From nutraceutical to clinical trial: frontiers in Ganoderma development

Ganoderma spp. are medical mushrooms with various pharmacological compounds which are regarded as a nutraceutical for improving health and treating diseases. This review summarizes current progress in the studies of Gamoderma ranging from bioactive metabolites, bioactivities, production techniques to clinical trials. Traditionally, polysaccharides and ganoderic acids have been reported as the major bioactive metabolites of Ganoderma possessing anti-tumor and immunomodulation functions. Moreover, recent studies indicate that Gandoerma also exerts other bioactivities such as skin lighting, gut microbiota regulation, and anti-virus effects. However, since these medical fungi are rare in natural environment, and that the cost of cultivation of fruiting bodies is high, industrial submerged fermentation of Ganoderma mycelia promotes the development of Ganoderma by dint of an increase of biomass and bioactive metabolites used for further application. In addition, various strategies for production of different metabolites are well developed, such as gene regulation, bi-stage pH, and oxygen control. To date, Ganoderma not only has become one of the most popular nutraceuticals worldwide but also has been applied to clinical trials for advanced diseases such as breast and non-small-cell lung cancer.

     

Visualizing the knowledge structure and evolution of bioinformatics

Background

Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics.

Results

First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics.

Conclusions

The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.

     

Mono- and binuclear non-heme iron chemistry from a theoretical perspective

In this minireview, we provide an account of the current state-of-the-art developments in the area of mono- and binuclear non-heme enzymes (NHFe and NHFe₂) and the smaller NHFe₍₂₎ synthetic models, mostly from a theoretical and computational perspective. The sheer complexity, and at the same time the beauty, of the NHFe₍₂₎ world represents a challenge for experimental as well as theoretical methods. We emphasize that the concerted progress on both theoretical and experimental side is a conditio sine qua non for future understanding, exploration and utilization of the NHFe₍₂₎ systems. After briefly discussing the current challenges and advances in the computational methodology, we review the recent spectroscopic and computational studies of NHFe₍₂₎ enzymatic and inorganic systems and highlight the correlations between various experimental data (spectroscopic, kinetic, thermodynamic, electrochemical) and computations. Throughout, we attempt to keep in mind the most fascinating and attractive phenomenon in the NHFe₍₂₎ chemistry, which is the fact that despite the strong oxidative power of many reactive intermediates, the NHFe₍₂₎ enzymes perform catalysis with high selectivity. We conclude with our personal viewpoint and hope that further developments in quantum chemistry and especially in the field of multireference wave function methods are needed to have a solid theoretical basis for the NHFe₍₂₎ studies, mostly by providing benchmarking and calibration of the computationally efficient and easy-to-use DFT methods.     

An Atlas of Network Topologies Reveals Design Principles for Caenorhabditis elegans Vulval Precursor Cell Fate Patterning

The vulval precursor cell (VPC) fate patterning in Caenorhabditis elegans is a classic model experimental system for cell fate determination and patterning in development. Despite its apparent simplicity (six neighboring cells arranged in one dimension) and many experimental and computational efforts, the patterning strategy and mechanism remain controversial due to incomplete knowledge of the complex biology. Here, we carry out a comprehensive computational analysis and obtain a reservoir of all possible network topologies that are capable of VPC fate patterning under the simulation of various biological environments and regulatory rules. We identify three patterning strategies: sequential induction, morphogen gradient and lateral antagonism, depending on the features of the signal secreted from the anchor cell. The strategy of lateral antagonism, which has not been reported in previous studies of VPC patterning, employs a mutual inhibition of the 2° cell fate in neighboring cells. Robust topologies are built upon minimal topologies with basic patterning strategies and have more flexible and redundant implementations of modular functions. By simulated mutation, we find that all three strategies can reproduce experimental error patterns of mutants. We show that the topology derived by mapping currently known biochemical pathways to our model matches one of our identified functional topologies. Furthermore, our robustness analysis predicts a possible missing link related to the lateral antagonism strategy. Overall, we provide a theoretical atlas of all possible functional networks in varying environments, which may guide novel discoveries of the biological interactions in vulval development of Caenorhabditis elegans and related species.     

Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction

Background

Researchers discover LncRNA–miRNA regulatory paradigms modulate gene expression patterns and drive major cellular processes. Identification of lncRNA-miRNA interactions (LMIs) is critical to reveal the mechanism of biological processes and complicated diseases. Because conventional wet experiments are time-consuming, labor-intensive and costly, a few computational methods have been proposed to expedite the identification of lncRNA-miRNA interactions. However, little attention has been paid to fully exploit the structural and topological information of the lncRNA-miRNA interaction network.

Results

In this paper, we propose novel lncRNA-miRNA prediction methods by using graph embedding and ensemble learning. First, we calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network. Second, we adopt several graph embedding methods to learn embedded representations of lncRNAs and miRNAs from the heterogeneous network, and construct the ensemble models using two ensemble strategies. For the former, we consider individual graph embedding based models as base predictors and integrate their predictions, and develop a method, named GEEL-PI. For the latter, we construct a deep attention neural network (DANN) to integrate various graph embeddings, and present an ensemble method, named GEEL-FI. The experimental results demonstrate both GEEL-PI and GEEL-FI outperform other state-of-the-art methods. The effectiveness of two ensemble strategies is validated by further experiments. Moreover, the case studies show that GEEL-PI and GEEL-FI can find novel lncRNA-miRNA associations.

Conclusion

The study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task. In conclusion, GEEL-PI and GEEL-FI are promising for lncRNA-miRNA interaction prediction.

     

Global profiling of protein complexes: current approaches and their perspective in biomedical research

Introduction: Despite the rapid evolution of proteomic methods, protein interactions and their participation in protein complexes – an important aspect of their function – has rarely been investigated on the proteome-wide level. Disease states, such as muscular dystrophy or viral infection, are induced by interference in protein-protein interactions within complexes. The purpose of this review is to describe the current methods for global complexome analysis and to critically discuss the challenges and opportunities for the application of these methods in biomedical research.

Areas covered: We discuss advancements in experimental techniques and computational tools that facilitate profiling of the complexome. The main focus is on the separation of native protein complexes via size exclusion chromatography and gel electrophoresis, which has recently been combined with quantitative mass spectrometry, for a global protein-complex profiling. The development of this approach has been supported by advanced bioinformatics strategies and fast and sensitive mass spectrometers that have allowed the analysis of whole cell lysates. The application of this technique to biomedical research is assessed, and future directions are anticipated.

Expert commentary: The methodology is quite new, and has already shown great potential when combined with complementary methods for detection of protein complexes.     

Mathematical study on kinetics of hematopoietic stem cells – theoretical conditions for successful transplantation

Numerous haematological diseases occur due to dysfunctions during homeostasis processes of blood cell production. Haematopoietic stem cell transplantation (HSCT) is a therapeutic option for the treatment of haematological malignancy and congenital immunodeficiency. Today, HSCT is widely applied as an alternative method to bone marrow transplantation; however, HSCT can be a risky procedure because of potential side effects and complications after transplantations. Although an optimal regimen to achieve successful HSCT while maintaining quality of life is to be developed, even theoretical considerations such as the evaluations of successful engraftments and proposals of clinical management strategies have not been fully discussed yet.

In this paper, we construct and investigate mathematical models that describe the kinetics of hematopoietic stem cell self-renewal and granulopoiesis under the influence of growth factors. Moreover, we derive theoretical conditions for successful HSCT, primarily on the basis of the idea that the basic reproduction number R ₀ represents a threshold condition for a population to successfully grow in a given steady-state environment. Successful engraftment of transplanted haematopoietic stem cells (HSCs) is subsequently ensured by employing a concept of dynamical systems theory known as ‘persistence’. On the basis of the implications from the modelling study, we discuss how the conditions derived for a successful HSCT are used to link to experimental studies.     

GNNQQNY—Investigation of Early Steps during Amyloid Formation

Protein aggregation has been implicated in the pathology of several neurodegenerative diseases, and a better understanding of how it proceeds is essential for the development of therapeutic strategies. Recently, the amyloidogenic heptapeptide GNNQQNY has emerged as a molecule of choice for fundamental studies of protein aggregation. A number of experimental and computational studies have examined the structure of the GNNQQNY aggregate. Less work, however, has been aimed at understanding its aggregation pathway. In this study, we present a detailed computational analysis of such a pathway. To that end, transition path sampling Monte Carlo simulations are used to examine the dimerization process. A statistical analysis of the reaction pathways shows that the dimerization reaction proceeds via a zipping mechanism, initiated with the formation of distinct contacts at the third residue (N). Asparagine residues are found to play a key role in the early stages of aggregation. And, contrary to previous belief, it is also shown that the tyrosine terminal group is not required to stabilize the dimer. In fact, an asparagine residue leads to faster aggregation of the peptide.     

Recruiting machine learning methods for molecular simulations of proteins

Abstract

Molecular dynamics (MD) simulations are critical to understanding the movements of proteins in time. Yet, MD simulations are limited due to the availability of high-resolution protein structures, accuracy of the underlying force-field, computational expense, and difficulty in analysing big data-sets. Machine learning algorithms are now routinely used to circumvent many of these limitations and computational biophysicists are continuously making progress in developing novel applications. Here, we discuss some of these methods, varying from traditional dimensionality reduction approaches to more recent abstractions such as transfer learning and reinforcement learning, and how they have been used to deal with the challenges in MD. We conclude with the prospective issues in the application of machine learning methods in MD, to increase accuracy and efficiency of protein dynamics studies in general.     

