GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference.

The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor-gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN.

ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction.

Efficient and accurate recognition of protein-DNA interactions is vital for understanding the molecular mechanisms of related biological processes and further guiding drug discovery. Although the current experimental protocols are the most precise way to determine protein-DNA binding sites, they tend to be labor-intensive and time-consuming. There is an immediate need to design efficient computational approaches for predicting DNA-binding sites. Here, we proposed ULDNA, a new deep-learning model, to deduce DNA-binding sites from protein sequences. This model leverages an LSTM-attention architecture, embedded with three unsupervised language models that are pre-trained on large-scale sequences from multiple database sources. To prove its effectiveness, ULDNA was tested on 229 protein chains with experimental annotation of DNA-binding sites. Results from computational experiments revealed that ULDNA significantly improves the accuracy of DNA-binding site prediction in comparison with 17 state-of-the-art methods. In-depth data analyses showed that the major strength of ULDNA stems from employing three transformer language models. Specifically, these language models capture complementary feature embeddings with evolution diversity, in which the complex DNA-binding patterns are buried. Meanwhile, the specially crafted LSTM-attention network effectively decodes evolution diversity-based embeddings as DNA-binding results at the residue level. Our findings demonstrated a new pipeline for predicting DNA-binding sites on a large scale with high accuracy from protein sequence alone.

MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.

MOTIVATION: Drug-target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target prediction have received much attention and made many advances, they are still a challenging task that requires further research. The main challenges are manifested as follows: (i) most graph neural network-based methods only consider the information of the first-order neighboring nodes (drug and target) in the graph, without learning deeper and richer structural features from the higher-order neighboring nodes. (ii) Existing methods do not consider both the sequence and structural features of drugs and targets, and each method is independent of each other, and cannot combine the advantages of sequence and structural features to improve the interactive learning effect. RESULTS: To address the above challenges, a Multi-view Integrated learning Network that integrates Deep learning and Graph Learning (MINDG) is proposed in this study, which consists of the following parts: (i) a mixed deep network is used to extract sequence features of drugs and targets, (ii) a higher-order graph attention convolutional network is proposed to better extract and capture structural features, and (iii) a multi-view adaptive integrated decision module is used to improve and complement the initial prediction results of the above two networks to enhance the prediction performance. We evaluate MINDG on two dataset and show it improved DTI prediction performance compared to state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: https://github.com/jnuaipr/MINDG.

Clara cell 10 (CC10) protein attenuates allergic airway inflammation by modulating lung dendritic cell functions.

Allergic asthma is a complex inflammatory disorder predominantly orchestrated by T helper 2 (Th2) lymphocytes. The anti-inflammatory protein Clara Cell 10-kDa (CC10), also known as secretoglobin family 1A member 1 (SCGB1A1), shows promise in modulating respiratory diseases. However, its precise role in asthma remains unclear. This study examines the potential of CC10 to suppress allergic asthma inflammation, specifically assessing its regulatory effects on Th2 cell responses and dendritic cells (DCs). Lower CC10 levels in asthma were observed and correlated with increased IgE and lymphocytes. Cc10-/- mice exhibited exacerbated allergic airway inflammation marked by increased inflammatory cell infiltration, Th2 cytokines, serum antigen-specific IgE levels, and airway hyperresponsiveness (AHR) in house dust mite (HDM)-induced models. Conversely, recombinant CC10 significantly attenuated these inflammatory responses. Intriguingly, CC10 did not directly inhibit Th cell activation but significantly downregulated the population of CD11b+CD103- DCs subsets in lungs of asthmatic mice and modulated the immune activation functions of DCs through NF-κB signaling pathway. The mixed lymphocyte response assay revealed that DCs mediated the suppressive effect of CC10 on Th2 cell responses. Collectively, CC10 profoundly mitigates Th2-type allergic inflammation in asthma by modulating lung DC phenotype and functions, highlighting its therapeutic potential for inflammatory airway conditions and other related immunological disorders.

Zbtb16 increases susceptibility of atrial fibrillation in type 2 diabetic mice via Txnip-Trx2 signaling.

