tailieunhanh - Báo cáo sinh học: "Efficient unfolding pattern recognition in single molecule force spectroscopy data"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Efficient unfolding pattern recognition in single molecule force spectroscopy data. | Andreopoulos and Labudde Algorithms for Molecular Biology 2011 6 16 http content 6 1 16 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Efficient unfolding pattern recognition in single molecule force spectroscopy data Bill Andreopoulos1 and Dirk Labudde2 Abstract Background Single-molecule force spectroscopy SMFS is a technique that measures the force necessary to unfold a protein. SMFS experiments generate Force-Distance F-D curves. A statistical analysis of a set of F-D curves reveals different unfolding pathways. Information on protein structure conformation functional states and inter- and intra-molecular interactions can be derived. Results In the present work we propose a pattern recognition algorithm and apply our algorithm to datasets from SMFS experiments on the membrane protein bacterioRhodopsin bR . We discuss the unfolding pathways found in bR which are characterised by main peaks and side peaks. A main peak is the result of the pairwise unfolding of the transmembrane helices. In contrast a side peak is an unfolding event in the alpha-helix or other secondary structural element. The algorithm is capable of detecting side peaks along with main peaks. Therefore we can detect the individual unfolding pathway as the sequence of events labeled with their occurrences and co-occurrences special to bR s unfolding pathway. We find that side peaks do not co-occur with one another in curves as frequently as main peaks do which may imply a synergistic effect occurring between helices. While main peaks co-occur as pairs in at least 50 of curves the side peaks co-occur with one another in less than 10 of curves. Moreover the algorithm runtime scales well as the dataset size increases. Conclusions Our algorithm satisfies the requirements of an automated methodology that combines high accuracy with efficiency in analyzing SMFS datasets. The algorithm tackles the force spectroscopy analysis bottleneck leading to more consistent and reproducible