ConSole: using modularity of Contact maps to locate Solenoid domains in protein structures

Abstract

BackgroundPeriodic proteins, characterized by the presence of multiple repeats of short motifs, form an interesting and seldom-studied group. Due to often extreme divergence in sequence, detection and analysis of such motifs is performed more reliably on the structural level. Yet, few algorithms have been developed for the detection and analysis of structures of periodic proteins.ResultsConSole recognizes modularity in protein contact maps, allowing for precise identification of repeats in solenoid protein structures, an important subgroup of periodic proteins. Tests on benchmarks show that ConSole has higher recognition accuracy as compared to Raphael, the only other publicly available solenoid structure detection tool. As a next step of ConSole analysis, we show how detection of solenoid repeats in structures can be used to improve sequence recognition of these motifs and to detect subtle irregularities of repeat lengths in three solenoid protein families.ConclusionsThe ConSole algorithm provides a fast and accurate tool to recognize solenoid protein structures as a whole and to identify individual solenoid repeat units from a structure. ConSole is available as a web-based, interactive server and is available for download at http://console.sanfordburnham.org.

TAPO: A combined method for the identification of tandem repeats in protein structures

“…Although ProStrip [26] and CE-Symm [30] have relatively high rate of True Positive cases, they also generate an unsatisfactory high number of false positives. The benchmark also showed that RAPHAEL [19] and ConSole [20], in general, have lower rates of both True Positive and False Positive prediction. This can be explained by the fact that they are mainly designed to predict specific classes of 3D TRs, therefore, fail to find the other existing types of TR structures.…”

“…In recent years, efforts have been made to develop bioinformatics tools for the detection and analysis of repetitive elements in protein structures (3D TRs) such as feature-based learning methods RAPHAEL [19] and ConSole [20], a method for tiling structural space [21], a Fourier analysis method by Taylor et al [22], wavelet transforms [23], signal analysis methods: DAVROS [24] and OPASS [25], methods that use conformational alphabets (ProStrip [26] and Swelfe [27]), and miscellaneous methods such as AnkPred [28], IRIS [29] and CE-Symm [30]. In the next section, we survey these approaches.…”

“…ConSole [20] is another feature based learning algorithm for the identification of solenoid proteins. ConSole uses image-processing, known as template-matching, to enable the differentiation between solenoids and non-solenoids.…”

PRIGSA2: Improved version of Protein Repeat Identification by Graph Spectral Analysis

“…Swelfe (Abraham et al 2008) and ProSTRIP (Sabarinathan et al 2010)). RAPHAEL generates geometric profiles based on Cα coordinates and uses them in combination with support vector machine (SVM) to mimic visual interpretation of a manual expert and classifies a protein into solenoid/non-solenoid class (Walsh et al 2012), while graph based approach, ConSole (Hrabe and Godzik 2014), uses a rule based machine learning technique to identify solenoid repeats in proteins. TAPO (TAndem PrOtein detector) (Do Viet et al 2015) considers various structural features such as periodicities of atomic coordinates, strings generated by conformational alphabets, residue contact maps and arrangements of vectors of secondary structure elements to build a prediction model using SVM for identification of structural repeats.…”

“…of PRIGSA2 algorithm is compared with four state-of-the-art repeat detection algorithms, namely, REPETITA(Marsella et al 2009), ConSole(Hrabe and Godzik 2014),TAPO (Do Viet et al 2015) and RepeatsDB-lite, and its previous version (PRIGSA(Chakrabarty and Parekh 2014c)). ConSole applies image processing on contact map representation of protein structure and uses a trained SVM to identify repeats, while TAPO ranks the predictions from various sequence and structure based methods on a SVM classifier to identify repeats.…”