Browsing by Author "Department of BioHealth Informatics, IU School of Informatics and Computing"
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Item Direct prediction of profiles of sequences compatible to a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles(Wiley Online Library, 2014-10) Li, Zhixiu; Yang, Yuedong; Faraggi, Eshel; Zhou, Jian; Zhou, Yaoqi; Department of BioHealth Informatics, IU School of Informatics and ComputingLocating sequences compatible with a protein structural fold is the well-known inverse protein-folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy-optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment-derived sequence profiles and structure-derived energy profiles. SPIN improves over the fragment-derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild-type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single-body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks-lab.org.Item ExSurv: A Web Resource for Prognostic Analyses of Exons Across Human Cancers Using Clinical Transcriptomes(SAGE, 2016-08-07) Hashemikhabir, Seyedsasan; Budak, Gungor; Janga, Sarath Chandra; Department of BioHealth Informatics, IU School of Informatics and ComputingSurvival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients' clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing - a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. To address this gap and to provide a platform for rational identification of prognostic exons from cancer transcriptomes, we developed ExSurv (https://exsurv.soic.iupui.edu), a web-based platform for predicting the survival contribution of all annotated exons in the human genome using RNA sequencing-based expression profiles for cancer samples from four cancer types available from The Cancer Genome Atlas. ExSurv enables users to search for a gene of interest and shows survival probabilities for all the exons associated with a gene and found to be significant at the chosen threshold. ExSurv also includes raw expression values across the cancer cohort as well as the survival plots for prognostic exons. Our analysis of the resulting prognostic exons across four cancer types revealed that most of the survival-associated exons are unique to a cancer type with few processes such as cell adhesion, carboxylic, fatty acid metabolism, and regulation of T-cell signaling common across cancer types, possibly suggesting significant differences in the posttranscriptional regulatory pathways contributing to prognosis.Item Identification of ultramodified proteins using top-down tandem mass spectra(American Chemical Society, 2013-12-06) Liu, Xiaowen; Hengel, Shawna; Wu, Si; Tolić, Nikola; Pasa-Tolić, Ljiljana; Pevzner, Pavel A.; Department of BioHealth Informatics, IU School of Informatics and ComputingPost-translational modifications (PTMs) play an important role in various biological processes through changing protein structure and function. Some ultramodified proteins (like histones) have multiple PTMs forming PTM patterns that define the functionality of a protein. While bottom-up mass spectrometry (MS) has been successful in identifying individual PTMs within short peptides, it is unable to identify PTM patterns spreading along entire proteins in a coordinated fashion. In contrast, top-down MS analyzes intact proteins and reveals PTM patterns along the entire proteins. However, while recent advances in instrumentation have made top-down MS accessible to many laboratories, most computational tools for top-down MS focus on proteins with few PTMs and are unable to identify complex PTM patterns. We propose a new algorithm, MS-Align-E, that identifies both expected and unexpected PTMs in ultramodified proteins. We demonstrate that MS-Align-E identifies many proteoforms of histone H4 and benchmark it against the currently accepted software tools.Item Medication-related cognitive artifacts used by older adults with heart failure(Elsevier, 2015-12-01) Mickelson, Robin S.; Willis, Matt; Holden, Richard J.; Department of BioHealth Informatics, IU School of Informatics and ComputingOBJECTIVE: To use a human factors perspective to examine how older adult patients with heart failure use cognitive artifacts for medication management. METHODS: We performed a secondary analysis of data collected from 30 patients and 14 informal caregivers enrolled in a larger study of heart failure self-care. Data included photographs, observation notes, interviews, video recordings, medical record data, and surveys. These data were analyzed using an iterative content analysis. RESULTS: Findings revealed that medication management was complex, inseparable from other patient activities, distributed across people, time, and place, and complicated by knowledge gaps. We identified fifteen types of cognitive artifacts including medical devices, pillboxes, medication lists, and electronic personal health records used for: 1) measurement/evaluation; 2) tracking/communication; 3) organization/administration; and 4) information/sensemaking. These artifacts were characterized by fit and misfit with the patient's sociotechnical system and demonstrated both advantages and disadvantages. We found that patients often modified or "finished the design" of existing artifacts and relied on "assemblages" of artifacts, routines, and actors to accomplish their self-care goals. CONCLUSIONS: Cognitive artifacts are useful but sometimes are poorly designed or are not used optimally. If appropriately designed for usability and acceptance, paper-based and computer-based information technologies can improve medication management for individuals living with chronic illness. These technologies can be designed for use by patients, caregivers, and clinicians; should support collaboration and communication between these individuals; can be coupled with home-based and wearable sensor technology; and must fit their users' needs, limitations, abilities, tasks, routines, and contexts of use.Item Prediction and Validation of Transcription Factors Modulating the Expression of Sestrin3 Gene Using an Integrated Computational and Experimental Approach(Plos, 2016-07-28) Srivastava, Rajneesh; Zhang, Yang; Xiong, Xiwen; Zhang, Xiaoning; Pan, Xiaoyan; Dong, X. Charlie; Liangpunsakul, Suthat; Janga, Sarath Chandra; Department of BioHealth Informatics, IU School of Informatics and ComputingSESN3 has been implicated in multiple biological processes including protection against oxidative stress, regulation of glucose and lipid metabolism. However, little is known about the factors and mechanisms controlling its gene expression at the transcriptional level. We performed in silico phylogenetic footprinting analysis of 5 kb upstream regions of a diverse set of human SESN3 orthologs for the identification of high confidence conserved binding motifs (BMo). We further analyzed the predicted BMo by a motif comparison tool to identify the TFs likely to bind these discovered motifs. Predicted TFs were then integrated with experimentally known protein-protein interactions and experimentally validated to delineate the important transcriptional regulators of SESN3. Our study revealed high confidence set of BMos (integrated with DNase I hypersensitivity sites) in the upstream regulatory regions of SESN3 that could be bound by transcription factors from multiple families including FOXOs, SMADs, SOXs, TCFs and HNF4A. TF-TF network analysis established hubs of interaction that include SMAD3, TCF3, SMAD2, HDAC2, SOX2, TAL1 and TCF12 as well as the likely protein complexes formed between them. We show using ChIP-PCR as well as over-expression and knock out studies that FOXO3 and SOX2 transcriptionally regulate the expression of SESN3 gene. Our findings provide an important roadmap to further our understanding on the regulation of SESN3.