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Protein function detection software tools | Sequence data analysis

Protein function is a concept that can have different interpretations in different biological contexts. Generally, it describes biochemical, cellular, and phenotypic aspects of the molecular events that involve the protein, including how they interact with the environment (e.g. small compounds or pathogens).

Source text:
(Radivojac et al., 2013) A large-scale evaluation of computational protein function prediction. Nat Methods.

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Mouse BAC finder
Provides a solution for protein tagging in mammalian tissue culture cells. Mouse BAC finder is an efficient, generic and scalable approach for bacterial artificial chromosomes (BACs)-based transgenesis in mammalian tissue culture cells, which we term ‘BAC TransgeneOmics’. The use of bacterial artificial chromosomes (BACs) for transgenesis enables the expression of the transgene from its native genomic environment. The method is applicable to very large genes, which are difficult to obtain as cDNAs.
A structure-based method for biological function annotation of protein molecules. To use COFACTOR, user needs to provide a 3D-structural model of the protein of interest. COFACTOR will thread the structure through the BioLiP protein function database by local and global structure matches to identify functional sites and homologies. Functional insights, including ligand-binding site, gene-ontology terms, and enzyme classification, will be derived from the best functional homology template.
EFICAz / Enzyme Function Inference by a Combined Approach
Allows users to study the proteomic scale inference of enzyme function. EFICAz can identify functionally discriminating residue (FDR) as residues that discriminate the members of a homo-functional family from a hetero-functional family. It combines the prediction from four independent methods, namely: (1) CHIEFc family-based (FDR) identification, (2) multiple PFAM-based FDR recognition, (3) CHIEFc SIT evaluation and (4) high-specificity multiple PROSITE patterns.
PFP / Protein Function Prediction
Provides a web server that predicts Gene Ontology (GO) terms from a list of query sequences. PFP manages a large prediction coverage by retrieving annotations widely including from weakly similar sequences. The software uses predict function method which allows to consider sequences with a lack of annotated homologs in the database, extract and infers functional information. The tool can be combined with Extended Similarity Group (ESG) in a single interface.
ESG / Extended Similarity Group
Provides an improving specificity by accumulating contribution of consistently predicted Gene Ontology (GO) terms in an iterative search. ESG predicts a GO term with a high score if it appears many times consistently in the multiple searches including the initial search and the second level searches. The method can be applied in several multiple-domains and aims to improve predictions about functions derived from different domains. The tool can be combined with PFP (Protein Functional Prediction) in the a single interface.
SIFTS / Structure integration with function taxonomy and sequence
Includes cross-references to other biological resources such as Pfam, SCOP, CATH, GO, InterPro and the NCBI taxonomy database. The Structure Integration with Function, Taxonomy and Sequences resource (SIFTS) is focused on standardization of taxonomy information in the PDB based on the NCBI taxonomy database, and on adding cross-references to UniProtKB for all the protein sequences in the PDB that are present in the UniProt database. It has two main components—the semi-automated process that identifies the correct and up-to-date UniProtKB cross-reference for protein chains in the PDB and the automated pipeline that generates residue-level correspondences between proteins in the PDB and the corresponding UniProtKB sequence.
Allows prediction of protein-to-protein and phenotype-to-protein functional associations based on phylogenetic profiling. ProtPhylo achieves flexibility and state-of-the-art taxonomic and functional coverage by generating phylogenetic profiles. It concerns more than 9 million non-redundant protein sequences across over 2000 organisms and implements four independent orthology detection algorithms. In summary, this tool allows prediction of subcellular localization, protein domains, membrane spanning regions, and complementary evidence of protein-protein interactions (PPIs).
INGA / Interaction Network GO Annotator
A web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. The method has been evaluated by the CAFA assessors (2014) among the best predictors.
GASS-WEB / Genetic Active Site Search-WEB
Can use catalytic and binding sites templates to search similar sites in a protein. GASS-WEB is a free and a user-friendly web server created for searching similar active sites based on data from the PDB, CSA and NCBI-VAST. This resource could be an invaluable tool for assisting protein function prediction and active site annotation. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures.
MaRIboES / Metabolite and Reaction Inference based on Enzyme Specificities
Predicts possible enzymatic transformations as well as the resulting output compounds, given a set of input compounds. MaRIboES is a system for metabolite and reaction inference based on enzyme specificities. It was validated using a metabolome-wide leave-one out procedure. It also predicts enzymatic alternatives for reactions thought to be autocatalytic, interesting bypasses within and crosslinks between pathways.
