Sudhir A. Kulkarni

A comprehensive structure–activity analysis of protein kinase B-alpha (Akt1) inhibitors

Protein kinase B (PKB, also known as Akt) belongs to the AGC subfamily of the protein kinase superfamily. Akt1 has been reported as a central player in regulation of metabolism, cell survival, motility, transcription and cell-cycle progression, among the signalling proteins that respond to a large variety of signals. In this study an attempt was made to understand structural requirements for Akt1 inhibition using conventional QSAR, k-nearest neighbour QSAR and novel GQSAR methods. With this intention, a wide variety of structurally diverse Akt1 inhibitors were collected from various literature reports. The conventional QSAR analyses revealed the key role of Baumanns alignment independent topological descriptors along with other descriptors such as the number of hydrogen bond acceptors, hydrogen bond donors, rotatable bonds and aromatic oxygen (SaaOcount) along with molecular branching (chi3Cluster), alkene carbon atom type (SdsCHE-index) in governing activity variation. Further, the GQSAR analyses show that chemical variations like presence of hetero-aromatic ring, flexibility, polar surface area and fragment length present in the hinge binding fragment (in the present case fragment D) are highly influential for achieving highly potent Akt1 inhibitors. In addition, this study resulted in a k-nearest neighbour classification model with three descriptors suggesting the key role of oxygen (SssOE-index) and aromatic carbon (SaaCHE-index and SaasCE-index) atoms electro-topological environment that differentiate molecules binding to Akt1 kinase or PH domain. The developed models are interpretable, with good statistical and predictive significance, and can be used for guiding ligand modification for the development of potential new Akt1 inhibitors.

Application of GQSAR for Scaffold Hopping and Lead Optimization in Multitarget Inhibitors

Many literature reports suggest that drugs against multiple targets may overcome many limitations of single targets and achieve a more effective and safer control of the disease. However, design of multitarget drugs presents a great challenge. The present study demonstrates application of a novel Group based QSAR (GQSAR) method to assist in lead optimization of multikinase (PDGFR‐beta, FGFR‐1 and SRC) and scaffold hopping of multiserotonin target (serotonin receptor 1A and serotonin transporter) inhibitors. For GQSAR analysis, a wide variety of structurally diverse multikinase inhibitors (225 molecules) and multiserotonin target inhibitors (162 molecules) were collected from various literature reports. Each molecule in the data set was divided into four fragments (kinase inhibitors) and three fragments (serotonin target inhibitors) and their corresponding two‐dimensional fragment descriptors were calculated. The multiresponse regression GQSAR models were developed for both the datasets. The developed GQSAR models were found to be useful for scaffold hopping and lead optimization of multitarget inhibitors. In addition, the developed GQSAR models provide important fragment based features that can form the building blocks to guide combinatorial library design in the search for optimally potent multitarget inhibitors.

Rationalizing Protein–Ligand Interactions for PTP1B Inhibitors Using Computational Methods

Protein tyrosine phosphatase 1B inhibitors were reported to have anti‐diabetic properties and hence this enzyme has become interesting drug target in the recent time. Huge amount of data is available in public domain about the PTP1B inhibitors in the form of X‐ray structures. This study is an attempt to transform this data into useful knowledge which can be directly used to design more effective protein tyrosine phosphatase inhibitors. In this study, we have built quantitative models for activity of co‐crystallized protein tyrosine phosphatase inhibitors using two new approaches developed in our group, i.e. receptor–ligand interaction and Structure‐based compound optimization, prioritization and evolution based on receptor–ligand interaction descriptors and residue‐wise interaction energies as descriptors, respectively. These models have given insights into the receptor–ligand interactions essential for modulating the activity of PTP1B inhibitors. An external validation set of 22 molecules was used to test predictive power of these models on external set molecules.