AIMC Topic: Proteins

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Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network.

Interdisciplinary sciences, computational life sciences
Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this...

Automated Protein Secondary Structure Assignment from C Positions Using Neural Networks.

Biomolecules
The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E ...

Sequence-based drug-target affinity prediction using weighted graph neural networks.

BMC genomics
BACKGROUND: Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based d...

Explainable deep drug-target representations for binding affinity prediction.

BMC bioinformatics
BACKGROUND: Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug-target interactions and new leads. However, most of these methodologies have been overlooking the importance of prov...

Predicting Protein-Ligand Docking Structure with Graph Neural Network.

Journal of chemical information and modeling
Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both...

Screening membraneless organelle participants with machine-learning models that integrate multimodal features.

Proceedings of the National Academy of Sciences of the United States of America
Protein self-assembly is one of the formation mechanisms of biomolecular condensates. However, most phase-separating systems (PS) demand multiple partners in biological conditions. In this study, we divided PS proteins into two groups according to th...

Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.

BMC bioinformatics
BACKGROUND: Computer-aided drug design provides an effective method of identifying lead compounds. However, success rates are significantly bottlenecked by the lack of accurate and reliable scoring functions needed to evaluate binding affinities of p...

Sequence-assignment validation in cryo-EM models with checkMySequence.

Acta crystallographica. Section D, Structural biology
The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models w...

OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets.

BMC bioinformatics
BACKGROUND: Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typic...

Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images.

Analytical and bioanalytical chemistry
Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited cryst...