Binding-Site Match Maker (BSMM): A Computational Method for the Design of Multi-Target Ligands.

Jinming Zhou,Jian Hui Wu

doi:10.3390/molecules25081821

Abstract

Multi-target ligand strategies provide a valuable method of drug design. However, to develop a multi-target drug with the desired profile remains a challenge. Herein, we developed a computational method binding-site match maker (BSMM) for the design of multi-target ligands based on binding site matching. BSMM was built based on geometric hashing algorithms and the representation of a binding-site with physicochemical (PC) points. The BSMM software was used to detect proteins with similar binding sites or subsites. In particular, BSMM is independent of protein global folds and sequences and is therefore applicable to the matching of any binding sites. The similar sites between protein pairs with low homology and/or different folds are generally not obvious to the visual inspection. The detection of such similar binding sites by BSMM could be of great value for the design of multi-target ligands.

Highlights

Many signaling networks in mammalian cells are likely to be wired with redundant pathways, such that optimal therapeutic interventions can be achieved through perturbing multiple nodes of the networks [1]
The binding-site was described as a set of the physicochemical type points, such as the hydrogen acceptor (HA), hydrogen donor (HD), mixed hydrogen acceptor and donor (HAD), aliphatic hydrophobic (ALI) and aromatic properties (ARO)
To evaluate how well the match has been made by binding-site match maker (BSMM), the A/L crystal structure is aligned with the matched points of protein A, whereas the B/L

Summary

Introduction

Many signaling networks in mammalian cells are likely to be wired with redundant pathways, such that optimal therapeutic interventions can be achieved through perturbing multiple nodes of the networks [1]. Most modern searches for new drugs take place within the terrain of the “One-drug one-target” paradigm. Such a reductional approach is fruitful but it does not exploit the network complexity and pathway redundancy. It is generally accepted that activity at a single receptor is insufficient for a complex disease involving multiple factors such as diabetes, neurodegenerative syndromes, cardiovascular diseases, or cancer. Tyrosine kinase inhibitor imatinib inhibits BCR-ABL, PDGF receptor and c-kit simultaneously [5]. Many currently marketed drugs act via multiple targets, the discovery of their multi-targeting properties is usually serendipitous [4].

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Results

Conclusion