Journal of Computational Chemistry

This work presents DSSNA (Define Spatial Structure of Nucleic Acids) v2026, a free and open-source standalone GROMACS module for the automated analysis of nucleic acid structure based on atomic coordinates. DSSNA reproduces the core functionality of the X3DNA and DSSR approach while extending it to the analysis of molecular dynamics trajectories through integration with the GROMACS software package. The algorithm identifies and classifies structural features, including canonical and noncanonical base pairs, hydrogen-bond networks, stacking interactions, helices, and many other elements of nucleic acid secondary and tertiary structure. Its modular output system enables users to select the structural metrics required for a particular analysis, while the use of neighbor-search algorithms and data-processing capabilities facilitates the analysis of large trajectory datasets. The performance and applicability of DSSNA were demonstrated using a molecular dynamics trajectory of a GUAA tetraloop mutant of the sarcin-ricin domain from Escherichia coli 23S ribosomal RNA (PDB ID: 1MSY). The analysis showed that only a subset of the observed base pairs and stacking interactions remains stable throughout the trajectory. Persistent canonical base pairs formed the structural core responsible for maintaining the secondary-structure profile, whereas most noncanonical base pairs and stacking interactions were transient. The results demonstrate that DSSNA can be used to characterize both static nucleic acid structures and the temporal evolution of structural interactions in molecular dynamics simulations. The work also provides a glossary of terms relevant to the analysis and interpretation of nucleic acid structures.

Nucleic acid secondary structure thermodynamic models have great utility for the analysis and design of nucleic acid structures, devices, and systems, yet their complexity and piecemeal development over decades pose challenges to further innovation. Employing different functional forms for each loop type within a structure, existing nearest neighbor (NN) models necessitate numerous loop-specific dynamic programming recursions in order to calculate physical quantities of interest. Here, we derive and implement a new class of models based on transfer matrices, yielding a single functional form for the free energy of all loop types (as the trace of a matrix product) and a single dynamic programming recursion for calculating physical quantities of interest. This new class of models has additional attractive properties: 1) incorporating a subensemble of states describing proximal interactions within each loop, 2) approximating logarithmic entropic scaling for all closed loops, 3) offering the opportunity to model correlations between the subensembles of neighboring loops by generalizing transfer matrices to tensors, and 4) providing a basis for systematically adjusting the number of model parameters to enhance accuracy as new empirical data become available for parameter regression. We use literature experimental data to regress parameters for representative new transfer matrix models, observing accuracy as good as or better than that of existing NN models, yet using dramatically fewer parameters (e.g., dozens vs hundreds or thousands). For complex ensembles containing an arbitrary number of interacting nucleic acid strands totaling N nucleotides, the new single recursion leads to exact dynamic programs for calculating diverse physical quantities (including the partition function, equilibrium base-pairing probabilities, minimum free energy and suboptimal proxy structures, Boltzmann-sampled secondary structures, and gradient of the partition function) with O(N3) time complexity. The simplicity of the new partition function dynamic program enables overflow-safe partition function calculations using renormalization to achieve scalability for large complexes (e.g., 50,000 nt) as well as formulation using a global matrix multiplication primitive that facilitates GPU acceleration, yielding speedups of an order of magnitude relative to NN partition function algorithms for large complexes. The new class of transfer matrix and tensor models provides an elegant framework for future efforts to advance the accuracy, speed, and scope of computational nucleic acid analysis and design.

Oncogenic mutations are known to occur at non-random rates and specific hotspots, yet the structural and thermodynamic factors underlying these hotspots remain poorly understood. This study presents molecular dynamics simulations of 22 cancer hotspots across 10 oncogene families, simulated as histone-DNA complexes. Across 18 of 22 mutations, thermodynamic effects were predominantly localized to within 6 [A] of the DNA-histone interface (mean capture 104%), with van der Waals interactions driving the effect at the thermodynamic extremes, indicating that oncogenic mutations alter nucleosome binding through precise local contact changes rather than global structural rearrangements. Further, analysis revealed a gene-family correlated pattern in thermodynamic stability. RAS family mutations showed a consistent trend toward nucleosome stabilization (mean {Delta}{Delta}G = -4.82 kcal mol-1), while kinase domain mutations trended toward destabilization (mean {Delta}{Delta}G = +40.12 kcal mol-1), a difference reaching statistical significance in this exploratory analysis (Mann-Whitney U, p = 0.008). This interface-specific mechanism, combined with the gene-family-correlated thermodynamic pattern, provides a biophysical framework for understanding nucleosome-level contributions to cancer hotspot mutation biology.

