Nature https://doi.org/10.1038/s41586-020-2313-x Published online 7 May 2020 - Check for updates Kangpeng Xiao, Junqiong Zhai, Yaoyu Feng, Niu Zhou, Xu Zhang, Jie-Jian Zou, Na Li, Yaqiong Guo, ...Xiaobing Li, Xuejuan Shen, Zhipeng Zhang, Fanfan Shu, Wanyi Huang, Yu Li, Ziding Zhang, Rui-Ai Chen, Ya-Jiang Wu, Shi-Ming Peng, Mian Huang, Wei-Jun Xie, Qin-Hui Cai, Fang-Hui Hou, Wu Chen, Lihua Xiao & Yongyi Shen In this Article, data in Extended Data Table 3 and Extended Data Fig. 4 were mislabelled and attributed incorrectly. Dr Chen, who archives data from clinical specimens by animal ID, provided all samples and data for the Nature Article with matched IDs, including some metagenomic data. Because the numbering system for the metagenomic data was different from that used for the Viruses paper, the informaticians of our team believed that metagenomic data from M2, M3, M4 and M8 provided by Dr Chen were new. The lack of face-to-face meetings imposed by various restrictions due to COVID-19 among the four research groups involved with the study led to a delay in finding out the problem.
Protein kinase CIPK23 and channel subunit AtKC1 are both crucial for the modulation of potassium channel AKT1 as well as for the response to low-potassium stress.
In Arabidopsis (
Arabidopsis ...thaliana
), the Shaker K
+
channel AKT1 conducts K
+
uptake in root cells, and its activity is regulated by CBL1/9-CIPK23 complexes as well as by the AtKC1 channel subunit. CIPK23 and AtKC1 are both involved in the AKT1-mediated low-K
+
(
LK
) response; however, the relationship between them remains unclear. In this study, we screened suppressors of
low-K
+
sensitive
lks1
(
cipk23
) and isolated the
suppressor of lks1
(
sls1
) mutant, which suppressed the leaf chlorosis phenotype of
lks1
under
LK
conditions. Map-based cloning revealed a point mutation in
AtKC1
of
sls1
that led to an amino acid substitution (G322D) in the S6 region of AtKC1. The G322D substitution generated a gain-of-function mutation, AtKC1
D
, that enhanced K
+
uptake capacity and
LK
tolerance in Arabidopsis. Structural prediction suggested that glycine-322 is highly conserved in K
+
channels and may function as the gating hinge of plant Shaker K
+
channels. Electrophysiological analyses revealed that, compared with wild-type AtKC1, AtKC1
D
showed enhanced inhibition of AKT1 activity and strongly reduced K
+
leakage through AKT1 under
LK
conditions. In addition, phenotype analysis revealed distinct phenotypes of
lks1
and
atkc1
mutants in different
LK
assays, but the
lks1 atkc1
double mutant always showed a
LK
-sensitive phenotype similar to that of
akt1
. This study revealed a link between CIPK-mediated activation and AtKC1-mediated modification in AKT1 regulation. CIPK23 and AtKC1 exhibit distinct effects; however, they act synergistically and balance K
+
uptake/leakage to modulate AKT1-mediated
LK
responses in Arabidopsis.
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► We compared two linear gap penalties with two profile-based gap penalties. ► We examine alignment accuracy improvements brought by profile-based gap penalties. ► Profile-based gap ...penalties get limited improvements than linear gap penalties. ► Secondary structure is less powerful in gap penalties than in scoring functions. ► The maximal potential of the indel frequency profiles remains to be discovered.
Profile–profile alignment algorithms have proven powerful for recognizing remote homologs and generating alignments by effectively integrating sequence evolutionary information into scoring functions. In comparison to scoring function, the development of gap penalty functions has rarely been addressed in profile–profile alignment algorithms. Although indel frequency profiles have been used to construct profile-based variable gap penalties in some profile–profile alignment algorithms, there is still no fair comparison between variable gap penalties and traditional linear gap penalties to quantify the improvement of alignment accuracy. We compared two linear gap penalty functions, the traditional affine gap penalty (AGP) and the bilinear gap penalty (BGP), with two profile-based variable gap penalty functions, the Profile-based Gap Penalty used in SP5 (SPGP) and a new Weighted Profile-based Gap Penalty (WPGP) developed by us, on some well-established benchmark datasets. Our results show that profile-based variable gap penalties get limited improvements than linear gap penalties, whether incorporated with secondary structure information or not. Secondary structure information appears less powerful to be incorporated into gap penalties than into scoring functions. Analysis of gap length distributions indicates that gap penalties could stably maintain corresponding distributions of gap lengths in their alignments, but the distribution difference from reference alignments does not reflect the performance of gap penalties. There is useful information in indel frequency profiles, but it is still not good enough for improving alignment accuracy when used in profile-based variable gap penalties. All of the methods tested in this work are freely accessible at http://protein.cau.edu.cn/gppat/.
A protein (domain) is usually classified into one of the following four structural classes: all‐α, all‐β, α/β and α + β. In this paper, a new formulation is proposed to predict the structural class ...of a protein (domain) from its primary sequence. Instead of the amino‐acid composition used widely in the previous structural class prediction work, the auto‐correlation functions based on the profile of amino‐acid index along the primary sequence of the query protein (domain) are used for the structural class prediction. Consequently, the overall predictive accuracy is remarkably improved. For the same training database consisting of 359 proteins (domains) and the same component‐coupled algorithm Chou, K.C. & Maggiora, G.M. (1998) Protein Eng.11, 523–538, the overall predictive accuracy of the new method for the jackknife test is 5–7% higher than the accuracy based only on the amino‐acid composition. The overall predictive accuracy finally obtained for the jackknife test is as high as 90.5%, implying that a significant improvement has been achieved by making full use of the information contained in the primary sequence for the class prediction. This improvement depends on the size of the training database, the auto‐correlation functions selected and the amino‐acid index used. We have found that the amino‐acid index proposed by Oobatake and Ooi, i.e. the average nonbonded energy per residue, leads to the optimal predictive result in the case for the database sets studied in this paper. This study may be considered as an alternative step towards making the structural class prediction more practical.
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