When it comes to developing new drugs, one of the most important considerations is how well a candidate drug will bind to its target protein. This binding ability, also known as affinity, plays a critical role in determining the efficacy of a drug. In the past, determining the binding affinity of a drug candidate was a time-consuming and expensive process that relied heavily on experimental techniques. However, advancements in computer modeling have made it possible to predict binding affinities with greater accuracy and efficiency.

In the same feat, researchers at the University of Arizona have utilized a combination of computational physics and experimental data to create computer models that can predict a drug candidate’s ability to bind to specific proteins within cells. An accurate estimator could potentially eliminate the need for experimental researchers to test millions of chemical compounds by computationally demonstrating binding affinity. This could significantly reduce the cost and time required for developing new drugs.

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Measuring the exact binding affinity between proteins and ligands is a crucial issue in computational biophysics. Ideally, precise binding energy calculations should be the foundation of any research on protein-ligand interactions. However, these calculations often require a lot of computational resources, making it necessary to improve the methods used to analyze these interactions. Experimentally determined binding affinity is often used as a standard to evaluate the accuracy of different computational methods used to estimate binding affinity.

Various methods can be employed to investigate the equilibrium of protein-ligand binding, such as isothermal titration calorimetry (ITC), fluorescence spectroscopy, and surface plasmon resonance. Research has shown that the binding affinities obtained through experimentation can differ depending on the technique used. As a result, it is crucial to comprehensively understand the conditions under which reference data is obtained when comparing computational binding affinities with experimental results.

Scientists have established a theoretical approach for determining the binding affinity between a ligand and a protein. The proposed method improves upon previous stratification techniques that incorporate umbrella sampling (US) or other enhanced sampling simulations with added restraints based on collective variables by making them more general and straightforward. The method involves assigning an energy value to the ligand at each point on a grid system, with the grid’s origin being the predicted location of the ligand when it is bound to the protein.

A ligand is a substance, typically an ion or molecule, that binds to a specific site on a target molecule, known as a receptor. The binding of a ligand to its receptor can lead to various biological responses, such as the activation or inhibition of enzymes, the regulation of gene expression, or the modulation of cellular signaling pathways. This ability of the ligand to bind to receptors makes it useful in various fields, such as medicine and diagnostics.

The scientists employed simulations that were not neutral and also employed methods of adjusting for the bias to generate a binding estimator that was both precise and computationally practical. They then utilized a robust mathematical method called the orientation quaternion formalism to more precisely depict the changes in the shape of the ligand as it attached to targeted proteins.

The method proposed in the study is similar to the stratification strategy proposed by previous research but includes several differences, such as providing a general scheme that can be easily adapted to any number of restraints, the non-parametric reconstruction of the grid potential of mean force (PMF), and the use of the unidimensional orientation angle of the ligand with respect to the protein as a collective variable for restraining. The method was used to calculate the binding affinity for the interaction of human fibroblast growth factor 1 (hFGF1) with heparin hexasaccharide, a glycosaminoglycan (GAG) binding partner. The results were in good agreement with binding affinity data from ITC experiments and were more accurate than FEP simulations when similar simulation times were used.

The umbrella sampling (US) method presented in this article provides a physics-based enhanced sampling approach for calculating binding affinities.The results obtained through the new method were in good agreement with binding affinity data from ITC experiments. Moreover, it was found to be more accurate than FEP simulations when similar simulation times were used. This method provides a promising approach for calculating binding affinities and can be applied to various protein-ligand interactions.

Article Source: Reference Paper – Nature Computational Science | Reference Article

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Dr. Tamanna Anwar is a Scientist and Co-founder of the Centre of Bioinformatics Research and Technology (CBIRT). She is a passionate bioinformatics scientist and a visionary entrepreneur. Dr. Tamanna has worked as a Young Scientist at Jawaharlal Nehru University, New Delhi. She has also worked as a Postdoctoral Fellow at the University of Saskatchewan, Canada. She has several scientific research publications in high-impact research journals. Her latest endeavor is the development of a platform that acts as a one-stop solution for all bioinformatics related information as well as developing a bioinformatics news portal to report cutting-edge bioinformatics breakthroughs.


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