黑料网

Shape is a crucial molecular characteristic that typically dictates a compound's fate in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small molecule-protein, peptide-receptor, antigen-antibody, and protein-protein interactions is critical not just for life and survival, but also for locating bioactive compounds. We look at how shape is used in biological systems including substrate recognition, ligand specificity/selectivity, and antibody recognition using computational methods like docking, quantitative structure-activity connections, classification models, and similarity search algorithms. The usefulness and applicability of defining molecular shape in drug discovery, virtual screening, and prediction toxicology has recently been established using these in silico pharmacology methodologies. Shape and shapebased descriptors are at least as valuable as other standard molecular descriptors, according to findings from recent studies.

The importance of determining molecular shape and variations in this feature in order to comprehend molecules engaged in chemical reactions has long been acknowledged. Shape recognition allows enzymes to distinguish between functional groups in a molecule; natural chemicals created via biosynthetic pathways also use shape recognition for selective oxidation [1]. More recently, it was discovered that chiral recognition involves mutually induced conformational modifications at the single-molecule level. Ribozymes from Escherichia coli and Bacillus subtitles detect cloverleaf form RNAs rather than hairpin shape RNAs at lower magnesium ion concentrations, demonstrating shape recognition. In the senses, chemical shape interaction plays a crucial role. Smell (through hundreds of olfactory receptors), sight (by color receptors), and taste (via bitter, sour, sweet, salt, and umami receptors), and all of these receptors are G-protein coupled receptors. Several investigations have confirmed that molecule structure has a significant impact on biological activity. Other aspects of molecular shape, such as complementary electrostatic or steric interactions, would be expected to boost specificity [2].

The essence of shape is therefore particularly useful in describing molecule(s) by itself or the nature of interactions between molecules, regardless of the type of definition used. As a result of its relevance in the drug design process in silico approaches commonly used to reduce the costs of drug discovery and development, the study of form in molecular pharmacology has gained prominence [3]. These computational approaches can quickly compare small compounds, or small molecules with protein receptor sites, depending on shape and other features like electrostatics.. This article examines the numerous definitions of shape that are commonly used when defining a molecule or a molecule-molecule interaction, as well as examples of biological systems where the idea of form is important. The usefulness of form in in silico approaches, as well as potential advancements in in silico pharmacology, is also discussed. Several published research show that shape-based approaches and descriptors are just as helpful as traditional molecular features like 2D descriptors in various classification and Modelling schemes [4].

The research of nuclear hormone receptors that identify bile salts has looked into the co-evolution of molecule-protein interactions in terms of form. In vertebrate species, bile salts are the primary endmetabolites of cholesterol. Planar 5 bile salts are used by early vertebrates such as jawless fish. Many other vertebrates, such as humans and most mammals, use 5 bile salts with a bend at the intersection of the A and B steroid rings [5]. Cross-species comparisons of the foresaid X receptor's selectivity for structurally diverse bile salts revealed that FXR's selectivity for bile salts has changed from a preference for 5 bile salts ('ancestral' pattern in sea lamprey and zebra fish) to a preference for 5 bile salts ('recent' pattern in humans and mice). The shape and size of the ligand binding pocket were projected to vary, according to computational homology models. Using comparable computational methodologies, it was discovered that the ligand specificity of vertebrate liver X receptors (LXR, 'ox sterol receptor') differs from that of invertebrate LXR. A ligand-based technique, such as a pharmacophore, can be used to determine cross-species differences in receptor binding sites [6]. Pregnant X receptors implicated in liver metabolism regulation, for example, reveal considerable cross-species changes in ligand specificity, with ligand specificity broadening from teleost fish through mammals and birds. These NHRs serve as reliable model systems for studying the shape and size evolution of receptors and ligands.

Shape and charge complementarity between the ligand and the receptor micro domain were discovered to have a vital impact in the functioning of 1,4-disubstituted aromatic piperidines and piperazine inhibitors in a recent work on discovering highly selective dopamine D4 receptor agonists and antagonists.

The shape of ligands interacting with receptors, ion channels, enzymes and transporters, as well as a variety of other proteins and complex biological processes, is a crucial molecular property. To locate molecules with complementarity, use the crystal structure conformation of the ligands in the PDB as a shape-based search query, or use the shape of the protein or a (pseudo)receptor binding site. This could indicate to potential off-targets or alternate targets that could be used to repurpose existing medications. When it comes to in silico pharmacology, shapebased techniques have a lot of room for improvement.

1. Lingenfelder M, Tomba G, Costantin Gi, Ciacchi LC, De Vitaet A, et al. (2007) . Angew Chem Int Ed Engl 46:4492–4495.

, ,

2. Ballester PJ, Richards WG (2007) . J Comput Chem 28:1711–1723.

, ,

3. Di Gennaro P, Sello G, Bianchi D, D'Amico P (1997) . J Biol Chem 272:30254–30260.

,   

4. Ekins S, Balakin KV, Savchuk N, Ivanenkov Y (2006) . J Med Chem 49:5059–5071.

, ,

5. Reschly EJ, Ai N, Welsh WJ, Ekins S, Hagey LR, et al. (2008) . J Steroid Biochem Mol Biol 110:83–94.

, ,

6. Kortagere S, Gmeiner P, Weinstein H, Schetz JA (2004) . Mol Pharmacol 66:1491–1499.

, ,