Recent advances in computational and experimental protein-ligand affinity determination techniques

ABSTRACT

Introduction

Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved.

Areas covered

In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein – small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design.

Expert opinion

The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.

KEYWORDS:

• Drug design
• inhibitor
• binding constant
• dissociation constant
• machine learning
• docking
• ITC
• TSA

Article highlights

• Modern drug discovery heavily relies on assessing target – ligand affinities.

• A great variety of computational and experimental affinity determination techniques, each with their own pros and cons, are being developed and constantly improved.

• Recent years saw an explosive growth of promising Machine Learning-based scoring and docking methods, but there is still room for improvement.

• The traditional binding affinity prediction methods have also seen a significant advance, notably methods based on Molecular Dynamics and Quantum Mechanics.

• A number of stability, mobility, and spectroscopic shift assays have been increasing in throughput and decreasing in sample consumption.

• Thermodynamic and kinetic data produced by some of the affinity determination techniques is invaluable for optimizing drug candidates.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.