RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification

BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier

The use of bacteriocins has emerged as a propitious strategy in the development of new drugs to combat antibiotic resistance, given their ability to kill bacteria with both broad and narrow natural spectra. Hence, a compelling requirement arises for a precise and efficient computational model that can accurately predict novel bacteriocins. Machine learning's ability to learn patterns and features from bacteriocin sequences that are difficult to capture using sequence matching-based methods makes it a potentially superior choice for accurate prediction. A web application for predicting bacteriocin was created in this study, utilizing a machine learning approach. The feature sets employed in the application were chosen using alternating decision tree (ADTree), genetic algorithm (GA), and linear support vector classifier (linear SVC)-based feature evaluation methods. Initially, potential features were extracted from the physicochemical, structural, and sequence-profile attributes of both bacteriocin and non-bacteriocin protein sequences. We assessed the candidate features first using the Pearson correlation coefficient, followed by separate evaluations with ADTree, GA, and linear SVC to eliminate unnecessary features. Finally, we constructed random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), k-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB) models using reduced feature sets. We obtained the overall top performing model using SVM with ADTree-reduced features, achieving an accuracy of 99.11% and an AUC value of 0.9984 on the testing dataset. We also assessed the predictive capabilities of our best-performing models for each reduced feature set relative to our previously developed software solution, a sequence alignment-based tool, and a deep-learning approach. A web application, titled BPAGS (Bacteriocin Prediction based on ADTree, GA, and linear SVC), was developed to incorporate the predictive models built using ADTree, GA, and linear SVC-based feature sets. Currently, the web-based tool provides classification results with associated probability values and has options to add new samples in the training data to improve the predictive efficacy. BPAGS is freely accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/.

Protein-DNA Binding Residues Prediction Using a Deep Learning Model With Hierarchical Feature Extraction

Biologically important effects occur when proteins bind to other substances, of which binding to DNA is a crucial one. Therefore, accurate identification of protein-DNA binding residues is important for further understanding of the protein-DNA interaction mechanism. Although wet-lab methods can accurately obtain the location of bound residues, it requires significant human, financial and time costs. There is thus an urgent need to develop efficient computational-based methods. Most current state-of-the-art methods are two-step approaches: the first step uses a sliding window technique to extract residue features; the second step uses each residue as an input to the model for prediction. This has a negative impact on the efficiency of prediction and ease of use. In this study, we propose a sequence-to-sequence (seq2seq) model that can input the entire protein sequence of variable length and use two modules, Transformer Encoder Block and Feature Extracting Block, for hierarchical feature extraction, where Transformer Encoder Block is used to extract global features, and then Feature Extracting Block is used to extract local features to further improve the recognition capability of the model. The comparison results on two benchmark datasets, namely PDNA-543 and PDNA-41, prove the effectiveness of our method in identifying protein-DNA binding residues.

AI Makes Crypto Evolve

The recent literature reveals a dichotomy formed by a coevolution between cryptography and Artificial Intelligence (AI). This dichotomy consists of two sides, namely Crypto-Influenced AI (CIAI) and AI-Influenced Cryptography (AIIC). While it is pertinent to investigate this dichotomy from both sides, the first side has already been studied. In this review, we focused on AIIC. We identified and analyzed the stages on the evolutionary path of AIIC. Moreover, we attempted to anticipate what the future may hold for AIIC given the impact of quantum computing on the present and the future of AI.