From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

Purifying Selection in Deeply Conserved Human Enhancers Is More Consistent than in Coding Sequences

(c) 2014 De Silva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Exploring hypotheses of the actions of TGF-beta 1 in epidermal wound healing using a 3D computational multiscale model of the human epidermis

In vivo and in vitro studies give a paradoxical picture of the actions of the key regulatory factor TGF-beta 1 in epidermal wound healing with it stimulating migration of keratinocytes but also inhibiting their proliferation. To try to reconcile these into an easily visualized 3D model of wound healing amenable for experimentation by cell biologists, a multiscale model of the formation of a 3D skin epithelium was established with TGF-beta 1 literature-derived rule sets and equations embedded within it. At the cellular level, an agent-based bottom-up model that focuses on individual interacting units ( keratinocytes) was used. This was based on literature-derived rules governing keratinocyte behavior and keratinocyte/ECM interactions. The selection of these rule sets is described in detail in this paper. The agent-based model was then linked with a subcellular model of TGF-beta 1 production and its action on keratinocytes simulated with a complex pathway simulator. This multiscale model can be run at a cellular level only or at a combined cellular/subcellular level. It was then initially challenged ( by wounding) to investigate the behavior of keratinocytes in wound healing at the cellular level. To investigate the possible actions of TGF-beta 1, several hypotheses were then explored by deliberately manipulating some of these rule sets at subcellular levels. This exercise readily eliminated some hypotheses and identified a sequence of spatial-temporal actions of TGF-beta 1 for normal successful wound healing in an easy-to-follow 3D model. We suggest this multiscale model offers a valuable, easy-to-visualize aid to our understanding of the actions of this key regulator in wound healing, and provides a model that can now be used to explore pathologies of wound healing

A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions

High Diversity of the Fungal Community Structure in Naturally-Occurring Ophiocordyceps sinensis

BACKGROUND: Ophiocordyceps sinensis (syn. Cordyceps sinensis), which is a parasite of caterpillars and is endemic to alpine regions on the Tibetan Plateau, is one of the most valuable medicinal fungi in the world. "Natural O. sinensis specimens" harbor various other fungi. Several of these other fungi that have been isolated from natural O. sinensis specimens have similar chemical components and/or pharmaceutical effects as O. sinensis. Nevertheless, the mycobiota of natural O. sinensis specimens has not been investigated in detail. METHODOLOGY/PRINCIPAL FINDINGS: Based on the technique of PCR-single-strand conformation polymorphism (PCR-SSCP), the mycobiota of three different sections (stromata, sclerotia, and mycelial cortices) from natural O. sinensis specimens were investigated using both culture-dependent and -independent methods. For the culture-dependent method, 572 fungal strains were isolated, and 92 putative operational taxonomic units (OTUs) were identified from 226 sequenced strains with the threshold of 97%. For the culture-independent method, 490 fungal clones were identified from about 3000 clones of ITS fragments from the whole-community DNA; based on PCR-SSCP analyses, 266 of these clones were selected to be sequenced, and 118 putative OTUs were detected. The overwhelming majority of isolates/clones and OTUs were detected from mycelial cortices; only a few were detected from stromata and sclerotia. The most common OTUs detected with both methods belonged to Ascomycota; however, only 13 OTUs were detected simultaneously by both methods. Potential novel lineages were detected by each of the two methods. CONCLUSIONS/SIGNIFICANCE: A great number of fungal species present in the mycobiota of naturally-occurring O. sinensis specimens were detected, and many of them may represent undescribed lineages. That only a few of the same OTUs were detected by both methods indicated that different methods should be used. This study increased our understanding about the fungal community structure of this valuable medicinal herb

LC/MS-Based Quantitative Proteomic Analysis of Paraffin-Embedded Archival Melanomas Reveals Potential Proteomic Biomarkers Associated with Metastasis

