Department/Unit poster (BioMolecular Sciences). Corresponding author: Sushil Mishra (sushil@olemiss.edu)https://egrove.olemiss.edu/pharm_annual_posters_2022/1012/thumbnail.jp

Doerksen, Robert J.

Mishra, Sushil

Samanta, Priyanka

English

eGrove (Univ. of Mississippi)

University of Mississippi eGrove Annual Poster Session 2022 Annual Poster Session 10-11-2022 Computational Chemistry and Bioinformatics Research Core (CCBRC) Sushil Mishra University of Mississippi, sushil@olemiss.edu Priyanka Samanta University of Mississippi Robert J. Doerksen University of Mississippi Follow this and additional works at: https://egrove.olemiss.edu/pharm_annual_posters_2022 Recommended Citation Mishra, Sushil; Samanta, Priyanka; and Doerksen, Robert J., "Computational Chemistry and Bioinformatics Research Core (CCBRC)" (2022). Annual Poster Session 2022. 13. https://egrove.olemiss.edu/pharm_annual_posters_2022/13 This Book is brought to you for free and open access by the Annual Poster Session at eGrove. It has been accepted for inclusion in Annual Poster Session 2022 by an authorized administrator of eGrove. For more information, please contact egrove@olemiss.edu. Follow us:            @UM_glycore @um_glycore @GlyCOREComputational Chemistry and Bioinformatics Research CORE (CCBRC)Sushil K. Mishra1, Priyanka Samanta1, Robert J. Doerksen*1,21Glycoscience Center of Research Excellence, Department of BioMolecular Sciences; 2Research Institute of Pharmaceutical Sciences, The University of Mississippi, University, MS, 38677-1848 USAProtein–Glycan Interactions[1] H. Lis, N. Sharon, Chem. Rev. 98, 637–674 (1998).[2] D. Solís, N. V. Bovin, A. P. Davis, J. Jiménez-Barbero, et al., Biochim. Biophys. Acta. 1850, 186–235 (2015).[3] N. Taniguchi, Y. Kizuka, Advances in Cancer Research., (eds. Drake, R. R. & Ball, L. E.) 126, 11–51 (Academic Press, 2015).[4] C. Yu, K. Gao, L. Zhu, W. Wang, L. Wang, F. Zhang, C. et al., Sci. Rep. 6, 20029 (2016).[5] S. Cunningham, E. Starr, I. Shaw, J. Glavin, M. Kane, L. Joshi, Anal. Chem. 85, 949–955 (2013).[6] H. Clausen, S. Hakomori, Vox Sang. 56, 1–20 (1989).CORE CompetenciesqProviding all sorts of computational support for GlyCORE Jis, PPPIs. qResearch on computational glycosciences.qPerforming computations as part of service or collaboration.qConsultation on glycoscience research.qProviding access to high performance and GPU computing resources.qProviding access to software for molecular modeling.qTraining and workshops on computational glycosciences.AcknowledgementsFunding from the US National Institute of General Medical Sciences of the National Institutes of Health (NIH) under Award Number P20GM130460. The research is solely the responsibility of the researchers and does not necessarily represent the official views of the NIH.Glycosylation is the most commonpost-translational modification.Protein–glycan interactions areinvolved in many biologicalprocesses including cell signalling,immune response, and cell–celladhesion [1-2]. Cancer cells frequentlygo though altered glycosylation, andthese modifications are unique forcancer cells or cancer types [3].Such modified glycans are known as tumor-associated carbohydrateantigens (TACAs). Furthermore, exposure to non-self type glycans canresult in an immunological response in humans. For example, blood groupantigens A, B or non-human antigen Galα1,3Gal or Neu5Gc can trigger asevere allergic reaction [4-6]. Thus it is essential not only to understandprotein–glycan interactions but also to design recognition molecules todetect glycan patterns with high specificity.Glycoantigens and GlycomarkersABBlood Group typesβ3/4α2Oβ3β3/4α2α3Aβ3β3/4α2α3Bβ3Normal Cell Cancer CellsN-glycan FucosylatedN-glycanTnT sTnO-glycanα3β3 β6α3α6β3β4α6α3β4β2/4β4α2,6D-GalD-GalNAcD-ManD-GlcNAcD-NeuAcL-FucGal⍺1,3Galβ4α6α3β4β2/4β4α2,6α2Protein-glycan interactions are key to numerous physiological and pathological processes (Fig[2])Why Glycoscience?CORE FacilitiesProf. Robert J. Doerksen, Core Director: rjd@olemiss.edu CCBRC:Dr. Sushil K. Mishra, Core Manager: sushil@olemiss.eduLicensed software:• Schrödinger Drug Discovery Platform• Flex, Flare Forge (Cresset Group)• Amber20, Desmond• PyMOL v.2Other software:• Molecular Dynamics: Amber20, NAMD• Docking: AutoDock Vina, Vina-Carb, MGLTools• Structure Prediction: MODELLER, ROSETTA• Binding Free-energy Calculation: Sire, FESetup• Molecular Viewers: VMD, PyMOL, Maestro• Sequencing: Guppy, Flye, PycoQC, Flitlong, etc.