PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.

Structure-based virtual screening (SBVS) is a widely used approach in in silico drug discovery, requiring a receptor structure or binding site to predict a ligand's binding pose and affinity. Consequently, the performance of SBVS depends on the protein's conformation. The most common SBVS method is protein-ligand docking, which relies on physics-based, empirical, or knowledge-based scoring functions to estimate binding affinity. However, these methods are highly sensitive to structural variations and conformational changes of the receptor structure. The performance of SBVS often drops substantially when the apo form of the receptor used for screening is different from its holo form. To address this issue, we previously introduced an SBVS method called PL-PatchSurfer. This method uses a surface representation to describe a binding site of receptors and drug molecules, which is more robust to structural changes, offering more stable screening performance. The surface of the molecules is segmented into patches, and their shapes and physicochemical properties, such as the electrostatic potential, are represented by the 3D Zernike descriptor, a compact and rotationally invariant mathematical representation. Here, we introduce PL-PatchSurfer3, an improved version of PL-PatchSurfer, which incorporates two key enhancements: a refined definition of hydrogen bond complementarity and the integration of visibility, which captures curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while maintaining its robustness against receptor structure variations.Scientific contributionThis study presents PL-PatchSurfer3, an improved surface patch-based virtual screening method that incorporates molecular surface shape, physicochemical properties, hydrogen-bonding compatibility, and visibility, capturing local curvature using rotationally invariant 3D Zernike descriptors. PL-PatchSurfer3 demonstrates robust performance across holo, apo, modeled, and AlphaFold-predicted receptor structures and is superior to or competitive with conventional approaches and recent deep learning-based methods.