Computational Biology and Chemistry最新文献

Global public health is confronted with significant challenges due to the prevalence of cancer and the emergence of treatment resistance. This work focuses on the identification of cyclin-dependent kinase 2 (CDK2) through a systematic computational approach to discover novel cancer therapeutics. A ligand-based pharmacophore model was initially developed using a training set of seven potent CDK2 inhibitors. The obtained most robust model was characterized by three features: one donor (|Don|) and two acceptors (|Acc|). Screening this model against the ZINC database resulted in identifying 108 hits, which underwent further molecular docking studies. The docking results indicated binding affinity, with energy values ranging from −6.59 kcal mol⁻¹ to −7.40 kcal mol⁻¹ compared to the standard Roscovitine. The top 10 compounds (Z1-Z10) selected from the docking data were further screened for ADMET profiling, ensuring their compliance with pharmacokinetic and toxicological criteria. The top 3 compounds (Z1-Z3) chosen from the docking were subjected to Density Functional Theory (DFT) studies. They revealed significant variations in electronic properties, providing insights into the reactivity, stability, and polarity of these compounds. Molecular dynamics simulations confirmed the stability of the ligand-protein complexes, with acceptable RMSD and RMSF values. Specifically, compound Z1 demonstrated stability, around 2.4 Å, and maintained throughout the 100 ns simulation period with minimal conformational changes, stable RMSD, and consistent protein-ligand interactions.

Nipah virus (NiV) remains a significant global concern due to its impact on both the agricultural industry and human health, resulting in substantial economic and health consequences. Currently, there is no cure or commercially available vaccine for the virus. Therefore, it is crucial to prioritize the discovery of new and effective treatment options to prevent its continued spread. Streptomyces spp. are rich sources of metabolites known for their bioactivity against certain diseases; however, their potential as antiviral drugs against the Nipah virus remain unexplored. In this study, 6524 Streptomyces spp. metabolites were screened through in silico methods for their inhibitory effects against the Nipah virus matrix (NiV-M) protein, which assists in virion assembly of Nipah virus. Different computer-aided tools were utilized to carry out the virtual screening process: ADMET profiling revealed 913 compounds with excellent safety and efficacy profiles, molecular docking predicted the binding poses and associated docking scores of the ligands in their respective targets, MD simulations confirmed the binding stability of the top ten highest-scoring ligands in a 100 ns all-atom simulation, PCA elucidated simulation convergence, and MMPB(GB)SA calculations estimated the binding energies of the final candidate compounds and determined the key residues crucial for complex formation. Using in silico methods, we identified six metabolites targeting the main substrate-binding site and five targeting the dimerization site that exhibited excellent stability and strong binding affinity. We recommend testing these compounds in the next stages of drug development to confirm their effectiveness as therapeutic agents against Nipah virus.

In the global fight against the COVID-19 pandemic caused by the highly transmissible SARS-CoV-2 virus, the search for potent medications is paramount. With a focused investigation on the SARS-CoV-2 papain-like protease (PL^pro) as a promising therapeutic target due to its pivotal role in viral replication and immune modulation, the catalytic triad of PL^pro comprising Cys111, His272, and Asp286, highlights Cys111 as an intriguing nucleophilic center for potential covalent bonds with ligands. The detailed analysis of the binding site unveils crucial interactions with both hydrophobic and polar residues, demonstrating the structural insights of the cavity and deepening our understanding of its molecular landscape. The sequence of PL^pro among variants of concern (Alpha, Beta, Gamma, Delta and Omicron) and the recent variant of interest, JN.1, remains conserved with no mutations at the active site. Moreover, a thorough exploration of apo, non-covalently bound, and covalently bound PL^pro conformations exposes significant conformational changes in loop regions, offering invaluable insights into the intricate dynamics of ligand-protein complex formation. Employing strategic in silico medication repurposing, this study swiftly identifies potential molecules for target inhibition. Within the domain of covalent docking studies and molecular dynamics, using reported inhibitors and clinically tested molecules elucidate the formation of stable covalent bonds with the cysteine residue, laying a robust foundation for potential therapeutic applications. These details not only deepen our comprehension of PL^pro inhibition but also play a pivotal role in shaping the dynamic landscape of COVID-19 treatment strategies.

Purpose

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels. This study aimed to analyze the changes underlying heterogeneities and communication properties of CMs in diabetes mellitus (DM).

Methods

GSE213337 dataset was retrieved from NCBI Gene Expression Omnibus, containing the single-cell RNA sequencing data of hearts from the control and streptozotocin-induced diabetic mice. GSEA and GSVA were used to explore the function enrichment of DEGs in CM. Cell communication analysis was carried out to study the altered signals and significant ligand-receptor interactions.

Results

Seventeen cell types were identified between DM and the controls. The increasing ratio of CM suggested the occurrence of diabetes induces potential pathological changes of CM proliferation. A total of 1144 DEGs were identified in CM. GSEA and GSVA analysis indicated the enhancing lipid metabolism involving in DM. The results of cell communication analysis suggested that high glucose activated the ability of CM receiving fibroblast and LEC, while inhibited the capacity of receiving ECC and pericyte. Furthermore, GAS and ANGPTL were significantly decreased under DM, which was consistent with the results of GSEA and GSVA. Finally, the ligand-receptor interactions such as vegfc-vegfr2, angptl1 were changes in CM.

Conclusions

The CM showed the significant heterogeneities in DM, which played an important role in myocardial fibrosis induce by hyperglycemia.

The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and −8.66 kcal mol⁻¹, respectively), when compared to the control drug, Ribavirin (-7.01 and −6.52 kcal mol⁻¹, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol⁻¹) and NiV M (-40.26 kJ mol⁻¹) proteins in comparison with the control drug Ribavirin (-13.12 and −13.94 kJ mol⁻¹, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection.

Retrosynthesis is vital in synthesizing target products, guiding reaction pathway design crucial for drug and material discovery. Current models often neglect multi-scale feature extraction, limiting efficacy in leveraging molecular descriptors. Our proposed SB-Net model, a deep-learning architecture tailored for retrosynthesis prediction, addresses this gap. SB-Net combines CNN and Bi-LSTM architectures, excelling in capturing multi-scale molecular features. It integrates parallel branches for processing one-hot encoded descriptors and ECFP, merging through dense layers. Experimental results demonstrate SB-Net’s superiority, achieving 73.6 % top-1 and 94.6 % top-10 accuracy on USPTO-50k data. Versatility is validated on MetaNetX, with rates of 52.8 % top-1, 74.3 % top-3, 79.8 % top-5, and 83.5 % top-10. SB-Net’s success in bioretrosynthesis prediction tasks indicates its efficacy. This research advances computational chemistry, offering a robust deep-learning model for retrosynthesis prediction. With implications for drug discovery and synthesis planning, SB-Net promises innovative and efficient pathways.

DEMETER-Like DNA demethylases (DMLs) are epigenetic regulators of many developmental and biological processes in plants. No comprehensive information about the DML gene family in citrus is available to date. Here, a total of three DML genes in the genomes of Citrus sinensis (named CsDML1–3) and C. clementina (named CcDML1–3) were identified and analyzed. They encode hydrophilic and relatively large proteins, with prediction of nuclear localization, containing the conserved domains and motifs typical of plant DMLs. Protein interaction network analysis suggested that they interact primarily with proteins related to the maintenance of DNA methylation and remodeling of chromatin. Analysis of their promoter regions led to the identification of several cis-acting regulatory elements involved in stress response, including drought, heat and cold stresses. The presence of several miRNA targets and potential phosphorylation sites suggest that their expression is also regulated at post-transcriptional and post-translational levels. RNA-Seq data and quantitative real-time PCR analysis showed a low and drought-regulated gene expression of the citrus DMLs in different plant tissues. CsDML1 and CsDML3 were also differentially regulated by deficit irrigation in fruits at different developmental stages, with a positive and significant correlation found between CsDML1 and PHYTOENE SYNTHASE (PSY) and between CsDML3 and ATP CITRATE LYASEs (ACLs) and ZETA-CAROTENE DESATURASE (ZDS) gene expression. These results indicate that the citrus DMLs are potentially functional enzymes involved in developmental processes and drought stress-adaptive responses, providing a useful reference for further investigation of their functions and applications on the citrus improvement.

Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics.

Mitochondrial disorders are a class of heterogeneous disorders caused by genetic variations in the mitochondrial genome (mtDNA) as well as the nuclear genome. The spectrum of mtDNA variants remains unexplored in the Indian population. In the present study, we have cataloged 2689 high confidence single nucleotide variants, small insertions and deletions in mtDNA in 1029 healthy Indian individuals. We found a major proportion (76.5 %) of the variants being rare (AF<=0.005) in the studied population. Intriguingly, we found two ‘confirmed’ pathogenic variants (m.1555 A>G and m.14484 T>C) with a frequency of ∼1 in 250 individuals in our dataset. The high carrier frequency underscores the need for screening of the mtDNA pathogenic mutations in newborns in India. Interestingly, our analysis also revealed 202 variants in our dataset which have been ‘reported’ in disease cases as per the MITOMAP database. Additionally, we found the frequency of haplogroup M (52.2 %) to be the highest among all the 18 top-level haplogroups found in our dataset. In comparison to the global population datasets, 20 unique mtDNA variants are found in the Indian population. We hope the whole genome sequencing based compendium of mtDNA variants along with their allele frequencies and heteroplasmy levels in the Indian population will drive additional genome scale studies for mtDNA. Furthermore, the identification of clinically relevant variants in our dataset will aid in better clinical interpretation of the variants in mitochondrial disorders.

Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on information derived from protein-protein interaction (PPI) networks. Despite attempts by some researchers to integrate biological data and PPI networks for predicting essential proteins, designing effective integration methods remains a challenge. In response to these challenges, this paper presents the ACDMBI model, specifically designed to overcome the aforementioned issues. ACDMBI is comprised of two key modules: feature extraction and classification. In terms of capturing relevant information, we draw insights from three distinct data sources. Initially, structural features of proteins are extracted from the PPI network through community division. Subsequently, these features are further optimized using Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Moving forward, protein features are extracted from gene expression data utilizing Bidirectional Long Short-Term Memory networks (BiLSTM) and a multi-head self-attention mechanism. Finally, protein features are derived by mapping subcellular localization data to a one-dimensional vector and processing it through fully connected layers. In the classification phase, we integrate features extracted from three different data sources, crafting a multi-layer deep neural network (DNN) for protein classification prediction. Experimental results on brewing yeast data showcase the ACDMBI model’s superior performance, with AUC reaching 0.9533 and AUPR reaching 0.9153. Ablation experiments further reveal that the effective integration of features from diverse biological information significantly boosts the model’s performance.