A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features

Roha Arif; Sameera Kanwal; Saeed Ahmed; Muhammad Kabir

doi:10.1007/s12539-024-00628-9

A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features

Interdiscip Sci. 2024 May 11. doi: 10.1007/s12539-024-00628-9. Online ahead of print.

Authors

Roha Arif¹, Sameera Kanwal¹, Saeed Ahmed¹, Muhammad Kabir²

Affiliations

¹ School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan.
² School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan. kabiricp@gmail.com.

PMID: 38733473
DOI: 10.1007/s12539-024-00628-9

Abstract

Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as they allow for tailored medication delivery towards cancer cells. These peptides have a high affinity enabling particular receptors present upon tumor surfaces, allowing for the creation of precision medications that reduce off-target consequences and enhance cancer patient treatment results. Wet-lab techniques are considered essential tools for studying THPs; however, they're labor-extensive and time-consuming, therefore making prediction of THPs a challenging task for the researchers. Computational-techniques, on the other hand, are considered significant tools in identifying THPs according to the sequence data. Despite many strategies have been presented to predict new THP, there is still a need to develop a robust method with higher rates of success. In this paper, we developed a novel framework, THP-DF, for accurately identifying THPs on a large-scale. Firstly, the peptide sequences are encoded through various sequential features. Secondly, each feature is passed to BiLSTM and attention layers to extract simplified deep features. Finally, an ensemble-framework is formed via integrating sequential- and deep features which are fed to a support vector machine which with 10-fold cross-validation to carry to validate the efficiency. The experimental results showed that THP-DF worked better on both [Formula: see text] and [Formula: see text] datasets by achieving accuracy of > 95% which are higher than existing predictors both datasets. This indicates that the proposed predictor could be a beneficial tool to precisely and rapidly identify THPs and will contribute to the cutting-edge cancer treatment strategies and pharmaceuticals.

Keywords: Bidirectional LSTM; Deep features; Learning algorithms; Sequence-based models; Tumor homing peptides.