Matching Cavities in G Protein-Coupled Receptors to Infer Ligand-Binding Sites

Identification of Protein-Ligand Binding Sites by Sequence Information and Ensemble Classifier

Identifying protein-ligand binding sites is an important process in drug discovery and structure-based drug design. Detecting protein-ligand binding sites is expensive and time-consuming by traditional experimental methods. Hence, computational approaches provide many effective strategies to deal with this issue. Recently, lots of computational methods are based on structure information on proteins. However, these methods are limited in the common scenario, where both the sequence of protein target is known and sufficient 3D structure information is available. Studies indicate that sequence-based computational approaches for predicting protein-ligand binding sites are more practical. In this paper, we employ a novel computational model of protein-ligand binding sites prediction, using protein sequence. We apply the Discrete Cosine Transform (DCT) to extract feature from Position-Specific Score Matrix (PSSM). In order to improve the accuracy, Predicted Relative Solvent Accessibility (PRSA) information is also utilized. The predictor of protein-ligand binding sites is built by employing the ensemble weighted sparse representation model with random under-sampling. To evaluate our method, we conduct several comprehensive tests (12 types of ligands testing sets) for predicting protein-ligand binding sites. Results show that our method achieves better Matthew's correlation coefficient (MCC) than other outstanding methods on independent test sets of ATP (0.506), ADP (0.511), AMP (0.393), GDP (0.579), GTP (0.641), Mg²⁺ (0.317), Fe³⁺ (0.490) and HEME (0.640). Our proposed method outperforms earlier predictors (the performance of MCC) in 8 of the 12 ligands types.

Computational Approaches for Decoding Select Odorant-Olfactory Receptor Interactions Using Mini-Virtual Screening

Olfactory receptors (ORs) belong to the class A G-Protein Coupled Receptor superfamily of proteins. Unlike G-Protein Coupled Receptors, ORs exhibit a combinatorial response to odors/ligands. ORs display an affinity towards a range of odor molecules rather than binding to a specific set of ligands and conversely a single odorant molecule may bind to a number of olfactory receptors with varying affinities. The diversity in odor recognition is linked to the highly variable transmembrane domains of these receptors. The purpose of this study is to decode the odor-olfactory receptor interactions using in silico docking studies. In this study, a ligand (odor molecules) dataset of 125 molecules was used to carry out in silico docking using the GLIDE docking tool (SCHRODINGER Inc Pvt LTD). Previous studies, with smaller datasets of ligands, have shown that orthologous olfactory receptors respond to similarly-tuned ligands, but are dramatically different in their efficacy and potency. Ligand docking results were applied on homologous pairs (with varying sequence identity) of ORs from human and mouse genomes and ligand binding residues and the ligand profile differed among such related olfactory receptor sequences. This study revealed that homologous sequences with high sequence identity need not bind to the same/ similar ligand with a given affinity. A ligand profile has been obtained for each of the 20 receptors in this analysis which will be useful for expression and mutation studies on these receptors.

CholMine: Determinants and Prediction of Cholesterol and Cholate Binding Across Nonhomologous Protein Structures

Identifying physiological ligands is necessary for annotating new protein structures, yet this presents a significant challenge to biologists and pharmaceutical chemists. Here we develop a predictor of cholesterol and cholate binding that works across diverse protein families, extending beyond sequence motif-based prediction. This approach combines SimSite3D site comparison with the detection of conserved interactions in cholesterol/cholate bound crystal structures to define three-dimensional interaction motifs. The resulting predictor identifies cholesterol sites with an ∼82% unbiased true positive rate in both membrane and soluble proteins, with a very low false positive rate relative to other predictors. The CholMine Web server can analyze users' structures, detect those likely to bind cholesterol/cholate, and predict the binding mode and key interactions. By deciphering the determinants of binding for these important steroids, CholMine may also aid in the design of selective inhibitors and detergents for targets such as G protein coupled receptors and bile acid receptors.

SNPs of melanocortin 4 receptor (MC4R) associated with body weight in Beagle dogs

Melanocortin 4 receptor (MC4R), which is associated with inherited human obesity, is involoved in food intake and body weight of mammals. To study the relationships between MC4R gene polymorphism and body weight in Beagle dogs, we detected and compared the nucleotide sequence of the whole coding region and 3'- and 5'- flanking regions of the dog MC4R gene (1214 bp). In 120 Beagle dogs, two SNPs (A420C, C895T) were identified and their relation with body weight was analyzed with RFLP-PCR method. The results showed that the SNP at A420C was significantly associated with canine body weight trait when it changed amino acid 101 of the MC4R protein from asparagine to threonine, while canine body weight variations were significant in female dogs when MC4R nonsense mutation at C895T. It suggested that the two SNPs might affect the MC4R gene's function which was relative to body weight in Beagle dogs. Therefore, MC4R was a candidate gene for selecting different size dogs with the MC4R SNPs (A420C, C895T) being potentially valuable as a genetic marker.

Recent structural advances of β1 and β2 adrenoceptors yield keys for ligand recognition and drug design

Because they represent attractive drug targets, adrenoceptors have been widely studied. Recent progress in structural data of β-adrenoceptors allows us to understand and predict key interactions in ligand recognition and receptor activation. Nevertheless, an important aspect of this process has only begun to be explored: the stabilization of a conformational state of these receptors upon contact with a ligand and the capacity of a ligand to influence receptor conformation through allosteric modulation, biased signaling, and selectivity. The aim of the present Perspective is to identify the well-defined orthosteric binding site and possible allosteric sites and to analyze the importance of the ligand-receptor interaction in the stabilization of certain receptor conformations. For this purpose, we have reviewed recent advances made through the use of X-ray data from ligand-β-adrenoceptor (including ADRB1 and ADRB2) crystal structures. Most importantly, implications in the medicinal chemistry field are explored in relation to drug design.

Subpocket analysis method for fragment-based drug discovery

Although two binding sites might be dissimilar overall, they might still bind the same fragments if they share suitable subpockets. Information about shared subpockets can be therefore used in fragment-based drug design to suggest new fragments or to replace existing fragments within an already known compound. A novel computational method called SubCav is described which allows the similarity searching and alignment of subpockets from a PDB-wide database against a user-defined query. The method is based on pharmacophoric fingerprints combined with a subpocket alignment algorithm. SubCav was shown to be effective in producing reasonable alignments for subpockets with low sequence similarity and be able to retrieve relevant subpockets from a large database of structures including those with different folds. It can also be used to analyze subpockets inside a protein family to facilitate drug design and to rationalize compound selectivity.

On the origins of drug polypharmacology

The ability of many drugs, unintended most often, to interact with multiple proteins is commonly referred to as polypharmacology. Could this be a reminiscent chemical signature of early protein evolution?