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Biocomputing 2012 - Proceedings Of The Pacific Symposium.

Bibliographic Details
Main Author: Klein, Teri E.
Other Authors: Jung, Tiffany A., Hunter, Lawrence., Dunker, A Keith., Altman, Russ B.
Format: eBook
Language:English
Published: Singapore : World Scientific Publishing Company, 2011.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Contents
  • Preface
  • IDENTIFICATION OF ABERRANT PATHWAY AND NETWORK ACTIVITY FROM HIGH-THROUGHPUT DATA
  • Session Introduction Rachel Karchin, Michael F. Ochs, Joshua M. Stuart, and Joel S. Bader
  • Introduction
  • Genetic interaction networks in model organisms
  • Human data and local subnetworks
  • Converging problems and challenges
  • References
  • SSLPred : Predicting Synthetic Sickness Lethality Nirmalya Bandyopadhyayy, Sanjay Ranka, and Tamer Kahveci
  • 1. Introduction
  • 2. Background
  • 3. Methods
  • 3.1. Problem Formulation and Notation
  • 3.2. Between Pathway Conjectures
  • 3.3. Regression based solution
  • 4. Experiments
  • 4.1. Datasets
  • 4.2. Comparison with Hescott's Method
  • 5. Conclusion
  • References
  • Predicting the Effects of Copy-Number Variation in Double and Triple Mutant Combinations Gregory W. Carter, Michelle Hays, Song Li, and Timothy Galitski
  • 1. Introduction
  • 2. Network Model Inference
  • 2.1.1. Yeast Gene Expression Profiling
  • 2.1.2. Singular Value Decomposition Analysis
  • 2.1.3. Genetic Influences Decomposition
  • 2.2. Predictions and Validation for a Multicopy Perturbation
  • 2.2.1. Prediction for Multi-Copy Strains
  • 2.2.2. Experimental Test of Predictions
  • 3. Discussion and Conclusions
  • 4. Supplementary Material
  • 5. Acknowledgments
  • References
  • Integrative Network Analysis to Identify Aberrant Pathway Networks in Ovarian Cancer Li Chen, Jianhua Xuan, Jinghua Gu, Yue Wang, Li Chen, Zhen Zhang, Tian-Li Wang, and Ie-Ming Shih
  • 1. Introduction
  • 2. Materials and method
  • 2.1. Integrative framework
  • 2.2. Data description
  • 2.3. DNA copy number consensus region detection
  • 2.4. Network identification by bootstrapping MRF (BMRF)
  • 2.5. Network constrained support vector machines (NetSVM)
  • 2.6. Classification performance merits and survival analysis
  • 3. Results and discussion.
  • 4. Conclusion
  • 5. Acknowledgments
  • References
  • Role of Synthetic Genetic Interactions in Understanding Functional Interactions Among Pathways Shahin Mohammadi, Giorgos Kollias, and Ananth Grama
  • 1. Introduction
  • 2. Methods
  • 2.1. Notations
  • 2.2. Performance of local methods for predicting functional similarity of gene pairs
  • 2.3. Constructing the neighborhood overlap graph (NOG)
  • 2.4. Identifying interaction ports and inferring cross-pathway dependencies
  • 3. Results
  • 3.1. Datasets
  • 3.1.1. Genetic interaction network
  • 3.1.2. Functional annotations
  • 3.1.3. Availability
  • 3.2. Similarity of genetic neighborhood as a predictor of functional similarity
  • 3.3. Constructing KEGG crosstalk map
  • 4. Discussion
  • 5. Acknowledgments
  • References
  • Discovery of Mutated Subnetworks Associated with Clinical Data in Cancer Fabio Vandin, Patrick Clay, Eli Upfal, and Benjamin J. Raphael
  • 1. Introduction
  • 2. Methods
  • 2.1. Generalized HotNet
  • 2.2. Adaptation to Clinical Data
  • 2.2.1. Gene Scores
  • 2.2.2. Selection of parameters t and
  • 2.2.3. The Null Hypothesis Distribution
  • 3. Results
  • 3.1. Simulated data
  • 3.2. Ovarian TCGA data
  • 4. Discussion
  • 5. Acknowledgements
  • References
  • INTRINSICALLY DISORDERED PROTEINS: ANALYSIS, PREDICTION, SIMULATION, AND BIOLOGY
  • Session Introduction Jianhan Chen, Jianlin Cheng, and A. Keith Dunker
  • 1. Introduction
  • 2. Papers in this Session
  • Analysis of IDPs' function and evolution
  • Simulation of IDPs' conformation
  • Prediction of IDPs
  • Acknowledgements
  • Quasi-Anharmonic Analysis Reveals Intermediate States in the Nuclear Co-Activator Receptor Binding Domain Ensemble Virginia M. Burger, Arvind Ramanathan, Andrej J. Savol, Christopher B. Stanly, Pratul K. Agarwal, and Chakra S. Chennubhotla
  • 1. Introduction
  • 2. Approach
  • 3. Molecular Simulations for NCBD.
