DeepIR-Pred: identification of insulin receptors using metaheuristic optimization of biologically informed multi-view features with deep recurrent learning.

Authors

Matee Ullah, Zhen Li, Yixiao Zhai, Changwei Lv, Kashif Ahmad Khan, Quan Zou, Xu Tan

Published in

Briefings in bioinformatics. Volume 27. Issue 5. Sep 01, 2026.

Abstract

The insulin receptor (IR) proteins play a central role in cellular growth and metabolic regulation. Disruption of IRs is linked to several fatal neurodegenerative and cancer-like diseases. Wet-lab experiments are time-consuming, costly, and resource-intensive. Therefore, accurately predicting IR proteins using computational methods is critical for understanding the IR-associated biological mechanism and advancing therapeutic research. In this work, we propose a biologically informed deep learning method, DeepIR-Pred, which is the first rigorous model to accurately identify IR proteins. To ensure model robustness, two rigorously curated non-redundant sequence-based training and testing datasets (IR2476 and IR274) were first constructed. A newly proposed transformed image-based feature descriptor [Position-Specific Scoring Matrix (PSSM)-rotation-invariant co-occurrence among adjacent local binary patterns], derived from PSSMs, was integrated with biologically informed qualitative characteristics and protein language model embeddings (Evolutionary Scale Modeling 2 and ProtTrans-T5). Next, an enhanced metaheuristic-based spatial bound whale optimization algorithm was employed to reduce and select the most discriminative features. Finally, DeepIR-Pred was learned using a deep recurrent neural network (Bidirectional Gated Recurrent Unit) architecture. Extensive experimental results, using 10-fold cross-validation and independent testing, demonstrated the significant performance of the DeepIR-Pred in terms of both robustness and generalization capability on unseen samples. Furthermore, interpretability analysis provided insights into feature contributions and discriminative characteristics associated with IR proteins, while the uniform manifold approximation and projection (UMAP) analysis highlighted improved class separability. Collectively, DeepIR-Pred paves the way for researchers to investigate computational-based methods for IR proteins in depth and unlock new potential in therapeutic discovery. All data and source codes for DeepIR-Pred are available at https://github.com/MateeullahKhan/DeepIR-Pred for academic use.

PMID:
42803630
Bibliographic data and abstract were imported from PubMed on 28 Sep 2026.

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