Public Repositories Research Articles

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation.

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

Exploration of programmed cell death-associated characteristics and immune infiltration in neonatal sepsis: new insights from bioinformatics analysis and machine learning

BackgroundNeonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition.MethodsFrom the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. To further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes.ResultsWithin the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay.ConclusionIn our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887–0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000).

DNA Barcode-Assisted Inventory of the Marine Macroalgae from the Azores, Including New Records

Up to the present study, only 8.5% of the 522 macroalgal species reported at the Azores have sequences deposited in GenBank and BOLD public repositories. The sequences of four genetic markers (cox1, rbcL, UPA, tufA) were obtained for recently collected samples from two Azorean islands. DNA barcode-assisted identification was conducted on newly generated and unpublished sequences from public repositories. A literature review of recently published studies, including the molecular identifications of Azorean macroalgae, was also performed. The results confirm the occurrence of 51 species (including subspecific ranks) and provide four new records, namely, three cryptogenic species (Olokunia boudouresquei, Padina gymnospora, and Ulva lacinulata) and one introduced species (Ulva australis). This study contributes 23 DNA barcodes generated for the first time to the Azores, which now has 10.5% of its marine flora represented in public repositories. Additionally, UPA sequences were generated for the first time for the five taxa.

Finding Candida auris in public metagenomic repositories.

Candida auris is a newly emerged multidrug-resistant fungus capable of causing invasive infections with high mortality. Despite intense efforts to understand how this pathogen rapidly emerged and spread worldwide, its environmental reservoirs are poorly understood. Here, we present a collaborative effort between the U.S. Centers for Disease Control and Prevention, the National Center for Biotechnology Information, and GridRepublic (a volunteer computing platform) to identify C. auris sequences in publicly available metagenomic datasets. We developed the MetaNISH pipeline that uses SRPRISM to align sequences to a set of reference genomes and computes a score for each reference genome. We used MetaNISH to scan ~300,000 SRA metagenomic runs from 2010 onwards and identified five datasets containing C. auris reads. Finally, GridRepublic has implemented a prospective C. auris molecular monitoring system using MetaNISH and volunteer computing.

Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions.

Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.

Identifying the process and agency characteristics associated with poor utilization outcomes in home healthcare

ABSTRACT This study identified the process and agency characteristics associated with poor utilization outcomes – higher percentages of patients (i) admitted to an acute care organization and (ii) visited an emergency room (ER) unplanned without hospitalization – for home health agencies (HHAs) in the United States. We conducted a secondary analysis of data about HHAs’ various characteristics, process adherence levels, and utilization outcomes collected from disparate public repositories for 2010–2022. We developed descriptive tree-based models using HHAs’ hospital admission or ER visit percentages as response variables. Across the board, hospital admission percentages have steadily improved while ER percentages deteriorated for an extended period. Recently, checking for fall risks and depression was associated with improved outcomes for urban agencies. In general, rural HHAs had worse utilization outcomes than urban HHAs. Targeted investments and improvement initiatives can help rural HHAs close the urban-rural gap in the future.

Accessibility of clinical study reports supporting medicine approvals: a cross-sectional evaluation

