Biology subjects

Mondal, K.

Publications and source records attributed to Mondal, K..

10 recordsLinked to original sources

bioRxiv2026

XAMPGen: An Interpretable SI Pipeline For Novel Antimicrobial Peptide Discovery

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of activity against bacterial pathogens, representing a promising avenue for combating the growing threat of antibiotic resistance. This study focuses on two main components: 1) generating novel AMPs with desired properties using generative SI (GenSI) and 2) utilizing explainable SI (XSI) methods to validate the GenSI model and derive biological insights. In this work, we fine-tuned ProGen, a decoder-only protein language model, on known AMPs to generate novel peptide sequences with strong antimicrobial properties. To advance the interpretability and reliability of the generative model, we applied Explainable SI (XSI) techniques. Specifically, we used a probing classifier and a regressor to identify cornerstone layers that encode functionally critical information. Self-attention analysis within these layers revealed residue level interdependencies and patterns underlying antimicrobial functions, which we validated against well-characterized AMPs from the literature. Overall, these methods provide mechanistic insight into the model's decision-making process, offering a principled basis for trusting the sequences it generates rather than treating the model as a black box. Candidate sequences were filtered based on predicted Minimum Inhibitory Concentration (MIC) along with human insights into the structural activities of the AMPs in bacterial membranes, and a final set of peptides was selected for experimental testing. Biological assays for determining MIC confirmed antimicrobial activity in all generated and tested peptides, with fluorescence and circular dichroism measurements providing structural insight. With our XSI approach, we are able to reveal correlations between amino acid sequence patterns and binding properties of these sequences to bacterial membrane mimics, that directly correlate with biological activities in cell-based assays. This builds confidence in that the learned amino acid interdependencies as applied to AMPs carries physico-chemical meaning and is relevant for discoveries of novel AMPs with therapeutic potential. This work demonstrates that interpretable generative language models can serve as effective and transparent tools for rational AMP discovery.

bioinformatics↗

bioRxiv2026

Big-team science reveals that patterns of inhibitory control variation in teleost fishes differ from those in mammals and birds

Understanding the evolution of cognition is fundamental to understanding the origins and nature of our own cognitive abilities, with cross-species comparisons providing a cornerstone of this endeavor. Yet, comparative cognition so far has largely focused on endothermic vertebrates, particularly mammals and birds, while ectotherms--representing a substantial proportion of vertebrate diversity--remain relatively understudied. Moreover, variation in experimental methods makes existing data difficult to compare across species. Big-team science offers a way to overcome these limitations by enabling standardized cognitive testing across broad taxonomic scales. Here, we apply this approach to fishes, using inhibitory control as a proof of concept for standardized comparative testing in 444 individuals across 22 teleost species spanning 19 genera. We demonstrate that standardized cognitive testing across such diversity is feasible, while also revealing substantial heterogeneity in the sample. Performance varied markedly among species, but, strikingly, showed only a weak phylogenetic signal, contrasting with patterns previously reported in mammals and birds. In addition, we detected great within-species variation, including differences across research groups, suggesting that performance may reflect not only species-level evolutionary history but also ontogenetic experience and the capacity to adjust decision rules to local conditions. Together, our findings highlight the importance of both evolutionary and environmental sources of cognitive variation and demonstrate that extending standardized comparative approaches beyond traditionally studied endotherms can reveal a more dynamic and complex picture of cognitive evolution across the vertebrate lineage.

animal behavior and cognition↗

bioRxiv2026

Species- and Topic-aware Representation Learning for Antimicrobial Peptide Discovery

