Biology subjects

Ramasamy, P.

Publications and source records attributed to Ramasamy, P..

10 recordsLinked to original sources

bioRxiv2026

Scop3P-Toolkit: executable structure-aware workflows linking PTMs, peptides, and mutations to protein function

Post-translational modifications (PTMs) and genetic variants regulate protein function, signalling, and disease, but their interpretation requires integration of sequence annotations with structural, interaction, and biophysical context. Although resources such as Scop3P, UniProt, the Protein Data Bank, and AlphaFold provide extensive annotations and structural information, integrating these data into reproducible structure-aware analyses still requires custom scripting and manual coordination between multiple independent tools. To address this challenge, we developed Scop3P-Toolkit, an open-source executable analytical environment for interactive analysis of PTMs, mutations, and proteomics-derived peptides in their structural context. The toolkit integrates protein annotation retrieval with structural mapping, residue interaction network analysis, comparative structural analysis, and residue-level biophysical profiling within a unified framework. Experimentally supported phosphosites, phosphopeptides, and phosphoproteomics evidence are provided for human proteins through Scop3P, with optional integration of curated UniProt PTM annotations. UniProt-derived PTMs, sequence features, and genetic variants are available for proteins from any species, extending the framework beyond the human phosphoproteome. Scop3P-Toolkit supports structure-centric analyses including interpretation of PTMs and disease-associated variants, analysis of residue interaction networks and their rewiring across alternative conformations, structural localisation of peptides, and exploration of protein-protein, protein-ligand, and host-pathogen interfaces. Interactive visualisation links sequence annotations, three-dimensional structures, residue interaction networks, and biophysical profiles, enabling coordinated exploration across multiple molecular representations. The toolkit is distributed as Jupyter notebooks, browser-based Voila applications, and a Galaxy interactive tool, providing transparent, accessible, and reproducible workflows for both computational and experimental researchers. By integrating biological annotation resources into executable, structure-aware workflows, Scop3P-Toolkit enables reproducible interpretation of PTMs, mutations, and proteomics data.

bioinformatics↗

bioRxiv2026

Scop3P in 2026: an expanded proteomics-informed resource contextualizing phosphorylation sites through sequence, structure, mutation, and experimental provenance

Protein phosphorylation is a central regulatory mechanism controlling protein activity, interactions, and cellular signalling, and its dysregulation is implicated in numerous diseases. Advances in mass spectrometry-based phosphoproteomics have led to a rapid expansion in the number of reported phosphorylation sites; however, interpretation of these data remains challenging due to fragmented evidence, limited structural context, and the lack of uniform experimental provenance across resources. Interpretation is further complicated by the fact that the biological meaning of reported phosphosites can vary substantially across tissues, cell lines, perturbations, and disease settings. Here, we present a major update of Scop3P, a proteomics-informed knowledgebase that contextualizes human phosphorylation sites within integrated sequence, structural, biophysical, evolutionary, and mutational frameworks. The current release incorporates uniformly reprocessed human phosphoproteomics data from 116 PRIDE datasets alongside curated UniProt annotations, retaining peptide-spectrum matches, site localization confidence, and direct links to primary mass spectrometry evidence via Universal Spectrum Identifiers. This integration yields 152,350 unique serine, threonine, and tyrosine phosphorylation sites across 16,533 human proteins, supported by full experimental provenance. Beyond site identification, Scop3P provides residue-level contextual annotations derived from experimentally determined protein structures and proteome-wide AlphaFold models, enabling near-complete structural coverage of phosphorylation sites. Structural context is further complemented by residue-level biophysical, evolutionary, and mutational annotations, supporting integrated assessment of phosphorylation in functional and disease-related settings. The current release also introduces residue interaction network representations derived from AlphaFold-predicted structures, capturing spatial connectivity and local interaction environments of phosphorylation and mutation sites. A redesigned web interface enables interactive exploration through coordinated 1D, 2D, 2.5D, and 3D visualizations, peptide-level coverage views, and direct access to original spectra via PRIDE. By bridging experimental phosphoproteomics with structural, functional, and disease-related context, Scop3P provides a scalable and provenance-aware resource for phosphosite interpretation, hypothesis generation, and data-driven modelling of phosphorylation-dependent regulation.

