Using predicted shape string to enhance the accuracy of γ-turn prediction.

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.

Improving the performance of ß-turn prediction using predicted shape strings and a two-layer support vector machine model.

BACKGROUND: The ß-turn is a secondary protein structure type that plays an important role in protein configuration and function. Development of accurate prediction methods to identify ß-turns in protein sequences is valuable. Several methods for ß-turn prediction have been developed; however, the prediction quality is still a challenge and there is substantial room for improvement. Innovations of the proposed method focus on discovering effective features, and constructing a new architectural model. RESULTS: We utilized predicted secondary structures, predicted shape strings and the position-specific scoring matrix (PSSM) as input features, and proposed a novel two-layer model to enhance the prediction. We achieved the highest values according to four evaluation measures, i.e. Q(total) = 87.2%, MCC = 0.66, Q(observed) = 75.9%, and Q(predicted) = 73.8% on the BT426 dataset. The results show that our proposed two-layer model discriminates better between ß-turns and non-ß-turns than the single model due to obtaining higher Q(predicted). Moreover, the predicted shape strings based on the structural alignment approach greatly improve the performance, and the same improvements were observed on BT547 and BT823 datasets as well. CONCLUSION: In this article, we present a comprehensive method for the prediction of ß-turns. Experiments show that the proposed method constitutes a great improvement over the competing prediction methods.

Sustained effects of one-time sewage sludge addition on rice yield and heavy metals accumulation in salt-affected mudflat soil.

High-yielding and sustainable production of rice in salt-affected mudflat is restricted by high soil salinity. Although sewage sludge can be used for mudflat amendment especially soil salt reduction, the possibility of potential heavy metal contamination in sludge-amended mudflat especially under paddy cultivation remains unclear, which hinders the further utilization of sewage sludge. In this study, a field experiment was conducted in a newly reclaimed mudflat to assess the sustained effects of one-time sludge input with different addition rates (0, 30, 60, 120, and 180 t ha-1) on soil salinity, rice yield, and potential metal contamination under paddy cultivation. The results indicated that sewage sludge addition (SSA) significantly decreased soil salinity and increased soil fertility. The increasing SSA rates and amending years led to the gradual increase of rice yield in salt-affected mudflat. The maximum increases in rice yield were 125.1%, 124.7%, and 127.9% in 2016, 2017, and 2018, and the average annual increase in rice yield in sludge-treated mudflat was 1.7%. Sludge addition increased metals accumulation in mudflat soil and metals uptake by rice tissues except Cr, Cu, and Pb in rice grain. The maximum increments in metal concentrations in soil and rice plant all occurred at 180 t ha-1 sludge addition rate. However, the metal concentrations in rice grain were below the safety limits even in the treatment with the highest sludge addition rate. Metal concentrations in sludge-treated soil and rice plant showed downward trend during the 3-year trial, and the decreases in total amount of soil metals were mainly concentrated in the first amending year, accounting for more than 50%. In summary, one-time sludge input achieved sustained mudflat amendment and efficient rice production. In addition, controlling the total amount of sludge input realized safe utilization of sewage sludge in salt-affected mudflat under paddy cultivation.

Sewage sludge application enhances soil properties and rice growth in a salt-affected mudflat soil.

The most important measures for salt-affected mudflat soil reclamation are to reduce salinity and to increase soil organic carbon (OC) content and thus soil fertility. Salinity reduction is often accomplished through costly freshwater irrigation by special engineering measures. Whether fertility enhancement only through one-off application of a great amount of OC can improve soil properties and promote plant growth in salt-affected mudflat soil remains unclear. Therefore, the objective of our indoor pot experiment was to study the effects of OC amendment at 0, 0.5%, 1.0%, 1.5%, and 2.5%, calculated from carbon content, by one-off application of sewage sludge on soil properties, rice yield, and root growth in salt-affected mudflat soil under waterlogged conditions. The results showed that the application of sewage sludge promoted soil fertility by reducing soil pH and increasing content of OC, nitrogen and phosphorus in salt-affected mudflat soil, while soil electric conductivity (EC) increased with increasing sewage sludge (SS) application rates under waterlogged conditions. In this study, the rice growth was not inhibited by the highest EC of 4.43 dS m-1 even at high doses of SS application. The SS application increased yield of rice, promoted root growth, enhanced root activity and root flux activity, and increased the soluble sugar and amino acid content in the bleeding sap of rice plants at the tillering, jointing, and maturity stages. In conclusion, fertility enhancement through organic carbon amendment can "offset" the adverse effects of increased salinity and promote plant growth in salt-affected mudflat soil under waterlogged conditions.