Biology subjects

Valdes-Garcia, G.

Publications and source records attributed to Valdes-Garcia, G..

4 recordsLinked to original sources

bioRxiv2022

The Effect of Polymer Length in Phase Separation

Understanding the thermodynamics that drives liquid-liquid phase separation (LLPS) is quite important given the many numbers of diverse biomolecular systems undergoing this phenomenon. Regardless of the diversity, the processes underlying the formation of condensates exhibit physical similarities. Many studies have focused on condensates of long polymers, but very few systems of short polymer condensates have been observed and yet studied. Here we study a short polymer system of various lengths of poly-Adenine RNA and peptide formed by the RGRGG sequence repeats to understand the underlying thermodynamics of LLPS. We carried out MD simulations using the recently developed COCOMO coarse-grained (CG) model which revealed the possibility of condensates for lengths as short as 5-10 residues, which was then confirmed by experiment, making this one of the smallest LLPS systems yet observed. Condensation depends on polymer length and concentration, and phase boundaries were identified. A free energy model was also developed. Results show that the length dependent condensation is driven solely by entropy of confinement and identifies a negative free energy (-{Delta}G) of phase separation, indicating the stability of the condensates. The simplicity of this system will provide the basis for understanding more biologically realistic systems.

biophysics↗

bioRxiv2022

Modeling concentration-dependent phase separation processes involving peptides and RNA via residue-based coarse-graining

Biomolecular condensation, especially liquid-liquid phase separation, is an important physical process with relevance for a number of different aspects of biological functions. Key questions of what drives such condensation, especially in terms of molecular composition, can be addressed via computer simulations, but the development of computationally efficient, yet physically realistic models has been challenging. Here, the coarse-grained model COCOMO is introduced that balances the polymer behavior of peptides and RNA chains with their propensity to phase separate as a function of composition and concentration. COCOMO is a residue-based model that combines bonded terms with short- and long-range terms, including a Debye-Huckel solvation term. The model is highly predictive of experimental data on phase-separating model systems. It is also computationally efficient and can reach the spatial and temporal scales on which biomolecular condensation is observed with moderate computational resources.

biophysics↗

bioRxiv2022

Characterizing Transient Protein-Protein Interactions by Trp-Cys Quenching and Computer Simulations

Transient protein-protein interactions occur frequently under the crowded conditions encountered in biological environments, yet they remain poorly understood. Here, tryptophan-cysteine quenching is introduced as an experimental approach that is ideally suited to characterize such interactions between proteins with minimal labeling due to its sensitivity to nano- to microsecond dynamics on sub-nanometer length scales. The experiments are paired with computational modeling at different resolutions including fully atomistic molecular dynamics simulations to provide interpretation of the experimental observables and add further insights at the molecular level. This approach is applied to model systems, villin variants and the drkN SH3 domain, in the presence of protein G crowders. It is demonstrated that Trp-Cys quenching experiments are able to not only distinguish between overall attractive and repulsive interactions between different proteins, but they can also discern variations in interaction preferences at different protein surface locations. The close integration between experiment and simulations also provides an opportunity to evaluate different molecular force fields for the simulation of concentrated protein solutions. Significance StatementBiological environments typically involve a variety of different proteins at very high concentrations where non-specific interactions are unavoidable. These interactions may go beyond simple crowding effects and involve transient contacts that may impact structure, dynamics, and ultimately function of proteins in vivo. While computer simulations have partially characterized such interactions, experimental data remain limited because established techniques are generally not well-suited to the characterization of dynamic processes on microsecond time and nanometer length scales. Tryptophan quenching by cysteine is introduced here as a new approach for studying transient protein encounters under concentrated conditions with the support of computational modeling. The study demonstrates that such experiments can resolve not just differences between different proteins but also residue-specific interaction preferences.

biophysics↗

bioRxiv2022

Direct Generation of Protein Conformational Ensembles via Machine Learning

Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied. Here, we demonstrate that machine learning can be trained with simulation data to directly generate physically realistic conformational ensembles of proteins without the need for any sampling and at negligible computational cost. As a proof-of-principle a generative adversarial network based on a transformer architecture with self-attention was trained on coarse-grained simulations of intrinsically disordered peptides. The resulting model, idpGAN, can predict sequence-dependent ensembles for any sequence demonstrating that transferability can be achieved beyond the limited training data. idpGAN was also retrained on atomistic simulation data to show that the approach can be extended in principle to higher-resolution conformational ensemble generation.

biophysics↗