bioinformatics↗

bioRxiv2025

InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics

Phosphorylation, a crucial post-translational modification (PTM), plays a central role in cellular signaling and disease mechanisms. Mass spectrometry-based phosphoproteomics is widely used for system-wide characterization of phosphorylation events. However, traditional methods struggle with accurate phosphorylated site localization, complex search spaces, and detecting sequences outside the reference database. Advances in de novo peptide sequencing offer opportunities to address these limitations, but have yet to become integrated and adapted for phosphoproteomics datasets. Here, we present InstaNovo-P, a phosphorylation specific version of our transformer-based InstaNovo model, fine-tuned on extensive phosphoproteomics datasets. InstaNovo-P significantly surpasses existing methods in phosphorylated peptide detection and phosphorylated site localization accuracy across multiple datasets, including complex experimental scenarios. Our model robustly identifies peptides with single and multiple phosphorylated sites, effectively localizing phosphorylation events on serine, threonine, and tyrosine residues. We experimentally validate our model predictions by studying FGFR2 signaling, further demonstrating that InstaNovo-P uncovers phosphorylated sites previously missed by traditional database searches. These predictions align with critical biological processes, confirming the models capacity to yield valuable biological insights. InstaNovo-P adds value to phosphoproteomics experiments by effectively identifying biologically relevant phosphorylation events without prior information, providing a powerful analytical tool for the dissection of signaling pathways.

bioinformatics↗

bioRxiv2025

Assessing the relation between protein phosphorylation, AlphaFold3 models and conformational variability

Proteins perform diverse functions critical to cellular processes. Transitions between functional states are often regulated by post-translational modifications (PTMs) such as phosphorylation, which dynamically influence protein structure, function, folding, and interactions. Dysregulation of PTMs can therefore contribute to diseases such as cancer and Alzheimers. However, the structure-function relationship between proteins and their modifications remains poorly understood due to a lack of experimental structural data, the inherent diversity of PTMs, and the dynamic nature of proteins. Recent advances in deep learning, particularly AlphaFold, have transformed protein structure prediction with near-experimental accuracy. However, it remains unclear whether these models can effectively capture PTM-driven conformational changes, such as those induced by phosphorylation. Here, we systematically evaluated AlphaFold models (AF2, AF3-non phospho, and AF3-phospho) to assess their ability to predict phosphorylation-induced structural diversity. By analysing experimentally derived conformational ensembles, we found that all models predominantly aligned with dominant structural states, often failing to capture phosphorylation-specific conformations. Despite its phosphorylation-aware design, AF3-phospho predictions provided only modest improvement over AF2 and AF3-non phospho predictions. Our findings highlight key challenges in modelling PTM-driven structural landscapes and underscore the need for more adaptable structure prediction frameworks capable of capturing modification-induced conformational variability.

bioinformatics↗

bioRxiv2025

Development of a General Purpose Targeted LC-MS Method for Accurate Quantification of the SARS-CoV-2 Spike Protein Expression

The COVID-19 pandemic has catalyzed interest in immuno-multiple reaction monitoring (immuno-MRM) methods, with the detection of peptides unique to the nucleocapsid protein in nasopharyngeal swabs. While current applications predominantly focus on disease biomarkers, the pandemic has unveiled new opportunities, namely for the quantification of antigen expression following mRNA vaccination. Here, we present an optimized immuno-MRM method for quantifying SARS-CoV-2 spike protein fusion peptide, SFIEDLLFNK, for several practical applications. The method is versatile, applicable to multiple biological matrices, including plasma, and can be extended to nasopharyngeal swabs. It also offers a high-precision tool for assessing protein expression following plasmid and mRNA transfection. Moreover, in parallel to enabling accurate antigen quantification, the flow-through can be used to determine the proteome profile of the infected cells, providing insights into the intracellular immune response. This dual capability supports the rapid optimization of mRNA vaccines, thereby driving advancements in vaccine development strategies.

