[HTML][HTML] Homology modeling in the time of collective and artificial intelligence
Homology modeling is a method for building protein 3D structures using protein primary
sequence and utilizing prior knowledge gained from structural similarities with other …
sequence and utilizing prior knowledge gained from structural similarities with other …
Learning from protein structure with geometric vector perceptrons
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 …
learning, but there has yet to emerge a unifying network architecture that simultaneously …
Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13
Predicting residue‐residue distance relationships (eg, contacts) has become the key
direction to advance protein structure prediction since 2014 CASP11 experiment, while …
direction to advance protein structure prediction since 2014 CASP11 experiment, while …
GraphQA: protein model quality assessment using graph convolutional networks
Motivation Proteins are ubiquitous molecules whose function in biological processes is
determined by their 3D structure. Experimental identification of a protein's structure can be …
determined by their 3D structure. Experimental identification of a protein's structure can be …
Estimation of model accuracy in CASP13
J Cheng, MH Choe, A Elofsson, KS Han… - Proteins: Structure …, 2019 - Wiley Online Library
Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental
part of most protein folding pipelines and important for reliable identification of the best …
part of most protein folding pipelines and important for reliable identification of the best …
Geometry-complete perceptron networks for 3d molecular graphs
A Morehead, J Cheng - Bioinformatics, 2024 - academic.oup.com
Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …
several scientific domains such as protein structure prediction and design, leading to …
Protein model quality assessment using 3D oriented convolutional neural networks
Motivation Protein model quality assessment (QA) is a crucial and yet open problem in
structural bioinformatics. The current best methods for single-model QA typically combine …
structural bioinformatics. The current best methods for single-model QA typically combine …
Artificial intelligence for template-free protein structure prediction: a comprehensive review
Protein structure prediction (PSP) is a grand challenge in bioinformatics, drug discovery, and
related fields. PSP is computationally challenging because of an astronomically large …
related fields. PSP is computationally challenging because of an astronomically large …
3D-equivariant graph neural networks for protein model quality assessment
Motivation Quality assessment (QA) of predicted protein tertiary structure models plays an
important role in ranking and using them. With the recent development of deep learning end …
important role in ranking and using them. With the recent development of deep learning end …
Geometric potentials from deep learning improve prediction of CDR H3 loop structures
Motivation Antibody structure is largely conserved, except for a complementarity-determining
region featuring six variable loops. Five of these loops adopt canonical folds which can …
region featuring six variable loops. Five of these loops adopt canonical folds which can …