NMR-based metabolomics reveals that plant-derived smoke stimulates root growth via affecting carbohydrate and energy metabolism in maize.

INTRODUCTION: It is well known that plant-derived smoke stimulates seed germination and seedling growth in many plants. Although a number of transcriptomics and proteomics studies have been carried out to understand the mode of action of smoke, less is known about the biochemical alterations associated with smoke exposure in plants. OBJECTIVES: The aims of this study were (1) to determine the metabolic alterations in maize roots pre-treated with various concentrations of smoke solution, and (2) to identify the smoke-responsive metabolic pathways during early root growth period. METHODS: Maize seeds were pre-treated with different concentrations of smoke solutions for 24 h and then grown for 10 days. 600-MHz 1H NMR spectroscopy was performed on the aqueous root extracts of maize seedlings. The metabolite data obtained from the NMR spectra were analyzed by several statistical and functional methods, including one-way ANOVA, PCA, PLS-DA and pathway analysis. RESULTS: Our study identified a total of 29 metabolites belonging to various chemical groups. Concentrations of 20 out of these 29 metabolites displayed significant (p < 0.05) changes after at least one smoke pre-treatment compared to the control. Moreover, functional analyses revealed that smoke pre-treatments markedly affected the carbohydrate- and energy-related metabolic pathways, such as galactose metabolism, glycolysis, glyoxylate metabolism, tricarboxylic acid cycle, and starch/sucrose metabolism. CONCLUSIONS: To our knowledge, this is the first study that investigates smoke-induced biochemical alterations in early root growth period using NMR spectroscopy. Our findings clearly indicate that smoke either directly or indirectly influences many metabolic processes in maize roots.

NMRmix: A Tool for the Optimization of Compound Mixtures in 1D (1)H NMR Ligand Affinity Screens.

NMR ligand affinity screening is a powerful technique that is routinely used in drug discovery or functional genomics to directly detect protein-ligand binding events. Binding events can be identified by monitoring differences in the 1D (1)H NMR spectrum of a compound with and without protein. Although a single NMR spectrum can be collected within a short period (2-10 min per sample), one-by-one screening of a protein against a library of hundreds or thousands of compounds requires a large amount of spectrometer time and a large quantity of protein. Therefore, compounds are usually evaluated in mixtures ranging in size from 3 to 20 compounds to improve the efficiency of these screens in both time and material. Ideally, the NMR signals from individual compounds in the mixture should not overlap so that spectral changes can be associated with a particular compound. We have developed a software tool, NMRmix, to assist in creating ideal mixtures from a large panel of compounds with known chemical shifts. Input to NMRmix consists of an (1)H NMR peak list for each compound, a user-defined overlap threshold, and additional user-defined parameters if default settings are not used. NMRmix utilizes a simulated annealing algorithm to optimize the composition of the mixtures to minimize spectral peak overlaps so that each compound in the mixture is represented by a maximum number of nonoverlapping chemical shifts. A built-in graphical user interface simplifies data import and visual evaluation of the results.

The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data.

We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simulated annealing. The protein sequence, chemical shift assignments, and NOE spectra are the only required inputs. Distance constraints generated automatically from ambiguously assigned NOE peaks are validated during the structure calculation against information from an enlarged version of the freely available PACSY database that incorporates information on protein structures deposited in the Protein Data Bank (PDB). This approach yields robust sets of distance constraints and 3D structures. We evaluated the performance of AUDANA with input data for 14 proteins ranging in size from 6 to 25 kDa that had 27-98 % sequence identity to proteins in the database. In all cases, the automatically calculated 3D structures passed stringent validation tests. Structures were determined with and without database support. In 9/14 cases, database support improved the agreement with manually determined structures in the PDB and in 11/14 cases, database support lowered the r.m.s.d. of the family of 20 structural models.

Structure and function of human DnaJ homologue subfamily a member 1 (DNAJA1) and its relationship to pancreatic cancer.

Pancreatic cancer has a dismal 5 year survival rate of 5.5% that has not been improved over the past 25 years despite an enormous amount of effort. Thus, there is an urgent need to identify truly novel yet druggable protein targets for drug discovery. The human protein DnaJ homologue subfamily A member 1 (DNAJA1) was previously shown to be downregulated 5-fold in pancreatic cancer cells and has been targeted as a biomarker for pancreatic cancer, but little is known about the specific biological function for DNAJA1 or the other members of the DnaJ family encoded in the human genome. Our results suggest the overexpression of DNAJA1 suppresses the stress response capabilities of the oncogenic transcription factor, c-Jun, and results in the diminution of cell survival. DNAJA1 likely activates a DnaK protein by forming a complex that suppresses the JNK pathway, the hyperphosphorylation of c-Jun, and the anti-apoptosis state found in pancreatic cancer cells. A high-quality nuclear magnetic resonance solution structure of the J-domain of DNAJA1 combined with a bioinformatics analysis and a ligand affinity screen identifies a potential DnaK binding site, which is also predicted to overlap with an inhibitory binding site, suggesting DNAJA1 activity is highly regulated.

