Sumit Mittal

Transthyretin (TTR) amyloidosis is initiated by dissociation of the native tetramer into lower‐order species, yet the molecular determinants governing dimer stability remain incompletely understood. Here, we present an atomistic investigation of wild‐type (WT) and amyloidogenic V30M TTR dimers using multiple long‐range all‐atom molecular dynamics simulations combined with principal component analysis, free‐energy landscape mapping, umbrella sampling, and MM‐PBSA calculations. Both dimers retain a β‐sheet–rich architecture; however, V30M induces subtle but systematic perturbations at the interface, including increased solvent exposure, reduced inter‐chain contact density, and reorganization of the hydrogen‐bonding network. Enhanced sampling using steered molecular dynamics followed by umbrella sampling revealed a lower free‐energy barrier for dimer dissociation into individual monomers in V30M (∼13 kcal/mol) than in WT (∼17 kcal/mol), indicating reduced dimer stability and a greater propensity of the V30M variant to dissociate into monomeric subunits. Consistently, MM‐PBSA calculations indicate an overall ∼7 kcal/mol reduction in effective inter‐chain binding free energy for the mutant, arising from long‐range perturbations rather than direct contributions of residue 30. Collectively, V30M weakens the TTR dimer interface, lowers the dissociation barrier, and promotes aggregation‐prone intermediates, highlighting stabilization of native dimer contacts as a therapeutic strategy.

The mechanisms driving amyloid assembly have long intrigued structural biologists, as they offer insights into systemic fibrotic changes and the dynamic behavior of transthyretin (TTR) aggregation, crucial for developing amyloid‐targeted therapies. In TTR‐associated amyloidosis, amyloid fibrils form via destabilization of the tetramer into dimers and monomers. While many TTR mutations have been studied, the atomistic impact of multiple mutations on amyloid transthyretin (ATTR) self‐assembly remains underexplored. To the best of our knowledge, this is the first computational analysis reporting the impact of the L110M mutation on ATTR peptide aggregation. Using triplicate 1 μs all‐atom molecular dynamics (MD) simulations, totaling 18 μs, the conformational dynamics of cross‐β amyloid fibrils in the ATTR(105–115) segment were examined for both wild‐type and L110M mutant TTR. The L110M mutation consistently enhanced the β‐sheet content in all oligomers, with increases of ~1%, ~5%, and ~4% over the wild‐type in the 2‐, 4‐, and 8‐peptide systems, respectively. Molecular mechanics Poisson–Boltzmann surface area (MM‐PBSA) calculations revealed higher effective binding free energy for the L110M mutant, with residue M110 contributing significantly to stabilization. These results suggest that L110M modestly enhances conformational order and stability in the TTR peptide assemblies without major structural disruption, deepening our understanding of amyloidogenesis in TTR‐related disorders.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein–nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9–2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Remdesivir (RDV) emerged as an effective drug against the SARS‐CoV‐2 virus pandemic. One of the crucial steps in the mechanism of action of RDV is its incorporation into the growing RNA strand. RDV, an adenosine analogue, forms Watson‐Crick (WC) type hydrogen bonds with uridine in the complementary strand and the strength of this interaction will control efficacy of RDV. While there is a plethora of structural and energetic information available about WC H‐bonds in natural base pairs, the interaction of RDV with uridine has not been studied yet at the atomic level. In this article, we aim to bridge this gap, to understand RDV and its hydrogen bonding interactions, by employing density functional theory (DFT) at the M06‐2X/cc‐pVDZ level. The interaction energy, QTAIM analysis, NBO and SAPT2 are performed for RDV, adenosine, and their complex with uridine to gain insights into the nature of hydrogen bonding. The computations show that RDV has similar geometry, energetic, molecular orbitals, and aromaticity as adenosine, suggesting that RDV is an effective adenosine analogue. The important geometrical parameters, such as bond distances and red‐shift in the stretching vibrational modes of adenosine, RDV and uridine identify two WC‐type H‐bonds. The relative strength of these two H‐bonds is computed using QTAIM parameters and the computed hydrogen bond energy. Finally, the SAPT2 study is performed at the minima and at non‐equilibrium base pair distances to understand the dominant intermolecular physical force. This study, based on a thorough analysis of a variety of computations, suggests that both adenosine and RDV have similar structure, energetic, and hydrogen bonding behaviour.

Cellular forces regulate an untold spectrum of living processes such as cell migration, gene expression, and ion conduction. However, a quantitative description of mechanical control remains elusive due to the lack of general, live-cell tools to measure discrete forces between biomolecules. Here we introduce a computational pipeline for force measurement that leverages well-defined, tunable release of a mechanically activated small molecule fluorophore. These sensors are characterized using a consensus approach combining equilibrium and steered QM/MM molecular dynamics models to capture the chemical, mechanical, and conformational transitions underlying force activation thresholds on a pico-to-nanonewton scale. We find that chemical modification of the mechanophore and variation of its biomolecular tethers can tune the rate-determining step for fluorophore release and adjust the mechanochemical activation barrier. The models offer a new molecular framework for calibrated, programmable biomolecular force reporting within the live-cell regime, opening new opportunities to study mechanical phenomena in biological systems