Biology subjects

Bacic Toplek, F.

Publications and source records attributed to Bacic Toplek, F..

3 recordsLinked to original sources

bioRxiv2026

Assessing multi-eGO Predictions of PDZ2-Peptide Binding across Mutations

Accurately predicting how mutations alter protein-peptide binding remains challenging for molecular simulations because both conformational sampling and binding kinetics are computationally demanding. Here, we investigate whether multi-eGO, a hybrid transferable/structure-based atomistic-resolution model previously developed and validated for protein-small molecule interactions, can be transferred to protein-peptide binding without peptide-specific retraining. Using the PDZ2 domain of protein tyrosine phosphatase basophil-like in complex with the peptide EQVTAV as a benchmark, we first show that multi-eGO reproduces the structural dynamics of PDZ2 and the equilibrium binding thermodynamics of the wild-type complex. The model substantially accelerates both binding and unbinding relative to experiment but accurately preserves the resulting equilibrium dissociation constant. We then introduce conservative mutations in PDZ2 and in the peptide and evaluate their effects on binding without repeating the computationally expensive training procedure. Multi-eGO reproduces the experimentally observed changes in equilibrium dissociation constants, with strong agreement across PDZ2 mutants and moderate agreement when the peptide is also mutated. In contrast, the individual association and dissociation rate constants show substantially weaker agreement with experiment. The results indicate that the simplified energy landscape of multi-eGO limits the quantitative prediction of absolute kinetics while preserving thermodynamic information relevant to relative binding affinity. These findings establish multi-eGO as a computationally efficient approach for protein-peptide recognition and for predicting and rank-ordering the effects of conservative mutations on binding affinity.

biochemistry↗

bioRxiv2025

Electric Field-Induced Destabilization and Surface Modulation of Aβ42 Fibrils in Molecular Simulations: Theoretical Implications for DC Stimulation in Alzheimer's Disease

The amyloid-{beta} peptide 42 (A{beta}42) forms fibrillar aggregates that are a hallmark of Alzheimers disease. While recent pharmacologic therapeutic strategies targeting A{beta}42 fibrils and oligomers have shown promising results, safer and more effective approaches are still needed. Non-invasive brain stimulation (NIBS) techniques such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have been increasingly explored as a possible complementary intervention, but the molecular mechanisms by which static electric fields could influence amyloid aggregation remain poorly understood. Here, we use atomistic molecular dynamics simulations to investigate the effects of static electric fields on A{beta}42 fibrils. We examine the response of an ex vivo fibril structure, with reconstructed N-terminal regions, to increasing field strengths under different structural restraint conditions. Our results show that the electric field perturbs the disordered N-terminal "fuzzy coat," altering its conformational dynamics and reducing its interactions with the fibril core. This reorganization modifies the surface properties of the fibril, potentially impairing secondary nucleation. Additionally, simulations with unrestrained fibril ends reveal increased fluctuations in core residues, particularly near the N-terminus, indicating a destabilizing effect that may hinder fibril elongation. While the field strengths used here exceed those typical of NIBS, our findings provide a molecular-level rationale for how electric fields could modulate fibril propagation and support further experimental investigations under physiologically relevant conditions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/678267v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@9b6a01org.highwire.dtl.DTLVardef@14ca26forg.highwire.dtl.DTLVardef@3c42ccorg.highwire.dtl.DTLVardef@458b4f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution

Protein aggregation into amyloid fibrils is the archetype of aberrant biomolecular self-assembly processes, with more than 50 diseases associated that are mostly uncurable. Understanding aggregation mechanisms is thus of fundamental importance and goes in parallel with the characterization of the structures of the transient oligomers formed in the process. Oligomers have been proven elusive to high-resolution structural techniques, while the large sizes and long-time scales typical of aggregation processes have limited, so far, the use of computational methods. To surmount these limitations, we introduce here multi-eGO, an atomistic, hybrid structure-based model, which leveraging on the knowledge of monomers conformational dynamics and of fibril structures, can efficiently capture the essential structural and kinetics aspects of protein aggregation. Multi-eGO molecular dynamics simulations can describe the aggregation kinetics of thousands of monomers. The concentration dependence of the simulated kinetics, as well as the structural features of the resulting fibrils, are in qualitative agreement with in vitro experiments on an amyloidogenic peptide of Transthyretin, a protein responsible for one of the most common cardiac amyloidosis. Multi-eGO simulations allow to observe in time and at atomic resolution the formation of primary nuclei in a sea of transient lower order oligomers, to follow their growth and the subsequent secondary nucleation events, till the maturation of multiple fibrils. Multi-eGO, combined with the many experimental techniques deployed to study protein aggregation, can provide the structural basis needed to advance the design of molecules targeting amyloidogenic diseases. Significance StatementAlzheimers and Parkinsons diseases are uncurable pathologies associated to the aberrant aggregation of specific proteins into amyloid fibrils. Understanding the mechanism leading to protein aggregation, by characterizing the structures of the oligomeric species populated in the process, would have a tremendous impact on the design of therapeutic molecules. We propose that a structure-based approach to molecular dynamics simulations can allow following at high resolution the aggregation kinetics of thousands of monomers. Having shown that simulations can describe the aggregation of a Transthyretin amyloidogenic peptide, we demonstrate how their efficiency allows acquiring a wealth of structural information. We foresee that integrating the latter with the many techniques developed to study protein aggregation will support the design of molecules to modulate amyloidogenesis.

biochemistry↗