Atomistic simulations of intrinsically disordered proteins (IDPs) are notoriously sensitive to force field inaccuracies, either regarding the protein or the water model, yielding inaccurate observables such as compactness, secondary structure propensities, or kinetics. The currently most widely used IDP force fields are Amber99sb-disp (A99disp) and Charmm36m (C36m). A99disp includes a new water model and thus optimized both, the protein and the water interactions. In contrast, C36m used the Tip3p water model and optimized only protein interactions. In many cases, C36m+Tip3p underestimates radii of gyration compared to FRET or SAXS experiments. Such overly compact structural ensembles are believed to arise from an imbalance between protein-protein, protein-water, and water-water interactions, which might be due to Tip3p inaccuracies. Here, we aim at re-balancing these interactions by combining C36m with the Optimal Point Charge (OPC) water model. Recently, this C36m+OPC combination showed improved accuracy for the disordered domain of the measles virus nucleoprotein. Here we present a systematic assessment, comparing C36m+OPC, C36m+Tip3p, C36m+Tip4p, C22*, A03ws, A99sb-ws, and A99disp for five IDPs, as well as a subset of those for five globular proteins, a set of disordered AGQ-repeat peptides, and the fast folding miniprotein CLN025. We compared extensive MD simulations (> 8.5 ms) with SAXS, NMR, circular dichroism, photo-induced electron transfer (PET), T-jump infrared spectroscopy, and X-ray crystallography measurements. We found that combining C36m with OPC improved accuracy over C36m+Tip3p for IDP ensembles without compromising its accuracy for globular proteins. While also the kinetics of the AGQ-peptides were more accurate for C36m+OPC, those of CLN025 folding were less accurate. Overall, C36m+OPC showed similar accuracy as A99sb-ws and A99disp, the latter is currently considered among the most accurate protein force fields.

Over the past decade, aptamers have emerged as promising therapeutics, with cancer therapeutics as a major research area. Existing computational methods, however, either predict aptamer-target pairs or design aptamers against a specific target. In this study, we present AntiCapt, a computational method for identifying single-stranded DNA (ssDNA) anticancer aptamers (ACAs) using sequence descriptors and fine-tuned nucleotide language models. The main dataset comprises 1,021 experimentally validated ACAs and an equal number of non-aptamer sequences. Comparative analysis revealed distinct patterns in the nucleotide, dinucleotide, and trinucleotide compositions associated with ACAs. We developed machine learning (ML) models using composition, autocorrelation, binary profiles and structural features. Among these, the best-performing composition-based model achieved an AUC of 0.93 on an independent dataset, while chemical descriptor-and structural feature-based models achieved a maximum AUC of 0.89. ML models using pretrained and fine-tuned NLM embeddings were also developed, with fine-tuned HyenaDNA embeddings achieving the highest performance, with an independent AUC of 0.94. Furthermore, we developed a model for discriminating anticancer aptamers from general aptamers. The best-performing models were integrated into AntiCapt, a web server and a standalone tool for predicting, designing, and genome-scale scanning anticancer aptamers (https://webs.iiitd.edu.in/raghava/anticapt/).

Simulations have emerged as a pillar in biophysics to understand behavior at the molecular scale, in particular for proteins such as intrinsically disordered proteins. These often show transient folding with long-lived states that are challenging to efficiently sample using conventional simulations. Here, we show that Hamiltonian replica-exchange at the level of hydrogen bonds can be used to systematically explore the conformational landscape of proteins. Investigating the IM30 protein, a partially alpha helical protein, under the PLUM coarse-grain force-field, we show that this approach straightforwardly allows characterization of local helix stability, which would be otherwise hard to obtain. For instance, we show that some regions of the conserved coiled-coil structure are less stable than others. By combining the different replicas with principal component analysis, we are able to dissect the conformational landscape of the IM30 protein. Taken together, this indicates that enhanced sampling remains an important part of simulations, allowing insights that cannot be easily obtained in traditional simulations.

