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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 accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi‐scale network, i.e., Res2Net, for improved prediction of inter‐residue geometries, including distance and orientations. The second is an attention‐based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM‐score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi‐scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/. [ABSTRACT FROM AUTHOR]

Arbuscular mycorrhizal fungi (AMF) form a mutualistic association with plant roots by improving phosphorus (P) uptake of the host plant. Previous studies demonstrated that AMF exert various influences on soil enzyme activity; however, quantification of these effects has not been published to date. This study explored the effect of AMF on soil enzyme activity by meta‐analysis of a current dataset. The AMF inoculation increased the activities of most soil enzymes, with the exception of polyphenol oxidase. Across all observations, AMF enhanced soil enzyme activity optimally at smaller soil available P and neutral soil pH conditions. This effect was positively correlated with the increasing ratios of soil available P and plant biomass. The results of this study indicate that AMF can enhance the release of soil nutrients required for plant growth in response to increased soil enzyme activity. The results obtained emphasize that the effect of AMF on soil enzyme activity is strongly abiotic context‐dependent and coupled with beneficial effects for plant growth. This has relevant implications for AMF application for sustainable agriculture. Highlights: Meta‐analysis of 56 studies shows that AMF usually increase soil enzyme activity.Neutral pH and low available phosphorus lead to optimal AMF influence on soil enzyme activity.Plant growth promotion by AMF can lead to an increase of soil enzyme activity.AMF inoculation offers positive implications for agricultural application. [ABSTRACT FROM AUTHOR]

We provide first large scale analysis of the peculiarities of surface areas of 5658 dissimilar (below 50% sequence similarity) proteins with known 3D-structures that bind to proteins, DNA or RNAs. We show here that area of the protein surface is highly correlated with the protein length. The size of the interface surface is only modestly correlated with the protein size, except for RNA-binding proteins where larger proteins are characterized by larger interfaces. Disordered proteins with disordered interfaces are characterized by significantly larger per-residue areas of their surfaces and interfaces when compared to the structured proteins. These result are applicable for proteins involved in interaction with DNA, RNA, and proteins and suggest that disordered proteins and binding regions are less compact and more likely to assume extended shape. We demonstrate that disordered protein binding residues in the interfaces of disordered proteins drive the increase in the per residue area of these interfaces. Our results can be used to predict in silico whether a given protomer from the DNA, RNA or protein complex is likely to be disordered in its unbound form. [ABSTRACT FROM AUTHOR]

ABSTRACT Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/.Proteins 2014; 82:145-158. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]