Journal of Bioinformatics and Computational Biology最新文献

Recent advances in deep learning have driven significant progress in drug-target affinity (DTA) prediction. However, many models do not effectively utilize drug molecular graphs or capture long-range protein features, limiting their predictive accuracy. To address these limitations, a novel COLDLNA model is designed for robust DTA prediction. The model employs the Long-range Node Attention Module to refine drug structure representations, while leveraging the Convolutional Attention Module to elucidate critical binding sites by extracting pivotal long-range information from protein amino acid sequences. Compared with the baseline model GraphDTA, COLDLNA reduced the MSE by 12.2% and 11.5% on the Davis and KIBA datasets, respectively. Additionally, its strong generalization ability was further validated on the Human dataset, C. elegans dataset, and in cold-start scenarios.

In exploring optimal strategies for immunotherapy in glioblastoma (GBM), one of the main challenges is enhancing treatment response. To better understand the dynamics of tumor-immune interactions, one applied Bayesian methods to estimate the parameters of glioblastoma immune model by using experimental data. One compared the effects of using uniform prior distributions versus improved prior distributions, which were adjusted based on posterior information, during parameter estimation. In addition, a comparative analysis of the results obtained by using four Markov Chain Monte Carlo (MCMC) sampling algorithms which respectively are Metropolis, DEMetropolis, DEMetropolisZ and NUTS, were performed. The results showed that the improved prior distribution significantly enhanced the accuracy of the model parameter estimates, and reduced the variance of the posterior distribution, but increased computational time and resource demands. Furthermore, DEMetropolisZ provided such efficient sampling and narrower confidence intervals within a shorter time frame, which outperformed the others. In contrast, the efficiency and stability of the Metropolis method were relatively poor. Therefore, the importance of selecting appropriate prior distributions and sampling algorithms to improve both the accuracy and efficiency of model inference were studied. The study provides valuable insights for optimizing GBM immunotherapy strategies and serves as a reference for modeling and parameter estimation of complex biological systems.

Current metagenomic classification methods are limited by short k-mer lengths and database dependency, resulting in insufficient taxonomic resolution at the species and genus level. This study proposes the first method integrating Locality-Sensitive Hashing (LSH) and Bidirectional Long-Short Term Memory (Bi-LSTM) networks for metagenomic sequence classification. The approach reduces runtime reliance on reference databases by learning discriminative features directly from sequences, while supporting long k-mers. The method consists of three key steps: (1) k-mer representation via locality-sensitive hashing, (2) k-mer embedding implementation using the skip-gram model, (3) label assignment to embedded vectors, followed by training in a Bi-LSTM network. Experimental results demonstrate superior classification performance at the genus level compared to existing models. Future work will explore the application of this method in the rapid detection of clinical pathogens.

To date, the study of single-stranded DNA (ssDNA) similarity has focused mainly on the similarity of bases in the same position in the nucleic acid sequence. However, focusing only on the similarity of base sequences has limitations. This similarity evaluation considers only the one-dimensional similarity of ssDNA and cannot fully capture the three-dimensional (3D) structural consistency of aptamers for nucleic acids with 3D structures. Therefore, it is necessary to develop a program that can quickly and accurately evaluate the 3D spatial consistency of ssDNA. To this end, we designed a Visual-SELEX rapid response program, which uses a screening ssDNA sequence set enriched in the DKK1 protein for analysis. The program directly generates a stable 3D structure of ssDNA through coarse-grained simulation and molecular dynamics (MD) simulation, converts the structure into a point cloud model, and then analyzes the similarity of the spatial structure of ssDNA through point cloud model alignment and superposition. The analysis results show that Visual-SELEX can accurately match ssDNAs with dissimilar base fragments but similar 3D spatial structures, providing richer 3D spatial similarity information than sequence similarity comparison alone.

In nanopore sequencing, especially in SELEX-based aptamer discovery, the correct ligation of artificial sequences (primers, adapters, barcodes) is crucial for library quality. Errors at this stage can lead to misidentification of sequences and loss of valuable information. Existing quality control tools lack focused capabilities to assess the positioning and prevalence of these artificial sequences. NanoporeInspect is a web-based tool designed to fill this gap by providing targeted insights into ligation efficacy and systematic biases within sequencing data. NanoporeInspect operates as a user-friendly, web-based platform that leverages a modern software stack with Flask, Celery and Redis to handle scalable and asynchronous task processing, and Plotly to deliver interactive visualizations. Evaluation of NanoporeInspect on various nanopore datasets demonstrated its effectiveness in discerning differences in ligation quality. Libraries with inefficient ligation showed irregular adapter and barcode distributions, indicating preparation issues, while high-quality libraries displayed uniform patterns, reflecting effective ligation.

Studying miRNA activity at the single-cell level presents a significant challenge due to the limitations of existing single-cell technologies in capturing miRNAs. To address this, we introduce two deep learning models: Cross-modality (CM) and single-modality (SM), both based on encoder-decoder architectures. These models predict miRNA expression at both bulk and single-cell levels using mRNA data. We evaluated the performance of CM and SM against the state-of-the-art miRSCAPE approach, using both bulk and single-cell datasets. Our results demonstrate that both CM and SM outperform miRSCAPE in accuracy. Furthermore, incorporating miRNA target information substantially enhanced performance compared to models that utilized all genes. These models provide powerful tools for predicting miRNA expression from single-cell mRNA data.

Shotgun proteomics coupled with high-performance liquid chromatography and mass spectrometry has been instrumental in identifying proteins in complex mixtures. Effective computational approaches are required to automate the spectra interpretation process to handle the vast amount of data collected in a single Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) run. De novo sequencing from MS/MS has emerged as a vital technology for peptide sequencing in proteomics. To enhance the accuracy and practicality of de novo sequencing, previous algorithms have utilized multiple spectra to identify peptide sequences. Here, our study focuses on de novo sequencing of multiple tandem mass spectra of peptides with stable isotope labeling with amino acids in cell culture (SILAC) by incorporating different isotope-labeled amino acids into newly synthesized proteins. Multiple MS/MS spectra for the same peptide sequence are produced by the spectrometer after the SILAC samples undergo processing by LC-MS/MS shotgun proteomics. Taking into consideration the factors such as retention time and precursor ion mass, we aim to identify the peptide sequence with specific SILAC modifications and their locations. To do so, we propose de novo sequencing algorithms to compute the potential candidate peptide sequence by using similarity scores, followed by refinement algorithms to evaluate them. We also use real experimental data to test the algorithms.

Ribonucleic acid (RNA) lies at the heart of the central dogma. It spans the breadth of biological functions, from information storage to gene regulation and catalysis. RNA molecules must attain specific structures to perform these functions, and their structures depend on their sequences. Predicting the structure of RNA has been a central problem in computational biology. Various methods have been developed for this purpose - while some consider the thermodynamics of folding, others abstract away the details behind neural networks (NN). This paper presents a brief overview of the existing tools for predicting RNA secondary structures from a given single RNA sequence. Furthermore, a comparative analysis of the different prediction software packages is also presented. Performance is analyzed by running each of the available software packages on a novel dataset developed using 3D crystal structures of RNA. Software packages considered include those that can predict pseudoknots along with those that cannot. Variation in software performance based on the length and type of RNA is described.

The helical coil is ubiquitous in biological and natural systems, and it is often the basic form in complex structures. This paper considers the question of its dimensionality, D, in biological information as the helical coil goes through recursive coiling as in DNA and RNA molecules in chromatin, in which the D-value is a function of the lengthening of the curve. It is shown that the dimensionality of coiled coils is virtually equal to e. Of the three forms of DNA, the dimensionality of the B-form is nearest to the optimal value, and this might be the reason why it is most common.

Bacillus subtilis (B. subtilis), a bacterium known to enter a competent state spontaneously, has garnered significant attention due to its intricate internal regulatory mechanisms. This study proposes a six-dimensional continuous delay differential equation (DDE) model incorporating two-time delays and a stochastic model that accounts for noise, aimed at delving deeper into the dynamic behaviors of the B. subtilis competence gene regulation circuit. Our investigation reveals that time delays play a crucial role in inducing oscillatory behavior within the continuous DDE model. Analyzing the dynamics of multiple time delays proves to be more intricate than studying a single delay. Furthermore, certain parameter adjustments significantly influence the system's dynamic characteristics. The introduction of noise also triggers oscillations, with the irregular oscillation patterns closely aligning with real-world observations. Intriguingly, the effects of parameters and noise regulation undergo significant changes when time delays are jointly considered. This analysis offers a fresh perspective on understanding B. subtilis competence and provides essential theoretical support for subsequent experimental endeavors in this domain of biomathematics.