Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach

Abstract

Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have got interest following the recent publication of their pharmacologically beneficial effects. Recently it comes out that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In the past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered but, unfortunately, none of them is in the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches were already performed for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. 14,492 compounds were retrieved from this database and filtered through a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 79–245 nM. ADMET properties prediction were performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives; from these analyses, three molecules resulted as excellent candidates for becoming new drugs.

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“…A previous study has shown that Phe19, Met20, Ala33, Pro38, Lys58, Phe57, Ala75, Glu72, Arg106, and Arg126 are important amino acid residues in the interaction process between FABP4 and its [11]. The presence of mutations in Phe57 residue shows a decrease in the binding function of the FABP4 protein to fatty acids, indicating the important role of this residue in FABP4 function.…”

“…The smaller binding energy value between testing ligands and proteins, the better the binding affinity and the more stable the interaction between complexes [10]. The position of testing ligands is indicated by the amino acid residues surrounding the ligands, while the ligand affinity to its receptor can be seen from the interaction between the ligands (native and testing ligands) with the amino acid residues that are responsible for the catalytic side of each protein [11].…”

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“…While the computer-aided structure-based drug design (e.g., docking) depend on the actual 3D structure of the targeted binding site of the targeted receptor protein to understand the stabilizing interactions at the molecular level between the studied ligand/receptor system, the ligand based-drug design (e.g., 3D-QSAR modeling) approach relies on the recognition of a database of already know ligands interacting with the studied target receptor. Both structure and ligand bases technologies have several success stories and pursue a key role in the drug modern drug discovery process [9][10][11][12][13]. In this context, several of these approaches have been recently employed to research novel drug candidates against COVID-19.…”