I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction
Most proteins in cells are composed of multiple folding units (or domains) to perform
complex functions in a cooperative manner. Relative to the rapid progress in single-domain …
complex functions in a cooperative manner. Relative to the rapid progress in single-domain …
Protein design: From the aspect of water solubility and stability
Water solubility and structural stability are key merits for proteins defined by the primary
sequence and 3D-conformation. Their manipulation represents important aspects of the …
sequence and 3D-conformation. Their manipulation represents important aspects of the …
Illuminating protein space with a programmable generative model
Three billion years of evolution has produced a tremendous diversity of protein molecules,
but the full potential of proteins is likely to be much greater. Accessing this potential has …
but the full potential of proteins is likely to be much greater. Accessing this potential has …
Improved protein structure prediction using potentials from deep learning
Protein structure prediction can be used to determine the three-dimensional shape of a
protein from its amino acid sequence. This problem is of fundamental importance as the …
protein from its amino acid sequence. This problem is of fundamental importance as the …
State-of-the-art estimation of protein model accuracy using AlphaFold
JP Roney, S Ovchinnikov - Physical Review Letters, 2022 - APS
The problem of predicting a protein's 3D structure from its primary amino acid sequence is a
longstanding challenge in structural biology. Recently, approaches like alphafold have …
longstanding challenge in structural biology. Recently, approaches like alphafold have …
[HTML][HTML] Advances in protein structure prediction and design
The prediction of protein three-dimensional structure from amino acid sequence has been a
grand challenge problem in computational biophysics for decades, owing to its intrinsic …
grand challenge problem in computational biophysics for decades, owing to its intrinsic …
[HTML][HTML] CADD, AI and ML in drug discovery: A comprehensive review
D Vemula, P Jayasurya, V Sushmitha, YN Kumar… - European Journal of …, 2023 - Elsevier
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest
because of its potential to expedite and lower the cost of the drug development process …
because of its potential to expedite and lower the cost of the drug development process …
Recent advances in de novo protein design: Principles, methods, and applications
X Pan, T Kortemme - Journal of Biological Chemistry, 2021 - ASBMB
The computational de novo protein design is increasingly applied to address a number of
key challenges in biomedicine and biological engineering. Successes in expanding …
key challenges in biomedicine and biological engineering. Successes in expanding …
The Rosetta all-atom energy function for macromolecular modeling and design
Over the past decade, the Rosetta biomolecular modeling suite has informed diverse
biological questions and engineering challenges ranging from interpretation of low …
biological questions and engineering challenges ranging from interpretation of low …
Protein sequence design by conformational landscape optimization
The protein design problem is to identify an amino acid sequence that folds to a desired
structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as …
structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as …