RepeatsDB: a database of tandem repeat protein structures

Domenico, Tomás Di; Potenza, Emilio; Walsh, Ian; Parra, R. Gonzalo; Giollo, Manuel; Minervini, Giovanni; Piovesan, Damiano; Ihsan, Awais; Ferrari, Carlo; Kajava, Andrey V.; Tosatto, Silvio C. E.

doi:10.1093/nar/gkt1175

Cited by 55 publications

(50 citation statements)

References 45 publications

(38 reference statements)

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“…For this purpose, we developed a substitution matrix for the conformational alphabet CA-1. Our matrix was built based on 341 MStAs of 3D repetitive units of 321 proteins from ''Detailed" annotated set of RepeatsDB [18], generated using MUSTANG [44]. The procedure for scoring was similar to BLOSUM [45].…”

Section: Detection Of Trs By Using T-reks and Trust The T-reks Pro-mentioning

confidence: 99%

“…Dataset-TR contains 321 proteins with <40% sequence identity from the ''Detailed" annotation of the RepeatsDB [18]. In addition, this dataset contains 281 proteins with 3D TRs that were selected from the PDB with <40% sequence identity (as of July 1st, 2011) based on the presence of TRs in their sequences.…”

Section: Datasetsmentioning

confidence: 99%

“…The positive subset of it contains 206 proteins with 3D TRs (153 manually selected from proteins with TRs found in their sequences as described in [53] and 53 proteins with TRs having repeat units of less than 25 residues from the Detailed Annotation of RepeatsDB [18]. The negative subset contains 101 proteins without 3D TRs that were manually selected from the PDB with <70% sequence identity (as of July 1st, 2011).…”

Section: Datasetsmentioning

confidence: 99%

“…To test the ability of TAPO to find new types of repeats, TAPO was run against the complete set of 141 307 protein structures (59 621 entries) from PDB (as of July 1st, 2011), the release that was used to build RepeatsDB [18]. For this purpose, the optimal SVM-score threshold was used to separate TR-containing and non-TR-containing structures.…”

Section: Analysis Of the Pdb Using Tapomentioning

confidence: 99%

“…Most of these unpredicted proteins are not classified and annotated in RepeatsDB, indicating that some of them may not have 3D TRs. At the same time, TAPO predicts 19 039 new TR-containing proteins that can be integrated in RepeatsDB [18] and be used in a more complete classification of proteins with 3D TRs. A big portion of these proteins contains two repeats in the TR regions.…”

Section: Analysis Of the Pdb Using Tapomentioning

confidence: 99%

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

2015

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a b s t r a c tIn recent years, there has been an emergence of new 3D structures of proteins containing tandem repeats (TRs), as a result of improved expression and crystallization strategies. Databases focused on structure classifications (PDB, SCOP, CATH) do not provide an easy solution for selection of these structures from PDB. Several approaches have been developed, but no best approach exists to identify the whole range of 3D TRs. Here we describe the TAndem PrOtein detector (TAPO) that uses periodicities of atomic coordinates and other types of structural representation, including strings generated by conformational alphabets, residue contact maps, and arrangements of vectors of secondary structure elements. The benchmarking shows the superior performance of TAPO over the existing programs. In accordance with our analysis of PDB using TAPO, 19% of proteins contain 3D TRs. This analysis allowed us to identify new families of 3D TRs, suggesting that TAPO can be used to regularly update the collection and classification of existing repetitive structures.

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Section: Detection Of Trs By Using T-reks and Trust The T-reks Pro-mentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Analysis Of the Pdb Using Tapomentioning

confidence: 99%

Section: Analysis Of the Pdb Using Tapomentioning

confidence: 99%

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

2015

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Structures of single‐layer β‐sheet proteins evolved from β‐hairpin repeats

Biancalana

Grant

et al. 2019

Protein Science

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Free‐standing single‐layer β‐sheets are extremely rare in naturally occurring proteins, even though β‐sheet motifs are ubiquitous. Here we report the crystal structures of three homologous, single‐layer, anti‐parallel β‐sheet proteins, comprised of three or four twisted β‐hairpin repeats. The structures reveal that, in addition to the hydrogen bond network characteristic of β‐sheets, additional hydrophobic interactions mediated by small clusters of residues adjacent to the turns likely play a significant role in the structural stability and compensate for the lack of a compact hydrophobic core. These structures enabled identification of a family of secreted proteins that are broadly distributed in bacteria from the human gut microbiome and are putatively involved in the metabolism of complex carbohydrates. A conserved surface patch, rich in solvent‐exposed tyrosine residues, was identified on the concave surface of the β‐sheet. These new modular single‐layer β‐sheet proteins may serve as a new model system for studying folding and design of β‐rich proteins.

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Comparative analysis of permanent and transient domain–domain interactions in multi‐domain proteins

Sidhanta,

Sowdhamini,

Srinivasan

2023

Proteins

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Protein domains are structural, functional, and evolutionary units. These domains bring out the diversity of functionality by means of interactions with other co‐existing domains and provide stability. Hence, it is important to study intra‐protein inter‐domain interactions from the perspective of types of interactions. Domains within a chain could interact over short timeframes or permanently, rather like protein–protein interactions (PPIs). However, no systematic study has been carried out between two classes, namely permanent and transient domain–domain interactions. In this work, we studied 263 two‐domain proteins, belonging to either of these classes and their interfaces on the basis of several factors, such as interface area and details of interactions (number, strength, and types of interactions). We also characterized them based on residue conservation at the interface, correlation of residue motions across domains, its involvement in repeat formation, and their involvement in particular molecular processes. Finally, we could analyze the interactions arising from domains in two‐domain monomeric proteins, and we observed significant differences between these two classes of domain interactions and a few similarities. This study will help to obtain a better understanding of structure–function and folding principles of multi‐domain proteins.

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RepeatsDB: a database of tandem repeat protein structures

Cited by 55 publications

References 45 publications

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

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

Structures of single‐layer β‐sheet proteins evolved from β‐hairpin repeats

Comparative analysis of permanent and transient domain–domain interactions in multi‐domain proteins

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