Biology subjects

Tsaban, T.

Publications and source records attributed to Tsaban, T..

7 recordsLinked to original sources

bioRxiv2025

Signal peptides as potential structural modulators of human splice isoforms

Signal peptides (SPs) are short protein segments responsible for protein localization, typically cleaved from mature proteins and rapidly degraded after fulfilling their targeting function. Beyond localization however, these sequentially and structurally diverse elements may play additional roles. We explore how alternative splicing potentially creates structural contexts where SPs become integral components of folded domains. Employing AlphaFold and additional computational approaches, we examined a known functional immune protein splice isoform of human SLAMF6 that retains biological activity, despite forming a truncated domain that lacks the central elements of the canonical interaction interface. We revealed striking characteristic similarities between the SLAMF6 SP and the absent segment, indicating its ability to complement and stabilize the isoform domain. An in silico screen of 235,000 reported expressed human isoforms identified several dozen additional candidates with potential SP complementation, previously dismissed as modeling artifacts. Notably, immunoglobulin and carbonic anhydrase domains show particular enrichment among these candidates. SPs are commonly regarded as dispensable elements that can be altered or eliminated when investigating proteins and their structures. Our research proposes an alternative perspective wherein SPs might perform integral roles in stabilizing their source proteins, or others. These findings extend the growing body of evidence for moonlighting SPs, suggesting that we have only begun to uncover their true functional scope. Specifically, SPs emerge as unique modulatory elements essential for understanding the structural and functional behavior of protein splice isoforms.

bioinformatics↗

bioRxiv2024

Peptide therapeutic leads for multi-target inhibition of inflammatory cytokines in Inflammatory Bowel Disease - computational design and in-vitro validation

Inflammatory Bowel Disease (IBD) are chronic and recurrent inflammatory disorders affecting the gastrointestinal tract, characterized by the involvement of numerous pro-inflammatory cytokines. These conditions profoundly impact both immune system dynamics and intestinal tissue integrity. Current therapeutic approaches predominantly rely on monoclonal antibodies, and frequently encounter limitations such as non-responsiveness, loss of efficacy over time, immunogenicity, adverse effects, and substantial cost. Consequently, there is a critical need for novel, targeted anti-inflammatory strategies. We present the computational structure guided design of peptidic inhibitors aimed at attenuating the activity of pivotal pro-inflammatory cytokines implicated in IBD pathogenesis, namely TNF, IL-1{beta}, and IL-6. These peptides were engineered to disrupt specific cytokine - receptor interactions, to block the release of pro-inflammatory cytokines. We structurally characterized key features in the studied interactions and used these to guide two computational design strategies, one based on the identification of dominant segments using our PeptiDerive approach, and one based on complementing fragments detected using our PatchMAN protocol. The designed peptides were synthesized and their efficacy was validated on Caco-2 intestinal epithelial cells and THP-1 macrophages, representative of the epithelial and immunological alterations typical of active IBD. The majority of the novel peptides effectively suppressed release of pro-inflammatory cytokines by both macrophages and intestinal epithelial cells, thereby reducing the risk of inflammation. This study underscores the efficacy of a rational design approach rooted in structural insights into inflammatory signaling complexes. Our findings demonstrate the potential of targeting key cytokines and receptor interaction with designed peptides as a promising therapeutic avenue for managing IBD and other inflammatory disorders.

bioinformatics↗

bioRxiv2023

Protein language models can capture protein quaternary state

BackgroundDetermining a proteins quaternary state, i.e. how many monomers assemble together to form the functioning unit, is a critical step in protein characterization, and deducing it is not trivial. Many proteins form multimers for their activity, and over 50% are estimated to naturally form homomultimers. Experimental quaternary state determination can be challenging and require extensive work. To complement these efforts, a number of computational tools have been developed for quaternary state prediction, often utilizing experimentally validated structural information. Recently, dramatic advances have been made in the field of deep learning for predicting protein structure and other characteristics. Protein language models that apply computational natural-language models to proteins successfully capture secondary structure, protein cell localization and other characteristics, from a single sequence. Here we hypothesize that information about the protein quaternary state may be contained within protein sequences as well, allowing us to benefit from these novel approaches in the context of quaternary state prediction. ResultsWe generated embeddings for a large dataset of quaternary state labels, extracted from the curated QSbio dataset. We then trained a model for quaternary state classification and assessed it on a non-overlapping set of distinct folds (ECOD family level). Our model, named QUEEN (QUaternary state prediction using dEEp learNing), performs worse than approaches that include information from solved crystal structures. However, we show that it successfully learned to distinguish multimers from monomers, and that the specific quaternary state is predicted with moderate success, better than a simple model that transfers annotation based on sequence similarity. Our results demonstrate that complex, quaternary state related information is included in these embeddings. ConclusionsQUEEN is the first to investigate the power of embeddings for the prediction of the quaternary state of proteins. As such, it lays out the strength as well as limitations of a sequence-based protein language model approach compared to structure-based approaches. Since it does not require any structural information and is fast, we anticipate that it will be of wide use both for in-depth investigation of specific systems, as well as for studies of large sets of protein sequences. A simple colab implementation is available at: https://colab.research.google.com/github/Orly-A/QUEEN_prediction/blob/main/QUEEN_prediction_notebook.ipynb.

