Biology subjects

Reyes-Figueroa, A. D.

Publications and source records attributed to Reyes-Figueroa, A. D..

5 recordsLinked to original sources

bioRxiv2024

Addressing the problem of lysine glycation prediction in proteins via Recurrent Neural Networks

A distinguishing feature of the metabolic disorder diabetes involves elevated damage to cellular proteins. The primary form of alteration arises from the chemical interaction between glycating agents such as methylglyoxal and proteinaceous arginine/lysine residues, causing structural and functional disruptions in target proteins. In this study, a curated version of the CPLM database to implement a recurrent neural network strategy for the classification of lysine glycation has been utilized. By using one physical property for the characterization of amino acids next to lysine sites (i.e., isoelectric point), it was possible to obtain a 59.6% accuracy for correctly predicting lysine glycation. When two properties were combined, i.e., mass and torsion angle, the accuracy increased to 59.9%. Overall, this approach can aid the task of narrowing down possible sites of lysine glycation in protein targets for further analysis.

biochemistry↗

bioRxiv2022

On the prediction of arginine glycation using artificial neural networks

One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence, thus making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here we used a recently published high-quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine-containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a possible solution for predicting arginine glycation. Our approach will greatly aid researchers in narrowing down possible glycation sites in protein targets. This strategy could improve the structural and functional characterization of proteins of interest.

biochemistry↗

bioRxiv2021

The structure of the enigmatic ripple phase in saturated bilayers resolved: Machine learning reveals four lipid populations

A new mixed radial-angular, three-particle correlation function method in combination with unsupervised machine learning (ML) was applied to examine the emergence of the ripple phase in dipalmitoyphosphatidylcholine (DPPC) lipid bilayers using data from atomistic molecular dynamics (MD) simulations of system sizes ranging from 128 to 4,096 lipids. Based on the acyl tail conformations, the analysis revealed the presence of four distinct conformational populations of lipids in the ripple phases of the DPPC lipid bilayers. The expected gel- (ordered; Lo) and fluid-like (disordered; Ld) lipids are found along with their splayed tail equivalents (Lo,s and Ld,s). These lipids differ based on their gauche distribution and tail packing. The disordered (Ld) and disordered splayed (Ld,s) lipids spatially cluster in the ripple in the groove side, that is, in an asymmetric manner across the bilayer leaflets. The ripple phase does not contain large numbers of Ld lipids, instead they only exist on the interface of the groove side of the undulation. The bulk of the groove side is a complex coexistence of Lo, Lo,s and Ld,s lipids. The convex side of the undulation contains predominantly Lo lipids. Thus, the structure of the ripple phase is neither a simple coexistence of ordered and disordered lipids nor a coexistence of ordered interdigitating gel-like (Lo) and ordered splayed (Lo,s) lipids, but instead a coexistence of an ordered phase and a complex mixed phase. Principal component analysis (PCA) further confirmed the existence of the four lipid groups.

biophysics↗

bioRxiv2021

Molecular dynamics simulation of a model brain membrane with and without a 14-3-3 tau protein

An unbalanced composition of lipids and proteins in brain membranes is related to the appearance neurodegenrative diseases and recent investigations show that the 14-3-3 tau protein might relate to some of these diseases. This article reports results from a coarse-grained model brain membrane with and without a 14-3-3{tau} /{theta} protein inside the membrane. We investigated the symmetrized partial density, thickness, diffusion coefficients, and deuterium order parameters of the membrane with and without protein. We observe a slight increase in heads and linkers in the symmetrized partial density of the membrane with the protein inserted and higher values of the deuterium order parameters for the brain model membrane with protein. We observe a reduction in the diffusion coefficient of the fluid membrane in the presence of the transmembrane tau protein. Our findings show that the protein can modify the structural and dynamical properties of the membrane. This work will serve as a guide for future investigations on the interactions of tau proteins with brain membrane models and their relation to neurodegenerative diseases.

biophysics↗

bioRxiv2020

Cholesterol Sequestration by Xenon Leads to Lipid Raft Destabilization

Combined coarse-grained (CG) and atomistic molecular dynamics (MD) simulations were performed to study the interactions of xenon with model lipid rafts consisting of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC) and cholesterol (Chol). At a concentration of 2 Xe/lipid we observed an unexpected result: Spontaneous nucleation of Xe nanoclusters which then rapidly plunged into the bilayer. In this process Chol, essential for raft stabilization, was pulled out from the raft into the hydrophobic zone. When concentration was further increased (3 Xe/lipid), the clusters disrupted both the membrane and raft. We computed the radial distribution functions, pair-wise potentials, second virial coefficients and Schlit-ter entropy to scrutinize the nature of the interactions. Our findings suggest that the well-known anaesthetic effect of Xe could be mediated by sequestration of Chol, which, in turn, compromises the stability of rafts where specialized proteins needed to produce the nervous signal are anchored.

biophysics↗