Biology subjects

Tsitsa, I.

Publications and source records attributed to Tsitsa, I..

4 recordsLinked to original sources

bioRxiv2025

Missense3D-PTMdb an interactive web resource to explore and visualize genetic variants and post-translational modifications sites (PTMs) using AlphaFold 3D models

Only a fraction of the >11 million missense variants identified in the human population has a known damaging or tolerated clinical impact. Post-translational modifications (PTMs), such as phosphorylation, glycosylation and ubiquitination, are key regulators of protein function and structure, and are critical for protein localisation, stability and interactions with other molecules. The ability of a protein to undergo PTMs, is subject to a correctly folded protein structure, and the recognition and binding of enzymes to specific amino acid motifs in close proximity to residues that undergo PTMs [PTM residue]. AlphaFold models provide an unprecedented opportunity to perform sequence-structure mapping of variants, which are in close linear or spatial proximity to a PTM site and should have their impact on protein function experimentally investigated. We present Missense3D-PTMdb, a "one-stop-shop" interactive web tool that provides a user-friendly sequence-structure mapping of 20,235 human proteins to 11,544,303 naturally occurring human missense variants, 203,775 PTM sites and their neighbours in sequence and 3D structure space using AlphaFold generated 3D models of the human proteome. Additionally, the sequence-structure mapping tool allows visualization and exploration of any human variant not currently stored in the database. Missense3D-PTMDb is freely available at https://missense3d.bc.ic.ac.uk/ptmdb.

genetics↗

bioRxiv2025

The AlphaFold Database Ages

The AlphaFold database provides 200M protein structures predicted by AlphaFold2 and released in 2022 from sequences in UniProt in April 2021. However, of the 20,504 full-length human structures in the AlphaFold database, 631 entries conflict with UniProt (version 2025_03 i.e. release 3 of 2025 published Jun 18, 2025); and there is a similar discrepancy for other species. This highlights how bioinformatics resources, as exemplified by the AlphaFold database can rapidly age. Time flies in bioinformatics.

bioinformatics↗

bioRxiv2024

Defining short linear motif binding determinants by phage-based multiplexed deep mutational scanning

Deep mutational scanning (DMS) has emerged as a powerful approach for evaluating the effects of mutations on binding or function. Here, we developed a multiplexed DMS by phage display protocol to define the binding determinants of short linear motifs (SLiMs) binding to peptide binding domains. We first designed a benchmarking DMS library to evaluate the performance of the approach on well-known ligands for eleven different peptide binding domains, including the talin-1 PTB domain. Systematic benchmarking against a gold-standard set of motifs from the eukaryotic linear motif (ELM) database confirmed that the DMS by phage analysis correctly identifies known motif binding determinants. The DMS analysis further defined a non-canonical PTB binding motif, with a putative extended conformation. A second DMS library was designed aiming to provide information on the binding determinants for 19 SLiM-based interactions between human and SARS-CoV-2 proteins. The analysis confirmed the affinity determining residues of viral peptides binding to host proteins, and refined the consensus motifs in human peptides binding to five domains from SARS-CoV-2 proteins, including the non-structural protein (NSP) 9. The DMS analysis further pinpointed mutations that increased the affinity of ligands for NSP3 and NSP9. An affinity improved cell-permeable NSP9-binding peptide was found to exert stronger antiviral effects as compared to the initial wild-type peptide. Our study demonstrates that DMS by phage display can efficiently be multiplexed and applied to refine binding determinants, and shows how DMS by phage display can guide peptide-engineering efforts.

biochemistry↗

bioRxiv2024

High resolution profiling of cell cycle-dependent protein and phosphorylation abundance changes in non-transformed cells

The cell cycle governs a precise series of molecular events, regulated by coordinated changes in protein and phosphorylation abundance, that culminates in the generation of two daughter cells. Here, we present a proteomic and phosphoproteomic analysis of the human cell cycle in hTERT-RPE-1 cells using deep quantitative mass spectrometry by isobaric labelling. Through analysing non-transformed cells, and improving the temporal resolution and coverage of key cell cycle regulators, we present a dataset of cell cycle-dependent protein and phosphorylation site oscillation that offers a foundational reference for investigating cell cycle regulation. These data reveal uncharacterised regulatory intricacies including proteins and phosphorylation sites exhibiting previously unreported cell cycle-dependent oscillation, and novel proteins targeted for degradation during mitotic exit. Integrated with complementary resources, our data link cycle-dependent abundance dynamics to functional changes and are accessible through the Cell Cycle database (CCdb), an interactive web-based resource for the cell cycle community. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/599917v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@bb2a69org.highwire.dtl.DTLVardef@1dd55ecorg.highwire.dtl.DTLVardef@34073eorg.highwire.dtl.DTLVardef@1c7a8c9_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