Capacities of computer evaluation of hidden potential of phytochemicals of medicinal plants of the traditional Indian Ayurvedic medicine

Applicability of our computer programs PASS and PharmaExpert for prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both separately and in combinations has been evaluated. For this purpose we have created the web-resource containing known information about structural formulas and biological activity of 1906 phytocomponents of 50 Ayurvedic medicinal plants used in Traditional Indian Medicine (TIM) (http://ayurveda.pharmaexpert.ru). The PASS training set was updated by addition of information about structure and biological activity of 946 natural compounds; then the training procedure and validation were performed, to estimate the quality of PASS prediction. It was shown that the differences between the average accuracy of prediction obtained in leave-5%-out cross-validation (94.467%) and in leave-one-out cross-validation (94.605%) are very small thus demonstrating high predictive ability of the program. Results of biological activity spectra prediction for all phytocomponents included in our database coincided in 83.5% of cases with known experimental data. Additional types of biological activity predicted with high probability indicate further promising directions for further studies of certain phytocomponents of some medicinal plants. The analysis of prediction results of sets of phytocomponents in each of 50 medicinal plants was made by the PharmaExpert software. Based on this analysis, we found that the combination of phytocomponents from Passiflora incarnata may exhibit nootropic, anticonvulsant, and antidepressant effects. Experiments carried out in mice models confirmed the predicted effects of P. incarnata extracts.     

The program Proteocat as a tool for planning of proteomic experiments

ProteoCat is a computer program that has been designed to help researchers in the planning of large-scale proteomic experiments. The central part of this program is the unit of hydrolysis simulation that supports 4 proteases (trypsin, lysine C, endoproteinases Asp-N and GluC). For peptides obtained after virtual hydrolysis or loaded from data files a number of properties important in mass-spectrometric experiments can be calculated and predicted; the resultant data can be analyzed or filtered (to reduce a set of peptides). The program is using new and improved modifications of own earlier developed methods for pI prediction, which can be also predicted by means of popular pKa scales proposed by other reseachers. The algorithm for prediction of peptide retention time has been realized similarly to the algorithm used in the SSRCalc program. Using ProteoCat it is possible to estimate the coverage of amino acid sequences of analyzed proteins under defined limitation on peptides detection, as well as the possibility of assembly of peptide fragments with user-defined minimal sizes of “sticky” ends. The program has a graphical user interface, written on JAVA and available at http://www.ibmc.msk.ru/LPCIT/ProteoCat.     

Prediction of linear B-cell epitopes

The use of antigenicity scales based on physicochemical properties and the sliding window method in combination with an averaging algorithm and subsequent search for the maximum value is the classical method for B-cell epitope prediction. However, recent studies have demonstrated that the best classical methods provide a poor correlation with experimental data. We review both classical and novel algorithms and present our own implementation of the algorithms. The AAPPred software is available at http://www.bioinf.ru/aappred/.     

Monitoring proteolytic processing events by quantitative mass spectrometry

Introduction: Protease activity plays a key role in a wide variety of biological processes including gene expression, protein turnover and development. misregulation of these proteins has been associated with many cancer types such as prostate, breast, and skin cancer. thus, the identification of protease substrates will provide key information to understand proteolysis-related pathologies.

Areas covered: Proteomics-based methods to investigate proteolysis activity, focusing on substrate identification, protease specificity and their applications in systems biology are reviewed. Their quantification strategies, challenges and pitfalls are underlined and the biological implications of protease malfunction are highlighted.

Expert commentary: Dysregulated protease activity is a hallmark for some disease pathologies such as cancer. Current biochemical approaches are low throughput and some are limited by the amount of sample required to obtain reliable results. Mass spectrometry based proteomics provides a suitable platform to investigate protease activity, providing information about substrate specificity and mapping cleavage sites.     

