Biology subjects

Saravanan, K. M.

Publications and source records attributed to Saravanan, K. M..

10 recordsLinked to original sources

bioRxiv2025

Examining Sleep Signals at the Cradle of Life: Can phylogenomic analysis of the Last Universal Common Ancestor (LUCA) reveal the fundamental role of sleep?

In common with most physiological activities, sleep is a highly evolutionarily conserved function. Nevertheless, the purpose of sleep remains inadequately investigated. One possible cause of this deficiency is the limitations of the traditional methods used for examining sleep. Up to this time, the mainstay tool used to look at the evolutionary basis of sleep has been phylogenetic analysis. This approach has provided many valuable insights into sleep, yet it has left many questions unanswered. The present study uses a relatively new hybrid technique at the interface of phylogenetics and genomics, known as phylogenomic analysis. This study is the first to use phylogenomic analysis to investigate the basis of sleep by evaluating the presence and conservation of sleep-related genes in the reconstructed genome of the Last Universal Common Ancestor (LUCA). Our gene set enrichment analysis of humans and LUCA indicates that the conserved sleep genes are linked to signaling, metabolism, and circadian rhythm pathways, suggesting that these genes possess primordial roles in essential physiological functions. These findings indicate that the component genes carry out essential physiological tasks that were subsequently repurposed to regulate sleep in more advanced organisms throughout evolution. This study lays the foundation for a systematic phylogenomic exploration of sleep-related genes, connecting molecular evolution with sleep science. By tracing the biological history of sleep to its deep evolutionary origins, our research presents novel insights into sleeps nature, origin, and evolutionary function, paving the way for further interdisciplinary exploration of the biology of sleep. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/644522v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@17adc89org.highwire.dtl.DTLVardef@124de86org.highwire.dtl.DTLVardef@18d3e5corg.highwire.dtl.DTLVardef@1f93b06_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Studying sleep orthologs in Epsilonproteobacteria through an evolutionary lens: Investigating sleep mysteries through phylogenomics

The current study employed phylogenomic methods to examine the evolutionary role and significance of sleep-related genes in Sulfurimonas paralvinellae of the Epsilonproteobacteria class. This has facilitated the identification of conserved sleep orthologs, including DnaK, serine hydroxymethyltransferase, and potassium channel family proteins, exhibiting sequence similarities ranging from 39.13% to 61.45%. These findings align with prior research indicating that chaperones and ion channels are conserved during sleep. This was demonstrated by the observation that proteins with fewer domains exhibited more significant conservation than others, such as adenylate kinase, which is substantial under selective pressure. Distinct adaptations in bifunctional protein - serine/threonine kinases and phosphatases were linked to S. paralvinellae, an extremophilic organism adapted to high-pressure and/or high-temperature conditions, indicating functional divergence influenced by the organisms environment. The Gene Ontology study results indicated catalytic activity, potassium channel function, and cellular processes, underscoring the significance of ion channels in regulating the sleep-wake cycle. Furthermore, the categories not recognized as particularly significant for the over-represented genes encompassed metabolic and signal transduction categories, suggesting enhanced functional flexibility within this protein subfamily. The findings emphasize that orthologous interactions are complex and influenced by subfunctionalization and neofunctionalization of ecology and evolution. These findings enhance the existing understanding of the evolution of sleep-related genes and their association with metabolic and environmental changes, providing a foundation for subsequent experimental investigations and cross-taxonomic comparisons.

genomics↗

bioRxiv2024

Unraveling the Mysteries of Sleep: Exploring Phylogenomic Sleep Signals in the Recently Characterized Archaeal Phylum Lokiarchaeota near Loki's Castle

