[HTML][HTML] Computational design of novel protein–protein interactions–An overview on methodological approaches and applications
A Marchand, AK Van Hall-Beauvais… - Current Opinion in …, 2022 - Elsevier
Protein–protein interactions (PPIs) govern numerous cellular functions in terms of signaling,
transport, defense and many others. Designing novel PPIs poses a fundamental challenge …
transport, defense and many others. Designing novel PPIs poses a fundamental challenge …
Antibody-antigen docking and design via hierarchical structure refinement
Computational antibody design seeks to automatically create an antibody that binds to an
antigen. The binding affinity is governed by the 3D binding interface where antibody …
antigen. The binding affinity is governed by the 3D binding interface where antibody …
Deep learning methods for designing proteins scaffolding functional sites
Current approaches to de novo design of proteins harboring a desired binding or catalytic
motif require pre-specification of an overall fold or secondary structure composition, and …
motif require pre-specification of an overall fold or secondary structure composition, and …
Antibody-antigen docking and design via hierarchical equivariant refinement
Computational antibody design seeks to automatically create an antibody that binds to an
antigen. The binding affinity is governed by the 3D binding interface where antibody …
antigen. The binding affinity is governed by the 3D binding interface where antibody …
Deep learning for flexible and site-specific protein docking and design
M McPartlon, J Xu - BioRxiv, 2023 - biorxiv.org
Protein complexes are vital to many biological processes and their understanding can lead
to the development of new drugs and therapies. Although the structure of individual protein …
to the development of new drugs and therapies. Although the structure of individual protein …
Deep learning of protein sequence design of protein–protein interactions
R Syrlybaeva, EM Strauch - Bioinformatics, 2023 - academic.oup.com
Motivation As more data of experimentally determined protein structures are becoming
available, data-driven models to describe protein sequence–structure relationships become …
available, data-driven models to describe protein sequence–structure relationships become …
Machine learning for protein engineering
Directed evolution of proteins has been the most effective method for protein engineering.
However, a new paradigm is emerging, fusing the library generation and screening …
However, a new paradigm is emerging, fusing the library generation and screening …
[HTML][HTML] Tpgen: a language model for stable protein design with a specific topology structure
X Min, C Yang, J Xie, Y Huang, N Liu, X Jin, T Wang… - BMC …, 2024 - Springer
Background Natural proteins occupy a small portion of the protein sequence space,
whereas artificial proteins can explore a wider range of possibilities within the sequence …
whereas artificial proteins can explore a wider range of possibilities within the sequence …
Randomized gates eliminate bias in sort‐seq assays
Sort‐seq assays are a staple of the biological engineering toolkit, allowing researchers to
profile many groups of cells based on any characteristic that can be tied to fluorescence …
profile many groups of cells based on any characteristic that can be tied to fluorescence …
Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation
TE Kim, K Tsuboyama, S Houliston… - Proceedings of the …, 2022 - National Acad Sciences
Designing entirely new protein structures remains challenging because we do not fully
understand the biophysical determinants of folding stability. Yet, some protein folds are …
understand the biophysical determinants of folding stability. Yet, some protein folds are …