Four novel proteasome inhibitors, TMC-95A-D (1-4) have been isolated from the fermentation broth of Apiospora montagnei Sacc. TC 1093, isolated from a soil sample. All of the molecular formulas of 1-4 were established as C(33)H(38)N(6)O(10) by high-resolution FAB-MS. Their planar structures were determined on the basis of extensive analyses of 1D and 2D NMR, and degradation studies. Compounds 1-4 have the same planar structures to each other, and are unique highly modified cyclic peptides containing L-tyrosine, L-aspargine, highly oxidized L-tryptophan, (Z)-1-propenylamine, and 3-methyl-2-oxopentanoic acid units. The absolute configuration at C-11 and C-36 of 1-4 was determined based on chiral TLC and HPLC analyses of their chemical degradation products. The ROESY analysis along with (1)H-(1)H coupling constants clarified the absolute stereochemistry at C-6, -7, -8, and -14 of the cyclic moieties. These studies revealed the relationships of 1-4 to be diastereomers at C-7 and C-36.

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BackgroundMolecular descriptors have been widely used to predict biological activities and physicochemical properties or to analyze chemical libraries on the basis of similarity. Although fingerprints and properties are generally used as descriptors, neither is perfect for these purposes. A fingerprint can distinguish between molecules, whereas a property may not do the same in certain cases, and vice versa. When the number of the training set is especially small, the construction of good predictive models is difficult. Herein, a novel descriptor integrating mutually compensating fingerprint and property characteristics is described. The format of this descriptor is not conventional. It has two dimensions with variable length in one dimension to represent one molecule. This format is not acceptable for any machine learning methods. Therefore the distance between molecules has been newly defined for application to machine learning techniques. The evaluation of this descriptor, as applied to classification tasks, was performed using a support vector machine after the features of the descriptor had been optimized by a genetic algorithm.ResultsBecause the optimizing feature is time-intensive due to the complicated calculation of distances between molecules, the optimization was forced to stop before it was completed. As a result, no remarkable improvement was observed in the classification results for the new descriptor compared with those for other descriptors in any evaluation set used in this work. However, extremely low accuracies were also not found for any set.ConclusionsThe novel descriptor proposed in this work can potentially be used to make highly accurate predictive models. This new concept in descriptors is expected to be useful for developing novel predictive methods with quick training and high accuracy.

The energy landscape of a peptide [Ace-Lys-Gln-Cys-Arg-Glu-Arg-Ala-Nme] in explicit water was studied with a multicanonical molecular dynamics simulation, and the AMBER parm96 force field was used for the energy calculation. The peptide was taken from the recognition helix of the DNA-binding protein, c-Myb. A rugged energy landscape was obtained, in which the random-coil conformations were dominant at room temperature. The CD spectra of the synthesized peptide revealed that it is in the random state at room temperature. However, the 300 K canonical ensemble, Q(300K), contained ␣-helix, 3 10 -helix, ␤-turn, and ␤-hairpin structures with small but notable probabilities of existence. The complete ␣-helix, imperfect ␣-helix, and random-coil conformations were separated from one another in the conformational space. This means that the peptide must overcome energy barriers to form the ␣-helix. The overcoming process may correspond to the hydrogen-bond rearrangements from peptide-water to peptide-peptide interactions. The ␤-turn, imperfect 3 10 -helix, and ␤-hairpin structures, among which there are no energy barriers at 300 K, were embedded in the ensemble of the random-coil conformations. Two types of ␤-hairpin with different ␤-turn regions were observed in Q(300K). The two ␤-hairpin structures may have different mechanisms for the ␤-hairpin formation. The current study proposes a scheme that the random state of this peptide consists of both ordered and disordered conformations. In contrast, the energy landscape obtained from the parm94 force field was funnel like, in which the peptide formed the helical conformation at room temperature and random coil at high temperature.Keywords: Folding; rugged surface; funnel; ␤-hairpin; ␣-helix; random state; multicanonical; force fieldThe thermodynamic stability and the folding process of a polypeptide chain are determined by the energy landscape of the system. One picture for the landscape is a funnel-like surface, which has the advantage of quickly folding the chain into a unique tertiary structure. The other picture is a rugged surface, in which a number of energy local minima are widely spread, and the chain thermally fluctuates among the minima at a given temperature.The folding funnel has been studied with simplified models (Bryngelson et al. 1995;Chan and Dill 1998;Dill 1999;Istrail et al. 1999;Nakamura and Sasai 1999), in which the chain was designed to fold into a unique, stable structure at the ground state or at a low temperature. An advantage of the simplified model is that the thermodynamically important states can be counted relatively precisely, and thus the free energies of the states are evaluated. The simplified model is also used to specify the determinant factors to fold the chain into unique tertiary structures (Irback and Potthast

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