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With BAR 3.0 and a sequence you can annotate when possible: function (Gene Ontology), structure (Protein Data Bank), protein domains (Pfam). Also if your sequence falls into a cluster with a structural/some structural template/s we provide an alignment towards the template/templates based on the Cluster-HMM (HMM profile) that allows you to ...
A graph-based algorithm that constructs a graph of the sequence. no [11] SubSeqer: 2008 web: A graph-based approach for the detection and identification of repetitive elements in low–complexity sequences. no [12] ANNIE: 2009 web: This method creates an automation of the sequence analytic process. no [13] LPS-annotate 2011 on request
PredictProtein (PP) is an automatic service that searches up-to-date public sequence databases, creates alignments, and predicts aspects of protein structure and function. Users send a protein sequence and receive a single file with results from database comparisons and prediction methods.
Separate modules extend Biopython's capabilities to sequence alignment, protein structure, population genetics, phylogenetics, sequence motifs, and machine learning. Biopython is one of a number of Bio* projects designed to reduce code duplication in computational biology. [6]
Proteome Analyst (PA) is a freely available web server and online toolkit for predicting protein subcellular localization, or where a protein resides in a cell. [1] [2] In the field of proteomics, accurately predicting a protein's subcellular localization, or where a specific protein is located inside a cell, is an important step in the large scale study of proteins.
sequence - Overall generic and amyloidogenic regions based on the consensus PASTA 2.0 [30] 2014 Web Server - PASTA 2.0: Secondary structure-related. Predicts the most aggregation-prone portions and the corresponding β-strand inter-molecular pairing for multiple input sequences. sequence top pairings and energies, mutations and protein-protein
PSPredictor is a machine learning approach to predict proteins that phase separate, trained on a set of experimentally validated protein sequences in the LLPSDB database. [6] PSAP [7] 2021 PSAP is a random forest classifier to predict the probability of proteins to mediate phase separation.
Similarly, Light-Attention uses machine learning methods to predict ten different common subcellular locations. [ 12 ] The first model to generalize protein subcellular localization to all cell line does so by leveraging images of subcellular landmark stains (i.e., nuclear, plasma membrane, and endoplasmic reticulum markers) across multiple ...