Engineered sequence-specific zinc finger nucleases (ZFNs) make the highly efficient modification of eukaryotic genomes possible.However,most current strategies for developing zinc finger nucleases with customized sequ...Engineered sequence-specific zinc finger nucleases (ZFNs) make the highly efficient modification of eukaryotic genomes possible.However,most current strategies for developing zinc finger nucleases with customized sequence specificities require the construction of numerous tandem arrays of zinc finger proteins (ZFPs),and subsequent largescale in vitro validation of their DNA binding affinities and specificities via bacterial selection.The labor and expertise required in this complex process limits the broad adoption of ZFN technology.An effective computational assisted design strategy will lower the complexity of the production of a pair of functional ZFNs.Here we used the FoldX force field to build 3D models of 420 ZFP-DNA complexes based on zinc finger arrays developed by the Zinc Finger Consortium using OPEN (oligomerized pool engineering).Using nonlinear and linear regression analysis,we found that the calculated protein-DNA binding energy in a modeled ZFP-DNA complex strongly correlates to the failure rate of the zinc finger array to show significant ZFN activity in human cells.In our models,less than 5% of the three-finger arrays with calculated protein-DNA binding energies lower than 13.132 kcal mol 1 fail to form active ZFNs in human cells.By contrast,for arrays with calculated protein-DNA binding energies higher than 5 kcal mol 1,as many as 40% lacked ZFN activity in human cells.Therefore,we suggest that the FoldX force field can be useful in reducing the failure rate and increasing efficiency in the design of ZFNs.展开更多
A Chinese Holstein population with daughter design was analyzed using 14 microsatellites covering a map distance of 55.7 cM on chromosome 6 to fine map QTL for five milk production traits. 26 paternal half-sib familie...A Chinese Holstein population with daughter design was analyzed using 14 microsatellites covering a map distance of 55.7 cM on chromosome 6 to fine map QTL for five milk production traits. 26 paternal half-sib families with 2356 daughters were involved. Two different approaches, linear regression approach and variance component ap-proach, were employed, with a one-QTL model and two-QTL model fitted. With a one-QTL model, the linear regression approach revealed a QTL near BMS470 with effects on milk yield, fat yield, protein yield, and fat percentage, and another QTL near BMS2460 for protein percentage. The variance component approach confirmed the results of linear regres-sion approach for the three yield traits, with the exception that the QTL for fat yield was mapped to a different position near BMS1242. The 95% confidence intervals resulted from linear regression, obtained by bootstrapping, were generally large, ranging from 31 to 53 cM, whereas the variance com-ponent approach revealed very small confidence intervals, calculated by LOD drop-off method, for the three yield traits, only 4―5 cM. With a two-QTL model, both approaches pro-vided strong evidence for the existence of two QTLs for the three yield traits. Along with the QTLs identified in one-QTL model analyses, the linear regression approach revealed a second QTL near BP7 with effects on all the three yield traits, whereas the variance component approach located the sec-ond QTL near ILSS035, BMS470, and BP7 for the three traits, respectively.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.30901018)the China Postdoctoral Science Foundation (Grant No.201003388)
文摘Engineered sequence-specific zinc finger nucleases (ZFNs) make the highly efficient modification of eukaryotic genomes possible.However,most current strategies for developing zinc finger nucleases with customized sequence specificities require the construction of numerous tandem arrays of zinc finger proteins (ZFPs),and subsequent largescale in vitro validation of their DNA binding affinities and specificities via bacterial selection.The labor and expertise required in this complex process limits the broad adoption of ZFN technology.An effective computational assisted design strategy will lower the complexity of the production of a pair of functional ZFNs.Here we used the FoldX force field to build 3D models of 420 ZFP-DNA complexes based on zinc finger arrays developed by the Zinc Finger Consortium using OPEN (oligomerized pool engineering).Using nonlinear and linear regression analysis,we found that the calculated protein-DNA binding energy in a modeled ZFP-DNA complex strongly correlates to the failure rate of the zinc finger array to show significant ZFN activity in human cells.In our models,less than 5% of the three-finger arrays with calculated protein-DNA binding energies lower than 13.132 kcal mol 1 fail to form active ZFNs in human cells.By contrast,for arrays with calculated protein-DNA binding energies higher than 5 kcal mol 1,as many as 40% lacked ZFN activity in human cells.Therefore,we suggest that the FoldX force field can be useful in reducing the failure rate and increasing efficiency in the design of ZFNs.
基金This work was supported by the National Natural Science Foundation for Distin-guished Young Scholars(Grand No.30025003)the National Key Basic Research Program(Grant No.G200001603)the Hi-Tech Research and Development Program of China(Grand No.2001AA24301l).
文摘A Chinese Holstein population with daughter design was analyzed using 14 microsatellites covering a map distance of 55.7 cM on chromosome 6 to fine map QTL for five milk production traits. 26 paternal half-sib families with 2356 daughters were involved. Two different approaches, linear regression approach and variance component ap-proach, were employed, with a one-QTL model and two-QTL model fitted. With a one-QTL model, the linear regression approach revealed a QTL near BMS470 with effects on milk yield, fat yield, protein yield, and fat percentage, and another QTL near BMS2460 for protein percentage. The variance component approach confirmed the results of linear regres-sion approach for the three yield traits, with the exception that the QTL for fat yield was mapped to a different position near BMS1242. The 95% confidence intervals resulted from linear regression, obtained by bootstrapping, were generally large, ranging from 31 to 53 cM, whereas the variance com-ponent approach revealed very small confidence intervals, calculated by LOD drop-off method, for the three yield traits, only 4―5 cM. With a two-QTL model, both approaches pro-vided strong evidence for the existence of two QTLs for the three yield traits. Along with the QTLs identified in one-QTL model analyses, the linear regression approach revealed a second QTL near BP7 with effects on all the three yield traits, whereas the variance component approach located the sec-ond QTL near ILSS035, BMS470, and BP7 for the three traits, respectively.
