Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Revisiting Pyrimidine-Embedded Molecular Frameworks to Probe the Unexplored Chemical Space for Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential in numerous biological processes and diseases, making them attractive yet challenging drug targets. While many advances have been made in traditional drug discovery, targeting PPIs has been difficult due to a lack of specialized chemical libraries designed to modulate these interactions. Current libraries mainly focus on conventional target proteins like enzymes or receptors as substrate analogs rather than small-molecule modulators targeting PPIs. These traditional drug targets behave differently from PPIs. Conventional druggable targets have relatively small surfaces and binding pockets that have allowed them to be targeted with current libraries, but PPIs behave differently than these traditional drug targets. As a result, there is an urgent need for an innovative approach to expand the druggable space. To address this, we developed a privileged substructure-based diversity-oriented synthesis (pDOS) strategy, aimed at creating maximal skeletal diversity to explore broader biochemical space. Pyrimidine serves as the privileged substructure in our approach, which employs several strategies: (i) silver-catalyzed or iodine-mediated tandem cyclizations to generate pyrimidine-embedded polyheterocycles; (ii) diverse pairing strategies to produce pyrimidodiazepine-containing polyheterocyclic skeletons with enhanced scaffold saturation; (iii) skeletal transformation to develop pyrimidine-fused medium-sized azacycles via chemoselective cleavages or migrations of N-N or C-N bond; (iv) design of small-molecule peptidomimetics that systematically mimic three pivotal protein secondary structures using pyrimidodiazepine-based scaffolds; and (v) identification of pyrimidodiazepine-based small-molecules that allosterically inhibits the interaction between human ACE2 and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein to block viral entry into host cells. Through these approaches, we generated 39 distinct pyrimidine-embedded frameworks, demonstrating significant molecular diversity validated by chemoinformatic analyses such as Tanimoto similarity and principal moment of inertia (PMI) analysis. This molecular diversity extends pyrimidine structures beyond traditional linear or bicyclic forms, creating polyheterocycles with enhanced 3D structural diversity. These novel frameworks overcome the limitation of simpler privileged scaffolds, offering promising tools for modulating PPIs. Our pDOS approach highlights how privileged structure-embedded polyheterocycles, particularly those based on pyrimidine, can effectively target previously undruggable PPIs. This strategy provides a new direction for drug discovery, allowing for the development of small molecules that operate beyond traditional drug-like rules. In addition to expanding the chemical space for PPI modulation, our pDOS strategy enables the creation of scaffolds that are particularly suited for targeting complex and dynamic protein interfaces. This innovation could significantly impact therapeutic development, offering solutions for previously intractable drug targets. By expanding the scope of pyrimidine-based scaffolds, we have opened up new possibilities for targeting PPIs and advancing chemical biology. This perspective demonstrates the potential outlines of our pDOS strategy in creating structurally diverse frameworks, offering a platform for the discovery of PPI modulators and facilitating the exploration of untapped biochemical spaces in drug development, potentially transforming the way we approach these complex biological interactions.

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.