• The Glycoscience Center of Research Excellence (GlyCORE) is a NIHCOBRE Phase 1 center.• GlyCORE seeks to develop, support and foster glycoscience researchthroughout the Mid-South region of USA (including Mississippi,Alabama, Arkansas, Louisiana, Tennessee, Kentucky and Missouri).• The CCBRC is a core facility of GlyCORE which focuses oncomputational glycoscience and provides training, access to hardwareand software, and collaboration to help solve scientific problems.Hardware:§ GAG: 8x A5000 GPUs, 384 GB memory, 40 TB storage§ High end GPU workstations: Nvidia RTX 308024TGPUs, 40 cores, 196 GB memory, 24 TB storage§ GPU workstations: 24 cores, 32 GB memory, RTX3080/A5000 GPUs§ Storage server: 80 TB of storage (6x 16 TB; RAID5)NVIDIA RTX 3080      RTX A5000 19.75.461.63556.33198.6151.420 200 400 600JAC_NPT-23,558 atoms PME 2fsFACTOR_IX_NPT-90,906 atomsCELLULOSE_NPT- 408,609 atomsMD speed (ns/day)RTX-3080 AMD Ryzen 3990 (64C)CCBRCReferencesIdentifying HitsHits to lead Lead optimizationStudying DynamicsMolecularRecognitionBinding Affinity Calculations∆𝐺?Structure Guided Drug DesigningGPU Cluster3D Structure Prediction1,6 branch1,3 branchServices Recent Publications of CCBRC:1. Shofolawe-Bakare, O. T.; de Mel, J. U.; Mishra, S. K.; Hossain, M.; Hamadani, C. M.; Pride, M. C.; Dasanayake, G. S.; Monroe, W.; Roth, E. W.; Tanner, E. E. L.; Doerksen, R. J.; Smith, A. E.; Werfel, T. A. ROS-Responsive Glycopolymeric Nanoparticles for Enhanced Drug Delivery to Macrophages. Macromol. Biosci, e2200281.2. Nagae, M.; Hirata, T.; Tateno, H.; Mishra, S. K.; Manabe, N.; Osada, N.; Tokoro, Y.; Yamaguchi, Y.; Doerksen, R. J.; Shimizu, T.; Kizuka, Y. Discovery of a Lectin Domain That Regulates Enzyme Activity in Mouse N-Acetylglucosaminyltransferase-IVa (MGAT4A). Commun. Biol. 2022, 5 (1), 1–11. 3. Vibhute, A. M.; Tanaka, H.; Mishra, S. K.; Osuka, R. F.; Nagae, M.; Yonekawa, C.; Korekane, H.; Doerksen, R. J.; Ando, H.; Kizuka, Y. Structure-Based Design of UDP-GlcNAc Analogs as Candidate GnT-V Inhibitors. Biochim. Biophys. Acta BBA - Gen. Subj. 2022, 1866 (6), 130118.4. Khaje, N. A.; Eletsky, A.; Biehn, S. E.; Mobley, C. K.; Rogals, M. J.; Kim, Y.; Mishra, S. K.; Doerksen, R. J.; Lindert, S.; Prestegard, J. H.; Sharp, J. S. Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling. Commun. Biol. 2022, 5 (1), 1–8.5. Kim, S. B.; Zoepfl, M.; Samanta, P.; Zhang, F.; Xia, K.; Thara, R.; Linhardt, R. J.; Doerksen, R. J.; McVoy, M. A.; Pomin, V. H. Fractionation of Sulfated Galactan from the Red Alga Botryocladia Occidentalis Separates Its Anticoagulant and Anti-SARS-CoV-2 Properties. J. Biol. Chem. 2022, 298.6. Dwivedi, R.; Samanta, P.; Sharma, P.; Zhang, F.; Mishra, S. K.; Kucheryavy, P.; Kim, S. B.; Aderibigbe, A. O.; Linhardt, R. J.; Tandon, R.; Doerksen, R. J.; Pomin, V. H. Structural and Kinetic Analyses of Holothurian Sulfated Glycans Suggest Potential Treatment for SARS-CoV-2 Infection. J. Biol. Chem. 2021, 297(4) 101207.o Glycam-web o Homology modelingo ab Initio: Rosettao Machine learning basedo Replica Exchange MDo Molecular dynamics (MD)o Replica Exchange MDo Steered MDo Metadynamicso Umbrella samplingo Dockingo Protein–proteino Protein–ligando Protein–glycano Protein–glycosaminoglycano Binding-energy calculationo Scoring functionso End-point approacheso Alchemical approacheso Potential of Mean ForceCCBRC Contact

Computational Chemistry and Bioinformatics Research Core (CCBRC)

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Computational Chemistry and Bioinformatics Research Core (CCBRC)

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