AIMC Topic: Protein Folding

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Predicting residue cooperativity during protein folding: A combined, molecular dynamics and unsupervised learning approach.

The Journal of chemical physics
Allostery in proteins involves, broadly speaking, ligand-induced conformational transitions that modulate function at active sites distal to where the ligand binds. In contrast, the concept of cooperativity (in the sense used in phase transition theo...

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.

Methods in molecular biology (Clifton, N.J.)
Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The thres...

AlphaFold predicts the most complex protein knot and composite protein knots.

Protein science : a publication of the Protein Society
The computer artificial intelligence system AlphaFold has recently predicted previously unknown three-dimensional structures of thousands of proteins. Focusing on the subset with high-confidence scores, we algorithmically analyze these predictions fo...

GraphVAMPNet, using graph neural networks and variational approach to Markov processes for dynamical modeling of biomolecules.

The Journal of chemical physics
Finding a low dimensional representation of data from long-timescale trajectories of biomolecular processes, such as protein folding or ligand-receptor binding, is of fundamental importance, and kinetic models, such as Markov modeling, have proven us...

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors.

Briefings in bioinformatics
Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer libra...