QC500 ProteinDF 2014p
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ProteinDF is a program for performing canonical molecular orbital calculations on large proteins using density functional theory. It uses Gaussian-type orbitals and is based on a 3rd generation density functional calculation method. ProteinDF has been used to study systems such as cytochrome c with 9,600 orbitals and insulin hexamers with 26,790 orbitals. The program achieves good self-consistent field convergence through an initial guess generated by the Quasi-canonical localized orbital method.
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Many-body Green functions theory for electronic and optical properties of or...
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(are physicists any good at chemistry?)
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structural biology-Protein structure function relationship
Structural biology determines protein structures using x-ray crystallography. X-rays are diffracted by regular arrays of atoms in protein crystals to produce patterns that reveal atomic structures. Protein structures determine their functions, such as catalytic activity. Understanding protein structures is essential to elucidating their roles in cellular processes.
PROTEIN STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINE
Support Vector Machine (SVM) is used for predict the protein structural. Bioinformatics method use to protein structure prediction mostly depends on the amino acid sequence. In this paper, work predicted of 1-
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Protein Structure Prediction Using Support Vector Machine
Support Vector Machine (SVM) is used for predict the protein structural. Bioinformatics method use to protein structure prediction mostly depends on the amino acid sequence. In this paper, work predicted of 1-D, 2-D, and 3-D protein structure prediction. Protein structure prediction is one of the most important problems in modern computation biology. Support Vector Machine haves shown strong generalization ability protein structure prediction. Binary classification techniques of Support Vector Machine are implemented and RBF kernel function is used in SVM. This Radial Basic Function (RBF) of SVM produces better accuracy in terms of classification and the learning results.
Recent trends in bioinformatics
This document discusses recent trends in bioinformatics, including the analysis of cDNA microarray data, protein tertiary structure prediction using Ramachandran plots, and the Protein Data Bank (PDB) which contains experimentally determined protein structures. It also discusses protein structure prediction techniques like CASP and TMW which aim to predict protein structures theoretically based on sequence. Predictions start from an initial conformation and use internal coordinates and planar geometry to model the structure as a tree. Further proteomics research can study protein function once a structure is obtained.
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+
53.13485
−
27.82088
with a host spectroscopic redshift of
2.903
±
0.007
. The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (
�
(
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−
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)
∼
0.9
) despite a host galaxy with low-extinction and has a high Ca II velocity (
19
,
000
±
2
,
000
km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
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Ca-rich population. Although such an object is too red for any low-
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cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
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1
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) with
Λ
CDM. Therefore unlike low-
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Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
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truly diverge from their low-
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counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
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Directdamage
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QC500 ProteinDF 2014p
- 1. ProteinDF: a canonical MO program for protein Toshiyuki HIRANO Institute of Industrial Science The University of Tokyo
- 2. Features of ProteinDF originally developed to attain canonical molecular orbital (CMO) calculations for extremely large molecules, such as metal complex proteins. uses Gauss-type orbitals as basis functions. based on the 3rd generation density functional (DF) calculation method. – see TH and FS, PCCP, 16, 14496 (2014). written by C++ using MPI & OpenMP. 2
- 3. What is 3rd gen DF method High-cost molecular integrals (including ERIs) are evaluated just once before SCF loop. The Coulomb (J) and exact-exchange (K) terms can be built by just matrix operation (DDOT and DGEMM) of the Cholesky vectors and density matrix. The exchange-correlation (XC) term in pure- DFT can be also build by matrix operation based on the grid-free DFT method. The details were written in “TH and FS, PCCP, 16, 14496 (2014)”. 3
- 4. cases using ProteinDF Cytochrome c (9,600 orb.) – F. Sato, et. al., Chem. Phys. Lett., 341, 645 (2001). Insulin hexamer (26 790 orb.) – T. Inaba, et. al., Chem. Phys. Lett., 434, 331 (2007). Photoconductor / Photodiode – Y. Tokita, et.al., Angew. Chem., 50, 11663-6 (2011) CO-binding myoglobin – K. Chiba, et. al., Int., J., Quant. Chem., DOI: 10.1002/qua.24459 (2013). 4
- 5. Another important point to achieve good SCF convergence Precise initial guess is needed to achieve the good SCF convergence. We have developed the Quasi-canonical localized orbital (QCLO) method to solve the problem. – see TH and FS et.al., JCP, 127, 184106 (2007). 5
- 6. Comparison of the QCLO method and density matrices in initial guess created by density matrices created by the QCLO method 6
- 7. If you are interested in ProteinDF, You can get the source code from: http://proteindf.github.io/ – GPL v3 – Sorry to delay writing the English manual. 7