Combining computational physics and experimental data, researchers at the University of Arkansas have developed a computer model to determine the ability of drug candidates to target and bind to proteins within cells.
If accurate, such estimators could demonstrate binding affinities computationally, saving experimental researchers from investigating millions of compounds. This work could significantly reduce the costs and time associated with developing new drugs.
Mahmoud Moradi, Associate Professor of Chemistry and Biochemistry at Fulbright College of Arts and Sciences, said: “The proposed method assigns the effective energy to the ligand at every grid point of the coordinate system. If the ligand is in a bonded state, the origin is at the most probable location 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. For this project, he collaborated with Suresh Tarapuranam, Professor of Biochemistry and Cooper Chair of Bioinformatics Research.
Moradi and Thallapuranam used bias-aware simulations and nonparametric reweighting techniques to account for biases 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 conformational changes upon ligand binding to target proteins.
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 I compared the experimental and calculated data, I had a moment of joy when the two numbers were in near perfect agreement.”
Researcher’s work has been published natural computational science.
Moradi was previously noted for developing computational simulations of the behavior of the SARS-CoV-2 spike protein prior to fusion with its human cell receptor. SARS-CoV-2 is the virus that causes COVID-19.
