![]() However, these motifs include only information on the protein side, and they do not represent the non-covalent interactions established between the ligand and the receptor.ĭesaphy et al. can be used to identify, compare, classify and even predict binding sites. The motifs computed by the methods designed by Gonçalves-Almeida et al. This signature does not require any ligand information, and it is independent of molecular orientations. Each binding site is represented by a feature vector that encodes a cumulative edge count of contact graphs defined for different cut-off distances, which are used as input data for learning algorithms. used graphs that consider physicochemical properties of atoms and their contacts to represent protein pockets, generating a signature that perceives distance patterns from protein pockets. Centroids were used to summarize the connected components of this graph, and conserved centroids, termed hydrophobic patches, were used to characterize, detect and predict cross-inhibition. ![]() IFRs were modeled as a graph in which hydrophobic atoms were the nodes and the contacts between them were the edges. ![]() developed a method based on hydrophobic patch centroids to predict cross-inhibition, also known as inhibitor promiscuity, in serine proteases. ![]() In general, classical methods, such as sequence/structural alignments, are not appropriate for conservation detection when proteins have dissimilar sequences and/or structures. These strategies work well for rigid ligands as they result in structural alignments of good quality due to ligand-induced superimposition. Previous solutions for detecting structural binding motifs for a set of diverse proteins and a common ligand involved protein superimposition based on the ligand and subsequent clustering of the conserved residues or atoms interacting with this ligand. Here, we briefly review some recent works that are representative examples of the diverse techniques that have already been proposed. Several methods have been proposed to identify three-dimensional binding motifs. Thus, it is reasonable to expect that methods used to detect conserved interactions between proteins and ligands should be able to address both protein and ligand promiscuity. On the other hand, ligands can also be promiscuous, such as when one ligand is recognized by different proteins. On one hand, proteins can be promiscuous, as they interact with different ligands. We consider ligands to be small non-protein molecules. In this paper, we are interested in the interface between proteins and ligands. Therefore, IFR can be an invaluable source of information to support the identification of conserved interactions across a set of complexes. , protein structures are more conserved than their sequences, and IFRs are even more conserved than whole protein structures. Interface forming residues (IFR) are residues in the molecular interface region between proteins. Identifying conserved interactions between proteins and ligands that are reused across a protein family is a key factor for understanding molecular recognition processes and can contribute to rational drug design, target identification, lead discovery and ligand prediction. Understanding how protein-ligand interactions take place has been the goal of many research studies, as molecular recognition is pivotal in biological processes, including signal transduction, catalysis and the regulation of biological function, to name a few examples. A variety of protein functions can be attributed to or regulated by these interactions. At the molecular level, protein receptors constantly interact with small-molecule ligands, such as metabolites or drugs.
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