title: Mathematical model for empirically optimizing large scale production of soluble protein domains.
abstract: BACKGROUND
  Efficient dissection of large proteins into their structural domains is critical for high throughput proteome analysis. So far, no study has focused on mathematically modeling a protein dissection protocol in terms of a production system. Here, we report a mathematical model for empirically optimizing the cost of large-scale domain production in proteomics research.
  
  
  RESULTS
  The model computes the expected number of successfully producing soluble domains, using a conditional probability between domain and boundary identification. Typical values for the model's parameters were estimated using the experimental results for identifying soluble domains from the 2,032 Kazusa HUGE protein sequences. Among the 215 fragments corresponding to the 24 domains that were expressed correctly, 111, corresponding to 18 domains, were soluble. Our model indicates that, under the conditions used in our pilot experiment, the probability of correctly predicting the existence of a domain was 81% (175/215) and that of predicting its boundary was 63% (111/175). Under these conditions, the most cost/effort-effective production of soluble domains was to prepare one to seven fragments per predicted domain.
  
  
  CONCLUSIONS
  Our mathematical modeling of protein dissection protocols indicates that the optimum number of fragments tested per domain is actually much smaller than expected a priori. The application range of our model is not limited to protein dissection, and it can be utilized for designing various large-scale mutational analyses or screening libraries.
authors.affiliation: Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp
authors.forename: Eisuke
authors.initials: E
authors.lastname: Chikayama
authors.forename: Atsushi
authors.initials: A
authors.lastname: Kurotani
authors.forename: Takanori
authors.initials: T
authors.lastname: Tanaka
authors.forename: Takashi
authors.initials: T
authors.lastname: Yabuki
authors.forename: Satoshi
authors.initials: S
authors.lastname: Miyazaki
authors.forename: Shigeyuki
authors.initials: S
authors.lastname: Yokoyama
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Kuroda
mesh_terms: D030562:Databases, Protein; D008962:Models, Theoretical; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D020543:Proteome / Q000737:chemistry; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins; D020543:Proteome