Supervised learning of Plasmodium falciparum life cycle stages using single-cell transcriptomes identifies crucial proteins

Shukla, Swarnim; Choudhuri, Soham; Iragavarapu, Gayathri Priya; Ghosh, Bratin

doi:10.26502/jbsb.5107047

Cited by 4 publications

(3 citation statements)

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“…According to Graph theory, PPI network's topological structure gives relevant connections and information regarding the associated biological functions 49 . In network theory, degree(D), Connectivity degree (k), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and eccentricity are the fundamental measures 4 . Degree of a node denotes the number of edges connected with that node.…”

Section: Construction and Analysis Of Protein-protein Interaction Net...mentioning

confidence: 99%

“…The development of parasite strains that are resistant to drugs has further complicated the treatment procedure, necessitating innovative approaches in the search for effective antimalarial drugs. The incorporation of single-cell transcriptome analysis represents a major development in this endeavor 4 . Malaria parasites can evade the host immune system by modulating host cell gene expression, rendering the infection process extremely flexible and dynamic.…”

Section: Introductionmentioning

confidence: 99%

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Computational approach for decoding Malaria Drug Targets from Single-Cell Transcriptomics and finding potential drug molecule

Choudhuri,

Ghosh

2024

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Malaria is a deadly disease caused by Plasmodium parasites. While potent drugs are available in the market for malaria treatment, over the years, Plasmodium parasites have successfully developed resistance against many, if not all, front-line drugs. This poses a serious threat to global malaria eradication efforts, and the continued discovery of new drugs is necessary to tackle this debilitating disease. With the advent of recent unprecedented progress in machine learning techniques, single-cell transcriptomic in Plasmodium offers a powerful tool for identifying crucial proteins as a drug target and subsequent computational prediction of potential drugs. In this study, We have implemented a mutual-information-based feature reduction algorithm with a classification algorithm to select important proteins from transcriptomic datasets (sexual and asexual stages) for Plasmodium falciparum and then constructed the protein-protein interaction (PPI) networks of the proteins. The analysis of this PPI network revealed key proteins vital for the survival of Plasmodium falciparum. Based on the function and identification of a few strong binding sites on a couple of these key proteins, we computationally predicted a set of potential drug molecules using a deep learning-based technique. Lead drug molecules that satisfy ADMET and drug-likeliness properties are finally reported out of the generated drugs. The study offers a general computational pipeline to identify crucial proteins using scRNA-seq data sets and further development of potential new drugs.

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Section: Construction and Analysis Of Protein-protein Interaction Net...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational approach for decoding Malaria Drug Targets from Single-Cell Transcriptomics and finding potential drug molecule

Choudhuri,

Ghosh

2024

Preprint

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“…Beyond immediate mortality, malaria can have lasting detrimental effects on individuals, even in non-fatal cases. Recurrent infections can contribute to anemia, cognitive decline (particularly in children), and other complications, ultimately diminishing quality of life and economic productivity 24 . Drug resistance presents a central obstacle in malaria control.…”

Section: Introductionmentioning

confidence: 99%

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

Vishva Saravanan,

Choudhuri,

Ghosh

2024

Preprint

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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 forde novodesign 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 receptor’s binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

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Evaluation of Exosomal Proteins as Potential Biomarkers from RBC Stages of Plasmodium falciparum 3D7

Joshi,

Shah,

Gupta

et al. 2024

ACS Infect. Dis.

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Supervised learning of Plasmodium falciparum life cycle stages using single-cell transcriptomes identifies crucial proteins

Cited by 4 publications

References 0 publications

Computational approach for decoding Malaria Drug Targets from Single-Cell Transcriptomics and finding potential drug molecule

Computational approach for decoding Malaria Drug Targets from Single-Cell Transcriptomics and finding potential drug molecule

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

Evaluation of Exosomal Proteins as Potential Biomarkers from RBC Stages of Plasmodium falciparum 3D7

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