Biomedical subjects

Archana Kotu

Publications and source records attributed to Archana Kotu.

2 recordsLinked to original sources

PubMed2005

The automatic detection of known beta-propeller structural motifs from protein tertiary structure.

Following our previous work on the analysis of 'structural plasticity' associated with the beta-propeller structural motifs, we have now developed a simple method that can automatically detect all the known beta-propellers in protein tertiary structure, given a list of Protein Data Bank (PDB) codes as input to the computer program. Our beta-propeller detection (BPD) method identifies the location of beta-propellers in the protein structure, specifies the beta-propeller type, the beta-sheet associated beta-strand pattern and the structurally similar beta-propellers observed in other proteins. When tested on 21,566 proteins in the PDB, the BPD method was capable of correctly identifying all the known 245 beta-propellers described in the structural classification of proteins (SCOP) with the number of false positives detected being less than 0.2%. Forty-one false positives were detected that correspond to eight known protein families. When compared with some of the popular web-based programs that can automatically detect 'structural similarities' between the query and target proteins, our method has the advantage of also being capable of detecting beta-propellers associated with 'structural plasticity' and in situations where the target and query proteins differ in amino acid sequence length.

Amino Acid Motifs↗

PubMed2004

The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides.

We present a methodology for solving the inverse-quantitative structure-activity relationship (QSAR) problem using the molecular descriptor called signature. This methodology is detailed in four parts. First, we create a QSAR equation that correlates the occurrence of a signature to the activity values using a stepwise multilinear regression technique. Second, we construct constraint equations, specifically the graphicality and consistency equations, which facilitate the reconstruction of the solution compounds directly from the signatures. Third, we solve the set of constraint equations, which are both linear and Diophantine in nature. Last, we reconstruct and enumerate the solution molecules and calculate their activity values from the QSAR equation. We apply this inverse-QSAR method to a small set of LFA-1/ICAM-1 peptide inhibitors to assist in the search and design of more-potent inhibitory compounds. Many novel inhibitors were predicted, a number of which are predicted to be more potent than the strongest inhibitor in the training set. Two of the more potent inhibitors were synthesized and tested in-vivo, confirming them to be the strongest inhibiting peptides to date. Some of these compounds can be recycled to train a new QSAR and develop a more focused library of lead compounds.

Drug Design↗