CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.

Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several in silico models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/CNNArginineMe.

Electro-optic chaotic system based on time delay feature hiding and key space enhancement based on chaotic post-processing.

To improve the output performance of the classical all-optical chaotic system and solve the security problems of its key exposure and small key space, a new chaotic system, to the best of our knowledge, based on logistic map post-processing is proposed. In terms of the general output performance of the system, the spectrum of the proposed system is flatter than the classical system. Through a bifurcation diagram and permutation entropy analysis, it is found that the output of the system is extremely complex. In terms of security, the simulation results show that, with a reasonable selection of system parameters, key hiding can be achieved under a large parameter range. Moreover, through the sensitivity analysis of logistic parameters, it can be seen that the introduction of logistic parameters can improve the key space of the system and further improve the security of the system.

Development of an experiment-split method for benchmarking the generalization of a PTM site predictor: Lysine methylome as an example.

Many computational classifiers have been developed to predict different types of post-translational modification sites. Their performances are measured using cross-validation or independent test, in which experimental data from different sources are mixed and randomly split into training and test sets. However, the self-reported performances of most classifiers based on this measure are generally higher than their performances in the application of new experimental data. It suggests that the cross-validation method overestimates the generalization ability of a classifier. Here, we proposed a generalization estimate method, dubbed experiment-split test, where the experimental sources for the training set are different from those for the test set that simulate the data derived from a new experiment. We took the prediction of lysine methylome (Kme) as an example and developed a deep learning-based Kme site predictor (called DeepKme) with outstanding performance. We assessed the experiment-split test by comparing it with the cross-validation method. We found that the performance measured using the experiment-split test is lower than that measured in terms of cross-validation. As the test data of the experiment-split method were derived from an independent experimental source, this method could reflect the generalization of the predictor. Therefore, we believe that the experiment-split method can be applied to benchmark the practical performance of a given PTM model. DeepKme is free accessible via https://github.com/guoyangzou/DeepKme.

Electro-optic chaos system with time delay signature concealment based on XOR operation and multi-bit PRBS.

A novel chaos system with XOR operations and multi-bit PRBS is proposed to improve the sequence complexity and the security of the classic electro-optic intensity chaos system. Through the bifurcation diagram and permutation entropy analysis, the PE can be increased to 0.99. The key space is enlarged because additional DSP parameters and PRBS are introduced. The impacts of ADC/DAC characteristics and PRBS characteristics are analyzed in detail. The simulation results show that the time delay signature can be concealed with the appropriate DSP parameters.