Protein Residue Contacts and Prediction Methods.
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.
Department of Computer Science, University of Missouri, 201 Engineering Building West, Columbia, MO, 65211, USA.
Badri
B
Adhikari
Department of Computer Science, University of Missouri, 201 Engineering Building West, Columbia, MO, 65211, USA. chengji@missouri.edu.
Jianlin
J
Cheng
D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D000069550:Machine Learning; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D064878:Web Browser
D011506:Proteins
Deep learning; Protein contact prediction methods