Специфика статистики упругих и деформационно-прочностных свойств высокопрочных волокон полипропилена

Regularities of statistical distributions of a complex of mechanical properties, including the module of elasticity (E), strength () and strain at break (b), high-strength industrial oriented polypropylene (PP) fibers have been analyzed using the Weibull and Gauss models based on large a wide array of measurements (50 identical samples in each series). The values of the statistical Weibull modulus (m) - a parameter characterizing the scatter of the measured values of the data arrays of E,  and b – have been estimated for the PP samples of two types: single fibers (monofilaments) and multifilament fibers consisting from several hundred single fibers. For the PP multifilament fibers, a more correct description of the distributions of E,  and b has been received both in the framework of the normal distribution (Gaussian distribution) and in the framework of the Weibull distribution in comparison with the description of such distributions for the PP monofilaments. The influence of the polymer chain conformation on the regularities of the statistical distributions of E,  and b for the high-strength oriented polymeric materials with different chemical chain structures and the correctness of their descriptions in the framework of the Gauss and Weibull models have been analyzed. For this purpose, the values of m calculated in this work for PP with a helical chain conformation have been compared with the values of m determined by us earlier for ultra-high molecular weight polyethylene and polyamide-6 with the chain conformations in the form of an in-plane trans-zigzag.

OPUS-Rota4: A Gradient-Based Protein Side-Chain Modeling Framework Assisted by Deep Learning-Based Predictors

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs; and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. In summary, OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the constraints derived from OPUS-RotaCM. In this case, we convert the protein side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms into its side-chain modeling procedure. In other words, OPUS-Rota4 provides a platform in which the protein side-chain conformation can be dynamically adjusted under the influence of other processes, such as protein-protein interaction. We apply OPUS-Rota4 on 15 FM predictions submitted by Alphafold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than the side chains predicted by Alphafold2.