Dataset Information

The specific applications of the TSR-based method in identifying Zn²⁺ binding sites of proteases and ACE/ACE2.

ABSTRACT: We have developed an alignment-free TSR (Triangular Spatial Relationship)-based computational method for protein structural comparison and motif identification and discovery. To demonstrate the potential applications of the method, we have generated two datasets. One dataset contains five classes: Actin/Hsp70, serine protease (chymotrypsin/trypsin/elastase), ArsC/Prdx2, PKA/PKB/PKC, and AChE/BChE at the hierarchical level 1 and twelve groups at the level 2. The other dataset includes representative proteases and ACE/ACE2. The x,y, z coordinates of the structures were obtained from PDB. We calculated the keys (or features) that represent each structure using the TSR-based method. The dataset and data presented here include additional information that help the readers become aware of specific applications of the TSR-based method in protein clustering, identification and discovery of metal ion binding sites as well as to understand the effect of amino acid grouping on protein 3D structural relationships at both global and local levels.

SUBMITTER: Sarkar T

PROVIDER: S-EPMC9679521 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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The specific applications of the TSR-based method in identifying Zn<sup>2+</sup> binding sites of proteases and ACE/ACE2.

Sarkar Titli T Reaux Camille R CR Li Jianxiong J Raghavan Vijay V VV Xu Wu W

Data in brief 20220923

We have developed an alignment-free TSR (Triangular Spatial Relationship)-based computational method for protein structural comparison and motif identification and discovery. To demonstrate the potential applications of the method, we have generated two datasets. One dataset contains five classes: Actin/Hsp70, serine protease (chymotrypsin/trypsin/elastase), ArsC/Prdx2, PKA/PKB/PKC, and AChE/BChE at the hierarchical level 1 and twelve groups at the level 2. The other dataset includes representat ...[more]

PMID: 36426009

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Project description:Development of protein 3-D structural comparison methods is important in understanding protein functions. At the same time, developing such a method is very challenging. In the last 40 years, ever since the development of the first automated structural method, ~200 papers were published using different representations of structures. The existing methods can be divided into five categories: sequence-, distance-, secondary structure-, geometry-based, and network-based structural comparisons. Each has its uniqueness, but also limitations. We have developed a novel method where the 3-D structure of a protein is modeled using the concept of Triangular Spatial Relationship (TSR), where triangles are constructed with the Cα atoms of a protein as vertices. Every triangle is represented using an integer, which we denote as "key," A key is computed using the length, angle, and vertex labels based on a rule-based formula, which ensures assignment of the same key to identical TSRs across proteins. A structure is thereby represented by a vector of integers. Our method is able to accurately quantify similarity of structure or substructure by matching numbers of identical keys between two proteins. The uniqueness of our method includes: (i) a unique way to represent structures to avoid performing structural superimposition; (ii) use of triangles to represent substructures as it is the simplest primitive to capture shape; (iii) complex structure comparison is achieved by matching integers corresponding to multiple TSRs. Every substructure of one protein is compared to every other substructure in a different protein. The method is used in the studies of proteases and kinases because they play essential roles in cell signaling, and a majority of these constitute drug targets. The new motifs or substructures we identified specifically for proteases and kinases provide a deeper insight into their structural relations. Furthermore, the method provides a unique way to study protein conformational changes. In addition, the results from CATH and SCOP data sets clearly demonstrate that our method can distinguish alpha helices from beta pleated sheets and vice versa. Our method has the potential to be developed into a powerful tool for efficient structure-BLAST search and comparison, just as BLAST is for sequence search and alignment.

Dataset Information

The specific applications of the TSR-based method in identifying Zn²⁺ binding sites of proteases and ACE/ACE2.

Publications

The specific applications of the TSR-based method in identifying Zn<sup>2+</sup> binding sites of proteases and ACE/ACE2.

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Dataset Information

The specific applications of the TSR-based method in identifying Zn2+ binding sites of proteases and ACE/ACE2.

Publications

The specific applications of the TSR-based method in identifying Zn<sup>2+</sup> binding sites of proteases and ACE/ACE2.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

The specific applications of the TSR-based method in identifying Zn²⁺ binding sites of proteases and ACE/ACE2.