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Summary: It is well understood that agricultural management influences arbuscular mycorrhizal (AM) fungi, but there is controversy about whether farmers should manage for AM symbiosis.We assessed AM fungal communities colonizing wheat roots for three consecutive years in a long‐term (> 14 yr) tillage and fertilization experiment. Relationships among mycorrhizas, crop performance, and soil ecosystem functions were quantified.Tillage, fertilizers and continuous monoculture all reduced AM fungal richness and shifted community composition toward dominance of a few ruderal taxa. Rhizophagus and Dominikia were depressed by tillage and/or fertilization, and their abundances as well as AM fungal richness correlated positively with soil aggregate stability and nutrient cycling functions across all or no‐tilled samples. In the field, wheat yield was unrelated to AM fungal abundance and correlated negatively with AM fungal richness. In a complementary glasshouse study, wheat biomass was enhanced by soil inoculum from unfertilized, no‐till plots while neutral to depressed growth was observed in wheat inoculated with soils from fertilized and conventionally tilled plots.This study demonstrates contrasting impacts of low‐input and conventional agricultural practices on AM symbiosis and highlights the importance of considering both crop yield and soil ecosystem functions when managing mycorrhizas for more sustainable agroecosystems. [ABSTRACT FROM AUTHOR]

The disruption of the energy or nutrient balance triggers endoplasmic reticulum (ER) stress, a process that mobilizes various strategies, collectively called the unfolded protein response (UPR), which reestablish homeostasis of the ER and cell. Activation of the UPR stress sensor IRE1α (inositol-requiring enzyme 1α) stimulates its endoribonuclease activity, leading to the generation of the mRNA encoding the transcription factor XBP1 (X-box binding protein 1), which regulates the transcription of genes encoding factors involved in controlling the quality and folding of proteins. We found that the activity of IRE1α was regulated by the ER oxidoreductase PDIA6 (protein disulfide isomerase A6) and the microRNA miR-322 in response to disruption of ER Ca2+ homeostasis. PDIA6 interacted with IRE1α and enhanced IRE1α activity as monitored by phosphorylation of IRE1α and XBP1 mRNA splicing, but PDIA6 did not substantially affect the activity of other pathways that mediate responses to ER stress. ER Ca2+ depletion and activation of store-operated Ca2+ entry reduced the abundance of the microRNA miR-322, which increased PDIA6 mRNA stability and, consequently, IRE1α activity during the ER stress response. In vivo experiments with mice and worms showed that the induction of ER stress correlated with decreased miR-322 abundance, increased PDIA6 mRNA abundance, or both. Together, these findings demonstrated that ER Ca2+, PDIA6, IRE1α, and miR-322 function in a dynamic feedback loop modulating the UPR under conditions of disrupted ER Ca2+ homeostasis.

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein–protein and protein–nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/. [ABSTRACT FROM AUTHOR]

Motivation Significant progress has been achieved in distance-based protein folding, due to improved prediction of inter-residue distance by deep learning. Many efforts are thus made to improve distance prediction in recent years. However, it remains unknown what is the best way of objectively assessing the accuracy of predicted distance. Results A total of 19 metrics were proposed to measure the accuracy of predicted distance. These metrics were discussed and compared quantitatively on three benchmark datasets, with distance and structure models predicted by the trRosetta pipeline. The experiments show that a few metrics, such as distance precision , have a high correlation with the model accuracy measure TM-score (Pearson's correlation coefficient >0.7). In addition, the metrics are applied to rank the distance prediction groups in CASP14. The ranking by our metrics coincides largely with the official version. These data suggest that the proposed metrics are effective for measuring distance prediction. We anticipate that this study paves the way for objectively monitoring the progress of inter-residue distance prediction. A web server and a standalone package are provided to implement the proposed metrics. Availability and implementation http://yanglab.nankai.edu.cn/APD. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. Results We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L /10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. Availability and implementation The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Threading is one of the most effective methods for protein structure prediction. In recent years, the increasing accuracy in protein contact map prediction opens a new avenue to improve the performance of threading algorithms. Several preliminary studies suggest that with predicted contacts, the performance of threading algorithms can be improved greatly. There is still much room to explore to make better use of predicted contacts. Results We have developed a new contact-assisted threading algorithm named CATHER using both conventional sequential profiles and contact map predicted by a deep learning-based algorithm. Benchmark tests on an independent test set and the CASP12 targets demonstrated that CATHER made significant improvement over other methods which only use either sequential profile or predicted contact map. Our method was ranked at the Top 10 among all 39 participated server groups on the 32 free modeling targets in the blind tests of the CASP13 experiment. These data suggest that it is promising to push forward the threading algorithms by using predicted contacts. Availability and implementation http://yanglab.nankai.edu.cn/CATHER/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects. Results Based on the improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning-based contact prediction method. MapPred consists of two component methods, DeepMSA and DeepMeta, both trained with the residual neural networks. DeepMSA was inspired by the recent method DeepCov, which was trained on 441 matrices of covariance features. By considering the symmetry of contact map, we reduced the number of matrices to 231, which makes the training more efficient in DeepMSA. Experiments show that DeepMSA outperforms DeepCov by 10–13% in precision. DeepMeta works by combining predicted contacts and other sequence profile features. Experiments on three benchmark datasets suggest that the contribution from the metagenome sequence data is significant with P-values less than 4.04E-17. MapPred is shown to be complementary and comparable the state-of-the-art methods. The success of MapPred is attributed to three factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA and optimized training by the residual neural networks. Availability and implementation http://yanglab.nankai.edu.cn/mappred/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available for RNA solvent accessibility prediction. However, its performance is unsatisfactory for protein-free RNAs. Results We developed RNAsol, a new algorithm to predict RNA solvent accessibility. RNAsol was built based on improved sequence profiles from the covariance models and trained with the long short-term memory (LSTM) neural networks. Independent tests on the same datasets from RNAsnap show that RNAsol achieves the mean Pearson's correlation coefficient (PCC) of 0.43/0.26 for the protein-bound/protein-free RNA molecules, which is 26.5%/136.4% higher than that of RNAsnap. When the training set is enlarged to include both types of RNAs, the PCCs increase to 0.49 and 0.46 for protein-bound and protein-free RNAs, respectively. The success of RNAsol is attributed to two aspects, including the improved sequence profiles constructed by the sequence-profile alignment and the enhanced training by the LSTM neural networks. Availability and implementation http://yanglab.nankai.edu.cn/RNAsol/ Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The interactions between protein and nucleic acids play a key role in various biological processes. Accurate recognition of the residues that bind nucleic acids can facilitate the study of uncharacterized protein–nucleic acids interactions. The accuracy of existing nucleic acids-binding residues prediction methods is relatively low. Results In this work, we introduce NucBind, a novel method for the prediction of nucleic acids-binding residues. NucBind combines the predictions from a support vector machine-based ab-initio method SVMnuc and a template-based method COACH-D. SVMnuc was trained with features from three complementary sequence profiles. COACH-D predicts the binding residues based on homologous templates identified from a nucleic acids-binding library. The proposed methods were assessed and compared with other peering methods on three benchmark datasets. Experimental results show that NucBind consistently outperforms other state-of-the-art methods. Though with higher accuracy, similar to many other ab-initio methods, cross prediction between DNA and RNA-binding residues was also observed in SVMnuc and NucBind. We attribute the success of NucBind to two folds. The first is the utilization of improved features extracted from three complementary sequence profiles in SVMnuc. The second is the combination of two complementary methods: the ab-initio method SVMnuc and the template-based method COACH-D. Availability and implementation http://yanglab.nankai.edu.cn/NucBind Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: As protein structure is more conserved than sequence during evolution, multiple structure alignment can be more informative than multiple sequence alignment, especially for distantly related proteins. With the rapid increase of the number of protein structures in the Protein Data Bank, it becomes urgent to develop efficient algorithms for multiple structure alignment. Results: A new multiple structure alignment algorithm (mTM-align) was proposed, which is an extension of the highly efficient pairwise structure alignment program TM-align. The algorithm was benchmarked on four widely used datasets, HOMSTRAD, SABmark_sup, SABmark_twi and SISY-multiple, showing that mTM-align consistently outperforms other algorithms. In addition, the comparison with the manually curated alignments in the HOMSTRAD database shows that the automated alignments built by mTM-align are in general more accurate. Therefore, mTM-align may be used as a reliable complement to construct multiple structure alignments for real-world applications. [ABSTRACT FROM AUTHOR]