Name
Title | ||
|---|---|---|
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies. |
Abstract | ||
|---|---|---|
In the current “genomic era” the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibitors, allosteric regulators, etc.) is pivotal for protein functions in the vast majority of the cases and knowledge of the region where these processes take place is essential for protein function prediction and drug design. In this regard, computational methods represent essential tools to tackle this problem. A significant number of software tools have been developed in the last few years which exploit either protein sequence information, structure information or both. This review describes the most recent developments in protein function recognition and binding site prediction, in terms of both freely-available and commercial solutions and tools, detailing the main characteristics of the considered tools and providing a comparative analysis of their performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10822-019-00235-7 | Journal of Computer-Aided Molecular Design |
Keywords | Field | DocType |
Binding site recognition, Software, Binding region, Protein–ligand interactions | Binding site,Protein sequencing,Chemistry,Small molecule,Exploit,Function (biology),Software,Artificial intelligence,Protein function prediction,Machine learning,Exponential growth | Journal |
Volume | Issue | ISSN |
33 | 10 | 0920-654X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Macari | 1 | 0 | 0.68 |
| Daniele Toti | 2 | 105 | 13.86 |
| Fabio Polticelli | 3 | 68 | 8.97 |