Sleep is a universally conserved behavior with an elusive origin and an uncertain evolutionary purpose. Leveraging phylogenomics, we investigate the evolutionary foundations of sleep by identifying orthologs of Human sleep-related genes in the Lokiarchaeota of the Asgard superphylum. Our findings indicate a conserved suite of genes associated with energy metabolism and cellular repair, suggesting a primordial role of sleep in cellular maintenance. This data lends credence to the idea that sleep improves organismal fitness across evolutionary time by acting as a restorative process. Notably, our approach demonstrates that phylogenomics is more useful than standard phylogenetics for clarifying common evolutionary traits. By offering insight into the evolutionary history of sleep and putting forth a novel model framework for sleep research across taxa, these findings contribute to our growing understanding of the molecular foundation of sleep. This study lays the groundwork for further investigations into the importance of sleep in various organisms, which could have consequences for human health and a deeper comprehension related to the fundamental processes of life. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/621620v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@4dd502org.highwire.dtl.DTLVardef@1db82c5org.highwire.dtl.DTLVardef@6662d3org.highwire.dtl.DTLVardef@1e2268b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Cross-Kingdom Genomic Conservation of Human Sleep-Related Gene Orthologs: Phylogenomic Evidence from Chlamydomonas reinhardtii

Sleep is a widespread and evolutionarily conserved process observed in diverse organisms, from jellyfish to mammals, hinting at its origin as a life-supporting mechanism over 500 million years ago. Although its fundamental purpose and mechanisms remain unclear, the evolution and adaptive significance of sleep continue to be debated. This study explores the evolutionary origins of sleep using Chlamydomonas reinhardtii as a model organism, identifying 145 orthologs analogous to known sleep-related genes across species, highlighting the evolutionary conservation of sleep-regulating pathways. Additionally, discovering uncharacterized proteins with high sequence similarity and significant e-values suggests unexplored roles in sleep regulation, underscoring the potential of C. reinhardtii to reveal new insights into the molecular basis of sleep. This work provides a foundation for identifying previously unknown sleep-associated proteins, particularly within single-celled organisms, which may offer novel perspectives on the biological role of sleep. The study demonstrates that phylogenomic analysis of diverse model organisms can expand our understanding of the evolutionary trajectory of sleep and its fundamental function, paving the way for further research in sleep biology and its health implications. Overall, the fundamental functions of sleep observed in higher animal phyla originated from its primordial activities, demonstrating an evolutionary continuum wherein more specialized tasks were integrated with sleeps essential restorative properties.

bioinformatics↗

bioRxiv2024

Revolutionizing GPCR-Ligand Predictions: DeepGPCR with experimental Validation for High-Precision Drug Discovery

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks (GCN) and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrodinger Dock with an AUC of 0.72, accuracy of 0.68, and TPR of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and RMSE of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with 3 (F545-1970, K297-0698, S948-0241) out of 8 candidates. Furthermore, we also successfully obtained 6 active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

bioinformatics↗

bioRxiv2023

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Accurate Protein-Ligand Interaction Prediction

The core of large-scale drug virtual screening is to accurately and efficiently select the binders with high affinity from large libraries of small molecules in which nonbinders are usually dominant. The protein pocket, ligand spatial information, and residue types/atom types play a pivotal role in binding affinity. Here we used the pocket residues or ligand atoms as nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, we find that the model with pre-trained molecular vectors performs better than the onehot representation. The main advantage of DeepBindGCN is that it is non-dependent on docking conformation and concisely keeps the spatial information and physical-chemical feature. Notably, the DeepBindGCN_BC has high precision in many DUD.E datasets, and DeepBindGCN_RG achieve a very low RMSE value in most DUD.E datasets. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline by integrating DeepBindGCN_BC, DeepBindGCN_RG, and other methods to identify strong binding affinity compounds. In addition, a DeepBindGCN_RG_x model has been used for comparing performance with other methods in PDBbind v.2016 and v.2013 core set. It is the first time that a non-complex dependent model achieves an RMSE value of 1.3843 and Pearson-R value of 0.7719 in the PDBbind v.2016 core set, showing comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. Our DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

bioinformatics↗

bioRxiv2023

Deep-learning based bioactive peptides generation and screening against Xanthine oxidase

