Biology subjects

Cofas-Vargas, L. F.

Publications and source records attributed to Cofas-Vargas, L. F..

4 recordsLinked to original sources

bioRxiv2026

Exploring Conformational Transitions of RNA Dimers via Machine Learning Potentials

RNA is a flexible biopolymer that adopts diverse conformations while forming structural motifs essential for its function. Classical RNA force fields often show limited transferability and inefficient sampling of transitions between stable states, particularly in moderately large RNA. To address these limitations, quantum-informed machine learning (ML) potentials have recently emerged as a promising alternative, offering improved accuracy and transferability relative to classical force fields. Here, we assess ML potentials for exploring RNA conformations using the adenine-adenine dinucleoside monophosphate (ApA) dimer, a fundamental RNA building block. We generated an extensive quantum-mechanical (QM) dataset for ApA conformations obtained from temperature replica exchange molecular dynamics (TREMD) simulations. Despite its small size, the ApA dimer exhibits six conformations in which quantum effects and solvent-mediated interactions play crucial roles. Using this dataset, we parameterized ML potentials based on the equivariant MACE architecture and informed by both ab-initio and semi-empirical data. The resulting potentials reproduce key conformational features of the ApA system, including base stacking, sugar puckering, and backbone flexibility, and provide broader coverage of structural transitions than the general-purpose SO3LR and MACE-OFF24 models. These findings highlight the importance of quantum-accurate RNA force fields towards the structural and energetic characterization of RNA complexes.

biophysics↗

bioRxiv2025

An optimized contact map for GoMartini 3 enabling conformational changes in protein assemblies

Advances in structural biology, particularly cryo-electron microscopy, have enabled high-resolution characterization of complex biomolecular assemblies. These developments emphasize the need for computational approaches capable of describing biologically relevant conformational changes over extended timescales. G[o]Martini 3 is a coarse-grained approach that demonstrates computational efficiency and versatility across several systems, from protein-binding membranes and soluble proteins to intrinsically disordered proteins, while preserving key physicochemical features. In this work, we introduce an optimized approach that integrates dynamic contact information from AA-MD simulations to refine the contact map in G[o]Martini simulations. Specifically, we define high-frequency contacts (HFC), which reduce the number of original G[o] contacts set by {approx}20-30%, thereby improving the representation of conformational states beyond the original approach. Benchmarking different contact-selection criteria revealed that including intra- and interchain HFC captures structural flexibility and domain dynamics. The method was tested on three small soluble proteins and on the SARS-CoV-2 spike protein. Overall, the optimized contact map improves sampling efficiency and expands the accessible conformational landscape relative to the original G[o]Martini 3 implementation. The full framework is available as an open-source resource for large-scale simulations of biomolecular assemblies.

biophysics↗

bioRxiv2025

A Comparative Nanomechanical Study of Antibody and Nanobody Binding to SARS-CoV-2 Variants

The receptor-binding domain of the SARS-CoV-2 spike protein is the principal target of neutralizing antibodies (Abs) and nanobodies (Nbs). Although their thermodynamic binding properties have been extensively characterized, their stability under mechanical force remains less understood. Here, we perform a comparative nanomechanical analysis of three Abs (PDI-231, S2X259, and R1-32) and three Nbs (R14, C1, and n3113.1) bound to the RBD from the WT strain and the Omicron BA.4 and JN.1 variants. Using coarse-grained steered molecular dynamics within the G[o]Martini 3 framework, we identified distinct force-response behaviors shaped by epitope topology, binding architecture, and variant-specific mutations. Ab/RBD dissociation was characterized by asymmetric rupture events, variant-dependent unfolding of RBD segments, and occasional deformation of antibody constant domains. Analysis of single-chain systems revealed that the heavy chain acts as the main load-bearing element, while the light chain sustains a consistent but weaker mechanical response. For the two-chain Ab system, the cooperative action of both chains enhances stability, enabling complexes to withstand rupture forces in the range of 500 pN. By contrast, Nb/RBD complexes dissociated primarily through rigid-body mechanisms, transmitting force more directly to the RBD interface with minimal structural disruption. Together, these results demonstrate that mechanical resilience emerges from immune complex topology and inter-chain cooperation, providing complementary insights beyond affinity into the design of therapeutics resilient to viral evolution.

biophysics↗

bioRxiv2024

GoMartini 3: From large conformational changes in proteins to environmental bias corrections

Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The G[o]Martini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced G[o]Martini model, which combines a virtual-site implementation of G[o] models with Martini 3. The implementation has been extensively tested by the community since the release of the new version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with G[o] models.

biophysics↗