[The Applications of qNMR in Drug Quality Control].

NMR is well known as one of the most important methods for elucidating the structure of organic compounds. Furthermore, it has recently been recognized as a powerful tool for quantitative analysis. The quantitative NMR (qNMR) has become an official analytical method described in detail in the Japanese Pharmacopoeia. And today, it is widely applied in drug development. The qNMR method offers many new advantages over traditional and conventional quantitative analysis methods. For example, this method requires only a few milligrams of the analyte and allows absolute quantitation of the analyte without using a qualified reference standard as a control sample. Then, it can be easily applied to most chemicals without expending significant time and resources on method development. In addition, residual solvent can be determined using qNMR methods. The peak area of an NMR spectrum is directly proportional to the number of protons contributing to the resonance. Based on this principle, the residual solvent can be determined by counting the signal corresponding to the residual solvent in the sample solution. We have applied qNMR as an alternative to GC. Thus, qNMR is an innovative and promising analytical technique that is expected to make significant progress in the future. Recently, the analytical research and quality control departments have been working together to expand this technology to a wide range of areas in the pharmaceutical industry.

Cloning of the cycloisomaltotetraose-forming enzymes using whole genome sequence analyses of Agreia sp. D1110 and Microbacterium trichothecenolyticum D2006.

We performed whole genome sequence analyses of Agreia sp. D1110 and Microbacterium trichothecenolyticum D2006 that secrete enzymes to produce cyclo-{→6)-α-d-Glcp-(1→6)-α-d-Glcp-(1→6)-α-d-Glcp-(1→6)-α-d-Glcp-(1→} (CI4) from dextran. Full-length amino acid sequences of CI4-forming enzymes were identified by matching known N-terminal amino acid sequences with products of the draft genome. Domain searches revealed that the CI4-forming enzymes are composed of Glycoside Hydrolase family 66 (GH66) domain, Carbohydrate Binding Module family 35 (CBM35) domain, and CBM13 domain, categorizing the CI4-forming enzymes in the GH66. Furthermore, the amino acid sequences of the two CI4-forming enzymes were 71% similar to each other and up to 51% similar to cycloisomaltooligosaccharide glucanotransferases (CITases) categorized in GH66. Differences in sequence between the CI4-forming enzymes and the CITases suggest mechanisms to produce specific cycloisomaltooligosaccharides, and whole genome sequence analyses identified a gene cluster whose gene products likely work in concert with the CI4-forming enzymes.

Targeted enzyme gene re-positioning: A computational approach for discovering alternative bacterial enzymes for the synthesis of plant-specific secondary metabolites.

Plant-biosynthesised secondary metabolites are unique sources of pharmaceuticals, food additives, and flavourings, among other industrial uses. However, industrial production of these metabolites is difficult because of their structural complexity, dangerousness and unfriendliness to natural environment, so the development of new methods to synthesise them is required. In this study, we developed a novel approach to identifying alternative bacterial enzyme to produce plant-biosynthesised secondary metabolites. Based on the similarity of enzymatic reactions, we searched for candidate bacterial genes encoding enzymes that could potentially replace the enzymes in plant-specific secondary metabolism reactions that are contained in the KEGG database (enzyme re-positioning). As a result, we discovered candidate bacterial alternative enzyme genes for 447 plant-specific secondary metabolic reaction. To validate our approach, we focused on the ability of an enzyme from Streptomyces coelicolor strain A3(2) strain to convert valencene to the grapefruit metabolite nootkatone, and confirmed its enzymatic activity by gas chromatography-mass spectrometry. This enzyme re-positioning approach may offer an entirely new way of screening enzymes that cannot be achieved by most of other conventional methods, and it is applicable to various other metabolites and may enable microbial production of compounds that are currently difficult to produce industrially.

Cloning and starch degradation profile of maltotriose-producing amylases from Streptomyces species.

