Title: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
Volume: 23
Issue: 3
Author(s): Akanksha Behl, Nagendra Nath Das, Krishna Kant Sharma, Namita Sharma, Prity Gulia and Anil Kumar Chhillar*
Affiliation:
- Centre for Biotechnology, Maharshi Dayanand University, Rohtak, Haryana-124001, India
Keywords:
Heteroclitic peptides, anti-MUC1, breast cancer, subunit vaccine potency, MHC interactions, neural network.
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.
