ActTRANS: Functional classification in active transport proteins based on transfer learning and contextual representations

Integrating Pre-Trained protein language model and multiple window scanning deep learning networks for accurate identification of secondary active transporters in membrane proteins

Secondary active transporters play pivotal roles in regulating ion and molecule transport across cell membranes, with implications in diseases like cancer. However, studying transporters via biochemical experiments poses challenges. We propose an effective computational approach to identify secondary active transporters from membrane protein sequences using pre-trained language models and deep learning neural networks. Our dataset comprised 290 secondary active transporters and 5,420 other membrane proteins from UniProt. Three types of features were extracted - one-hot encodings, position-specific scoring matrix profiles, and contextual embeddings from the ProtTrans language model. A multi-window convolutional neural network architecture scanned the ProtTrans embeddings using varying window sizes to capture multi-scale sequence patterns. The proposed model combining ProtTrans embeddings and multi-window convolutional neural networks achieved 86% sensitivity, 99% specificity and 98% overall accuracy in identifying secondary active transporters, outperforming conventional machine learning approaches. This work demonstrates the promise of integrating pre-trained language models like ProtTrans with multi-scale deep neural networks to effectively interpret transporter sequences for functional analysis. Our approach enables more accurate computational identification of secondary active transporters, advancing membrane protein research.

Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to pre-trained language models

Membrane proteins play a crucial role in various cellular processes and are essential components of cell membranes. Computational methods have emerged as a powerful tool for studying membrane proteins due to their complex structures and properties that make them difficult to analyze experimentally. Traditional features for protein sequence analysis based on amino acid types, composition, and pair composition have limitations in capturing higher-order sequence patterns. Recently, multiple sequence alignment (MSA) and pre-trained language models (PLMs) have been used to generate features from protein sequences. However, the significant computational resources required for MSA-based features generation can be a major bottleneck for many applications. Several methods and tools have been developed to accelerate the generation of MSAs and reduce their computational cost, including heuristics and approximate algorithms. Additionally, the use of PLMs such as BERT has shown great potential in generating informative embeddings for protein sequence analysis. In this review, we provide an overview of traditional and more recent methods for generating features from protein sequences, with a particular focus on MSAs and PLMs. We highlight the advantages and limitations of these approaches and discuss the methods and tools developed to address the computational challenges associated with features generation. Overall, the advancements in computational methods and tools provide a promising avenue for gaining deeper insights into the function and properties of membrane proteins, which can have significant implications in drug discovery and personalized medicine.

Identification of efflux proteins based on contextual representations with deep bidirectional transformer encoders

Efflux proteins are the transport proteins expressed in the plasma membrane, which are involved in the movement of unwanted toxic substances through specific efflux pumps. Several studies based on computational approaches have been proposed to predict transport proteins and thereby to understand the mechanism of the movement of ions across cell membranes. However, few methods were developed to identify efflux proteins. This paper presents an approach based on the contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) with the Support Vector Machine (SVM) classifier. BERT is the most effective pre-trained language model that performs exceptionally well on several Natural Language Processing (NLP) tasks. Therefore, the contextualized representations from BERT were implemented to incorporate multiple interpretations of identical amino acids in the sequence. A dataset of efflux proteins with annotations was first established. The feature vectors were extracted by transferring protein data through the hidden layers of the pre-trained model. Our proposed method was trained on complete training datasets to identify efflux proteins and achieved the accuracies of 94.15% and 87.13% in the independent tests on membrane and transport datasets, respectively. This study opens a research avenue for the implementation of contextualized word embeddings in Bioinformatics and Computational Biology.

TRP-BERT: Discrimination of transient receptor potential (TRP) channels using contextual representations from deep bidirectional transformer based on BERT

Transient receptor potential (TRP) channels are non-selective cation channels that act as ion channels and are primarily found on the plasma membrane of numerous animal cells. These channels are involved in the physiology and pathophysiology of a wide variety of biological processes, including inhibition and progression of cancer, pain initiation, inflammation, regulation of pressure, thermoregulation, secretion of salivary fluid, and homeostasis of Ca²⁺ and Mg²⁺. Increasing evidences indicate that mutations in the gene encoding TRP channels play an essential role in a broad array of diseases. Therefore, these channels are becoming popular as potential drug targets for several diseases. The diversified role of these channels demands a prediction model to classify TRP channels from other channel proteins (non-TRP channels). Therefore, we presented an approach based on the Support Vector Machine (SVM) classifier and contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) to represent protein sequences. BERT is a deeply bidirectional language model and a neural network approach to Natural Language Processing (NLP) that achieves outstanding performance on various NLP tasks. We apply BERT to generate contextualized representations for every single amino acid in a protein sequence. Interestingly, these representations are context-sensitive and vary for the same amino acid appearing in different positions in the sequence. Our proposed method showed 80.00% sensitivity, 96.03% specificity, 95.47% accuracy, and a 0.56 Matthews correlation coefficient (MCC) for an independent test set. We suggest that our proposed method could effectively classify TRP channels from non-TRP channels and assist biologists in identifying new potential TRP channels.