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This list of protein subcellular localisation prediction tools includes software, databases, and web services that are used for protein subcellular localization prediction. Some tools are included that are commonly used to infer location through predicted structural properties, such as signal peptide or transmembrane helices , and these tools ...
Protein–lipid interaction is the influence of membrane proteins on the lipid physical state or vice versa.. The questions which are relevant to understanding of the structure and function of the membrane are: 1) Do intrinsic membrane proteins bind tightly to lipids (see annular lipid shell), and what is the nature of the layer of lipids adjacent to the protein?
PSI-blast based secondary structure PREDiction (PSIPRED) is a method used to investigate protein structure. It uses artificial neural network machine learning methods in its algorithm. [ 2 ] [ 3 ] [ 4 ] It is a server-side program, featuring a website serving as a front-end interface, which can predict a protein's secondary structure ( beta ...
AlphaFold gave the best prediction for 25 out of 43 protein targets in this class, [33] [34] [35] achieving a median score of 58.9 on the CASP's global distance test (GDT) score, ahead of 52.5 and 52.4 by the two next best-placed teams, [36] who were also using deep learning to estimate contact distances.
Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein structure prediction, this proved difficult. [4] Machine learning techniques such as deep learning can learn features of data sets rather than requiring the programmer to define them individually.
Amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation. sequence - Amyloidogenic regions ArchCandy [37] 2015 Download- BiSMM: Secondary structure-related
Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal ...
In 2006, random forest, an example of a supervised technique, was found to be the most-effective machine learning method for protein interaction prediction. [57] Such methods have been applied for discovering protein interactions on human interactome, specifically the interactome of Membrane proteins [ 58 ] and the interactome of Schizophrenia ...