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Author : Rosa M. Vitale
Publisher :
Page : pages
File Size : 43,91 MB
Release : 2011
Category : Computers
ISBN :
Computational Methods in Mass Spectrometry-Based Protein 3D Studies.
Author : Heitor Lopes
Publisher : IntechOpen
Page : 458 pages
File Size : 45,4 MB
Release : 2011-09-02
Category : Computers
ISBN : 9789533076294
Nowadays it is difficult to imagine an area of knowledge that can continue developing without the use of computers and informatics. It is not different with biology, that has seen an unpredictable growth in recent decades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnology and information technology. More recently, the development of high throughput techniques, such as microarray, mass spectrometry and DNA sequencing, has increased the need of computational support to collect, store, retrieve, analyze, and correlate huge data sets of complex information. On the other hand, the growth of the computational power for processing and storage has also increased the necessity for deeper knowledge in the field. The development of bioinformatics has allowed now the emergence of systems biology, the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of a living being. This book presents some theoretical issues, reviews, and a variety of bioinformatics applications. For better understanding, the chapters were grouped in two parts. In Part I, the chapters are more oriented towards literature review and theoretical issues. Part II consists of application-oriented chapters that report case studies in which a specific biological problem is treated with bioinformatics tools.
Author : Ingvar Eidhammer
Publisher : John Wiley & Sons
Page : 290 pages
File Size : 21,4 MB
Release : 2012-12-10
Category : Mathematics
ISBN : 111849377X
The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author’s carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.
Author : Ingvar Eidhammer
Publisher : John Wiley & Sons
Page : 296 pages
File Size : 49,46 MB
Release : 2008-02-28
Category : Medical
ISBN : 9780470724293
Proteomics is the study of the subsets of proteins present in different parts of an organism and how they change with time and varying conditions. Mass spectrometry is the leading technology used in proteomics, and the field relies heavily on bioinformatics to process and analyze the acquired data. Since recent years have seen tremendous developments in instrumentation and proteomics-related bioinformatics, there is clearly a need for a solid introduction to the crossroads where proteomics and bioinformatics meet. Computational Methods for Mass Spectrometry Proteomics describes the different instruments and methodologies used in proteomics in a unified manner. The authors put an emphasis on the computational methods for the different phases of a proteomics analysis, but the underlying principles in protein chemistry and instrument technology are also described. The book is illustrated by a number of figures and examples, and contains exercises for the reader. Written in an accessible yet rigorous style, it is a valuable reference for both informaticians and biologists. Computational Methods for Mass Spectrometry Proteomics is suited for advanced undergraduate and graduate students of bioinformatics and molecular biology with an interest in proteomics. It also provides a good introduction and reference source for researchers new to proteomics, and for people who come into more peripheral contact with the field.
Author : Heitor Lopes
Publisher : BoD – Books on Demand
Page : 460 pages
File Size : 11,80 MB
Release : 2011-09-02
Category : Computers
ISBN : 9533076291
Nowadays it is difficult to imagine an area of knowledge that can continue developing without the use of computers and informatics. It is not different with biology, that has seen an unpredictable growth in recent decades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnology and information technology. More recently, the development of high throughput techniques, such as microarray, mass spectrometry and DNA sequencing, has increased the need of computational support to collect, store, retrieve, analyze, and correlate huge data sets of complex information. On the other hand, the growth of the computational power for processing and storage has also increased the necessity for deeper knowledge in the field. The development of bioinformatics has allowed now the emergence of systems biology, the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of a living being. This book presents some theoretical issues, reviews, and a variety of bioinformatics applications. For better understanding, the chapters were grouped in two parts. In Part I, the chapters are more oriented towards literature review and theoretical issues. Part II consists of application-oriented chapters that report case studies in which a specific biological problem is treated with bioinformatics tools.
