{"doi":"10.17615/tmqe-f823","title":"Molecular dynamics simulations to explore the structure and rheological properties of normal and hyperconcentrated airway mucus","abstract":"We develop the first molecular dynamics model of airway mucus based on the detailed physical properties and chemical structure of the predominant gel-forming mucin MUC5B. Our airway mucus model leverages the LAMMPS open-source code [https://lammps.sandia.gov], based on the statistical physics of polymers, from single molecules to networks. On top of the LAMMPS platform, the chemical structure of MUC5B is used to superimpose proximity-based, non-covalent, transient interactions within and between the specific domains of MUC5B polymers. We explore feasible ranges of hydrophobic and electrostatic interaction strengths between MUC5B domains with 9 nanometer spatial and 1 nanosecond temporal resolution. Our goal here is to propose and test a mechanistic hypothesis for a striking clinical observation with respect to airway mucus: a 10-fold increase in non-swellable, dense structures called flakes during progression of cystic fibrosis disease. Among the myriad possible effects that might promote self-organization of MUC5B networks into flake structures, we hypothesize and confirm that the clinically confirmed increase in mucin concentration, from 1.5 to 5 mg/mL, alone is sufficient to drive the structure changes observed with scanning electron microscopy images from experimental samples. We post-process the LAMMPS simulated datasets at 1.5 and 5 mg/mL, both to image the structure transition and compare with scanning electron micrographs and to show that the 3.33-fold increase in concentration induces closer proximity of interacting electrostatic and hydrophobic domains, thereby amplifying the proximity-based strength of the interactions.","journal":"UNC Libraries","year":2024,"id":499943,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8584,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":105530,"name":"Richard C. Boucher","orcid":"0000-0002-5745-3989","position":1,"is_corresponding":false},{"id":735868,"name":"Micah Papanikolas","orcid":"0000-0003-2691-5113","position":2,"is_corresponding":false},{"id":353583,"name":"Ronit Freeman","orcid":"0000-0001-5960-6689","position":3,"is_corresponding":false},{"id":736598,"name":"Andrew G. Ford","orcid":null,"position":4,"is_corresponding":false},{"id":370980,"name":"M. Gregory Forest","orcid":"0000-0002-7718-4456","position":5,"is_corresponding":false},{"id":394495,"name":"David B. Hill","orcid":"0000-0002-9270-777X","position":6,"is_corresponding":false},{"id":570715,"name":"Matthew R. Markovetz","orcid":"0000-0003-3931-0594","position":7,"is_corresponding":false},{"id":105501,"name":"Takafumi Kato","orcid":"0000-0003-2248-3376","position":8,"is_corresponding":false},{"id":735867,"name":"Xue‐Zheng Cao","orcid":"0000-0002-0409-6324","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:10:04.829419Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}