PONDEROSA-C/S: client-server based software package for automated protein 3D structure determination.

Peak-picking Of Noe Data Enabled by Restriction Of Shift Assignments-Client Server (PONDEROSA-C/S) builds on the original PONDEROSA software (Lee et al. in Bioinformatics 27:1727-1728. doi: 10.1093/bioinformatics/btr200, 2011) and includes improved features for structure calculation and refinement. PONDEROSA-C/S consists of three programs: Ponderosa Server, Ponderosa Client, and Ponderosa Analyzer. PONDEROSA-C/S takes as input the protein sequence, a list of assigned chemical shifts, and nuclear Overhauser data sets ((13)C- and/or (15)N-NOESY). The output is a set of assigned NOEs and 3D structural models for the protein. Ponderosa Analyzer supports the visualization, validation, and refinement of the results from Ponderosa Server. These tools enable semi-automated NMR-based structure determination of proteins in a rapid and robust fashion. We present examples showing the use of PONDEROSA-C/S in solving structures of four proteins: two that enable comparison with the original PONDEROSA package, and two from the Critical Assessment of automated Structure Determination by NMR (Rosato et al. in Nat Methods 6:625-626. doi: 10.1038/nmeth0909-625 , 2009) competition. The software package can be downloaded freely in binary format from http://pine.nmrfam.wisc.edu/download_packages.html. Registered users of the National Magnetic Resonance Facility at Madison can submit jobs to the PONDEROSA-C/S server at http://ponderosa.nmrfam.wisc.edu, where instructions, tutorials, and instructions can be found. Structures are normally returned within 1-2 days.

Application of NMR and molecular docking in structure-based drug discovery.

Drug discovery is a complex and costly endeavor, where few drugs that reach the clinical testing phase make it to market. High-throughput screening (HTS) is the primary method used by the pharmaceutical industry to identify initial lead compounds. Unfortunately, HTS has a high failure rate and is not particularly efficient at identifying viable drug leads. These shortcomings have encouraged the development of alternative methods to drive the drug discovery process. Specifically, nuclear magnetic resonance (NMR) spectroscopy and molecular docking are routinely being employed as important components of drug discovery research. Molecular docking provides an extremely rapid way to evaluate likely binders from a large chemical library with minimal cost. NMR ligand-affinity screens can directly detect a protein-ligand interaction, can measure a corresponding dissociation constant, and can reliably identify the ligand binding site and generate a co-structure. Furthermore, NMR ligand affinity screens and molecular docking are perfectly complementary techniques, where the combination of the two has the potential to improve the efficiency and success rate of drug discovery. This review will highlight the use of NMR ligand affinity screens and molecular docking in drug discovery and describe recent examples where the two techniques were combined to identify new and effective therapeutic drugs.

Correlation between protein function and ligand binding profiles.

We report that proteins with the same function bind the same set of small molecules from a standardized chemical library. This observation led to a quantifiable and rapidly adaptable method for protein functional analysis using experimentally derived ligand binding profiles. Ligand binding is measured using a high-throughput NMR ligand affinity screen with a structurally diverse chemical library. The method was demonstrated using a set of 19 proteins with a range of functions. A statistically significant similarity in ligand binding profiles was only observed between the two functionally identical albumins and between the five functionally similar amylases. This new approach is independent of sequence, structure, or evolutionary information and, therefore, extends our ability to analyze and functionally annotate novel genes.

Solution structure and function of YndB, an AHSA1 protein from Bacillus subtilis.

The solution structure of the Bacillus subtilis protein YndB has been solved using NMR to investigate proposed biological functions. The YndB structure exhibits the helix-grip fold, which consists of a ß-sheet with two small and one long α-helix, forming a hydrophobic cavity that preferentially binds lipid-like molecules. Sequence and structure comparisons with proteins from eukaryotes, prokaryotes, and archaea suggest that YndB is very similar to the eukaryote protein Aha1, which binds to the middle domain of Hsp90 and induces ATPase activity. On the basis of these similarities, YndB has been classified as a member of the activator of Hsp90 ATPase homolog 1-like protein (AHSA1) family with a function that appears to be related to stress response. An in silico screen of a compound library of ∼ 18,500 lipids was used to identify classes of lipids that preferentially bind YndB. The in silico screen identified, in order of affinity, the chalcone/hydroxychalcone, flavanone, and flavone/flavonol classes of lipids, which was further verified by 2D (1) H-(15) N HSQC NMR titration experiments with trans-chalcone, flavanone, flavone, and flavonol. All of these compounds are typically found in plants as precursors to various flavonoid antibiotics and signaling molecules. The sum of the data suggests an involvement of YndB with the stress response of B. subtilis to chalcone-like flavonoids released by plants due to a pathogen infection. The observed binding of chalcone-like molecules by YndB is likely related to the symbiotic relationship between B. subtilis and plants.

Caloric Restriction Engages Hepatic RNA Processing Mechanisms in Rhesus Monkeys.