  • 4. dQAA: Quasi-anharmonic analysis in the dihedral angle space
  • 5. Hierarchical clustering in the dQAA-space to identify meta-stable states
  • 6. Intermediate states of ligand-free NCBD access ligand-bound conformations
  • 7. Conclusions and Future Work
  • References
  • Efficient Construction of Disordered Protein Ensembles in a Bayesian Framework with Optimal Selection of Conformations Charles K. Fisher, Orly Ullman, and Collin M. Stultz
  • 1. Introduction
  • 2. Theory
  • 2.1. Optimal Structure Selection
  • 2.2. Variational Bayesian Weighting
  • 2.3. Variational Bayes with Structure Selection
  • 2.3. Approximate Confidence Intervals
  • 3. Results and Discussion
  • 3.1. Validation with Reference Ensembles
  • 3.2. α-Synuclein Ensemble
  • 4. Conclusions
  • 5. Acknowledgements
  • References
  • Correlation Between Posttranslational Modification and Intrinsic Disorder in Protein Jianjiong Gao and Dong Xu
  • 1. Background
  • 2. Results
  • 2.1. Correlation of PTM sites and their predicted disorder scores
  • 2.2. Correlation of PTM sites and their spatial fluctuations in NMR 3-D structures
  • 2.3. Spatial fluctuation changes in 3-D structure due to PTM
  • 3. Discussion
  • Acknowledgments
  • References
  • Intrinsic Disorder Within and Flanking the DNA-Binding Domains of Human Transcription Factors Xin Guo, Martha L. Bulyk, and Alexander J. Hartemink
  • 1. Introduction
  • 2. Materials and Methods
  • 2.1. Constructing the TF and non-TF control sets of proteins
  • 2.2. Comparing the TF and non-TF control sets of proteins
  • 2.3. Identifying DNA-binding domains (DBDs) and their locations within proteins
  • 2.4. Predicting intrinsically disordered regions (IDRs) and their locations within proteins using multiple existing methods
  • 2.5. Defining disorder features: spatial relationships of IDRs relative to DBDs within TFs.
