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Updated: Jan 31, 2026
Determining Binding Affinity KD of Radiolabeled Antibodies to Immobilized Antigens
Published on: June 23, 2022
High-throughput binding affinity calculations at extreme scales
Jumana Dakka1, Matteo Turilli1, David W Wright2
1Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA.
Background:
Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.
Results:
We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.
Conclusions:
As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.
Insights
The High-throughput Binding Affinity Calculator (HTBAC) enables rapid, scalable analysis of drug-target interactions, aiding personalized cancer treatment and drug discovery by predicting binding affinity and residence time.
Area of Science:
- Computational Chemistry
- Pharmacology
- Bioinformatics
Background:
- Cancer treatment effectiveness is limited by drug resistance, often due to genetic changes in target proteins.
- Understanding molecular determinants of drug binding is crucial for overcoming resistance.
- Molecular simulations offer insights into ligand binding free energy and residence time.
Purpose of the Study:
- To introduce a scalable, adaptive, and automated computational tool for binding free energy calculations.
- To enhance the flexibility and performance of molecular simulation workflows.
- To support personalized cancer treatment and drug discovery.
Main Methods:
- Development of the High-throughput Binding Affinity Calculator (HTBAC).
- Utilizing a multi-stage pipeline approach for binding affinity calculations.
- Leveraging high-performance computing resources for automated calculations.
Main Results:
- Demonstrated near-perfect weak scaling for concurrent binding affinity calculation pipelines.
- Achieved a rapid time-to-solution, largely independent of calculation protocol, ligand size, and simulation ensemble size.
- Validated the performance and scalability of the HTBAC platform.
Conclusions:
- HTBAC represents an advancement in binding affinity calculation methods and protocols.
- The platform facilitates the study of diverse cancer drugs and ligands.
- Enables personalized clinical decisions informed by genomic data and accelerates drug discovery.
Related Concept Videos
01:41Affinity and Avidity
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02:41pH Scale
02:46Calculating the Equilibrium Constant
For example, gaseous nitrogen dioxide forms dinitrogen tetroxide according to this equation:
02:49Calculating Standard Free Energy Changes