Biology subjects

Cagiada, M.

Publications and source records attributed to Cagiada, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Nanosecond methyl dynamics in the eukaryotic RNA exosome core

Dynamics in proteins occur on a wide range of timescales and are crucial for protein function. On the fast end of that timescale, pico- to nanosecond dynamics have been extensively employed as proxies for entropy and their amplitude can be described by order parameters. Experimentally, NMR can be used to determine order parameters of the protein backbone and, via deuterium relaxation, of methyl groups, yet such experiments cannot be applied to large protein assemblies. In contrast, relaxation-violated coherence transfer experiments, that allow for the determination of side chain order parameters in highly deuterated, methyl-labeled proteins, are more sensitive. Here, we demonstrate that such experiments can be applied to very large, asymmetric protein assemblies by determining axial methyl order parameters for the 300 kDa fully asymmetric core of the eukaryotic RNA exosome complex. Ile-{delta}1[13CH3] methyl groups adopt a wide range of order parameters but highly flexible side chains are infrequent. High quality data, which we obtain for flexible regions, is required to observe subtle effects of RNA binding on order parameters. Local cryo-EM Q-scores correlate moderately with order parameters suggesting that Q-scores contain information on nanosecond motions. AF2{chi}, a recently described prediction tool for side-chain variability, provides good estimates of methyl order parameters, which are, in favorable cases, strongly correlated with experimental values. We thus demonstrate that relaxation-violated coherence transfer experiments can be employed to determine order parameters in large, asymmetric protein complexes that are difficult to capture by other methods, yet are crucial for the understanding of protein function. SignificanceNanosecond side chain dynamics contribute to the entropy of proteins and are therefore proxies for protein stability and binding. Here, we demonstrate that NMR can be employed to experimentally quantify nanosecond dynamics in large, asymmetric proteins paving the way to assess contributions of fast dynamics to the quality of static protein structures. Furthermore, we employ the experimental data to validate computational methods that provide structural insights into nanosecond dynamics.

biophysics↗

bioRxiv2026

Just Add Structure: Protein Language Models Combined with Structural Equivariance Excel at Protein Tasks

Accurate in silico prediction of protein properties, functional fitness, and mutational effects remains a central challenge in protein engineering and therapeutic design. While Protein Language Models (PLMs) successfully capture rich evolutionary and functional constraints from sequence data, they only indirectly encode the spatial and geometric information that fundamentally governs protein function. Consequently, state-of-the-art approaches typically rely on extensive fine-tuning, ensembling, or the incorporation of handcrafted structural features to achieve competitive accuracy, making them computationally expensive and difficult to scale. In this work, we demonstrate that explicit geometric modeling can substitute for, and in most cases outperform, large-scale PLM fine-tuning, with much higher parameter efficiency. Our approach, ProtEGNN, pairs PLM residue representations with a lightweight E(3)-Equivariant Graph Neural Network, competing with or achieving state-of-the-art performance across eight different benchmarks in protein property, mutational effect and function prediction, while needing 100-1000x fewer parameters than competing methods. Even when protein structure is combined with representations from ESM2-T6, a small 8M-parameter PLM, ProtEGNN matches fine-tuned sequence-only approaches based on substantially larger PLM backbones, while training orders of magnitude fewer parameters. Together, these results highlight geometric inductive bias as a powerful and scalable alternative to task-specific fine-tuning of large PLMs for protein modeling.

bioinformatics↗

bioRxiv2026

Reverse engineering the fatally cross-reactive A3A TCR to decouple potency and specificity

