KC, D. B. | Search bioRxiv

bioRxiv2024

pLM-DBPs: Enhanced DNA-Binding Protein Prediction in Plants Using Embeddings From Protein Language Models

DNA-binding proteins (DBPs) play critical roles in gene regulation, development, and environmental response across various species, including plants, animals, and microorganisms. While various machine learning and deep learning models have been developed to distinguish DNA-binding proteins (DBPs) from non-DNA-binding proteins (NDBPs), most available tools have focused on human and mouse datasets. As a result, there are limited studies specifically addressing plant-based DNA-binding proteins, which restricts our understanding of their unique roles and functions in plant biology. Developing an efficient framework for improving DBP prediction in plants would enhance our knowledge and enable precise gene expression control, accelerate crop improvement, enhance stress resilience, and optimize metabolic engineering for agricultural advancement. In this work, we developed a tool that uses a protein language model (pLM) pre-trained on millions of sequences. We evaluated several leading models, including ProtT5, Ankh, and ESM-2, and leveraged their high-dimensional, information-rich representations to improve the accuracy of DNA-binding protein prediction in plants significantly. Our final model, pLM-DBPs, a feed-forward neural network classifier utilizing ProtT5-based representations, outperformed existing approaches with a Matthews Correlation Coefficient (MCC) of 83.8% on the independent test set. This represents a 10% improvement over the previous state-of-the-art model for plant-based DBP prediction, highlighting its superior performance compared to the existing approaches.

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bioRxiv2024

CaLMPhosKAN: Prediction of General Phosphorylation Sites in Proteins via Fusion of Codon Aware Embeddings with Amino Acid Aware Embeddings and Wavelet-based Kolmogorov Arnold Network

The mapping from codon to amino acid is surjective due to the high degeneracy of the codon alphabet, suggesting that codon space might harbor higher information content. Embeddings from the codon language model have recently demonstrated success in various downstream tasks. However, predictive models for phosphorylation sites, arguably the most studied Post-Translational Modification (PTM), and PTM sites in general, have predominantly relied on amino acid-level representations. This work introduces a novel approach for prediction of phosphorylation sites by incorporating codon-level information through embeddings from a recently developed codon language model trained exclusively on protein-coding DNA sequences. Protein sequences are first meticulously mapped to reliable coding sequences and encoded using this encoder to generate codon-aware embeddings. These embeddings are then integrated with amino acid-aware embeddings obtained from a protein language model through an early fusion strategy. Subsequently, a window-level representation of the site of interest is formed from the fused embeddings within a defined window frame. A ConvBiGRU network extracts features capturing spatiotemporal correlations between proximal residues within the window, followed by a Kolmogorov-Arnold Network (KAN) based on the Derivative of Gaussian (DoG) wavelet transform function to produce the prediction inference for the site. We dub the overall model integrating these elements as CaLMPhosKAN. On independent testing with Serine-Threonine (combined) and Tyrosine test sets, CaLMPhosKAN outperforms existing approaches. Furthermore, we demonstrate the models effectiveness in predicting sites within intrinsically disordered regions of proteins. Overall, CaLMPhosKAN emerges as a robust predictor of general phosphosites in proteins. CaLMPhosKAN will be released publicly soon.

bioinformatics↗