Drug-target association and dissociation rates often determine in vivo efficacy more than affinity alone. The association rate constant, however, is computationally challenging to predict since the productive encounter is a rare event in a large translational and orientational search space, which lies beyond the reach of conventional atomistic simulations. We present PySTARC (Python Simulation Toolkit for Association Rate Constants), a GPU-accelerated Brownian dynamics engine for estimating bimolecular association rate constants. PySTARC is a Python reimplementation of the BrownDye engine that converges even small reaction probabilities on a single GPU, at a throughput that would otherwise require a large CPU cluster. The current framework resolves reactions between integration steps with a closed-form Brownian bridge, models the internal flexibility of the solute through a coarse-grained bead chain, distributes trajectories across multiple GPUs, checkpoints long runs, monitors convergence, and automates the entire workflow from input structures to rate estimates. PySTARC is validated against protein-ligand and protein-protein complexes spanning five orders of magnitude in the association rate constant, with most estimates within one order of magnitude of experiment.

Structure-guided mutation design requires relating a proposed substitution to its molecular environment to anticipate its effects. Existing evaluations of large language models (LLMs) in protein engineering emphasize sequence-based prediction or tool-integrated workflows, leaving unclear whether LLMs themselves can infer mutation effects from structural evidence without dedicated predictors. We evaluate physical intuition-qualitative reasoning that connects structural observations with molecular interaction principles-using a benchmark that tests the same substitution in the same protein across different binding partners, with predictions based on local atomic coordinates and no dedicated mutation-effect predictors. In the tested comparisons, the best-performing LLM achieved group accuracy comparable to the evaluated energy predictors and Biomni, an LLM-driven tool workflow, while the methods differed in their prediction patterns. Most evaluated LLMs achieved higher group accuracy for larger affinity improvements. Structural interventions support selective dependence on mutation-site and partner information, while assessable structural claims largely agree with the supplied coordinates. These findings support direct LLM structural reasoning as a basis for developing rational mutation-design tools that connect molecular observations with partner-specific effects.

Phase separating biopolymers are often described by the sticker-spacer model, which accounts for non-attractive linkers that connect discrete attractive moieties. "Associative polymer" theories describe these systems as two coexisting fluids of stickers and spacers, respectively. In such theories the stickers and spacers are decoupled in the sense that sticker binding does not affect spacer conformations or vice versa. Informed by coarse-grained simulations, we develop a phenomenological mean-field theory to determine the microscopic coupling between the sticker and spacer degrees of freedom. Accounting for linkers connecting adjacent spacers introduces free energy contributions due to conformational entropy of the spacer and the binding combinatorics of the stickers. These contributions have opposite dependencies on spacer length because longer spacers allow a greater volume to search for binding partners but incur a greater entropy cost upon bond formation. The length dependent terms from these contributions cancel leaving a length-independent elastic term arising from the fact that a small amount of spacer stretching lowers the free energy by increasing the number of possible binding partners. The cancellation of spacer length from the theory justifies the decoupling approximation and supports mean-field treatments of heterogeneous spacers.

Ligand transport through proteins underlies many aspects of molecular function, but its detailed mechanisms remain largely inaccessible to direct observation. Molecular dynamics simulations could close this gap, but are computationally too demanding for practical purposes, including filtering stages of enzyme design pipelines. Simplified docking-based approaches might be informative, but sometimes the oversimplifications required for high throughput applications might lead to unreliable predictions. Here we present RosettaLigand PathFinder (RLPF), a fast and practical tool explicitly built to ensure conformational continuity of the ligand trajectory, protein and ligand flexibility, and robustness of every run. Built using a flexible RosettaScripts XML interface, it enables fine-grained control over all simulation parameters and adaptability for a wide range of use cases. Applied to several representative systems, RLPF reproduced established transport features and mutation effects, highlighting its ability to produce biologically relevant insights and serve as a filter in enzyme design campaigns. Wide customizability and well-defined scope of RLPF make it a useful addition to the family of tools to study the dynamics of interactions between small molecules and proteins.

