Researchers at the University of Arkansas have combined computational physics with experimental data to develop computer models that determine the ability of drug candidates to target and bind proteins within cells.
If accurate, such an estimator could computationally demonstrate binding affinity, avoiding the need for experimental researchers to study millions of compounds. This work could drastically reduce the costs and time associated with developing new drugs.
“We developed a theoretical framework for estimating ligand-protein binding,” said Mahmoud Moradi, associate professor of chemistry and biochemistry in the Fulbright College of Arts and Sciences. “The proposed method assigns an effective energy to the ligand at each grid point in a coordinate system whose origin is located at the most probable position of the ligand when it is in a bound state.”
A ligand is a substance — an ion or a molecule — such as a drug that binds to another molecule, such as a protein, to form a complex system that can cause or prevent a biological function.
Moradi’s research focuses on computational simulations of disease, including coronaviruses. On this project, he collaborated with Suresh Thallapuranam, professor of biochemistry and Cooper Chair in Bioinformatics Research.
Moradi and Thallapuranam used biased simulations—and a nonparametric reweighting technique to account for the bias—to create a computationally efficient and accurate binding estimator. They then used a mathematically robust technique called directed quaternion formalism to further describe the conformational changes of the ligand upon binding to the target protein.
The researchers tested this approach by estimating the binding affinity between human fibroblast growth factor 1, a specific signaling protein, and heparin hexasaccharide 5, a popular drug.
The project was conceived because Moradi and Thallapuranam were studying the human fibroblast growth factor 1 protein and its mutants in the absence and presence of heparin. They found strong qualitative agreement between simulation and experimental results.
“When it comes to binding affinity, we know that the typical methods available to us are not applicable to such a difficult problem,” Moradi said. “That’s why we decided to develop a new method. We had a great time when the experimental and calculated data were compared with each other, and the two figures matched almost perfectly.”
The researchers’ work was published in Natural Computational Science.
Moradi previously gained attention for developing computational simulations of how the SARS-CoV-2 spike protein behaves before it fuses with human cell receptors. SARS-CoV-2 is the virus that causes COVID-19.
