AI Medical Compendium Topic:
Protein Conformation

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evERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

The Journal of chemical physics
Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that s...

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13).

Proteins
We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free-modeling (FM) methods which combine the predictions of three neural networks. All three systems were guide...

Machine learning can be used to distinguish protein families and generate new proteins belonging to those families.

The Journal of chemical physics
Proteins are classified into families based on evolutionary relationships and common structure-function characteristics. Availability of large data sets of gene-derived protein sequences drives this classification. Sequence space is exponentially lar...

Graph kernels combined with the neural network on protein classification.

Journal of bioinformatics and computational biology
At present, most of the researches on protein classification are based on graph kernels. The essence of graph kernels is to extract the substructure and use the similarity of substructures as the kernel values. In this paper, we propose a novel graph...

refineD: improved protein structure refinement using machine learning based restrained relaxation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraint...

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fie...

canSAR: update to the cancer translational research and drug discovery knowledgebase.

Nucleic acids research
canSAR (http://cansar.icr.ac.uk) is a public, freely available, integrative translational research and drug discovery knowlegebase. canSAR informs researchers to help solve key bottlenecks in cancer translation and drug discovery. It integrates genom...

Note: Variational encoding of protein dynamics benefits from maximizing latent autocorrelation.

The Journal of chemical physics
As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the time scale of the latent space while inferring a red...

Blind prediction of protein B-factor and flexibility.

The Journal of chemical physics
The Debye-Waller factor, a measure of X-ray attenuation, can be experimentally observed in protein X-ray crystallography. Previous theoretical models have made strong inroads in the analysis of beta (B)-factors by linearly fitting protein B-factors f...

CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction.