Moe software

Manufactured by Chemical Computing Group

Sourced in Canada

MOE (Molecular Operating Environment) is a software package developed by Chemical Computing Group. It provides a comprehensive set of tools for molecular modeling, simulation, and analysis. MOE's core function is to assist researchers and scientists in the study of molecular structures and their properties.

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Lab products found in correlation

Human database, by Chemical Computing Group (3 mentions) Pymol software, by Schrödinger (2 mentions) Schrodinger suite 2009, by Schrödinger (1 mentions) Pymol software version 4, by Schrödinger (1 mentions) High pure viral rna kit, by Roche (1 mentions)

Close spelling variants of this product

MOE) software 2020.0901 MOE software suite MOE) software version 2008.10 MOE software (version 2018, MOE software package

55 protocols using moe software

Docking Analysis of Dox and Sdox Interactions

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Interactions of Dox and Sdox with 85 different targets were analyzed by computational docking using MOE software (version 2019.01, Chemical Computing Group, ULC, Montreal, QC, Canada). For β-tubulin, interactions at both the colchicine binding site (CBS) and the Vinca binding site (VBS) were investigated. Crystallographic structure of targets was obtained from PDB (list of PDB target IDs available in Supplementary Information) (Berman et al., 2000 (link)). Protein structures were energetically minimized using Amber10 force field with EHT parameters for small molecules, R-field solvation model, and dielectric constant of 1 for the protein interior and 80 for exterior. Dox and Sdox structures were drawn in MOE software (version 2019.01, Chemical Computing Group, ULC, Montreal, QC, Canada), and their energies were minimized according to the above parameters using as stop criterion an RMS gradient lower than 0.01 kcal/mol/Å. For docking calculations, the Triangle Matcher algorithm with the London dG scoring function in the placement stage was used. The receptor was rigid, and the GBVI/WSA dG scoring function was used in the refinement stage.
Results were expressed as docking score (DS) values and difference in DS (ΔDS) values obtained for Dox and Sdox against the same target in order to provide more relevant information in their mechanism of action (Palmeira et al., 2012 (link)).

Alov P., Al Sharif M., Aluani D., Chegaev K., Dinic J., Divac Rankov A., Fernandes M.X., Fusi F., García-Sosa A.T., Juvonen R., Kondeva-Burdina M., Padrón J.M., Pajeva I., Pencheva T., Puerta A., Raunio H., Riganti C., Tsakovska I., Tzankova V., Yordanov Y, & Saponara S. (2022). A Comprehensive Evaluation of Sdox, a Promising H2S-Releasing Doxorubicin for the Treatment of Chemoresistant Tumors. Frontiers in Pharmacology, 13, 831791.

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NMDA Receptor Molecular Docking

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Molecular docking was carried out using the default parameters of MOE software (version 2016. 08, Chemical Computing Group Inc., Montreal, QC, Canada). The molecular model of NMDA receptor was built from the X-ray co-crystal structure of NMDA receptor (PDB: 5UOW) in complex with one ligand, the ion channel blocker (MK-801). The crystallographic structure was prepared using QuickPrep program of MOE using default setting. DT010 was organized in MOE database file. The co-crystallized ligand MK-801 for the identification for the binding site.

Hu S., Hu H., Mak S., Cui G., Lee M., Shan L., Wang Y., Lin H., Zhang Z, & Han Y. (2018). A Novel Tetramethylpyrazine Derivative Prophylactically Protects against Glutamate-Induced Excitotoxicity in Primary Neurons through the Blockage of N-Methyl-D-aspartate Receptor. Frontiers in Pharmacology, 9, 73.

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Antibody Sequence Alignment and Clustering

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Example 4

The Molecular Operating Environment (MOE) software developed by Chemical Computing Group (CCG) are used to generate alignments between the rabbit antibody clone 132, clone 154 and clone 163 and pairs of variable light and heavy chains, VL and VH, respectively from two databases:

- (1) The Abysis human database: a database of about 2000 known human VL/VH sequence pairs from IMGT-LigM DB; and
- (2) A human germline database: a database of germline sequences.

Humanized models show the best sequence alignments (highest identity to both the VL and VH domains) with fewest gaps. The top 100 antibody pairs from each human database are exported and clustered using kClust (Hauser, Mayer, & Soding, (2013) BMC Bioinformatics, 248).

US11834654B2. Antigen binding molecules and methods of use thereof (2023-12-05). Kite Pharma, Inc. [US]. Inventors: Jed J. W. Wiltzius [US], Stuart A. Sievers [US].

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High-Throughput Virtual Screening for PCNA Inhibitors

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We performed a virtual screen of libraries of chemical structures based on the known crystal structure of the PCNA/FEN1 complex that is available from the RCSB protein database. We specifically focused on the binding pocket in PCNA delineated in part by residues between L126 and Y133 of PCNA (see Fig 2g). We screened chemical databases available at the Albany Molecular Research Institute (AMRI, Albany, NY), containing 300,000 chemical compounds available directly from AMRI in at least 2 mg quantities, and more than 6.5 million additional compounds which were available in similar quantities from external vendors. For more than 3 million drug-like compounds in the databases, we pre-computed multiple conformations and performed a combination of substructure and pharmacophore searches using tools in the MOE software (Chemical Computing Group, Montreal, Canada, MOE v2008.05). The initial virtual screen yielded more than 8000 hits. We further analyzed these hits by molecular docking studies using the computer program, Glide (Schrödinger, LLC, New York, NY, Impact v 50207) (17 (link)), and identified 57 compounds, (including AOH39), for acquisition and experimental testing.

Gu L., Lingeman R., Yakushijin F., Sun E., Cui Q., Chao J., Hu W., Li H., Hickey R.J., Stark J.M., Yuan Y.C., Chen Y., Vonderfecht S.L., Synold T.W., Shi Y., Reckamp K.L., Horne D, & Malkas L.H. (2018). The anti-cancer activity of a first-in-class small molecule targeting PCNA. Clinical cancer research : an official journal of the American Association for Cancer Research, 24(23), 6053-6065.

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Antibody Sequence Alignment and Clustering

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Example 4

(1) The Abysis human database: a database of about 2000 known human VL/VH sequence pairs from IMGT-LigM DB; and

(2) A human germline database: a database of germline sequences.

US10844371B2. Antigen binding molecules and methods of use thereof (2020-11-24). Kite Pharma, Inc. [US]. Inventors: Jed J. W. Wiltzius [US], Stuart A. Sievers [US].

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Humanizing Rabbit Antibody Sequences

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Example 3

The Molecular Operating Environment (MOE) software developed by Chemical Computing Group (CCG) may be used to generate alignments between the rabbit antibody clones and pairs of variable light and heavy chains, VL and VH, respectively from two databases:

- (1) The Abysis human database: a database of about 2000 known human VL/VH sequence pairs from IMGT-LigM DB; and
- (2) A human germline database: a database of germline sequences.

US11820823B2. T cell receptor antigen binding molecules and methods of use thereof (2023-11-21). Kite Pharma, Inc. [US]. Inventors: Stephanie Astrow [US], Stuart Sievers [US], Jed Wiltzius [US].

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Protein Isoelectric Point Prediction

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Full-length homology models of all IgG molecules were generated and structure-based protein pI (Pro pI 3D) was computed using MOE software (Chemical Computing Group, 2022 ). The Pro pI 3D descriptor was calculated using PROPKA method to determine residue pKa values which are then used in the sequence-based pI formula (Li et al., 2005 (link); Meireles Ribeiro and Sillero, 1991 (link); Thorsteinson et al., 2021 (link)).

Feng J., Jiang M., Shih J, & Chai Q. (2022). Antibody apparent solubility prediction from sequence by transfer learning. iScience, 25(10), 105173.

