Biology subjects

Miletic, V.

Publications and source records attributed to Miletic, V..

3 recordsLinked to original sources

bioRxiv2026

Capturing secondary structure in coarse grained intrinsically disordered proteins with simulations driven by chemical shifts.

A major challenge when investigating intrinsically disordered proteins (IDPs) pertains to understanding how secondary structure formation across otherwise disordered ensembles, relates to function. While coarse-grained (CG) simulation methods are instrumental to describe ensemble-to-function relationships in IDPs, they remain largely incapable of sampling secondary structure. We here present NapshiftC, an artificial neural network capable of modelling NMR chemical shifts from C-mapped CG protein conformations, leveraging its predictions to restrain simulations and yield experimentally-informed IDP ensembles that contain secondary structure. We applied NapshiftC on a large set of proteins in isolation or condensed phase, and found that, when secondary structure is present, CS incorporation yields improved agreements with smFRET spectroscopy and resolves the over-reliance of CG models on near linear conformers. By incorporating observables that are routinely collected for IDPs but never coupled to CG simulations, NapshiftC ultimately informs on both local and global conformational features of function-defining IDP ensembles.

biophysics↗

bioRxiv2025

Integrating NMR restraints into coarse-grained simulations: toward accurate conformational ensembles of complex protein systems

Structural dynamics play critical roles for the biological activity of protein molecules. Characterising the inherent conformational landscapes of these macromolecules remains a major experimental and computational challenge, particularly for heterogeneous and transient systems such as intrinsically disordered proteins, membrane-associated assemblies and disordered fuzzy coats of amyloid aggregates. In this context, coarse-grained (CG) molecular dynamics simulations have enabled accessing to extended timescales and large system sizes, however, their reduced resolution and simplified interaction potentials often limit the structural accuracy. Here, we introduce Martini3-NMR, an integrative framework that incorporates nuclear magnetic resonance (NMR) observables directly into CG protein force fields. Using artificial neural networks to model NMR chemical shifts at the CG level, and integrating these data with NOE restraints, we define an approach to significantly enhance the accuracy of CG simulations while maintaining their elevated sampling efficiency, thereby resulting in a substantially improved description of protein conformational ensembles. We demonstrate the broad applicability of Martini3-NMR by generating CG ensembles for a diverse range of systems and complex processes such as protein folding from misfolded states, oligomer disassembly within lipid bilayers and the topology of disordered fuzzy regions at amyloid fibril surfaces, which were found to display condensate-like structural and dynamical properties. By enabling an experimentally driven and computationally efficient exploration of protein conformational landscapes, Martini3-NMR provides a novel general framework for investigating dynamic, heterogeneous and multiscale biomolecular processes. This approach opens to significant new opportunities for extending CG simulations towards a more quantitative understanding of the relationship between molecular structure, dynamics and biological function.

biophysics↗

bioRxiv2020

Structural Analysis of Simultaneous Activation and Inhibition of Gamma-Secretase Activity in Development of Drugs for Alzheimers disease

SignificanceThe majority of drugs that target membrane-embedded protease {gamma}-secretase show unusual biphasic activation-inhibition dose-response in cells, model animals, and humans. Semagacestat and avagacestat are two biphasic-drugs that can facilitate cognitive decline in patients with Alzheimers disease. Initial mechanistic studies showed that the biphasic-drugs, and pathogenic mutations, can produce the same type of changes in {gamma}-secretase activity. ResultsDAPT, semagacestat LY-411,575, and avagacestat are four drugs that show different binding constants, and biphasic activation-inhibition dose-response curves, for amyloid-{beta}-40 products in SHSY-5 cells. Multiscale molecular dynamics studies showed that all four drugs bind to the most mobile parts in presenilin structure, at different ends of the 29 [A] long active site tunnel. Combined results from structure-activity studies, showed that the biphasic dose-response curves are a result of modulation of {gamma}-secretase activity by concurrent binding of multiple drug molecules at each end of the active site tunnel. The drugs activate {gamma}-secretase by forcing the active site tunnel to open, when the rate-limiting step is the tunnel opening, and formation of the enzyme-substrate complex. The drugs inhibit {gamma}-secretase as uncompetitive inhibitors, by binding next to the substrate to dynamic enzyme structures that regulate processive catalysis. The drugs can modulate the production of different amyloid-{beta} catalytic intermediates, by penetrating into the active site tunnel to different depth with different binding affinity. The drugs and pathogenic mutations affect the same dynamic processes in {gamma}-secretase structure. ConclusionsBiphasic-drugs like disease-causing mutations can reduce the catalytic capacity of {gamma}-secretase and facilitate pathogenic changes in amyloid metabolism.

neuroscience↗