scholarly journals Enrichment of Remote Homology Detection using Cascading Maximum Entropy Markov Model

2021 ◽  
Vol 13 (19) ◽  
pp. 80-84
Author(s):  
Manikandan P ◽  
Ramyachitra D ◽  
Muthu C ◽  
Sajithra N
2015 ◽  
Vol 32 (3) ◽  
pp. 338-344
Author(s):  
Swati Kaushik ◽  
Anu G. Nair ◽  
Eshita Mutt ◽  
Hari Prasanna Subramanian ◽  
Ramanathan Sowdhamini

Author(s):  
N. Srinivasan ◽  
G. Agarwal ◽  
R. M. Bhaskara ◽  
R. Gadkari ◽  
O. Krishnadev ◽  
...  

In the post-genomic era, biological databases are growing at a tremendous rate. Despite rapid accumulation of biological information, functions and other biological properties of many putative gene products of various organisms remain either unknown or obscure. This paper examines how strategic integration of large biological databases and combinations of various biological information helps address some of the fundamental questions on protein structure, function and interactions. New developments in function recognition by remote homology detection and strategic use of sequence databases aid recognition of functions of newly discovered proteins. Knowledge of 3-D structures and combined use of sequences and 3-D structures of homologous protein domains expands the ability of remote homology detection enormously. The authors also demonstrate how combined consideration of functions of individual domains of multi-domain proteins helps in recognizing gross biological attributes. This paper also discusses a few cases of combining disparate biological datasets or combination of disparate biological information in obtaining new insights about protein-protein interactions across a host and a pathogen. Finally, the authors discuss how combinations of low resolution structural data, obtained using cryoEM studies, of gigantic multi-component assemblies, and atomic level 3-D structures of the components is effective in inferring finer features in the assembly.


Sign in / Sign up

Export Citation Format

Share Document