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rstoolbox - a Python library for large-scale analysis of computational protein design data and structural bioinformatics

Publications associées (155)

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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

Keap 1: The new Janus word on the block

Yimon Aye

Here we draw insights from the latest serendipitous findings made on the opposing roles of a proposed drug-target protein Keap1. We weigh up how natural reactive electrophiles and electrophilic small-molecule drugs in clinical use directly impinge on seemi ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Proteins are fundamental components in biological systems and crucial for a variety of biological functions. Over the last decades, significant progress in structural biology has facilitated the study of proteins and their interactions with molecular partn ...

EPFL2021

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction

Jaume Bonet Martinez

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that grou ...

OXFORD UNIV PRESS2021

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

Jaume Bonet Martinez

BackgroundStatistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we deco ...

BMC2021

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

The work presented in this thesis combines supervised and unsupervised machine learning to examine structure-property relationships in databases of materials. While either supervised learning or unsupervised learning alone can be a powerful tool for assess ...

EPFL2021

New Methods for the Integrative Dynamic Modeling of Biomolecular Structures

Sylvain Träger

The true understanding of most cellular functions is only really achievable through the structural determination of their underlying macromolecular assemblies. However, their size, large number of individual components and metastable states make their eluc ...

EPFL2021

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Imaging of proteins at the single-molecule level can reveal conformational variability, which is essential for the understanding of biomolecules. To this end, a biologically relevant state of the sample must be retained during both sample preparation and i ...

NATL ACAD SCIENCES2021

A Rosetta-based protein design protocol converging to natural sequences

Bruno Emanuel Ferreira De Sousa Correia, Zander Harteveld, Stéphane Rosset, Giulia Sormani

Computational protein design has emerged as a powerful tool capable of identifying sequences compatible with pre-defined protein structures. The sequence design protocols, implemented in the Rosetta suite, have become widely used in the protein engineering ...

AMER INST PHYSICS2021