We present SMartini, an automated pipeline for deriving Martini 3 coarse-grained force-field parameters for arbitrary small molecules. Starting from a molecular structure or a SMILES string, the pipeline maps the all-atom molecule onto a coarse-grained bead representation and automatically fits both bonded and non-bonded parameters. Bonded parameters are obtained via Boltzmann inversion of atomistic molecular dynamics trajectories; successive rounds of coarse-grained simulation and distribution-matching updates then refine these estimates until the coarse-grained conformational ensemble reproduces the all-atom reference within tolerance. We validate the pipeline on a diverse set of small molecules including drug-like compounds, metabolites, and cofactors. Implemented as a modular sequence of scripts that can be orchestrated on high-performance computing clusters, SMartini makes automated parametrization of large ligand libraries practical and reduces the manual effort required for coarse-grained model development, enabling high-throughput coarse-grained simulations of protein--ligand systems within the Martini 3 ecosystem

Hit-to-lead optimization is a multi-objective molecular-design problem in which binding-related scores must be considered together with drug-likeness and synthetic accessibility. We developed a closed-loop language-model medicinal-chemistry agent that combines target and starting-hit context, PubMed retrieval, molecular-property tools, docking, and feedback from previously scored analogues. In a matched computational benchmark comprising six kinase targets, six methods, a 40-candidate budget, and six independent campaigns per method, the agent achieved the highest mean aggregate score under a pre-specified composite objective on four targets. Re-analysis of the same candidate sets with drug-likeness-gated docking and docking-drug-likeness hypervolume produced different target-level winners, showing that evaluation rules can change the comparative interpretation of an optimization campaign. Candidate distributions and component ablations indicated that the agent's advantage was associated primarily with retention of scored-candidate feedback and higher-QED regions rather than with docking alone. An EGFR case study illustrates a scaffold-preserving in-silico optimization trajectory from erlotinib. The study is a computational benchmark and evaluation framework; the prioritized molecules are hypotheses for subsequent experimental testing, not experimentally validated leads.

We present SOP-Multi-{pi}, an enhanced version of the two-bead-per-amino-acid resolution Self-Organized Polymer (SOP) model designed to investigate the conformational dynamics of Intrinsically Disordered Proteins (IDPs) and their phase behavior in biomolecular condensates. The original SOP-IDP and SOP-Multi models work well for chains in good solvents that predominantly adopt expanded conformations. However, they fail to reproduce the single-chain properties and phase-separation behavior of IDPs such as hnRNPA1 and its variants, which have a distinct pattern of aromatic residues. We therefore reparameterized the SOP-IDP force field to explicitly capture {pi}-{pi} and cation-{pi} interactions at the coarse-grained (CG) resolution. These interactions are weak but important for biomolecular recognition and for driving and stabilizing biomolecular condensates. Dimensionless rescaling prefactors ({lambda}) were applied exclusively to the aromatic {pi}-{pi} (Phe, Tyr, Trp) and cation-{pi} (Arg/Lys with Phe, Tyr, Trp) pairs. They were optimized iteratively against experimental radii of gyration of the hnRNPA1 low-complexity domain (A1-LCD) and its 16 aromatic and charge mutants. This restored a physically meaningful Tyr > Phe interaction hierarchy, and the Pearson correlation for Rg improved from r = 0.07 (SOP-IDP) to r = 0.83 (RMSD = 0.08 nm). The same parameters, without further tuning, gave r = 0.93 for 34 additional IDPs (24-198 residues) and reproduced Flory scaling ( {nu} ~ 0.588). We back-mapped microsecond-scale CG ensembles to all-atom representations and computed SAXS and Kratky profiles for direct comparison with experiments on IDPs and on the multidomain proteins hnRNPA1, TDP-43, G3BP1, and FUS. In direct-coexistence slab simulations, the model captures the phase behavior of A1-LCD condensates and their variants, with critical temperatures that correlate strongly with experiment (r = 0.88) but are somewhat underestimated in absolute terms. The model is implemented in LAMMPS, and a detailed user manual is provided for setting up single-chain and multi-chain condensate simulations of full-length multidomain proteins.

