Automated genome mining for natural products
© Li et al; licensee BioMed Central Ltd. 2009
Received: 05 December 2008
Accepted: 16 June 2009
Published: 16 June 2009
Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassay-guided identification and natural product structure elucidation. Increasingly rapid and more cost-effective genome sequencing technologies coupled with advanced computational power have converged to transform this trend toward a more rational and predictive pursuit.
We have developed a rapid method of scanning genome sequences for multiple polyketide, nonribosomal peptide, and mixed combination natural products with output in a text format that can be readily converted to two and three dimensional structures using conventional software. Our open-source and web-based program can assemble various small molecules composed of twenty standard amino acids and twenty two other chain-elongation intermediates used in nonribosomal peptide systems, and four acyl-CoA extender units incorporated into polyketides by reading a hidden Markov model of DNA. This process evaluates and selects the substrate specificities along the assembly line of nonribosomal synthetases and modular polyketide synthases.
Using this approach we have predicted the structures of natural products from a diverse range of bacteria based on a limited number of signature sequences. In accelerating direct DNA to metabolomic analysis, this method bridges the interface between chemists and biologists and enables rapid scanning for compounds with potential therapeutic value.
Many of the pharmaceuticals currently in clinical use have natural product origins, illustrating the effectiveness of compounds found in nature as antibiotic, anticancer, cholesterol-lowering, antiparasitic and anti-fungal agents . Testing for therapeutic activities remains conceptually similar compared to the time of the discovery of penicillin by Alexander Fleming. Scientists today still approach identification of medicinal compounds by screening chemicals for their ability to inhibit cellular growth. This was a landmark step for science compared to using medicines passed down through collective human experience, but one from which we have not yet significantly advanced .
The arrival of a powerful graphic user interface (GUI) in the mid 1990s coincided with the first whole genome sequence of the microorganism Haemophilus influenza . This synergy of computational technology and molecular biology was the beginning of whole genome analysis aiding drug discovery by searching for robust protein targets. Genome scanning was subsequently recognized as a means of discovering useful secondary metabolites such as nonribosomal peptides (NRPs), polyketides (PKs), and terpenoids .
In analysis of secondary metabolism, computing abilities such as multiple sequence alignment and crystallographic analysis tools enable researchers to elucidate the functions and characteristics of key enzymes involved in assembly and tailoring of natural products including nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs). NRPs and PKs have long been utilized by bacteria for cellular defense and offense against competitors . They are prolific in nature and generate many of the antibiotics used clinically such as penicillin, erythromycin, vancomycin, and daptomycin, among numerous others . Their ability to target important biomolecules highlights their significance in the central scheme of cellular metabolism.
PKSs select simple acyl-CoAs and other extender units with an acyltransferase (AT) domain, catalyze linkage of the activated acyl chain onto a thiolation (T) domain, and decarboxylate and condense the extender units using a ketosynthase (KS) domain. Similarly, NRPSs bind and activate amino acids and other carboxylic acids through adenylation (A) and thiolation (T) domains and link them together with a condensation (C) domain to create a linear proteinogenic molecule that is either cleaved to release a linear or cyclized molecule through the action of a thioesterase (TE) , or in a few other cases, a reductase (RE) domain . Each KS, AT, and T domain or C, A, and T domain for the PKSs and NRPSs, respectively, represent the core of one module. Most of these systems have multiple modules that each catalyzes the binding and activation of an acyl-CoA or amino acid extender unit .
A and AT domains are considered the gatekeepers of monomer selection of the starter and extender units that are essential for NRP and PK biosynthesis. Signature amino acid motifs determine which A or AT domain are likely to activate a particular monomer, similar in concept to how key residues, termed anticodons, in tRNAs determine which cognate amino acid it binds . This stored specificity from signature residues in a linear protein sequence may be considered a hidden Markov model (HMM) . Much like how multiple tRNAs may bind to the same amino acid, the NRPS A and PKS AT domain amino acid binding sequence is degenerate, such that multiple signature sequences can code for binding of the NRPS or PKS domain to the same amino acid or polyketide subunit, respectively. Multiple modifying domains including those for methylation, oxidation, epimerization, and reduction may sculpt the molecule into a highly functionalized structure. A NRPS, PKS, or hybrid NRPS-PKS enzymatic assembly-line can generate an immense chemical library that is multiplied further by enzymes within the natural product cluster to structurally modify a NRP, PK, or mixed NRP-PK molecule after its assembly through reactions such as hydroxylation, glycosylation, cyclization, and halogenation .
We tested the program using natural product biosynthetic pathways with accessible DNA sequences from two well-established databases: ASMPKS  for PKs and NORINE  for primarily NRPs and mixed NRP/PK derived systems. To enhance our natural products search engine, we also incorporated detection, though not structure generation, for trans-AT PKS systems as well as for terpenoids, that represent new niches where many more useful natural products are likely to be discovered .
