TACCSTER 2021 Proceedings

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    Quantum Mechanical Studies of Topiroxostat Analogs for XO inhibition
    (2021) James, Samantha; Dong, Chao
    Gout is a chronic, inflammatory condition due to slow urate metabolism. The xanthine oxidase inhibitors, such as allopurinol and febuxostat, are recommended to reduce the uric acid level and prevent gout to attack in adult patients. The emerging new generation of xanthine inhibitor, topiroxostat (FYX051), displays a high cost effectiveness compared with febuxostat therapy in chronic gout patients. Here, we designed several modified inhibitors with 1,2,4-triazole ring being replaced by furan, thiophene, pyrrole, pyrazole. The quantum mechanical computational method is performed to obtain binding energy of these topiroxostat analogs with xanthine oxidase active site. the preminary data indicates there is a interesting linear relationship between binding energy and polarity of topiroxostat analogs.
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    Exploring the Role of Interfacial Cation in F Ion Channel using MD Simulation: Application of Computational Chemistry
    (2021) Chezhian, Aru; Momin, Zabin; Torabifard, Hedieh
    For many microbes, fluoride ion (F-) is toxic in high concentrations. To resist Ftoxicity, microbes have evolved a resistance mechanism, in which the Fluc channel exports Fions with high selectivity. Fluc has several unique features including a dual topology dimeric architecture. It has been shown that a Na+ ion is located at the interface of the dimer, however, the proposed Na+ is tetrahedrally coordinated while Na+ usually coordinates with 5 or 6 ligands. This study provides details about the role of a tetrahedrally–coordinated sodium ion in the structural stability and aid in identifying the contributing residues in high Fselectivity. We are modeling Fluc with various cations including Mg2+ and Mn4+ to provide a comprehensive comparison of Fluc structural stability and conformational changes. This research proposes an alternate interfacial ion for Fluc and could have larger implications for future study of this channel and other cation-coupled transporters for antimicrobial drug design.
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    Numerical Simulations against Experimental Data: A Comparison of Atmospheric Flow over Real Terrain Topography
    (2021) Bernardoni, Federico; Ciri, Umberto; Leonardi, Stefano
    The application of reduced order models for the estimations of local atmospheric flows in presence of complex terrain topography is challenged by the variety of the terrain geometrical features and of the atmospheric flow parameters. As an example, during the assessment of new wind farm site there is the need to accurately estimate how the terrain topography affects the local flow features and, as a consequence, the Annual Energy Production. In this study we compare numerical and experimental data of the flow over real terrain topography. We consider the complex terrain of the Perdigão site, in Portugal. This site consists of two parallel ridges with a wind turbine located on the top of one of the ridges. In 2017 a field campaign collected data at several locations. We carried out Large Eddy Simulations of the flow over the site topography and compared them with experimental data. In particular, wind velocity profile and turbulence intensities from numerical simulations are compared at the locations of the field campaign met-towers inside the valley and on top of the ridges. The vegetation is also reproduced in the numerical simulations showing its significant effect on the wind velocity profile close to the ground when comparing numerical simulations with experimental data.
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    Flow Stabilization in a 3D Channel flow
    (2021) Balogh, Andras; Vasquez, Camille
    We present the results of numerical simulations for the boundary feedback stabilization of the parabolic steady state profile of the incompressible Navier-Stokes Equations in a 3D channel flow. The computation is based on a MPI code written in FORTRAN that uses a hybrid pseudospectral-finite difference discretization and fractional step technique. The decentralized, static boundary feedback control laws are derived using Lyapunov technique. While the theoretical results are limited to stability enhancement for small Reynolds numbers, the numerical results demonstrate the effectiveness of the proposed feedback law even in cases when the uncontrolled flow is turbulent.
