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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

Journal of Molecular biology 2025年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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BPS2025 - Biophysical insights into allosteric modulation and inhibition of viral proteases using MD simulations

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Biophysical Journal 2025年第3期124卷 61a-61a页

作者： Marion Q. LoPresti Kelsie M. King Anne M. Brown Virginia Tech Blacksburg VA USA Genetics Bioinformatics and Computational Biology Program Virginia Tech Christiansburg VA USA Department of Biochemistry Virginia Tech Blacksburg VA USA

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Insights into the binding interactions of Macitentan to bovine serum Albumin: A Spectroscopic, electrochemical and computational study

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Journal of Molecular Liquids 2025年 429卷

作者： Sadak, Selenay Dindar, Çiğdem Kanbeş Kabir, Md.Zahirul Rehman, Fazal Mohamad, Saharuddin B. Yildirim, Sercan Uslu, Bengi Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara06560 Turkey Ankara University The Graduate School of Health Sciences Ankara06110 Turkey Biochemistry Program Institute of Biological Sciences Faculty of Science University Malaya Kuala Lumpur Malaysia Faculty of Science Bioinformatics Programme Institute of Biological Sciences Malaysia Centre of Research for Computational Sciences and Informatics for Biology Bioindustry Environment Agriculture and Healthcare University of Malaya Kuala Lumpur Malaysia Karadeniz Technical University Faculty of Pharmacy Department of Analytical Chemistry Trabzon Turkey

Macitentan (MACI) is an endothelial receptor antagonist used to treat pulmonary arterial hypertension (PAH). The pharmacokinetics and therapeutic efficacy of MACI are significantly influenced by its interaction with plasma proteins, especially bovine serum albumin (BSA). In this study, spectroscopic (fluorescence and UV–visible absorption spectroscopy), electrochemical, and computational techniques were applied to explore the binding properties and interaction mechanisms between MACI and BSA. The results indicated that the MACI quenched BSA fluorescence through a static mechanism. The interaction possessed a moderate binding affinity with Ka values ranging from 1.27 × 104 to 7.25 × 104 M−1 at different temperatures. Hydrophobic forces and hydrogen bonds were identified as critical factors in stabilizing the complex based on thermodynamic parameters. The local environment of tryptophan residues in BSA was found to be altered following the addition of MACI, as revealed by synchronous and three-dimensional fluorescence spectra. The spectroscopic studies were supported by electrochemical analyses, which indicated the formation of an electro-inactive complex between MACI and BSA. From the molecular docking experiments, the most likely binding site for MACI was found to be site I of BSA. Several hydrogen bonds and hydrophobic contacts were observed during the interaction of MACI with site I of BSA. These findings provided detailed insight to understand the binding properties of MACI to BSA, which may be beneficial for medical and pharmaceutical applications in the future. © 2025 Elsevier B.V.

关键词： Hydrogen bonds

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It takes glue to tango: MeDICi integration framework's data-intensive computing pipeline

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Scientific Computing 2008年第7期25卷 16-19页

作者： Gorton, Ian Oehmen, Christopher S. McDermott, Hason E. PNNL's Data Intensive Computing Program United States Laboratory's Bioinformatics and Computational Biology Group Fundamental and Computational Sciences Directorate United States

Researchers at the Department of Energy's (DOE) Pacific Northwest National Laboratory (PNNL) in Richland, WA, are creating computing environments for biologists that seamlessly integrate collections of data and computational resources. MeDICi is an evolving middleware platform for building complex, high-performance analytical applications. MIF components are constructed using Java programming interfaces that support inter-component communication using asynchronous messaging. Local components execute inside the MIF container. Remote components create distributed solutions and integrate with non-Java code. Mule provides the MIF container environment. MIF extends the Mule interface to make component and pipeline construction easier and to create an encapsulation device for component creation. The MIF interface is agnostic of the underlying Java messaging platform. This allows deployments to configure MIF applications using technologies that meet individual quality-of-service requirements.

关键词： Containers

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Breaking the high-throughput bottleneck

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Scientific Computing 2006年第4期23卷 22-26页

作者： Waters, Katrina M. Singhal, Mudita Webb-Robertson, Bobbie-Jo M. Stephan, Eric G. Gephart, Julie M. Computational Biology and Bioinformatics Groups United States Lab's Systems Biology Program Biological Sciences Division United States PNNL United States

The development of high-throughput (HTP) technologies such as transcriptomics (microarrays) and proteomics has made a revolution in the medical industry. PNNL is developing a systems biology computational framework using a problem-solving environment called bioinformatics Resource Manager (BRM) that automates routine data-merging and data-mining methods, and provides seamless linkage to data visualization tools. The BRM is designed to be operated by biologists looking for greater insights into their experimental results. BRM uses Remote Method Invocation (RMI) to launch several external data analysis tools, including NCBI's PubMed, the Conserved Domain Architectural Retrieval Tool (CDART) and the Basic Local Alignment Search Tool (BLAST). PQuad is a proteomics visualization tool that combines DNA or RNA sequence and proteomics data at multiple resolutions. It enables visualization of the data at the chromosome, gene and sequence levels.

