Quasi-QSPR to Predict Proteins Behavior Under Various Concentrations of Drug Using Nanoconductometric Assay

Abstract

Conductometric monitoring of drug-gene and drug-protein interactions is of fundamental importance in functional proteomics. Here, we model our previously obtained findings and characterizations of an important antiblastic used in neuro-oncology (Temozolomide), interacting with selected proteins that represent predictive biomarkers of the rate survival of the patients, of the outcome of chemotherapy and resistance to drug itself (namely, BRIP1 and MLH1) acquired with Nucleic Acid Programmable Protein Arrays (NAPPA)based nanoconductometric sensor. Quasi-SMILES which are analogies of the traditional SMILES (simplified molecular input-line entry systems) used to represent molecular structure are suggested as a tool to represent complex substances which are acting under different conditions (dose, different peptides). By means of the optimal descriptors quasi-QSPR for conductance and frequency are established. Statistical quality of these models is satisfactory.

Quasi-SMILES-Based Nano-Quantitative Structure–Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells

“…The external validation results showed a good prediction ability with R 2 pred between 0.81 and 0.88. Previous studies − ,,,,,,− ,,,− that used the quasi-SMILES approach for toxicity prediction only used external validation data obtained by random division of the total data set. Compared with this study, our models could simulate the real-world situation that occurs in the cases of completely new data and use the models for the prediction of end points.…”

“…On the basis of these suggestions, they indicated the descriptors that were closely related to toxicity indirectly. Instead of using traditional descriptors (i.e., physicochemical properties and molecular information), a new approach using eclectic information as descriptors to predict the toxicity of organic and nanoscale materials was developed by Toropova and Toropov through many publications. − In this approach, physicochemical properties and the exposure conditions of nanoparticles are represented by so-called “quasi-SMILES”, which are character-based representations derived from traditional SMILES . A data sample that consisted of the physicochemical properties and the exposure conditions of nanoparticles can be represented by a series of characters.…”

“…Thus, a main advantage of the quasi-SMILES is to increase the number of available data sets by using the eclectic data in developing Nano-QSARs. Nevertheless, the QSAR models for nanomaterials employed in these publications ,,,,− ,, were limited in terms of data quantity and external validation; the number of data samples was generally small (several tens of data rows), and the largest number of data rows was 109 . Other studies ,,,,, had only a few tens of data rows.…”