Biology subjects

Fraternali, F.

Publications and source records attributed to Fraternali, F..

At least 19 recordsLinked to original sources

bioRxiv2025

Tumor-infiltrating CD27-IgD- regulatory B cells suppress cytotoxic CD8+T cell responses in renal cell carcinoma

B cells play a pivotal role in shaping the tumor microenvironment (TME) and tertiary lymphoid structures (TLS), but the functions of specific B cell subsets in cancer pathogenesis remain unclear. Using a novel tissue-centric single cell RNA-sequencing (scRNA-seq) bioinformatic workflow aimed at unraveling cancer-specific clusters, combined with flow cytometry and high-plex spatial imaging, we identify a renal cell carcinoma (RCC)-specific enrichment of CD27-IgD-CD21+CD11c- double negative 1 (DN1) B cells associated with worse prognosis and regulatory function. Spatial profiling localizes DN1 Bregs within immature TLSs, in close proximity to IL-10 and TGF{beta}CD8T cells. RCC-resident DN1 B cells are enriched for endosomal Toll-like receptor (TLR) signaling pathways and stimulation of tumor-infiltrating lymphocytes (TILs) with TLR agonists induces the differentiation of IL-10+ and TGF{beta}+DN Bregs suppressing CD8+T cell cytotoxicity, partially via IL-10 and TGF{beta}. These findings identify a pro-tumorigenic B cell population with potential diagnostic and therapeutic relevance in RCC.

immunology↗

bioRxiv2025

Multi-step antibody class switching in a primary human response is restricted after IGHG2 and dependent on B cell maturation stage.

Class switch recombination (CSR) allows the formation of functionally specialized antibodies. Understanding of CSR dynamics is key for better design and prediction of vaccines to protect mucosal surfaces. To investigate CSR in a primary human immune response under a controlled setting, we sampled healthy volunteers without COVID-19 history every other day during the first three weeks after SARS-CoV-2 vaccination, with additional time points up to six months. Leveraging bulk and single-cell B cell receptor repertoires, single-cell transcriptomics, and immunophenotyping, we uncover paradigm-shifting insights into CSR. Newly activated B cells produce sterile transcripts of all antibody isotype constant regions (IGHC) simultaneously up to IGHG2, challenging the view that sterile transcription occurs for only a single IGHC gene at a time. CSR follows a multistep progression along the IGHC locus; in this challenge vaccine-induced B cells switch from IGHM to IGHG3 and IGHG1, followed by a subsequent switching to IGHA1 and IGHG2 after secondary immunization. IGHA2 clones require pre-switching to IGHA1 clones. Notably, switching tendency, measured by IGHC sterile transcription, is memory B cell subtype dependent, particularly beyond IGHG2. Contrary to other vaccines, antigen-specific B cells are enriched in DN2, Cmem2 and DN4 subtypes after immunization. We also observe a temporal decoupling of CSR and somatic hypermutation (SHM), with the latter detectable only after six months post-immunization. Our data describes the dynamics between CSR, SHM, sterile transcription and B cell memory development during a human primary response, challenges textbook models of CSR and offers new insights to aid control of CSR direction. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/652638v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@785769org.highwire.dtl.DTLVardef@b2c54eorg.highwire.dtl.DTLVardef@b78c72org.highwire.dtl.DTLVardef@65b5aa_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryClass switch recombination occurs independently of somatic hypermutation and in a multistep fashion up to IGHG2 during a primary response in humans.

immunology↗

bioRxiv2025

Assessing structure-function impacts on Vitellogenin by leveraging allelic variant occurring in honey bee subspecies Apis mellifera meliffera

