Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication.

BACKGROUND: Liver cancer is one of the deadliest malignant tumors worldwide. Immunotherapy has provided hope to patients with advanced liver cancer, but only a small fraction of patients benefit from this treatment due to individual differences. Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies, thereby improving patient survival rates. Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer, the impact of cell-cell interactions in the tumor microenvironment has not been adequately considered. AIM: To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy. METHODS: Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways. Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells. The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features, and a least absolute shrinkage and selection operator (LASSO) regression model was constructed to screen for diagnostic-related features. Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model. Finally, 3 genes (stathmin 1, cofilin 1, and C-C chemokine ligand 5) significantly associated with survival were identified and used to construct an immune-related gene signature. RESULTS: The immune-related gene signature composed of stathmin 1, cofilin 1, and C-C chemokine ligand 5 was identified through cell-cell communication. The effectiveness of the identified gene signature was validated based on experimental results of predictive immunotherapy response, tumor mutation burden analysis, immune cell infiltration analysis, survival analysis, and expression analysis. CONCLUSION: The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment, providing insights for personalized treatment strategies.

ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.

Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.

Fully Bio-Based 2,5-Furandicarboxylic Acid Polyester toward Plastics with Mechanically Robust, Excellent Gas Barrier and Fast Degradation.

Aliphatic-aromatic copolyesters offer a promising solution to mitigate plastic pollution, but high content of aliphatic units (>40 %) often suffer from diminished comprehensive performances. Poly(butylene oxalate-co-furandicarboxylate) (PBOF) copolyesters were synthesized by precisely controlling the oxalic acid content from 10 % to 60 %. Compared with commercial PBAT, the barrier properties of PBOF for H2O and O2 increased by more than 6 and 26 times, respectively. The introduction of the oxalic acid units allowed the water contact angle to be reduced from 82.5° to 62.9°. Superior hydrophilicity gave PBOF an excellent degradation performance within a 35-day hydrolysis. Interestingly, PBO20F and PBO30F also displayed obvious decrease of molecular weight during hydrolysis, with elastic modulus >1 GPa and tensile strength between 35-54 MPa. PBOF achieved the highest hydrolysis rates among the reported PBF-based copolyesters. The hydrolytic mechanism was further explored based on Fukui function analysis and density functional theory (DFT) calculation. Noncovalent analysis indicated that the water molecules formed hydrogen bonding interaction with adjacent ester groups and thus improved the reactivity of carbonyl carbon. PBOF not only meet the requirements of the high-performance packaging market but can quickly degrade after the end of their usage cycles, providing a new choice for green and environmental protection.

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

Structural vaccinology, molecular simulation and immune simulation approaches to design multi-epitopes vaccine against John Cunningham virus.

The JCV (John Cunningham Virus) is known to cause progressive multifocal leukoencephalopathy, a condition that results in the formation of tumors. Symptoms of this condition such as sensory defects, cognitive dysfunction, muscle weakness, homonosapobia, difficulties with coordination, and aphasia. To date, there is no specific and effective treatment to completely cure or prevent John Cunningham polyomavirus infections. Since the best way to control the disease is vaccination. In this study, the immunoinformatic tools were used to predict the high immunogenic and non-allergenic B cells, helper T cells (HTL), and cytotoxic T cells (CTL) epitopes from capsid, major capsid, and T antigen proteins of JC virus to design the highly efficient subunit vaccines. The specific immunogenic linkers were used to link together the predicted epitopes and subjected to 3D modeling by using the Robetta server. MD simulation was used to confirm that the newly constructed vaccines are stable and properly fold. Additionally, the molecular docking approach revealed that the vaccines have a strong binding affinity with human TLR-7. The codon adaptation index (CAI) and GC content values verified that the constructed vaccines would be highly expressed in E. coli pET28a (+) plasmid. The immune simulation analysis indicated that the human immune system would have a strong response to the vaccines, with a high titer of IgM and IgG antibodies being produced. In conclusion, this study will provide a pre-clinical concept to construct an effective, highly antigenic, non-allergenic, and thermostable vaccine to combat the infection of the John Cunningham virus.

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction.

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.

Improving the substrate binding of acetyl-CoA carboxylase (AccB) from Streptomyces antibioticus through computational enzyme engineering.

Malonyl-CoA serves as the main building block for the biosynthesis of many important polyketides, as well as fatty acid-derived compounds, such as biofuel. Escherichia coli, Corynebacterium gultamicum, and Saccharomyces cerevisiae have recently been engineered for the biosynthesis of such compounds. However, the developed processes and strains often have insufficient productivity. In the current study, we used enzyme-engineering approach to improve the binding of acetyl-CoA with ACC. We generated different mutations, and the impact was calculated, which reported that three mutations, that is, S343A, T347W, and S350W, significantly improve the substrate binding. Molecular docking investigation revealed an altered binding network compared to the wild type. In mutants, additional interactions stabilize the binding of the inner tail of acetyl-CoA. Using molecular simulation, the stability, compactness, hydrogen bonding, and protein motions were estimated, revealing different dynamic properties owned by the mutants only but not by the wild type. The findings were further validated by using the binding-free energy (BFE) method, which revealed these mutations as favorable substitutions. The total BFE was reported to be -52.66 ± 0.11 kcal/mol for the wild type, -55.87 ± 0.16 kcal/mol for the S343A mutant, -60.52 ± 0.25 kcal/mol for T347W mutant, and -59.64 ± 0.25 kcal/mol for the S350W mutant. This shows that the binding of the substrate is increased due to the induced mutations and strongly corroborates with the docking results. In sum, this study provides information regarding the essential hotspot residues for the substrate binding and can be used for application in industrial processes.

Structural and molecular investigation of the impact of S30L and D88N substitutions in G9R protein on coupling with E4R from Monkeypox virus (MPXV).

Understanding the pathogenesis mechanism of the Monkeypox virus (MPXV) is essential to guide therapeutic development against the Monkeypox virus. In the current study, we investigated the impact of the only two reported substitutions, S30L, D88N, and S30L-D88N on the G9R of the replication complex in 2022 with E4R using structural modeling, simulation, and free energy calculation methods. From the molecular docking and dissociation constant (KD) results, it was observed that the binding affinity did not increase in the mutants, but the interaction paradigm was altered by these substitutions. Molecular simulation data revealed that these mutations are responsible for destabilization, changes in protein packing, and internal residue fluctuations, which can cause functional variance. Additionally, hydrogen bonding analysis revealed that the estimated number of hydrogen bonds are almost equal among the wild-type G9R and each mutant. The total binding free energy for the wild-type G9R with E4R was -85.00 kcal/mol while for the mutants the TBE was -42.75 kcal/mol, -43.68 kcal/mol, and -48.65 kcal/mol respectively. This shows that there is no direct impact of these two reported mutations on the binding with E4R, or it may affect the whole replication complex or any other mechanism involved in pathogenesis. To explore these variations further, we conducted PCA and FEL analyses. Based on our findings, we speculate that within the context of interaction with E4R, the mutations in the G9R protein might be benign, potentially leading to functional diversity associated with other proteins.Communicated by Ramaswamy H. Sarma.

Structural basis for the mechanism of interaction of SARS-CoV-2 B.1.640.2 variant RBD with the host receptors hACE2 and GRP78.

The inflicted chaos instigated by the SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) globally continues with the emergence of novel variants. The current global outbreak is aggravated by the manifestation of novel variants, which affect the effectiveness of the vaccine, attachment with hACE2 (human Angiotensin-converting enzyme 2) and immune evasion. Recently, a new variant named University Hospital Institute (IHU) (B.1.640.2) was reported in France in November 2021 and is spreading globally affecting public healthcare. The B.1.640.2 SARS-CoV-2 strain revealed 14 mutations and 9 deletions in spike protein. Thus, it is important to understand how these variations in the spike protein impact the communication with the host. A protein coupling approach along with molecular simulation protocols was used to interpret the variation in the binding of the wild type (WT) and B.1.640.2 variant with hACE2 and Glucose-regulating protein 78 (GRP78) receptors. The initial docking scores revealed a stronger binding of the B.1.640.2-RBD with both the hACE2 and GRP78. To further understand the crucial dynamic changes, we looked at the structural and dynamic characteristics and also explored the variations in the bonding networks between the WT and B.1.640.2-RBD (receptor-binding domain) in association with hACE2 and GRP78, respectively. Our findings revealed that the variant complex demonstrated distinct dynamic properties in contrast to the wild type due to the acquired mutations. Finally, to provide conclusive evidence on the higher binding by the B.1.640.2 variant the TBE was computed for each complex. For the WT with hACE2 the TBE was quantified to be-61.38 ± 0.96 kcal/mol and for B.1.640.2 variant the TBE was estimated to be -70.47 ± 1.00 kcal/mol. For the WT-RBD-GRP78 the TBE -was computed to be 32.32 ± 0.56 kcal/mol and for the B.1.640.2-RBD a TBE of -50.39 ± 0.88 kcal/mol was reported. This show that these mutations are the basis for higher binding and infectivity produced by B.1.640.2 variant and can be targeted for drug designing against it.Communicated by Ramaswamy H. Sarma.

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for developing immunotherapies, including neoantigen vaccine and drugs. Accurate prediction of TCR-epitope binding specificity via deep learning remains challenging, especially in test cases which are unseen in the training set. Here, we propose TEPCAM (TCR-EPitope identification based on Cross-Attention and Multi-channel convolution), a deep learning model that incorporates self-attention, cross-attention mechanism, and multi-channel convolution to improve the generalizability and enhance the model interpretability. Experimental results demonstrate that our model outperformed several state-of-the-art models on two challenging tasks including a strictly split dataset and an external dataset. Furthermore, the model can learn some interaction patterns between TCR and epitope by extracting the interpretable matrix from cross-attention layer and mapping them to the three-dimensional structures. The source code and data are freely available at https://github.com/Chenjw99/TEPCAM.

Phenotype versus genotype discordant rifampicin susceptibility testing in tuberculosis: implications for a diagnostic accuracy.

IMPORTANCE: An accurate diagnosis of drug resistance in clinical isolates is an important step for better treatment outcomes. The current study observed a higher discordance rate of rifampicin resistance on Mycobacteria Growth Indicator Tube (MGIT) drug susceptibility testing (DST) than Lowenstein-Jenson (LJ) DST when compared with the rpoB sequencing. We detected a few novel mutations and their combination in rifampicin resistance isolates that were missed by MGIT DST and may be useful for the better management of tuberculosis (TB) treatment outcomes. Few novel deletions in clinical isolates necessitate the importance of rpoB sequencing in large data sets in geographic-specific locations, especially high-burden countries. We explored the discordance rate on MGIT and LJ, which is important for the clinical management of rifampicin resistance to avoid the mistreatment of drug-resistant TB. Furthermore, MGIT-sensitive isolates may be subjected to molecular methods of diagnosis for further confirmation and treatment options.

Omicron variant (B.1.1.529) challenge the integrity of blood brain barrier: Evidence from protein structural analysis.

Studies on nonhuman primates, wild-type and transgenic mice have shown the presence of SARS-CoV-2 RNA components in the brains. Despite the Blood-Brain Barrier (BBB) provides protection there are less evidences on how the SARS-CoV-2 crosses the BBB. Given that there is an increase of Omicron reinfection rates, transmissibility rate and involvement to cause neurological dysfunctions, we hypothesized to investigate how the Omicron variant (B.1.1.529) binds structurally to key BBB-maintaining proteins and thus can possibly challenge the integrity and transportation to the brain. By using molecular dynamics simulation approaches we examined the interaction of Omicron variant (B.1.1.529) with different structural and transporter proteins located at the BBB. Our results show that in Zona Ocludin 1-RBD complex, we observe a distinct pattern. Omicron demonstrates a docking score of -88.9 ± 6.8 kcal/mol and six interactions, while the wild type (WT) presents a higher score of -94.0 ± 2.3 kcal/mol, forming eight interactions. Comparing affinities, the WT-RBD displays a stronger preference for Claudin-5, boasting a docking score of -110.2 ± 3.0 and nine interactions, versus Omicron-RBD's slightly reduced engagement, with a docking score of -105.6 ± 0.2 and seven interactions. Interestingly, the Omicron variant exhibits heightened stability in interactions with Glucose Transporter and ABC transporters, registering docking scores of -110.6 ± 1.9 and -112.0 ± 3.6 kcal/mol, respectively. This surpasses the WT's respective scores of -95.2 ± 2.2 and -104.0 ± 6.2 kcal/mol, reflecting a unique interaction profile. Rigorous molecular dynamics simulations validate our findings. Our study emphasizes the Omicron variant's increased affinity towards transporter proteins, illuminating potential implications for BBB integrity and brain transportation. While these insights offer a valuable framework, comprehensive experimental validation is indispensable for a comprehensive understanding.

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions.

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

High-Molecular-Weight and Light-Colored Disulfide-Bond-Embedded Polyesters: Accelerated Hydrolysis Triggered by Redox Responsiveness.

Disulfide bonds have attracted considerable attention due to their reduction responsiveness, but it is crucial and challenging to prepare disulfide-bond-based polyesters by melt polycondensation. Herein, the inherently poor thermal stability of the S-S bond in melting polycondensation was overcome. Moreover, poly(butylene succinate-co-dithiodipropionate) (PBSDi) with a light color and high molecular weights (Mn values up to 84.7 kg/mol) was obtained. These polyesters can be applied via melt processing with Td,5% > 318 °C. PBSDi10-PBSDi40 shows good crystallizability (crystallinity 56-38%) and compact lamellar thickness (2.9-3.2 nm). Compared with commercial poly(butylene adipate-co-terephthalate) (PBAT), the elevated mechanical and barrier performances of PBSDi make them better packaging materials. For the degradation behavior, the disulfide monomer obviously accelerates the enzyme degradation but has a weaker effect on hydrolysis. In 0.1 mol/L or higher concentrations of H2O2 solutions, the oxidation of disulfide bonds to sulfoxide and sulfone groups can be realized. This process results in a stronger nucleophilic attack, as confirmed by the Fukui function and DFT calculations. Additionally, the greater polarity and hydrophilicity of oxidation products, proved by noncovalent interaction analysis, accelerate the hydrolysis of polyesters. Moreover, glutathione-responsive breakage, from polymers to oligomers, is confirmed by an accelerated decline in molecular weight. Our research offers fresh perspectives on the effective synthesis of the disulfide polyester and lays a solid basis for the creation of high-performance biodegradable polyesters that degrade on demand.

Biobased Biodegradable Copolyesters from 2,5-Thiophenedicarboxylic Acid: Effect of Aliphatic Diols on Barrier Properties and Degradation.

The demand for sustainable development has led to increasing attention in biobased polyesters due to their adjustable thermal and mechanical properties and biodegradability. In this study, we used a novel bioderived aromatic diacid, 2,5-thiophenedicarboxylic acid (TDCA) to synthesize a list of novel aromatic-aliphatic poly(alkylene adipate-co-thiophenedicarboxylate) (PAATh) copolyesters through a facile melt polycondensation method. PAAThs are random copolyesters with weight-average molecular weights of 58400 to 84200 g·mol-1 and intrinsic viscosities of 0.80 to 1.27 dL·g-1. All PAAThs exhibit sufficiently high thermal stability as well as the highest tensile strength of 6.2 MPa and the best gas barrier performances against CO2 and O2, 4.3- and 3.3-fold better than those of poly(butylene adipate-co-terephthalate) (PBAT). The biodegradability of PAAThs was fully evaluated through a degradation experiment and various experimental parameters, including residue weights, surface morphology, and molecular compositions. The state-of-the-art molecular dynamics (MD) simulations were applied to elucidate the different enzymatic degradation behaviors of PAAThs due to the effect of diols with different chain structures. The sterically hindered carbonyl carbon of the PHATh-enzyme complex was more susceptible to nucleophilic attack and exhibited a higher tendency to enter a prereaction state. This study has introduced a group of novel biobased copolyesters with their structure-property relationships investigated thoroughly, and the effect of diol components on the enzymatic degradation was revealed by computational analysis. These findings may lay the foundation for the development of promising substitutes for commercial biodegradable polyesters and shed light on their complicated degradation mechanisms.

DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model.

MOTIVATION: Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information. RESULTS: In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite. AVAILABILITY AND IMPLEMENTATION: The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.

Exploring microproteins from various model organisms using the mip-mining database.

Microproteins, prevalent across all kingdoms of life, play a crucial role in cell physiology and human health. Although global gene transcription is widely explored and abundantly available, our understanding of microprotein functions using transcriptome data is still limited. To mitigate this problem, we present a database, Mip-mining ( https://weilab.sjtu.edu.cn/mipmining/ ), underpinned by high-quality RNA-sequencing data exclusively aimed at analyzing microprotein functions. The Mip-mining hosts 336 sets of high-quality transcriptome data from 8626 samples and nine representative living organisms, including microorganisms, plants, animals, and humans, in our Mip-mining database. Our database specifically provides a focus on a range of diseases and environmental stress conditions, taking into account chemical, physical, biological, and diseases-related stresses. Comparatively, our platform enables customized analysis by inputting desired data sets with self-determined cutoff values. The practicality of Mip-mining is demonstrated by identifying essential microproteins in different species and revealing the importance of ATP15 in the acetic acid stress tolerance of budding yeast. We believe that Mip-mining will facilitate a greater understanding and application of microproteins in biotechnology. Moreover, it will be beneficial for designing therapeutic strategies under various biological conditions.

Novel mutations in structural proteins of dengue virus genomes.

BACKGROUND: Genomic characterization of the dengue virus (DENV) is useful for understanding its molecular evolution, transmission, pathogenicity and infectivity. The DENV genomic RNA encodes three structural proteins, capsid (C) envelope (E) and membrane (M) proteins mediating viral entry and assembly during host infection. The current study aims to explore the DENV serotypes and mutations in the E and M proteins. METHODS: Twenty-three samples of DENV-positive patients were processed and selected for whole genome sequencing (WGS) from the Punjab Province of Pakistan. RESULTS: Among the 23 WGS, 19 samples showed numerous mutations (BioProject ID PRJNA943555). DENV1 and DENV2 are the most prevalent serotypes. A total of 179 mutations were detected in the E protein, in which K203E, T88A, I114L, and I293T are novel. The I270L, T272A, S273L, and T277A were found in the "kl" ß-hairpin (aa 270-279). The M protein harbors 74 mutations, of which 24 were novel. Three prominent complementary regions in the prM and E protein complex formations include R6, E46, D47, D63, and D65 on 'pr' peptide, and E84, K64, and H244, K247 on E, remain conserved except R6C. To our knowledge, it is the first comprehensive study of mutations in structural proteins. CONCLUSION: Genomic epidemiology is critical for analyzing emerging mutations and designing new policies therapeutic efforts for future outbreaks.

MATT-DDI: Predicting multi-type drug-drug interactions via heterogeneous attention mechanisms.

The joint use of multiple drugs can result in adverse drug-drug interactions (DDIs) and side effects that harm the body. Accurate identification of DDIs is crucial for avoiding accidental drug side effects and understanding potential mechanisms underlying DDIs. Several computational methods have been proposed for multi-type DDI prediction, but most rely on the similarity profiles of drugs as the drug feature vectors, which may result in information leakage and overoptimistic performance when predicting interactions between new drugs. To address this issue, we propose a novel method, MATT-DDI, for predicting multi-type DDIs based on the original feature vectors of drugs and multiple attention mechanisms. MATT-DDI consists of three main modules: the top k most similar drug pair selection module, heterogeneous attention mechanism module and multi­type DDI prediction module. Firstly, based on the feature vector of the input drug pair (IDP), k drug pairs that are most similar to the input drug pair from the training dataset are selected according to cosine similarity between drug pairs. Then, the vectors of k selected drug pairs are averaged to obtain a new drug pair (NDP). Next, IDP and NDP are fed into heterogeneous attention modules, including scaled dot product attention and bilinear attention, to extract latent feature vectors. Finally, these latent feature vectors are taken as input of the classification module to predict DDI types. We evaluated MATT-DDI on three different tasks. The experimental results show that MATT-DDI provides better or comparable performance compared to several state-of-the-art methods, and its feasibility is supported by case studies. MATT-DDI is a robust model for predicting multi-type DDIs with excellent performance and no information leakage.

Free energy landscape and thermodynamics properties of novel mutations in PncA of pyrazinamide resistance isolates of Mycobacterium tuberculosis.

Pyrazinamide (PZA) is one of the first-line antituberculosis therapy, active against non-replicating Mycobacterium tuberculosis (Mtb). The conversion of PZA into pyrazinoic acid (POA), the active form, required the activity of pncA gene product pyrazinamidase (PZase) activity. Mutations occurred in pncA are the primary cause behind the PZA resistance. However, the resistance mechanism is important to explore using high throughput computational approaches. Here we aimed to explore the mechanism of PZA resistance behind novel P62T, L120R, and V130M mutations in PZase using 200 ns molecular dynamics (MD) simulations. MD simulations were performed to observe the structural changes for these three mutants (MTs) compared to the wild types (WT). Root means square fluctuation, the radius of gyration, free energy landscape, root means square deviation, dynamic cross-correlation motion, and pocket volume were found in variation between WT and MTs, revealing the effects of P62T, L120R, and V130M. The free energy conformational landscape of MTs differs significantly from the WT system, lowering the binding of PZA. The geometric shape complementarity of the drug (PZA) and target protein (PZase) further confirmed that P62T, L120R, and V130M affect the protein structure. These effects on PZase may cause vulnerability to convert PZA into POA.Communicated by Ramaswamy H. Sarma.