Classification of the Adenylation and Acyl-Transferase Activity of NRPS and PKS Systems Using Ensembles of Substrate Specific Hidden Markov Models

There is a growing interest in the Non-ribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) of microbes, fungi and plants because they can produce bioactive peptides such as antibiotics. The ability to identify the substrate specificity of the enzyme''s adenylation (A) and acyl-transferase (AT) domains is essential to rationally deduce or engineer new products. We here report on a Hidden Markov Model (HMM)-based ensemble method to predict the substrate specificity at high quality. We collected a new reference set of experimentally validated sequences. An initial classification based on alignment and Neighbor Joining was performed in line with most of the previously published prediction methods. We then created and tested single substrate specific HMMs and found that their use improved the correct identification significantly for A as well as for AT domains. A major advantage of the use of HMMs is that it abolishes the dependency on multiple sequence alignment and residue selection that is hampering the alignment-based clustering methods. Using our models we obtained a high prediction quality for the substrate specificity of the A domains similar to two recently published tools that make use of HMMs or Support Vector Machines (NRPSsp and NRPS predictor2, respectively). Moreover, replacement of the single substrate specific HMMs by ensembles of models caused a clear increase in prediction quality. We argue that the superiority of the ensemble over the single model is caused by the way substrate specificity evolves for the studied systems. It is likely that this also holds true for other protein domains. The ensemble predictor has been implemented in a simple web-based tool that is available at http://www.cmbi.ru.nl/NRPS-PKS-substrate-predictor/.     

EDAS—A database of alternatively spliced human genes

EDAS, an alternatively spliced human gene database, contains data on alignment of proteins, mRNAs, and ESTs. For 8324 human genes, the database contains information on all observed exons and introns and also elementary alternatives formed therefrom. The database allows one to filter the output data by varying the cutoff threshold according to the significance level. The database is available at http://www.genebee.msu.ru/edas/.     

Habitat use and tree selection of a declining Afro-Palaearctic migrant at sub-Saharan staging and wintering sites

Capsule: Wood Warblers Phylloscopus sibilatrix showed significant selection for tree species and woodland characteristics at staging and wintering sites in sub-Saharan Africa.

Aims: To investigate home range size, habitat and tree species selection of Wood Warblers at a staging site in Burkina Faso (Koubri) and a wintering site in Ghana (Pepease).

Methods: Comparing habitat recorded at locations of radio-tagged birds and at control points, we investigated whether there was habitat and tree species selection. We also compared home range size of individual birds between the two sites.

Results: Home range size did not differ between the two sites. There was significant selection for tree species at both Koubri and Pepease: Anogeissus leiocarpus and Albizia zygia, respectively. At Koubri, there was significant avoidance of the most common tree species (Azadirachta indica, Mangifera indica (both non-native), Vitellaria paradoxa and Acacia spp.). In addition, there was a preference for taller trees and greater tree density at both sites. However, the probability of a point being used declined with increasing number of taller (>14?m) trees.

Conclusion: Fine-scale selection of woodland habitats suggests that Wood Warblers are likely to suffer the consequences of ongoing land-use change in their West African wintering grounds.     

Application of proteomics in research on traditional Chinese medicine

Introduction: Traditional Chinese medicine (TCM) is a widely used complementary alternative medicine approach. Although many aspects of its effectiveness have been approved clinically, rigorous scientific techniques are highly required to translate the promises from TCM into powerful modern therapies. In this respect, proteomics is useful because of its ability to unveil the underlying target proteins and/or protein biomarkers.

Areas covered: In this review, we summarize the recent interplay between proteomics and research on TCM, ranging from exploration of the medicinal materials to the biological basis of TCM concepts, and from pathological studies to pharmacological investigations. We show that proteomic analyses provide preliminary biological evidence of the promises in TCM, and the integration of proteomics with other omics and bioinformatics offers a comprehensive methodology to address the complications of TCM.

Expert commentary: Currently, only limited information can be obtained regarding TCM issues and thus more work is required to resolve the ambiguity. As such, more collaborations between proteomics and other techniques (other omics, network pharmacology, etc.) are essential for deciphering the underlying biological basis in TCM topics.     

PASS: prediction of activity spectra for biologically active substances

The concept of the biological activity spectrum was introduced to describe the properties of biologically active substances. The PASS (prediction of activity spectra for substances) software product, which predicts more than 300 pharmacological effects and biochemical mechanisms on the basis of the structural formula of a substance, may be efficiently used to find new targets (mechanisms) for some ligands and, conversely, to reveal new ligands for some biological targets. We have developed a WWW interface for the PASS software. A WWW server for the on-line prediction of the biological activity spectra of substances has been constructed.     