Computational advances involving artificial intelligence (AI) and successful experimental state-of-the-art structure determination can provide detailed pictures of large and complex protein structures, and their variations. A standout case is Vitellogenin (Vg) derived from the honey bee (Apis mellifera). Vg is an essential protein for reproduction in almost all egg-laying animals, and can in addition regulate behavior and provide immunological support in some species, including the honey bee. Information is limited in terms of the structure-function relationships that underlie Vgs pleiotropic functions, at least in part because this protein is not expressed in the best developed gene-editing models, such as fruit flies and mice. However, naturally occurring allelic variation in Vg can provide some insight, i.e., into which changes (mutations) are allowable (present at some frequency) vs. likely not allowable (not present or only at low frequency). Here, we leverage a unique dataset of 1,086 fully sequenced Vg alleles from honey bees in 15 countries. We identify a population-specific 9 nucleotide deletion in a locally endangered honey bee subspecies (A. m. mellifera) that impacts a loop structure in a central Vg domain. Due to the A. m. mellifera population history of near extinction and human intervention, an assessment of this Vg variant is not only theoretically interesting but also relevant for subspecies conservation efforts. Using structural bioinformatics, molecular dynamics simulations, and a transformer-based indel predictor (IndeLLM), we demonstrate that Vg protein structure and stability can be maintained despite the deletion. Our approach also reveals the dynamic nature of specific regions in Vg for the first time. Generalizable results may extend to other egg-laying animals of ecological and economic importance.

bioinformatics↗

bioRxiv2025

Tuning antibody stability and function by rational designs of framework mutations

Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens, however these approaches often focus on the antibody complementarity determining region (CDR) whilst ignoring the immunoglobulin framework (FW) which provides structural rigidity and support for the flexible CDR loops. Here we present an integrated computational-experimental workflow, combining static structure analyses, molecular dynamics simulations and in vitro physicochemical and functional assays to generate rational designs of FW mutations for modulating antibody stability and activity. We first showed that recent antibody-specific language models lacked insights in FW mutagenesis, in comparison to approaches which utilised antibody structure information. Using the widely-used breast cancer therapeutic trastuzumab as a use case, we designed stabilising mutants which were distal to the CDR and preserved the antibodys functionality to engage its cognate antigen (HER2) and induce antibody-dependent cellular cytotoxicity (ADCC). Interestingly, guided by local backbone motions predicted using molecular dynamics simulations, we designed a FW mutation on the trastuzumab light chain which retained antigen-binding effects but lost Fab-mediated and Fc-mediated effector functions. This highlighted effects of FW on immunological functions engendered in distal areas of the antibody, and the importance to consider attributes other than binding affinity when assessing antibody function. Our approach incorporates interdomain dynamics and distal effects between FW and the Fc domains, expands the scope of antibody engineering beyond the CDR, and underscores the importance of a holistic perspective that considers the entire antibody structure as a whole in optimising antibody stability, developability and function.

immunology↗

bioRxiv2025

Leveraging protein language models and scoring function for Indel characterisation and transfer learning

1.Protein language models (PLMs) are increasingly used to assess the impact of genetic variation on proteins. By leveraging sequence information alone, PLMs achieve high performance and accuracy and can outperform traditional pathogenicity predictors specifically designed to identify harmful variants contributing to diseases. PLMs can perform zero-shot inference, making predictions without task-specific fine-tuning, offering a simpler and less overfitting-prone alternative to complex methods. However, studying in-frame insertions and deletions (indels) with PLMs remains challenging. Indels alter protein length, making direct comparisons between wildtype and mutant sequences not straightforward. Additionally, indel pathogenicity is less studied than other genetic variants, such as single nucleotide variants, resulting in a lack of annotated datasets. Despite these challenges, approaches that leverage PLMs through transfer learning have emerged, making it possible to capture the features needed for more accurate predictions. Still, the current approaches are limited in terms of allowed organisms, indel length, and interpretability. In this work, we devise a new scoring approach for indel pathogenicity prediction (IndeLLM) that provides a solution for the difference in protein lengths. Our method only uses sequence information and zero-shot inference with a fraction of computing time while achieving performances similar to other indel pathogenicity predictors. We used our approach to construct a simple transfer learning approach for a Siamese network, which outperformed all tested indel pathogenicity prediction methods (Matthews correlation coefficient = 0.77). IndeLLM is universally applicable across species since PLMs are trained on diverse protein sequences. To enhance accessibility, we designed a plug-and-play Google Colab notebook that allows easy use of IndeLLM and visualisation of the impact of indels on protein sequence and structure. The tool is available on GitHub https://github.com/OriolGraCar/IndeLLM and Colab https://colab.research.google.com/drive/1CgwprttaNFR_KeJGyFzP0a0C9Y wc4P. Graphical Abstract, if needed, or logo til include on Google Colab O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/642715v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@e5d1b0org.highwire.dtl.DTLVardef@299b19org.highwire.dtl.DTLVardef@1859f02org.highwire.dtl.DTLVardef@18a7ba9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains

The development of stable antibodies formed by compatible heavy (H) and light (L) chain pairs is crucial in both the in vivo maturation of antibody-producing cells and the ex vivo designs of therapeutic antibodies. We present here a novel machine learning framework, ImmunoMatch, for deciphering the molecular rules governing the pairing of antibody chains. Fine-tuned on an antibody-specific language model, ImmunoMatch learns from paired H and L sequences from single human B cells to distinguish cognate H-L pairs and randomly paired sequences. We find that the predictive performance of ImmunoMatch can be augmented by training separate models on the two types of antibody L chains in humans,{kappa} and{lambda} , in line with the in vivo mechanism of B cell development in the bone marrow. Using ImmunoMatch, we illustrate that refinement of H-L chain pairing is a hallmark of B cell maturation in both healthy and disease conditions. We find further that ImmunoMatch is sensitive to sequence differences at the H-L interface. ImmunoMatch focusses on H-L chain pairing as a specific, under-explored problem in antibody developability, and facilitates the computational assessment and modelling of stably assembled immunoglobulins towards large-scale optimisation of efficacious antibody therapeutics.

bioinformatics↗

bioRxiv2024

Corylus avellana non-specific lipid-transfer protein Cor a 8 is a moonlighting enzyme with a new lipase activity

The high-fat content of hazelnuts, mainly triglycerides, makes them prone to lipid oxidation during storage, which has a big impact on their sensory and nutritional quality. The chemical pathways leading to hazelnut oxidative rancidity have been well characterized and it are faster on free fatty acids. Lipase(s) enzymes are required, in oilseed, to hydrolyze the ester bond to free the single molecule of fatty acids. This step, necessary for germination, is the first event to trigger rancidity. Identifying the lipase(s) enzyme and the biochemical pathways involved in rancidity would lead to an effective strategy to prevent fat deterioration. Different proteins have been characterized in hazelnut seed and great interest has been risen towards the non-specific lipid transfer protein family because they were identified as human allergens. Here we show that Cor a 8 - a member of nsLTP - is a novel non-regiospecific lipase that is able to bind to oil-water interfaces and hydrolyze the triacylglycerol (TAGs) ester bonds by a non-canonical active site (non-serine dependent). Molecular modelling and molecular dynamics suggest that Cor a 8 is a moonlighting enzyme not only able to catalyze the hydrolysis of TAGs but also to stabilize the resulting free fatty acids and transport it. Cor a 8 homologues are present in all land plants, but the specific catalytic amino acids are found only in angiosperms, suggesting an evolutionary adaptation for lipid metabolism unique to flowering plants. This study sets the foundation for understanding this new lipid metabolism in plants and its role in rancidity development.

biochemistry↗

bioRxiv2024

Single Distal Mutation Enhances Activity of known PETases via Stabilisation of PET-binding

As a major source of plastic pollution, PET has attracted significant interest for biodegradation due to its potential in the circular economy. Finding effective enzymes still remains a challenge as screening methods are limited by either the low throughput or dependence on alternative non-PET substrates due to PETs insolubility. Here, we report a highly active, stable and robust enzyme, Fast_2.9, identified while directly screening for PET-degrading activity in mesophilic conditions using droplet-based encapsulation of PET nanoparticles with the throughput above 1 kHz. We identified a distal S269T mutation that improves activity in the majority of all known PETases with up to 400 times over wildtype, and more than twice of known engineered PETases, as tested on untreated post-consumer plastics. Microsecond time scale molecular dynamics analyses indicate that this distant mutation possibly influences residues near the substrate-binding cleft via a common mechanism across PETases. Compared to the state-of-the-art FastPETase and LCCICCG enzymes, the engineered Fast_2.9 enzyme requires up to 8 and 42 times lower enzyme concentrations to reach the same enzymatic activity, ultimately requiring significantly less enzyme. As such our engineered enzyme degrades multiple post-consumer PET substrates, including polyester textiles, within as least as just 2 days with up to nearly 100% terephthalic acid conversion using as little as 0.72 mgenzyme/gPET at 50. Our study presents a universal methodology for direct screening of insoluble substrates at ultra-high-throughput and highlights the techno-economic potential of Fast_2.9 for PET depolymerisation.

