Research progress in the etiology of hepatitis type E virus / 中华肝脏病杂志

Hepatitis type E virus (HEV) is a significant infectious zoonotic disease that causes hepatitis E. The disease is primarily transmitted via the fecal-oral route through contaminated water or food and is transmissible between species and genera. The causative agent for the disease is the hepatitis type E virus, which is a member of the Hepadnaviridae family and a single-stranded RNA virus. Its 7.2 kb genome mainly contains three open reading frames (ORFs): ORF1 encodes a non-structural polyprotein that mediates viral replication and transcription; ORF2 encodes a capsid protein and free antigen that induce neutralizing antibodies; ORF3 partially overlaps with ORF2 and encodes a small multifunctional protein involved in virion formation and release. HEV has a unique dual life cycle: it is excreted into feces in the form of naked virions but circulates in the blood in the form of "quasi-enveloped" particles. The two kinds of virus particles adsorb and penetrate the host cell in distinct ways, then internalize and decapsulate to replicate the genome, thereby producing more virion and releasing it outside the cell to mediate the virus's spread. This paper reviews the morphological characteristics, genome structure, encoded proteins, and function of HEV virus-like particles in order to provide a theoretical basis for basic research and comprehensive disease prevention and control.

Advances in machine learning for predicting protein functions / 生物工程学报

Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in green synthesis has been of great interest, but the high cost of obtaining specific functional enzymes as well as the variety of enzyme types and functions hamper their application. At present, the specific functions of proteins are mainly determined through tedious and time-consuming experimental characterization. With the rapid development of bioinformatics and sequencing technologies, the number of protein sequences that have been sequenced is much larger than those can be annotated, thus developing efficient methods for predicting protein functions becomes crucial. With the rapid development of computer technology, data-driven machine learning methods have become a promising solution to these challenges. This review provides an overview of protein function and its annotation methods as well as the development history and operation process of machine learning. In combination with the application of machine learning in the field of enzyme function prediction, we present an outlook on the future direction of efficient artificial intelligence-assisted protein function research.

Intrinsically disordered proteins (IDPs) and the impact on cell stress resistance / 生物工程学报

Intrinsically disordered proteins (IDPs) are proteins or protein regions that fail to get folded into definite three-dimensional structures but participate in various biological processes and perform specific functions. Defying the traditional protein "sequence-structure-function" paradigm, they enrich the protein "structure-function" diversity. Ubiquitous in organisms, they show extreme hydrophilicity, charged amino acids, and highly repetitive amino acid sequences, with simple arrangement. As a result, they feature highly variable binding affinities and high coordination, which facilitate their functions. IDPs play an important role in cell stress response, which can improve the tolerance to a variety of stresses, such as freezing, high salt, heat shock, and desiccation. In this study, we briefed the characteristics, classifications, and identification of IDPs, summarized the molecular mechanism in improving cell stress resistance, and described the potential applications.

Heritable genomic diversity in breast cancer driver genes and associations with risk in a Chilean population

BACKGROUND: Driver mutations are the genetic components responsible for tumor initiation and progression. These variants, which may be inherited, influence cancer risk and therefore underlie many familial cancers. The present study examines the potential association between SNPs in driver genes SF3B1 (rs4685), TBX3 (rs12366395, rs8853, and rs1061651) and MAP3K1 (rs72758040) and BC in BRCA1/2-negative Chilean families. METHODS: The SNPs were genotyped in 486 BC cases and 1258 controls by TaqMan Assay. RESULTS: Our data do not support an association between rs4685:C > T, rs8853:T > C, or rs1061651:T > C and BC risk. However, the rs12366395-G allele (A/G + G/G) was associated with risk in families with a strong history of BC (OR = 1.2 [95% CI 1.0-1.6] p = 0.02 and OR = 1.5 [95% CI 1.0-2.2] p = 0.02, respectively). Moreover, rs72758040-C was associated with increased risk in cases with a moderate-to-strong family history of BC (OR = 1.3 [95% CI 1.0-1.7] p = 0.02 and OR = 1.3 [95% CI 1.0-1.8] p = 0.03 respectively). Finally, risk was significantly higher in homozygous C/C cases from families with a moderate-to-strong BC history (OR = 1.8 [95% CI 1.0-3.1] p = 0.03 and OR = 1.9 [95% CI 1.1-3.4] p = 0.01, respectively). We also evaluated the combined impact of rs12366395-G and rs72758040-C. Familial BC risk increased in a dose-dependent manner with risk allele count, reflecting an additive effect (p-trend = 0.0002). CONCLUSIONS: Our study suggests that germline variants in driver genes TBX3 (rs12366395) and MAP3K1 (rs72758040) may influence BC risk in BRCA1/2-negative Chilean families. Moreover, the presence of rs12366395-G and rs72758040-C could increase BC risk in a Chilean population.

