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Current Cancer Drug Targets

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ISSN (Print): 1568-0096
ISSN (Online): 1873-5576

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Research Article

Computational Design for Identification of Human Anti-MUC1 Heteroclitic Peptides in the Treatment of HER2-Positive Breast Cancer through Neural Network Training and Monomeric based Design

Author(s): Akanksha Behl, Nagendra Nath Das, Krishna Kant Sharma, Namita Sharma, Prity Gulia and Anil Kumar Chhillar*

Volume 23, Issue 3, 2023

Published on: 14 October, 2022

Page: [235 - 241] Pages: 7

DOI: 10.2174/1568009622666220921110605

Price: $65

Abstract

Aims: Generation of the human anti-MUC1 peptide through neural network training and monomeric design method. Analyzing 9-mer peptide potential computationally for treatment of HER2-positive breast cancer.

Background: With the advancements of cancer genome atlas project (TCGA), cancer dependancy project (DepMap) and human protein atlas (HPA), large-scale datasets are generated for oncology studies. However, after development of redefined breast cancer drug targets, there are key issues in successful breast cancer treatments that needed to be pursued which paved the pathway for new approaches or strategies. In that respect, our research data aimed to represent a new aspect of breast cancer drug development studies.

Objective: Extract human MUC1 sequences from various databases. Perform neural networking method for novel peptides sequences. Analyze the potentiality of generated heteroclitic peptide sequences for suitable vaccine candidate for breast cancer treatment.

Methods: Input scaffolds of protein database (PDB) files for human MUC1 were retrieved and loaded into Evo design server with monomeric based design option. Further, neural network training approaches were followed and other computational tools were used for alignment-independent prediction of protective antigens and subunit vaccines potency of designed heteroclitic peptides.

Results: Study findings revealed two human anti-MUC1 heteroclitic peptides of 9mers (WAVWTYVSV, FMSFYIMNL), which showed the lowest energy cluster and sequence identity, normalized relative error rate of secondary structure, solvent accessibility, backbone torsion angles for neural networking and RMSD values in evolutionary profiling, and online MHCPred IC50 interaction values. VaxiGen v2.0 server revealed subunit vaccine potency values of in-silico designed two heteroclitic peptides were 0.1551 (WAVWTYVSV) and 0.3508 (FMSFYIMNL) with a threshold value of 0.5 followed by AllerTOP v2.0 for their allergenicity nature in immunogenic reactions.

Conclusion: Computationally designed heteroclitic peptide WAVWTYVSV indicated promising values which can be utilised as drug delivery or tumour marker candidate in the treatment of human breast cancer by eliciting lyse of tumor cells.

Keywords: Heteroclitic peptides, anti-MUC1, breast cancer, subunit vaccine potency, MHC interactions, neural network.

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