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    Using Supercomputing Resources in Genomic Research
    (2021) Bacolla, Albino; Tsai, Chi-Lin; Ye, Zu; De-Paula, Ruth B; Moiani, Davide; Ahmed, Zamal; Tainer, John A
    TACC resources have proven to be critical and enabling to mine cancer genomic data, genomic variants associated with human disease and polymorphic human traits, addressing biological questions otherwise non-approachable by conventional experiments. We have developed computational scripts that we use in a parallel environment to harness the capabilities of TACC HPCs, and which we have made publicly available on GitHub. In selected peer-review publications acknowledging TACC support, we have reported the association of DNA sequences able to form alternative DNA structures (or non-B DNA) with sites of chromosomal breaks leading to gross chromosomal translocations in cancer genomes, with sites of gene duplication predisposing to Parkinson’s disease, and most recently with regions of increased polymorphism in the human population. We found an exquisite correlation between the expression of selected genes and the mutational burden in cancer patients. While solving the crystal structure of a poorly characterized exonuclease, named EXO5, TACC resources enabled the assignment of a role for EXO5 in the cellular response to DNA damage, a vital pathway used by tumors to survive and grow, along with key genes whose high expression is linked to poor survival in cancer patients. Most recently, during the discovery of a nuclear role for GRB2, an adaptor protein previously thought to act only in the cytoplasm, TACC resources enabled us to test hypotheses derived from laboratory data. We were gratified to confirm the laboratory prediction that high expression of GRB2, together with its binding partner the MRE11 nuclease, carries accurate prognostic power for poor patient survival in breast cancer patients proficient in DNA homology-directed repair. These composite findings, significantly facilitated by TACC resources, have been critical to further our understanding in biological processes relevant to human disease, and to provide knowledge for the development of more precise therapeutic tools aimed at improving human health.
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    Unified Robust Estimation via the COCO
    (2021) Wang, Zhu
    Robust estimation is primarily concerned with how to provide reliable parameter estimates in the presence of outliers. Numerous robust loss functions have been proposed in regression and classification, along with various computing algorithms. This article proposes a unified framework for loss function construction and parameter estimation. The CC-family contains a composite of concave and convex functions. The properties of the CC-family are investigated, and CC-estimation is innovatively conducted via composite optimization by conjugation operator (COCO). The weighted estimators are simple to implement, demonstrate robust quality in penalized generalized linear models and support vector machines, and can be conveniently extended to even more broad applications with existing software. The data analysis in this work was conducted on TACC systems utilizing parallel computing power.
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    Posterior-vertical White Matter Tracts Cluster with Ventral Stream Tracts in Development and Predict Behavioral Variability
    (2021) Vinci-Booher, S; Caron, B; Bullock, D; James, K H; Pestilli, F
    A relatively unexplored group of white matter tracts has been recently described and made available for scientific investigation. These posterior vertical white matter tracts connect cortical regions associated with the ventral and dorsal visual streams, making them distinct from the well-studied, canonical tracts that connect anterior and posterior cortical regions. We utilized diffusion MRI and open-source cloud computing to characterize the development of vertical white matter tracts in a cross-sectional sample of 24 children (5-8 years old) and 12 adults (18-22 years old). Horizontal tracts within the ventral visual stream (ILF, IFOF) had FA that was more adult-like than horizontal tracts in the dorsal cortex (SLF1/2 and SLF3), consistent with prior work. Results from a clustering analysis demonstrated that the mean FA of the posterior vertical tracts (pArc, TPC, MdLF-spl, MDLF-ang) was more similar to that of the ventral tracts than the dorsal tracts. Performance on a perceptual matching task predicted FA in the pArc in the child sample. Our results suggest that posterior vertical tracts develop later than ventral stream tracts but earlier than tracts in the dorsal cortex and that the development of posterior vertical tracts may be related to the influence of perceptual processing on dorsally mediated action in childhood.
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    Prediction of Ligand Activity at Subcellular Location
    (2021) Varshney, Manikya; Verma, Srijan; KC, Govinda; Bocci, Giovanni; Oprea, Tudor I; Sirimulla, Suman
    Understanding subcellular distribution and the mechanism of xenobiotics can help in modulating subcellular dysfunction mediated diseases. Therefore, with improved knowledge of how xenobiotics are distributed across subcellular locations and the mechanism for a specific molecule can play a crucial role in assessing drug efficacy and toxicity. Such knowledge would widen therapeutic windows by allowing specific receptors to be targeted efficiently. Based on datasets that provide information on the subcellular locations of proteins and their ligands, we developed machine learning models for 42 subcellular locations. Such models were trained and validated based on the grid search method and best models based on Cohen’s Kappa scores were selected. With the help of the state-of-the-art supercomputing facilities provided by the Texas Advanced Computing Center(TACC), we were able to develop a suite of more than 22300+ machine learning models. These machine learning models were built using 19 different fingerprints-based features for 42 different subcellular locations using 28 different ML classifiers. The web-application is available on an open portal and can be accessed at https://drugdiscovery.utep.edu/subcell/ by anyone in order to perform high-throughput cheminformatics simulations. All the data and models generated from the project are made available as open-source.