Expression of Mammalian BM88/CEND1 in Drosophila Affects Nervous System Development by Interfering with Precursor Cell Formation / 神经科学通报·英文版

We used Drosophila melanogaster as an experimental model to express mouse and pig BM88/CEND1 (cell cycle exit and neuronal differentiation 1) in order to investigate its potential functional effects on Drosophila neurogenesis. BM88/CEND1 is a neuron-specific protein whose function is implicated in triggering cells to exit from the cell cycle and differentiate towards a neuronal phenotype. Transgenic flies expressing either mouse or pig BM88/CEND1 in the nervous system had severe neuronal phenotypes with variable expressivity at various stages of embryonic development. In early embryonic stage 10, BM88/CEND1 expression led to an increase in the neural-specific antigenicity of neuroectoderm at the expense of precursor cells [neuroblasts (Nbs) and ganglion mother cells (GMCs)] including the defective formation and differentiation of the MP2 precursors, whereas at later stages (12-15), protein accumulation induced gross morphological defects primarily in the CNS accompanied by a reduction of Nb and GMC markers. Furthermore, the neuronal precursor cells of embryos expressing BM88/CEND1 failed to carry out proper cell-cycle progression as revealed by the disorganized expression patterns of specific cell-cycle markers. BM88/CEND1 accumulation in the Drosophila eye affected normal eye disc development by disrupting the ommatidia. Finally, we demonstrated that expression of BM88/CEND1 modified/reduced the levels of activated MAP kinase indicating a functional effect of BM88/CEND1 on the MAPK signaling pathway. Our findings suggest that the expression of mammalian BM88/CEND1 in Drosophila exerts specific functional effects associated with neuronal precursor cell formation during embryonic neurogenesis and proper eye disc development. This study also validates the use of Drosophila as a powerful model system in which to investigate gene function and the underlying molecular mechanisms.

In Silico Characterization of Meloidogyne Genus Nematode Cellulose Binding Proteins

Abstract Root-knot nematodes are a group of endoparasites species that induce the formation of giant cells in the hosts, by which they guarantee their feeding and development. Meloidogyne species infect over 2000 plant species, and are highly destructive, causing damage to many crops around the world. M. enterolobii is considered the most aggressive species in tropical regions, such as Africa and South America. Phytonematodes are able to penetrate and migrate within plant tissues, establishing a sophisticated interaction with their hosts through parasitism factors, which include a series of cell wall degradation enzymes and plant cell modification. Among the parasitism factors documented in the M. enterolobii species, cellulose binding protein (CBP), a nematode excretion protein that appears to be associated with the breakdown of cellulose present in the plant cell wall. In silico analysis can be of great importance for the identification, structural and functional characterization of genomic sequences, besides making possible the prediction of structures and functions of proteins. The present work characterized 12 sequences of the CBP protein of nematodes of the genus Meloidogyne present in genomic databases. The results showed that all CBP sequences had signal peptide and that, after their removal, they had an isoelectric point that characterized them as unstable in an acid medium. The values of the average hydrophilicity demonstrated the hydrophilic character of the analyzed sequences. Phylogenetic analyzes were also consistent with the taxonomic classification of the nematode species of this study. Five motifs were identified, which are present in all sequences analyzed. These results may provide theoretical grounds for future studies of plant resistance to nematode infection.

Progress in the effect of polyamines on proteins / 生物工程学报

Polyamines are a kind of aliphatic amines that exist widely in nearly all organisms. Polyamines interact with biological macromolecules through ionic interactions and hydrogen bonds, thereby they could affect the cell growth via regulating the function of macro-molecules. The impact of polyamines on nucleic acids has been thoroughly studied. However, their effects on protein structure and functions are not well established. This review summarizes the recent progress on how polyamines affect proteins, including metabolic enzymes, ion channel proteins and other important proteins. The interaction between polyamines and proteins is discussed, and the review also summarizes the challenges in studying polyamine-protein interaction as well as the potential application of these studies on the therapy of correlated diseases.

Molecular flexibility of Mycobacterium tuberculosis ribosome recycling factor and its functional consequences: An exploration involving mutants.

