Identification of the druggable concavity, in which drug-like molecules are highly inclined to bind, is an important step in structure-based drug design. We previously proposed an index named PLB (propensity for ligand binding), which is based on the amino acid composition characteristically observed at the small molecule binding sites in the X-ray structures of the complexes between proteins and drug-like small molecules. The PLB index was proven to be useful in identifying the druggable concavities in the quality X-ray structures of proteins. Here, we apply the PLB to predicting the druggable concavity in target proteins using the structures of homologous proteins constructed by homology modeling. In this study, we assembled a set of reference proteins that were accurately determined by X-ray analysis in forms of complexes with drug-like small molecules. Homology models for the reference protein were constructed using multiple homologous proteins as templates. The PLB index was then used to predict the druggable concavity. If the template protein in a complex with a drug-like small molecule was used, the druggable concavity was predicted well, with a prediction rate of 78%. When only the apo protein was available as the template, the practical prediction rate was 71%. Interestingly, even when the percent sequence identity between the reference and template proteins was lower than 30, the PLB index could successfully identify the druggable concavity in some cases. This study demonstrates the practical value of applying the PLB index to identifying the drugabble concavity in the homology model.
[1]
Noriaki Hirayama,et al.
A simple method to improve the odds in finding 'lead-like' compounds from chemical libraries.
,
2007,
Chemical & pharmaceutical bulletin.
[2]
T. N. Bhat,et al.
The Protein Data Bank
,
2000,
Nucleic Acids Res..
[3]
W. Kabsch,et al.
Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features
,
1983,
Biopolymers.
[4]
Hiroki Shirai,et al.
Use of Amino Acid Composition to Predict Ligand-Binding Sites
,
2007,
J. Chem. Inf. Model..
[5]
András Fiser,et al.
Molecular Biophysics
,
2022
.
[6]
E. Myers,et al.
Basic local alignment search tool.
,
1990,
Journal of molecular biology.