The end products from starch hydrolysis by amylases have important applications in various industries. Here, two amylases derived from two Streptomyces species that hydrolyze soluble starch from potato produced maltotriose as the primary maltooligosaccharide product. The genes, annotated as putative glycoside hydrolases, were cloned and expressed in Streptomyces lividans. These amylases displayed hydrolysis activity from pH 3 to 9.5 and were not affected by Ca(2+.) Optimal production of maltotriose was between 20 and 30 °C at pH 6.5. At the optimal temperature, both amylases produced maltotriose-rich end products rather than either maltose or maltotetraose.

ESPRESSO: a system for estimating protein expression and solubility in protein expression systems.

Recombinant protein technology is essential for conducting protein science and using proteins as materials in pharmaceutical or industrial applications. Although obtaining soluble proteins is still a major experimental obstacle, knowledge about protein expression/solubility under standard conditions may increase the efficiency and reduce the cost of proteomics studies. In this study, we present a computational approach to estimate the probability of protein expression and solubility for two different protein expression systems: in vivo Escherichia coli and wheat germ cell-free, from only the sequence information. It implements two kinds of methods: a sequence/predicted structural property-based method that uses both the sequence and predicted structural features, and a sequence pattern-based method that utilizes the occurrence frequencies of sequence patterns. In the benchmark test, the proposed methods obtained F-scores of around 70%, and outperformed publicly available servers. Applying the proposed methods to genomic data revealed that proteins associated with translation or transcription have a strong tendency to be expressed as soluble proteins by the in vivo E. coli expression system. The sequence pattern-based method also has the potential to indicate a candidate region for modification, to increase protein solubility. All methods are available for free at the ESPRESSO server (http://mbs.cbrc.jp/ESPRESSO).

Statistical analysis of features associated with protein expression/solubility in an in vivo Escherichia coli expression system and a wheat germ cell-free expression system.

Recombinant protein technology is an important tool in many industrial and pharmacological applications. Although the success rate of obtaining soluble proteins is relatively low, knowledge of protein expression/solubility under 'standard' conditions may increase the efficiency and reduce the cost of proteomics studies. In this study, we conducted a genome-scale experiment to assess the overexpression and the solubility of human full-length cDNA in an in vivo Escherichia coli expression system and a wheat germ cell-free expression system. We evaluated the influences of sequence and structural features on protein expression/solubility in each system and estimated a minimal set of features associated with them. A comparison of the feature sets related to protein expression/solubility in the in vivo Escherichia coli expression system revealed that the structural information was strongly associated with protein expression, rather than protein solubility. Moreover, a significant difference was found in the number of features associated with protein solubility in the two expression systems.

Development and evaluation of data-driven designed tags (DDTs) for controlling protein solubility.

Production of proteins is an important issue in protein science and pharmaceutical studies. Numerous protein expression systems using living cells and cell-free methods have been developed to date. In these systems, a promising strategy for improving the success rate of obtaining soluble proteins is the attachment of various tags into target proteins based on empirical rules. This paper presents a method for the production of data-driven designed tags (DDTs) based on highly frequent sequence property patterns in an experimentally assessed protein solubility dataset in a wheat germ cell-free system. We constructed seven proteins combined with 12 kinds of DDTs (six for enhancing solubility and six for insolubility) at the N-terminal region as tags. Then we investigated their behavior using SDS-PAGE. Results show that three and four proteins respectively showed a trend toward solubilization and insolubilization, which indicates the possibility that the theoretically designed sequence can control protein solubility.

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction.

BACKGROUND: Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information. RESULTS: In this work, we present a novel method for predicting two kinds of protein motions in ordered structures. The prediction uses only information from the amino acid sequence. We prepared a dataset of the internal and external motions of segments in many proteins by application of NMA. Subsequently, we analyzed the relation between thermal motion assessed from X-ray crystallographic B-factor and internal/external motions calculated by NMA. Results show that attributes of amino acids related to the internal motion have different features from those related to the B-factors, although those related to the external motion are correlated strongly with the B-factors. Next, we developed a method to predict internal and external motions from amino acid sequences based on the Random Forest algorithm. The proposed method uses information associated with adjacent amino acid residues and secondary structures predicted from the amino acid sequence. The proposed method exhibited moderate correlation between predicted internal and external motions with those calculated by NMA. It has the highest prediction accuracy compared to a naïve model and three published predictors. CONCLUSIONS: Finally, we applied the proposed method predicting the internal motion to a set of 20 proteins that undergo large conformational change upon protein-protein interaction. Results show significant overlaps between the predicted high internal motion regions and the observed conformational change regions.

