AI Medical Compendium Topic:
Proteins

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SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction.

Interdisciplinary sciences, computational life sciences
PURPOSE: The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein-protein interaction sites (PPI...

Collectively encoding protein properties enriches protein language models.

BMC bioinformatics
Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such protein language mod...

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.

Journal of chemical information and modeling
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and ma...

Prediction of drug-target interactions through multi-task learning.

Scientific reports
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. Ho...

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis.

Journal of visualized experiments : JoVE
The retinal pigment epithelium (RPE) and retina are functionally and structurally connected tissues that work together to regulate light perception and vision. Proteins on the RPE apical surface are tightly associated with proteins on the photorecept...

Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening.

Journal of chemical information and modeling
In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein-ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a standardized pocket Pfam-based clustering (Pfam-cluster) appr...

Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA.

Nature communications
Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, ...

PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability.

International journal of molecular sciences
Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful f...

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.

eLife
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allo...

Evaluation of a Self-Supervised Machine Learning Method for Screening of Particulate Samples: A Case Study in Liquid Formulations.

Journal of pharmaceutical sciences
Imaging is commonly used as a characterization method in the pharmaceuticals industry, including for quantifying subvisible particles in solid and liquid formulations. Extracting information beyond particle size, such as classifying morphological sub...