{"title":"Wavelet Analysis of Protein Motion.","authors":"Noah C Benson, Valerie Daggett","doi":"10.1142/S0219691312500403","DOIUrl":null,"url":null,"abstract":"<p><p>As high-throughput molecular dynamics simulations of proteins become more common and the databases housing the results become larger and more prevalent, more sophisticated methods to quickly and accurately mine large numbers of trajectories for relevant information will have to be developed. One such method, which is only recently gaining popularity in molecular biology, is the continuous wavelet transform, which is especially well-suited for time course data such as molecular dynamics simulations. We describe techniques for the calculation and analysis of wavelet transforms of molecular dynamics trajectories in detail and present examples of how these techniques can be useful in data mining. We demonstrate that wavelets are sensitive to structural rearrangements in proteins and that they can be used to quickly detect physically relevant events. Finally, as an example of the use of this approach, we show how wavelet data mining has led to a novel hypothesis related to the mechanism of the protein <i>γδ</i> resolvase.</p>","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"10 4","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S0219691312500403","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Wavelets Multiresolution and Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1142/S0219691312500403","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 11
Abstract
As high-throughput molecular dynamics simulations of proteins become more common and the databases housing the results become larger and more prevalent, more sophisticated methods to quickly and accurately mine large numbers of trajectories for relevant information will have to be developed. One such method, which is only recently gaining popularity in molecular biology, is the continuous wavelet transform, which is especially well-suited for time course data such as molecular dynamics simulations. We describe techniques for the calculation and analysis of wavelet transforms of molecular dynamics trajectories in detail and present examples of how these techniques can be useful in data mining. We demonstrate that wavelets are sensitive to structural rearrangements in proteins and that they can be used to quickly detect physically relevant events. Finally, as an example of the use of this approach, we show how wavelet data mining has led to a novel hypothesis related to the mechanism of the protein γδ resolvase.
期刊介绍:
International Journal of Wavelets, Multiresolution and Information Processing (hereafter referred to as IJWMIP) is a bi-monthly publication for theoretical and applied papers on the current state-of-the-art results of wavelet analysis, multiresolution and information processing.
Papers related to the IJWMIP theme are especially solicited, including theories, methodologies, algorithms and emerging applications. Topics of interest of the IJWMIP include, but are not limited to:
1. Wavelets:
Wavelets and operator theory
Frame and applications
Time-frequency analysis and applications
Sparse representation and approximation
Sampling theory and compressive sensing
Wavelet based algorithms and applications
2. Multiresolution:
Multiresolution analysis
Multiscale approximation
Multiresolution image processing and signal processing
Multiresolution representations
Deep learning and neural networks
Machine learning theory, algorithms and applications
High dimensional data analysis
3. Information Processing:
Data sciences
Big data and applications
Information theory
Information systems and technology
Information security
Information learning and processing
Artificial intelligence and pattern recognition
Image/signal processing.