Layer-wise relevance analysis for motif recognition in the activation pathway of the ß2-adrenergic GPCR receptor

G-protein-coupled receptors (GPCRs) are cell membrane proteins of relevance as therapeutic targets, and are associated to the development of treatments for illnesses such as diabetes, Alzheimer’s, or even cancer. Therefore, comprehending the underlying mechanisms of the receptor functional properties is of particular interest in pharmacoproteomics and in disease therapy at large. Their interaction with ligands elicits multiple molecular rearrangements all along their structure, inducing activation pathways that distinctly influence the cell response. In this work, we studied GPCR signaling pathways from molecular dynamics simulations as they provide rich information about the dynamic nature of the receptors. We focused on studying the molecular properties of the receptors using deep-learning-based methods. In particular, we designed and trained a one-dimensional convolution neural network and illustrated its use in a classification of conformational states: active, intermediate, or inactive, of the ß2 -adrenergic receptor when bound to the full agonist BI-167107. Through a novel explainability-oriented investigation of the prediction results, we were able to identify and assess the contribution of individual motifs (residues) influencing a particular activation pathway. Consequently, we contribute a methodology that assists in the elucidation of the underlying mechanisms of receptor activation–deactivation.This research was funded by Spanish PID2019-104551RB-I00 research project.Peer ReviewedPostprint (published version

Obstruction level detection of sewers videos using convolutional neural networks

Worldwide, sewer networks are designed to transport wastewater to a centralized treatment plant to be treated and returned to the environment. This is a critical process for preventing waterborne illnesses, providing safe drinking water and enhancing general sanitation in society. To keep a perfectly operational sewer network several inspections are manually performed by a Closed-Circuit Television system to report the obstruction level which may trigger a cleaning operative. In this work, we design a methodology to train a Convolutional Neural Network (CNN) for identifying the level of obstruction in pipes. We gathered a database of videos to generate useful frames to fed into the model. Our resulting classifier obtains deployment ready performances. To validate the consistency of the approach and its industrial applicability, we integrate the Layer-wise Relevance Propagation (LPR) algorithm, which endows a further understanding of the neural network behavior. The proposed system provides higher speed, accuracy, and consistency in the sewer process examination.This work is partially supported by the Consejo Nacional de Ciencia y Tecnologia (CONACYT), Estudiante No. CVU: 630716, by the RIS3CAT Utilities 4.0 SENIX project (COMRDI16-1-0055), cofounded by the European Regional Development Fund (FEDER) under the FEDER Catalonia Operative Programme 2014- 2020. It is also partially supported by the Spanish Government through Programa Severo Ochoa (SEV2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2017-SGR-1414).Peer ReviewedPostprint (published version

Métodos y técnicas de monitoreo y predicción temprana en los escenarios de riesgos socionaturales

Esta obra concentra los métodos y las técnicas fundamentales para el seguimiento y monitoreo de las dinámicas de los escenarios de riesgos socionaturales (geológicos e hidrometeorológicos) y tiene como objetivo general orientar, apoyar y acompañar a los directivos y operativos de protección civil en aterrizar las acciones y políticas públicas enfocadas a la gestión del riesgo local de desastre

A deep learning-based method for uncovering GPCR ligand-induced conformational states using interpretability techniques

There is increasing interest in the development of tools for investigating the protein ligand space. Understanding the underlying mechanisms of G protein-coupled receptors (GPCR) in the ligand-binding process is of particular interest due to their role in pharmacoproteomics. In this work, we propose the study of GPCR ligand-induced conformational variations from Molecular Dynamics (MD) simulations using Deep Learning (DL)-based methods. We devise and train a Convolutional Neural Network (CNN) for classifying the states for both ligand-free structure and the bound of agonists in the ß2-adrenergic receptor. We also study the transformation of MD data into an interaction network matrix to further improve and facilitate the analyses without significant loss of information. Our method introduces a framework for the study of the effect of ligand-receptor binding activity that includes a novel analysis based on interpretability algorithms, allowing the quantification of the contribution of individual residues to structural re-arrangements.This research is partially funded by research grant PID2019-104551RB-I00.Peer ReviewedPostprint (author's final draft

Recognition of conformational states of a G protein coupled receptor from molecular dynamic simulations using sampling techniques

This version of the contribution has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-34953-9_1. Use of this Accepted Version is subject to the publisher's Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsProtein structures are complex and dynamic entities relevant to many biological processes. G-protein-coupled receptors in particular are a functionally relevant family of cell membrane proteins of interest as targets in pharmacology. Nevertheless, the limited knowledge about their inherent dynamics hampers the understanding of the underlying functional mechanisms that could benefit rational drug design. The use of molecular dynamics simulations and their analysis using Machine Learning methods may assist the discovery of diverse molecular processes that would be otherwise beyond our reach. The current study builds on previous work aimed at uncovering relevant motifs (groups of residues) in the activation pathway of the ß2-adrenergic (ß2AR) receptor from molecular dynamics simulations, which was addressed as a multi-class classification problem using Deep Learning methods to discriminate active, intermediate, and inactive conformations. For this problem, the interpretability of the results is particularly relevant. Unfortunately, the vast amount of intermediate transformations, in contrast to the number of re-orderings establishing active and inactive conditions, handicaps the identification of relevant residues related to a conformational state as it generates a class-imbalance problem. The current study aims to investigate existing Deep Learning techniques for addressing such problem that negatively influences the results of the predictions, aiming to unveil a trustworthy interpretation of the information revealed by the models about the receptor functional mechanics.Peer ReviewedPostprint (author's final draft

Endothelial Progenitor Cells May Be Related to Major Amputation after Angioplasty in Patients with Critical Limb Ischemia

Background: Critical limb ischemia represents an advanced stage of peripheral arterial disease. Angioplasty improves blood flow to the limb; however, some patients progress irreversibly to lower limb amputation. Few studies have explored the predictive potential of biomarkers during postangioplasty outcomes. Aim: To evaluate the behavior of endothelial progenitor cells in patients with critical limb ischemia, in relation to their postangioplasty outcome. Methods: Twenty patients with critical limb ischemia, candidates for angioplasty, were enrolled. Flow-mediated dilation, as well as endothelial progenitor cells (subpopulations CD45+/CD34+/CD133+/CD184+ and CD45+/CD/34+/KDR[VEGFR-2]+ estimated by flow cytometry) from blood flow close to vascular damage, were evaluated before and after angioplasty. Association with lower limb amputation during a 30-day follow-up was analyzed. Results: Endothelial progenitor cells were related with flow-mediated dilation. A higher number of baseline EPCs CD45+CD34+KDR+, as well as an impaired reactivity of endothelial progenitor cells CD45+CD34+CD133+CD184+ after angioplasty, were observed in cases further undergoing major limb amputation, with a significant discrimination ability and risk (0.75, specificity 0.83 and RR 4.5 p +CD34+KDR+, as well as an impaired reactivity of subpopulation CD45+CD34+CD133+CD184+ after angioplasty, showed a predictive ability for major limb amputation in patients with critical limb ischemia