T cell receptor (TCR) affinity enhancement can introduce off-target cross-reactivity with life-threatening consequences, as illustrated by the MAGE-A3-specific A3A TCR, which caused fatal cardiotoxicity through recognition of a Titin-derived peptide. Here, we reconstructed the cross-reactivity landscape by reverse-engineering A3A toward its wild-type precursor, generating intermediate variants in which engineered CDR2 residues are systematically reverted to the wild-type sequence. Reverting just two engineered residues yields a receptor, v9, that retains MAGE-A3 cytotoxicity comparable to A3A while eliminating Titin and other acquired cross-reactivities. Structurally, these substitutions reduce CDR2-MHC contacts and disrupt an intra-TCR CDR2-CDR3{beta} interaction, propagating conformational changes across CDR3 loops that reshape peptide engagement without altering docking geometry. These results demonstrate that mutations outside the peptide-contacting CDR3 loops can allosterically reconfigure antigen specificity and establish simple stepwise reverse engineering to wild-type as a strategy for correcting TCR cross-reactivity.

immunology↗

bioRxiv2026

Sampling antibody conformational ensembles withABodyBuilder4-STEROIDS

Conformational flexibility is fundamental to the function of many proteins and in the case of antibodies can impact key properties such as affinity and specificity. While it is possible to predict single, static protein structures with high accuracy, predicting conformational ensemble remains challenging. Molecular dynamics simulations suffer from high computational costs, while deep learning methods are yet to achieve the same level of accuracy. Here, we introduce ABB4-STEROIDS a generative structure prediction model that samples conformational ensembles of antibodies. We trained our model on 4.2 million structural frames derived from [~]136,000 coarse-grained and a set of 83 new all-atom antibody MD simulations. We benchmarked our model on reproducing MD ensembles and evaluated the diversity of sampled structures and the covered conformational space against experimental evidence. ABB4-STEROIDS achieves state-of-the-art accuracy, particularly within the experimental benchmarks. The model is openly available and provides a robust resource for large-scale investigations of antibody conformational ensembles.

bioinformatics↗

bioRxiv2025

Structural basis for synergistic antibody protection against the essential malaria invasion complex protein RIPR

Plasmodium falciparum RH5-interacting protein (RIPR) is central to the essential PTRAMP-CSS-RIPR-CyRPA-RH5 (PCRCR)-complex; a leading target of blood-stage malaria vaccines. However, mechanisms whereby anti-RIPR antibodies inhibit parasite invasion are poorly understood. Here, we characterise 83 human IgG mAbs from RIPR-vaccinated Kymouse platform mice. Single mAbs have minimal neutralising activity, however, high-level synergistic inhibition is observed with pools of mAbs targeting the RIPR-Tail region. Structural characterisation and molecular dynamics simulations of RIPR-Tail show that mAbs targeting EGF-like domains 6-8 (RIPREGF (6-8)), but not EGF-like domains 9-10 or the C-terminal domain (RIPREGF (9-10)-CTD), synergise to constrain the RIPR-Tail conformation. The same antibodies dissociate PTRAMP-CSS from RIPR, thereby enabling anti-RIPREGF (9-10)-CTD mAbs or anti-CSS sdAbs to bind and potentiate anti-RIPREGF (6-8) IgG. Addition of these mAbs to IgG from humans immunised with the R78C (RIPREGF (7-8)-CyRPA) candidate vaccine enhances malaria growth inhibition. These data provide a framework to guide next-generation blood-stage malaria vaccine design.

immunology↗

bioRxiv2025

Uncovering the flexibility of CDR loops in antibodies and TCRs through large-scale molecular dynamics

Antibody structures are composed of framework regions that adopt a conserved fold and complementarity determining regions (CDR) loops which are far more variable. Flexibility of CDR loops has been linked to key properties such as affinity and specificity. However, owing to the scarcity of available data it has not been possible to study the functional implications of their dynamics in detail. To overcome these data limitations, we introduce CALVADOS 3-Fv, a customised set-up of the residue-based CALVADOS 3 model, utilizing restraints and parameterization tailored for immune receptor simulations. CALVADOS 3-Fv reproduces ensemble metrics in all atom simulations and experimental data with high accuracy. Having validated our protocol, we created FlAbDab and FTCRDab, two databases containing simulations of more than 150,000 antibodies and T-cell receptors. The databases are released open source to enable the study of CDR dynamics and as a large data source for training machine learning models.

