DASP3: identification of protein sequences belonging to functionally relevant groups

Abstract

BackgroundDevelopment of automatable processes for clustering proteins into functionally relevant groups is a critical hurdle as an increasing number of sequences are deposited into databases. Experimental function determination is exceptionally time-consuming and can’t keep pace with the identification of protein sequences. A tool, DASP (Deacon Active Site Profiler), was previously developed to identify protein sequences with active site similarity to a query set. Development of two iterative, automatable methods for clustering proteins into functionally relevant groups exposed algorithmic limitations to DASP.ResultsThe accuracy and efficiency of DASP was significantly improved through six algorithmic enhancements implemented in two stages: DASP2 and DASP3. Validation demonstrated DASP3 provides greater score separation between true positives and false positives than earlier versions. In addition, DASP3 shows similar performance to previous versions in clustering protein structures into isofunctional groups (validated against manual curation), but DASP3 gathers and clusters protein sequences into isofunctional groups more efficiently than DASP and DASP2.ConclusionsDASP algorithmic enhancements resulted in improved efficiency and accuracy of identifying proteins that contain active site features similar to those of the query set. These enhancements provide incremental improvement in structure database searches and initial sequence database searches; however, the enhancements show significant improvement in iterative sequence searches, suggesting DASP3 is an appropriate tool for the iterative processes required for clustering proteins into isofunctional groups.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1295-z) contains supplementary material, which is available to authorized users.

An approach to functionally relevant clustering of the protein universe: Active site profile‐based clustering of protein structures and sequences

“…Each discrete cluster is examined for validity as a functionally relevant cluster by creating a profile for the cluster members and analyzing if a DASP search of PDB distinctly identifies only cluster members. To support TuLIP, a new version of DASP, DASP2, was developed to improve the efficiency of database searching and overcome certain edge case anomalies noted in the original DASP implementation (see Methods).…”

“… DASP method using ASPs to search for sequences that contain functional site features similar to those represented in the original profile. Sequence databases can be searched for proteins containing active site features similar to those in the original ASP using a tool called DASP; DASP2 was developed to improve input and algorithmic details. As described in Methods, the ASP is split into individual motifs (representing the colored, structurally continuous fragments in Fig.…”

K-mer based classifiers extract functionally relevant features to support accurate Peroxiredoxin subgroup distinction

“…Table 4 indicates the features of five different methods that can be used to classify Prx proteins to the subgroup level. These methods include HMM search against the SFLD database [10], use of the MISST algorithm [4] which builds on DASPs [20], BLAST search against the PREX database [3], search against the NCBI Conserved Domains database (CDD) [21], and the method described in this work named Prx_3-merSVM.…”

“…The classification process employed by Harper et al makes use of alignment against active site profiles (ASP) [20,22], where an active site profile consists of multiple (usually 4 to 5) sequence fragments for which the residues are within 10 angstroms in structural space of known active site key residues. The Harper annotations [4] are considered the current gold standard for Prx annotations.…”

ASM-Clust: classifying functionally diverse protein families using alignment score matrices

“…There are various automatic tools available for classification of proteins into isofunctional families using sequence similarity, active site characteristics, and phylogenetic relationships (Brown, Krishnamurthy, and Sjölander 2007; Lee, Rentzsch, and Orengo 2010; de Melo-Minardi, Bastard, and Artiguenave 2010; Leuthaeuser et al 2016; Knutson et al 2017). Alternatively, the structure of a protein family can be interactively explored using sequence similarity networks (SSNs) (Atkinson et al 2009; Copp et al 2018).…”