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and recent epidemiological studies suggested type 2 diabetes mellitus (T2DM) is an independent risk factor for the development of AF. Zinc finger and BTB (broad-complex, tram-track and bric-a-brac) domain containing 16 (Zbtb16) serve as transcriptional factors to regulate many biological processes. However, the potential effects of Zbtb16 in AF under T2DM condition remain unclear. Here, we reported that db/db mice displayed higher AF vulnerability and Zbtb16 was identified as the most significantly enriched gene by RNA sequencing (RNA-seq) analysis in atrium. In addition, thioredoxin interacting protein (Txnip) was distinguished as the key downstream gene of Zbtb16 by Cleavage Under Targets and Tagmentation (CUT&Tag) assay. Mechanistically, increased Txnip combined with thioredoxin 2 (Trx2) in mitochondrion induced excess reactive oxygen species (ROS) release, calcium/calmodulin-dependent protein kinase II (CaMKII) overactivation, and spontaneous Ca2+ waves (SCWs) occurrence, which could be inhibited through atrial-specific knockdown (KD) of Zbtb16 or Txnip by adeno-associated virus 9 (AAV9) or Mito-TEMPO treatment. High glucose (HG)-treated HL-1 cells were used to mimic the setting of diabetic in vitro. Zbtb16-Txnip-Trx2 signaling-induced excess ROS release and CaMKII activation were also verified in HL-1 cells under HG condition. Furthermore, atrial-specific Zbtb16 or Txnip-KD reduced incidence and duration of AF in db/db mice. Altogether, we demonstrated that interrupting Zbtb16-Txnip-Trx2 signaling in atrium could decrease AF susceptibility via reducing ROS release and CaMKII activation in the setting of T2DM.

Review of predicting protein stability changes upon variations.

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

Analyzing domain features of small proteins using a machine-learning method.

Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary. In this study, the InterPro tool and the maximum correlation method were utilized to analyze functional domains of SPs. The purpose was to identify important functional domains that can indicate the essential differences between small and large protein sequences. First, the small and large proteins were represented by their functional domains via a one-hot scheme. Then, the MaxRel method was adopted to evaluate the relationships between each domain and the target variable, indicating small or large protein. The top 36 domain features were selected for further investigation. Among them, 14 were deemed to be highly related to SPs because they were annotated to SPs more frequently than large proteins. We found the involvement of functional domains, such as ubiquitin-conjugating enzyme/RWD-like, nuclear transport factor 2 domain, and alpha subunit of guanine nucleotide-binding protein (G-protein) in regulating the biological function of SPs. The involvement of these domains has been confirmed by other recent studies. Our findings indicate that protein functional domains may regulate small protein-related functions and predict their biological activity.

Fluorinated Nonporous Adaptive Cages for the Efficient Removal of Perfluorooctanoic Acid from Aqueous Source Phases.

Per- and polyfluoroalkyl substances (PFAS) accumulate in water resources and pose serious environmental and health threats due to their nonbiodegradable nature and long environmental persistence times. Strategies for the efficient removal of PFAS from contaminated water are needed to address this concern. Here, we report a fluorinated nonporous adaptive crystalline cage (F-Cage 2) that exploits electrostatic interaction, hydrogen bonding, and F-F interactions to achieve the efficient removal of perfluorooctanoic acid (PFOA) from aqueous source phases. F-Cage 2 exhibits a high second-order kobs value of approximately 441,000 g mg-1 h-1 for PFOA and a maximum PFOA adsorption capacity of 45 mg g-1. F-Cage 2 can decrease PFOA concentrations from 1500 to 6 ng L-1 through three rounds of flow-through purification, conducted at a flow rate of 40 mL h-1. Elimination of PFOA from PFOA-loaded F-Cage 2 is readily achieved by rinsing with a mixture of MeOH and saturated NaCl. Heating at 80 °C under vacuum then makes F-Cage 2 ready for reuse, as demonstrated across five successive uptake and release cycles. This work thus highlights the potential utility of suitably designed nonporous adaptive crystals as platforms for PFAS remediation.

Cyclo[2]pyridine[8]pyrrole: An Expanded Porphyrinoid with Three Closed-Shell Oxidation States.

