Biology subjects

Poma, A. B.

Publications and source records attributed to Poma, A. B..

6 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

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

Benchmarking coarse-grained simulation methods for investigation of transport tunnels in enzymes

Enzymes are pivotal to numerous biological processes, often featuring buried active sites linked to the surrounding solvent through intricate and dynamic tunnels. These tunnels are vital for facilitating substrate access, enabling product release, and regulating solvent exchange, which collectively influence enzymatic function and efficiency. Consequently, knowledge of tunnels is key for a holistic understanding of the effect of mutations as well as predicting drug residence times. Unfortunately, most transport tunnels are transient, i.e., equipped by molecular gates, rendering their opening a rare event that is often notoriously hard to study with conventional molecular dynamics simulations. To overcome the sampling limitation of such simulations, this study investigated the efficacy of three different coarse-grained (CG) molecular dynamics simulation methods for inferring enzyme tunnel structure and dynamics. Here, we covered the Martini and SIRAH models with different restraint protocols providing stability to CG proteins while to some extent biasing the sampling towards a reference structure. By contrasting CG results with all-atom simulations, we benchmarked the ability of CG methods to replicate ensemble characteristics of complex tunnel networks in haloalkane dehalogenase LinB and two of its mutants with engineered tunnel networks. The assessed tunnel parameters are essential for prioritizing functionally relevant tunnels and delineating the effect of mutations on transport tunnels. Our findings reveal that while CG methods significantly enhance the efficiency of tunnel analyses, some of them, like Martini with Elastic network restraints, were limited in recapitulating all-atom tunnel dynamics due to the structural bias applied. In contrast, the Martini G[o] model even captured the intricate details of mutation perturbing tunnel dynamics. All studied CG methods performed well in capturing the geometry of tunnel ensembles in line with all-atom simulations. Additionally, the wider applicability of CG methods was verified by analyzing tunnel networks of nine enzymes from different combinations of structural and functional classes, demonstrating their potential to uncover new tunnel phenomena and validate their utility in broader biological and functional contexts. This comprehensive evaluation underscores the strengths and constraints of CG simulations in capturing enzyme tunnels and benefiting from their computational speed for studying huge datasets of enzymes. These insights are valuable for enzyme engineering, drug design, and understanding enzyme function while benefitting from the efficiency of coarse-grained models.

bioinformatics↗

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↗

bioRxiv2021

Optimizing mechanostable anchor points of engineered lipocalin in complex with CTLA-4

We used single-molecule AFM force spectroscopy (AFM-SMFS) to screen residues along the backbone of a non-antibody protein binding scaffold (lipocalin/anticalin), and determine the optimal anchor point that maximizes binding strength of the interaction with its target (CTLA-4). By incorporating non-canonical amino acids into anticalin, and using click chemistry to attach an Fg{beta} peptide at internal sequence positions, we were able to mechanically dissociate anticalin from CTLA-4 by pulling from eight different anchoring residues using an AFM cantilever tip. We found that pulling on the anticalin from residue 60 or 87 resulted in significantly higher rupture forces and a decrease in koff by 2-3 orders of magnitude over a force range of 50-200 pN. Five of the six internal pulling points tested were significantly more stable than N- or C-terminal anchor points, rupturing at up to 250 pN at loading rates of 0.1-10 nN sec-1. Anisotropic network modelling and molecular dynamics simulations using the G[o]-MARTINI approach explained the mechanism underlying the geometric dependency of mechanostability. These results suggest that optimization of attachment residue position for therapeutic and diagnostic cargo can provide large improvements in binding strength, allowing affinity maturation without requiring genetic mutation of binding interface residues.

biophysics↗

bioRxiv2020

Characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein

The novel coronavirus disease 2019 (COVID-19) pandemic has disrupted modern societies and their economies. The resurgence in COVID-19 cases as part of the second wave is observed across Europe and the Americas. The scientific response has enabled a complete structural characterization of the Severe Acute Respiratory Syndrome - novel Coronavirus 2 (SARS-CoV-2). Among the most relevant proteins required by the novel coronavirus to facilitate the cell entry mechanism is the spike protein trimer. This protein possesses a receptor-binding domain (RBD) that binds the cellular angiotensin-converting enzyme 2 (ACE2) and then triggers the fusion of viral and host cell membranes. In this regard, a comprehensive characterization of the structural stability of the spike protein is a crucial step to find new therapeutics to interrupt the process of recognition. On the other hand, it has been suggested the participation of more than one RBD as a possible mechanism to enhance cell entry. Here we discuss the protein structural stability based on the computational determination of the dynamic contact map and the energetic difference of the spike protein conformations via the mapping of the hydration free energy by the Poisson-Boltzmann method. We expect our result to foster the discussion of the number of RBD involved during recognition and the repurposing of new drugs to disable the recognition by discovering new hotspots for drug targets apart from the flexible loop in the RBD that binds the ACE2.

biophysics↗