It is getting familiar that pathway information greatly contributes to elucidate the molecular basis of human disease with large-scale biological data. We developed a pathway database for molecular pathology in period...
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Background. Francisella tularensis is the etiologic agent of tularemia and is classified as a select agent by the Centers for Disease Control and Prevention. Currently four known subspecies of F. tularensis that diffe...
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MicroRNAs (miRNAs) are a class of small noncoding RNAs that have important regulatory roles in multicellular organisms. However, miRNA has never been identified experimentally in protist. Direct cloning of 438 express...
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Misfit sidechains in protein crystal structures are a stumbling block in using those structures to direct further scientific inference. Problems due to surface disorder and poor electron density are very difficult to ...
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Misfit sidechains in protein crystal structures are a stumbling block in using those structures to direct further scientific inference. Problems due to surface disorder and poor electron density are very difficult to address, but a large class of systematic errors are quite common even in well-ordered regions, resulting in sidechains fit backwards into local density in predictable ways. The MolProbity web site is effective at diagnosing such errors, and can perform reliable automated correction of a few special cases such as 180° flips of Asn or Gln sidechain amides, using all-atom contacts and H-bond networks. However, most at-risk residues involve tetrahedral geometry, and their valid correction requires rigorous evaluation of sidechain movement and sometimes backbone shift. The current work extends the benefits of robust automated correction to more sidechain types. The Autofix method identifies candidate systematic, flipped-over errors in Leu, Thr, Val, and Arg using MolProbity quality statistics, proposes a corrected position using real-space refinement with rotamer selection in Coot, and accepts or rejects the correction based on improvement in MolProbity criteria and on χ angle change. Criteria are chosen conservatively, after examining many individual results, to ensure valid correction. To test this method, Autofix was run and analyzed for 945 representative PDB files and on the 50S ribosomal subunit of file 1YHQ. Over 40% of Leu, Val, and Thr outliers and 15% of Arg outliers were successfully corrected, resulting in a total of 3,679 corrected sidechains, or 4 per structure on average. Summary Sentences: A common class of misfit sidechains in protein crystal structures is due to systematic errors that place the sidechain backwards into the local electron density. A fully automated method called "Autofix" identifies such errors for Leu, Val, Thr, and Arg and corrects over one third of them, using MolProbity validation criteria and Coot real-space refinement
Transcription factor activating enhancer-binding protein 4 (AP-4) is a basic helix-loop-helix protein that binds to E-box elements. AP-4 has received increasing attention for its regulatory role in cell growth and dev...
Transcription factor activating enhancer-binding protein 4 (AP-4) is a basic helix-loop-helix protein that binds to E-box elements. AP-4 has received increasing attention for its regulatory role in cell growth and development, including transcriptional repression of the human homolog of murine double minute 2 (HDM2), an important oncoprotein controlling cell growth and survival, by an unknown mechanism. Here we demonstrate that AP-4 binds to an E-box located in the HDM2-P2 promoter and represses HDM2 transcription in a p53-independent manner. Incremental truncations of AP-4 revealed that the C-terminal Gln/Pro-rich domain was essential for transcriptional repression of HDM2. To further delineate the molecular mechanism(s) of AP-4 transcriptional control and its potential implications, we used DNA-affinity purification followed by complementary quantitative proteomics, cICAT and iTRAQ labeling methods, to identify a previously unknown E-box-bound AP-4 protein complex containing 75 putative components. The two labeling methods complementarity quantified differentially AP-4-enriched proteins, including the most significant recruitment of DNA damage response proteins, followed by transcription factors, transcriptional repressors/corepressors, and histone-modifying proteins. Specific interaction of AP-4 with CCCTC binding factor, stimulatory protein 1, and histone deacetylase 1 (an AP-4 corepressor) was validated using AP-4 truncation mutants. Importantly, inclusion of trichostatin A did not alleviate AP-4-mediated repression of HDM2 transcription, suggesting a previously unidentified histone deacetylase-independent repression mechanism. In contrast, the complementary quantitative proteomics study suggested that transcription repression occurs via coordination of AP-4 with other transcription factors, histone methyltransferases, and/or a nucleosome remodeling SWI-SNF complex. In addition to previously known functions of AP-4, our data suggest that AP-4 participates in a
Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are them...
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Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are themselves vector valued, corresponding to sampled values in a time-series. Here, we present a classification methodology to accommodate multiple time-series using boosting. This method constructs an additive model by adaptively selecting basis functions consisting of a discriminating feature's full time-series. We present the necessary modifications to fisher linear discriminant analysis and least-squares, as base learners, to accommodate the weighted data in the proposed boosting procedure. We conclude by presenting the performance of our proposed method against a synthetic stochastic differential equation data set and a real world data set involving prediction of cancer patient susceptibility for a particular chemoradiotherapy.
Achievement of the best balance between the accuracy and efficiency is always an important issue when searching a tree space of large data sets. In the 5th issue in 2009, Rodrigo et al used bootstrapped topologies as ...
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TDP-43 is a pathogenic protein: its normal function in binding to UG-rich RNA is related to cystic fibrosis, and inclusion of its C-terminal fragments in brain cells is directly linked to frontotemporal lobar degenera...
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TDP-43 is a pathogenic protein: its normal function in binding to UG-rich RNA is related to cystic fibrosis, and inclusion of its C-terminal fragments in brain cells is directly linked to frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). We
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