Name
Abstract | ||
|---|---|---|
Proteins are complex macromolecules which contribute to maintaining cellular environments and thus have fundamental roles in biological processes of living organisms. Understanding the conformational transitions of proteins represents an important stage towards comprehending protein function and would help to identify situations when mutations can occur. In this paper we use clustering as an unsupervised classification method in order to study the relevance of the residues’ relative solvent accessibility (RSA) values to analyze protein internal transitions. With the main goal of studying the evolution of RSA values between conformational transitions, we experimentally show that RSA values are slowly modifying as the protein undergoes conformational changes. The study conducted in this paper is aimed to provide a better apprehension of how proteins’ conformational transitions are evolving in time, with the broader goal of better understanding protein internal dynamics. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.07.205 | Procedia Computer Science |
Keywords | Field | DocType |
Protein analysis,Unsupervised learning,Clustering 2000 MSC: 92D20,68T05,62H30 | Computer science,Unsupervised learning,Protein function,Artificial intelligence,Cluster analysis,Machine learning,Protein structure | Conference |
Volume | ISSN | Citations |
126 | 1877-0509 | 1 |
PageRank | References | Authors |
0.39 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mihai Teletin | 1 | 4 | 2.53 |
| Gabriela Czibula | 2 | 80 | 19.53 |
| Silvana Albert | 3 | 3 | 1.47 |
| Maria-Iuliana Bocicor | 4 | 6 | 1.52 |