Bo He

Predicting intrinsic disorder in proteins: an overview

The discovery of intrinsically disordered proteins (IDP) (i.e., biologically active proteins that do not possess stable secondary and/or tertiary structures) came as an unexpected surprise, as the existence of such proteins is in contradiction to the traditional “sequence→structure→function” paradigm. Accurate prediction of a proteins predisposition to be intrinsically disordered is a necessary prerequisite for the further understanding of principles and mechanisms of protein folding and function, and is a key for the elaboration of a new structural and functional hierarchy of proteins. Therefore, prediction of IDPs has attracted the attention of many researchers, and a number of prediction tools have been developed. Predictions of disorder, in turn, are playing major roles in directing laboratory experiments that are leading to the discovery of ever more disordered proteins, and thereby leading to a positive feedback loop in the investigation of these proteins. In this review of algorithms for intrinsic disorder prediction, the basic concepts of various prediction methods for IDPs are summarized, the strengths and shortcomings of many of the methods are analyzed, and the difficulties and directions of future development of IDP prediction techniques are discussed.

Analysis method of epigenetic DNA methylation to dynamically investigate the functional activity of transcription factors in gene expression

BackgroundDNA methylation is a fundamental component of epigenetic modification, which is intimately involved in the regulation of gene expression. One important DNA methylation pathway reduces the abilities of transcription factors to bind to gene promoter regions. Although many experiments have been designed to measure genome-wide DNA methylation levels at high resolution, the meaning of these different DNA methylation levels on transcription factor binding abilities remains poorly understood. We have, therefore, developed a method to quantitatively explore the extent to which DNA methylation levels can significantly reduce or even abolish the binding of certain transcription factors, resulting in reduced or non-expression of flanking genes. This method allows transcription factors that are functionally active in gene expression to be investigated.ResultsThe method is based on a general model that depicts the relationship between DNA methylation and transcription factor binding ability based on intrinsic component properties, and the model parameters can be optimized through relative analysis of recognized transcription factor binding status and gene expression profiling. With fixed models, transcription factors functionally active in the regulation of gene expression and affected by epigenetic DNA methylation can be identified and subsequently confirmed. The method identified eleven apparently functionally active transcriptional factors in SH-SY5Y neuroblastoma cells.ConclusionsCompared with gene regulatory elements, epigenetic modifications are able to change to dynamically respond to signals from physical, biological and social environments. Our proposed method is therefore designed to provide a dynamic assessment to investigate functionally active transcription factors. With the information deduced from our method, we can predict transcription factor binding status in promoter regions to further investigate how a particular gene is regulated by a specific group of transcription factors organized in a particular pattern. This will be helpful in the diagnosis and development of treatment for numerous diseases, including cancer. Although the method only investigates DNA methylation, it has the potential to be applied to more epigenetic factors, such as histone modification.

Lipopolysaccharide treatment induces genome-wide pre-mRNA splicing pattern changes in mouse bone marrow stromal stem cells.

BackgroundLipopolysaccharide (LPS) is a gram-negative bacterial antigen that triggers a series of cellular responses. LPS pre-conditioning was previously shown to improve the therapeutic efficacy of bone marrow stromal cells/bone-marrow derived mesenchymal stem cells (BMSCs) for repairing ischemic, injured tissue.ResultsIn this study, we systematically evaluated the effects of LPS treatment on genome-wide splicing pattern changes in mouse BMSCs by comparing transcriptome sequencing data from control vs. LPS-treated samples, revealing 197 exons whose BMSC splicing patterns were altered by LPS. Functional analysis of these alternatively spliced genes demonstrated significant enrichment of phosphoproteins, zinc finger proteins, and proteins undergoing acetylation. Additional bioinformatics analysis strongly suggest that LPS-induced alternatively spliced exons could have major effects on protein functions by disrupting key protein functional domains, protein-protein interactions, and post-translational modifications.ConclusionAlthough it is still to be determined whether such proteome modifications improve BMSC therapeutic efficacy, our comprehensive splicing characterizations provide greater understanding of the intracellular mechanisms that underlie the therapeutic potential of BMSCs.