Antimicrobial resistance poses a major global health challenge, necessitating efficient strategies to discover potent antimicrobial peptides (AMPs). While recent generative models can produce many candidate sequences, experimentally validating all generated peptides in wet labs is impractical due to the high costs and time involved in such measurements. As a result, there is a strong demand for accurate predictions of peptide efficacy, typically measured as the minimum inhibitory concentration (MIC). We introduce STAMP, a framework for Species- and Topic-aware Representation Learning in AMP Discovery. This unified machine learning framework allows for cross-species predictions of AMP activity. STAMP integrates protein language model embeddings with species conditioning and topic-aware representations that capture sequence-level patterns, enabling generalizable predictions across multiple bacterial species within a single model. We evaluated STAMP on three benchmark datasets, which include two previously published datasets and a newly curated dataset derived from DBAASP, addressing duplicates and inconsistencies systematically. STAMP achieved strong predictive performance across these datasets, demonstrating a Pearson correlation coefficient (PCC) of 0.837 and an R2 of 0.70, outperforming several baseline models. Importantly, we further validated our prediction model using peptides that were experimentally tested for their antimicrobial activity against E.coli. and S.epidermidis bacteria, demonstrating its real-world applicability. Furthermore, residue-level importance analyses provide insights into the sequence determinants governing antimicrobial activity. Together, these results establish STAMP as a scalable framework for MIC prediction and an effective computational tool for accelerating AMP discovery and optimization.

bioinformatics↗

bioRxiv2024

Threshold-dependent negative autoregulation of PIF4 gene expression optimizes growth and fitness in Arabidopsis

PHYTOCHROME INTERACTING FACTOR 4 (PIF4) is a vital transcription factor that controls plant growth by integrating environmental signals like light and temperature. Though upstream regulators of PIF4 are known, transcriptional regulation of PIF4 is poorly understood. Here, we demonstrate that the PIF4 undergoes negative autoregulation. We show that PIF4 promoter activity is more in the pif4 mutant but significantly reduced in PIF4 overexpression transgenic lines. Moreover, CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1), which enhances PIF4 protein stability, promotes PIF4 autoinhibition. However, Phytochrome B (phyB), a photoreceptor that decreases PIF4 stability, inhibits autoinhibition. We further develop a network-based mathematical model incorporating the PIF4 autoinhibition and other key interactions. Our modeling and data analysis reveals that PIF4 autoregulation depends on a threshold of cellular PIF4 concentration. Our model also successfully predicts the hypocotyl growth and PIF4 promoter activity in various light and temperature conditions. Moreover, we show that the transgenic lines with enhanced PIF4 function negatively influence biomass and yield, irrespective of photoperiod and temperature. Together, the negative feedback of PIF4 dampens its own function and restraints unregulated growth. Our study thus elucidates the mechanisms of how the phyB-COP1/DET1-PIF4 module controls PIF4 transcription in tune with the endogenous PIF4 level.

plant biology↗

bioRxiv2024

Physically Interpretable Performance Metrics for Clustering

Clustering is a type of machine learning (ML) technique which is used to group huge amounts of data based on their similarity into separate groups or clusters. Clustering is a very important task which is nowadays used to analyze the huge and diverse amount of data coming out of molecular dynamics (MD) simulations. Typically, the data from the MD simulations in terms of their various frames in the trajectory are clustered into different groups and a representative element from each group is studied separately. Now a very important question coming in this process is what is the quality of the clusters that are obtained. There are several performance metrics that are available in literature like Silhouette index and Davies-Bouldin Index that are often used to analyze the quality of clustering. However, most of these metrics focus on the overlap or the similarity of the clusters in the reduced dimension that is used for clustering and do not focus on the physically important properties or the parameters of the system. To address this issue, we have developed two physically interpretable scoring metrics that focus on the physical parameters of the system that we are analyzing. We have used and tested our algorithm on three different systems (1) Ising model, (2) peptide folding and unfolding of WT HP35, (3) a protein-ligand trajectory of an enzyme and substrate and (4) a protein-ligand dissociated trajectory. We show that the scoring metrics provide us clusters that match with our physical intuition about the systems.

bioinformatics↗

bioRxiv2024

Effect of Mutations on Smlt1473 Binding to Various Substrates Using Molecular Dynamics Simulations