Strategy in manipulating transglycosylation activity of glycosyl hydrolase for oligosaccharide production

Background: The increasing market demand for oligosaccharides has intensified the need for efficient biocatalysts. Glycosyl hydrolases (GHs) are still gaining popularity as biocatalyst for oligosaccharides synthesis owing to its simple reaction and high selectivity.

Purpose: Over the years, research has advanced mainly directing to one goal; to reduce hydrolysis activity of GHs for increased transglycosylation activity in achieving high production of oligosaccharides.

Design and methods: This review concisely presents the strategies to increase transglycosylation activity of GHs for oligosaccharides synthesis, focusing on controlling the reaction equilibrium, and protein engineering. Various modifications of the subsites of GHs have been demonstrated to significantly modulate the hydrolysis and transglycosylation activity of the enzymes. The clear insight of the roles of each amino acid in these sites provides a platform for designing an enzyme that could synthesize a specific oligosaccharide product.

Conclusions: The key strategies presented here are important for future improvement of GHs as a biocatalyst for oligosaccharide synthesis.     

Recent advances in engineering propionyl-CoA metabolism for microbial production of value-added chemicals and biofuels

Diminishing fossil fuel reserves and mounting environmental concerns associated with petrochemical manufacturing practices have generated significant interests in developing whole-cell biocatalytic systems for the production of value-added chemicals and biofuels. Although acetyl-CoA is a common natural biogenic precursor for the biosynthesis of numerous metabolites, propionyl-CoA is unpopular and non-native to most organisms. Nevertheless, with its C3-acyl moiety as a discrete building block, propionyl-CoA can serve as another key biogenic precursor to several biological products of industrial importance. As a result, engineering propionyl-CoA metabolism, particularly in genetically tractable hosts with the use of inexpensive feedstocks, has paved an avenue for novel biomanufacturing. Herein, we present a systematic review on manipulation of propionyl-CoA metabolism as well as relevant genetic and metabolic engineering strategies for microbial production of value-added chemicals and biofuels, including odd-chain alcohols and organic acids, bio(co)polymers and polyketides.

     

Glycosylation and its implications in breast cancer

Introduction: For decades, the role of glycans and glycoproteins in the progression of breast cancer and other cancers have been evaluated. Through extensive studies focused on elucidating the biological functions of glycosylation, researchers have been able to implicate alterations in these functions to tumor formation and metastasis.

Areas covered: In this review, we summarize how changes in glycosylation are associated with tumorigenesis, with emphasis on breast cancers. An overview of the changes in N-linked and O-linked glycans associated with breast cancer tumors and biofluids are described. Recent advances in glycomics are emphasized in the context of continuing to decipher the glycosylation changes associated with breast cancer progression.

Expert opinion: While changes in glycosylation have been studied in breast cancer for many years, the clinical relevance of these studies has been limited. This reflects the inherent biological and clinical heterogeneity of breast cancers. Glycomics analysis lags behind the advances in genomics and proteomics, but new approaches are emerging. A summary of known glycosylation changes associated with breast cancer is necessary to implement new findings in the context of clinical outcomes and therapeutic strategies. A better understanding of the dynamics of tumor and immune glycosylation is critical to improving emerging immunotherapeutic treatments.     

Molybdenum-containing nitrite reductases: Spectroscopic characterization and redox mechanism

Objectives: This review summarizes the spectroscopic results, which will provide useful suggestions for future research. In addition, the fields that urgently need more information are also advised.

Background: Nitrite-NO-cGMP has been considered as an important signaling pathway of NO in human cells. To date, all the four known human molybdenum-containing enzymes, xanthine oxidase, aldehyde oxidase, sulfite oxidase, and mitochondrial amidoxime-reducing component, have been shown to function as nitrite reductases under hypoxia by biochemical, cellular, or animal studies. Various spectroscopic techniques have been applied to investigate the structure and catalytic mechanism of these enzymes for more than 20 years.

Methods: We summarize the published data on the applications of UV-vis and EPR spectroscopies, and X-ray crystallography in studying nitrite reductase activity of the four human molybdenum-containing enzymes.

