Abstract
The canonical molecular orbital (CMO) calculation can help to understand chemical properties and reactions in proteins. However, it is difficult to perform the CMO calculation of proteins because of its self-consistent field (SCF) convergence problem and expensive computational cost. To certainly obtain the CMO of proteins, we work in research and development of high-performance CMO applications and perform experimental studies. We have proposed the third-generation density-functional calculation method of calculating the SCF, which is more advanced than the FILE and direct method. Our method is based on Cholesky decomposition for two-electron integrals calculation and the modified grid-free method for the pure-XC term evaluation. By using the third-generation density-functional calculation method, the Coulomb, the Fock-exchange, and the pure-XC terms can be given by simple linear algebraic procedure in the SCF loop. Therefore, we can expect to get a good parallel performance in solving the SCF problem by using a well-optimized linear algebra library such as BLAS on the distributed memory parallel computers. The third-generation density-functional calculation method is implemented to our program, ProteinDF. To achieve computing electronic structure of the large molecule, not only overcoming expensive computation cost and also good initial guess for safe SCF convergence are required. In order to prepare a precise initial guess for the macromolecular system, we have developed the quasi-canonical localized orbital (QCLO) method. The QCLO has the characteristics of both localized and canonical orbital in a certain region of the molecule. We have succeeded in the CMO calculations of proteins by using the QCLO method. For simplified and semi-automated calculation of the QCLO method, we have also developed a Python-based program, QCLObot.
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