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31.
P.A. Abhinand Faraz Shaikh Soumendranath Bhakat Ashish Radadiya L.V.K.S. Bhaskar Anamik Shah 《Journal of biomolecular structure & dynamics》2016,34(4):892-905
Methylenetetrahydrofolate reductase (MTHFR) protein catalyzes the only biochemical reaction which produces methyltetrahydrofolate, the active form of folic acid essential for several molecular functions. The Ala222Val polymorphism of human MTHFR encodes a thermolabile protein associated with increased risk of neural tube defects and cardiovascular disease. Experimental studies have shown that the mutation does not affect the kinetic properties of MTHFR, but inactivates the protein by increasing flavin adenine dinucleotide (FAD) loss. The lack of completely solved crystal structure of MTHFR is an impediment in understanding the structural perturbations caused by the Ala222Val mutation; computational modeling provides a suitable alternative. The three-dimensional structure of human MTHFR protein was obtained through homology modeling, by taking the MTHFR structures from Escherichia coli and Thermus thermophilus as templates. Subsequently, the modeled structure was docked with FAD using Glide, which revealed a very good binding affinity, authenticated by a Glide XP score of ?10.3983 (kcal mol?1). The MTHFR was mutated by changing Alanine 222 to Valine. The wild-type MTHFR-FAD complex and the Ala222Val mutant MTHFR-FAD complex were subjected to molecular dynamics simulation over 50 ns period. The average difference in backbone root mean square deviation (RMSD) between wild and mutant variant was found to be ~.11 Å. The greater degree of fluctuations in the mutant protein translates to increased conformational stability as a result of mutation. The FAD-binding ability of the mutant MTHFR was also found to be significantly lowered as a result of decreased protein grip caused by increased conformational flexibility. The study provides insights into the Ala222Val mutation of human MTHFR that induces major conformational changes in the tertiary structure, causing a significant reduction in the FAD-binding affinity. 相似文献
32.
Zachary D. Stephens Skylar Y. Lee Faraz Faghri Roy H. Campbell Chengxiang Zhai Miles J. Efron Ravishankar Iyer Michael C. Schatz Saurabh Sinha Gene E. Robinson 《PLoS biology》2015,13(7)
Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.We compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Astronomy has faced the challenges of Big Data for over 20 years and continues with ever-more ambitious studies of the universe. YouTube burst on the scene in 2005 and has sparked extraordinary worldwide interest in creating and sharing huge numbers of videos. Twitter, created in 2006, has become the poster child of the burgeoning movement in computational social science [6], with unprecedented opportunities for new insights by mining the enormous and ever-growing amount of textual data [7]. Particle physics also produces massive quantities of raw data, although the footprint is surprisingly limited since the vast majority of data are discarded soon after acquisition using the processing power that is coupled to the sensors [8]. Consequently, we do not include the domain in full detail here, although that model of rapid filtering and analysis will surely play an increasingly important role in genomics as the field matures.To compare these four disparate domains, we considered the four components that comprise the “life cycle” of a dataset: acquisition, storage, distribution, and analysis (
Data Phase
Astronomy
Twitter
YouTube
Genomics
Acquisition
25 zetta-bytes/year 0.5–15 billion tweets/year 500–900 million hours/year 1 zetta-bases/year
Storage
1 EB/year 1–17 PB/year 1–2 EB/year 2–40 EB/year
Analysis
In situ data reduction Topic and sentiment mining Limited requirements Heterogeneous data and analysis Real-time processing Metadata analysis Variant calling, ~2 trillion central processing unit (CPU) hours Massive volumes All-pairs genome alignments, ~10,000 trillion CPU hours
Distribution
Dedicated lines from antennae to server (600 TB/s) Small units of distribution Major component of modern user’s bandwidth (10 MB/s) Many small (10 MB/s) and fewer massive (10 TB/s) data movement