Smlt1473 is a polysaccharide lyase from Stenotrophomonas maltophilia whose crystal structure was solved recently using X-ray crystallography. There was an effort to study the effect of mutations on the activity of Smlt1473 binding to various substrates like hyaluronic acid(HA), mannuronic acid(ManA), and alginate. In this study, we use molecular docking and molecular dynamics simulations to investigate the effect of binding of various substrates (HA and ManA) to Smlt1473 and two of its mutants H221F and R312L. We further studied the stability in the binding of Smlt1473 to its various substrates as well as the role of fluctuations. Machine-Learning based clustering algorithms were used to group the entire simulation trajectory into various stable states. The molecular interactions Smlt1473 to the substrates were calculated and the importance of specific residues were tested with observed activity assays due to residue mutations. Overall, we find that the R218 plays an important role in substrate binding and thus impacting the activity due to the H221F mutant and R/L312 itself plays an important role in the R312 mutation. In addition, we have also found three more residues K56, R107, and R164 important for substrate binding which we further proceed to confirm using wet lab mutagenesis studies.

biophysics↗

bioRxiv2024

Exploring Probiotic Potential: A Comparative Genomics and In Silico Assessment of Genes within the Genus Geobacillus

The pursuit of new probiotic targets has seen a surge, aided by next-generation sequencing, facilitating a thorough exploration of bacterial traits. The genus Geobacillus stands out as a promising target for uncovering its potential as a probiotic. The study explored the genetic attributes of the genus Geobacillus for their resilience to gastrointestinal conditions, nutrient production, and immunomodulatory compound creation, revealing potential probiotic traits. Additionally, the research undertook predictive analyses of genomic elements such as prophages, CRISPR-Cas systems, insertion sequences, genomic islands, antibiotic resistance genes, and CAZymes. These evaluations aimed to assess the safety aspects associated with the genus Geobacillus. A comparative genomic analysis was also carried out using 18 validly published genomes of the genus Geobacillus and a few other genomes of Lactobacillus and Bifidobacterium were taken as control. Genes associated with probiotic traits like adhesion, stress tolerance (acid/bile, osmotic, oxidative), immune modulation, and molecular chaperones were uniformly detected in the Geobacillus genus. Notably, mobile genetic elements such as plasmids, prophages, and insertion sequences were absent, as were virulence factors, toxins, and Antibiotic resistance genes. Additionally, CRISPR-Cas systems and CAZymes were present. The pan-genome encompassed 25,284 protein-coding genes with translation. Comparative genomic analysis revealed an open pan-genome for Geobacillus. Pan-genome exhibited variability, particularly in genes linked to environmental interaction and secondary metabolite synthesis. In conclusion, Geobacillus appears potentially safe and well-suited for the gut habitat. However, further in vitro studies are essential to add to the knowledge of the probiotic potential of Geobacillus species. ImportanceThis comprehensive study highlights the significant probiotic potential and genetic makeup of the Geobacillus genus, shedding light on its unique attributes in adapting to extreme environmental conditions. Understanding the probiotic properties of Geobacillus is crucial amidst growing concerns over antibiotic resistance, offering promising alternatives for combating pathogenic microbes. Additionally, exploring the genetic diversity and adaptive mechanisms of Geobacillus through genomic and metagenomic approaches provides valuable insights into its biotechnological applications and evolutionary history. By employing in-silico methods and comparative analyses with established probiotic genera, this study contributes to elucidating the probiotic characteristics of Geobacillus, paving the way for further research in harnessing its beneficial traits for various applications in health, biotechnology, and environmental remediation.