synthetic biology↗

bioRxiv2024

VCAb: A web-tool for structure-guided antibody engineering

Effective responses against different immune challenges require secretion of antibodies with various isotypes performing specific effector functions. Structural information on these isotypes is essential to engineer antibodies with desired physico-chemical features of their antigen-binding properties, and optimal stability and developability as potential therapeutic antibodies. In silico mutational scanning profiles on antibody structures would further pinpoint candidate mutations for enhancing antibody stability and function. Although a number of antibody structure databases exist, a public data resource which provides clear, consistent annotation of isotypes, species coverage of 3D antibody structures and their deep mutation profiles is currently lacking. The V and C region bearing antibody (VCAb) web tool is established with the purpose to clarify these annotations and provide an accessible and easily consultable resource to facilitate antibody engineering. VCAb currently provides data on 6,948 experimentally determined antibody structures including both V and C regions from different species. Additionally, VCAb provides annotations of species and isotypes with both V and C region numbering schemes applied, which can be interactively queried or downloaded in batch. Multiple in silico mutational scanning methods are applied on VCAb structures to provide an easily accessible interface for querying the impact of mutations on antibody stability. These features are implemented in a R shiny application to enable interactive data interrogation. VCAb is freely accessible at https://fraternalilab.cs.ucl.ac.uk/VCAb/. The source code to generate the VCAb database and the online R shiny application is available at https://github.com/Fraternalilab/VCAb, enabling users to set up local VCAb instances.

bioinformatics↗

bioRxiv2024

TITINdb2 - Expanding Annotation and Structural Information for Protein Variants in the Giant Sarcomeric Protein Titin

Structured AbstractO_ST_ABSSummaryC_ST_ABSWe present TITINdb2, an update to the TITINdb database previously constructed to facilitate the identification of pathogenic missense variants in the giant protein titin, which are associated with a variety of skeletal and cardiac myopathies. The database and web portal have been substantially revised and include the following new features: (i) an increase in computational annotation from 4 to 20 variant impact predictors, available through a new custom data table dialogue; (ii) thorough structural coverage of single domains with AlphaFold2 predicted models; (iii) newly predicted domain-domain interface annotations; (iv) an expanded in silico saturation mutagenesis incorporating 4 variant impact predictors; (v) a comprehensive overhaul of available data, including population data sources and variants reported pathogenic in the literature; (vi) A curated mapping of existing protein, transcript and chromosomal sequence positions and a new variant conversion tool to translate variants in one format to any other format. Availability and ImplementationDatabase accessible via titindb.kcl.ac.uk/TITINdb/ ContactFranca Fraternali (f.fraternali@ucl.ac.uk) Supplementary InformationAvailable

bioinformatics↗

bioRxiv2023

sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data

Class-switch recombination (CSR) is an integral part of B cell maturation. Steady-state analyses of isotype distribution (e.g. B cell receptor [BCR] repertoire analysis of snapshots during an immune response) do not directly measure CSR dynamics, which is crucial in understanding how B cell maturation is regulated across time. We present sciCSR (pronounced scissor, single-cell inference of class switch recombination), a computational pipeline which analyses CSR events and dynamics of B cells from single-cell RNA-sequencing (scRNA-seq) experiments. sciCSR re-analyses transcriptomic sequence alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline "sterile" transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built by the pipeline to infer the dynamics and direction of CSR. Applying sciCSR on SARS-CoV-2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier timepoint in the collected time-course, the isotype distribution of BCR repertoires of subsequent timepoints with high accuracy (cosine similarity [~] 0.9). sciCSR also recapitulates CSR patterns in mouse models where B cell maturation was perturbed using gene knockouts. sciCSR infers cell state transitions using processes specific to B cells, identifies transitions which are often missed by conventional RNA velocity analyses, and can reveal insights into the regulation of CSR and the dynamics of B cell maturation during an immune response.

bioinformatics↗

bioRxiv2021

Pandemic, epidemic, endemic: B cell repertoire analysis reveals unique anti-viral responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus

Immunoglobulin gene heterogeneity reflects the diversity and focus of the humoral immune response towards different infections, enabling inference of B cell development processes. Detailed compositional and lineage analysis of long read IGH repertoire sequencing, combining examples of pandemic, epidemic and endemic viral infections with control and vaccination samples, demonstrates general responses including increased use of IGHV4-39 in both EBOV and COVID-19 infection cohorts. We also show unique characteristics absent in RSV infection or yellow fever vaccine samples: EBOV survivors show unprecedented high levels of class switching events while COVID-19 repertoires from acute disease appear underdeveloped. Despite the high levels of clonal expansion in COVID-19 IgG1 repertoires there is a striking lack of evidence of germinal centre mutation and selection. Given the differences in COVID-19 morbidity and mortality with age, it is also pertinent that we find significant differences in repertoire characteristics between young and old patients. Our data supports the hypothesis that a primary viral challenge can result in a strong but immature humoral response where failures in selection of the repertoire risks off-target effects.

immunology↗

bioRxiv2021

Protein structural consequences of DNA mutational signatures: A meta-analysis of somatic variants and deep mutational scanning data

AO_SCPLOWBSTRACTC_SCPLOWSignatures of DNA motifs associated with distinct mutagenic exposures have been defined for somatic variants, but little is known about the consequences different mutational processes pose to the cell, especially how mutagens exert damage on specific proteins and their three-dimensional structures. Here we identify a DNA mutational signature which corresponds to damaging protein variants. We show that this mutational signature is under-sampled in sequencing data from tumour cohorts, constituting the "dark matter" of the mutational landscape which could only be accessed using deep mutational scanning (DMS) data. By training a set of gradient boosting classifiers, we illustrate that DMS data from only a handful ({approx} 10) of experiments can accurately predict variant impact, and that DNA mutational signatures embed information about the protein-level impact of variants. We bridge the gap between DNA sequence variations and protein-level consequences, discuss the significance of this signature in informing protein design and molecular principles of protein stability, and clarify the relationship between disease association and the true impact mutations bring to protein function.

bioinformatics↗

bioRxiv2020

Extended in vitro inactivation of SARS-CoV-2 by titanium dioxide surface coating

SARS-CoV-2 transmission occurs via airborne droplets and surface contamination. We show tiles coated with TiO2 120 days previously can inactivate SARS-CoV-2 under ambient indoor lighting with 87% reduction in titres at 1h and complete loss by 5h exposure. TiO2 coatings could be an important tool in containing SARS-CoV-2.

microbiology↗

bioRxiv2020

Single-cell transcriptomic analyses define distinct peripheral B cell subsets and discrete development pathways

Separation of B cells into different subsets has been useful to understand their different functions in various immune scenarios. In some instances, the subsets defined by phenotypic FACS separation are relatively homogeneous and so establishing the functions associated with them is straightforward. Other subsets, such as the "Double negative" (DN, CD19+CD27-IgD-) population, are more complex with reports of differing functionality which could indicate a heterogeneous population. Recent advances in single-cell techniques enable an alternative route to characterise cells based on their transcriptome. To maximise immunological insight, we need to match prior data from phenotype-based studies with the finer granularity of the single-cell transcriptomic signatures. We also need to be able to define meaningful B cell subsets from single cell analyses performed on PBMCs, where the relative paucity of a B cell signature means that defining B cell subsets within the whole is challenging. Here we provide a reference single-cell dataset based on phenotypically sorted B cells and an unbiased procedure to better classify functional B cell subsets in the peripheral blood, particularly useful in establishing a baseline cellular landscape and in extracting significant changes with respect to this baseline from single-cell datasets. We find 10 different clusters of B cells and applied a novel, geometry-inspired, method to RNA velocity estimates in order to evaluate the dynamical transitions between B cell clusters. This indicated the presence of two main developmental branches of memory B cells. One involves IgM memory cells and two DN subpopulations, culminating in a population thought to be associated with Age related B cells and the extrafollicular response. The other branch involves a third DN cluster which appears to be a precursor of classical memory cells. In addition, we identify a novel DN4 population, which is IgE rich and on its own developmental branch but with links to the classical memory branch.