POODLE-I: disordered region prediction by integrating POODLE series and structural information predictors based on a workflow approach.

Under physiological conditions, many proteins that include a region lacking well-defined three-dimensional structures have been identified, especially in eukaryotes. These regions often play an important biological cellular role, although they cannot form a stable structure. Therefore, they are biologically remarkable phenomena. From an industrial perspective, they can provide useful information for determining three-dimensional structures or designing drugs. For these reasons, disordered regions have attracted a great deal of attention in recent years. Their accurate prediction is therefore anticipated to provide annotations that are useful for wide range of applications. POODLE-I (where "I" stands for integration) is a web-based disordered region prediction system. POODLE-I integrates prediction results obtained from three kinds of disordered region predictors (POODLEs) developed from the viewpoint that the characteristics of disordered regions change according to their length. Furthermore, POODLE-I combines that information with predicted structural information by application of a workflow approach. When compared with server teams that showed best performance in CASP8, POODLE-I ranked among the top and exhibited the highest performance in predicting unfolded proteins. POODLE-I is an efficient tool for detecting disordered regions in proteins solely from the amino acid sequence. The application is freely available at http://mbs.cbrc.jp/poodle/poodle-i.html.

POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions.

MOTIVATION: Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes, such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high-throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications. RESULTS: We developed Prediction Of Order and Disorder by machine LEarning (POODLE-L; L stands for long), the Support Vector Machines (SVMs) based method for predicting long disordered regions using 10 kinds of simple physico-chemical properties of amino acid. POODLE-L assembles the output of 10 two-level SVM predictors into a final prediction of disordered regions. The performance of POODLE-L for predicting long disordered regions, which exhibited a Matthew's correlation coefficient of 0.658, was the highest when compared with eight well-established publicly available disordered region predictors. AVAILABILITY: POODLE-L is freely available at http://mbs.cbrc.jp/poodle/poodle-l.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.

UNLABELLED: Protein disorder is characterized by a lack of a stable 3D structure, and is considered to be involved in a number of important protein functions such as regulatory and signalling events. We developed a web application, the POODLE-S, which predicts the disordered region from amino acid sequences by using physicochemical features and reduced amino acid set of a position-specific scoring matrix. AVAILABILITY: POODLE-S is available from http://mbs.cbrc.jp/poodle/poodle-s.html and can be used by both academic and commercial users.

Predicting mostly disordered proteins by using structure-unknown protein data.

BACKGROUND: Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences. RESULTS: When the proposed method was evaluated on data that included 82 disordered proteins and 526 ordered proteins, its sensitivity was 0.723 and its specificity was 0.977. It resulted in a Matthews correlation coefficient 0.202 points higher than that obtained using FoldIndex, 0.221 points higher than that obtained using the method based on plotting hydrophobicity against the number of contacts and 0.07 points higher than that obtained using support vector machines (SVMs). To examine robustness against training data sparseness, we investigated the correlation between two results obtained when the method was trained on different datasets and tested on the same dataset. The correlation coefficient for the proposed method is 0.14 higher than that for the method using SVMs. When the proposed SGT-based method was compared with four per-residue predictors (VL3, GlobPlot, DISOPRED2 and IUPred (long)), its sensitivity was 0.834 for disordered proteins, which is 0.052-0.523 higher than that of the per-residue predictors, and its specificity was 0.991 for ordered proteins, which is 0.036-0.153 higher than that of the per-residue predictors. The proposed method was also evaluated on data that included 417 partially disordered proteins. It predicted the frequency of disordered proteins to be 1.95% for the proteins with 5%-10% disordered sequences, 1.46% for the proteins with 10%-20% disordered sequences and 16.57% for proteins with 20%-40% disordered sequences. CONCLUSION: The proposed method, which utilizes the information of structure-unknown data, predicts disordered proteins more accurately than other methods and is less affected by training data sparseness.