microbiology↗

bioRxiv2023

The role of sphingosine-1-phosphate receptor 2 in mouse retina light responses

The bioactive sphingolipid sphingosine-1-phosphate (S1P) acts as a ligand for a family of G protein-coupled S1P receptors (S1PR1-5) to participate in a variety of signaling pathways. However, their specific roles in the neural retina remain unclear. We previously showed that S1P receptor subtype 2 (S1PR2) is expressed in murine retinas, primarily in photoreceptors and bipolar cells, and its expression is altered by retinal stress. This study aims to elucidate the role of S1PR2 in the mouse retina. We examined light responses by electroretinography (ERG), structural differences by optical coherence tomography (OCT), and protein levels by immunohistochemistry (IHC) in wild-type (WT) and S1PR2 knockout (KO) mice at various ages between 3 and 6 months. We found that a- and b-wave responses significantly increased at flash intensities between 400[~]2000 and 4[~]2,000 cd.s/m2 respectively, in S1PR2 KO mice relative to those of WT controls at baseline. S1PR2 KO mice also exhibited significantly increased retinal nerve fiber layer (RNFL) and outer plexiform layer (OPL) thickness by OCT relative to the WT. Finally, in S1PR2 KO mice, we observed differential labeling of synaptic markers by immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction (RT-qPCR). These results suggest a specific involvement of S1PR2 in the structure and synaptic organization of the retina and a potential role in light-mediated functioning of the retina.

physiology↗

bioRxiv2023

Spatiotemporal dynamics and emergent ordering in a mixture of morphologically distinct bacteria having different cell motility

Microbial communities exhibit complex behaviors driven by species interactions and individual characteristics. In this study, we delve into the dynamics of a mixed bacterial population comprising two distinct species with different morphology and motility aspects. Employing agent-based modeling and computer simulations, we analyze the impacts of size ratios and packing fractions on dispersal patterns, aggregate formation, clustering, and spatial ordering. Notably, we find that motility and anisotropy of elongated bacteria significantly influence the distribution and spatial organization of nonmotile spherical species. Passive spherical cells display superdiffusive behavior, particularly at smaller size ratios, while active rod-like cells exhibit normal diffusive behavior in the diffusion regime. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. Additionally, we identify the pivotal role of passive cell area fraction in influencing the response of active cells toward nematicity, with its dependence on size ratio. These findings shed light on the significance of morphology and motility in shaping the collective behavior of microbial communities, providing valuable insights into complex microbial behaviors with implications for ecology, biotechnology, and bioengineering.

biophysics↗

bioRxiv2022

Comparative genomics and integrated system biology approach unveiled undirected phylogeny patterns, mutational hot spots, functional patterns and molecule repurposing for monkey pox virus

Monkeypox is a viral zoonosis with symptoms that are reminiscent to those experienced in previous smallpox cases. GSAID databases (Global Initiative on Sharing Avian Influenza Data) was used to assess 630 genomes of MPXV. Six primary clades were inferred from the phylogenetic study, coupled with a lesser percentage in radiating clades. Individual clades that make up various nationalities may have formed as a result of a particular SNP hotspot type, which may have mutated in a particular population type. The most significant mutation, based on a mutational hotspot analysis, was found at G3729A and G5143A. The gene ORF138, which encodes the Ankyrin repeat (ANK) protein, was found to have the most mutations. This protein is known to mediate molecular recognition via protein-protein interactions. It was shown that 243 host proteins interacted with 10 monkeypox proteins identified as the hub proteins E3, SPI2, C5, K7, E8, G6, N2, B14, CRMB, and A41 through 262 direct connections. The interaction with chemokine system-related proteins provides further evidence that the human protein is being suppressed by the monkey pox virus in order to facilitate its survival against innate immunity. A few FDA-approved molecules were likely used as possible inhibitors after being researched for blocking F13, a significant envelope protein on the membrane of extracellular versions of virus. A total of 2500 putative ligands were docked individually with the F13 protein. The F13 protein and these molecules interaction may help prevent the monkey pox virus from spreading. As a result, after being confirmed by experiments, these putative inhibitors might have an impact on the activity of these proteins and be utilised in monkeypox treatments.

genomics↗