New insight into cataract formation: Enhanced stability through mutual attraction

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Advancing first principle-based molecular dynamics of biological systems with machine learning

François Louis Mouvet

Molecular dynamics (MD) simulations have emerged as a transformative approach to analyse molecular systems at the atomic level, offering valuable insights into complex biological processes. Many biological phenomena can only accurately be described by inco ...

EPFL2023

Antenna-coupled infrared nanospectroscopy of intramolecular vibrational interaction

Felipe Herrera, Hatice Altug, Aurélian Michel John-Herpin

Many photonic and electronic molecular properties, as well as chemical and biochemical reactivities are controlled by fast intramolecular vibrational energy redistribution (IVR). This fundamental ultrafast process limits coherence time in applications from ...

National Academy of Sciences2023

Systems and methods for de novo design of protein interactions with learned surface fingerprints

Anthony Marchand, Zander Harteveld, Pablo Gainza Cirauqui, Freyr Sverrisson, Andreas Scheck, Sarah Wehrle, Alexandra Krina Van Hall-Beauvais, Michael Bronstein

The present application relates to a computer-implemented systems and methods for protein interaction design using surface fingerprints. The method comprises predicting at least one target interface site with high binding propensity, wherein, optionally, t ...

2023

De novo design of protein interactions with learned surface fingerprints

Henning Paul-Julius Stahlberg, Didier Trono, Priscilla Turelli, Charlène Mireille Raymonde Raclot, Sandrine Madeleine Suzanne Georgeon, Beat Fierz, Aaron Simone Petruzzella, Anthony Marchand, Bruno Emanuel Ferreira De Sousa Correia, Elisa Oricchio, Zander Harteveld, Pablo Gainza Cirauqui, Freyr Sverrisson, Andreas Scheck, Sarah Wehrle, Stéphane Rosset, Alexandra Krina Van Hall-Beauvais, Alexandra Teslenko, Jane Marsden, Michael Bronstein, Casper Alexander Goverde, Stephen Michael Buckley, Dongchun Ni, Martin Pacesa

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This k ...

2023

Protein Dielectrophoresis with Gradient Array of Conductive Electrodes Sheds New Light on Empirical Theory

Olivier Martin, Siarhei Zavatski

Dielectrophoresis (DEP) is a versatile tool for the precise microscale manipulation of a broad range of substances. To unleash the full potential of DEP for the manipulation of complex molecular-sized particulates such as proteins requires the development ...

AMER CHEMICAL SOC2023

Bridging Native and Intrinsic Structures of Microhydrated Biomolecules by Cold Ion Spectroscopy

Andrei Zviagin

Solving native structures of such large molecules, like biomolecules, is often challenging, particularly due to the potentially infinite number of non-covalent interactions with water. In this thesis, we report the use of cold ion gas-phase action spectros ...

EPFL2023

Hierarchy of the Components in Spray-Dried, Protein-Excipient Particles Using DNP-Enhanced NMR Spectroscopy

David Lyndon Emsley, Baptiste Busi, Pierrick Berruyer, Andrea Bertarello

Protein-based drugs are becoming increasingly important, but there are challenges associated with their formulation (for example, formulating stable inhalable aerosols while maintaining the proper long-term stability of the protein). Determining the morpho ...

Amer Chemical Soc2023

Towards automating de novo protein design for novel functionalities: controlling protein folds and protein-protein interactions

Zander Harteveld

The sheer size of the protein sequence space is massive: a protein of 100 residues can have 20^100 possible sequence combinations; and knowing that this exceeds the number of atoms in the universe, the chance of randomly discovering a stable new sequence w ...

EPFL2022

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

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

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

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Stefano Zamuner

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference ...

PUBLIC LIBRARY SCIENCE2022

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