Regulatory T Cells are Abundant in Glioblastoma Multiforme Tumors and Exhibit Enhanced Suppression in the Presence of Glioblastoma Multiforme Tumor Cells
Gregory L. CvetanovichCharles AshleyDavid E. AndersonRichard B. AndersonJeffrey N. BruceClare Baecher‐Allan
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The synthesis of Diketopyrrolopyrrole (DPP) having secondary interaction in the side chain explores its possibility to use in electronic and sensing applications. Herein we report easy method to engineer side chains of DPP. The hydrogen bonding is introduced on the side chain by substitution of urethane side chains on Diketopyrrolopyrrole (DPPurethane).The urethane side chain comprises a branched alkyl chain with good yields and purities. The DPPurethane characterized by NMR and IR, optical properties along with energy minimized structure were studied.
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We continue our study of the common features present in drug molecules by looking in detail at drug side chains. Using shape description methods, we divide a database of commercially available drugs into a list of common drug side chains. On the basis of the atom pair shape descriptor (taking into account atom type, hybridization, and bond order), there are 1,246 different side chains among the 5,090 compounds analyzed. The average number of side chains per molecule is 4, and the average number of heavy atoms per side chain is 2. If we ignore the carbonyl side chain, then there are approximately 15,000 occurrences of side chains. Of these 15,000 approximately 11,000 are from the "top 20" group of side chains. This suggests that the diversity that side chains provide to drug molecules is quite low. We discuss ways that this work could be used to provide guidance for molecular design efforts.
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The basic differences between the 20 natural amino acid residues are due to differences in their side-chain structures. This characteristic design of protein building blocks implies that side-chain-side-chain interactions play an important, even dominant role in 3D-structural realization of amino acid codes. Here we present the results of a comparative analysis of the contributions of side-chain-side-chain (s-s) and side-chain-backbone (s-b) interactions to the stabilization of folded protein structures within the framework of the CHARMm molecular data model. Contrary to intuition, our results suggest that side-chain-backbone interactions play the major role in side-chain packing, in stabilizing the folded structures, and in differentiating the folded structures from the unfolded or misfolded structures, while the interactions between side chains have a secondary effect. An additional analysis of electrostatic energies suggests that combinatorial dominance of the interactions between opposite charges makes the electrostatic interactions act as an unspecific folding force that stabilizes not only native structure, but also compact random conformations. This observation is in agreement with experimental findings that, in the denatured state, the charge-charge interactions stabilize more compact conformations. Taking advantage of the dominant role of side-chain-backbone interactions in side-chain packing to reduce the combinatorial problem, we developed a new algorithm, ChiRotor, for rapid prediction of side-chain conformations. We present the results of a validation study of the method based on a set of high resolution X-ray structures.
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Code (set theory)
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article Free Access Share on Remarks on Algorithm 332: Jacobi polynomials: Algorithm 344: student's t-distribution: Algorithm 351: modified Romberg quadrature: Algorithm 359: factoral analysis of variance Author: Arthur H. J. Sale Univ. of Sydney, Sydney, Australia Univ. of Sydney, Sydney, AustraliaView Profile Authors Info & Claims Communications of the ACMVolume 13Issue 7July 1970 https://doi.org/10.1145/362686.362700Published:01 July 1970Publication History 0citation275DownloadsMetricsTotal Citations0Total Downloads275Last 12 Months10Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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The Raman optical activity (ROA) spectra of proteins show distinct patterns arising from the secondary structure. It is generally believed that the spectral contributions of the side-chains largely cancel out because of their flexibility and the occurrence of many side-chains with different conformations. Yet, the influence of the side-chains on the ROA patterns assigned to different secondary structures is unknown. Here, the first systematic study of the influence of all amino acid side-chains on the ROA patterns is presented based on density functional theory (DFT) calculations of an extensive collection of peptide models that include many different side-chain and secondary structure conformations. It was shown that the contributions of the side-chains to a large extent average out with conformational flexibility. However, specific side-chain conformations can have significant contributions to the ROA patterns. It was also shown that α-helical structure is very sensitive to both the exact backbone conformation and the side-chain conformation. Side-chains with χ1 ≈-60° generate ROA patterns alike those in experiment. Aromatic side-chains strongly influence the amide III ROA patterns. Because of the huge structural sensitivity of ROA, the spectral patterns of proteins arise from extensive conformational averaging of both the backbone and the side-chains. The averaging results in the fine spectral details and relative intensity differences observed in experimental spectra.
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Abstract The problem of protein side‐chain packing for a given backbone trace is investigated using 3 different prediction models. The first requires an exhaustive search of all possible combinations of side‐chain conformers, using the dead‐end elimination theorem. The second considers only side‐chain‐backbone interactions, whereas the third neglects side‐chain‐backbone interactions and instead keeps side‐chain‐side‐chain interactions. Predictions of side‐chain conformations for 11 proteins using all 3 models show that removal of side‐chain‐side‐chain interactions does not cause a large decrease in the prediction accuracy, whereas the model having only side‐chain‐side‐chain interactions still retains a significant level of accuracy. These results suggest that the 2 classes of interactions, side‐chain‐backbone and side‐chain‐side‐chain, are consistent with each other and work concurrently to stabilize the native conformations. This is confirmed by analyses of energy spectra of the side‐chain conformations derived from the fourth prediction model, the Independent model, which gives almost the same quality of the prediction as the dead‐end elimination. The analyses indicate that the 2 classes of interactions simultaneously increase the energy difference between the native and nonnative conformations.
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The document first gives definitions of basic terms related to liquid-crystalline and mesomorphic states of matter and then terms specific to the classification of liquid-crystal polymers. The terms have been restricted to those most commonly encountered in the structural description of the latter class of materials.
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Mesogen
Pendant group
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