ObjectivesClinical study reports (CSRs) are highly detailed documents that play a pivotal role in medicine approval processes. Though not historically publicly available, in recent years, major entities including the European Medicines Agency (EMA), Health Canada, and the US Food and Drug Administration (FDA) have highlighted the importance of CSR accessibility. The primary objective herein was to determine the proportion of CSRs that support medicine approvals available for public download as well as the proportion eligible for independent researcher request via the study sponsor. Study Design and SettingThis cross-sectional study examined the accessibility of CSRs from industry-sponsored clinical trials whose results were reported in the FDA-authorized drug labels of the top 30 highest-revenue medicines of 2021. We determined (1) whether the CSRs were available for download from a public repository, and (2) whether the CSRs were eligible for request by independent researchers based on trial sponsors’ data sharing policies. ResultsThere were 316 industry-sponsored clinical trials with results presented in the FDA-authorized drug labels of the 30 sampled medicines. Of these trials, CSRs were available for public download from 70 (22%), with 37 available at EMA and 40 at Health Canada repositories. While pharmaceutical company platforms offered no direct downloads of CSRs, sponsors confirmed that CSRs from 183 (58%) of the 316 clinical trials were eligible for independent researcher request via the submission of a research proposal. Overall, 218 (69%) of the sampled clinical trials had CSRs available for public download and/or were eligible for request from the trial sponsor. ConclusionCSRs were available from 69% of the clinical trials supporting regulatory approval of the 30 medicines sampled. However, only 22% of the CSRs were directly downloadable from regulatory agencies, the remaining required a formal application process to request access to the CSR from the study sponsor.

BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.

The African Human Microbiome Portal: a public web portal of curated metagenomic metadata.

There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL: https://microbiome.h3abionet.org/.

Modeling multisource multifrequency acoustic wavefields by a multiscale Fourier feature physics-informed neural network with adaptive activation functions

Recently, the physics-informed neural network (PINN) was adopted to solve partial differential equation (PDE)-based forward and inverse problems. Compared to numerical differentiation, a PINN calculates derivatives by mesh-free automatic differentiation without dispersion artifacts. The Fourier feature PINN was applied to solve the frequency-domain acoustic wave equation to model multifrequency scattered wavefields. Although solving for scattered wavefields avoids the source singularity problem, it has drawbacks (e.g., requiring an analytic formula for computing the background wavefield, which only exists for the wave equation for simple models). We evaluated an approach for modeling multisource multifrequency acoustic wavefields using a multiscale Fourier feature mapping (MFFM) PINN with adaptive activations, directly solving for full wavefields instead of scattered wavefields and naturally avoiding the drawbacks of solving the scattered wave equation. For the MFFM, we explored the determination of the maximum and number of Fourier scales. Our inputs to the MFFM were only the spatial coordinates of the subsurface model; this result is lower than that of previous work (improving the efficiency of the PINN while maintaining its accuracy). Because the activation function is extremely important for a PINN, we use an existing technique and adapt it to a new architecture and develop an adaptive amplitude-scaled and phase-shifted sine activation function, which performs the best among the studied activation functions. Experiments show that the MFFM, adaptive activation, an appropriate learning rate, a linearly shrinking NN, and transfer learning greatly improve the convergence rate, accuracy, and efficiency of the PINN for simulating multisource multifrequency wavefields, laying the foundation for applying a PINN to wave equation-based inversion and imaging. We shared our codes, data, and results via a public repository.

Practical Aspects of Physics-Informed Neural Networks Applied to Solve Frequency-Domain Acoustic Wave Forward Problem

Abstract Physics-informed neural networks (PINNs) have been used by researchers to solve partial differential equation (PDE)-constrained problems. We evaluate PINNs to solve for frequency-domain acoustic wavefields. PINNs can solely use PDEs to define the loss function for optimization without the need for labels. Partial derivatives of PDEs are calculated by mesh-free automatic differentiations. Thus, PINNs are free of numerical dispersion artifacts. It has been applied to the scattered acoustic wave equation, which relied on boundary conditions (BCs) provided by the background analytical wavefield. For a more direct implementation, we solve the nonscattered acoustic wave equation, avoiding limitations related to relying on the background homogeneous medium for BCs. Experiments support our following insights. Although solving time-domain wave equations using PINNs does not require absorbing boundary conditions (ABCs), ABCs are required to ensure a unique solution for PINNs that solve frequency-domain wave equations, because the single-frequency wavefield is not localized and contains wavefield information over the full domain. However, it is not trivial to include the ABC in the PINN implementation, so we develop an adaptive amplitude-scaled and phase-shifted sine activation function, which performs better than the previous implementations. Because there are only two outputs for the fully connected neural network (FCNN), we validate a linearly shrinking FCNN that can achieve a comparable and even better accuracy with a cheaper computational cost. However, there is a spectral bias problem, that is, PINNs learn low-frequency wavefields far more easily than higher frequencies, and the accuracy of higher frequency wavefields is often poor. Because the shapes of multifrequency wavefields are similar, we initialize the FCNN for higher frequency wavefields by that of the lower frequencies, partly mitigating the spectral bias problem. We further incorporate multiscale positional encoding to alleviate the spectral bias problem. We share our codes, data, and results via a public repository.

