Academic Appointments

Contact

  • Academic
    poulami@stanford.edu
    University - Academic staff Department: Chemistry Position: Physical Science Research Scientist

Additional Info

  • Mail Code: 5080

All Publications

  • Unveiling of a messenger: Gut microbes make a neuroactive signal. Cell Chatterjee, P., Dassama, L. M. 2024; 187 (12): 2903-2904

    Abstract

    Gut microbes are known to impact host physiology in several ways. However, key molecular players in host-commensal interactions remain to be uncovered. In this issue of Cell, McCurry et al. reveal that gut bacteria perform 21-dehydroxylation to convert abundant biliary corticoids to neurosteroids using readily available H2 in their environment.

    View details for DOI 10.1016/j.cell.2024.05.014

    View details for PubMedID 38848674

  • Rapid proteome-wide prediction of lipid-interacting proteins through ligand-guided structural genomics. bioRxiv : the preprint server for biology Chou, J. C., Decosto, C. M., Chatterjee, P., Dassama, L. M. 2024

    Abstract

    Lipids are primary metabolites that play essential roles in multiple cellular pathways. Alterations in lipid metabolism and transport are associated with infectious diseases and cancers. As such, proteins involved in lipid synthesis, trafficking, and modification, are targets for therapeutic intervention. The ability to rapidly detect these proteins can accelerate their biochemical and structural characterization. However, it remains challenging to identify lipid binding motifs in proteins due to a lack of conservation at the amino acids level. Therefore, new bioinformatic tools that can detect conserved features in lipid binding sites are necessary. Here, we present Structure-based Lipid-interacting Pocket Predictor (SLiPP), a structural bioinformatics algorithm that uses machine learning to detect protein cavities capable of binding to lipids in experimental and AlphaFold-predicted protein structures. SLiPP, which can be used at proteome-wide scales, predicts lipid binding pockets with an accuracy of 96.8% and a F1 score of 86.9%. Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. Use of the algorithm to detect lipid binding proteins in the proteomes of various bacteria, yeast, and human have produced hits annotated or verified as lipid binding proteins, and many other uncharacterized proteins whose functions are not discernable from sequence alone. Because of its ability to identify novel lipid binding proteins, SLiPP can spur the discovery of new lipid metabolic and trafficking pathways that can be targeted for therapeutic development.

    View details for DOI 10.1101/2024.01.26.577452

    View details for PubMedID 38352308

    View details for PubMedCentralID PMC10862712