AI Medical Compendium Topic:
Proteins

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TMPpred: A support vector machine-based thermophilic protein identifier.

Analytical biochemistry
MOTIVATION: The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics.

DLPacker: Deep learning for prediction of amino acid side chain conformations in proteins.

Proteins
Prediction of side chain conformations of amino acids in proteins (also termed "packing") is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have...

Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network machine learning model.

BMC bioinformatics
BACKGROUND: The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates...

Residue-Frustration-Based Prediction of Protein-Protein Interactions Using Machine Learning.

The journal of physical chemistry. B
The study of protein-protein interactions (PPIs) is important in understanding the function of proteins. However, it is still a challenge to investigate the transient protein-protein interaction by experiments. Hence, the computational prediction for...

Prediction of liquid-liquid phase separating proteins using machine learning.

BMC bioinformatics
BACKGROUND: The liquid-liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function. Dysregulation...

ProtPlat: an efficient pre-training platform for protein classification based on FastText.

BMC bioinformatics
BACKGROUND: For the past decades, benefitting from the rapid growth of protein sequence data in public databases, a lot of machine learning methods have been developed to predict physicochemical properties or functions of proteins using amino acid se...

Machine learning modeling of family wide enzyme-substrate specificity screens.

PLoS computational biology
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their nat...

A backbone-centred energy function of neural networks for protein design.

Nature
A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type...

Machine learning based predictive model for the analysis of sequence activity relationships using protein spectra and protein descriptors.

Journal of biomedical informatics
Accurately establishing the connection between a protein sequence and its function remains a focal point within the field of protein engineering, especially in the context of predicting the effects of mutations. From this, there has been a continued ...

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.

ACS biomaterials science & engineering
Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino ac...