bioinformatics↗

bioRxiv2021

Tandem WW/PPxY motif interactions in WWOX: the multifaceted role of the second WW domain

Class I WW domains mediate protein interactions by binding short linear PPxY motifs. They occur predominantly as tandem repeats, and their target proteins often contain multiple PPxY motifs, but the interplay of WW/peptide interactions is not always intuitive. WW domain-containing oxidoreductase (WWOX) protein harbors two WW domains: unstable WW1 capable of PPxY binding, and well-folded but mutated WW2 that cannot bind such motifs. WW2 is considered to act as a WW1 chaperone, but the underlying mechanism remains to be revealed. Here we combine NMR, ITC and structural modeling to elucidate the role of both WW domains in WWOX binding to single and double motif peptides derived from its substrate ErbB4. Using NMR we identified an interaction surface between the two domains that supports a WWOX conformation that is compatible with peptide substrate binding. ITC and NMR measurements reveal that while binding affinity to a single motif is marginally increased in the presence of WW2, affinity to a dual motif peptide increases tenfold, and that WW2 can directly bind double motif-peptides using its canonical binding site. Finally, differential binding of peptides in a mutagenesis study is consistent with a parallel orientation binding to the WW1-WW2 tandem domain, agreeing with structural models of the interaction. Our results reveal the complex nature of tandem WW domain organization and substrate binding, highlighting the contribution of WWOX WW2 to both stability and binding. This opens the way to assess how evolution can utilize the multivariate nature of binding to fine-tune interactions for specific biological functions.

biophysics↗

bioRxiv2021

PatchMAN docking: Modeling peptide-protein interactions in the context of the receptor surface

Peptide docking can be perceived as a subproblem of protein-protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation between the two partners, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about the peptide sequence to generate a representative conformer ensemble, which can then be rigid body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely that the protein surface defines the peptide bound conformation.We present PatchMAN (Patch-Motif AligNments), a novel peptide docking approach which uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a non-redundant set of protein-peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide-protein complexes in 62% / 81% within 2.5[A] / 5[A] RMSD, with corresponding sampling in 81% / 100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the conformation of the bound peptide is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide-protein association, and to the design of new peptide binders.

bioinformatics↗

bioRxiv2021

Harnessing protein folding neural networks for peptide-protein docking

Highly accurate protein structure predictions by the recently published deep neural networks such as AlphaFold2 and RoseTTAFold are truly impressive achievements, and will have a tremendous impact far beyond structural biology. If peptide-protein binding can be seen as a final complementing step in the folding of a protein monomer, we reasoned that these approaches might be applicable to the modeling of such interactions. We present a simple implementation of AlphaFold2 to model the structure of peptide-protein interactions, enabled by linking the peptide sequence to the protein c-terminus via a poly glycine linker. We show on a large non-redundant set of 162 peptide-protein complexes that peptide-protein interactions can indeed be modeled accurately. Importantly, prediction is fast and works without multiple sequence alignment information for the peptide partner. We compare performance on a smaller, representative set to the state-of-the-art peptide docking protocol PIPER-FlexPepDock, and describe in detail specific examples that highlight advantages of the two approaches, pointing to possible further improvements and insights in the modeling of peptide-protein interactions. Peptide-mediated interactions play important regulatory roles in functional cells. Thus the present advance holds much promise for significant impact, by bringing into reach a wide range of peptide-protein complexes, and providing important starting points for detailed study and manipulation of many specific interactions.

bioinformatics↗

bioRxiv2021

Uncovering the modified immunopeptidome reveals insights into principles of PTM-driven antigenicity

Antigen processing and presentation are critical for modulating tumor-host interactions. While post-translational modifications (PTMs) can alter the binding and recognition of antigens, their identification remains challenging. Here we uncover the role PTMs may play in antigen presentation and recognition in human cancers by profiling 29 different PTM combinations in immunopeptidomics data from multiple clinical samples and cell lines. We established and validated an antigen discovery pipeline and showed that newly identified modified antigens from a murine cancer model are cancer-specific and can elicit T cell killing. Systematic analysis of PTMs across multiple cohorts defined new haplotype preferences and binding motifs in association with specific PTM types. By expanding the antigenic landscape with modifications, we uncover disease-specific targets, including thousands of novel cancer-specific antigens and reveal insight into PTM-driven antigenicity. Collectively, our findings highlight an immunomodulatory role for modified peptides presented on HLA I, which may have broad implications for T-cell mediated therapies in cancer and beyond. SignificanceMajor efforts are underway to identify cancer-specific antigens for personalized immunotherapy. Here, we enrich the immunopeptidome landscape by uncovering thousands of novel putative antigens that are post-translationally modified. We define unique preferences for PTM-driven alterations affecting HLA binding and TCR recognition, which in turn alter tumor-immune interactions. Conflict of interest statementAuthors declare no conflicts of interest.

cancer biology↗