Research into the binding affinities of potential drugs by scientists at the University of Arkansas could help reduce the costs and time associated with developing new drugs.
Researchers at the University of Arkansas, USA, have combined computational physics and experimental data to develop a computer model that determines the ability of drug candidates to target and bind to proteins in cells.This work was recently published natural computational science.
If accurate, such estimators can be used to calculate binding affinities, saving experimental researchers from investigating millions of compounds. This work has the potential to significantly reduce the costs and time associated with developing new drugs.
“We have developed a theoretical framework for predicting ligand-protein binding,” said Associate Professor Mahmoud Moradi. “The proposed method assigns the effective energy to the ligand at every lattice point of the coordinate system. If the ligand is in a bonded state, the origin is at the most probable position of the ligand. “
A ligand is a substance (ion or molecule) such as a drug that binds to another molecule, such as a protein, to form a complex system that can cause or interfere with a biological function.
Moradi’s research focuses on computational simulations of diseases, including coronaviruses. We collaborated with Professor Suresh Thallapuranam on this project.
Moradi and Thallapuranam used biased simulations and nonparametric reweighting techniques to account for the bias to produce a computationally efficient and accurate binding estimator. We then used a mathematically robust technique called the oriented quaternion formalism to further account for the conformational changes upon ligand binding to the target protein.
The researchers tested this approach by estimating the binding affinity between a specific signaling protein, human fibroblast growth factor 1, and a common drug, heparin hexasaccharide 5.
This project was devised because Moradi and Thallapuranam were studying the human fibroblast growth factor 1 protein and its variants in the absence and presence of heparin. They found strong qualitative agreement between simulation and experimental results.
“When it comes to binding affinities, we knew that the typical methods at our disposal would not work for such a difficult problem,” Moradi said. When we compared the experimental and calculated data, we were very happy when the two numbers were almost in perfect agreement.”
