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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 ...
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.
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
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]
BLASTp, or Protein BLAST, is used to compare protein sequences. You can input one or more protein sequences that you want to compare against a single protein sequence or a database of protein sequences. This is useful when you're trying to identify a protein by finding similar sequences in existing protein databases. [17]
A profile HMM modelling a multiple sequence alignment. HMMER is a free and commonly used software package for sequence analysis written by Sean Eddy. [2] Its general usage is to identify homologous protein or nucleotide sequences, and to perform sequence alignments.
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 ...
Casanovo is a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide, enabling de novo peptide sequencing without prior information. [34] InstaNovo Open source