MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

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.

MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein-ligand fragment interaction, pathways and SNPs.

Protein-ligand interaction analysis is an important step of drug design and protein engineering in order to predict the binding affinity and selectivity between ligands to the target proteins. To date, there are more than 100 000 structures available in the Protein Data Bank (PDB), of which ∼30% are protein-ligand (MW below 1000 Da) complexes. We have developed the integrative web server MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) with the aim of providing a user-friendly web interface to assist structural study and design of protein-ligand interactions.