Choudhuri, S. | Search bioRxiv

bioRxiv2024

A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation

The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA: a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used for de novo design of peptide binders given target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) genes. The ability of our model to generate peptides conditioned on the target receptors binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

bioinformatics↗

bioRxiv2022

Machine learning approaches for classificationof Plasmodium falciparum life cycle stagesusing single-cell transcriptomes

Malaria, spread by the female Anopheles mosquito, is a highly fatal disease widespread in many parts of the world, causing 0.4 million deaths globally. Vital gene expressions form the basis in the detection of malaria infection levels. Quantification of malaria parasite infected RBCs and classification of its life cycle stages are done at macroscopic level by experts, for making informed decisions. Off late multiple computational approaches have been proposed to circumvent the problem of dimensionality leading to accurate predicted results. In this work a dimensionality reduction technique based on Genetic Algorithm (GA) is applied on P. falciparum single-cell transcriptomics to arrive at an optimized subset of features from the larger dataset. Features are chosen based on their class variants considering increased efficiency and accuracy, to separately transform the selected elements into a lower dimension. For the classification of the life cycle of malaria parasite based on single cell transcriptome data, a three-pronged approach employing the multiclass Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) techniques is used. Distribution of cells was visualised and mapped using the R-based Seurat package. Further, we constructed protein interaction networks of the genes identified by the feature selection method and elucidated the role of the proteins in progression of the parasite through its life cycle. Our approach presents a novel protocol to implement ML techniques on scRNA seq datasets and subsequently harnessing the extracted information for biomarker/drug target detection.

bioinformatics↗