Protein Residue Contacts and Prediction Methods

Abstract

In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch.In this chapter, we briefly discuss many elements of protein residue-residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated.

Keywords: Deep learning; Protein contact prediction methods.

Fig. 1

Two globular proteins with some contacts in them shown in black dotted lines along with the contact distance in Armstrong. The alpha helical protein 1bkr (left) has many long-range contacts and the beta sheet protein 1c9o (right) has more short- and medium-range contacts

Fig. 2

The process in predicting protein residue contacts

Fig. 3

A screenshot of DNcon web server at http://iris.rnet.missouri.edu/dncon/. By default, top L contacts are predicted

Fig. 4

Predicted top 14 long- and medium-range contacts highlighted in the native structure. The lines were shown using distance commands in USEF Chimera