immunology↗

bioRxiv2020

SARS-CoV-2 spike protein predicted to form stable complexes with host receptor protein orthologues from mammals, but not fish, birds or reptiles

SARS-CoV-2 has a zoonotic origin and was transmitted to humans via an undetermined intermediate host, leading to infections in humans and other mammals. To enter host cells, the viral spike protein (S-protein) binds to its receptor, ACE2, and is then processed by TMPRSS2. Whilst receptor binding contributes to the viral host range, S-protein:ACE2 complexes from other animals have not been investigated widely. To predict infection risks, we modelled S-protein:ACE2 complexes from 215 vertebrate species, calculated changes in the energy of the complex caused by mutations in each species, relative to human ACE2, and correlated these changes with COVID-19 infection data. We also analysed structural interactions to better understand the key residues contributing to affinity. We predict that mutations are more detrimental in ACE2 than TMPRSS2. Finally, we demonstrate phylogenetically that human SARS-CoV-2 strains have been isolated in animals. Our results suggest that SARS-CoV-2 can infect a broad range of mammals, but few fish, birds or reptiles. Susceptible animals could serve as reservoirs of the virus, necessitating careful ongoing animal management and surveillance.

bioinformatics↗

bioRxiv2019

Missense variants in health and disease target distinct functional pathways and proteomics features

1Missense variants are present amongst the healthy population, but some of them are causative of human diseases. Therefore, a classification of variants associated with \"healthy\" or \"diseased\" states is not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences, is essential to better distinguish pathogenic from population variants. Here we quantify variant enrichment across full-length proteins, their domains and 3D-structure defined regions. We integrate this with available transcriptomic and proteomic (protein half-life, thermal stability, abundance) data. Using this approach we have mined a rich set of molecular features which enable us to understand the differences underlying pathogenic and population variants: pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing, localise to protein cores and interaction interfaces, and are enriched in more abundant proteins. In terms of their molecular properties, we find that common population variants and pathogenic variants show the greatest contrast. Additionally, in contrary to other studies, we find that rare population variants display features closer to common than pathogenic variants. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants. Such details could be harnessed to predict variant deleteriousness, and prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at http://fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate a large number of missense variants with protein structural information, and to calculate variant enrichment in different protein structural regions.\n\nSignificance StatementOne of the greatest challenges in understanding the genetic basis of diseases is to discriminate between likely harmless and potentially disease-causing sequence variants. To better evaluate the pathogenic potential of missense variants, we developed a strategy to quantitatively measure the enrichment of both disease and non disease-related variants within a protein based on its structural and domain organisation. By integrating available transcriptomics and proteomics data, our approach distinguishes pathogenic from population variants far more clearly than previously possible, and reveals hitherto unknown details of how different proteins exhibit resilience and/or sensitivity towards genetic variants. Our results will help to prioritise variant-enriched proteins for therapeutic targeting; we have created the ZoomVar database, accessible at http://fraternalilab.kcl.ac.uk/ZoomVar, for programmatic mapping of user-defined variants to protein structural and domain information.

bioinformatics↗

bioRxiv2018

Functional cross-talk between allosteric effects of activating and inhibiting ligands underlies PKM2 regulation

Allosteric regulation is central to the role of the glycolytic enzyme pyruvate kinase M2 (PKM2) in cellular metabolism. Multiple activating and inhibitory allosteric ligands regulate PKM2 activity by controlling the equilibrium between high activity tetramers and low activity dimers and monomers. However, it remains elusive how allosteric inputs upon simultaneous binding of different ligands are integrated to regulate PKM2 activity. Here, we show that, in the presence of the allosteric inhibitor L-phenylalanine (Phe), the activator fructose 1,6-bisphosphate (FBP) can induce PKM2 tetramerisation, but fails to maximally increase enzymatic activity. Guided by a new computational framework we developed to identify residues that mediate FBP-induced allostery, we generated two PKM2 mutants, A327S and C358A, in which activation by FBP remains intact but cannot be attenuated by Phe. Our findings demonstrate a role for residues involved in FBP-induced allostery in enabling the integration of allosteric input from Phe and reveal a mechanism that underlies the co-ordinate regulation of PKM2 activity by multiple allosteric ligands.

biochemistry↗