arXiv subjects

Ariane Nunes-Alves

Publications and source records attributed to Ariane Nunes-Alves.

5 recordsLinked to original sources

arXiv2026

Recent advances in modeling and simulation of biological phenomena in crowded and cellular environments

While experiments and computer simulations to study biological phenomena are usually performed in diluted in vitro conditions, such phenomena happen inside the cell, an environment densely packed with diverse macromolecules. Here, we revise recent computational methods to investigate crowded and cellular environments. Protein crowders, inert crowders and small molecules were used to mimic crowding. Simulations were performed for models of the cytoplasm. New methods were developed to simulate crowded systems, reaching up to 200 microseconds of simulation time. Apart from the challenges, modeling and simulations to investigate biological phenomena inside cells is a growing field, and has a lot of potential to improve our understanding of how such phenomena happen in vivo.

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arXiv2025

Molecular glues stabilize water-mediated hydrogen bonds in ternary complexes

By stabilizing weak and transient protein-protein interactions (PPIs), molecular glues address the challenge of targeting proteins previously considered undruggable. Rapamycin and WDB002 are molecular glues that bind to FK506-binding protein (FKBP12) and target the FKBP12-rapamycin-associated protein (FRAP) and the centrosomal protein 250 (CEP250), respectively. Here, we used molecular dynamics simulations to gain insights into the effects of molecular glues on protein conformation and PPIs. The molecular glues modulated protein flexibility, leading to less flexibility in some regions, and changed the pattern and stability of water-mediated hydrogen bonds between the proteins. Our findings highlight the importance of considering water-mediated hydrogen bonds in developing strategies for the rational design of molecular glues.

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arXiv2022

Recent advances in computational methods for studying ligand binding kinetics

Binding kinetic parameters can be correlated with drug efficacy, which led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms in recent years. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

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arXiv2020

Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from tauRAMD simulations

The protein-ligand residence time, tau, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict tau, computational methods must overcome the problem that tau often exceeds the timescales accessible to conventional molecular dynamics (MD) simulation. Here, we apply the tau-Random Acceleration Molecular Dynamics (tauRAMD) method to a set of kinetically characterized complexes of T4 lysozyme mutants with engineered binding cavities. tauRAMD yields relative ligand dissociation rates in good accordance with experiments and thereby allows a comprehensive characterization of the ligand egress routes and determinants of tau. Although ligand dissociation by multiple egress routes is observed, we find that egress via the predominant route determines the value of tau. We also find that the presence of metastable states along egress pathways slows down protein-ligand dissociation. These physical insights could be exploited in the rational optimization of the kinetic properties of drug candidates.

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arXiv2020

Recent progress in molecular simulation methods for drug binding kinetics

Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.

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