ResSeq: enhancing short-read sequencing alignment by rescuing error-containing reads

Next-generation short-read sequencing is widely utilized in genomic studies. Biological applications require an alignment step to map sequencing reads to the reference genome, before acquiring expected genomic information. This requirement makes alignment accuracy a key factor for effective biological interpretation. Normally, when accounting for measurement errors and single nucleotide polymorphisms, short read mappings with a few mismatches are generally considered acceptable. However, to further improve the efficiency of short-read sequencing alignment, we propose a method to retrieve additional reliably aligned reads (reads with more than a pre-defined number of mismatches), using a Bayesian-based approach. In this method, we first retrieve the sequence context around the mismatched nucleotides within the already aligned reads; these loci contain the genomic features where sequencing errors occur. Then, using the derived pattern, we evaluate the remaining (typically discarded) reads with more than the allowed number of mismatches, and calculate a score that represents the probability that a specific alignment is correct. This strategy allows the extraction of more reliably aligned reads, therefore improving alignment sensitivity. Implementation: The source code of our tool, ResSeq, can be downloaded from: https://github.com/hrbeubiocenter/Resseq.

Using Integrated Bioinformatics Strategy to Identify Critical Factors for the Structural Integrity of Salmonella T3SS

Type-III secretion system of Gram-negative bacteria is the major molecular machine responsible for the infection of host cells and the in-host survival of the bacteria. The T3SS is composed of three structural components: basal body, needle, and export apparatus. The needle is an extracellular protein complex that recognizes host cells and transport bacterial effector proteins into the host cells. The basal body forms a channel across the bacterial membranes and also provides structural support to the needle. The export apparatus selects effector proteins and initiates the transportation of these proteins. Since all these three structural components are formed by specific proteins, abolishing the interaction of these proteins will disrupt the structural integrity of one or more structural components of T3SS, and eventually affect the infection and/or virulence of the bacteria. In this study, we analyzed the sequential, structural, and interactomic features of Salmonella T3SS structural proteins. We found that these structural proteins have abundant short and/or long disordered regions that overlap with other structured/functional regions. We identified critical interaction patterns and hub proteins SipB, SpaO, and SpaS, in the interactome of T3SS structural proteins. We also predicted novel binding motifs for six T3SS structural proteins of which the interaction partners are unknown. These results are expected to shed light on future studies in the fields of T3SS structural integrity and drug discovery. *Corresponding author: Bin Xue, Department of Cell Biology, Microbiology, and Molecular Biology, College of Arts and Sciences, University of South Florida, 4202 E. Fowler Ave, ISA 2015, Tampa, FL, USA 33620, Tel: (813) 974-6007; E-mail: binxue@ usf.edu Received date: January 18, 2016 Accepted date: January 29, 2016 Published date: February 03, 2016

Research on preprocessing of DNase signal in DNAprotein binding sites detection

There are many functional proteins in human cells, which bind in genes and regulate their expression. Accurately identifying binding sites of these proteins is an important and difficult job in life science. To solve this problem, a new DNA protein binding site detection technology, named DNase-Seq emerges. DNase-Seq has great advantages, but its detection signal is quite complex, which makes accurately identifying DNA protein binding site a challenging task. We in-depth analyze DNase-Seq signals and propose a novel preprocessing method. The corresponding experiment proves validity of the method.

Predicting intrinsically disordered proteins based on multi-scale characteristics fusion

Due to the importance of functions, it has already become a hotter and hotter topic to predict intrinsically disordered regions in proteins. To consider the information from long and short disordered regions simultaneously and accurately predict both of the two regions, a new method based on multi-scale characteristics fusion was proposed in this article. First, characteristics based on different scales were extracted from amino acid sequences and used to build several basic models by SVM. Then the Q-statistics method was introduced to measure the diversity among all basic models. The basic models with the larger diversity were chose out and built the integrated predictor. Finally, majority voting method was used to make decision fusion and output the final predicting results. Subsequent simulation suggests that the proposed method can consider the information from the long and short disordered regions simultaneously and get a good predicting accuracy for IDPs, especially short disordered regions.

A general model to analyze DNA methylation effect on transcription factors binding ability

DNA methylation is identified as an elaborate epigenetic element to regulate binding of transcription factor to gene promoter region. With latest highthroughput technology, it is convenient to accurately test methylation level in experiment, which opens a door to investigate how methylation affects transcription factor. In this study, a general model is presented to depict methylation function, where parameters of model are fixed by analysis of relativity between transcription factor binding scores in promoter regions and gene expression levels. In neuroblastoma cell, with the deduced model, 10 transcriptional factors were found to be apparently affected by methylation of promoter regions.