Learning from protein structure with geometric vector perceptrons

B Jing, S Eismann, P Suriana… - International …, 2020 - openreview.net
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine
learning, but there has yet to emerge a unifying network architecture that simultaneously …

Machine learning approaches for quality assessment of protein structures

J Chen, SWI Siu - Biomolecules, 2020 - mdpi.com
Protein structures play a very important role in biomedical research, especially in drug
discovery and design, which require accurate protein structures in advance. However …

Protein model quality assessment using rotation-equivariant, hierarchical neural networks

S Eismann, P Suriana, B Jing, RJL Townshend… - arXiv preprint arXiv …, 2020 - arxiv.org
Proteins are miniature machines whose function depends on their three-dimensional (3D)
structure. Determining this structure computationally remains an unsolved grand challenge …

Obtaining tertiary protein structures by the ab initio interpretation of small angle X-ray scattering data

C Prior, OR Davies, D Bruce, E Pohl - Journal of chemical theory …, 2020 - ACS Publications
Small angle X-ray scattering (SAXS) is an important tool for investigating the structure of
proteins in solution. We present a novel ab initio method representing polypeptide chains as …

Contact area-based structural analysis of proteins and their complexes using CAD-score

K Olechnovič, Č Venclovas - Structural Bioinformatics: Methods and …, 2020 - Springer
Quantifying discrepancies between computationally derived and native (reference)
structures is an essential step in the development and comparison of protein modeling and …

[PDF][PDF] Multi-objective evolutionary strategy approaches for protein structure prediction

宋双宝 - 2020 - toyama.repo.nii.ac.jp
The problem of predicting the three-dimensional structure of a protein from its
onedimensional sequence has been called the “holy grail of molecular biology”, and it has …