Author : Weiwei Tong
Publisher :
Page : 282 pages
File Size : 35,45 MB
Release : 2008
Category :
ISBN :
Abstract: Nearly all major processes in living cells are carried out by complex apparatus consisting of protein molecules. This thesis describes computational tools developed to help investigate two fundamental questions about proteins that underlie cell functions: how they interact with each other and form complex structures; and how they are expressed and modified in different cell states. In order to address the first question, several methods are developed to predict protein-protein complex structures. Protein interactions are energy driven processes. The prediction of protein complex structures is the search for the global minimum on the binding free-energy landscape. An approach is described that uses Van der Wools energy, desolvation energy and shape complementarity as the scoring functions and a five-dimensional fast Fourier transform algorithm to expedite the search. Two methods to screen and optimize the predicted protein complex structures are also introduced. They incorporate additional energy terms and clustering algorithms to provide more precise estimations of the binding free-energy. The same methods can also be used to predict hot spots, the mutations of which significantly alter the binding kinetics. To study the protein expression profiles, a two-step approach for protein identification using peptide mass fingerprinting data is developed. Peptide mass fingerprinting uses peptide masses determined by mass spectrometry to identify the peptides and subsequently, the proteins in the sample Peaks in the mass spectrum are associated with known peptide sequences in the database based on log-likelihood ratio test. A statistical algorithm is then used to identify proteins by comparing the probability of each protein's presence in the sample, given the peak assignments with the background probability. This method also discovers post-translational modifications in the identified proteins. The protein binding prediction program successfully predicts protein complex structures that closely resemble their native forms, as observed by x-ray crystallography or NMR. The refinements and hot spot predictions also give accurate and consistent results. The database search program that interprets mass spectrometry data is evaluated with artificial and experimental data. The program identifies proteins in the sample with high sensitivity and specificity. The results presented in this thesis demonstrate that computational methods help to better understand the structure and the composition of the protein machineries. All of the methods described herein have been implemented and made available for the research community over the Internet.
Author : Ying Xu
Publisher : Springer Science & Business Media
Page : 408 pages
File Size : 39,76 MB
Release : 2007-08-24
Category : Science
ISBN : 0387683720
Volume One of this two-volume sequence focuses on the basic characterization of known protein structures, and structure prediction from protein sequence information. Eleven chapters survey of the field, covering key topics in modeling, force fields, classification, computational methods, and structure prediction. Each chapter is a self contained review covering definition of the problem and historical perspective; mathematical formulation; computational methods and algorithms; performance results; existing software; strengths, pitfalls, challenges, and future research.
Author :
Publisher :
Page : pages
File Size : 29,98 MB
Release : 2018
Category :
ISBN :
Author : Matthew James Sinnott
Publisher :
Page : pages
File Size : 35,48 MB
Release : 2021
Category :
ISBN :
Author : Rachana Jain
Publisher :
Page : 129 pages
File Size : 31,20 MB
Release : 2010
Category :
ISBN :
Mass spectrometry (MS) is used routinely to identify proteins in biological samples. Peptide Mass Fingerprinting (PMF) uses peptide masses and a pre-specified search database to identify proteins. It is often used as a complementary method along with Peptide Fragment Fingerprinting (PFF) or de-novo sequencing for increasing confidence and coverage of protein identification during mass spectrometric analysis. At the core of a PMF database search algorithm lies a similarity measure or quality statistics that is used to gauge the level to which an experimentally obtained peaklist agrees with a list of theoretically observable mass-to-charge ratios for a protein in a database. In this dissertation, we use publicly available gold standard data sets to show that the selection of search criteria such as mass tolerance and missed cleavages significantly affects the identification results. We propose, implement and evaluate a statistical (Kolmogorov-Smirnov-based) test which is computed for a large mass error threshold thus avoiding the choice of appropriate mass tolerance by the user. We use the mass tolerance identified by the Kolmogorov-Smirnov test for computing other quality measures. The results from our careful and extensive benchmarks suggest that the new method of computing the quality statistics without requiring the end-user to select a mass tolerance is competitive. We investigate the similarity measures in terms of their information content and conclude that the similarity measures are complementary and can be combined into a scoring function to possibly improve the over all accuracy of PMF based identification methods. We describe a new database search tool, PRIMAL, for protein identification using PMF. The novelty behind PRIMAL is two-fold. First, we comprehensively analyze methods for measuring the degree of similarity between experimental and theoretical peaklists. Second, we employ machine learning as a means of combining the individual similarity measures into a scoring function. Finally, we systematically test the efficacy of PRIMAL in identifying proteins using highly curated and publicly available data. Our results suggest that PRIMAL is competitive if not better than some of the tools extensively used by the mass spectrometry community. A web server with an implementation of the scoring function is available at http://bmi.cchmc.org/primal. We also note that the methodology is directly extensible to MS/MS based protein identification problem. We detail how to extend our approaches to the more complex MS/MS data.