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Each entry contains referenced information about the RNA, including sequences, structural information, genomic context, expression, subcellular localization, conservation, functional evidence and other relevant information. lncRNAdb can be searched by querying published RNA names and aliases, sequences, species and associated protein-coding ...
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.
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 ...
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.
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
CS-BLAST starts by predicting the expected mutation probabilities for each position. For a certain residue, a sequence window of ten total surrounding residues is selected as seen in the image. Then, Biegert and Söding compared the sequence window to a library with thousands of context profiles.
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 sheets, alpha helixes and coils) from the primary sequence. PSIPRED is available as a web service and as software.
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