biophysics↗

bioRxiv2025

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2

The flexibility of protein side chains is an essential contributor of conformational entropy and affects processes such as folding, stability and molecular interactions. Structure determination experiments and prediction tools such as AlphaFold generally fail to capture or represent the conformational heterogeneity of proteins in solution. Experiments can be used to study side-chain flexibility, but cannot be applied at scale, and most prediction methods focus on reconstructing the minimum free energy state rather than an ensemble representing side-chain configurations. Here, we use AlphaFold2 and its internal side-chain representations to develop AF2{chi} that predicts side-chain {chi}-angle distributions and generates structural ensembles. We extensively benchmark AF2{chi} predictions using experimental NMR 3J-couplings and s2 order parameters, as well as dihedral angle distributions derived from collections of experimental structures, demonstrating the accuracy of AF2{chi} in generating accurate side-chain ensembles. We also compare the accuracy of AF2{chi} with molecular dynamics simulations and recent machine learning models aimed to generate conformational ensembles and show that AF2{chi} provides state-of-the-art accuracy orders of magnitude faster than molecular simulations. With its speed and accuracy, AF2{chi} offers a strong complementary option to simulations and rotamer library approaches, making it particularly valuable for applications such as protein design, ligand docking and interpretation of biophysical experiments.

biophysics↗

bioRxiv2025

Supervised learning of protein variant effects across large-scale mutagenesis datasets

The increasing availability of data from multiplexed assays of variant effects (MAVEs) enables supervised model training against large quantities of experimental data to learn sequence-function relationships. Variant effect scores from MAVEs can, however, be influenced by the experimental method to create experiment-to-experiment differences in the mapping from molecular level-variant effects to MAVE readout, which presents a challenge for supervised learning across datasets. We here propose a framework for performing supervised learning with MAVE data that takes the influence of the experimental protocol into account, thus enabling variant effects to be learned across datasets produced in independent experiments. We apply the framework to train a model against variant effect scores collected with VAMP-seq, a MAVE technique that quantifies the steady-state cellular abundance of protein variants. We show that mapping variant abundance to VAMP-seq readout in a dataset-specific manner during model training improves the learned abundance model and moreover allows the learned model to predict variant effects on an interpretable scale. Our work highlights the importance of validating MAVE results with low-throughput methods to facilitate MAVE score interpretation and supervised model training.

biophysics↗

bioRxiv2024

Decoding molecular mechanisms for loss of function variants in the human proteome

Proteins play a critical role in cellular function by interacting with other biomolecules; missense variants that cause loss of protein function can lead to a broad spectrum of genetic disorders. While much progress has been made on predicting which missense variants may cause disease, our ability to predict the underlying molecular mechanisms remain limited. One common mechanism is that missense variants cause protein destabilization resulting in decreased protein abundance and loss of function, while other variants directly disrupt key interactions with other molecules. We have here leveraged machine-learning models for protein sequence and structure to disentangle effects on protein function and abundance, and applied our resulting model to all missense variants in the human proteome. We find that approximately half of all missense variants that lead to loss of function and disease do so because they disrupt protein stability. We predicted functionally important positions in all human proteins and found that they cluster on protein structures and are often found on the protein surface. Our work provides a resource for interpreting both predicted and experimental variant effects across the human proteome, and a mechanistic starting point for developing therapies towards genetic diseases.

bioinformatics↗

bioRxiv2024

Predicting absolute protein folding stability using generative models

While there has been substantial progress in our ability to predict changes in protein stability due to amino acid substitutions, progress has been slower in methods to predict the absolute stability of a protein. Here we show how a generative model for protein sequence can be leveraged to predict absolute protein stability. We benchmark our predictions across a broad set of proteins and find a mean error of 1.5 kcal/mol and a correlation coefficient of 0.7 for the absolute stability across a range of natural, small-medium sized proteins up to ca. 150 amino acid residues. We analyse current limitations and future directions including how such model may be useful for predicting conformational free energies. Our approach is simple to use and freely available via an online implementation.

