We developed a new sampling method, MuSTAR MD which is an extension of temperature accelerated MD and can also be considered as a variation of replica-exchange MD. In the MuSTAR MD, each replica contains an all-atom model, at least one coarse-grained model, and a collective variable model that interacts with the other models through coupling terms. The coarse-grained model is introduced to drive efficient sampling of large conformational space and the all-atom model can serve to conduct accurate conformational sampling. The coupling strengths are exchanged between neighboring replicas in some interval obeying the Metropolis method. MuSTAR MD was applied to Ala-dipeptide and metenkephalin. Comparison with existing methods shows the efficiency and accuracy of MuSTAR MD. 2P074α-シヌクレイン繊維形成に対する分子混雑の影響 Macromolecular crowding effect on fibril formation of α-An aggregated form of α-synucleins are found in the Lewy body, which is the pathological hallmark of Parkinson's disease. The native state of αsynucleins are in a stable α-helical tetrameric state, while their monomeric state is disordered state. An in vitro experiment showed that fibril formation of α-synucleins occurs in their disordered state. These fibrils are considered as seeds for the aggregated form. In order to investigate the mechanism of transition from stable tetrameric state to monomeric disordered state, we constructed a simple lattice gas model considering the effects of macromolecular crowding. We found that decrease of macromolecular density causes tetramer-monomer transition and leads to subsequent fibril formation. 2P075サルモネラべん毛繊維の多型変換におけるフラジェリン Arg 431の役割The role of Arg431 of flagellin in the polymorphic transformation of Salmonella flagellar filament Fumio Hayashi, Kenji Oosawa (Div. Mol. Sci., Fac.Sci. and Tech, Gunma Univ.)Salmonella flagellar filament is a μm-length scale and helical structure composed of ~30,000 molecules of a single protein flagellin. The filament transforms among several helical structures, which are different in curvature and twist, and the morphological transformation is called polymorphic transformation. Elucidating the atomic mechanism of the transformation observed at μm scale provides a new insight into the actuating mechanism of large protein machines. Previously, we proposed that Arg431 of flagellin is one of the key residues for the polymorphic transformation by fliC-intragenic suppressor analysis. In the present study, we created mutants carrying mutations at Arg431 and investigated their filament shape and polymorphic transformation activity. 2P076 表面力測定によるシグナル伝達タンパク質間相互作用の研究 Interactions between signal transduction proteins studied by surface forces measurementSpoluation in Bacillus subtilis is controlled by phosphorelay signal transduction. In this series of phosphorylation reactions, KinA binds ATP and autophosphorylates at a histidine residue (KinA-P). The phosphoryl moiety on KinA is transferred to Spo0F and then to Spo0B. We have succeeded in measuring the specific interactions between KinA and Spo0F only ...
Neural Network (NN) are converted to output signal by 1/0 function such as the sigmoid function. The input signals at each layer unit is changed by the learning. Its change is depend on the slope of t h e 1/0 f u n c t i o n . I n t h e sensitive region as active region given by the 1/0 function, input signals move effectively and the movement increase with t h e i n c r e a s e of slope. A t t h e saturation region, however, it is very small. In this paper the limited linear function is used instead of the sigmoid function, because the active region is defined clearly and an examination of the movement easily. And the convergence mechanism of l e a r n i n g on Backpropagation is examined by the movement per a training cycle. Moreover, possibil i t y of t h e h i g h s p e e d l e a r n i n g is considered on the basis of its results. I NOn learning of the NN with Backpropagation, the output signals of units are calculated by the 1/0 function and they are used as new input signals at the next layer. This process is the same on it of the hidden layer and output layer. The output signals gotten at the o u t p u t l a y e r a r e compared with t h e s u p e r v i s e d s i g n a l s a n d t h e i n t e rconnecting weights are modified from the output layer to the input layer. This o p e r a t i o n is c o n t i n u e d u n t i l t h e difference between the output signals and the supervised signals are smaller than the minimum error. The modification of interconnecting weights decides the movement of input signals directly. In addition, 1/0 function is important factor for conversion of signals, because it decides the magnitude of movement of the input signals. This movement of input signals in sensitive region ("active region") is given by modification of interconnecting weights and 1/0 function. However its movement is s m a l l at the saturation region. For s t u d y i n g on c o n v e r g e n c e mechanism of l e a r n i n g , t h e sigmoid function which is used as 1/0 function is not suitable, because sigmoid function is non-linear function and the active region is not clear. Then limited linear function is used instead of sigmoid function. A s the limited linear function can define the active region and has a constant slope in t h e a c t i v e r e g i o n , t h e modeling of mechanism becomes simple and magnitudeof movement of input signals can be q u a n t i f i e d . I t is e x p e c t e d t h a t t h e increasing of movement in magnitude is effective mechanism of learning. And convergence mechanism becomes clear by the effect of the active region and the movement of t h e i n p u t s i g n a l s i n a training. .MethodThe modification of t h e i n t e rconnecting weights is given by Backpropagation and decides the movement of the input signals. Branch input signals (3) are led after each signal at units of the previous layer (Pj) are multiplied by the each interconnecting weight (wj). And the input signals (i) are given by summation of each branch input sQnals. ib:Branch input signal 1 8 1992 IEEE 523
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