Accurate Demarcation of Protein Domain Linkers based on Structural Analysis of Linker Probable Region

TL;DR

This paper introduces a structural analysis-based method for accurately identifying protein domain linkers by analyzing 3D structures and geometric invariants, improving linker demarcation in multi-domain proteins.

Contribution

A novel structural approach using geometric invariants and clustering to demarcate protein linkers, enhancing accuracy over existing sequence-based methods.

Findings

Achieved an F1 score of 0.745 on benchmark dataset.

Successfully identified novel linkers in large protein datasets.

Method complements sequence-based linker prediction techniques.

Abstract

In multi-domain proteins, the domains are connected by a flexible unstructured region called as protein domain linker. The accurate demarcation of these linkers holds a key to understanding of their biochemical and evolutionary attributes. This knowledge helps in designing a suitable linker for engineering stable multi-domain chimeric proteins. Here we propose a novel method for the demarcation of the linker based on a three-dimensional protein structure and a domain definition. The proposed method is based on biological knowledge about structural flexibility of the linkers. We performed structural analysis on a linker probable region (LPR) around domain boundary points of known SCOP domains. The LPR was described using a set of overlapping peptide fragments of fixed size. Each peptide fragment was then described by geometric invariants (GIs) and subjected to clustering process where the fragments corresponding to actual linker come up as outliers. We then discover the actual linkers by finding the longest continuous stretch of outlier fragments from LPRs. This method was evaluated on a benchmark dataset of 51 continuous multi-domain proteins, where it achieves F1 score of 0.745 (0.83 precision and 0.66 recall). When the method was applied on 725 continuous multi-domain proteins, it was able to identify novel linkers that were not reported previously. This method can be used in combination with supervised / sequence based linker prediction methods for accurate linker demarcation.