In our previous work, we have developed LSTM_Pep to generate de novo potential active peptides by finetuning with known active peptides and developed DeepPep to effectively identify protein-peptide interaction. Here, we have combined LSTM_Pep and DeepPep to successfully obtained an active de novo peptide (ARG-ALA-PRO-GLU) of Xanthine oxidase (XOD) with IC50 value of 3.76mg/mL, and XOD inhibitory activity of 64.32%. Consistent with the experiment result, the peptide ARG-ALA-PRO-GLU has the highest DeepPep score, this strongly supports that we can generate de novo potential active peptides by finetune training LSTM_Pep over some known active peptides and identify those active peptides by DeepPep effectively. Our work sheds light on the development of deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

bioinformatics↗

bioRxiv2022

Deep-learning based bioactive therapeutic peptides generation and screening

Many bioactive peptides demonstrated therapeutic effects over-complicated diseases, such as antiviral, antibacterial, anticancer, etc. Similar to the generating de novo chemical compounds, with the accumulated bioactive peptides as a training set, it is possible to generate abundant potential bioactive peptides with deep learning. Such techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. However, there are very few deep learning-based peptide generating models. Here, we have created an LSTM model (named LSTM_Pep) to generate de novo peptides and finetune learning to generate de novo peptides with certain potential therapeutic effects. Remarkably, the Antimicrobial Peptide Database has fully utilized in this work to generate various kinds of potential active de novo peptide. We proposed a pipeline for screening those generated peptides for a given target, and use Main protease of SARS-COV-2 as concept-of-proof example. Moreover, we have developed a deep learning-based protein-peptide prediction model (named DeepPep) for fast screening the generated peptides for the given targets. Together with the generating model, we have demonstrated iteratively finetune training, generating and screening peptides for higher predicted binding affinity peptides can be achieved. Our work sheds light on to the development of deep learning-based methods and pipelines to effectively generating and getting bioactive peptides with a specific therapeutic effect, and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

bioinformatics↗

bioRxiv2019

A novel machine learning based approach for iPS progenitor cell identification

Identification of induced pluripotent stem (iPS) progenitor cells, the iPS forming cells in early stage of reprogramming, could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only about 6 days after reprogramming initiation, iPS cells can be experimentally determined via fluorescent probes. What is more, the ratio of progenitor cells during early reprograming period is below 5%, which is too low to capture experimentally in the early stage.\n\nIn this paper, we propose a novel computational approach for the identification of iPS progenitor cells based on machine learning and microscopic image analysis. Firstly, we record the reprogramming process using a live cell imaging system after 48 hours of infection with retroviruses expressing Oct4, Sox2 and Klf4, later iPS progenitor cells and normal murine embryonic fibroblasts (MEFs) within 3 to 5 days after infection are labeled by retrospectively tracing the time-lapse microscopic image. We then calculate 11 types of cell morphological and motion features such as area, speed, etc., and select best time windows for modeling and perform feature selection. Finally, a prediction model using XGBoost is built based on the selected six types of features and best time windows. Our model allows several missing values/frames in the sample datasets, thus it is applicable to a wide range of scenarios.\n\nCross-validation, holdout validation and independent test experiments showed that the minimum precision is above 52%, that is, the ratio of predicted progenitor cells within 3 to 5 days after viral infection is above 52%. The results also confirmed that the morphology and motion pattern of iPS progenitor cells is different from that of normal MEFs, which helps with the machine learning methods for iPS progenitor cell identification.\n\nAuthor SummaryIdentification of induced pluripotent stem (iPS) progenitor cells could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only after about 6 days of induction, iPS cells can be experimentally determined via fluorescent probes. What is more, the percentage of the progenitor cells during the early induction period is below 5%, too low to capture experimentally in early stage. In this work, we proposed an approach for the identification of iPS progenitor cells, the iPS forming cells, based on machine learning and microscopic image analysis. The aim is to help biologists to enrich iPS progenitor cells during the early stage of induction, which allows experimentalists to select iPS progenitor cells with much higher probability, and furthermore to study the biomarkers which trigger the reprogramming process.

bioinformatics↗

bioRxiv2017

Sequence fingerprints distinguish erroneous from correct predictions of Intrinsically Disordered Protein Regions

More than sixty prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the world-wide web. Nearly, all of these predictors give balanced accuracies in the ~65% to ~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally-determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.

bioinformatics↗