Preclinical exploration of the DNA damage response pathway using the interactive neuroblastoma cell line explorer CLEAN.

Neuroblastoma (NB) is the most common cancer in infancy with an urgent need for more efficient targeted therapies. The development of novel (combinatorial) treatment strategies relies on extensive explorations of signaling perturbations in neuroblastoma cell lines, using RNA-Seq or other high throughput technologies (e.g. phosphoproteomics). This typically requires dedicated bioinformatics support, which is not always available. Additionally, while data from published studies are highly valuable and raw data (e.g. fastq files) are nowadays released in public repositories, data processing is time-consuming and again difficult without bioinformatics support. To facilitate NB research, more user-friendly and immediately accessible platforms are needed to explore newly generated as well as existing high throughput data. To make this possible, we developed an interactive data centralization and visualization web application, called CLEAN (the Cell Line Explorer web Application of Neuroblastoma data; https://ccgg.ugent.be/shiny/clean/). By focusing on the regulation of the DNA damage response, a therapeutic target of major interest in neuroblastoma, we demonstrate how CLEAN can be used to gain novel mechanistic insights and identify putative drug targets in neuroblastoma.

Metagenomic investigation of 16S rRNA marker gene samples to analyze the role of race, ethnicity, and location in preterm birth: A comprehensive vaginal microbiome meta-analysis

The vaginal microbiome is essential to women's reproductive health and numerous studies have demonstrated that any microbial dysbiosis can increase the risk of Preterm Birth (PTB). The present study utilized an integrated computational methodology to conduct a meta-analysis of PTB vaginal microbiome samples from women of different racial, ethnic, and geographical locations, across multiple studies derived from public repositories. Taxonomic classification, functional profiling, and statistical analysis of the PTB samples revealed that Finegoldia, Haemophilus, and Lactobacillus showed the highest variation in their microbial abundances while the Sensory system pathway, Substance dependence pathway, and Circulatory system pathway showed the highest variation in their expression levels based on race, ethnicity, and locations respectively. All of these factors cause subsequent variations in the etiology of PTB across the aforementioned demographic attributes. LEfSE and ANCOM-BC approaches were performed to identify race, ethnicity, and geographic location-specific microbial biomarkers. To the best of our knowledge, our work is one of the first to provide a detailed analysis of the complex delineations between socioeconomic constructs of race and ethnicity at a global scale, considering the eight racial groups and the geographical location of the participants who faced PTB. We believe these findings will have significant implications for the development of more targeted and efficacious strategies for the prevention and treatment of PTB.

Current status of global conservation and characterisation of wild and cultivated Brassicaceae genetic resources.

The economic importance of the globally distributed Brassicaceae family resides in the large diversity of crops within the family and the substantial variety of agronomic and functional traits they possess. We reviewed the current classifications of crop wild relatives (CWRs) in the Brassicaceae family with the aim of identifying new potential cross-compatible species from a total of 1,242 species using phylogenetic approaches. In general, cross-compatibility data between wild species and crops, as well as phenotype and genotype characterisation data, were available for major crops but very limited for minor crops, restricting the identification of new potential CWRs. Around 70% of wild Brassicaceae did not have genetic sequence data available in public repositories, and only 40% had chromosome counts published. Using phylogenetic distances, we propose 103 new potential CWRs for this family, which we recommend as priorities for cross-compatibility tests with crops and for phenotypic characterisation, including 71 newly identified CWRs for 10 minor crops. From the total species used in this study, more than half had no records of being in ex situ conservation, and 80% were not assessed for their conservation status or were data deficient (IUCN Red List Assessments). Great efforts are needed on ex situ conservation to have accessible material for characterising and evaluating the species for future breeding programmes. We identified the Mediterranean region as one key conservation area for wild Brassicaceae species, with great numbers of endemic and threatened species. Conservation assessments are urgently needed to evaluate most of these wild Brassicaceae.

