RNA threading with secondary structure and sequence profile.

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.

RNALigands: a database and web server for RNA-ligand interactions.

RNA molecules can fold into complex and stable 3D structures, allowing them to carry out important genetic, structural, and regulatory roles inside the cell. These complex structures often contain 3D pockets made up of secondary structural motifs that can be potentially targeted by small molecule ligands. Indeed, many RNA structures in PDB contain bound small molecules, and high-throughput experimental studies have generated a large number of interacting RNA and ligand pairs. There is considerable interest in developing small molecule lead compounds targeting viral RNAs or those RNAs implicated in neurological diseases or cancer. We hypothesize that RNAs that have similar secondary structural motifs may bind to similar small molecule ligands. Toward this goal, we established a database collecting RNA secondary structural motifs and bound small molecule ligands. We further developed a computational pipeline, which takes as input an RNA sequence, predicts its secondary structure, extracts structural motifs, and searches the database for similar secondary structure motifs and interacting small molecule. We demonstrated the utility of the server by querying α-synuclein mRNA 5' UTR sequence and finding potential matches which were validated as correct. The server is publicly available at http://RNALigands.ccbr.utoronto.ca The source code can also be downloaded at https://github.com/SaisaiSun/RNALigands.

A unified approach to protein domain parsing with inter-residue distance matrix.

MOTIVATION: It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing. RESULTS: Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis. AVAILABILITY AND IMPLEMENTATION: https://yanglab.nankai.edu.cn/UniDoc/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Compact and low-insertion-loss polarization beam-splitting multimode filter using pixelated waveguides.

A polarization beam-splitting multimode filter using pixelated waveguides has been presented and experimentally demonstrated in this paper. Finite difference time domain method and direct binary search optimization algorithm are employed to optimize pixelated waveguides to realize compact size, broad bandwidth, large extinction ratio, low insertion loss, and good polarization extinction ratio. Measurement results show that, in a wavelength range from 1520 to 1560 nm, for the fabricated device working at transverse-electric polarization, the measured insertion loss is less than 1.23 dB and extinction ratio is larger than 15.14 dB, while for transverse-magnetic polarization, the corresponding insertion loss lower than 0.74 dB and extinction ratio greater than 15.50 dB are realized. The measured polarization extinction ratio larger than 15.02 dB is achieved. The device's length is only 15.4 µm.

Sub-gigahertz bandwidth all-optical tunable differentiator with high-energy efficiency based on a micro-ring resonator.

We propose and demonstrate a tunable fractional-order photonic differentiator (DIFF) that can process input pulses with a sub-gigahertz bandwidth. Our scheme utilizes the self-induced optical modulation effect observed in a silicon-on-insulator micro-ring resonator. Gaussian-like pulses with varying pulse widths between 7.5 and 20 ns are employed for differentiation, achieving an energy efficiency over 45%, to the best of our knowledge, which surpasses all previously reported schemes for input pulses with a sub-gigahertz bandwidth. We simulate the temporal dynamics of pulses to gain insight into the physical mechanisms underlying the differentiated outputs and provide a method for differentiation order adjustment, which is experimentally realized using an all-optical pump-probe technique.

Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15.

We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequence alignments (MSAs). Top MSAs were then selected for the subsequent step of structure prediction. We utilized trRosettaX2 and AlphaFold2 for monomer structure prediction (group name Yang-Server), and AlphaFold-Multimer for multimer structure prediction (group name Yang-Multimer). Yang-Server and Yang-Multimer are ranked at the top and the fourth, respectively, for monomer and multimer structure prediction. For 94 monomers, the average TM-score of the predicted structure models by Yang-Server is 0.876, compared to 0.798 by the default AlphaFold2 (i.e., the group NBIS-AF2-standard). For 42 multimers, the average DockQ score of the predicted structure models by Yang-Multimer is 0.464, compared to 0.389 by the default AlphaFold-Multimer (i.e., the group NBIS-AF2-multimer). Detailed analysis of the results shows that several factors contribute to the improvement, including improved MSAs, iterated modeling for large targets, interplay between monomer and multimer structure prediction for intertwined structures, etc. However, the structure predictions for orphan proteins and multimers remain challenging, and breakthroughs in this area are anticipated in the future.

