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GRAMMCell: Docking-based Cell Modeling Resource

Journal of Molecular biology 2025年 169085页

作者： Singh, Amar Tytarenko, Andrii M. Ambati, Vineeth Kumar Copeland, Matthew M. Kundrotas, Petras J. Kasyanov, Pavlo O. Feinberg, Eugene A. Vakser, Ilya A. Computational Biology Program The University of Kansas Lawrence 66045 KS United States Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute Kyiv 03056 Ukraine Department of Applied Mathematics and Statistics Stony Brook University Stony Brook 11794 NY United States Department of Molecular Biosciences The University of Kansas Lawrence 66045 KS United States

The environment inside biological cells is densely populated by macromolecules and other cellular components. The crowding has a significant impact on folding and stability of macromolecules, and on kinetics of molecular interactions. computational approaches to cell modeling, such as molecular dynamics, provide details of macromolecular behavior in concentrated solutions. However, such simulations are either slow, when carried out at atomic resolution, or significantly coarse-grained. Protein docking has been widely used for predicting structures of protein complexes. Systematic docking approaches, such as those based on Fast Fourier Transform (FFT), map the entire intermolecular energy landscape by determining the position and depth of the energy minima. The GRAMMCell web server implements docking-based approach for simulating cell crowded environment by sampling the intermolecular energy landscape generated by GRAMM (Global RAnge Molecular Matching). GRAMM systematically maps the landscape by a spectrum of docking poses corresponding to stable (deep energy minima) and transient (shallow minima) protein interactions. The sampling of these energy landscapes of a large system of proteins is performed in GRAMMCell using highly optimized Markov Chain Monte Carlo protocol. The procedure allows simulation of extra-long trajectories of large, crowded protein systems with atomic resolution accuracy. GRAMMCell is available at https://***. © 2025 The Author(s)

关键词： cell-modelling energy landscape protein docking protein–protein interactions web-based resource

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

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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 - computational simulation of astral cytoskeletal networks reveals optimal architecture for mechanical strength

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Biophysical Journal 2025年第3期124卷 471a-471a页

作者： Brady Berg Jun F. Allard Mathematical Computational and Systems Biology Program University of California Irvine Irvine CA USA Department of Mathematics and Department of Physics and Astronomy University of California Irvine Irvine CA USA

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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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Vector encoding of phylogenetic trees by ordered leaf attachment

arXiv

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arXiv 2025年

作者： Richman, Harry Zhang, Cheng Matsen, Frederick A. Computational Biology Program Fred Hutchinson Cancer Center Seattle United States Mathematics Division National Center for Theoretical Sciences Taipei Taiwan School of Mathematical Sciences Center for Statistical Science Peking University Beijing China Department of Statistics University of Washington Seattle United States Department of Genome Sciences University of Washington Seattle United States Howard Hughes Medical Institute Fred Hutchinson Cancer Center Seattle United States

As part of work to connect phylogenetics with machine learning, there has been considerable recent interest in vector encodings of phylogenetic trees. We present a simple new "ordered leaf attachment" (OLA) method for uniquely encoding a binary, rooted phylogenetic tree topology as an integer vector. OLA encoding and decoding take linear time in the number of leaf nodes, and the set of vectors corresponding to trees is a simply-described subset of integer sequences. The OLA encoding is unique compared to other existing encodings in having these properties. The integer vector encoding induces a distance on the set of trees, and we investigate this distance in relation to the NNI and SPR *** Codes 92B10, 05C05, 05C85, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Vectors

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BPS2025 - Systematic embedding of biomechanics within dynamical graph grammars with application to actin networks in dendritic spine heads

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Biophysical Journal 2025年第3期124卷 206a-206a页

作者： Eric Mjolsness Matthew Hur Thomas M. Bartol Terrence J. Sejnowski Departments of Computer Science and Mathematics University of California Irvine Irvine CA USA Mathematical Computational and Systems Biology Program University of California Irvine Irvine CA USA Computational Neurobiology Laborartory Salk Institute for Biological Studies San Diego CA USA

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The grand biological universe: A comprehensive geometric construction of genome space

