Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs

A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction

Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications.

M6ATMR: identifying N6-methyladenosine sites through RNA sequence similarity matrix reconstruction guided by Transformer

Numerous studies have focused on the classification of N6-methyladenosine (m6A) modification sites in RNA sequences, treating it as a multi-feature extraction task. In these studies, the incorporation of physicochemical properties of nucleotides has been applied to enhance recognition efficacy. However, the introduction of excessive supplementary information may introduce noise to the RNA sequence features, and the utilization of sequence similarity information remains underexplored. In this research, we present a novel method for RNA m6A modification site recognition called M6ATMR. Our approach relies solely on sequence information, leveraging Transformer to guide the reconstruction of the sequence similarity matrix, thereby enhancing feature representation. Initially, M6ATMR encodes RNA sequences using 3-mers to generate the sequence similarity matrix. Meanwhile, Transformer is applied to extract sequence structure graphs for each RNA sequence. Subsequently, to capture low-dimensional representations of similarity matrices and structure graphs, we introduce a graph self-correlation convolution block. These representations are then fused and reconstructed through the local-global fusion block. Notably, we adopt iteratively updated sequence structure graphs to continuously optimize the similarity matrix, thereby constraining the end-to-end feature extraction process. Finally, we employ the random forest (RF) algorithm for identifying m6A modification sites based on the reconstructed features. Experimental results demonstrate that M6ATMR achieves promising performance by solely utilizing RNA sequences for m6A modification site identification. Our proposed method can be considered an effective complement to existing RNA m6A modification site recognition approaches.

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network

Predicting the protein sequence information of enzymes and non-enzymes is an important but a very challenging task. Existing methods use protein geometric structures only or protein sequences alone to predict enzymatic functions. Thus, their prediction results are unsatisfactory. In this paper, we propose a novel approach for predicting the amino acid sequences of enzymes and non-enzymes via Convolutional Neural Network (CNN). In CNN, the roles of enzymes are predicted from multiple sides of biological information, including information on sequences and structures. We propose the use of two-dimensional data via 2DCNN to predict the proteins of enzymes and non-enzymes by using the same fivefold cross-validation function. We also use an independent dataset to test the performance of our model, and the results demonstrate that we are able to solve the overfitting problem. We used the CNN model proposed herein to demonstrate the superiority of our model for classifying an entire set of filters, such as 32, 64, and 128 parameters, with the fivefold validation test set as the independent classification. Via the Dipeptide Deviation from Expected Mean (DDE) matrix, mutation information is extracted from amino acid sequences and structural information with the distance and angle of amino acids is conveyed. The derived feature maps are then encoded in DDE exploitation. The independent datasets are then compared with other two methods, namely, GRU and XGBOOST. All analyses were conducted using 32, 64 and 128 filters on our proposed CNN method. The cross-validation datasets achieved an accuracy score of 0.8762%, whereas the accuracy of independent datasets was 0.7621%. Additional variables were derived on the basis of ROC AUC with fivefold cross-validation was achieved score is 0.95%. The performance of our model and that of other models in terms of sensitivity (0.9028%) and specificity (0.8497%) was compared. The overall accuracy of our model was 0.9133% compared with 0.8310% for the other model.