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Numerous methods for predicting γ-turns in proteins have been developed. However, the results they generally provided are not very good, with a Matthews correlation coefficient (MCC)≤0.18. Here, an attempt has been made to develop a method to improve the accuracy of γ-turn prediction. First, we employ the geometric mean metric as optimal criterion to evaluate the performance of support vector machine for the highly imbalanced γ-turn dataset. This metric tries to maximize both the sensitivity and the specificity while keeping them balanced. Second, a predictor to generate protein shape string by structure alignment against the protein structure database has been designed and the predicted shape string is introduced as new variable for γ-turn prediction. Based on this perception, we have developed a new method for γ-turn prediction. After training and testing the benchmark dataset of 320 non-homologous protein chains using a fivefold cross-validation technique, the present method achieves excellent performance. The overall prediction accuracy Qtotal can achieve 92.2% and the MCC is 0.38, which outperform the existing γ-turn prediction methods. Our results indicate that the protein shape string is useful for predicting protein tight turns and it is reasonable to use the dihedral angle information as a variable for machine learning to predict protein folding. The dataset used in this work and the software to generate predicted shape string from structure database can be obtained from anonymous ftp site ftp://cheminfo.tongji.edu.cn/GammaTurnPrediction/ freely.

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Although inlet bent pipes are usually adopted due to limited installation space, the influences of different bend pipes on the inlet flow characteristics and performance of centrifugal compressors are still unclear. The numerical simulation of a centrifugal compressor is established and validated by experimental results with the case of a straight inlet pipe. Then, the internal flow characteristics of the centrifugal compressor with a 90-degree bent pipe (p90) and Z-shaped bent pipe (pz) are simulated and discussed. The results show that the adoption of two inlet bent pipes reduces the performance of the centrifugal compressor to a certain extent, which reduces more greatly with pz, with a maximum reduction of 6.82% in pressure ratio and 14.83% in efficiency, respectively. The pressure ratio and efficiency reduction of the centrifugal compressor both increase with the increment of distortion degree, which maintains the increasing trend as the flow rate increases, and the maximum distortion degree of p90 and pz reaches 0.0351 and 0.0479, respectively. The reduction degree of the pressure ratio shows a power–law relationship with the distortion degree, while the reduction degree of efficiency shows an exponential relationship with it. The flow characteristics at the outlet section of the inlet pipe affect the flow field distribution at the inlet of the impeller, and the distortion area ranges of the total pressure and axial velocity at the inlet of the impeller are near 72°–144° in the circumferential direction for p90, while those of pz are close to 108°–180° and 288°–360°. When the flow with a high distortion degree enters the impeller, a large area with high turbulent kinetic energy is formed in the downstream flow channel, resulting in an increase in the flow loss.