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Most cellular processes are governed by association and dissociation of protein molecules. A malfunction of protein molecules causes most of the diseases. Consequently, drug molecules are designed to interact with these target proteins (receptors) in our bodies and to alter their activities in a way that is beneficial to our health. Drug discovery is usually a very expensive and difficult task. Computer-Aided Drug Design (CADD) is a specialized discipline that uses computational methods to speed up the long and expensive process of drug discovery. Traditionally, these methods rely on databases of chemical compounds and small molecules that can bind to receptor molecules. However, analysis of the 3D structures of receptors themselves is also important. We have developed a set of computational algorithms for recognition and comparison of protein regions which can serve as binding sites and can interact with the drug molecules. We recognize local geometrical and physicochemical similarity, which can be present even in the absence of overall sequence or fold similarity of the protein molecules. Our first algorithm, SiteEngine, can search a complete surface of one protein for regions similar to a binding site of another. Our second algorithm, MultiBind, aligns between a set of protein binding sites and can recognize the common set of features which can be responsible for the binding and the function. The methods are extremely efficient and suitable for large scale database searches of the entire Protein Data Bank (PDB). There are several ways in which our methods can contribute to the drug discovery process. First, analysis of the small molecules bound to proteins with similar binding sites may provide hints of chemical groups and scaffolds that can be used to design a drug for the protein target. Second, proteins with similar interaction regions may bind the same drug and therefore may cause undesired side effects. Thorough investigation of such proteins and particularly their 3D structure during the drug design process is important for the development of better and more specific drug leads. In addition, we have developed methods for comparison between protein-protein interfaces (PPIs) which are regions of interaction between two protein molecules. We applied them to compare between all 2-chain interfaces in the PDB and to create a nonredundant dataset of PPIs. This data set is unique, since it contains clusters of interfaces with similar shapes and spatial organization of chemical functional groups. The data set allows statistical investigation of similar interfaces, as well as the identification and analysis of the chemical forces that account for the protein–protein associations.

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