  • 2.6. Calculating statistical significance of disorder features
  • 3. Results
  • 3.1. Comparing the three methods for predicting IDRs within proteins
  • 3.2. Assessing significance of order or disorder within and anking human TF DBDs
  • 3.3. Investigating detailed spatial relationships of IDRs relative to DBDs within TFs
  • 3.4. Analyzing spatial relationships for some DBD classes prevalent in human TFs
  • 3.4.1. Zinc ngers
  • 3.4.2. Homeobox
  • 3.4.3. HLH
  • 4. Discussion
  • 5. Acknowledgments
  • References
  • Intrinsic Protein Disorder and Protein-Protein Interactions Wei-Lun Hsu, Christopher Oldfield, Jingwei Meng, Fei Huang, Bin Xue, Vladimir N. Uversky, Pedro Romero, and A. Keith Dunker
  • 1. Introduction
  • 2. Results
  • 2.1 Disordered hub dataset
  • 2.2 Functional consequences of MoRF (or ELM) binding
  • 2.3 Binding to multiple partners, conservation at structure-matching sites
  • 3. Discussion
  • 4. Methods
  • 4.1 Disordered hub dataset
  • 4.2 Sequence and Structure analysis
  • References
  • Subclassifying Disordered Proteins by the CH-CDF Plot Method Fei Huang, Christopher Oldfield, Jingwei Meng, Wei-lun Hsu, Bin Xue, Vladimir N. Uversky, Pedro Romero, and A. Keith Dunker
  • 1. Introduction
  • 2. Results
  • 2.1 CH-CDF plot
  • 2.2 PDB coverage
  • 2.3 Sequence window CH-CDF analysis
  • 2.4 Match PDB coverage to disorder prediction
  • 2.5 Function analysis for each quadrant
  • 3. Discussion
  • 3.1 Overview
  • 3.2 Structural Partitioning by the CH-CDF plot
  • 3.2 The rare protein quadrant (Q1)
  • 3.3 Disorder subtypes and IDP functions
  • 4. Methods
  • 4.1 Protein data
  • 4.2 PDB Coverage
  • 4.2 GO term analysis
  • References
  • Coevolved Residues and the Functional Association for Intrinsically Disordered Protein Chan-Seok Jeong and Dongsup Kim
  • 1. Introduction
  • 2. Materials and methods
  • 2.1. Data set.
  • 2.2. Multiple sequence alignment construction
  • 2.3. Coevolution estimation
  • 2.4. Sequence conservation estimation
  • 2.5. Disorder conservation estimation
  • 2.6. Functional categories
  • 3. Results
  • 3.1. Distribution of coevolved residues for disordered proteins
  • 3.2. Relationship between coevolution and functions
  • 4. Discussion
  • Acknowledgments
  • References
  • Cryptic Disorder: An Order-Disorder Transformation Regulates the Function of Nucleophosmin Diana M. Mitrea and Richard W. Kriwacki
  • 1. Biological Function and Structural Features of Npm
  • 2. Alteration of the electrostatic features of Npm-N through phosphorylation
  • 3. In Silico site-directed mutagenesis
  • 4. Probing for structural strain in Npm-N
  • 5. Mechanistic insights on Npm's order-disorder polymorphism
  • 6. Materials and Methods
  • References
  • Functional Annotation of Intrinsically Disordered Domains by Their Amino Acid Content Using IDD Navigator Ashwini Patil, Shunsuke Teraguchi, Huy Dinh, Kenta Nakai, and Daron M Standley
  • 1. Introduction Intrinsically disordered domains
  • 2. Methodology
  • 2.1. Preparation of IDD dataset
  • 2.2. Similarity scores
  • 2.2.1. Similarity score based on Euclidean distance
  • 2.2.2. BLAST score
  • 2.3. Pfam domain and Gene Ontology term prediction
  • 2.4. Evaluation of function prediction
  • 2.5. Web server
  • 3. Results and Discussion
  • 3.1 IDD Navigator Function prediction
  • 3.2 Comparing different methods in IDD Navigator
  • 3.3 Function prediction for IDD clusters
  • 3.4 Case Studies
  • 3.4.1 GRA15 from T. gondii
  • 3.4.2 Cyclon from M. musculus
  • 3.4.3 STIM1 from M. musculus
  • 3.4.4 ROP16 from T. gondii
  • 4. Conclusions
  • 5. Acknowledgements
  • References
  • On the Complementarity of the Consensus-Based Disorder Prediction Zhenling Peng and Lukasz Kurgan
  • 1. Introduction
  • 2. Methods.
  • 2.1. Considered disorder predictors.

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