We report the results of two fully automated structure prediction pipelines, “Zhang‐Server” and “QUARK”, in CASP13. The pipelines were built upon the C‐I‐TASSER and C‐QUARK programs, which in turn are based on I‐TASSER and QUARK but with three new modules: (a) a novel multiple sequence alignment (MSA) generation protocol to construct deep sequence‐profiles for contact prediction; (b) an improved meta‐method, NeBcon, which combines multiple contact predictors, including ResPRE that predicts contact‐maps by coupling precision‐matrices with deep residual convolutional neural‐networks; and (c) an optimized contact potential to guide structure assembly simulations. For 50 CASP13 FM domains that lacked homologous templates, average TM‐scores of the first models produced by C‐I‐TASSER and C‐QUARK were 28% and 56% higher than those constructed by I‐TASSER and QUARK, respectively. For the first time, contact‐map predictions demonstrated usefulness on TBM domains with close homologous templates, where TM‐scores of C‐I‐TASSER models were significantly higher than those of I‐TASSER models with a P‐value <.05. Detailed data analyses showed that the success of C‐I‐TASSER and C‐QUARK was mainly due to the increased accuracy of deep‐learning‐based contact‐maps, as well as the careful balance between sequence‐based contact restraints, threading templates, and generic knowledge‐based potentials. Nevertheless, challenges still remain for predicting quaternary structure of multi‐domain proteins, due to the difficulties in domain partitioning and domain reassembly. In addition, contact prediction in terminal regions was often unsatisfactory due to the sparsity of MSAs. Development of new contact‐based domain partitioning and assembly methods and training contact models on sparse MSAs may help address these issues.
Novel layered microporous polymers with high surface area and gas storage were prepared by low-cost solvent knitting method.
A novel metalporphyrin-based microporous organic polymer (HUST-1-Co), which exhibits a high CO2 uptake and efficient chemical conversion of CO2 under ambient conditions, is reported.
We report the results of residue‐residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)‐based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact‐map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end‐to‐end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free‐modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long‐range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.
Motivation Comparison of RNA 3D structures can be used to infer functional relationship of RNA molecules. Most of the current RNA structure alignment programs are built on size-dependent scales, which complicate the interpretation of structure and functional relations. Meanwhile, the low speed prevents the programs from being applied to large-scale RNA structural database search. Results We developed an open-source algorithm, RNA-align, for RNA 3D structure alignment which has the structure similarity scaled by a size-independent and statistically interpretable scoring metric. Large-scale benchmark tests show that RNA-align significantly outperforms other state-of-the-art programs in both alignment accuracy and running speed. The major advantage of RNA-align lies at the quick convergence of the heuristic alignment iterations and the coarse-grained secondary structure assignment, both of which are crucial to the speed and accuracy of RNA structure alignments. Availability and implementation https://zhanglab.ccmb.med.umich.edu/RNA-align/. Supplementary information Supplementary data are available at Bioinformatics online.
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