Refinement of prognostication for IDH-mutant astrocytomas using DNA methylation-based classification.

The 2021 World Health Organization (WHO) grading system of isocitrate dehydrogenase (IDH)-mutant astrocytomas relies on histological features and the presence of homozygous deletion of the cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A/B). DNA methylation profiling has become highly relevant in the diagnosis of central nervous system (CNS) tumors including gliomas, and it has been incorporated into routine clinical diagnostics in some countries. In this study, we, therefore, examined the value of DNA methylation-based classification for prognostication of patients with IDH-mutant astrocytomas. We analyzed histopathological diagnoses, genome-wide DNA methylation array data, and chromosomal copy number alteration profiles from a cohort of 385 adult-type IDH-mutant astrocytomas, including a local cohort of 127 cases and 258 cases from public repositories. Prognosis based on WHO 2021 CNS criteria (histological grade and CDKN2A/B homozygous deletion status), other relevant chromosomal/gene alterations in IDH-mutant astrocytomas and DNA methylation-based subclassification according to the molecular neuropathology classifier were assessed. We demonstrate that DNA methylation-based classification of IDH-mutant astrocytomas can be used to predict outcome of the patients equally well as WHO 2021 CNS criteria. In addition, methylation-based subclassification enabled the identification of IDH-mutant astrocytoma patients with poor survival among patients with grade 3 tumors and patients with grade 4 tumors with a more favorable outcome. In conclusion, DNA methylation-based subclassification adds prognostic information for IDH-mutant astrocytomas that can further refine the current WHO 2021 grading scheme for these patients.

Open source and reproducible and inexpensive infrastructure for data challenges and education

Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure and expensive industry partnerships. Examples include challenges that use Google Cloud virtual machines and the Sage Bionetworks Dream Challenges platform. Such robust infrastructures can be financially prohibitive for investigators without substantial resources. Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, we developed a workflow that allows for reproducible model training, testing, and evaluation. We leveraged public GitHub repositories, open-source computational languages, and Docker technology. In addition, we conducted a data challenge using the infrastructure we developed. In this manuscript, we report on the infrastructure, workflow, and data challenge results. The infrastructure and workflow are likely to be useful for data challenges and education.

Streamlining remote nanopore data access with slow5curl.

As adoption of nanopore sequencing technology continues to advance, the need to maintain large volumes of raw current signal data for reanalysis with updated algorithms is a growing challenge. Here we introduce slow5curl, a software package designed to streamline nanopore data sharing, accessibility, and reanalysis. Slow5curl allows a user to fetch a specified read or group of reads from a raw nanopore dataset stored on a remote server, such as a public data repository, without downloading the entire file. Slow5curl uses an index to quickly fetch specific reads from a large dataset in SLOW5/BLOW5 format and highly parallelized data access requests to maximize download speeds. Using all public nanopore data from the Human Pangenome Reference Consortium (>22 TB), we demonstrate how slow5curl can be used to quickly fetch and reanalyze raw signal reads corresponding to a set of target genes from each individual in large cohort dataset (n = 91), minimizing the time, egress costs, and local storage requirements for their reanalysis. We provide slow5curl as a free, open-source package that will reduce frictions in data sharing for the nanopore community: https://github.com/BonsonW/slow5curl.