Proteins are molecular machines whose dynamics govern their function. Using time-resolved crystallography, the basis of protein mechanics can be uncovered by physical perturbation on protein crystals. Perturbations are often vector fields that, in crystals, break symmetry. Lacking existing tools for analyzing symmetry breaking, we built open-source software to do so. We applied this method to three time-resolved crystallography experiments and analyzed the effect of vectorial perturbations on macromolecular crystal structure. We demonstrate that our method can handle symmetry reduction in all 65 Sohncke space groups.

Characterizing the enzymatic accommodation of circular agricultural fertilizers is critical for informing strategies to remediate widespread soil sulfur hunger. Here, we present an exploratory in-silico investigation evaluating the active-site cleft of sulfate thiohydrolase (SoxB) across representative soil Proteobacteria. Following the crystallographic precedent of uncomplexed thiosulfate in PDB 2WDE, site-directed molecular docking indicated that the free thiosulfate polyanion binds favorably within the catalytic pocket (predicted affinity: -3.506 kcal/mol), yielding a substantially lower empirical energy barrier than hydrophobic elemental sulfur (S8, -1.409 kcal/mol). Comparative evaluation against the alkaline-adapted Thiobacillus denitrificans homolog revealed an elevated predicted binding affinity of -4.610 kcal/mol, suggesting a potential structural accommodation in high-pH calcareous soils (pH > 8.0). Unrestrained 5.0 ns all-atom molecular dynamics in explicit TIP3P solvent demonstrated initial structural stability of the unliganded host backbone (RMSD = 1.10 +/- 0.15 Angstrom). Energetic decomposition indicated that ligand association is governed predominantly by electrostatic interactions (delta-E_elec ~ -40 kcal/mol) with basic residues (His146, His269, Trp147; RMSF < 0.60 Angstrom), with a single-trajectory unbinding event observed at 3.2 ns. These computational observations provide an exploratory baseline characterizing the active-site electrostatic landscape of SoxB, generating working hypotheses for downstream empirical soil microcosm and in-planta trials.

Accurate prediction of mutation-induced changes in protein-protein affinity remains a central challenge in computational biophysics and protein engineering. Here we present a physics-based scoring method for predicting the effects of single amino acid substitutions on protein-protein and protein-peptide binding affinity. The method is based on MMPBSA energy calculations and implemented in a user-friendly, publicly available workflow (https://github.com/Hoecker-Lab/mmpbsa_scoring). To derive the scoring function, MMPBSA energy terms were fitted using a large and diverse dataset of experimentally measured binding affinity changes upon point mutation. The terms were then evaluated on independent systems. Specifically, we assessed the ability to predict mutational effects at the interface of SARS-CoV-2 spike protein with its cellular receptor, as well as computationally predicted Armadillo-repeat protein-peptide complexes. The approach performs well across diverse targets and shows promising predictive performance on both experimentally determined and computationally predicted structures. It matches or exceeds the accuracy of established physics-based protein design software and state-of-the-art deep learning-based scoring functions when applied to data outside their training domain. The method provides a computationally efficient and interpretable framework for prioritizing interface mutations in protein engineering and binding affinity optimization.

Ionization governs molecular behavior, yet predicting aqueous pKa accurately and tractably remains a fundamental challenge. Rigorous ensemble methods require enumerating a protonation-state space that grows exponentially with the number of ionizable sites, while fast graph predictors return numbers without thermodynamic consistency. Here we derive an exact thermodynamic identity showing that the macroscopic first-dissociation constant is fully determined by protonated-microstate populations and unique first-deprotonation events, eliminating the deprotonated ensemble algebraically and reducing aggregation to O(n+E) operations. We implement this identity in DTi-pKa, a dual-head graph neural network whose free-energy and dissociation heads are coupled by thermodynamic constraints. On four external benchmarks (355 molecules), DTi-pKa achieves a pooled MAE of 0.5665 pKa units, with 0.6285 on the 169 records lacking training-cache matches, while providing site-resolved micro-pKa values and protonation-state populations. Controlled ablations show that constraint placement at the level of the reported macroscopic equilibrium matters more than microscopic label precision--a design principle transferable to physics-informed machine learning beyond chemistry. Closure diagnostics expose numerical self-consistency as an open frontier, which we report transparently. By replacing an intractable computation with a physical identity, DTi-pKa unifies macroscopic accuracy and microscopic interpretability in a single framework.