Human host status inference from temporal microbiome changes via recurrent neural networks.

With the rapid increase in sequencing data, human host status inference (e.g. healthy or sick) from microbiome data has become an important issue. Existing studies are mostly based on single-point microbiome composition, while it is rare that the host status is predicted from longitudinal microbiome data. However, single-point-based methods cannot capture the dynamic patterns between the temporal changes and host status. Therefore, it remains challenging to build good predictive models as well as scaling to different microbiome contexts. On the other hand, existing methods are mainly targeted for disease prediction and seldom investigate other host statuses. To fill the gap, we propose a comprehensive deep learning-based framework that utilizes longitudinal microbiome data as input to infer the human host status. Specifically, the framework is composed of specific data preparation strategies and a recurrent neural network tailored for longitudinal microbiome data. In experiments, we evaluated the proposed method on both semi-synthetic and real datasets based on different sequencing technologies and metagenomic contexts. The results indicate that our method achieves robust performance compared to other baseline and state-of-the-art classifiers and provides a significant reduction in prediction time.

Toward the assessment of predicted inter-residue distance.

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.

Polarization-insensitive antisymmetric multimode waveguide Bragg grating filter based on an SiN-Si dual-layer stack.

A polarization-insensitive multimode antisymmetric waveguide Bragg grating (MASWBG) filter based on an SiN-Si dual-layer stack is demonstrated. Carefully optimized grating corrugations patterned on the sidewall of a silicon waveguide and SiN overlay are used to perturbate TE and TM modes, respectively. Furthermore, the lateral-shift apodization technique is utilized to improve the sidelobe suppression ratio (SLSR). A good overlap between the passbands measured in TE and TM polarization states is obtained. Insertion losses, SLSRs, and 3-dB bandwidths of measured passbands in TE/TM polarizations are 1/1.72 dB, 18.5/19.1 dB, and 5.1/3.5 nm, respectively.

Low-loss and broadband polarization-insensitive high-order mode pass filter based on photonic crystal and tapered coupler.

In this Letter, a polarization-insensitive high-order mode pass filter is presented, designed, and experimentally demonstrated. When TE0, TM0, TE1, and TM1 modes are injected into the input port, TM0 and TE0 modes are filtered, and TE1 and TM1 modes exit from the output port. To attain compactness, broad bandwidth, low insertion loss, excellent extinction ratio, and polarization-insensitive property, the finite difference time domain method and direct-binary-search or particle swarm optimization algorithm are employed for the optimization of structural parameters of the photonic crystal region and the coupling region in the tapered coupler. Measurement results reveal that, for the fabricated filter working at TE polarization, the extinction ratio and insertion loss are 20.42 and 0.32 dB at 1550 nm. In the case of TM polarization, the corresponding extinction ratio and insertion loss are 21.43 and 0.30 dB. Within a bandwidth from 1520 to 1590 nm, insertion loss smaller than 0.86 dB and extinction ratio larger than 16.80 dB are obtained for the fabricated filter working at TE polarization, while in the case of TM polarization, insertion loss lower than 0.79 dB and extinction ratio greater than 17.50 dB are realized.

Sparse 2-D optical phased array with large grating-lobe-free steering range based on an aperiodic grid.

Two-dimensional (2-D) optical phased arrays (OPAs) usually suffer from limited scan ranges and small aperture sizes. To overcome these bottlenecks, we utilize an aperiodic 32 × 32 grid to increase the beam scanning range and furthermore distribute 128 grating antennas sparsely among 1024 grid points so as to reduce the array element number. The genetic algorithm is used to optimize the uneven grid spacings and the sparse distribution of grating antennas. With these measures, a 128-channel 2-D OPA operating at 1550 nm realizes a grating-lobe-free steering range of 53° × 16°, a field of view of 24° × 16°, a beam divergence of 0.31° × 0.49°, and a sidelobe suppression ratio of 9 dB.

Improved protein structure prediction using predicted interresidue orientations.

The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.

Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network.

It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines.

Recognition of small molecule-RNA binding sites using RNA sequence and structure.

