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