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1. Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition

   https://doi.org/10.1093/nar/gkaf601  

   De Silva, Namindu; Perez, Alberto (July 2025, Nucleic Acids Research)

   Abstract DNA exhibits local conformational preferences that affect its ability to adopt biologically relevant conformations, such as those required for binding proteins. Traditional methods, like Markov state models and molecular dynamics (MD) simulations, have advanced our understanding but often struggle to capture these rare conformational states due to high computational demands. Here, we introduce a novel AI framework based on dynamical graphical models (DGMs), a generative machine learning approach trained on equilibrium MD data, to predict DNA conformational transitions that are never seen in the MD ensembles. By leveraging local DNA interactions, DGMs generate a comprehensive transition matrix that captures both thermodynamic and kinetic properties of unsampled states, enabling accurate predictions of rare global conformations without the need for extensive sampling. Applying this model to the B→A transition, we demonstrate that DGMs can efficiently predict sequence-dependent A-DNA preferences, achieving results that align closely with replica exchange umbrella sampling simulations. DGMs provide new insights into DNA sequence–structure relationships, paving the way for applications in DNA sequence design and optimization. 

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2. Transition Path Sampling Study of Engineered Enzymes That Catalyze the Morita–Baylis–Hillman Reaction: Why Is Enzyme Design so Difficult?

   https://doi.org/10.1021/acs.jcim.4c00045  

   Balasubramani, Sree Ganesh; Korchagina, Kseniia; Schwartz, Steven (March 2024, Journal of Chemical Information and Modeling)

   Merz, K (Ed.)

   It is hoped that artificial enzymes designed in laboratories can be efficient alternatives to chemical catalysts that have been used to synthesize organic molecules. However, the design of artificial enzymes is challenging and requires a detailed molecular-level analysis to understand the mechanism they promote in order to design efficient variants. In this study, we computationally investigate the mechanism of proficient Morita-Baylis-Hillman enzymes developed using a combination of computational design and directed evolution. The powerful transition path sampling method coupled with in-depth post-processing analysis has been successfully used to elucidate the different chemical pathways, transition states, protein dynamics, and free energy barriers of reactions catalyzed by such laboratory-optimized enzymes. This research provides an explanation for how different chemical modifications in an enzyme affect its catalytic activity in ways that are not predictable by static design algorithms. 

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3. Validation of DBFOLD: An efficient algorithm for computing folding pathways of complex proteins

   https://doi.org/10.1371/journal.pcbi.1008323  

   Bitran, Amir; Jacobs, William M.; Shakhnovich, Eugene (November 2020, PLOS Computational Biology)

   Kasson, Peter M. (Ed.)

   Atomistic simulations can provide valuable, experimentally-verifiable insights into protein folding mechanisms, but existing ab initio simulation methods are restricted to only the smallest proteins due to severe computational speed limits. The folding of larger proteins has been studied using native-centric potential functions, but such models omit the potentially crucial role of non-native interactions. Here, we present an algorithm, entitled DBFOLD, which can predict folding pathways for a wide range of proteins while accounting for the effects of non-native contacts. In addition, DBFOLD can predict the relative rates of different transitions within a protein’s folding pathway. To accomplish this, rather than directly simulating folding, our method combines equilibrium Monte-Carlo simulations, which deploy enhanced sampling, with unfolding simulations at high temperatures. We show that under certain conditions, trajectories from these two types of simulations can be jointly analyzed to compute unknown folding rates from detailed balance. This requires inferring free energies from the equilibrium simulations, and extrapolating transition rates from the unfolding simulations to lower, physiologically-reasonable temperatures at which the native state is marginally stable. As a proof of principle, we show that our method can accurately predict folding pathways and Monte-Carlo rates for the well-characterized Streptococcal protein G. We then show that our method significantly reduces the amount of computation time required to compute the folding pathways of large, misfolding-prone proteins that lie beyond the reach of existing direct simulation. Our algorithm, which is available online , can generate detailed atomistic models of protein folding mechanisms while shedding light on the role of non-native intermediates which may crucially affect organismal fitness and are frequently implicated in disease. 

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4. Exploring reaction dynamics involving post-transition state bifurcations based on quantum mechanical ambimodal transition states

   https://doi.org/10.1515/pac-2025-0462  

   Lam, Ching Ching; Houk, Kendall N (April 2025, Pure and Applied Chemistry)

   Abstract Computational methods for predicting product ratios in dynamically controlled reactions with shallow intermediates or bifurcating pathways after an ambimodal transition state are reviewed and benchmarked. The range of methods includes molecular dynamics simulations, machine learning-based models and recent advancements in correlational methods, all of which rely on quantum mechanical computations. Together, these approaches form a computational toolbox that enhances the efficiency and effectiveness of exploring reaction selectivity influenced by dynamic effects. 

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5. Aliphatic Polyester Recognition and Reactivity at the Active Cleft of a Fungal Cutinase

   https://doi.org/10.1021/acs.jcim.5c00739  

   Vidossich, Pietro; Manathunga, Madushanka; Götz, Andreas W; Merz, Kenneth M; De_Vivo, Marco (April 2025, Journal of Chemical Information and Modeling)

   Protein engineering of cutinases is a promising strategy for the biocatalytic degradation of non-natural polyesters. We report a mechanistic study addressing the hydrolysis of the aliphatic polyester poly(butylene succinate, or PBS) by the fungal Apergillus oryzae cutinase enzyme. Through atomistic molecular dynamics simulations and advanced alchemical transformations, we reveal how three units of a model PBS substrate fit the active site cleft of the enzyme, interacting with hydrophobic side chains. The substrate ester moiety approaches the Asp–His–Ser catalytic triad, displaying catalytically competent conformations. Acylation and deacylation hydrolytic reactions were modeled according to a canonical esterase mechanism using umbrella sampling simulations at the quantum mechanical/molecular mechanical DFT(B3LYP)/6–31G**/AMBERff level. The free energy profiles of both steps show a high-energy tetrahedral intermediate resulting from the nucleophilic attack on the ester’s carboxylic carbon. The free energy barrier of the acylation step is higher (20.2 ± 0.6 kcal mol–1) than that of the deacylation step (13.6 ± 0.6 kcal mol–1). This is likely due to the interaction of the ester’s carboxylic oxygen with the oxyanion hole in the reactive conformation of the deacylation step. In contrast, these interactions form as the reaction proceeds during the acylation step. The formation of an additional hydrogen bond interaction with the side chain of Ser48 is crucial to stabilizing the developing charge at the carboxylic oxygen, thus lowering the activation free energy barrier. These mechanistic insights will inform the design of enzyme variants with improved activity for plastic degradation. 

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