Recognizing substrate specificity
NP.searcher runs BLAST once to match a generic NRPS/PKS against an unknown NRPS/PKS. This enables alignment and extraction of the key residues of the unknown NRPS or PKS. The program runs BLAST again to align the discovered signatures with stored known signature sequences to determine substrate specificity. This is a fast, direct, and effective method of implementing BLAST to determine substrate specificity without resorting to any HMM-specific software. NP.searcher recognizes core and auxiliary NRPS and PKS domains if they align with an expectation value less than 0.1 and an identity greater than 120. Once an acyltransferase or adenylation domain of sufficient identity and length is recognized, the program parses its sequence for the signature residues as identified previously for the NRPS and additional ones found by compilation of various PKS sequences. We used a database of 187 NRPS signature sequences of ten residues and compiled PKS signature residues to build 18 PKS signature sequences of 18 residues from various published reports [20–25]. The program compares the extracted sequences and the database to reveal the substrate specificity of the catalytic domains. If a particular signature sequence is ambiguous regarding substrate selection, the program employs an auxiliary prediction system using an additive scoring algorithm to identify the most likely substrate. If this fails because of inconclusive scoring, then the NRPS or PKS residue is designated unknown and can be seen as a generic peptide or polyketide subunit in the structure.
Constructing the natural product
NP.searcher was written in the software language C++, and modeled the assembly of NRPs and PKs as a linked list. Upon recognition of the substrate specificity of a catalytic domain, the SMILES string of the predicted chain elongation intermediate is appended to the SMILES string of the previous intermediate. During the elongation process, polyketide or peptide monomers may be modified as they are incorporated into the growing chain. Once the construction of the core NRP or PK is completed, the small molecule is modified based on a reaction list, where cyclization and modifications specified by the user or directly by program-recognized auxiliary enzymes encoded within the natural product cluster are applied. The program computes every possible molecule given the number of modifiable sites and the reactions designated. All possible products computed by the program are added to a product list, where they may be filtered by molecular mass and included in the data output.
The breadth of recognition
Validation of PKS domain recognition
Validation of NRPS domain recognition
Aa/Total # of A domains
Eb domains detected/Total # of E domains
Uncertainties in prediction
Validation of PKS false positives
Various modifications could be performed on the linear molecule predicted from the DNA sequence. NP.searcher immediately generates a macrocyclized version of the molecule upon determining the linear sequence. Oxygen, nitrogen, and sulfur methylation can be applied to the natural product pre- and post-assembly of the NRPs or PKs derived core structure. NP.searcher allows epimerization, carbon methylation, and three types of PKS processing (DH, ER, KR) only during assembly with halogenation, hydroxylation, heterocyclization, and glycosylation only during post-assembly modifications. Thus, there are eight assembly reactions, seven post-assembly tailoring reactions, and two termination strategies depending upon off-loading of a linear product or macrocyclization. Upon assembly and post-assembly modifications, NP.searcher outputs molecules as SMILES. Both simple and highly complex structures can be displayed through this text format, which is recognized by various software in common use such as ChemDraw, Daylight Depict, Molinspiration, Smile23d, InChI, and PubChem.
In addition to assembling and predicting modular NRPS/PKS systems, the program is able to recognize trans-AT PKS and terpenoid gene clusters. The substrate sequence of trans-AT PKSs, unlike that of modular PKS derived AT domains is apparently determined by ketosynthases instead of acyltransferases . Currently, the program can detect, though not build, a trans-AT PK molecule. This is accomplished by searching for consecutive KSs separated by less than 15 kilobases without intervening ATs. The program can also recognize terpenoid clusters by searching for essential genes of the mevalonate and non-mevalonate pathways: ispH and mvd1, respectively . The innate ability of the program to perform post-assembly tailoring reactions allows users to enrich the database with additional core structure modifying enzymes. For example, incorporating cytochrome P450s and their corresponding hydroxylation and epoxidation reactions along with many other enzymes involved in natural product structure diversification is the next step for expanding the search engine's capabilities.
The next frontier in drug discovery
In aiding investigators to discover secondary metabolites by structure prediction, this new natural products search engine enables drug discovery efforts to move one step closer to prediction of molecules for lead optimization. The development of SMILES analysis for biological activity coupled with the advancement of docking software that can accept SMILES as input and dock them with proteins opens the door to a useful new tool for pharmaceutical research and development. Building in silico models of newly discovered natural products to predict activity and perform docking is another incremental step beyond the trial-and-error method of drug development. The predicted natural products in SMILES format can be used to generate both 2D and 3D representations in assorted chemical software. 3D models produced from SMILES can be used for docking with proteins using well-established methods .
Challenges to prediction
Currently, the best predicted structure of the molecule differs significantly in many cases from the actual molecule because of non-functional domains and unrecognized post-tailoring modifications. Thus, small molecule-target docking of the best predicted structure may not be representative of the actual molecular interaction. However, this is expected to improve with the deciphering of the determinants of inactive or silent auxiliary domains, along with further understanding of signature residues that determine the absence or presence of cyclization, the nature of cyclization, the type of post-assembly tailoring modifications, and their sites of action.
