Tandem mass spectrometry data analysis of human proteome for presence of point alterations. Subsequently, deduces DNA/mRNA alterations whenever possible.
FireProt-ASR is a web server for an automated calculation of ancestral sequences. Fireprot-ASR allows you to perform ancestral sequence reconstruction starting from a single protein sequence. The pipeline first compiles a dataset of catalytically similar protein sequences, aligns them, construct their phylogenetic tree, and then reconstruct ancestral nodes. It also allows user to input their own data and start from a different point in the pipeline.
Unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. Allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways.
EnzymeMiner identifies putative members of enzyme families or subfamilies and facilitates the selection of promising targets for experimental characterization. Two key selection criteria are (i) the predicted solubility and (ii) the sequence identities visualised using an interactive sequence similarity network. The search query can be a sequence from the Swiss-Prot database or a custom sequence with a custom description of essential residues. The output is an interactive selection table containing annotated identified sequences.
SoluProt is a web application for prediction of soluble protein expression in Escherichia coli.
SoluProt is one of the latest additions to the family of solubility predictors based on machine learning. The training set is based on the TargetTrack database, which was carefully filtered to keep only targets expressed in Escherichia coli. The negative and positive samples were balanced and equalized for the protein lengths. The independent validation set is derived from the NESG dataset.
The predictor is in its current version based on random forest regression model and employs 36 sequence-based features, e.g., amino acid content, predicted disorder, alpha-helix and beta-sheet content, sequence identity to PDB and several aggregated physico-chemical properties. SoluProt currently achieves accuracy 58.2%, higher than other comparable tools, and is a subject of further active development.