A chemogenomics view on protein-ligand spaces

Abstract

Background: Chemogenomics is an emerging inter-disciplinary approach to drug discovery that combines traditional ligand-based approaches with biological information on drug targets and lies at the interface of chemistry, biology and informatics. The ultimate goal in chemogenomics is to understand molecular recognition between all possible ligands and all possible drug targets. Protein and ligand space have previously been studied as separate entities, but chemogenomics studies deal with large datasets that cover parts of the joint protein-ligand space. Since drug discovery has traditionally focused on ligand optimization, the chemical space has been studied extensively. The protein space has been studied to some extent, typically for the purpose of classification of proteins into functional and structural classes. Since chemogenomics deals not only with ligands but also with the macromolecules the ligands interact with, it is of interest to find means to explore, compare and visualize protein-ligand subspaces.

Mapping of ligand‐binding cavities in proteins

“…To explore the structural and physicochemical space of cavities, their key properties were extracted from the descriptor matrix using Principal Component Analysis (PCA). This method has been widely used to explore the properties of ligands,39,40 protein-ligand space (from a chemogenomics perspective),45 whole proteins46,47 and binding sites of closely related proteins,48 but to our knowledge this is the first time that PCA has been applied to explore and compare cavities of a diverse set of proteins, applying an all-against-all comparison and mapping the resulting principal properties of the cavities. Here, the structural and physicochemical similarities and dissimilarities between ligand-binding cavities are visualised in PCA clustering trees, then analyzed and considered in relation to their respective domain classifications in the structural classification of proteins (SCOP)49 database.…”

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods, and Applications

“…In unsupervised learning, the objective is to extract and conjecture patterns and interactions among a series of input variables, and there is no outcome to train the input variables. The common approaches in unsupervised learning are clustering, data compression, and outlier detection, such as principal-component-based methods [74]. In supervised learning, the objective is to predict the value of an outcome variable based on the input variables [75].…”

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

“…In this way, the lack of known ligands for a given target can be compensated by the availability of known ligands for other targets. Chemogenomics has been becoming a prominent methodology for SAR learning [Klabunde, 2007;Rognan, 2007;Strö mbergsson and Kleywegt, 2009]. …”