AI Medical Compendium Topic:
Proteins

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iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions.

IEEE/ACM transactions on computational biology and bioinformatics
Among all the PTMs, the protein phosphorylation is pivotal for various pathological and physiological processes. About 30 percent of eukaryotic proteins undergo the phosphorylation modification, leading to various changes in conformation, function, s...

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction.

Biomolecules
The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence st...

Translating from Proteins to Ribonucleic Acids for Ligand-binding Site Detection.

Molecular informatics
Identifying druggable ligand-binding sites on the surface of the macromolecular targets is an important process in structure-based drug discovery. Deep-learning models have been shown to successfully predict ligand-binding sites of proteins. As a ste...

ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction.

Nature methods
Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two classes of methods prevail: machine learning models built...

On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks.

Journal of medicinal chemistry
Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in their free...

PHRONESIS: A One-Shot Approach for Sequential Assignment of Protein Resonances by Ultrafast MAS Solid-State NMR Spectroscopy.

Chemphyschem : a European journal of chemical physics and physical chemistry
Solid-state NMR (ssNMR) spectroscopy has emerged as the method of choice to analyze the structural dynamics of fibrillar, membrane-bound, and crystalline proteins that are recalcitrant to other structural techniques. Recently, H detection under fast...

Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities.

Communications biology
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dy...

Prediction of protein-protein interaction using graph neural networks.

Scientific reports
Proteins are the essential biological macromolecules required to perform nearly all biological processes, and cellular functions. Proteins rarely carry out their tasks in isolation but interact with other proteins (known as protein-protein interactio...

Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery.

Scientific reports
Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein-ligand with adversari...

Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Journal of chemical information and modeling
Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding...