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Investigation of Aframomum melegueta compounds as ERK5 inhibitor related to breast cancer via molecular docking and dynamic simulation

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In silico pharmacology 2025年第1期13卷 18页

作者： Paul Olamide Ottu Olorunfemi Oyewole Babalola Cecilia Oluwamodupe Ayodeji Folasade Oluwatobiloba Idayat Oyinkansola Kehinde Olufemi Adebisi Akinola Sulyman Olalekan Ibrahim Olusola Olalekan Elekofehinti Teady Bioscience Research Laboratory Ilara Mokin Ondo State Nigeria. Phytomedicine Molecular Biology and Bioinformatics lab Department of Chemical Science (Biochemistry program) Olusegun Agagu University of Science and Technology Okitipupa Ondo State Nigeria. Bioinformatics and Molecular Biology Lab Department of Biochemistry Federal University of Technology Akure Akure Ondo State Nigeria. Department of Industrial Chemistry University of Ilorin Ilorin Kwara State Nigeria.

Breast cancer remains a global health challenge, with rising cases predicted in the coming decades. The complexity of breast cancer treatment arises from its complex nature, often involving multiple therapeutic strategies. One promising approach is targeting the ERK5 pathway, a key regulator in cancer cell proliferation and survival. In this study, we explored the anticancer potential of bioactive compounds from Aframomum melegueta, a plant traditionally used in West African medicine. The 3D structure of ERK5 (PDB ID: 4B99) was prepared and optimized using the Schrödinger Protein Preparation Wizard. Six phytochemicals from Aframomum melegueta were screened for their binding affinities to ERK5 using GlideXP docking. Dihydrogingerenone A,1-(3,4-dihydroxy-5-methoxyphenyl)-7-(3,4-dihydroxyphenyl) heptane-3,5-diyldiacetate and Dihydrogingerenone C emerged as the lead compound, demonstrating a high docking score of − 9.659 kcal/mol, − 9.383 kcal/mol, and − 8.264 kcal/mol compared to standard anticancer drugs like Docetaxel (− 4.175 kcal/mol) and Temozolomide (− 5.443 kcal/mol). Post-docking analyses using MM-GBSA free energy calculations confirmed the compound’s high binding stability, with van der Waals interactions and hydrogen bonding at critical residues such as Met140 playing a significant role. Pharmacokinetic profiling using ADME analysis showed that our compounds exhibited favorable drug-likeness properties, adhering to Lipinski’s Rule of Five without violations. QSAR modeling and molecular dynamics (MD) simulations further validated its pharmacological potential. These findings suggest that Aframomum melegueta contains bioactive compounds with strong potential as ERK5 inhibitors, offering a novel approach to breast cancer treatment.

关键词： Aframomum melegueta Breast cancer Dihydrogingerenone A ERK5 Molecular dynamic simulation

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Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

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Genomics, Proteomics & bioinformatics 2022年第1期20卷 147-162页

作者： Yikai Luo Han Liang Graduate Program in Quantitative and Computational Biosciences Baylor College of MedicineHoustonTX 77030USA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA Department of Systems Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA

Genome-and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution,but its characteristics and implication in cancer evolution and therapy remain largely ***,we analyzed large-scale transcriptome/proteome profiles,such as The Cancer Genome Atlas(TCGA),the Genotype-Tissue Expression(GTEx),and the Clinical Proteomic Tumor Analysis Consortium(CPTAC),and found that compared to normal tissues,different cancer types showed a convergent pattern toward using biosynthetically low-cost amino *** a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids,termed energy cost per amino acid(ECPA).With this index,we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development,and revealed their reversed ***,focusing on the liver,a tissue with a dramatic increase in ECPA during development,we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single *** study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

关键词： Amino acid usage Tissue development Biosynthetic energy Diagnostic biomarker

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Building a unified model for drug synergy analysis powered by large language models

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Nature Communications 2025年第1期16卷 1-17页

作者： Liu, Tianyu Chu, Tinyi Luo, Xiao Zhao, Hongyu Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Biostatistics Yale University New Haven CT United States Department of Computer Science University of California Los Angeles Los Angeles CA United States

Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a unified Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models for the initial representation of drugs and cell lines. After demonstrating the relevance of these embeddings, we pre-train BAITSAO with a large-scale drug synergy database under a multi-task learning framework with rigorous selections of tasks. We demonstrate the superiority of the model architecture and the pre-trained strategies of BAITSAO over other methods through comprehensive benchmark analysis. Moreover, we investigate the sensitivity of BAITSAO and illustrate its promising functions including drug discoveries, drug combinations-gene interaction, and multi-drug synergy predictions. © The Author(s) 2025.

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Prediction of protein subcellular locations using a new measure of information discrepancy

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Journal of bioinformatics and computational biology 2005年第4期3卷 915-927页

作者： Jin, Lixia Tang, Huanwen Fang, Weiwu Bioinformatics and Computational Biology Department of Biochemistry Iowa State University Ames IA 50010 United States Institute of Computational Biology and Bioinformatics Dalian University of Technology Dalian 116025 China Institute of Applied Mathematics Chinese Academy of Sciences Beijing 100080 China

Given a raw protein sequence, knowing its subcellular location is an important step toward understanding its function and designing further experiments. A novel method is proposed for the prediction of protein subcellular locations from sequences. For four categories of eukaryotic proteins the overall predictive accuracy is 82.0%, 2.6% higher than that by using SVM approach. For three subcellular locations of prokaryotic proteins, an overall accuracy of 89.9% is obtained. In accordance with the architecture of cells, a hierarchical prediction approach is designed. Based on amino acid composition extracellular proteins and intracellular proteins can be identified with accuracy of 97%. © Imperial College Press.

关键词： Amino acid composition FDOD Physicochemical environment Subcellular locations

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ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types

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Genome biology 2025年第1期26卷 1-30页

作者： Dincer, Tevfik Umut Ernst, Jason Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles 90095 CA United States Department of Biological Chemistry University of California Los Angeles Los Angeles 90095 CA United States Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at University of California Los Angeles Los Angeles 90095 CA United States Computer Science Department University of California Los Angeles Los Angeles 90095 CA United States Jonsson Comprehensive Cancer Center University of California Los Angeles Los Angeles 90095 CA United States Molecular Biology Institute University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine University of California Los Angeles Los Angeles 90095 CA United States

We introduce ChromActivity, a computational framework for predicting and annotating regulatory activity across the genome through integration of multiple epigenomic maps and various functional characterization datasets. ChromActivity generates genomewide predictions of regulatory activity associated with each functional characterization dataset across many cell types based on available epigenomic data. It then for each cell type produces ChromScoreHMM genome annotations based on the combinatorial and spatial patterns within these predictions and ChromScore tracks of overall predicted regulatory activity. ChromActivity provides a resource for analyzing and interpreting the human regulatory genome across diverse cell types. © The Author(s) 2025.

关键词： CRISPR screens Epigenome Gene regulation Genome annotation Hidden Markov model Machine learning Massively parallel reporter assays

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Predicting intrinsic disorder in proteins: an overview

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细胞研究（英文版） 2009年第8期19卷 929-949页

作者： Bo He Kejun Wang Yunlong Liu Bin Xue Vladimir N Uversky A Keith Dunker College of Automation Harbin Engineering University Harbin Heilongjiang 150001 China Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN 46202 USA Division of Biostatistics Department of Medicine Indiana University School of Medicine Indianapolis IN 46202 USA Center for Medica Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN 46202 USA Department of Biochemistry and Molecular Biology Indiana University School of Medicine Indianapolis IN 46202 USA Institute for Intr

The discovery of intrinsically disordered proteins (IDP) (i.e., biologically active proteins that do not possess stable secondary and/or tertiary structures) came as an unexpected surprise, as the existence of such proteins is in contradiction to the traditional “sequence→structure→function” paradigm. Accurate prediction of a protein's predisposition to be intrinsically disordered is a necessary prerequisite for the further understanding of principles and mechanisms of protein folding and function, and is a key for the elaboration of a new structural and functional hierarchy of proteins. Therefore, prediction of IDPs has attracted the attention of many researchers, and a number of prediction tools have been developed. Predictions of disorder, in turn, are playing major roles in directing laboratory experiments that are leading to the discovery of ever more disordered proteins, and thereby leading to a positive feedback loop in the investigation of these proteins. In this review of algorithms for intrinsic disorder prediction, the basic concepts of various prediction methods for IDPs are summarized, the strengths and shortcomings of many of the methods are analyzed, and the difficulties and directions of future development of IDP prediction techniques are discussed.

关键词： protein intrinsic disorder prediction method

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Pitfalls of restriction enzyme analysis in identifying, characterizing,typing, and naming viral pathogens in the era of whole genome data, as illustrated by HAdV type 55

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Virologica Sinica 2016年第5期31卷 448-453页

作者： Qiwei Zhang Shoaleh Dehghan Donald Seto Biosafety Level-3 Laboratory School of Public HealthSouthern Medical University(Guangdong Provincial Key Laboratory of Tropical Disease Research) Chemistry Department American University Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason University

Restriction endonuclease analysis(REA),or restriction fragment length polymorphism(RFLP),was useful for identifying and determining the relatedness and putative identities of microbial strains(Tang et al.,1997)and for... 详细信息

关键词： and naming viral pathogens in the era of whole genome data DLL QS characterizing,typing Pitfalls of restriction enzyme analysis in identifying REA type

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U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation

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Molecular Cell 2025年第10期85卷 1968-1981.e7页

作者： Kim, GyeungYun Carroll, Christine L. Wakefield, Zachary Peters Tuncay, Mustafa Fiszbein, Ana Biology Department Boston University Boston 02215 United States Graduate Program in Molecular Biology Cell Biology & Biochemistry Boston University Boston 02215 United States Graduate Program in Cell and Molecular Biology Boston University Boston 02215 United States Bioinformatics Program Faculty of Computing and Data Science Boston University Boston 02215 United States Center for Computing & Data Sciences Boston University Boston 02215 United States

Emerging evidence indicates that splicing factors mediate the close link between transcription and splicing. However, the mechanisms underlying this coupling remain unclear. U1 small nuclear ribonucleoprotein particle (U1 snRNP) not only initiates splicing but also plays a crucial role in preventing premature cleavage and polyadenylation, facilitating long-distance transcriptional elongation. Here, we show that U1 snRNP regulates alternative promoter activity in human cells by inhibiting premature polyadenylation. In genes carrying premature polyadenylation sites between two promoters, U1 snRNP inhibition with antisense oligonucleotides leads to a significant decrease in downstream promoter activity. Conversely, restoring U1 snRNP activity or inhibiting premature polyadenylation rescues downstream promoter activity. Mechanistically, U1 snRNP inhibition correlates with reduced chromatin accessibility, decreased RNA polymerase II serine 5 phosphorylation, and increased promoter-proximal pause at downstream promoters. Our findings support a model in which U1 snRNP favors productive elongation from upstream promoters, triggering downstream promoter activation by destabilizing nucleosomes and promoting promoter escape. © 2025 The Author(s)

关键词： alternative promoters dowsntream promoters promoter-proximal pause splicing factors splicing transcription transcription cooperativity transcription initiation transcription interfierence transcription start sites U1 snRNP

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Molecular bases of morphologically diffused tumors across multiple cancer types

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National Science Review 2022年第11期9卷 65-67页

作者： Dingyun Liu Feiyang Xing Yueying Wang Jun Xiao Zheng An Ying Xu College of Computer Science and Technology Jilin University Center for Cancer Systems Biology China-Japan Union Hospital of Jilin University School of Life Sciences Jilin University China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of Georgia

Diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC) are the two main gastric cancer subtypes [1]. Morphologically, DGC consists of numerous disjoint tiny tumors while IGC forms one or a few solid tumors (Supplementary Supplementary Fig. S1)[1,2]. In-depth studies have discovered that DGCs generally have an ill-defined or missing glandular structure and reduced cell–cell adhesion,

关键词： identification enantioselective semisynthesis (−)-anisomelic acid oral agent anti-SARS-CoV-2

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Visualization of distribution of pathogenicity genomic islands between pathogenic, nosocomial and environmental bacteria

Visualization of distribution of pathogenicity genomic islan...

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2012 16th International Conference on Information Visualisation, IV 2012

作者： Reva, Oleg N. Bezuidt, Oliver Bioinformatics and Computational Biology Unit Biochemistry Department University of Pretoria Lynnwood Rd. Hillcrest Pretoria 0002 South Africa

ISBN: (纸本)9780769547718

The recurrent outbreaks of pathogens that possess new virulence factors and broad range antibiotic resistance gene cassettes reflect the importance of horizontal gene transfer (HGT) in the evolution of pathogenic bacteria. In many cases, the establishment of pathogens is mediated by the mobile genetic islands (GIs) which can easily be interchanged between bacterial taxa inhabiting the same or different environments. New computational tools to visualize and track down the network of distribution of GIs among bacteria are proposed in this work. © 2012 IEEE.

关键词： Bacteria

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