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Motivation RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory. Results Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3–40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection. Availability and implementation https://yanglab.qd.sdu.edu.cn/RNAthreader [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 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]

The aim is to reduce the elastic deformation of the web and side walls of low-stiffness thin-walled beams when the floating fixture method is used. This paper takes the number and position of fixture points as the optimization variables, establishes a calculation model of elastic deformation, and constructs the objective function of maximum total elastic deformation. An optimized solution utilizing the augmented multiplier method is employed, which forms the basis for the fixture layout optimization method to reduce the elastic deformation of low-stiffness thin-walled beams. A theoretical calculation, simulation analysis, and the fixture layout optimization of total maximum elastic deformation were completed using an aluminum alloy low-stiffness thin-walled beam as an example. The results show that based on the optimized layout, the average relative error between the calculated value and the simulated value of total maximum elastic deformation is 17.43%, and the simulated value of maximum elastic deformation is reduced by 48.49% after optimizing the fixture layout. The measured value is reduced by 0.02 mm on average, and deformation is reduced by 74.07%, which verifies the effectiveness of the floating fixture layout optimization control of machining elastic deformation proposed in this paper. [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]

The machining of monolithic components is prone to deformation, which causes a large amount of production resources to be wasted. Therefore, studying the process of initial configuration optimization method to reduce machining deformation is of great significance for the precision machining of monolithic components. In this paper, the concept and mathematical expression of the initial process configuration are proposed for aluminum alloy U-shaped cross-section stringer structural components. The initial configuration optimization problem is formulated with the initial process configuration as the variable and the objective of minimizing machining deformation. An iterative method is used to optimize the initial process configuration. The machining deformation after the optimized configuration was analyzed by finite element simulation and theoretical calculation, and the deformation data error of the two was less than 20%, which verified the correctness of the method proposed in this paper. Machining tests were carried out, and the results show that the proposed initial configuration optimization method can control the deformation of the U-shaped long truss parts within ± 0.06 mm/m, which is 80–90% less than that before optimization. This initial configuration optimization method has a positive effect on controlling machining deformation. [ABSTRACT FROM AUTHOR]