Evidence map›Paper›PMID 42461704›Full record

ArticleAccounts of chemical research2026

Atomistic Simulations Decode the Mechanisms of DNA and RNA Processing Enzymes: Function through Motion.

Marco De Vivo

Abstract read
In one paragraph

Article in Accounts of chemical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Marco De VivoMolecular Modeling and Drug Discovery Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163Genoa, Italy.ORCID 0000-0003-4022-5661

Funding

Fondazione AIRC per la ricerca sul cancro ETS IG 30631
6 · The paper itself

Abstract

Enzymes and nucleic acid processing machines are among the most sophisticated molecules evolved by nature. These include several vital enzymes such as polymerases, nucleases, topoisomerases, and RNA enzymes. These enzymes exhibit complex structures and multicomponent assemblies that enable an exceptional efficiency and specificity in carrying out the fundamental chemical reactions required for life. Yet, these structures pose a challenge to our understanding of their catalytic precision, as this requires atomistic insight into how they operate on DNA or RNA. Indeed, the static or quasi-static view from experiments fails to fully explore the conformational space that these enzymes traverse during catalysis. Thus, advanced molecular simulation techniques are increasingly integrated with experimental results to overcome such sampling limitations and to explore the configurational space, thereby defining the functional dynamics of structural motions and rearrangements that would otherwise remain unclear. In this Account, I will examine how atomistic multiscale molecular simulations and free energy calculations─recently coupled with AI-guided enhanced sampling methods─have contributed to clarifying enzymatic mechanisms that could only be hypothesized by examining structures, as evidenced by increasingly complex structural biology results. This is a time when we can simulate ultralarge realistic model systems comprising proteins, nucleic acids, ions, and water molecules, all of which are critical components for enzymatic function. Remarkably, such modeling and simulations can nowadays enable the prospective exploration of increasingly structurally complex systems. In this scenario, there is today a relevant body of work that defines function through motion as a key element in these enzymes. My group has generated a broad collection of results on a range of nucleic acid processing enzymes, from polymerases to nucleases and RNA enzymes, which altogether highlight how dynamics define function, with specific residues─often evolutionarily conserved and localized in the second coordination shell of the catalytic core─that are strategically positioned to operate in a cooperative and highly coordinated fashion for specific enzymatic actions on nucleic acids. These coordinated residue motions, near the reaction center, appear distinct from large allosteric movements of distal domains. Instead, they resemble finely tuned, small-scale mechanisms that marry complexity with the precise, clocklike efficiency evolution has built into the catalytic core. How such elaborate enzymatic architectures move-to-function, captured and demonstrated by the most recent computational work and structural data, will therefore be the core topic discussed in this Account. While the concept of "function through motion" can obviously be extended to other catalytic systems, what is particularly remarkable about these enzymes─even if not unique─is that their functional complexity is handled with extraordinary accuracy through precise motions, despite the system's architectural complexity, on the move. These recent mechanistic findings also provide insights into how to engineer or modulate these enzymes, with implications for several scientific activities centered on nucleic acid chemistry.

Indexed as

DNAMolecular Dynamics SimulationRNAMotionThermodynamicsDNARNA

Identifiers

PMID42461704
PMCPMC13492265

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.