BACKGROUND: Melanoma metastasis status is highly associated with the overall survival of patients; yet, little is known about proteomic changes during melanoma tumor progression. To better understand the changes in protein expression involved in melanoma progression and metastasis, and to identify potential biomarkers, we conducted a global quantitative proteomic analysis on archival metastatic and primary melanomas. METHODOLOGY AND FINDINGS: A total of 16 metastatic and 8 primary cutaneous melanomas were assessed. Proteins were extracted from laser captured microdissected formalin fixed paraffin-embedded archival tissues by liquefying tissue cells. These preparations were analyzed by a LC/MS-based label-free protein quantification method. More than 1500 proteins were identified in the tissue lysates with a peptide ID confidence level of >75%. This approach identified 120 significant changes in protein levels. These proteins were identified from multiple peptides with high confidence identification and were expressed at significantly different levels in metastases as compared with primary melanomas (q-Value<0.05). CONCLUSIONS AND SIGNIFICANCE: The differentially expressed proteins were classified by biological process or mapped into biological system networks, and several proteins were implicated by these analyses as cancer- or metastasis-related. These proteins represent potential biomarkers for tumor progression. The study successfully identified proteins that are differentially expressed in formalin fixed paraffin-embedded specimens of metastatic and primary melanoma

Development of a Three Dimensional Multiscale Computational Model of the Human Epidermis

Transforming Growth Factor (TGF-β1) is a member of the TGF-beta superfamily ligand-receptor network. and plays a crucial role in tissue regeneration. The extensive in vitro and in vivo experimental literature describing its actions nevertheless describe an apparent paradox in that during re-epithelialisation it acts as proliferation inhibitor for keratinocytes. The majority of biological models focus on certain aspects of TGF-β1 behaviour and no one model provides a comprehensive story of this regulatory factor's action. Accordingly our aim was to develop a computational model to act as a complementary approach to improve our understanding of TGF-β1. In our previous study, an agent-based model of keratinocyte colony formation in 2D culture was developed. In this study this model was extensively developed into a three dimensional multiscale model of the human epidermis which is comprised of three interacting and integrated layers: (1) an agent-based model which captures the biological rules governing the cells in the human epidermis at the cellular level and includes the rules for injury induced emergent behaviours, (2) a COmplex PAthway SImulator (COPASI) model which simulates the expression and signalling of TGF-β1 at the sub-cellular level and (3) a mechanical layer embodied by a numerical physical solver responsible for resolving the forces exerted between cells at the multi-cellular level. The integrated model was initially validated by using it to grow a piece of virtual epidermis in 3D and comparing the in virtuo simulations of keratinocyte behaviour and of TGF-β1 signalling with the extensive research literature describing this key regulatory protein. This research reinforces the idea that computational modelling can be an effective additional tool to aid our understanding of complex systems. In the accompanying paper the model is used to explore hypotheses of the functions of TGF-β1 at the cellular and subcellular level on different keratinocyte populations during epidermal wound healing

Non-irradiation-derived reactive oxygen species (ROS) and cancer: therapeutic implications

Owing to their chemical reactivity, radicals have cytocidal properties. Destruction of cells by irradiation-induced radical formation is one of the most frequent interventions in cancer therapy. An alternative to irradiation-induced radical formation is in principle drug-induced formation of radicals, and the formation of toxic metabolites by enzyme catalysed reactions. Although these developments are currently still in their infancy, they nevertheless deserve consideration. There are now numerous examples known of conventional anti-cancer drugs that may at least in part exert cytotoxicity by induction of radical formation. Some drugs, such as arsenic trioxide and 2-methoxy-estradiol, were shown to induce programmed cell death due to radical formation. Enzyme-catalysed radical formation has the advantage that cytotoxic products are produced continuously over an extended period of time in the vicinity of tumour cells. Up to now the enzymatic formation of toxic metabolites has nearly exclusively been investigated using bovine serum amine oxidase (BSAO), and spermine as substrate. The metabolites of this reaction, hydrogen peroxide and aldehydes are cytotoxic. The combination of BSAO and spermine is not only able to prevent tumour cell growth, but prevents also tumour growth, particularly well if the enzyme has been conjugated with a biocompatible gel. Since the tumour cells release substrates of BSAO, the administration of spermine is not required. Combination with cytotoxic drugs, and elevation of temperature improves the cytocidal effect of spermine metabolites. The fact that multidrug resistant cells are more sensitive to spermine metabolites than their wild type counterparts makes this new approach especially attractive, since the development of multidrug resistance is one of the major problems of conventional cancer therapy