Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

ACS Omega, Год журнала: 2024, Номер unknown

Опубликована: Фев. 8, 2024

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey field computational enzymology, highlighting key principles governing and discussing ongoing challenges promising advances. Over years, computer simulations have become indispensable in study mechanisms, with integration experimental exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies demonstrated power characterizing reaction pathways, transition states, substrate selectivity, product distribution, dynamic conformational changes various enzymes. Nevertheless, significant remain investigating multistep reactions, large-scale changes, allosteric regulation. Beyond mechanistic studies, modeling has emerged an tool computer-aided design rational discovery covalent drugs targeted therapies. Overall, design/engineering drug development can greatly benefit from our understanding detailed enzymes, such protein dynamics, entropy contributions, allostery, revealed by studies. Such convergence different research approaches expected continue, creating synergies research. This outlining ever-expanding research, aims provide guidance future directions facilitate new developments important evolving field.

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

---

Emerging Trends in Bioinspired Superhydrophobic and Superoleophobic Sustainable Surfaces

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Abstract Inspired by nature's ability to master materials for performance and sustainability, biomimicry has enabled the creation of bioinspired structural color, superadhesion, hydrophobicity hydrophilicity, among many others. This review summarizes emerging trends in novel sustainable fluorocarbon‐free designs creating superhydrophobic superoleophobic surfaces. It discusses methods, challenges, future directions, alongside impact computational modeling artificial intelligence accelerating experimental development more surface materials. While significant progress is made materials, surfaces remain a challenge. However, bioinspiration techniques supported platforms are paving way new renewable biodegradable repellent that meet environmental standards without sacrificing performance. Nevertheless, despite concerns, policies, several still continue apply fluorination other environmentally harmful achieve required standard repellency. As discussed this critical review, paradigm integrates advanced characterization, nanotechnology, additive manufacturing, modeling, coming, generate with tailored superhydrophobicity superoleophobicity while adhering standards.

Язык: Английский

---

Cell phenotypes can be predicted from propensities of protein conformations

Current Opinion in Structural Biology, Год журнала: 2023, Номер 83, С. 102722 - 102722

Опубликована: Окт. 21, 2023

Proteins exist as dynamic conformational ensembles. Here we suggest that the propensities of conformations can be predictors cell function. The states molecules preferentially visit viewed phenotypic determinants, and their mutations work by altering relative propensities, thus phenotype. Our examples include (i) inactive state variants harboring cancer driver present active state-like features, in K-Ras4B

Язык: Английский

---

Exploring the Conformational Ensembles of Protein–Protein Complex with Transformer-Based Generative Model

Journal of Chemical Theory and Computation, Год журнала: 2024, Номер 20(11), С. 4469 - 4480

Опубликована: Май 30, 2024

Protein–protein interactions are the basis of many protein functions, and understanding contact conformational changes protein–protein is crucial for linking structure to biological function. Although difficult detect experimentally, molecular dynamics (MD) simulations widely used study ensembles complexes, but there significant limitations in sampling efficiency computational costs. In this study, a generative neural network was trained on complex conformations obtained from directly generate novel with physical realism. We demonstrated use deep learning model based transformer architecture explore complexes through MD simulations. The results showed that learned latent space can be unsampled obtaining new complementing pre-existing ones, which as an exploratory tool analysis enhancement complexes.

Язык: Английский

---

SHP2 clinical phenotype, cancer, or RASopathies, can be predicted by mutant conformational propensities

Cellular and Molecular Life Sciences, Год журнала: 2023, Номер 81(1)

Опубликована: Дек. 12, 2023

Язык: Английский

---

Probing allosteric communication with combined molecular dynamics simulations and network analysis

Current Opinion in Structural Biology, Год журнала: 2024, Номер 86, С. 102820 - 102820

Опубликована: Апрель 29, 2024

Understanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots signal transduction can guide a rational approach to leverage modulation therapeutic purposes. While atomistic signatures processes are difficult determine experimentally, computational methods be remarkable resource. Network analysis built on Molecular Dynamics simulation data particularly suited this respect gradually becoming routine use. Herein, we collect recent literature field, discussing different aspects available options network construction analysis. We further highlight interesting refinements extensions, eventually providing our perspective topic.

Язык: Английский

---

All-Atom Biomolecular Simulation in the Exascale Era

Journal of Chemical Theory and Computation, Год журнала: 2024, Номер 20(5), С. 1777 - 1782

Опубликована: Фев. 21, 2024

Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds potential revolutionize our understanding of fundamental biological processes. Here we report on some major advances were discussed at a recent CECAM workshop in Pisa, Italy, topic with primary focus atomic-level simulations. First, highlight examples current large-scale simulations future possibilities enabled crossing exascale threshold. Next, discuss challenges be overcome optimizing usage these powerful resources. Finally, close listing several grand challenge problems could investigated this computer architecture.

Язык: Английский

---

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

---

AlphaFold2 in Molecular Discovery

Journal of Chemical Information and Modeling, Год журнала: 2023, Номер 63(19), С. 5947 - 5949

Опубликована: Окт. 9, 2023

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAlphaFold2 in Molecular DiscoveryAriane Nunes-Alves*Ariane Nunes-AlvesInstitute of Chemistry, Technische Universität Berlin, Berlin 10623, Germany*Ariane Nunes Alves - Institute Germany; email: [email protected]More by Ariane Nunes-Alveshttps://orcid.org/0000-0002-5488-4732 and Kenneth MerzKenneth MerzDepartment Michigan State University, East Lansing 48824, Michigan, United StatesMore Merzhttps://orcid.org/0000-0001-9139-5893Cite this: J. Chem. Inf. Model. 2023, 63, 19, 5947–5949Publication Date (Web):October 9, 2023Publication History Received11 September 2023Published online9 October inissue 9 2023https://doi.org/10.1021/acs.jcim.3c01459Copyright © 2023 American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views2112Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated reflect usage leading up last few days.Citations number other articles citing article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence for given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InReddit (929 KB) Get e-AlertscloseSUBJECTS:Computational modeling,Drug discovery,Homology,Molecular structure,Protein structure e-Alerts

Язык: Английский

---