A Semiautomated Structure-Based Method To Predict Substrates of Enzymes via Molecular Docking: A Case Study with Candida antarctica Lipase B

Versatile Lipases from the Candida rugosa-like Family: A Mechanistic Insight Using Computational Approaches

Lipases are enzymes able to catalyze the hydrolysis or synthesis of triglycerides, depending on the reaction conditions, whereas sterol esterases show the same ability on sterol esters. Structurally, both kinds of enzymes display an α/β-hydrolase fold, with a substrate-binding pocket formed by a hydrophobic cavity covered by a mobile lid. However, it has been reported that some lipases from the Candida rugosa-like family display wide substrate specificity on both triglycerides and sterol esters. Among them, enzymes with different biotechnological applications, such as the lipase isoenzymes produced by C. rugosa and the sterol esterase from Ophiostoma piceae, have been exhaustively characterized and their crystal structures are available. Differences in substrate affinity among these proteins have been attributed to changes in their hydrophobicity. In this work, we analyzed the full catalytic mechanisms of these proteins using molecular dynamics tools, gaining insight into their mechanistic properties. In addition, we developed an in silico protocol to predict the substrate specificity using C. rugosa and O. piceae lipases as model enzymes and triglycerides and cholesterol esters with different fatty acid chain lengths as model substrates. The protocol was validated by comparing the in silico results with those described in the literature. These results would be useful to perform virtual screening of substrates for enzymes of the C. rugosa-like family with unknown catalytic properties.

Substrate-imprinted docking of Agrobacterium tumefaciens uronate dehydrogenase for increased substrate selectivity

Agrobacterium tumefaciens uronate dehydrogenase (AtuUdh) belongs to the short-chain dehydrogenase superfamily, specifically those acting on the CH-OH group of donor with NAD⁺ or NADP⁺ as acceptor. It is apparently required for the production of D-glucaric acid. AtuUdh-catalyzed reaction is reversible with dual substrate-specific activity (D-galacturonic acid and D-glucuronic acid) in nature. In our study, 34 mutants were pre-screened from 155 mutants generated from AtuUdh (wild-type) and selected 10 structurally stable mutants with increased substrate selectivity. The specificity, efficiency, and selectivity of these mutants for different substrates and cofactors were predicted from 121 docked models using a substrate-imprinted docking approach. Q14F, S36L, and S75T mutants have shown a high binding affinity to D-glucuronic acid and its substrate intermediates such as D-glucaro-1,4-lactone and D-glucaro-1,5-lactone. These mutants exhibited a low binding affinity to the substrate and cofactor required for D-galactaric acid. D34S, N112E and S165E mutants found to show a high selectivity of D-galacturonic acid and its substrate intermediates for D-galactaric acid production. Ser75, Ser165, and Arg174 are active residues playing an imperative role in the substrate selectivity and also contributed in the conjecture the mechanism of transition state stabilization catalyzed by AtuUdh mutants. The present approach was used to reveal the substrate binding mechanism of AtuUdh mutants for a better understanding of the structural basis for selectivity and function.

Developing Hispolon-based novel anticancer therapeutics against human (NF-κβ) using in silico approach of modelling, docking and protein dynamics

Hispolon is a polyphenolic compound derived from black hoof mushroom (Phellinus linteus) or shaggy bracket mushroom (Inonotus hispidus) which induces the inhibition of cancer-promoting nuclear factor-kappa beta (NF-κβ) complex. To develop more potent lead molecules with enhanced anticancer efficiency, the mechanism of hispolon-mediated nuclear factor-κβ inhibition has been investigated by molecular modelling and docking. Ten derivatives of hispolon (DRG1-10) have been developed by pharmacophore-based design with a view to enhance the anticancer efficacy. Hispolon and its derivatives were further screened for different pharmacological parameters like binding free energy, drug likeliness, absorption-digestion-metabolism-excretion (ADME), permeability, mutagenicity, toxicity and inhibitory concentration 50 (IC50) to find a potent lead molecule. Based on pharmacological validation, comparative molecular dynamics (MD) simulations have been performed for three lead molecules: Hispolon, DRG2 and DRG7 complexed with human NF-κβ up to 50 ns. By analysing different factors like root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA) and principal component analysis (PCA), Gibb's free energy plots DRG2 have more binding efficiency compared to hispolon and DRG7. In RMSD plot, hispolon-bound NF-κβ has the most deviation within a range between 0.125 and 0.45 nm, and DRG2-bound complex showed the range between 0.125 and 0.25 nm. The residues of NF-κβ responsible for hydrophobic interactions with ligand, e.g. Met469, Leu522 and Cys533, have the lowest fluctuation values in DRG2-bound complex. The average Rg fluctuation for DRG2-bound NF-κβ has been recorded under 2.025 nm for most of the simulation time which is much less compared to hispolon and DRG7. Gibb's free energy plots also define the highest stability of DRG2-bound NF-κβ. Communicated by Ramaswamy H. Sarma.

Crius: A novel fragment-based algorithm of de novo substrate prediction for enzymes

The study of enzyme substrate specificity is vital for developing potential applications of enzymes. However, the routine experimental procedures require lot of resources in the discovery of novel substrates. This article reports an in silico structure-based algorithm called Crius, which predicts substrates for enzyme. The results of this fragment-based algorithm show good agreements between the simulated and experimental substrate specificities, using a lipase from Candida antarctica (CALB), a nitrilase from Cyanobacterium syechocystis sp. PCC6803 (Nit6803), and an aldo-keto reductase from Gluconobacter oxydans (Gox0644). This opens new prospects of developing computer algorithms that can effectively predict substrates for an enzyme.