Transforming growth factor-beta (TGF-beta), 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-beta1 receptor binding and TGF-beta1-induced cellular functions in vitro. Ursolic acid inhibited [(125)I]-TGF-beta1 receptor binding to Balb/c 3T3 mouse fibroblasts with an IC(50) value of 6.9+/-0.8 microM. Ursolic acid dose-dependently recovered reduced proliferation of Minc Mv1Lu cells in the presence of 5 nM of TGF-beta1 and attenuated TGF-beta1-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-beta1 to its receptor. All these findings taken together show that ursolic acid functions as an antagonist for TGF-beta1. This is the first report to show that a small molecule can inhibit TGF-beta1 receptor binding and influence functions of TGF-beta1.
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.
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