Quantum computing provides alternative encoding and sampling paradigms for protein structure prediction (PSP), but existing quantum-PSP methods are often limited by resource-scaling issues and by discrete or inefficient encodings for continuous coordinates. To address these limitations, we propose QSyncFold, a hybrid quantum-classical neural network framework that combines quantum superposition with differentiable learning. QSyncFold employs ProtaQode to simultaneously achieve reversible continuous-space encoding of residue coordinates and parameterized interaction modeling. This is realized by encoding residue-pair interactions in superposition via a decomposable Any-State RY (ASRY) operator that is efficient for a limited qubit budget. Algorithmically, QSyncFold trades register size for iteration count, reducing the qubit requirement for each iteration from $O(N)$ to $3+\lceil \log _{2} N \rceil $, where $N$ is the number of residues. This design ensures the framework is experimentally viable under NISQ constraints. On short peptide structure prediction, QSyncFold achieved a 5.25-fold improvement in the lDDT metric compared with the Variational Quantum Eigensolver baseline and demonstrated a clear trade-off between qubit budget and convergence speed. While using quantum baselines as the primary comparison, the method performance approaches AlphaFold2 in the short peptide domain, with classical methods serving as background reference. This study advances the precision and methodology of quantum computing in PSP, illustrating a viable pathway for quantum algorithms in biomolecular modeling.

Protein-nucleic acid interactions play a fundamental and critical role in a wide range of life activities. Accurate identification of nucleic acid-binding residues helps to understand the intrinsic mechanisms of the interactions. However, the accuracy and interpretability of existing computational methods for recognizing nucleic acid-binding residues need to be further improved. Here, we propose a novel method called GeSite based the domain-adaptive protein language model and E(3)-equivariant graph neural network. Prediction results across multiple benchmark test sets demonstrate that GeSite is superior or comparable to state-of-the-art prediction methods. The MCC values of GeSite are 0.522 and 0.326 for the one DNA-binding residue test set and one RNA-binding resi-due test set, which are 0.57 and 38.14% higher than that of the second-best method, respectively. Detailed experi-mental results suggest that the advanced performance of GeSite lies in the well-designed nucleic acid-binding pro-tein adaptive language model. Additionally, interpretabil-ity analysis exposes the perception of the prediction mod-el on various remote and close functional domains, which is the source of its discernment ability.

Predicting protein-nucleic acid binding sites is crucial for understanding the intrinsic mechanisms of protein-nucleic acid interactions, thereby aiding drug development and gene engineering. Although many computational methods have attempted to address this issue, accuracy and generalizability remain to be improved due to the lack of exploration of complex structure and sequence evolutionary information. Here, we present ChaiSite, a novel method that integrates the embedded knowledge of the large protein language model and a complex structure prediction model by a customized hybrid model that integrates an Equivariant Graph Neural Network and the Transformer for the first time. The proposed ChaiSite consistently outperforms existing approaches in predicting protein-nucleic acid binding sites across multiple datasets. On independent benchmark datasets DNA-129_Test, DNA-181_Test, RNA-117_Test, and RNA-285_Test, ChaiSite achieves AUC scores of$0.945,0.930$, 0.898, and 0.859, respectively, representing improvements of 0.43, 1.20, 4.30, and 0.94% over the second-best methods, demonstrating superior predictive accuracy.

Accurate identification of nucleic acid-binding residues is crucial for understanding protein-nucleic acid interactions, which play a key role in gene expression research and the discovery of regulatory mechanisms. Despite numerous computational efforts to address this challenge, achieving high accuracy remains difficult due to the complexity of extracting meaningful insights from proteins. Here, we introduce MegSite, a novel multimodal protein language model-informed method that integrates discriminative knowledge from protein sequence, structure, and function. This work presents the first integration of ESM3 multimodal features for nucleic acid-binding site prediction. MegSite significantly outperforms existing prediction methods, as evidenced by its performance on multiple independent test sets. The Matthews correlation coefficient values achieved by MegSite on DNA-129_Test, DNA-181_Test, RNA-117_Test, and RNA-285_Test are 0.567, 0.444, 0.411, and 0.421, representing the improvements of 2.72%, 7.66%, 1.22% and 6.58% over the second-best method separately. Notably, MegSite demonstrates robust performance even on proteins with low structural similarity, surpassing the previous structure-based methods. Furthermore, this method is seamlessly extendable to the predicted protein structure and a newly released RNA-binding residue test set with high accuracy, highlighting its broad applicability. Comprehensive experimental results reveal that the superior performance of MegSite is attributed to its effective integration of multimodal protein knowledge.

Protein-nucleic acid interactions play a very important role in a variety of biological activities. Accurate identification of nucleic acid-binding residues is a critical step in understanding the interaction mechanisms. Although many computationally based methods have been developed to predict nucleic acid-binding residues, challenges remain. In this study, a fast and accurate sequence-based method, called ESM-NBR, is proposed. In ESM-NBR, we first use the large protein language model ESM2 to extract discriminative biological properties feature representation from protein primary sequences; then, a multi-task deep learning model composed of stacked bidirectional long short-term memory (BiLSTM) and multi-layer perceptron (MLP) networks is employed to explore common and private information of DNA- and RNA-binding residues with ESM2 feature as input. Experimental results on benchmark data sets demonstrate that the prediction performance of ESM2 feature representation comprehensively outperforms evolutionary information-based hidden Markov model (HMM) features. Meanwhile, the ESM-NBR obtains the MCC values for DNA-binding residues prediction of 0.427 and 0.391 on two independent test sets, which are 18.61 and 10.45% higher than those of the second-best methods, respectively. Moreover, by completely discarding the time-cost multiple sequence alignment process, the prediction speed of ESM-NBR far exceeds that of existing methods (5.52s for a protein sequence of length 500, which is about 16 times faster than the second-fastest method). A user-friendly standalone package and the data of ESM-NBR are freely available for academic use at: https://github.com/wwzll123/ESM-NBR.

This paper presents the unscented Kalman filter (UKF) with reduced simplex sigma-point for the navigation system in a small scale unmanned helicopter. UKF is widely applied to nonlinear systems. However, the disadvantage of traditional UKF is the high computational cost caused by the unscented transformation step. The computational cost is proportional to the number of the constructed sigma-points. Therefore a reduced simplex sigma-point selection is proposed to be practically applied for the sensor fusion on the unmanned helicopter. The simulation and experimental results verify the computational load reduction.