Statistical potentials for 3D structure evaluation:From proteins to RNAs

Structure evaluation is critical to in silico 3-dimensional structure predictions for biomacromolecules such as proteins and RNAs.For proteins,structure evaluation has been paid attention over three decades along with protein folding problem,and statistical potentials have been shown to be effective and efficient in protein structure prediction and evaluation.In recent two decades,RNA folding problem has attracted much attention and several statistical potentials have been developed for RNA structure evaluation,partially with the aid of the progress in protein structure prediction.In this review,we will firstly give a brief overview on the existing statistical potentials for protein structure evaluation.Afterwards,we will introduce the recently developed statistical potentials for RNA structure evaluation.Finally,we will emphasize the perspective on developing new statistical potentials for RNAs in the near future.

A quick and effective multivariate statistical strategy for imaging mass spectrometry

A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry（IMS） techniques is reported.The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results.Instead of analyzing the whole datacube at one time,the strategy makes the analysis easier and decreases the computation time greatly.In this report,the IMS data are produced by the air flow-assisted ionization IMS（AFAI-IMS）.The strategy can be used in combination with most multivariate statistical analysis methods.In this paper,the strategy was combined with the principal component analysis（PCA） and partial least square analysis（PLS）.It was proven to be effective by analyzing the handwriting sample.By using the strategy,the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully.Moreover the analysis time grew linearly instead of exponentially as the size of sample increased.The strategy developed in this study has enormous potential for searching for the mjz of potential biomarkers quickly and effectively.

cgRNASP-CN: a minimal coarse-grained representation-based statistical potential for RNA 3D structure evaluation

Knowledge of RNA 3-dimensional(3 D) structures is critical to understand the important biological functions of RNAs, and various models have been developed to predict RNA 3 D structures in silico. However, there is still lack of a reliable and efficient statistical potential for RNA 3 D structure evaluation. For this purpose, we developed a statistical potential based on a minimal coarse-grained representation and residue separation, where every nucleotide is represented by C4’ atom for backbone and N1(or N9) atom for base. In analogy to the newly developed all-atom rsRNASP, cgRNASP-CN is composed of short-ranged and long-ranged potentials, and the short-ranged one was involved more subtly. The examination indicates that the performance of cgRNASP-CN is close to that of the all-atom rsRNASP and is superior to other top all-atom traditional statistical potentials and scoring functions trained from neural networks, for two realistic test datasets including the RNA-Puzzles dataset. Very importantly,cgRNASP-CN is about 100 times more efficient than existing all-atom statistical potentials/scoring functions including rsRNASP. cgRNASP-CN is available at website: https://github.com/Tan-group/cgRNASP-CN.

Impact of Climate Change on Maize Potential Productivity and the Potential Productivity Gap in Southwest China

The impact of climate change on maize potential productivity and the potential productivity gap in Southwest China（SWC） are investigated in this paper.We analyze the impact of climate change on the photosynthetic,light-temperature,and climatic potential productivity of maize and their gaps in SWC,by using a crop growth dynamics statistical method.During the maize growing season from 1961 to 2010,minimum temperature increased by 0.20℃ per decade（p 〈 0.01） across SWC.The largest increases in average and minimum temperatures were observed mostly in areas of Yunnan Province.Growing season average sunshine hours decreased by 0.2 h day^（-1） per decade（p 〈 0.01） and total precipitation showed an insignificant decreasing trend across SWC.Photosynthetic potential productivity decreased by 298 kg ha^（-1）per decade（p 〈 0.05）.Both light-temperature and climatic potential productivity decreased（p 〈 0.05） in the northeast of SWC,whereas they increased（p 〈 0.05） in the southwest of SWC.The gap between lighttemperature and climatic potential productivity varied from 12 to 2729 kg ha^（-1）,with the high value areas centered in northern and southwestern SWC.Climatic productivity of these areas reached only 10%-24%of the light-temperature potential productivity,suggesting that there is great potential to increase the maize potential yield by improving water management in these areas.In particular,the gap has become larger in the most recent 10 years.Sensitivity analysis shows that the climatic potential productivity of maize is most sensitive to changes in temperature in SWC.The findings of this study are helpful for quantification of irrigation water requirements so as to achieve maximum yield potentials in SWC.