26 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Protein Folding
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods.
We evaluated the accuracy and efficiency of optimizations on CPUs and GPUs, and showed the large-scale prediction capability by running ParaFold inferences of 19, 704 small proteins in five hours on one NVIDIA DGX-2.
The native results for the benchmark sequences and lattice polymers were compared with varying computational methods.
Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0. 5.
Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity
sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters.
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure
Predicting the structure of a protein from its sequence is a cornerstone task of molecular biology.