Caloric restriction (CR) extends lifespan and delays the onset of age-related disorders in diverse species. Metabolic regulatory pathways have been implicated in the mechanisms of CR, but the molecular details have not been elucidated. Here, we show that CR engages RNA processing of genes associated with a highly integrated reprogramming of hepatic metabolism. We conducted molecular profiling of liver biopsies collected from adult male rhesus monkeys (Macaca mulatta) at baseline and after 2 years on control or CR (30% restricted) diet. Quantitation of over 20,000 molecules from the hepatic transcriptome, proteome, and metabolome indicated that metabolism and RNA processing are major features of the response to CR. Predictive models identified lipid, branched-chain amino acid, and short-chain carbon metabolic pathways, with alternate transcript use for over half of the genes in the CR network. We conclude that RNA-based mechanisms are central to the CR response and integral in metabolic reprogramming.

Conserved Lipid and Small-Molecule Modulation of COQ8 Reveals Regulation of the Ancient Kinase-like UbiB Family.

Human COQ8A (ADCK3) and Saccharomyces cerevisiae Coq8p (collectively COQ8) are UbiB family proteins essential for mitochondrial coenzyme Q (CoQ) biosynthesis. However, the biochemical activity of COQ8 and its direct role in CoQ production remain unclear, in part due to lack of known endogenous regulators of COQ8 function and of effective small molecules for probing its activity in vivo. Here, we demonstrate that COQ8 possesses evolutionarily conserved ATPase activity that is activated by binding to membranes containing cardiolipin and by phenolic compounds that resemble CoQ pathway intermediates. We further create an analog-sensitive version of Coq8p and reveal that acute chemical inhibition of its endogenous activity in yeast is sufficient to cause respiratory deficiency concomitant with CoQ depletion. Collectively, this work defines lipid and small-molecule modulators of an ancient family of atypical kinase-like proteins and establishes a chemical genetic system for further exploring the mechanistic role of COQ8 in CoQ biosynthesis.

Identification of low-molecular-weight compounds inhibiting growth of corynebacteria: potential lead compounds for antibiotics.

The bacterial genus Corynebacteria contains several pathogenic species that cause diseases such as diphtheria in humans and "cheesy gland" in goats and sheep. Thus, identifying new therapeutic targets to treat Corynebacteria infections is both medically and economically important. CG2496, a functionally uncharacterized protein from Corynebacterium glutamicum, was evaluated using an NMR ligand-affinity screen. A total of 11 compounds from a library of 460 biologically active compounds were shown to selectively bind CG2496 in a highly conserved region of the protein. The best binder was identified to be methiothepin (KD =54 ± 19 µM), an FDA-approved serotonin receptor antagonist. Methiothepin was also shown to inhibit the growth of C. glutamicum, but not bacteria that lack CG2496 homologs. Our results suggest that CG2496 is a novel therapeutic target and methiothepin is a potential lead compound or structural scaffold for developing new antibiotics specifically targeting Corynebacteria.

Searching the protein structure database for ligand-binding site similarities using CPASS v.2.

BACKGROUND: A recent analysis of protein sequences deposited in the NCBI RefSeq database indicates that ~8.5 million protein sequences are encoded in prokaryotic and eukaryotic genomes, where ~30% are explicitly annotated as "hypothetical" or "uncharacterized" protein. Our Comparison of Protein Active-Site Structures (CPASS v.2) database and software compares the sequence and structural characteristics of experimentally determined ligand binding sites to infer a functional relationship in the absence of global sequence or structure similarity. CPASS is an important component of our Functional Annotation Screening Technology by NMR (FAST-NMR) protocol and has been successfully applied to aid the annotation of a number of proteins of unknown function. FINDINGS: We report a major upgrade to our CPASS software and database that significantly improves its broad utility. CPASS v.2 is designed with a layered architecture to increase flexibility and portability that also enables job distribution over the Open Science Grid (OSG) to increase speed. Similarly, the CPASS interface was enhanced to provide more user flexibility in submitting a CPASS query. CPASS v.2 now allows for both automatic and manual definition of ligand-binding sites and permits pair-wise, one versus all, one versus list, or list versus list comparisons. Solvent accessible surface area, ligand root-mean square difference, and Cß distances have been incorporated into the CPASS similarity function to improve the quality of the results. The CPASS database has also been updated. CONCLUSIONS: CPASS v.2 is more than an order of magnitude faster than the original implementation, and allows for multiple simultaneous job submissions. Similarly, the CPASS database of ligand-defined binding sites has increased in size by ~ 38%, dramatically increasing the likelihood of a positive search result. The modification to the CPASS similarity function is effective in reducing CPASS similarity scores for false positives by ~30%, while leaving true positives unaffected. Importantly, receiver operating characteristics (ROC) curves demonstrate the high correlation between CPASS similarity scores and an accurate functional assignment. As indicated by distribution curves, scores ≥ 30% infer a functional similarity. Software URL: http://cpass.unl.edu.

PROFESS: a PROtein function, evolution, structure and sequence database.

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are approximately 1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein-protein interaction networks. Database URL: http://cse.unl.edu/~profess/