Extracts localized features from sequence sets. CascadeDetect computes expected feature counts, screens artifacts and identifies statistically significant localized features of all orders in sets of short sequences, or features of orders up to a specified maximum in longer sequences. The method detects first-order features that are either overrepresented or underrepresented and higher order features that are overrepresented. The algorithm has been successfully tested on synthetic data, and then used to analyze amino acid sequence sets from problems in two different domains to demonstrate its broad utility: a study of HIV-1 protease (HIV PR) cleavage specificity applies the algorithm to a problem in which features play a transient sensing/recognition role, while an analysis of the Schellman loop motif in proteins evaluates features which are structural and therefore more permanent.
ProFET / Protein Feature Engineering Toolkit
Allows easy, universal discovery of new target proteins, as well as understanding the features underlying different high-level protein functions. ProFET extracts hundreds of features covering the elementary biophysical and sequence derived attributes. Most features capture statistically informative patterns. In addition, different representations of sequences and the amino acids (AA) alphabet provide a compact, compressed set of features. The results from ProFET were incorporated in data analysis pipelines, implemented in python, and adapted for multi-genome scale analysis.
UniProt-DAAC / UniProt Domain Architecture Alignment and Classification
A method for the automatic annotation of protein sequences in the UniProt Knowledgebase (UniProtKB) by comparing their domain architectures, classifying proteins based on the similarities and propagating functional annotation. The performance of UniProt-DAAC was measured through a cross-validation analysis using the Gene Ontology annotation of a sub-set of Uni-ProtKB/Swiss-Prot. The results indicate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85.
Utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. AptRank introduces an adaptive mechanism to the PageRank framework that computes an optimal set of weights for the first several steps of diffusion so as to maximize recovery of a subset of known function annotations. It outperforms the four existing state-of-the-art methods in almost all cases, and in particular, outperforms those methods that do not incorporate information about the functional hierarchy.
POSSUM / POsition-Specific Scoring matrix-based featUre generator for Machine learning
Generates a broad spectrum of Position-Specific Scoring Matrix (PSSM)-based numerical representation schemes for protein sequences. POSSUM implements a wide range of algorithms available in the literature, provides an easy-to-use interface, and allows users to derive and customize the descriptors. More than 20 types of PSSM profile-based feature descriptors can be generated. The software facilitates feature generation, analysis, training and benchmarking of machine-learning models and predictions.
Provides end-to-end feature extraction and classifier training method for enzyme function prediction. DEEPre is based on deep learning and utilizes the sequence information. It forces the model to learn to extract features by itself and adapt the parameters of the classifier simultaneously so that it can improve the performance in a virtuous circle. This tool predicts a score for each candidate value of a certain type II restriction enzyme (EC) digit, and can serve to detect the enzyme promiscuity.
SCI-PHY / Subfamily Classification In PHYlogenomics
A pipeline for automatic subfamily identification, followed by subfamily hidden Markov model (HMM) construction. A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies. Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression.
PrESOgenesis / Predict Embryo-, Spermato- and Oogenesis
Assists users in predicting fertility-related proteins. PrESOgenesis is a two-layer classifier based on the support vector machine (SVM) method. At the first layer, each protein was sorted by SVM classifier to determine whether it is a fertility-related protein or not. If not, the classifier is automatically stopped. If yes, the sequence is considered as a fertility-related protein candidate and is subsequently submitted into the second layer.
GPDE / Griss Proteomics Database Engine
A biological proteomic database specifically designed for clinical proteomics and biomarker discovery. GPDE combines experiments based on investigated cell types thereby supporting customizable biological meta-analyses. It is a powerful yet easy-to-use tool to support the fast identification and reliable evaluation of biomarker candidates. This tool allows the researcher to quickly see the complete details of all identifications of a certain peptide as well as the whole experiments’ information in which the peptide was identified.
DETECTER / DETErmining Clinically relevant Transmutations using Evolutionary Rationales
Determines whether an amino acid replacement at a site in a protein is more or less likely to have a significant impact on fitness, including causing a disease. DETECTER exploits contemporary sequence data to reconstruct the evolutionary history of the site using model-dependent mathematical heuristics. It is able to identify heterozygous recessive changes, with the potential to cause disease, within a carrier background.
Argot / Annotation Retrieval of Gene Ontology Terms
Allows to predict protein function. The Argot strategy treats the grouping of Gene Ontology (GO) terms by means of semantic similarity in order to infer protein function. Compared to the previous version, the 2.5 version allows users to realize the selection of the input data from sequence similarity searches performed against a clustered version of UniProt databank and a remodeling of the weights given to Pfam hits. It’s also possible with the application of taxonomic constraints to classify annotations that cannot be applied to proteins belonging to the species through investigation.
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