关键词： Data visualization

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Identification of novel TMEM16A blockers through integrated virtual screening, molecular dynamics, and experimental studies

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Scientific Reports 2025年第1期15卷 1-11页

作者： Sangkhawasi, Mattanun Pitaktrakul, Wichuda Khumjiang, Rungtiwa Shigeta, Yasuteru Muanprasat, Chatchai Hengphasatporn, Kowit Rungrotmongkol, Thanyada Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Chakri Naruebodindra Medical Institute Faculty of Medicine Ramathibodi Hospital Mahidol University Bang Pla Bang Pli Samut Prakan 10540 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba 305-8577 Japan Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok 10330 Thailand Division of Biochemistry Department of Preclinical Science Faculty of Medicine Thammasat University Pathumthani 12120 Thailand

The calcium-activated chloride channel TMEM16A is a promising drug target for treating hypertension, secretory diarrheas, and various cancers, including head and neck cancer. Despite its potential, no FDA-approved drugs have provided the structural basis for directly inhibiting TMEM16A. This study aims to identify a novel pore-blocker of TMEM16A by integrating virtual screening, molecular dynamics simulations, and in vitro studies. Using the calcium-bound structure of TMEM16A with and without the pore-blocker 1PBC, we performed virtual screening on nearly 90,000 compounds from the ChemDiv database. Approximately 67% of these compounds demonstrated better binding affinity than 1PBC. Among the top 20 compounds selected for short-circuit current assays using human lung adenocarcinoma cells (Calu-3), compounds N066-0059, N066-0060, and N066-0067 inhibited TMEM16A activity with IC₅₀ values of 0.24 µM, 0.41 µM, and 0.48 µM, respectively, which was lower than that of a positive control Ani9 (9 µM). Due to its highest potency in electrophysiological assays, N066-0059 was subjected to mechanistic studies. Molecular dynamics simulations elucidated its binding stability and strength, showing superior performance to 1PBC over 500 ns with 3 replicates. This study advances TMEM16A-targeted drug development, offering new insights for anticancer therapies. © The Author(s) 2025.

关键词： And electrophysiological assays Cytotoxicity Molecular dynamics Pore-blocker inhibitor TMEM16A Virtual screening

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BPS2025 - Probing the influence of sequence on cytotoxic and functional amyloid oligomer conformations

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Biophysical Journal 2025年第3期124卷 73a-73a页

作者： Kelsie M. King Hajar Zaheer Anne M. Brown Genetics Bioinformatics and Computational Biology Program Virginia Tech Christiansburg VA USA University of North Carolina Chapel Hill Chapel Hill NC USA Department of Biochemistry Virginia Tech University Blacksburg VA USA

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TACO： Taxonomic prediction of unknown OTUs through OTU co-abundance networks

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Frontiers of Electrical and Electronic Engineering in China 2016年第3期4卷 149-158页

作者： Zohreh Baharvand Irannia Ting Chen Program in Computational Biology and Bioinformatics Department of Biological Sciences University of Southern California Los Angeles CA 90089 USA Bioinformatics Division TNLIST Tsinghua University Beijing 100084 China

Background： A main goal of metagenomics is taxonomic characterization of microbial communities. Although sequence comparison has been the main method for the taxonomic classification, there is not a clear agreement on similarity calculation and similarity thresholds, especially at higher taxonomic levels such as phylum and class. Thus taxonomic classification of novel metagenomic sequences without close homologs in the biological databases poses a challenge. Methods： In this study, we propose to use the co-abundant associations between taxa/operational taxonomic units （OTU） across complex and diverse communities to assist taxonomic classification. We developed a Markov Random Field model to predict taxa of unknown microorganisms using co-abundant associations. Results： Although such associations are intrinsically functional associations, we demonstrate that they are strongly correlated with taxonomic associations and can be combined with sequence comparison methods to predict taxonomic origins of unknown microorganisms at phylum and class levels. Conclusions： With the ever-increasing accumulation of sequence data from microbial communities, we now take the first step to explore these associations for taxonomic identification beyond sequence similarity. Availability and Implementation： Source codes of TACO are freely available at the following URL： https：//***/ baharvand/OTU-Taxonomy-Identification implemented in C＋＋, supported on Linux and MS Windows.

关键词： metagenomics 16s rRNA gene taxonomic profiling taxonomic prediction Markov Random Field OTUco-abundance network

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HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data

arXiv

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arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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Refinement of NMR-determined protein structures with database derived distance constraints

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Journal of bioinformatics and computational biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database Structural bioinformatics

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