Spatial chemical distance based on atomic property fields.

Similarity of compound chemical structures often leads to close pharmacological profiles, including binding to the same protein targets. The opposite, however, is not always true, as distinct chemical scaffolds can exhibit similar pharmacology as well. Therefore, relying on chemical similarity to known binders in search for novel chemicals targeting the same protein artificially narrows down the results and makes lead hopping impossible. In this study we attempt to design a compound similarity/distance measure that better captures structural aspects of their pharmacology and molecular interactions. The measure is based on our recently published method for compound spatial alignment with atomic property fields as a generalized 3D pharmacophoric potential. We optimized contributions of different atomic properties for better discrimination of compound pairs with the same pharmacology from those with different pharmacology using Partial Least Squares regression. Our proposed similarity measure was then tested for its ability to discriminate pharmacologically similar pairs from decoys on a large diverse dataset of 115 protein-ligand complexes. Compared to 2D Tanimoto and Shape Tanimoto approaches, our new approach led to improvement in the area under the receiver operating characteristic curve values in 66 and 58% of domains respectively. The improvement was particularly high for the previously problematic cases (weak performance of the 2D Tanimoto and Shape Tanimoto measures) with original AUC values below 0.8. In fact for these cases we obtained improvement in 86% of domains compare to 2D Tanimoto measure and 85% compare to Shape Tanimoto measure. The proposed spatial chemical distance measure can be used in virtual ligand screening.

Stacking heterogeneity: a model for the sequence dependent melting cooperativity of duplex DNA.

A microscopic Potts-like one-dimensional model with many particle interactions [referred as the generalized model of polypeptide chains (GMPCs)] is developed to investigate cooperativity of DNA sequence dependent melting. For modeling sequence, regular homogeneous sequences were arranged in heterogeneous blocks of various lengths. Within the framework of the GMPC the authors show that the inclusion of stacking interaction heterogeneity relative to homogeneous hydrogen bond interactions leads to an unexpected and quite remarkable increase in melting cooperativity for small blocks. In some cases this tendency persists for long blocks having sharp sequence heterogeneity.

The helix-coil transition in heterogeneous double stranded DNA: microcanonical method.

A microscopic Potts-like one-dimensional model with many-particle interactions is developed to construct a statistical mechanical description of the melting of heterogeneous sequence duplex DNA. For this model, referred as the generalized model of polypeptide chains (GMPC), a closed-form expression for the free energy is derived. The characteristic equation of the model enables estimates on the melting temperature and transition interval, consistent with results obtained from more classical approaches. From the characteristic equation of the model, the temperature-dependent statistical weight parameter for helical states is evaluated. This parameter is shown to change throughout the transition from a harmonic form in early regions of the transition to an arithmetic form in later stages. The GMPC is extended to consider the influence of sequence heterogeneity in the melting of duplex DNA.