immunology↗

bioRxiv2022

PhosphoLingo: protein language models for phosphorylation site prediction

Protein post-translational modifications (PTMs) play an important role in numerous biological processes by significantly affecting protein structure and dynamics. Effective computational methods that provide a sequence-based prediction of PTM sites are desirable to guide functional experiments. Whereas these methods typically train neural networks on one-hot encoded amino acid sequences, protein language models carry higher-level pattern information that may improve sequence based prediction performance and hence constitute the current edge of the field. In this study, we first evaluate the training of convolutional neural networks on top of various protein language models for sequence based PTM prediction. Our results show substantial prediction accuracy improvements for various PTMs with current procedures of dataset compilation and model performance evaluation. We then used model interpretation methods to study what these advanced models actually base their learning on. Importantly for the entire field of PTM site predictors trained on proteomics-derived data, our model interpretation and transferability experiments reveal that the current approach to compile training datasets based on proteomics data leads to an artefactual protease-specific training bias that is exploited by the prediction models. This results in an overly optimistic estimation of prediction accuracy, an important caveat in the application of advanced machine learning approaches to PTM prediction based on proteomics data. We suggest a partial solution to reduce this data bias by implementing negative sample filtering, only allowing candidate PTM sites in matched peptides that are present in the experimental metadata. Availability and implementationThe prediction tool, with training and evaluation code, trained models, datasets, and predictions for various PTMs are available at https://github.com/jasperzuallaert/PhosphoLingo. Contactsven.degroeve@vib-ugent.be and nico.callewaert@vib-ugent.be Supplementary informationSupplementary materials are available at bioRxiv.

bioinformatics↗

bioRxiv2022

A panoramic perspective on human phosphosites

Protein phosphorylation is the most common post-translational reversible modification of proteins and is key in the regulation of many cellular processes. Due to this importance, phosphorylation is extensively studied, resulting in the availability of a large amount of mass spectrometry based phospho-proteomics data. Here, we leverage the information in these large-scale phospho-proteomics datasets, as contained in Scop3P, to analyze and characterize proteome-wide protein phosphorylation sites (P-sites). First, we set out to differentiate correctly observed P-sites from false positive sites using five complementary site properties. We then describe the context of these P-sites in terms of protein structure, solvent accessibility, structural transitions and disorder, and biophysical properties. We also investigate the relative prevalence of disease-linked mutations on and around P-sites. Moreover, we also assess structural dynamics of P-sites in their phosphorylated and unphosphorylated state. Our study shows that the residues that gets phosphorylated are more flexible than their equivalent non-phosphorylated residues. Our structural and biophysical analyses of P-sites in solvent inaccessible (buried) regions of proteins show that these sites are primarily found in multi-site phospho-proteins, where highly dynamic structural transitions can occur upon binding with another protein. Finally, our analysis of the biophysical properties of P-site mutations shows that P-site mutations that occur in structurally rigid regions are more often involved in disease.

bioinformatics↗

bioRxiv2020

Online biophysical predictions for SARS-CoV-2 proteins

The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. We here present a website (http://sars2.bio2byte.be/) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, {beta}-sheet aggregation and protein-protein interaction propensities. These predictions attempt to capture the emergent properties of the proteins, so the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide an indication of the biophysical variation that is observed in homologous proteins, which give an indication of the limits of the functionally relevant biophysical behaviour of these proteins. With this website, we therefore hope to provide researchers with further clues on the behaviour of SARS-CoV-2 proteins.

biophysics↗

bioRxiv2019

Scop3P: a comprehensive resource of human phosphosites within their full context

Protein phosphorylation is a key post-translational modification (PTM) in many biological processes and is associated to human diseases such as cancer and metabolic disorders. The accurate identification, annotation and functional analysis of phosphosites is therefore crucial to understand their various roles. Phosphosites (P-sites) are mainly analysed through phosphoproteomics, which has led to increasing amounts of publicly available phosphoproteomics data. Several resources have been built around the resulting phosphosite information, but these are usually restricted to protein sequence and basic site metadata. What is often missing from these resources, however, is context, including protein structure mapping, experimental provenance information, and biophysical predictions. We therefore developed Scop3P: a comprehensive database of human phosphosites within their full context. Scop3P integrates sequences (UniProtKB/Swiss-Prot), structures (PDB), and uniformly reprocessed phosphoproteomics data (PRIDE) to annotate all known human phosphosites. Furthermore, these sites are put into biophysical context by annotating each phosphoprotein with perresidue structural propensity, solvent accessibility, disordered probability, and early folding information. Scop3P, available at https://iomics.ugent.be/scop3p, presents a unique resource for visualization and analysis of phosphosites, and for understanding of phosphosite structure-function relationships.

bioinformatics↗

bioRxiv2018

Beyond the ribosome: proteome-wide secretability studies using SECRiFY

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here developed a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50 - 100 amino acids, we generated datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. SECRiFY is the first methodology that generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability predictors. The finding that secretability is indeed a learnable feature of protein sequences is of significant impact in the broad area of recombinant protein expression and de novo protein design.

molecular biology↗