Towards Prediction of Metabolic Products of Polyketide Synthases: An In Silico Analysis

Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS) family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis. We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences. Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.This work has also been supported by predoctoral fellowships FPU19/00608 and PRE2020-091825, and the PID2019-106370RBI00/AEI/10.13039/501100011033 grant from the Spanish Ministry of Science and Innovation.Peer ReviewedPostprint (author's final draft

Computer simulations explain mutation-induced effects on the DNA editing by adenine base editors.

Adenine base editors, which were developed by engineering a transfer RNA adenosine deaminase enzyme (TadA) into a DNA editing enzyme (TadA*), enable precise modification of A:T to G⋮C base pairs. Here, we use molecular dynamics simulations to uncover the structural and functional roles played by the initial mutations in the onset of the DNA editing activity by TadA*. Atomistic insights reveal that early mutations lead to intricate conformational changes in the structure of TadA*. In particular, the first mutation, Asp108Asn, induces an enhancement in the binding affinity of TadA to DNA. In silico and in vivo reversion analyses verify the importance of this single mutation in imparting functional promiscuity to TadA* and demonstrate that TadA* performs DNA base editing as a monomer rather than a dimer

Semi-rational design of nitroarene dioxygenase for catalytic ability toward 2,4-dichloronitrobenzene

Rieske non-heme dioxygenase family enzymes play an important role in the aerobic biodegradation of nitroaromatic pollutants, but no active dioxygenases are available in nature for initial reactions in the degradation of many refractory pollutants like 2,4-dichloronitrobenzene (24DCNB). Here, we report the engineering of hotspots in 2,3-dichloronitrobenzene dioxygenase from Diaphorobacter sp. strain JS3051, achieved through molecular dynamic simulation analysis and site-directed mutagenesis, with the aim of enhancing its catalytic activity toward 24DCNB. The computationally predicted activity scores were largely consistent with the detected activities in wet experiments. Among them, the two most beneficial mutations (E204M and M248I) were obtained, and the combined mutant reached up to a 62-fold increase in activity toward 24DCNB, generating a single product, 3,5-dichlorocatechol, which is a naturally occurring compound. In silico analysis confirmed that residue 204 affected the substrate preference for meta-substituted nitroarenes, while residue 248 may influence substrate preference by interaction with residue 295. Overall, this study provides a framework for manipulating nitroarene dioxygenases using computational methods to address various nitroarene contamination problems

The Agarolytic System of Microbulbifer elongatus PORT2, Isolated from Batu Karas, Pangandaran West Java Indonesia