Boosting antibody developability through rational sequence optimization

The application of monoclonal antibodies as commercial therapeutics poses substantial demands on stability and properties of an antibody. Therapeutic molecules that exhibit favorable properties increase the success rate in development. However, it is not yet fully understood how the protein sequences of an antibody translates into favorable in vitro molecule properties. In this work, computational design strategies based on heuristic sequence analysis were used to systematically modify an antibody that exhibited a tendency to precipitation in vitro. The resulting series of closely related antibodies showed improved stability as assessed by biophysical methods and long-term stability experiments. As a notable observation, expression levels also improved in comparison with the wild-type candidate. The methods employed to optimize the protein sequences, as well as the biophysical data used to determine the effect on stability under conditions commonly used in the formulation of therapeutic proteins, are described. Together, the experimental and computational data led to consistent conclusions regarding the effect of the introduced mutations. Our approach exemplifies how computational methods can be used to guide antibody optimization for increased stability.     

Boosting antibody developability through rational sequence optimization

The application of monoclonal antibodies as commercial therapeutics poses substantial demands on stability and properties of an antibody. Therapeutic molecules that exhibit favorable properties increase the success rate in development. However, it is not yet fully understood how the protein sequences of an antibody translates into favorable in vitro molecule properties. In this work, computational design strategies based on heuristic sequence analysis were used to systematically modify an antibody that exhibited a tendency to precipitation in vitro. The resulting series of closely related antibodies showed improved stability as assessed by biophysical methods and long-term stability experiments. As a notable observation, expression levels also improved in comparison with the wild-type candidate. The methods employed to optimize the protein sequences, as well as the biophysical data used to determine the effect on stability under conditions commonly used in the formulation of therapeutic proteins, are described. Together, the experimental and computational data led to consistent conclusions regarding the effect of the introduced mutations. Our approach exemplifies how computational methods can be used to guide antibody optimization for increased stability.     

Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production

Cyanobacteria are ideal metabolic engineering platforms for carbon-neutral biotechnology because they directly convert CO₂ to a range of valuable products. In this study, we present a computational assessment of biochemical production in Synechococcus sp. PCC 7002 (Synechococcus 7002), a fast growing cyanobacterium whose genome has been sequenced, and for which genetic modification methods have been developed. We evaluated the maximum theoretical yields (mol product per mol CO₂ or mol photon) of producing various chemicals under photoautotrophic and dark conditions using a genome-scale metabolic model of Synechococcus 7002. We found that the yields were lower under dark conditions, compared to photoautotrophic conditions, due to the limited amount of energy and reductant generated from glycogen. We also examined the effects of photon and CO₂ limitations on chemical production under photoautotrophic conditions. In addition, using various computational methods such as minimization of metabolic adjustment (MOMA), relative metabolic change (RELATCH), and OptORF, we identified gene-knockout mutants that are predicted to improve chemical production under photoautotrophic and/or dark anoxic conditions. These computational results are useful for metabolic engineering of cyanobacteria to synthesize value-added products.     

枯草芽孢杆菌在系统与合成生物技术中研究进展及工业应用

枯草芽孢杆菌Bacillus subtilis是微生物生理生化机理研究的模式菌株,也是工业应用生产小分子化合物、大宗化学品、工业酶、药物及保健品等生物制剂的良好底盘细胞.近些年,研究枯草芽孢杆菌的合成生物技术和代谢工程方法日新月异,为利用其作为底盘细胞生产目标产品提供了良好的工具和理论参考.文中综述了利用枯草芽孢杆菌为...     

Computational Chemistry in Lead Identification,Library Design and Lead Optimisation

Abstract

We describe a variety of the computational techniques which we use in the drug discovery and design process. Some of these computational methods are designed to support the new experimental technologies of high-throughput screening and combinatorial chemistry. We also consider some new approaches to problems of long-standing interest such as protein-ligand docking and the prediction of free energies of binding.     

Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.     

Functional group surface modifications for enhancing the formation and performance of exoelectrogenic biofilms on the anode of a bioelectrochemical system

Abstract

Various new energy technologies have been developed to reduce reliance on fossil fuels. The bioelectrochemical system (BES), an integrated microbial–electrochemical energy conversion process, is projected to be a sustainable and environmentally friendly energy technology. However, low power density is still one of the main limiting factors restricting the practical application of BESs. To enhance power output, functional group modification on anode surfaces has been primarily developed to improve the bioelectrochemical performances of BESs in terms of startup, power density, chemical oxygen demand (COD) removal and coulombic efficiency (CE). This modification could change the anode surface characteristics: roughness, hydrophobicity, biocompatibility, chemical bonding and electrochemically active surface area. This will facilitate bacterial adhesion, biofilm formation and extracellular electron transfer (EET). Additionally, some antibacterial functional groups are applied on air cathodes in order to suppress aerobic biofilms and enhance cathodic oxygen reduction reactions (ORRs). Various modification strategies such as: soaking, heat treatment and plasma modification have been reported to introduce functional groups typically as O-, N- and S-containing groups. In this review, the effects of anode functional groups on electroactive bacteria through the whole biofilm formation process are summarized. In addition, the application of those modification technologies to improve bioelectricity generation, resource recovery, bioelectrochemical analysis and the production of value-added chemicals and biofuels is also discussed. Accordingly, this review aims to help scientists select the most appropriate functional groups and up-to-date methods to improve biofilm formation.