Research, Год журнала: 2023, Номер 51(W1), С. W33 - W38

Опубликована: Апрель 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/.

Язык: Английский

---

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

Molecules, Год журнала: 2024, Номер 29(19), С. 4626 - 4626

Опубликована: Сен. 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.

Язык: Английский

---

Allostery

Quarterly Reviews of Biophysics, Год журнала: 2025, Номер 58

Опубликована: Янв. 1, 2025

Abstract Allostery describes the ability of biological macromolecules to transmit signals spatially through molecule from an allosteric site – a that is distinct orthosteric binding sites primary, endogenous ligands functional or active site. This review starts with historical overview and description classical example allostery hemoglobin other well-known examples (aspartate transcarbamoylase, Lac repressor, kinases, G-protein-coupled receptors, adenosine triphosphate synthase, chaperonin). We then discuss fringe allostery, including intrinsically disordered proteins inter-enzyme influence dynamics, entropy, conformational ensembles landscapes on mechanisms, capture essence field. Thereafter, we give over central methods for investigating molecular covering experimental techniques as well simulations artificial intelligence (AI)-based methods. conclude allostery-based drug discovery, its challenges opportunities: recent advent AI-based methods, compounds are set revolutionize discovery medical treatments.

Язык: Английский

---

Mapping cryptic binding sites of drug targets to overcome drug resistance

Drug Resistance Updates, Год журнала: 2023, Номер 67, С. 100934 - 100934

Опубликована: Янв. 23, 2023

Язык: Английский

---

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

ACS Chemical Neuroscience, Год журнала: 2024, Номер 15(4), С. 844 - 853

Опубликована: Фев. 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.

Язык: Английский

---