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

ACS Omega, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 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.

Language: Английский

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Machine learning approaches in predicting allosteric sites

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

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Emerging Trends in Bioinspired Superhydrophobic and Superoleophobic Sustainable Surfaces

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 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.

Language: Английский

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

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(11), P. 4469 - 4480

Published: May 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.

Language: Английский

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Cell phenotypes can be predicted from propensities of protein conformations

Current Opinion in Structural Biology, Journal Year: 2023, Volume and Issue: 83, P. 102722 - 102722

Published: Oct. 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

Language: Английский

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

Cellular and Molecular Life Sciences, Journal Year: 2023, Volume and Issue: 81(1)

Published: Dec. 12, 2023

Language: Английский

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Probing allosteric communication with combined molecular dynamics simulations and network analysis

Current Opinion in Structural Biology, Journal Year: 2024, Volume and Issue: 86, P. 102820 - 102820

Published: April 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.

Language: 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, Journal Year: 2023, Volume and Issue: 24(9), P. 7747 - 7747

Published: April 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.

Language: Английский

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All-Atom Biomolecular Simulation in the Exascale Era

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(5), P. 1777 - 1782

Published: Feb. 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.

Language: Английский

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The structural biology and dynamics of malate dehydrogenases

Essays in Biochemistry, Journal Year: 2024, Volume and Issue: 68(2), P. 57 - 72

Published: Aug. 8, 2024

Abstract Malate dehydrogenase (MDH) enzymes catalyze the reversible oxidoreduction of malate to oxaloacetate using NAD(P) as a cofactor. This reaction is vital for metabolism and exchange reducing equivalents between cellular compartments. There are more than 100 structures MDH in Protein Data Bank, representing species from archaea, bacteria, eukaryotes. conserved family shares common nucleotide-binding domain, substrate-binding subunits associate form dimeric or tetrameric enzyme. Despite variety crystallization conditions ligands experimental structures, conformation configuration similar. The quaternary structure active site dynamics account most conformational differences structures. Oligomerization appears essential activity despite each subunit having structurally independent site. two dynamic regions within that influence substrate binding possibly catalysis, with one these adjoining interface. In this review, we introduce reader general structural framework highlighting conservation certain features pointing out unique regulate enzyme activity.

Language: Английский

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