15 papers with code • 0 benchmarks • 0 datasets
We define the robustness measure for the predicted structure of a protein sequence to be the inverse of the root-mean-square distance (RMSD) in the predicted structure and the structure of its adversarially perturbed sequence.
We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding.
Although reconstruction algorithms typically model the 3D volume as a generic function parameterized as a voxel array or neural network, the underlying atomic structure of the protein of interest places well-defined physical constraints on the reconstructed structure.
Understanding the structure of loss landscape of deep neural networks (DNNs)is obviously important.
A protein is a linear chain containing a set of amino acids, which folds on itself to create a specific native structure, also called the minimum energy conformation.
Cellular automata with verbal rules are underexplored in biology.
The resulting kinetostatic control torque inputs will be close to the KCM-based reference vector field and guaranteed to be constrained by a predetermined bound; hence, high-entropy-loss routes during folding are avoided while the energy of the molecule is decreased.
Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design.
We develop quantum computational tools to predict how proteins fold in 3D, one of the most important problems in current biochemical research.
Protein Folding Quantum Physics