Peptides containing noncanonical amino acids (ncAAs) are becoming increasingly important in drug discovery, but their molecular dynamics (MD) simulation remains limited by labor-intensive residue parameterization and topology preparation. Here, we present TopFor (Topology and Residue Force-Field Parameter Preparation), an open-source, peptide-oriented workflow that automates the generation of ncAA residue templates and force-field parameter files for MD simulations with AMBER using established charge-derivation methods. TopFor supports single-residue, whole-peptide, and batch parameterization workflows. The workflow was validated using the MDM2-p53 complex by comparing native AMBER residue definitions with residues processed by TopFor using three charge-derivation methods, each evaluated in three independent 100 ns MD simulations. TopFor-generated residue definitions showed structural and conformational behavior comparable to the native AMBER representation. Applicability to more complex peptide architectures was demonstrated using the head-to-tail cyclic peptide cyclosporin A and the cyclic depsipeptide cyclohexylgriselimycin. At library scale, 1,138 of 1,214 cyclohexylgriselimycin derivatives (93.7%) successfully completed TopFor parameterization and the subsequent initial AMBER-based computational workflow across two derivatization campaigns. TopFor was further integrated into a structure-based peptide discovery workflow based on the DnaN-griselimycin complex. Six GM-derived peptides were computationally prioritized and experimentally evaluated, of which four produced SPR responses against Mycobacterium smegmatis (M. smegmatis) DnaN and three showed concentration-dependent responses. TopFor reduces the manual effort required to prepare ncAA-containing peptides for MD simulations and enables their integration into both library-scale computational screening and structure-based peptide discovery workflows. Keywords: noncanonical amino acids; peptide parameterization; molecular dynamics; AMBER; AmberTools; peptide design; high-throughput virtual screening

Protein dynamics is fundamental to understand mechanisms. Although atomistic Molecular Dynamics (MD) remains the gold standard for predicting protein motions, its computational cost limits applications at large scales. Here, we present a coarse-grained (CG) simulation framework that combines Elastic Network Models (ENMs) with implicit-solvent Brownian Dynamics (BD) to efficiently generate protein conformational ensembles from native states. Benchmarking the method on more than 8,500 proteins against large datasets of atomistic MD trajectories and multi-state ensembles from Nucleic Magnetic Resonance (NMR), we show that short ENM-BD simulations can reproduce the residue fluctuation profiles and the dominant protein motions with remarkable accuracy. Correlations of residue fluctuations often exceeded 80%, with many proteins showing agreements above 90%, while Principal Component Analysis (PCA) revealed strong correspondences between the essential motions in the ENM-BD simulations and those observed in MD and NMR ensembles. Following CG-to-all-atom reconstruction and short energy minimization, the ENM-BD conformers also achieve high stereochemical quality, which enables their usage in downstream atomistic applications. Finally, we show that the stochastic dynamics emerging from BD trajectories closely aligns with the normal modes encoded in the underlying elastic network, highlighting that most of the intrinsic dynamics of proteins is already embedded in their native 3D topology.

Lipid nanoparticles (LNPs) are a leading drug delivery platform for mRNA or other nucleic acid-based therapeutics. LNP platforms are typically composed of ionizable aminolipids, phospholipids, sterols, and lipids tagged with polyethylene glycol. The ionizable aminolipids are typically cationic at low pH, allowing them to encapsulate negatively charged mRNA. The pH at which the ionizable head groups change charge states, the apparent pKa, is crucial to understand the characteristics and formulation of LNP. However, this is difficult to measure as the apparent pKa differs from the pKa of the aminolipids in bulk solution due to the complex chemical and charge environment. In this study, we use molecular dynamics (MD) simulations with alchemical free energy perturbation to calculate the pKa shift as a function of lipid composition, treating LNP-like and vesicle-like environments. Our results show that the pH-dependent structure of the systems affects the trends in pKa shifts in complex ways, driven by factors including surface electrostatics and lipid internalization. Our all-atom simulation protocol allows us to efficiently estimate the apparent pKa of LNP-like models formed with novel ionizable aminolipids and estimate how it will change with composition.