MOTIVATION: RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule-RNA binding sites. RESULTS: We developed a novel method RNAsite to predict small molecule-RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure's flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/RNAsite. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA inter-nucleotide 3D closeness prediction by deep residual neural networks.

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.

Improved estimation of model quality using predicted inter-residue distance.

MOTIVATION: Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. RESULTS: Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single- and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive with other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/QDistance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Wavelet convolutional neural network for robust and fast temperature measurements in Brillouin optical time domain reflectometry.

In this paper, a wavelet convolutional neural network (WNN) consisting of a one-dimensional (1D) convolutional neural network and a self-adaptive wavelet neural network has been proposed and demonstrated experimentally for temperature measurement in a Brillouin optical time domain reflectometry (BOTDR) system. Based on the analysis of the system noise, it follows the Gaussian white noise distribution along the time-related sensing distance. The impact of the noise in time-domain on the measured Brillouin gain spectra (BGSs) could be neglected, so that the BGSs in the fiber can be regarded as a series of 1D input data of the proposed WNN. Different self-adaptive wavelet activation functions connected to each output of the full-connection network are adopted to realize the multi-scaled analysis and the scale translation, which can obtain more local characteristics in frequency-domain. The output extracted by the WNN is Brillouin frequency shift (BFS), which presents linearity correlation to the actual temperature. Considering the multi-parameters including different frequency ranges, signal-to-noise-ratios (SNRs), BFSs and spectral widths (SWs), a general model of the proposed WNN is trained to handle more extreme cases, in which it doesn't require retraining for different single-mode (SM) optical fibers in BOTDR sensing system. The performances of the WNN are compared with other two techniques, the Lorentzian curve fitting based on Levenberg-Marquardt (LM) algorithm and the basic neural network (NN) containing input and output layers together with two hidden layers. Both the simulated and measured results show that the WNN has better robustness and flexibility than the LM and the NN. Besides, the computational accuracy of the WNN is improved and the fluctuation of that is slighter, especially when the SNR is less than 11 dB. Moreover, the WNN takes approximately 0.54 s to measure the temperature from the 18,000 collected BGSs transmitted through the 18 km SM optical fiber. The calculating time of the WNN is greatly reduced by three orders of magnitude in comparison with that of the LM, and is comparable to that of the NN. It proves that the proposed WNN may provide a feasible or even better scheme for the robust and fast temperature measurement in BOTDR system.

High linearity silicon DC Kerr modulator enhanced by slow light for 112 Gbit/s PAM4 over 2 km single mode fiber transmission.

We demonstrate a high efficiency, high linearity and high-speed silicon Mach-Zehnder modulator based on the DC Kerr effect enhanced by slow light. The two modulation arms based on 500-µm-long grating waveguides are embedded with PN and PIN junctions, respectively. A comprehensive comparison between the two modulation arms reveals that insertion loss, bandwidth and modulation linearity are improved significantly after employing the DC Kerr effect. The complementary advantages of the slow light and the DC Kerr effect enable a modulation efficiency of 0.85 V·cm, a linearity of 115 dB·Hz2/3, and a bandwidth of 30 GHz when the group index of slow light is set to 10. Furthermore, 112 Gbit/s PAM4 transmission over 2 km standard single mode fiber (SSMF) with bit error ratio (BER) below the soft decision forward error correction (SD-FEC) threshold is also demonstrated.

Silicon-based high-power traveling wave photodetector with inductive gain peaking.

We demonstrate Ge/Si high-power and high-speed distributed traveling wave photodetectors (TWPD) by using the inductive gain peaking technique. Input terminals of TW electrodes are open to enhance RF output efficiencies to output loads. Furthermore, optimized on-chip spiral inductors are incorporated at output terminals of TW electrodes to alleviate bandwidth degradations caused by the absences of matching impedances. A comprehensive equivalent circuit model is developed to calculate the frequency response of this scheme. It is used to optimize the design, and then is validated by measurement results. After inducing on-chip inductors, the bandwidths of 4-stage and 8-stage TWPDs are improved from 32 to 44 GHz and 16 to 24 GHz, respectively. Maximum RF output powers of 4-stage and 8-stage TWPDs with on-chip inductors are measured to be 5.7 dBm and 9.4 dBm at 20 GHz, respectively.