Publications associées à Modélisation de protéines par enfilage

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

This study combined protein modeling methods to generate the prolamins' fractions as precise as possible. Hence, gliadins, zeins, kafirins, hordeins, secalins, avenins and oryzins were generated based on their characteristics and disulfide mapping. Finding ...

Elsevier Sci Ltd2024

Opportunities and challenges in design and optimization of protein function

Bruno Emanuel Ferreira De Sousa Correia, Casper Alexander Goverde

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calcu ...

Nature Portfolio2024

Supplementary dataset for Enzyme promiscuous profiles for protein sequence and reaction annotation

Vassily Hatzimanikatis, Ljubisa Miskovic, Homa Mohammadi Peyhani, Anastasia Sveshnikova

The data and scripts used to produce, analyze, and visualize the results of the manuscript Enzyme promiscuous profiles for protein sequence and reaction annotation by Homa MohammadiPeyhani, Anastasia Sveshnikova, Ljubisa Miskovic, and Vassily Hatzimanikati ...

Zenodo2023

Impact of phylogeny on structural contact inference from protein sequence data

Anne-Florence Raphaëlle Bitbol, Umberto Lupo, Nicola Dietler

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ance ...

ROYAL SOC2023

De novo designed proteins: a study in engineering novel folds and functions

Alexandra Krina Van Hall-Beauvais

Proteins control nearly every facet of life on a molecular level. Proteins are formed from linear strings of amino acids, which fold into three-dimensional structures that can enact functions. Evolution has created highly efficient proteins in diverse fold ...

EPFL2023

A generic framework for hierarchical de novo protein design

Bruno Emanuel Ferreira De Sousa Correia, Jaume Bonet Martinez, Fabian Sesterhenn, Zander Harteveld, Stéphane Rosset, Che Yang

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may ...

National Academy of Sciences2022

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

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

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Anne-Florence Raphaëlle Bitbol, Nicola Dietler, Andonis Samuel Rene Gerardos

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families ...

Public Library Science2022

Fast end-to-end learning on protein surfaces

Bruno Emanuel Ferreira De Sousa Correia, Freyr Sverrisson

Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional s ...

IEEE COMPUTER SOC2021