Biochemical profiling in silico—Predicting substrate specificities of large enzyme families

Design of a New Gemini Lipoaminoacid with Immobilized Lipases Based on an Eco-Friendly Biosynthetic Process

Lipoaminoacids (LAA) are an important group of biosurfactants, formed by a polar hydrophilic part (amino acid) and a hydrophobic tail (lipid). The gemini LAA structures allow the formation of a supramolecular complex with bioactive molecules, like DNA, which provides them with good transfection efficiency. Since lipases are naturally involved in lipid and protein metabolism, they are an alternative to the chemical production of LAA, offering an eco-friendly biosynthetic process option. This work aimed to design the production of novel cystine derived gemini through a bioconversion system using immobilized lipases. Three lipases were used: porcine pancreatic lipase (PPL); lipase from Thermomyces lanuginosus (TLL); and lipase from Rizhomucor miehei (RML). PPL was immobilized in sol-gel lenses. L-cystine dihydrochloride and dodecylamine were used as substrates for the bioreaction. The production of LAA was evaluated by thin layer chromatography (TLC), and colorimetric reaction with eosin. The identification and quantification was carried out by High Performance Liquid Chromatographer-Mass Spectrometry (HPLC-MS/MS). The optimization of media design included co-solvent (methanol, dimethylsulfoxide), biphasic (n-hexane and 2-propanol) or solvent-free media, in order to improve the biocatalytic reaction rates and yields. Moreover, a new medium was tested where dodecylamine was melted and added to the cystine and to the biocatalyst, building a system of mainly undissolved substrates, leading to 5 mg/mL of LAA. Most of the volume turned into foam, which indicated the production of the biosurfactant. For the first time, the gemini derived cystine lipoaminoacid was produced, identified, and quantified in both co-solvent and solvent-free media, with the lipases PPL, RML, and TLL.

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.

Docking of Covalent Ligands: Challenges and Approaches

Covalent ligands have recently regained considerable attention in drug discovery. The rational design of such ligands, however, is still faced with particular challenges, mostly related to the fact that covalent bond formation is a quantum mechanical phenomenon which cannot adequately be handled by the force fields or empirical approaches typically used for noncovalent protein-ligand interactions. Although the necessity for quantum chemical approaches is clear, they cannot yet routinely be applied on large data sets of ligands or for a broader exploration of binding modes in docking calculations. On the other hand, technical solutions for performing docking calculations with covalent ligands are available, but their scope is normally quite limited. Scoring functions typically neglect the contribution from covalent bond formation completely. In this situation, the question arises how to approach covalent ligands and which methods to choose for their docking and design.

Computational evaluation of factors governing catalytic 2-keto acid decarboxylation

Recent advances in computational approaches for creating pathways for novel biochemical reactions has motivated the development of approaches for identifying enzyme-substrate pairs that are attractive candidates for effecting catalysis. We present an improved structural-based strategy to probe and study enzyme-substrate binding based on binding geometry, energy, and molecule characteristics, which allows for in silico screening of structural features that imbue higher catalytic potential with specific substrates. The strategy is demonstrated using 2-keto acid decarboxylation with various pairs of 2-keto acids and enzymes. We show that this approach fitted experimental values for a wide range of 2-keto acid decarboxylases for different 2-keto acid substrates. In addition, we show that the structure-based methods can be used to select specific enzymes that may be promising candidates to catalyze decarboxylation of certain 2-keto acids. The key features and principles of the candidate enzymes evaluated by the strategy can be used to design novel biosynthesis pathways, to guide enzymatic mutation or to guide biomimetic catalyst design.

Engineering the meso-diaminopimelate dehydrogenase from Symbiobacterium thermophilum by site saturation mutagenesis for D-phenylalanine synthesis

In order to enlarge the substrate binding pocket of the meso-diaminopimelate dehydrogenase from Symbiobacterium thermophilum to accommodate larger 2-keto acids, four amino acid residues (Phe146, Thr171, Arg181, and His227) were targeted for site saturation mutagenesis. Among all mutants, the single mutant H227V had a specific activity of 2.39 ± 0.06 U · mg(-1), which was 35.1-fold enhancement over the wild-type enzyme.