Results: UV-vis has provided useful information on the redox active centers of these enzymes. The utilization of EPR spectroscopy has been critical in determining the coordination and redox status of the Mo center during catalysis. Despite the lack of substrate-bound crystal structures of these nitrite reductases, valuable structural information has been obtained by X-ray crystallography.

Conclusions: To fully understand the catalytic mechanisms of these physiologically/pathologically important nitrite reductases, structural studies on substrate-redox center interaction are needed.     

Defining the proteomic landscape of rheumatoid arthritis: progress and prospective clinical applications

Introduction: The heterogeneity of Rheumatoid Arthritis (RA) and the absence of clinical tests accurate enough to identify the early stages of this disease have hampered its management. Therefore, proteomics research is increasingly focused on the discovery of novel biological markers, which would not only be able make an early diagnosis, but also to gain insight into the different pathological mechanisms underlying the heterogeneity of RA and also to stratify patients, which is critical to enabling effective treatments.

Areas covered: The proteomic approaches that have been utilised to provide knowledge about RA pathogenesis, and to identify biomarkers for RA diagnosis, prognosis, disease monitoring and prediction of response to therapy, are summarized.

Expert commentary: Although each proteomic study is unique in its design, all of them have contributed to the understanding of RA pathogenesis and the discovery of promising biomarkers for patient stratification, which would improve clinical care of RA patients. Still, efforts need to be made to validate these findings and translate them into clinical practice.     

Epigenetic biomarkers in colorectal cancer: premises and prospects

Context: Colorectal cancer is one of the most common cancers worldwide. Epigenetic alterations play an important role in the pathogenesis of the colorectal cancer.

Objective: This review has focused on the most recent investigations, which has suggested potential epigenetic biomarkers in colorectal cancer.

Methods: Evidences were achieved by searching online medical databases including Google scholar, Pubmed, Scopus and Science Direct.

Results: Extensive studies have indicated that aberrant epigenetic modifications could serve as potential biomarkers for diagnosis, prognosis and prediction of colorectal cancer.

Conclusion: Advances in aberrant epigenetic modifications can open new avenues for exploration of reliable and robust biomarkers to improve the management of CRC patients.     

Utilizing proteomics to understand and define hypertension: where are we and where do we go?

Introduction: Hypertension is a complex and multifactorial cardiovascular disorder. With different mechanisms contributing to a different extent to an individual’s blood pressure, the discovery of novel pathogenetic principles of hypertension is challenging. However, there is an urgent and unmet clinical need to improve prevention, detection, and therapy of hypertension in order to reduce the global burden associated with hypertension-related cardiovascular diseases.

Areas covered: Proteomic techniques have been applied in reductionist experimental models including angiotensin II infusion models in rodents and the spontaneously hypertensive rat in order to unravel mechanisms involved in blood pressure control and end organ damage. In humans proteomic studies mainly focus on prediction and detection of organ damage, particularly of heart failure and renal disease. While there are only few proteomic studies specifically addressing human primary hypertension, there are more data available in hypertensive disorders in pregnancy, such as preeclampsia. We will review these studies and discuss implications of proteomics on precision medicine approaches.

Expert commentary: Despite the potential of proteomic studies in hypertension there has been moderate progress in this area of research. Standardized large-scale studies are required in order to make best use of the potential that proteomics offers in hypertension and other cardiovascular diseases.     

Standardization in synthetic biology: an engineering discipline coming of age

Background: Leaping DNA read-and-write technologies, and extensive automation and miniaturization are radically transforming the field of biological experimentation by providing the tools that enable the cost-effective high-throughput required to address the enormous complexity of biological systems. However, standardization of the synthetic biology workflow has not kept abreast with dwindling technical and resource constraints, leading, for example, to the collection of multi-level and multi-omics large data sets that end up disconnected or remain under- or even unexploited.

Purpose: In this contribution, we critically evaluate the various efforts, and the (limited) success thereof, in order to introduce standards for defining, designing, assembling, characterizing, and sharing synthetic biology parts. The causes for this success or the lack thereof, as well as possible solutions to overcome these, are discussed.

Conclusion: Akin to other engineering disciplines, extensive standardization will undoubtedly speed-up and reduce the cost of bioprocess development. In this respect, further implementation of synthetic biology standards will be crucial for the field in order to redeem its promise, i.e. to enable predictable forward engineering.