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Innovation 2025年

作者： Yu, Hongyu Sun, Nan Ren, Ruohan Zhou, Tao Guan, Mengcen Zhao, Leqi Yau, Stephen S.-T. Department of Mathematical Sciences Tsinghua University Beijing 100084 China Beijing Institute of Mathematical Sciences and Applications (BIMSA) Beijing 101408 China Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York 10065 NY United States Tri-Institutional Training Program in Computational Biology and Medicine Weill Cornell Medicine New York 10065 NY United States

Analyzing the geometric relationships among genomic sequences from a mathematical perspective and revealing the laws hidden within these relationships is a crucial challenge in bioinformatics. The natural vector method constructs a genome space by extracting statistical moments of k-mers to illuminate the relationships among genomes. This approach highlights a fundamental law in biology known as the convex hull principle, which states that natural vectors corresponding to different types of biological sequences form distinct, non-overlapping convex hulls. Previous studies have validated this important principle across various datasets. However, they often focused on specific kingdoms and did not thoroughly analyze the significance of the dimensions required for the convex hull separation. In this study, we integrate all reliable sequences from different kingdoms to construct the grand biological universe, within which we comprehensively validate the multi-level convex hull principle. We demonstrate that the separation of convex hulls arises from biological properties rather than mathematical characteristics of high-dimensional spaces. Furthermore, we develop suitable metrics within the grand biological universe to facilitate efficient sequence classification. This research advances the convex hull principle through both theoretical development and experimental validation, making significant contributions to the understanding of the geometric structure of genome space. © 2025 The Author(s)

关键词： genome space grand biological universe multi-level convex hull principle natural vector

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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 - Mechanism-based modeling of drug-resistant mutations in cancer

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Biophysical Journal 2025年第3期124卷 505a-505a页

作者： Sukrit Singh John D. Chodera Markus Seeliger Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA State University of New York Stony Brook Stony Brook NY USA

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A web-based histology atlas for the freshwater sentinel species Daphnia magna

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Science of the Total Environment 2025年 958卷 177930页

作者： Ngu, Mee S. Vanselow, Daniel J. Zaino, Carolyn R. Lin, Alex Y. Copper, Jean E. Beaton, Margaret J. Orsini, Luisa Colbourne, John K. Cheng, Keith C. Ang, Khai C. Department of Pathology Pennsylvania State University College of Medicine PA United States Jake Gittlen Laboratories for Cancer Research Pennsylvania State University College of Medicine PA United States Department of Biology Mount Allison University Sackville Canada Centre for Environmental Research and Justice The University of Birmingham Birmingham United Kingdom Institute for Computational and Data Sciences Pennsylvania State University State CollegePA United States Molecular and Precision Medicine Program Pennsylvania State University College of Medicine HersheyPA United States

Daphnia are keystone species of freshwater habitats used as model organisms in ecology and evolutionary biology. Their small size, wide geographic distribution, and sensitivity to chemicals make them useful as environmental sentinels in regulatory toxicology and chemical risk assessment. Biomolecular (-omic) assessments of responses to chemical toxicity, which reveal detailed molecular signatures, become more powerful when correlated with other phenotypic outcomes (such as behavioral, physiological, or histopathological) for comparative validation and regulatory relevance. However, the lack of histopathology or tissue phenotype characterization of this species presently limits our ability to assess cellular mechanisms of toxicity. Here, we address the central concept that interpreting aberrant tissue phenotypes requires a basic understanding of species normal microanatomy. We introduce the female and male Daphnia Histology Reference Atlas (DaHRA) for the baseline knowledge of Daphnia magna microanatomy. We also include developmental stages of female D. magna in the atlas. This interactive web-based resource of adult D. magna features overlaid vectorized demarcation of anatomical structures whose labels comply with an anatomical ontology created for this atlas. We demonstrate the potential utility of DaHRA for toxicological investigations by presenting aberrant phenotypes of acetaminophen-exposed D. magna. We envision DaHRA to facilitate the future integration of molecular and phenotypic data from the scientific community as we seek to understand how genes, chemicals, and environment interactions determine organismal phenotype. © 2024 The Authors

关键词： Biotic

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