Hajime Takashima

Ursolic acid, an antagonist for transforming growth factor (TGF)-β1

Transforming growth factor‐β (TGF‐β), a multifunctional cytokine which is involved in extracellular matrix modulation, has a major role in the pathogenesis and progression of fibrotic diseases. We now report the effects of ursolic acid on TGF‐β1 receptor binding and TGF‐β1‐induced cellular functions in vitro. Ursolic acid inhibited [125I]‐TGF‐β1 receptor binding to Balb/c 3T3 mouse fibroblasts with an IC50 value of 6.9 ± 0.8 μM. Ursolic acid dose‐dependently recovered reduced proliferation of Minc Mv1Lu cells in the presence of 5 nM of TGF‐β1 and attenuated TGF‐β1‐induced collagen synthesis and production in human fibroblasts. Molecular dynamics simulations suggest that ursolic acid may interact with the hydrophobic region of the dimeric interface and thereby inhibit the binding of TGF‐β1 to its receptor. All these findings taken together show that ursolic acid functions as an antagonist for TGF‐β1. This is the first report to show that a small molecule can inhibit TGF‐β1 receptor binding and influence functions of TGF‐β1.

A novel parallel algorithm for large‐scale Fock matrix construction with small locally distributed memory architectures: RT parallel algorithm

We developed a novel parallel algorithm for large‐scale Fock matrix calculation with small locally distributed memory architectures, and named it the “RT parallel algorithm.” The RT parallel algorithm actively involves the concept of integral screening, which is indispensable for reduction of computing times with large‐scale biological molecules. The primary characteristic of this algorithm is parallel efficiency, which is achieved by well‐balanced reduction of both communicating and computing volume. Only the density matrix data necessary for Fock matrix calculations are communicated, and the data once communicated are reutilized for calculations as many times as possible. The RT parallel algorithm is a scalable method because required memory volume does not depend on the number of basis functions. This algorithm automatically includes a partial summing technique that is indispensable for maintaining computing accuracy, and can also include some conventional methods to reduce calculation times. In our analysis, the RT parallel algorithm had better performance than other methods for massively parallel processors. The RT parallel algorithm is most suitable for massively parallel and distributed Fock matrix calculations for large‐scale biological molecules with more than thousands of basis functions.

Is large‐scale ab initio Hartree‐Fock calculation chemically accurate? Toward improved calculation of biological molecule properties

Numerical errors in total energy values in large‐scale Hartree–Fock calculations are discussed. To obtain total energy values within chemical accuracy, 0.01 kcal/mol, stricter numerical accuracy is required as basis size increases. In molecules with 10,000 basis sizes, such as proteins, numerical accuracy for total energy values must be retained to at least 11 digits (i.e., to the order of 1.0D‐10) to keep accumulation of numerical errors less than the chemical accuracy (0.01 kcal/mol). With this criterion, we examined the sensitivity analysis of numerical accuracy in Hartree–Fock calculation by uniformly replacing the last bit of the mantissa part of a double‐precision real number by zero in the Fock matrix construction step, the total energy calculation step, and the Fock matrix diagonalization step. Using a partial summation technique in the Fock matrix generation step, the numerical error for total energy value of molecules with basis size greater than 10,000 was within chemical accuracy (0.01 kcal/mol), whereas with the conventional method the numerical error with several thousand basis sets was larger than chemical accuracy. Computation of one Fock matrix element with parallel machines can include the partial summation technique automatically, so that parallel calculation yields not only high‐performance computing but also more precise numerical solutions than the conventional sequential algorithm. We also found that the numerical error of the Householder‐QR diagonalization routine is equal to or less than chemical accuracy, even with a matrix size of 10,000. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 443–454, 1999

Rapid computation of all sets of electron-repulsion integrals for large-scale molecules

Abstract A concept of nonredundant sets of primitive gaussian basis functions is described, which proved highly efficient for rapid computing all sets of electron-repulsion integrals. Combination of the RT parallel algorithm with the nonredundant method demonstrated performance faster than the G amess program by a margin greater than fivefold even with a single processor calculation, making it ideal for large-scale SCF calculations. Further performance gains require accelerating calculations of initial auxiliary integrals.