Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms
M AlQuraishi, PK Sorger - Nature methods, 2021 - nature.com
Deep learning using neural networks relies on a class of machine-learnable models
constructed using 'differentiable programs'. These programs can combine mathematical …
constructed using 'differentiable programs'. These programs can combine mathematical …
Structure‐based design of inhibitors of protein–protein interactions: mimicking peptide binding epitopes
M Pelay‐Gimeno, A Glas, O Koch… - Angewandte Chemie …, 2015 - Wiley Online Library
Protein–protein interactions (PPIs) are involved at all levels of cellular organization, thus
making the development of PPI inhibitors extremely valuable. The identification of selective …
making the development of PPI inhibitors extremely valuable. The identification of selective …
AlphaFold 2: why it works and its implications for understanding the relationships of protein sequence, structure, and function
AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment.
Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or …
Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or …
Modeling protein quaternary structure of homo-and hetero-oligomers beyond binary interactions by homology
Cellular processes often depend on interactions between proteins and the formation of
macromolecular complexes. The impairment of such interactions can lead to deregulation of …
macromolecular complexes. The impairment of such interactions can lead to deregulation of …
The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible
A system-wide understanding of cellular function requires knowledge of all functional
interactions between the expressed proteins. The STRING database aims to collect and …
interactions between the expressed proteins. The STRING database aims to collect and …
Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Predicting protein–protein interactions from the molecular to the proteome level
Identification of protein–protein interactions (PPIs) is at the center of molecular biology
considering the unquestionable role of proteins in cells. Combinatorial interactions result in …
considering the unquestionable role of proteins in cells. Combinatorial interactions result in …
AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
Accurate descriptions of protein-protein interactions are essential for understanding
biological systems. Remarkably accurate atomic structures have been recently computed for …
biological systems. Remarkably accurate atomic structures have been recently computed for …
Structure-based prediction of protein–protein interactions on a genome-wide scale
The genome-wide identification of pairs of interacting proteins is an important step in the
elucidation of cell regulatory mechanisms,. Much of our present knowledge derives from …
elucidation of cell regulatory mechanisms,. Much of our present knowledge derives from …
Template-based structure modeling of protein–protein interactions
A Szilagyi, Y Zhang - Current opinion in structural biology, 2014 - Elsevier
Highlights•The most accurate complex models are built by copying close homologous
templates.•Non-homologous templates can be detected by interface structure comparisons.• …
templates.•Non-homologous templates can be detected by interface structure comparisons.• …