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Blastocystis AOX Structural Modeling

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Blastocystis AOX was modeled to the TAO crystal structure (PDB:5GN2) using the Swiss-model software (http://swissmodel.expasy.org/; Arnold et al., 2006 (link); Benkert et al., 2011 (link); Biasini et al., 2014 (link)). The protein structure of Blastocystis AOX was loaded into MOE software (Molecular Operating Environment, version 2016.08, Chemical Computing Group Inc., Montreal, Canada) for some preparatory steps to correct any structural issues. Hence, the QuickPrep panel was used to optimize the hydrogen bond network using the Protonate 3D algorithm and to perform an energy minimization on the system. AMBER99 forcefield was used in assigning correct electronic charges and protonation of amino acid residues. The 3D structure for rhodoquinol was built within MOE and energy minimized using the Amber10:EHT forcefield. A second minimization was applied using the MOPAC semi-empirical energy functions (PM3 Hamiltonian). Rhodoquinol was docked into the binding site of the AOX using the Triangle Matcher placement method with London dG scoring. Subsequently, poses resulting from the placement stage were further refined using the Induced Fit method, which allows protein flexibility upon ligand binding, improving the prediction accuracy for the interaction. Poses were then rescored using the GBVI/WSA dG scoring function and the top five best scoring poses were retained.

Tsaousis A.D., Hamblin K.A., Elliott C.R., Young L., Rosell-Hidalgo A., Gourlay C.W., Moore A.L, & van der Giezen M. (2018). The Human Gut Colonizer Blastocystis Respires Using Complex II and Alternative Oxidase to Buffer Transient Oxygen Fluctuations in the Gut. Frontiers in Cellular and Infection Microbiology, 8, 371.

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Structural Modeling of mRNA and Translation Initiation

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The secondary structure of the complete mRNAs and its variants were predicted using the RNAfold [46 (link)] and mFOLD servers [47 (link)]. We used the default server conditions and simulated the 30S-bound structural layout by incorporating unpaired constrains from the SD to the start of the coding sequence (S1 File). To model the in vivo secondary structure, unpaired nucleotides resulting from chemical probing were set as unbound constrains [38 (link)] (S1 File).
Three-dimensional structures of the mRNAs were built using the RNAcomposer server, and the resulting pdb file was visualized in Pymol and MOE software [48 (link)] (Chemical Computing Group, Montreal, Canada). Structural alignments and RMSD between two structures were calculated using the MOE software (Chemical Computing Group, Montreal, Canada). For the structural modelling of the 30S IC and pre-IC, the predicted SR IIIb and TIR until +20 from the GUG were aligned with the bound mRNA of PDBs 5lmp and 5lmv, respectively [42 (link)]. mRNA structural alignments and visualization of the resulting models were performed with Pymol software (Schrödinger, New York, New York). Alignment and secondary structure prediction of the mTufA SETI of E. coli, Shigella dysenteriae, and Salmonella enterica were performed using the PETfold web server (S1 File) [49 (link)].

Vinogradova D.S., Zegarra V., Maksimova E., Nakamoto J.A., Kasatsky P., Paleskava A., Konevega A.L, & Milón P. (2020). How the initiating ribosome copes with ppGpp to translate mRNAs. PLoS Biology, 18(1), e3000593.

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Identifying Functional Patches on EBOV VP35

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Protein surface patches of EBOV (Variant Mayinga, species Zaire ebolavirus) VP35 IID (PDB code: 3FKE, chain B) were detected using Molecular Operating Environment (MOE) software (version 2018; Chemical Computing Group, Montreal, QC, Canada). Three hydrophobic patches were identified on the VP35 IID structure. To experimentally investigate whether these patches were functionally important, amino acid substitutions that eliminated each hydrophobic patch were determined by computational calculations with MOE. When considering a patch consisting of n residues, n × 19 mutants were generated at each patch by mutating a residue to the other 19 amino acids in silico. Then, for these mutants, we calculated the difference in the patch area and thermostability (dStability) from wild-type VP35 to each mutant (Tables S1–S3). Finally, we chose the mutants having the following three characteristics: (i) disappearance of the relevant patch area, (ii) no effects on other patches (i.e., numbers and area), and (iii) dStability within 2.0 kcal/mol.

Kasajima N., Matsuno K., Miyamoto H., Kajihara M., Igarashi M, & Takada A. (2021). Functional Importance of Hydrophobic Patches on the Ebola Virus VP35 IFN-Inhibitory Domain. Viruses, 13(11), 2316.

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