Toward the virtual screening of Cdc25A phosphatase inhibitors with the homology modeled protein structure

In Silico evaluation and identification of fungi capable of producing endo-inulinase enzyme

The enzyme endo-inulinase hydrolyzes inulin to short chain fructooligosaccharides (FOS) that are potential prebiotics with many health promoting benefits. Although the raw materials for inulin production are inexpensive and readily available, commercial production of FOS from inulin is limited due to inadequate availability of the enzyme source. This study aimed to identify the fungi capable of producing endo-inulinase based on the in silico analysis of proteins retrieved from non-redundant protein sequence database. The endo-inulinase of Aspergillus ficuum was used as reference sequence. The amino acid sequences with >90% sequence coverage, belonging to different fungi were retrieved from the database and used for constructing three-dimensional (3D) protein models using SWISS-MODEL and Bagheerath H. The 3D models of comparable quality as that of the reference endo-inulinase were selected based on QMEAN Z score. The selected models were evaluated and validated for different structural and functional qualities using Pro-Q, ProSA, PSN-QA, VERIFY-3D, PROCHECK, PROTSAV metaserver, STRAP, molecular docking, and molecular dynamic simulation analyses. A total of 230 proteins belonging to 53 fungal species exhibited sequence coverage >90%. Sixty one protein sequences with >60% sequence identity were modeled as endo-inulinase with higher QMEAN Z Score. The evaluations and validations of these 61 selected models for different structural and functional qualities revealed that 60 models belonging to 22 fungal species exhibited native like structure and unique motifs and residues as that of the reference endo-inulinase. Further, these models also exhibited similar kind of interaction between the active site around the conserved glutamate residue and substrate as that of the reference endo-inulinase. In conclusion, based on the current study, 22 fungal species could be identified as endo-inulinase producer. Nevertheless, further biological assessment of their capability for producing endo-inulinase is imminent if they are to be used for commercial endo-inulinase production for application in FOS industry.

Structure-based substrate screening for an enzyme

Background

Nowadays, more and more novel enzymes can be easily found in the whole enzyme pool with the rapid development of genetic operation. However, experimental work for substrate screening of a new enzyme is laborious, time consuming and costly. On the other hand, many computational methods have been widely used in lead screening of drug design. Seeing that the ligand-target protein system in drug design and the substrate-enzyme system in enzyme applications share the similar molecular recognition mechanism, we aim to fulfill the goal of substrate screening by in silico means in the present study.

Results

A computer-aided substrate screening (CASS) system which was based on the enzyme structure was designed and employed successfully to help screen substrates of Candida antarctica lipase B (CALB). In this system, restricted molecular docking which was derived from the mechanism of the enzyme was applied to predict the energetically favorable poses of substrate-enzyme complexes. Thereafter, substrate conformation, distance between the oxygen atom of the alcohol part of the ester (in some compounds, this oxygen atom was replaced by nitrogen atom of the amine part of acid amine or sulfur atom of the thioester) and the hydrogen atom of imidazole of His224, distance between the carbon atom of the carbonyl group of the compound and the oxygen atom of hydroxyl group of Ser105 were used sequentially as the criteria to screen the binding poses. 223 out of 233 compounds were identified correctly for the enzyme by this screening system. Such high accuracy guaranteed the feasibility and reliability of the CASS system.

Conclusion

The idea of computer-aided substrate screening is a creative combination of computational skills and enzymology. Although the case studied in this paper is tentative, high accuracy of the CASS system sheds light on the field of computer-aided substrate screening.