Biology subjects

Maia, J.

Publications and source records attributed to Maia, J..

3 recordsLinked to original sources

bioRxiv2020

AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spikes full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems. ACM Reference FormatLorenzo Casalino1{dagger}, Abigail Dommer1{dagger}, Zied Gaieb1{dagger}, Emilia P. Barros1, Terra Sztain1, Surl-Hee Ahn1, Anda Trifan2,3, Alexander Brace2, Anthony Bogetti4, Heng Ma2, Hyungro Lee5, Matteo Turilli5, Syma Khalid6, Lillian Chong4, Carlos Simmerling7, David J. Hardy3, Julio D. C. Maia3, James C. Phillips3, Thorsten Kurth8, Abraham Stern8, Lei Huang9, John McCalpin9, Mahidhar Tatineni10, Tom Gibbs8, John E. Stone3, Shantenu Jha5, Arvind Ramanathan2*, Rommie E. Amaro1*. 2020. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. In Supercomputing 20: International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI

biophysics↗

bioRxiv2020

Population Analysis of Extracellular Vesicles in Microvolumes of Biofluids

Extracellular Vesicles (EVs), membrane vesicles released by all cells, are emerging mediators of cell-cell communication. By carrying biomolecules from tissues to biofluids, EVs have attracted attention as non-invasive sources of clinical biomarkers in liquid biopsies. Although frequently employed for content characterization of EVs, the study of bulk preparations lacks information on sub-populations and the intrinsic heterogeneity of vesicles. Importantly, these strategies also difficult the characterization of EVs from small quantities of samples. We here present a Flow Cytometry strategy that enables detailed population analysis of EVs, at the same time decreasing sample volume requirements and accelerating the overall processing time. We show its unique application for quality control of isolates of EVs by comparing the proportion of vesicular and non-vesicular particles in samples prepared by different protocols. In addition, we demonstrate its suitability for the study of populations of EVs from samples characterized by challenging small volumes. To illustrate that, we perform longitudinal non-lethal analysis of EVs in mouse plasma and in single-animal collections of murine vitreous humor. By allowing for the analysis of EVs from minimal amounts of sample, our Flow Cytometry strategy has an unexplored potential in the study of EVs in clinical samples with intrinsically limited volumes. When compared to conventional methods, it also multiplies by several times the number of different analytes that can be studied from a single collection of biofluid.

cell biology↗

bioRxiv2018

A Public Database on Traumatic Brachial Plexus Injury

We hereby present the first worldwide public digital database centred on adult Traumatic Brachial Plexus Injury (TBPI). This initiative aims at reducing distance between clinical and experimental practice and encouraging data sharing and reuse. Detailed electronic questionnaires made with the free software LimeSurvey were designed to collect patients epidemiological, physical and clinical data. The freely available software Neuroscience Experiments System (NES) was employed to support data storage and management. First results of this effort concern data collected from 109 Brazilian adult TBPI patients with varying degrees of functional impairment. The sample is composed by large majority of men (84.4%), mean age of 32.1 (11.3 SD) years old, victims of motorcycle accidents (67%). The similarity of this dataset basic descriptors with those from previous reports in TBPI validates the strategies employed herein. Managing data from diverse provenance in TBPI may allow identifying functional markers related to the patients clinical improvement and foster the development of new investigative tools to unveil its mechanisms.

neuroscience↗