Machine learning for functional protein design
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and
structure data have radically transformed computational protein design. New methods …
structure data have radically transformed computational protein design. New methods …
Opportunities and challenges for machine learning-assisted enzyme engineering
Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …
Accurate structure prediction of biomolecular interactions with AlphaFold 3
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of
proteins and their interactions, enabling a huge range of applications in protein modelling …
proteins and their interactions, enabling a huge range of applications in protein modelling …
Opportunities and challenges in design and optimization of protein function
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 …
the low reliability of purely structure-based design methods limited their application, but …
Sparks of function by de novo protein design
Abstract Information in proteins flows from sequence to structure to function, with each step
causally driven by the preceding one. Protein design is founded on inverting this process …
causally driven by the preceding one. Protein design is founded on inverting this process …
Structure prediction of protein-ligand complexes from sequence information with Umol
Protein-ligand docking is an established tool in drug discovery and development to narrow
down potential therapeutics for experimental testing. However, a high-quality protein …
down potential therapeutics for experimental testing. However, a high-quality protein …
[HTML][HTML] De novo protein design—From new structures to programmable functions
T Kortemme - Cell, 2024 - cell.com
Methods from artificial intelligence (AI) trained on large datasets of sequences and
structures can now" write" proteins with new shapes and molecular functions de novo …
structures can now" write" proteins with new shapes and molecular functions de novo …
Binding and sensing diverse small molecules using shape-complementary pseudocycles
We describe an approach for designing high-affinity small molecule–binding proteins poised
for downstream sensing. We use deep learning–generated pseudocycles with repeating …
for downstream sensing. We use deep learning–generated pseudocycles with repeating …
A perspective on the prospective use of AI in protein structure prediction
R Versini, S Sritharan, B Aykac Fas… - Journal of Chemical …, 2023 - ACS Publications
AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as
highly reliable and effective methods for predicting protein structures. This article explores …
highly reliable and effective methods for predicting protein structures. This article explores …
Machine learning-aided design and screening of an emergent protein function in synthetic cells
Abstract Recently, utilization of Machine Learning (ML) has led to astonishing progress in
computational protein design, bringing into reach the targeted engineering of proteins for …
computational protein design, bringing into reach the targeted engineering of proteins for …