No abstract

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

The somatostatin subtype-4 receptor (sst4) is highly expressed in neocortical and hippocampal areas, which are affected by amyloid beta accumulation. Sst4 agonists enhance downstream activity of amyloid beta peptide catabolism through neprilysin and may slow the progression of Alzheimer’s disease (AD). Sst4 is a G protein coupled receptor (GPCR), the structure of which has yet to be resolved. A newly constructed sst4 homology model, along with a previously reported model-built sst4 receptor structure, were used in the present study to gain insights into binding requirements of sst4 agonists employing a set of compounds patented by Boehringer Ingelheim. Besides aiming at delineating binding at the macromolecular level of these recently disclosed compounds, our objectives included the generation of a quantitative structure-activity relationship (QSAR) global model to explore the relationship between chemical structure and affinity. Through the implementation of model building, docking, and QSAR, plausible correlations between structural properties and the binding affinity are established. This study sheds light on understanding binding requirements at the sst4 receptor. Graphical abstract

Sepsis is a serious medical condition characterized by bacterial infection and a subsequent massive systemic inflammatory response. In an effort to identify compounds that block lipopolysaccharide (LPS)-induced inflammation reported herein is the development of simple Lipid-A analogues that lack a disaccharide core yet still possess potent antagonistic activity against LPS. The structure of the new lead compound was developed based on predictive computational experiments. LPS antagonism by the lead compound was not straightforward, and a biphasic effect was observed suggesting a possibility of more than one binding site. An IC50 value of 13 nM for the new compound was determined for the possible high affinity site. The combination of computational, synthetic, and biological studies revealed new structural determinants of these simplified analogues. It is expected that the acquired information will aid future design of LPS targeting glycopharmaceuticals.

3,4,5-Trisubstituted-1,2,4-triazole somatostatin receptor-4 agonist SAR.