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LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets

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Abstract

Conventional cancer therapies are highly expensive and have serious complications. An alternative approach now emphasizes on the development of small, biologically active peptides without acute toxicity. Experimental screening to find curative anticancer peptides (ACP) often gives rise to multiple obstacles and is time dependent. Consequently, developing an effective computational technique to identify promising ACP candidates prior to preclinical research is in high demand. This study proposed a machine-learning framework that used the light gradient-boosting machine as a classifier and two compositional and two binary profile features as input. The ensemble model displayed an accuracy, MCC, and AUROC of 97.52%, 0.91, and 0.98, respectively, which outclassed most of the existing sequence-based computational tools. A distinct dataset of non-mutagenic, non-toxic, and non-inhibitory Cytochrome P-450 peptides was used to validate the hybrid model. The most relevant ACP in the alternative dataset was compared with two standard ACPs, beta defensin 2, and cecropin-A. Molecular docking of the predicted peptide revealed that it has a strong binding affinity with twenty-five anticancer drug targets, most notably phosphoenolpyruvate carboxykinase (− 7.2 kcal/mol). Additionally, molecular dynamics simulation and principal component analysis supported the stability of the peptide-receptor complex. Overall, the present findings will take a step forward in rational drug design through rapid identification and screening of therapeutic peptides.

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Funding

Departmental facilities and M. Tech Project funding of NIT Agartala.

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Authors and Affiliations

  1. Department of Bioengineering, NIT Agartala, Tripura, 799046, India

    Swarnava Garai, Juanit Thomas & Deeplina Das

  2. Civil Engineering Department, The ICFAI University, Tripura, 799210, India

    Palash Dey

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  1. Swarnava Garai
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  2. Juanit Thomas
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  3. Palash Dey
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  4. Deeplina Das
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Contributions

SG received the Bachelor’s degree in Computer Science and Engineering from the Maulana Abul Kalam Azad University of Technology, India. He is presently pursuing the M. Tech programme in Bioengineering department at National Institute of Technology Agartala, India. His research interests include bioinformatics and machine learning. JT received the B. Tech degree from the SRM Institute of Technology, India. She is currently enrolled in the M. Tech programme in Bioengineering department at National Institute of Technology Agartala, India. Her research interests include molecular docking and simulations. PD is presently working as an Assistant Professor at Civil Engineering department at The ICFAI University, Tripura, India. His research interests include Pattern recognition, machine learning, and artificial intelligence. DD is presently working as an Assistant Professor at Bioengineering department at National Institute of Technology Agartala, India. Her research interests include proteomics and bioinformatics.

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Correspondence to Deeplina Das.

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Garai, S., Thomas, J., Dey, P. et al. LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10602-0

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