AIMC Topic: Membrane Proteins

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Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation.

IEEE/ACM transactions on computational biology and bioinformatics
Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of structural biology, which call for accurate computational prediction approaches. H...

Prediction Enhancement of Residue Real-Value Relative Accessible Surface Area in Transmembrane Helical Proteins by Solving the Output Preference Problem of Machine Learning-Based Predictors.

Journal of chemical information and modeling
The α-helical transmembrane proteins constitute 25% of the entire human proteome space and are difficult targets in high-resolution wet-lab structural studies, calling for accurate computational predictors. We present a novel sequence-based method ca...

Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets.

IEEE/ACM transactions on computational biology and bioinformatics
Membrane proteins play important roles in various biological processes within organisms. Predicting the functional types of membrane proteins is indispensable to the characterization of membrane proteins. Recent studies have extended to predicting si...

Cheminformatics Based Machine Learning Models for AMA1-RON2 Abrogators for Inhibiting Plasmodium falciparum Erythrocyte Invasion.

Molecular informatics
Malaria remains a dreadful disease by putting every year about 3.4 billion people at risk and resulting into mortality of 627 thousand people worldwide. Existing therapies based upon Quinines and Artemisinin-based combination therapies have started s...

A Machine Learning Approach to Explain Drug Selectivity to Soluble and Membrane Protein Targets.

Molecular informatics
Improved understanding of the forces that determine drug specificity to their targets is important for drug design and discovery, as well as for gaining knowledge about molecular recognition. Here, we present a machine learning approach that includes...

Integrated bioinformatics analysis and machine learning identifies FZD4, SRPX2, and COL8A1 as angiogenesis hub genes in endometriosis.

Medicine
This study aims to identify angiogenesis-associated genes (AAGs) in endometriosis (EM) by integrating bioinformatics analysis with machine learning, and to investigate their underlying mechanisms. Differentially expressed genes (DEGs) were screened f...

pLMMoRF: A Web Server That Accurately Predicts Membrane-interacting Molecular Recognition Features by Employing a Protein Language Model.

Journal of molecular biology
Interactions between proteins and lipids are crucial for numerous cellular processes. Some of the lipid interacting segments in protein sequences are intrinsically disordered regions (IDRs), which may gain secondary structures upon binding. We collec...

Why Protein Modifications Matter for Digestibility: The Case of Ara h 1 Peanut Allergen and Trypsin Cleavage.

Journal of agricultural and food chemistry
Trypsin is the principal intestinal endopeptidase and proteomics digestion tool, yet the impact of protein modifications (PMs) on digestibility and allergenicity remains underexplored. We employed a proteomic approach to assess trypsin cleavage effic...

TmDet 4.0: determining membrane orientation of transmembrane proteins from 3D structure.

Nucleic acids research
During the structural determination of transmembrane proteins, one crucial piece of information is lost: the orientation of the protein within the lipid bilayer. The TmDet algorithm was developed in the early 2000s to determine the relative position ...