AIMC Topic: Proteins

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Anomaly Detection-Based Recognition of Near-Native Protein Structures.

IEEE transactions on nanobioscience
The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational appro...

LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening.

Journal of chemical information and modeling
Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased data sets. Recent investigations from numerous research groups unambiguously demonstrate that artificially constructed ...

DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction.

BMC bioinformatics
BACKGROUND: Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) a...

Recent advances in robotic protein sample preparation for clinical analysis and other biomedical applications.

Clinica chimica acta; international journal of clinical chemistry
Discovery of new protein biomarker candidates has become a major research goal in the areas of clinical chemistry, analytical chemistry, and biomedicine. These important species constitute the molecular target when it comes to diagnosis, prognosis, a...

Machine Learning Approaches for Quality Assessment of Protein Structures.

Biomolecules
Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly an...

Predicting protein-peptide binding sites with a deep convolutional neural network.

Journal of theoretical biology
MOTIVATION: Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide b...

Deep Generative Models for 3D Linker Design.

Journal of chemical information and modeling
Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of t...

Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.

Stroke and vascular neurology
The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures ...

Improving detection of protein-ligand binding sites with 3D segmentation.

Scientific reports
In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks espe...

DeeplyTough: Learning Structural Comparison of Protein Binding Sites.

Journal of chemical information and modeling
Protein pocket matching, or binding site comparison, is of importance in drug discovery. Identification of similar binding pockets can help guide efforts for hit-finding, understanding polypharmacology, and characterization of protein function. The d...