Divergent evolution in enolase superfamily: strategies for assigning functions

Nature's strategies for evolving catalytic functions can be deciphered from the information contained in the rapidly expanding protein sequence databases. However, the functions of many proteins in the protein sequence and structure databases are either uncertain (too divergent to assign function based on homology) or unknown (no homologs), thereby limiting the utility of the databases. The mechanistically diverse enolase superfamily is a paradigm for understanding the structural bases for evolution of enzymatic function. We describe strategies for assigning functions to members of the enolase superfamily that should be applicable to other superfamilies.

Studying enzyme-substrate specificity in silico: a case study of the Escherichia coli glycolysis pathway

In silico protein-ligand docking methods have proven to be useful in drug design and have also shown promise for predicting the substrates of enzymes, an important goal given the number of enzymes with uncertain function. Further testing of this latter approach is critical because (1) metabolites are on average much more polar than druglike compounds and (2) binding is necessary but not sufficient for catalysis. Here, we demonstrate that docking against the enzymes that participate in the 10 major steps of the glycolysis pathway in Escherichia coli succeeds in identifying the substrates among the top 1% of a virtual metabolite library.

Modelling substrate specificity and enantioselectivity for lipases and esterases by substrate-imprinted docking

Background

Previously, ways to adapt docking programs that were developed for modelling inhibitor-receptor interaction have been explored. Two main issues were discussed. First, when trying to model catalysis a reaction intermediate of the substrate is expected to provide more valid information than the ground state of the substrate. Second, the incorporation of protein flexibility is essential for reliable predictions.

Results

Here we present a predictive and robust method to model substrate specificity and enantioselectivity of lipases and esterases that uses reaction intermediates and incorporates protein flexibility. Substrate-imprinted docking starts with covalent docking of reaction intermediates, followed by geometry optimisation of the resulting enzyme-substrate complex. After a second round of docking the same substrate into the geometry-optimised structures, productive poses are identified by geometric filter criteria and ranked by their docking scores. Substrate-imprinted docking was applied in order to model (i) enantioselectivity of Candida antarctica lipase B and a W104A mutant, (ii) enantioselectivity and substrate specificity of Candida rugosa lipase and Burkholderia cepacia lipase, and (iii) substrate specificity of an acetyl- and a butyrylcholine esterase toward the substrates acetyl- and butyrylcholine.

Conclusion

The experimentally observed differences in selectivity and specificity of the enzymes were reproduced with an accuracy of 81%. The method was robust toward small differences in initial structures (different crystallisation conditions or a co-crystallised ligand), although large displacements of catalytic residues often resulted in substrate poses that did not pass the geometric filter criteria.

Analysis of the substrate-binding site of human carbonyl reductases CBR1 and CBR3 by site-directed mutagenesis

Human carbonyl reductase is a member of the short-chain dehydrogenase/reductase (SDR) protein superfamily and is known to play an important role in the detoxification of xenobiotics bearing a carbonyl group. The two monomeric NADPH-dependent human isoforms of cytosolic carbonyl reductase CBR1 and CBR3 show a sequence similarity of 85% on the amino acid level, which is definitely high if compared to the low similarities usually observed among other members of the SDR superfamily (15-30%). Despite the sequence similarity and the similar features found in the available crystal structures of the two enzymes, CBR3 shows only low or no activity towards substrates that are metabolised by CBR1. This surprising substrate specificity is still not fully understood. In the present study, we introduced several point mutations and changed sequences of up to 17 amino acids of CBR3 to the corresponding amino acids of CBR1, to gather insight into the catalytic mechanism of both enzymes. Proteins were expressed in Escherichia coli and purified by Ni-affinity chromatography. Their catalytic properties were then compared using isatin and 9,10-phenanthrenequinone as model substrates. Towards isatin, wild-type CBR3 showed a catalytic efficiency of 0.018 microM(-1)min(-1), whereas wild-type CBR1 showed a catalytic efficiency of 13.5 microM(-1)min(-1). In particular, when nine residues (236-244) in the vicinity of the catalytic center and a proline (P230) in CBR3 were mutated to the corresponding residues of CBR1 a much higher k(cat)/K(m) value (5.7 microM(-1)min(-1)) towards isatin was observed. To gain further insight into the protein-ligand binding process, docking simulations were perfomed on this mutant and on both wild-type enzymes (CBR1 and CBR3). The theoretical model of the mutant was ad hoc built by means of standard comparative modelling.

Molecular docking for substrate identification: the short-chain dehydrogenases/reductases

Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases. We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking >900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures. Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naive for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.