Biology subjects

Lohith, A.

Publications and source records attributed to Lohith, A..

3 recordsLinked to original sources

bioRxiv2025

MOAST: Mechanism of Action Similarity Tool

Determining the mechanism of action (MOA) for natural products remains a significant bottleneck in drug discovery, particularly for researchers with limited computational resources or small compound libraries. Traditional approaches require screening large numbers of annotated compounds alongside unknowns, which is cost-prohibitive, or depend on complex machine learning models that need substantial computational resources and large datasets. Here, we present a dissertation chapter excerpt: MOAST (Mechanism of Action Similarity Tool), a BLAST-inspired computational workflow that addresses these limitations by providing rapid MOA hypotheses for newly screened compounds. This chapter investigates two complementary approaches: a kernel density estimation (KDE) method providing statistical significance measures and E-values for MOA class membership, and a CatBoost machine learning classifier for multi-class prediction with ranked outputs. Using cytological profiling data from HeLa and A549 cell lines, MOAST achieved 22% accuracy for the top 5 predictions among [~] 300 MOA classes, with the CatBoost classifier reaching 10% balanced accuracy--significantly better than the [~] 3% reported in literature. The tool suggests a 0.8 prediction probability threshold for trustworthy results and demonstrates robust performance across multiple feature reduction strategies. MOAST provides a practical, accessible solution that bridges traditional phenotypic screening and modern computational approaches, making MOA determination feasible for researchers with limited resources while maintaining statistical rigor and interpretability.

bioinformatics↗

bioRxiv2025

Cell Painting in activated cells illuminates phenotypic dark space and uncovers novel drug mechanisms of action.

As drug and natural product libraries expand, assays for assessing mechanisms of action (MoA) are increasingly critical. Performing cytological profiling using the Cell Painting (CP) assay enables image-based profiling of cellular states upon treatment, yet many bioactive compounds remain uncharacterized due to undetectable cellular effects under standard conditions. To address this, we combined drug dosing with cell activation using the protein kinase C (PKC) agonist phorbol myristate acetate (PMA). Profiling A549 lung cancer cells treated with 8,387 compounds at two concentrations (1 and 10 {micro}M) in both resting and PMA-activated states allowed us to detect phenotypic effects for up to 40% of all screened compounds, effectively illuminating new phenotypic "dark space". Over 1,000 compounds exhibited phenotypes exclusively under PMA activation, establishing its advantage for MoA studies. We introduce novel quality control measures for CP screens and demonstrate that integrating phenotypic signatures enhances MoA discovery. Notably, 2-methoxycinnamaldehyde clustered with glucocorticoid receptor modulators and induced nuclear translocation, emphasizing the power of this approach in uncovering novel drug mechanisms and, therefore, aiding in improving therapeutic strategies.

systems biology↗

bioRxiv2019

High-Throughput Functional Annotation of Natural Products by Integrated Activity Profiling

Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform FUSION, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov-Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could also find target classes that were not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the novel azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery. Significance statementNew data-driven methods to aid in the discovery and biological characterization of natural products are necessary to advance the field. Assigning the mechanism of action (MOA) to novel bioactive compounds is an essential step in drug discovery and a major challenge in chemical biology. Despite technological advances in isolation, synthesis and screening strategies that make many bioactive substances readily available, in most cases their biological targets remain unknown. Additionally, a major bottleneck in natural products discovery efforts is de-replication of the large number of known compounds that predominate in crude extracts and fraction libraries. Advances in metabolomics has provided a better understanding of the constituents present in these libraries, but is not sufficient in itself to drive the discovery of novel biologically active metabolites. Here we describe an unbiased, data-driven strategy which integrates phenotypic screening with metabolomics into a single platform that provides rapid identification and functional annotation of natural products. This approach can be applied to any cohort of uncharacterized chemicals and represents a strategy that could significantly accelerate the process of drug discovery.

pharmacology and toxicology↗