We report here an expanded porphyrinoid, cyclo[2]pyridine[8]pyrrole, 1, that can exist at three closed-shell oxidation levels. Macrocycle 1 was synthesized via the oxidative coupling of two open chain precursors and fully characterized by means of NMR and UV-vis spectroscopies, MS, and X-ray crystallography. Reduction of the fully oxidized form (1, blue) with NaBH4 produced either the half-oxidized (2, teal) or fully reduced forms (3, pale yellow), depending on the amount of reducing agent used and the presence or absence of air. Reduced products 2 or 3 can be oxidized to 1 by various oxidants (quinones, FeCl3, and AgPF6). Macrocycle 1 also undergoes proton-coupled reductions with I-, Br-, Cl-, SO32-, or S2O32- in the presence of an acid. Certain thiol-containing compounds likewise reduce 1 to 2 or 3. This conversion is accompanied by a readily discernible color change, making cyclo[2]pyridine[8]pyrrole 1 able to differentiate biothiols, such as cysteine (Cys), homocysteine (Hcy), and glutathione (GSH).

MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Hydrophilic 1T-WS2 Nanosheet Arrays toward Conductive Textiles for High-Efficient and Continuous Hydroelectric Generation and Storage.

Flexible hydroelectric generators (HEGs) are promising self-powered devices that spontaneously derive electrical power from moisture. However, achieving the desired compatibility between a continuous operating voltage and superior current density remains a significant challenge. Herein, a textile-based van der Waals heterostructure is rationally designed between conductive 1T phase tungsten disulfide@carbonized silk (1T-WS2@CSilk) and carbon black@cotton (CB@Cotton) fabrics with an asymmetric distribution of oxygen-containing functional groups, which enhances the proton concentration gradients toward high-performance wearable HEGs. The vertically staggered 1T-WS2 nanosheet arrays on the CSilk fabric provide abundant hydrophilic nanochannels for rapid carrier transport. Furthermore, the moisture-induced primary battery formed between the active aluminum (Al) electrode and the conductive textiles introduces the desired electric field to facilitate charge separation and compensate for the decreased streaming potential. These devices exhibit a power density of 21.6 µW cm-2, an open-circuit voltage (Voc) of 0.65 V sustained for over 10 000 s, and a current density of 0.17 mA cm-2. This performance makes them capable of supplying power to commercial electronics and human respiratory monitoring. This study presents a promising strategy for the refined design of wearable electronics.

Immune to temperature interference sensor of carbon dioxide gas concentration based on a single modified fiber Bragg grating.

In this study, a novel method that can detect carbon dioxide (CO2) concentration and realize temperature immunity based on only one fiber Bragg grating (FBG) is proposed. The outstanding contribution lies in solving the temperature crosstalk issue of FBG and ensuring the accuracy of detection results under the condition of anti-temperature interference. To achieve immunity to temperature interference without changing the initial structure of FBG, the optical fiber cladding of FBG and adjacent optical fiber cladding at both ends of FBG are modified by a polymer coating. Moreover, a universal immune temperature demodulation algorithm is derived. The experimental results demonstrate that the temperature response sensitivity of the improved FBG is controlled within the range of 0.00407 nm/°C. Compared with the initial FBG (the temperature sensitivity of the initial FBG is 0.04 nm/°C), it decreases by nearly 10 times. Besides, the gas response sensitivity of FBG reaches 1.6 pm/ppm and has overwhelmingly ideal linearity. The detection error results manifest that the gas concentration error in 20 groups of data does not exceed 3.16 ppm. The final reproducibility research shows that the difference in detection sensitivity between the two sensors is 0.08 pm/ppm, and the relative error of linearity is 1.07%. In a word, the proposed method can accurately detect the concentration of CO2 gas and is efficiently immune to temperature interference. The sensor we proposed has the advantages of a simple production process, low cost, and satisfactory reproducibility. It also has the prospect of mass production.

Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

BACKGROUND: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. METHODS: We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. RESULTS: Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. CONCLUSION: Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Role of Additional MRI-Based Morphologic Measurements on the Performance of VI-RADS for Muscle-Invasive Bladder Cancer.

BACKGROUND: Vesical Imaging-Reporting and Data System (VI-RADS) is a pathway for the standardized imaging and reporting of bladder cancer staging using multiparametric (mp) MRI. PURPOSE: To investigate additional role of morphological (MOR) measurements to VI-RADS for the detection of muscle-invasive bladder cancer (MIBC) with mpMRI. STUDY TYPE: Retrospective. POPULATION: A total of 198 patients (72 MIBC and 126 NMIBC) underwent bladder mpMRI was included. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted imaging with fast-spin-echo sequence, spin-echo-planar diffusion-weighted imaging and dynamic contrast-enhanced imaging with fast 3D gradient-echo sequence. ASSESSMENT: VI-RADS score and MOR measurement including tumor location, number, stalk, cauliflower-like surface, type of tumor growth, tumor-muscle contact margin (TCM), tumor-longitudinal length (TLL), and tumor cellularity index (TCI) were analyzed by three uroradiologists (3-year, 8-year, and 15-year experience of bladder MRI, respectively) who were blinded to histopathology. STATISTICAL TESTS: Significant MOR measurements associated with MIBC were tested by univariable and multivariable logistic regression (LR) analysis with odds ratio (OR). Area under receiver operating characteristic curve (AUC) with DeLong's test and decision curve analysis (DCA) were used to compared the performance of unadjusted vs. adjusted VI-RADS. A P-value <0.05 was considered statistically significant. RESULTS: TCM (OR 9.98; 95% confidence interval [CI] 4.77-20.8), TCI (OR 5.72; 95% CI 2.37-13.8), and TLL (OR 3.35; 95% CI 1.40-8.03) were independently associated with MIBC at multivariable LR analysis. VI-RADS adjusted by three MORs achieved significantly higher AUC (reader 1 0.908 vs. 0.798; reader 2 0.906 vs. 0.855; reader 3 0.907 vs. 0.831) and better clinical benefits than unadjusted VI-RADS at DCA. Specially in VI-RADS-defined equivocal lesions, MOR-based adjustment resulted in 55.5% (25/45), 70.4% (38/54), and 46.4% (26/56) improvement in accuracy for discriminating MIBC in three readers, respectively. DATA CONCLUSION: MOR measurements improved the performance of VI-RADS in detecting MIBC with mpMRI, especially for equivocal lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Spreading and Capillary Imbibition of Viscous Oil Lens into an Open-Cell Porous Structure.

Oil pollution in the ocean is becoming more and more of a serious issue, which increases interest in both ways for combating its cause and methods for observing and monitoring how oil spreads. A promising approach based on an optical method with empirical relations for selected viscous oil-water systems is presented. Based on a modified melamine sponge (MMS), the microscopic spreading and oil capillary penetration phenomenon of the porous structure were investigated. The objective of this study is 2-fold: (i) to present a more thorough experimental description of the spreading of viscous oil lens on the water surface and capillary action of oil lens into MMS porous structure; and (ii) to provide a theoretical description that helps to explain some of the observed behavior. With knowledge of δ∞2=-2SρW/gρO(ρW-ρO), we can determine the spreading coefficient S. It needs to be pointed out that the oil lens floating on the water surface does satisfy Neumann's rule as the spreading coefficient of the air-oil-water system is negative (- 9.8 mN/m), indicating the ability to form a stable oil lens with thickness δO = 3.04 mm and radius RL = 38.64 mm after 60 min of spreading test. Furthermore, to better understand the capillary phenomena from a mechanical approach, an oil lens in contact with the surface of the MMS porous structure, by in-depth visualization, is properly defined as the balance of forces acting. Finally, as an illustration of this method, we utilized this approach to obtain the equilibrium height of the capillary rise and take it into account in terms of effective material thickness.

DEPDC1 affects autophagy-dependent glycolysis levels in human osteosarcoma cells by modulating RAS/ERK signaling through TTK.

The current treatment for osteosarcoma (OS) is based on surgery combined with systemic chemotherapy, however, gene therapy has been hypothesized to improve patient survival rates. The density-enhanced protein domain 1 protein (DEPDC1) functions as a crucial determinant in the advancement of OS, which is highly expressed in OS cells. The current study was designed to delve into the effect and mechanism of DEPDC1 and phosphotyrosine-picked threonine tyrosine kinase (TTK) in OS. The expression of DEPDC1 and TTK in OS cells was detected by western blotting. Furthermore, the assessment of glycolysis encompassed the quantification of extracellular acidification rate, glucose uptake rate, lactate concentration, and the expression of glucose transporter 1, hexokinase 2, and pyruvate kinase M2. Finally, the functions of DEPDC1 and TTK in autophagy and ras-extracellular signal-regulated kinase signaling were determined by western blotting after interfering with DEPDC1 in SaOS-2 cells. The results revealed that DEPDC1 and TTK were upregulated in OS cell lines and interfering with DEPDC1 inhibited glycolysis and autophagy in OS cells. Furthermore, the STRING database suggested that DEPDC1 and TTK perform targeted binding. Notably, the results of the present study revealed that DEPDC1 upregulated RAS expression through TTK and enhanced ERK activity, thereby affecting glycolysis and autophagy in OS cells. Collectively, the present investigation demonstrated that DEPDC1 affected autophagy-dependent glycolysis levels of OS cells by regulating RAS/ERK signaling through TTK.

A breakthrough of immunoassay format for hapten: recent insights into noncompetitive immunoassays to detect small molecules.

Immunoassay based on the antibodies specific for targets has advantages of high sensitivity, simplicity and low cost, therefore it has received more attention in recent years, especially for the rapid detection of small molecule chemicals present in foods, diagnostics and environments. However, limited by low molecular weight and only one antigenic determinant existed, immunoassays for these small molecule chemicals, namely hapten substances, were commonly performed in a competitive immunoassay format, whose sensitivities were obviously lower than the sandwich enzyme-linked immunosorbent assay generally adaptable for the protein targets. In order to break through the bottleneck of detection format, researchers have designed and established several novel noncompetitive immunoassays for the haptens in the past few years. In this review, we focused on the four representative types of noncompetitive immunoassay formats and described their characteristics and applications in rapid detection of small molecules. Meanwhile, a systematic discussion on the current technologies challenges and the possible solutions were also summarized. This review aims to provide an updated overview of the current state-of-the-art in noncompetitive immunoassay for small molecules, and inspire the development of novel designs for small molecule detection.

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework.

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

TM-search: An Efficient and Effective Tool for Protein Structure Database Search.

The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.

MetalTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in Protein Metal-Binding Sites.

The critical importance of accurately predicting mutations in protein metal-binding sites for advancing drug discovery and enhancing disease diagnostic processes cannot be overstated. In response to this imperative, MetalTrans emerges as an accurate predictor for disease-associated mutations in protein metal-binding sites. The core innovation of MetalTrans lies in its seamless integration of multifeature splicing with the Transformer framework, a strategy that ensures exhaustive feature extraction. Central to MetalTrans's effectiveness is its deep feature combination strategy, which merges evolutionary-scale modeling amino acid embeddings with ProtTrans embeddings, thus shedding light on the biochemical properties of proteins. Employing the Transformer component, MetalTrans leverages the self-attention mechanism to delve into higher-level representations. Utilizing mutation site information for feature fusion not only enriches the feature set but also sidesteps the common pitfall of overestimation linked to protein sequence-based predictions. This nuanced approach to feature fusion is a key differentiator, enabling MetalTrans to outperform existing methods significantly, as evidenced by comparative analyses. Our evaluations across varied metal binding site data sets (specifically Zn, Ca, Mg, and Mix) underscore MetalTrans's superior performance, which achieved the average AUC values of 0.971, 0.965, 0.980, and 0.945 on multiple 5-fold cross-validation, respectively. Remarkably, against the multichannel convolutional neural network method on a benchmark independent test set, MetalTrans demonstrated unparalleled robustness and superiority, boasting the AUC score of 0.998 on multiple 5-fold cross-validation. Our comprehensive examination of the predicted outcomes further confirms the effectiveness of the model. The source codes, data sets, and prediction results for MetalTrans can be accessed for academic usage at https://github.com/EduardWang/MetalTrans.