Publications associées à Changement conformationnel

From Sequence to Dynamics to Function: Computational Design of Allostery and Ligand Selectivity in G-Protein Coupled Receptors

The phenomenon of allostery, a general property in proteins that has been heralded as "the second secret of life" remains elusive to our understanding and even more challenging to incorporate into protein design. One example of allosteric proteins with gre ...

EPFL2024

Protein target highlights in CASP15: Analysis of models by structure providers

Bruno Lemaitre, Luciano Andres Abriata, Samuel Rommelaere, Kuan-Lin Wu, Han Xiao

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the su ...

WILEY2023

Computational design of dynamic receptor—peptide signaling complexes applied to chemotaxis

Patrick Daniel Barth, Aurélien Laurent Jean-Charles Oggier, Ana Paola Rico Villarreal, Mahdi Hijazi, Robert Everett Jefferson, Andreas Füglistaler

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well ...

Nature Research2023

Structural Basis of the Allosteric Inhibition of Human ABCG2 by Nanobodies

Henning Paul-Julius Stahlberg, Dongchun Ni

ABCG2 is an ATP-binding cassette transporter that exports a wide range of xenobiotic compounds and has been recognized as a contributing factor for multidrug resistance in cancer cells. Substrate and inhibitor interactions with ABCG2 have been extensively ...

Academic Press Ltd- Elsevier Science Ltd2023

Deep learning approaches for conformational flexibility and switching properties in protein design

Patrick Daniel Barth, Lucas Sebastian Powell-Rudden, Mahdi Hijazi

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel str ...

2022

Visualization, Interactive Handling and Simulation of Molecules in Commodity Augmented Reality in Web Browsers Using moleculARweb's Virtual Modeling Kits

Matteo Dal Peraro, Luciano Andres Abriata, Fabio Jose Cortes Rodriguez, Lucien Fabrice Krapp

moleculARweb (https://molecularweb.epfl.ch) began as a website for education and outreach in chemistry and structural biology through augmented reality (AR) content that runs in the web browsers of regular devices like smartphones, tablets, and computers. ...

SWISS CHEMICAL SOC2022

Mechanism ofcyclic beta-glucan export by ABC transporter Cgt of Brucella

Henning Paul-Julius Stahlberg, Dongchun Ni, Na Wu

Polysaccharides play critical roles in bacteria, including the formation of protective capsules and biofilms and establishing specific host cell interactions. Their transport across membranes is often mediated by ATP-binding cassette (ABC) transporters, wh ...

NATURE PORTFOLIO2022

Computationally designed GPCR quaternary structures bias signaling pathway activation

Patrick Daniel Barth, Robert Everett Jefferson

Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can be readily characterized, receptor associations ...

Nature Research2022

Amplitude and phase reconstruction for Low-Energy Electron Holography of individual proteins

Hannah Julia Ochner

Single-molecule imaging methods are of importance in structural biology, and specifically in the imaging of proteins, since they can elucidate conformational variability and structural changes that might be lost in imaging methods relying on averaging proc ...

EPFL2022

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Michele Ceriotti, Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Edgar Albert Engel, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...

2022