MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances
Issued Date
2022-01-06
Resource Type
ISSN
09692126
eISSN
18784186
Scopus ID
2-s2.0-85121985779
Pubmed ID
34614393
Journal Title
Structure
Volume
30
Issue
1
Start Page
181
End Page
189.e5
Rights Holder(s)
SCOPUS
Bibliographic Citation
Structure Vol.30 No.1 (2022) , 181-189.e5
Suggested Citation
Tanramluk D., Pakotiprapha D., Phoochaijaroen S., Chantravisut P., Thampradid S., Vanichtanankul J., Narupiyakul L., Akavipat R., Yuvaniyama J. MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances. Structure Vol.30 No.1 (2022) , 181-189.e5. 189.e5. doi:10.1016/j.str.2021.09.004 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/83859
Title
MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances
Other Contributor(s)
Abstract
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances, and active-site boundaries are used for the analysis of active sites. The algorithms derived provide model equations that can predict whether changes in distances, such as contraction or expansion, will result in improved binding affinity. The algorithm is confirmed using kinetic studies of dihydrofolate reductase (DHFR), together with two DHFR-TS crystal structures. Empirical analyses of 881 crystal structures involving 180 ligands are used to interpret protein-ligand binding affinities. MANORAA links to major biological databases for web-based analysis of drug design. The frequency of atoms inside the main protease structures, including those from SARS-CoV-2, shows how the rigid part of the ligand can be used as a probe for molecular design (http://manoraa.org).