Background The search of heavy atoms is crucial to the de novo determination of protein structures.Typically,the difference Patterson map is calculated as a first step to solve substructure.However,the pseudo-peaks an...Background The search of heavy atoms is crucial to the de novo determination of protein structures.Typically,the difference Patterson map is calculated as a first step to solve substructure.However,the pseudo-peaks and noises inherent in such maps arising from the high symmetry and large size of protein structures accompanied with the data collection errors inevitably pose a challenge in accurate real space-based substructure determination.Purpose In order to mitigate such pseudo-peaks and noises and further improve signal-to-noise ratio(SNR)of the difference Patterson map,the noise and artifact suppression using resampling(NASR)method originally proposed in nuclear magnetic resonance is introduced into protein crystallography in this work to optimize the difference Patterson map.Methods The NASR method makes use of the statistical learning theory,which in this work repeatedly samples a fixed portion of diffraction data(sub-dataset)randomly followed by a statistical analysis of the multiple calculated difference Patterson maps to discard pseudo-peaks and noises.Its feasibility is based on the fact that the true vector peaks of the heavy atoms keep static in the multiple random sub-datasets,whereas the pseudo-peaks and noises fluctuate remarkably.And the key of this method lies in the design of a weighting function to distinguish true vector peaks from pseudo-peaks and noises,as well as a proper selection of the parameters associated with the function.Results The introduced NASR method is both numerically and experimentally demonstrated to be feasible in suppressing spurious peaks and non-correlative noises intrinsic to the difference Patterson maps.As a result,the SNR of the difference Patterson maps can be enhanced to some extent to facilitate real space-based substructure determination.Conclusion It is therefore anticipated that the proposed method may provide a meaningful insight into how to denoise the difference Patterson maps,which in turn assists in locating heavy atoms and further facilitates de novo protein structure determination.展开更多
Background Recent advances in serial femtosecond crystallography(SFX)using X-ray free electron lasers(XFELs)have facilitated accurate structure determination for biological macromolecules.However,given the many fluctu...Background Recent advances in serial femtosecond crystallography(SFX)using X-ray free electron lasers(XFELs)have facilitated accurate structure determination for biological macromolecules.However,given the many fluctuations inherent in SFX,the acquisition of SFX data of sufficiently high quality still remains challenging.Method Aimed at enhancing the accuracy of SFX data,this study proposes an iterative refinement method to optimally match pairs of the observed and predicted reflections on the detector plane.This method features a combination of detector geometry optimization and diffraction model refinement in an alternate manner,concomitant with a cycle-by-cycle peak selection procedure.Result To demonstrate whether this iterative method is convergent and feasible,both numerical simulations and experimental tests have been performed.The results reveal that this method can gradually improve overall quality of the integrated SFX data and therefore accelerate the convergence of Monte Carlo integration,while simultaneously suppressing correlations inherent in certain parameters and precluding outliers to some extent during the refinement.Conclusion We have demonstrated that our iterative refinement method is applicable to both simulated and experimental SFX data.It is expected that this method could provide meaningful insights into the refinement of SFX data and take the step forward toward more accurate Monte Carlo integration.展开更多
基金the National Natural Science Foundation of China(31570744).
文摘Background The search of heavy atoms is crucial to the de novo determination of protein structures.Typically,the difference Patterson map is calculated as a first step to solve substructure.However,the pseudo-peaks and noises inherent in such maps arising from the high symmetry and large size of protein structures accompanied with the data collection errors inevitably pose a challenge in accurate real space-based substructure determination.Purpose In order to mitigate such pseudo-peaks and noises and further improve signal-to-noise ratio(SNR)of the difference Patterson map,the noise and artifact suppression using resampling(NASR)method originally proposed in nuclear magnetic resonance is introduced into protein crystallography in this work to optimize the difference Patterson map.Methods The NASR method makes use of the statistical learning theory,which in this work repeatedly samples a fixed portion of diffraction data(sub-dataset)randomly followed by a statistical analysis of the multiple calculated difference Patterson maps to discard pseudo-peaks and noises.Its feasibility is based on the fact that the true vector peaks of the heavy atoms keep static in the multiple random sub-datasets,whereas the pseudo-peaks and noises fluctuate remarkably.And the key of this method lies in the design of a weighting function to distinguish true vector peaks from pseudo-peaks and noises,as well as a proper selection of the parameters associated with the function.Results The introduced NASR method is both numerically and experimentally demonstrated to be feasible in suppressing spurious peaks and non-correlative noises intrinsic to the difference Patterson maps.As a result,the SNR of the difference Patterson maps can be enhanced to some extent to facilitate real space-based substructure determination.Conclusion It is therefore anticipated that the proposed method may provide a meaningful insight into how to denoise the difference Patterson maps,which in turn assists in locating heavy atoms and further facilitates de novo protein structure determination.
基金This work was financially supported by the grants from the Strategic Priority Research Program of CAS(XDB08030103)the National Natural Science Foundation of China(31570744,31670059).
文摘Background Recent advances in serial femtosecond crystallography(SFX)using X-ray free electron lasers(XFELs)have facilitated accurate structure determination for biological macromolecules.However,given the many fluctuations inherent in SFX,the acquisition of SFX data of sufficiently high quality still remains challenging.Method Aimed at enhancing the accuracy of SFX data,this study proposes an iterative refinement method to optimally match pairs of the observed and predicted reflections on the detector plane.This method features a combination of detector geometry optimization and diffraction model refinement in an alternate manner,concomitant with a cycle-by-cycle peak selection procedure.Result To demonstrate whether this iterative method is convergent and feasible,both numerical simulations and experimental tests have been performed.The results reveal that this method can gradually improve overall quality of the integrated SFX data and therefore accelerate the convergence of Monte Carlo integration,while simultaneously suppressing correlations inherent in certain parameters and precluding outliers to some extent during the refinement.Conclusion We have demonstrated that our iterative refinement method is applicable to both simulated and experimental SFX data.It is expected that this method could provide meaningful insights into the refinement of SFX data and take the step forward toward more accurate Monte Carlo integration.
