Publication
Protein design and folding: Template trapping of self-assembled helical bundles
Related publications (82)
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 Portfolio2024Investigating the intra-molecular and inter-molecular effects of post-translational modifications on intrinsically disordered protein regions and structured protein regions
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 ...
EPFL2024De novo protein design by inversion of the AlphaFold structure prediction network
Bruno Emanuel Ferreira De Sousa Correia, Hamed Khakzad, Casper Alexander Goverde, Stéphane Rosset, Benedict Dieter Gregor Wolf
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 ...
2023Generative 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 LTD2023A 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 ...
Cambridge2023De 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 ...
EPFL2023A Geometric Transformer for Structural Biology: Development and Applications of the Protein Structure Transformer
Proteins, the central building blocks of life, play pivotal roles in nearly every biological function. To do so, these macromolecular structures interact with their surrounding environment in complex ways, leading to diverse functional behaviors. The predi ...
EPFL2023Design of an artificial phage-display library based on a new scaffold improved for average stability of the randomized proteins
Scaffold-based protein libraries are designed to be both diverse and rich in functional/folded proteins. However, introducing an extended diversity while preserving stability of the initial scaffold remains a challenge. Here we developed an original approa ...
NATURE PORTFOLIO2023Towards automating de novo protein design for novel functionalities: controlling protein folds and protein-protein interactions
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 ...
EPFL2022Deep 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