The Role of Public and Private Interventions on the Evolution of Green Innovation in China

China's future economy is in jeopardy due to climate change, zero COVID policy, supply chain disruption, surging inflation, and the energy crisis due to Russia Ukraine war. Additionally, the government pledges to the Paris agreement and COP27: The UN Climate Change Conference to combat with climate change crisis through the reduction of carbon and greenhouse gases emission. In these uncertain conditions, climate pledges, and shifts to a greener economic system, it is essential to investigate the public and private interventions on the evolution of green innovation in China. Accordingly, we relied on public repositories, such as the World Bank, World Intellectual Property Organization, PatSnap, SIPR, China City Statistical Yearbook 2021, and the Peking University Digital Financial Inclusion Index of China from 2009 to 2021 in 280 Chinese cities, to investigate the role of public and private interventions in the growth of green innovation. Our findings indicate that one interquartile range increase in government intervention, imperative environment regulations, gross regional domestic product, investment in environmental pollution, actual foreign investment, market pull, and material increase the green innovation growth at the Chinese city level. At the same time, we did not find a significant relationship between energy and green innovation. Moreover, the study paves the path for policymakers to remodify and develop policies on the different authoritative levels, which can reshape the Chinese green economy in the long run.

A Distributed Software Architecture for IoT: Container Orchestration Impact and Evaluation

This paper proposes a Distributed Software Architecture (DSA) for Smart Building (SB) based on the Reactive Manifesto (RM) principles. To follow the RM principles, we analyze the usage of different deployment approaches, particularly the impact of using a container orchestrator on the application layer. After running performance tests on the different configurations, the container orchestrator usage led to enhanced distributed processing, lowering the latency, increasing flexibility, enhancing security, and providing cost-effectiveness and scalability. We introduce the implementation of a modern DSA, developed following state-of-the-art cloud patterns and compliant with the RM for the SB context. Furthermore, we have ensured the reproducibility of this implementation by making the initial tests and overall architecture code available in public repositories. The research follows the Design Science Research (DSR) methodology for elaborating each phase until we get the artifact (DSA) and, with this, contribute to the Knowledge Base. The architecture was properly tested, considering the performance as the principal test layer. This solution is tailored for application in domains of the Internet of Things (IoT), focusing on the SB and a case study involving the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) at São Paulo University. Moreover, its applicability extends to IoT domains like smart home, smart campus, smart city, and health-related applications.

Investigation of Cnidium monnieri compounds as phosphodiesterase-5 antagonists in erectile dysfunction via molecular docking and dynamic simulation

This study aimed at evaluating the inhibitory potential of bioactive compounds from C. monnieri and predicting lead compounds for achieving and maintaining penile erection through computational models. A total of 79 compounds were retrieved for the plant through public repositories, phosphodiesterase-5 (PDE-5) was retrieved from the data bank for proteins (PDB). The molecular docking, free binding energy calculation, pharmacophore modeling and molecular dynamic simulation were performed using Schrodinger suite (2017–1). The quantum chemical calculation was also carried out with Spartan 14 computational software. Among the 79 compounds virtually screened against Phosphodiesterase-5 (PDE-5), eight (8) bioactive compounds showed potent antagonist against PDE-5 with varying binding scores comparable to that of Sildenafil (co-crystalized ligand). Monnieriside (−10.625 kcal/mol), Diosmetin (−10.041 kcal/mol and Isogosferol (−10.038 kcal/mol) were observed to have the best docking score comparable with Sildenafil (−11.872 kcal/mol). The fitness score range of 0.982–1.941 confirmed the validation of the docking scores using e-pharmacophore hypothesis. The result of the MD simulation reveals better stability of hit compound-protein complex (Monnieriside A-PDE-5) compared with the Sildenafil-PDE-5 complex. None of the hit compounds violated more than one rule of five and within the reference range for pharmacokinetics parameters. The quantum mechanics estimation revealed the hit molecules possess proton accepting and donating potential therefore, possess antagonistic potential. Based on the results from this study, the three top binding compounds may be predicted as potent inhibitors of PDE-5 and could use as lead compound for discovery of potent drugs for erectile dysfunction.