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Active Site-Induced Evolutionary Constraints Follow Fold Polarity Principles in Soluble Globular Enzymes

Publications associées (53)

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Investigating the intra-molecular and inter-molecular effects of post-translational modifications on intrinsically disordered protein regions and structured protein regions

Zhidian Zhang

Post-translational modifications (PTMs) play a pivotal role in regulating protein structure, interaction, and function. Aberrant PTM patterns are associated with diseases. Moreover, individual PTMs have a complex interaction with each other, known as PTM c ...

EPFL2024

A new age in protein design empowered by deep learning

Bruno Emanuel Ferreira De Sousa Correia, Michael Bronstein, Hamed Khakzad, Casper Alexander Goverde, Arne Schneuing, Ilia Igashov

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Cambridge2023

Generative power of a protein language model trained on multiple sequence alignments

Anne-Florence Raphaëlle Bitbol, Damiano Sgarbossa, Umberto Lupo

Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating no ...

eLIFE SCIENCES PUBL LTD2023

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De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in comp ...

2023

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The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing ...

AMER ASSOC ADVANCEMENT SCIENCE2022

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

Patrick Daniel Barth, 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

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

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