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Be-03 Effects Of Dietary Iron On Taxonomic Composition And Function Of The Zebrafish Gut Microbiome, Megan D. Whisonant, Jeremiah L. Jackson, Sam L. Evans, Stuart Gordon Ph.D.

SC Upstate Research Symposium

A healthy gut microbiota is essential to promote host health and well-being, therefore, effects of dietary components on the gut microbiome are important to investigate as the gastrointestinal tract can be a major route of infection. Iron—an essential component of heme and iron-sulfur proteins—plays a central role in many biological activities, including oxygen transport and cellular respiration.

In particular, the iron homeostasis system is one of the best characterized due to iron's causative relationship with iron-deficiency anemia. Dietary iron supplementation is a commonly used treatment for iron deficiency anemia; however, the known direct impacts of iron on the gut microbiome …

Art Or Science?, Allison Marsh

Section 3: Imaging the Fast Moving

No abstract provided.

Seqnls: Nuclear Localization Signal Prediction Based On Frequent Pattern Mining And Linear Motif Scoring, J.-R. Lin, Jianjun Hu

Faculty Publications

Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking.

The experiment results on …

Conclusion Panel, Allison Marsh

Section 6: Conclusion

No abstract provided.

Mapping The Ocean Frontier, Allison Marsh

Section 4: Imaging the Concealed

No abstract provided.

Spotlight On Usc: South Carolina Institute For Anthropology And Archaeology, Allison Marsh

Section 4: Imaging the Concealed

No abstract provided.

Seeing With Sound, Allison Marsh

Section 4: Imaging the Concealed

No abstract provided.

World Ocean Floor, Courtesy Of The Library Of Congress, Allison Marsh

Section 4: Imaging the Concealed

No abstract provided.

Marie Tharp At Her Drafting Table, Courtesy Of The Lamont-Doherty Earth Observatory, Earth Institute, Columbia University, Allison Marsh

Section 4: Imaging the Concealed

No abstract provided.

Why Do We Collect?, Allison Marsh

Section 2: Imaging the Microscopic

No abstract provided.

Imaging And Aesthetics, Allison Marsh

Section 2: Imaging the Microscopic

No abstract provided.

Capturing Motion, Allison Marsh

Section 3: Imaging the Fast Moving

No abstract provided.

Understanding Fracture, Allison Marsh

Section 3: Imaging the Fast Moving

No abstract provided.

Listen, Allison Marsh

Section 3: Imaging the Fast Moving

No abstract provided.

Photographic Evidence, Allison Marsh

Section 3: Imaging the Fast Moving

No abstract provided.

Spotlight On Usc: Mechanical Engineering, Allison Marsh

Section 3: Imaging the Fast Moving

No abstract provided.

Usc Image Center, Allison Marsh

Section 1: Introduction

No abstract provided.

Powers Of Ten, Allison Marsh

Section 1: Introduction

No abstract provided.

Wall_1.6 — Acknowledgements, Allison Marsh

Section 1: Introduction

No abstract provided.

Questioning Images, Allison Marsh

Section 1: Introduction

No abstract provided.

How Small Is Small?, Allison Marsh

Section 1: Introduction

No abstract provided.

Imaging In 3-D, Allison Marsh

Section 1: Introduction

No abstract provided.

Influencing Art, Allison Marsh

Section 3: Imaging the Fast Moving

No abstract provided.

Spotlight On Usc: A.C. Moore Herbarium, Allison Marsh

Section 2: Imaging the Microscopic

No abstract provided.

Observing The Minuscule, Allison Marsh

Section 2: Imaging the Microscopic

No abstract provided.

Minimalist Ensemble Algorithms For Genome-Wide Protein Localization Prediction, J.-R. Lin, A. M. Mondal, R. Liu, Jianjun Hu

Faculty Publications

Background

Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms.

Results

This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature …

Hemebind: A Novel Method For Heme Binding Residue Prediction By Combining Structural And Sequence Information, R. Liu, Jianjun Hu

Faculty Publications

Background

Accurate prediction of binding residues involved in the interactions between proteins and small ligands is one of the major challenges in structural bioinformatics. Heme is an essential and commonly used ligand that plays critical roles in electron transfer, catalysis, signal transduction and gene expression. Although much effort has been devoted to the development of various generic algorithms for ligand binding site prediction over the last decade, no algorithm has been specifically designed to complement experimental techniques for identification of heme binding residues. Consequently, an urgent need is to develop a computational method for recognizing these important residues.

Results

Here …

Computational Prediction Of Heme-Binding Residues By Exploiting Residue Interaction Network, R. Liu, Jianjun Hu

Faculty Publications

Computational identification of heme-binding residues is beneficial for predicting and designing novel heme proteins. Here we proposed a novel method for heme-binding residue prediction by exploiting topological properties of these residues in the residue interaction networks derived from three-dimensional structures. Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network. HemeNet, a support vector machine (SVM) based predictor, was developed to identify heme-binding residues by combining topological features with existing sequence and structural features. The results showed that incorporation of network-based …

Prediction Of Discontinuous B-Cell Epitopes Using Logistic Regression And Structural Information, R. Liu, Jianjun Hu

Faculty Publications

Computational prediction of discontinuous B-cell epitopes remains challenging, but it is an important task in vaccine design. In this study, we developed a novel computational method to predict discontinuous epitope residues by combining the logistic regression model with two important structural features, B-factor and relative accessible surface area (RASA). We conducted five-fold cross-validation on a representative dataset composed of antigen structures bound with antibodies and independent testing on Epitome database, respectively. Experimental results indicate that besides the well-known RASA feature, B-factor can also be used to identify discontinuous epitopes. Furthermore, these two features are complementary and their combination can remarkably …

Bayesmotif: De Novo Protein Sorting Motif Discovery From Impure Datasets, Jianjun Hu, F. Zhang

Faculty Publications

Background

Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms.

Methods

We formulated the protein sorting motif discovery problem as a classification problem …