Internal mobility of the two domain molecule of ribosome recycling factor (RRF) is known to be important for its action. Mycobacterium tuberculosis RRF does not complement E. coli for its deficiency of RRF (in the presence of E. coli EF-G alone). Crystal structure had revealed higher rigidity of the M. tuberculosis RRF due to the presence of additional salt bridges between domains. Two inter-domain salt bridges and one between the linker region and the domain containing C-terminal residues were disrupted by appropriate mutations. Except for a C-terminal deletion mutant, all mutants showed RRF activity in E. coli when M. tuberculosis EF-G was also co-expressed. The crystal structures of the point mutants, that of the C-terminal deletion mutant and that of the protein grown in the presence of a detergent, were determined. The increased mobility resulting from the disruption of the salt bridge involving the hinge region allows the appropriate mutant to weakly complement E. coli for its deficiency of RRF even in the absence of simultaneous expression of the mycobacterial EF-G. The loss of activity of the C-terminal deletion mutant appears to be partly due to the rigidification of the molecule consequent to changes in the hinge region.

Review of Biological Network Data and Its Applications

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

Crowding, molecular volume and plasticity: An assessment involving crystallography, NMR and simulations.

The discrepancy between the X-ray and NMR structures of Mycobacterium tuberculosis peptidyl-tRNA hydrolase in relation to the functionally important plasticity of the molecule led to molecular dynamics simulations. The X-ray and the NMR studies along with the simulations indicated an inverse correlation between crowding and molecular volume. A detailed comparison of proteins for which X-ray and the NMR structures appears to confirm this correlation. In consonance with the reported results of the investigations in cellular compartments and aqueous solution, the comparison indicates that the crowding results in compaction of the molecule as well as change in its shape, which could specifically involve regions of the molecule important in function. Crowding could thus influence the action of proteins through modulation of the functionally important plasticity of the molecule.

In Silico Functional Assessment of Sequence Variations: Predicting Phenotypic Functions of Novel Variations

A multitude of protein-coding sequence variations (CVs) in the human genome have been revealed as a result of major initiatives, including the Human Variome Project, the 1000 Genomes Project, and the International Cancer Genome Consortium. This naturally has led to debate over how to accurately assess the functional consequences of CVs, because predicting the functional effects of CVs and their relevance to disease phenotypes is becoming increasingly important. This article surveys and compares variation databases and in silico prediction programs that assess the effects of CVs on protein function. We also introduce a combinatorial approach that uses machine learning algorithms to improve prediction performance.

Predicting enzyme class from protein structure using Bayesian classification

Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.

Analysis of the Relationship Between Era and YggG in E.coli by Double-promoter Expression Vector pDH2-YggG-P_(tac)-Era / 中国生物工程杂志

yggG, a Era-binding protein gene, was isolated and cloned from the E.coli genomic DNA library. Previous studies indicated that the product of yggG gene, YggG294(amino acids 1-294), strongly inhibited the growth of host bacteria and caused the death of bacteria cells. To elucidate whether Era is related to the death of bacterial cells expressed YggG294,A double promoter expression vector that can express YggG294 and Era proteins controllably in cells was constructed. Using this vector to express YggG294 and Era protein in the same E.coli cells, then analyzed the relation between YggG294 and Era. The results showed that the ratio of Era proteins to total proteins increased with the increase of induction time in E.coli cells without YggG294 expression and with little YggG294 expression;the ratio of Era proteins to total proteins seemed to be a constant level in E.coli cells overexpressing YggG294;but we could not detect any Era hydrolyzate in E.coli cells overexpressed YggG294 could not be detected. The results also showed that pre-expression of Era protein did not produce any effect on the growth inhibition of E.coli cells caused by YggG294. These results indicate that YggG294 can not hydrolyze Era protein in E.coli cells, and that YggG-Era interaction is not associated with the death of bacteria expressed YggG294. It is thus reasonable to draw a conclusion that Era is not associated with the growth inhibition of E.coli cells caused by YggG294. YggG294 inhibits the growth of bacteria by other way.

An Integrated Strategy for Functional Analysis in Large-scale Proteomic Research by Gene Ontology / 生物化学与生物物理进展

Data analysis poses a significant challenge to the large-scale proteomics studies. Based on the structured and controlled vocabularies-Gene Ontology (GO), and the GO annotation from related databases, a strategy composed of several programs and local databases is developed to identify the functional distribution and the significantly enriched functional categories of the proteomic expression profile. It would be helpful for understanding the overall functions of these identified proteins and supply the fundamental information for further bioinformatics exploration. This strategy has been successfully used in the Human Fetal Liver (HFL) proteomic research, which is available online at http://www.hupo.org.cn/GOfact/.