Important limitations remain in this initial version of NP.searcher and represent challenges for the future in this open source tool. For example, an incomplete ability to predict rare substrates because of a limited database of known natural product starter and extender units can lead to false or deficient predictions. However, finding compounds with unknown groups during genome mining might motivate the pursuit of predicted molecules as is the case with cryptophycin. These predictions were made from only a small number of known sequences and signatures stored by NP.searcher. Adding the signature sequences of these unique substrates to the search engine would reduce significantly the number of unknown subunits and increase program performance.
New sources for mining
The emerging views from marine invertebrate (e.g. sponges, tunicates, ascidians) and terrestrial microbial symbionts reveals that trans-AT PKS systems specify synthesis of a large proportion of novel natural products, and thus it is crucial to be able to genome-mine potential products from these non-traditional clusters . Furthermore, plant genomes should reveal a cornucopia of unexplored and diverse NRPs, PKs, and terpenoids that have potential therapeutic applications . With the development of heterologous expression of plant secondary metabolite pathways and better understanding of plant natural product metabolic systems, these organisms should increasingly become an attractive source of valuable compounds [31, 32]. Though terpenoid biosynthetic mechanisms are yet to be elucidated as well as that of NRPSs and PKSs, the abundance of medicines that may be produced from them and the scientific curiosity in plant biosynthesis might drive both industry and academia to tackle terpenoid pathways more aggressively in the future .
The keys to automated elucidation
With genome sequencing rapidly becoming more affordable, the bottleneck becomes the ever-elusive ability to predict small molecule structure, and more challengingly, protein three-dimensional structures from two-dimensional specifications. Although, there are sure to be an enormous number of novel enzymes to discover and characterize functionally, the existing database of known proteins and increasingly refined biochemical tools such as metabolic and gene expression profiling coupled with heterologous protein expression will accelerate solving enzymatic puzzles . Enzymes with low substrate specificity may be difficult to analyze using hidden Markov models and the time to use them in silico may not come until we can accurately simulate protein dynamics.
With better understanding of how natural products are genetically and enzymatically determined and the advance of rapid genomic scanning technologies, there is a need to extract chemical knowledge from genetic information more efficiently for potential applications. Taking advantage of the recent advances in natural products domain recognition, NP.searcher decodes natural product gene clusters into molecules and brings to the forefront the ability to recognize thousands of new secondary metabolites. On the more basic level, this program can function as an editing device to compose natural product molecules. With the development of greater protein engineering capabilities, this program will enable biologists and chemists to envision possible NRPs and PKs to design de novo pathways by metabolic engineering or synthetic biology approaches. At the most advanced and useful level, NP.searcher may read through thousands of gene clusters and automatically construct and screen potential natural product molecules from large databases of uncharacterized microbial genome or mixed metagenome sequences. In another future application, the development of better protein three-dimensional modeling, the program will seek to employ reverse engineering to provide DNA sequences required to prescribe the biosynthesis of a particular natural product molecule.
A natural products search engine
In addition to extensively searching through chemical space, NP.searcher performed intramolecular reactions in output structures as seen with echinomycin. Such dynamic in silico enzymology will be necessary to elucidate compounds with the complexity of the NRPS-derived antibiotic vancomycin . Given the diverse chemical arsenal found in Nature, such an extensive search capability promises to uncover interesting candidate metabolites from NP.searcher by performing various intramolecular reactions on a natural product molecule. Moreover, the SMILES output of the program can be applied to other software to analyze and predict biological activities relevant to selected drug targets. In addition to the challenges of predicting bioactivity from SMILES, resulting 2D and 3D structures, and predicting cytotoxicity and drug action for a predicted structure, other important challenges remain to be addressed such as elucidation of terpene biosynthesis and non-co-linear and dispersed synthetase systems in microbes and plants. Accordingly, the broad potential of cost-efficient genome sequencing coupled to rapid and accurate prediction of secondary metabolic products and biological activity provides an urgent motivation to accomplish these objectives more quickly and effectively.
NP.searcher was developed to scan rapidly microbial genomes for secondary metabolite biosynthetic gene clusters, and output candidate nonribosomal peptide and polyketide natural products in SMILES format, enabling immediate decoding of DNA to produce 2D and 3D structures in widely available software. The ability to recognize novel NRP and PK products will grow with continuous updating of the search engine's database of adenylation and acyltransferase signature sequences for various amino acid and polyketide starter and extender units. The value of NP.searcher is likely to improve with addition of algorithms built on further proteomic analysis that reveal the basis for post-assembly tailoring steps such as cyclization, glycosylation, methylation, and various other common reactions, further enhancing the output of structural predictions. With the development of faster and cheaper genome sequencing technologies, NP.searcher may be increasingly useful in the rapid screening of suitable natural product drug candidates directly from genomic information.
Availability and Requirements
Project name: Natural products search engine
Project home page: http://dna.sherman.lsi.umich.edu
Login: temp; Password: temp
Operating system: Linux and web-based
Programming language: C++
Any restrictions to use by non-academics: no, open-source
nonribosomal peptide synthetase
simplified molecular input line entry specification
hidden Markov model.
This work was supported by NIH grant GM076477 and the Hans W. Vahlteich Professorship (to D.H.S), the Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, and an NIH Cellular Biotechnology Training Grant.
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