Agar is a marine heteropolysaccharide with repeating units consisting of 3,6-α-anhydro-L-galactopyranose and D-galactopyranose linked by α-(1,3) and β-(1,4) linkages. It has been promoted as a prospective replacement for petroleum-based feedstocks and other applications. Enzymatic biotransformation of agar generates high specific products: It is also more environmentally friendly than chemical hydrolysis. In particular, agarolytic bacteria and their agarases are preferred for the processing of agar into sugar derivatives. Agar-producing macroalgae are one of Indonesia's national commodities. However, agar-based products and technology are rarely developed in Indonesia. This research is aimed to explore the potential of an Indonesian marine bacterium and its agarases as bioagents for agar bioprocessing. The research objectives are to identify the novelty of the isolate among known agarolytic bacteria using microbiology and molecular biology approaches, to elucidate the agarolytic system of the bacterium using in silico genome analysis, to express and characterize the recombinant agarases, and to elucidate their potential for producing agar-derived saccharides from Indonesian natural agar. Microbulbifer elongatus PORT2 is a gram-negative marine bacterium that had been isolated from Batu Karas seawater, Pangandaran, West Java Indonesia. PORT2 shows potential as biocatalysts for agar saccharides conversion by showing remarkable agar liquefaction. The annotation of the draft genome identifies six putative β-agarases consist of three GH50, two GH86, and one GH16 in M. elongatus PORT2. Those agarases are clustered at two different contigs. Besides agarases, other genes for D-galactose and 3,6 anhydro-L galactose metabolism, sugar transports and regulatory system are found in the vicinity of the agarases clusters. Despite the ability to utilize agar as a sole carbon sole, PORT2 lacks any putative α-agarase GH117 or GH96. Both are responsible for the cleavage of α-glycosidic bonds in agar. Indeed, several hypothetical proteins are in the neighborhood of the agarase gene clusters in M. elongatus PORT2. They probably could have a function as the alternative machinery or pathway for agar monomerization that needs clarification in future research work. Four recombinant β-agarases from PORT2; AgaA50, AgaB50, AgaC50, and AgaF16A have been successfully overexpressed in E.coli and characterized. The AgaA50 and AgaC50 exhibit metal-dependent activity. They perform exo-agarolytic modes and generates neoagarobiose (NA2). The AgaB50 can act as endo-and exo-β-agarase without any additional activator and produces neoagarohexaose (NA6), neoagarotetraose (NA4), and NA2. AgaF16 produces NA6 and NA4. The enzyme shows pure endo-catalytic action which thiol agents positively affect its activity. The synergetic reaction of AgaF16A and AgaA50 converts Indonesian Gelidium agar into NA2 and Gracilaria agar into modified NA2. The modified NA2 from Gracilaria agar could promise new potential bioactivity that is different from agarose-derived NA2 due to the presence of additional side chains on the saccharide backbone. The NA6, NA4, and NA2 products from agarose have shown potential pharmaceutical applications such as immunomodulator, anti-tumor, antioxidant, anti-diabetic, and moisturizer. Despite being isolated from a mesophilic marine bacterium, the recombinant agarases from M. elongatus PORT2 are active at 50 °C and pH between 6.5 to 8. They maintain more than 75% of their activities even after 1 h preincubation at 50 °C, except for AgaC50. Their thermostability gives advantages for the effective biocatalytic conversion of agar because the substrate is more accessible at mild pH and the temperature above the sol-gel condition (> 40 °C).:Contents 1. Introduction 1 1.1. Motivation and Scientific Goals 1 1.2. Literature Review 3 2. Materials and Methods 12 2.1. Materials 12 2.2. Methods 13 3. Agarolytic Bacterium Microbulbifer elongatus PORT2 22 3.1. Results 22 3.2. Discussion 28 4. Genome Profiling for In Silico Elucidation of the Agarolytic System 32 4.1. Results 32 4.2. Discussion 41 5. Recombinant Agarases from Microbulbifer elongatus PORT2 44 5.1. Results 44 5.2. Discussion 71 6. Conclusions and Outlooks 78 References 81 Appendices 97 Acknowledgements 11

ResBoost: characterizing and predicting catalytic residues in enzymes

Abstract Background Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed. Results We propose ResBoost, a new computational method to learn characteristics of catalytic residues. The method effectively selects and combines rules of thumb into a simple, easily interpretable logical expression that can be used for prediction. We formally define the rules of thumb that are often used to narrow the list of candidate residues, including residue evolutionary conservation, 3D clustering, solvent accessibility, and hydrophilicity. ResBoost builds on two methods from machine learning, the AdaBoost algorithm and Alternating Decision Trees, and provides precise control over the inherent trade-off between sensitivity and specificity. We evaluated ResBoost using cross-validation on a dataset of 100 enzymes from the hand-curated Catalytic Site Atlas (CSA). Conclusion ResBoost achieved 85% sensitivity for a 9.8% false positive rate and 73% sensitivity for a 5.7% false positive rate. ResBoost reduces the number of false positives by up to 56% compared to the use of evolutionary conservation scoring alone. We also illustrate the ability of ResBoost to identify recently validated catalytic residues not listed in the CSA

Reverse Conservation Analysis Reveals the Specificity Determining Residues of Cytochrome P450 Family 2 (CYP 2)

The concept of conservation of amino acids is widely used to identify important alignment positions of orthologs. The assumption is that important amino acid residues will be conserved in the protein family during the evolutionary process. For paralog alignment, on the other hand, the opposite concept can be used to identify residues that are responsible for specificity. Assuming that the function-specific or ligand-specific residue positions will have higher diversity since they are under evolutionary pressure to fit the target specificity, these function-specific or ligand-specific residues positions will have a lower degree of conservation than other positions in a highly conserved paralog alignment. This study assessed the ability of reverse conservation analysis to identify function-specific and ligand-specific residue positions in closely related paralog