biophysics↗

bioRxiv2023

Deep mutational scanning reveals a tight correlation between protein degradation and toxicity of thousands of non-native aspartoacylase protein variants

When the structural stability of a protein is compromised, the protein may form non-native interactions with other cell proteins and thus becomes a hazard to the cell. To mitigate this danger, destabilized proteins are targeted by the cellular protein quality control (PQC) network, which either corrects the folding defect or targets the protein for degradation. However, the details of how the protein folding and degradation systems collaborate to combat potentially toxic non-native proteins are unknown. To address this issue, we performed systematic studies on destabilized variants of the cytosolic aspartoacylase, ASPA, where loss-of-function variants are linked to Canavans disease, an autosomal recessive and lethal neurological disorder, characterized by the spongy degeneration of the white matter in the brain. Using Variant Abundance by Massively Parallel sequencing (VAMP-seq), we determined the abundance of 6152 out of the 6260 ([~]98%) possible single-site missense and nonsense ASPA variants in cultured human cells. The majority of the low abundance ASPA variants are degraded through the ubiquitin-proteasome system (UPS) and become toxic upon prolonged expression. Variant cellular abundance data correlates with predicted thermodynamic stability, evolutionary conservation, and separates most known disease-linked variants from benign variants. Systematic mapping of degradation signals (degrons) shows that inherent primary degrons in ASPA are located in buried regions, and reveals that the wild-type ASPA C-terminal region functions as a degron. Collectively, our data can be used to interpret Canavans disease variants and also offer mechanistic insight into how ASPA missense variants are targeted by the PQC system. These are essential steps towards future implementation of precision medicine for Canavans disease.

molecular biology↗

bioRxiv2023

A mutational atlas for Parkin proteostasis

The delicate balance of protein homeostasis can be disturbed by mutations that affect folding and stability of the encoded protein. More than half of disease-causing missense variants are thought to lead to protein degradation, but determining which and the molecular mechanisms involved remain enigmatic. To examine these issues, we selected the ubiquitin-protein ligase Parkin, where known missense variants result in an autosomal recessive, early onset Parkinsonism. We used the variant abundance by massively parallel sequencing (VAMP-seq) approach to quantify the abundance of Parkin missense variants in cultured human cells. The resulting mutational map, covering 9219 out of the 9300 possible single-site amino acid substitutions and nonsense Parkin variants, show that most low abundance variants are located within the structured domains of the protein, while the flexible linker regions are more tolerant. The vast majority of low abundance Parkin variants are degraded through the ubiquitin-proteasome system and are stabilized at a lowered temperature. The cellular abundance data correlate with thermodynamic stability, evolutionary conservation, and show that half of the known disease-linked variants are found at low abundance. Systematic mapping of degradation signals (degrons) shows that inherent primary degrons in Parkin largely overlap with regions that are buried, and highly sensitive to mutations. An exposed degron region proximal to the so-called "activation element" is enhanced by substitutions to hydrophobic residues and destroyed by introduction of hydrophilic residues. The data provide examples of how missense variants may cause degradation either via destabilization of the native protein, or by introducing local signals for degradation. Combined with the computational methods based on Parkin structure and conservation, our abundance map sheds light on the mechanisms that cause loss of function, and points to variants where function potentially can be restored.

biochemistry↗

bioRxiv2023

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms of human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. We assayed the abundance of 95% of GCK missense and nonsense variants, and found that 43% of hypoactive variants have a decreased cellular abundance. By combining our abundance scores with predictions of protein thermodynamic stability, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

biochemistry↗

bioRxiv2023

Rare catechol-O-methyltransferase (COMT) missense variants are structurally unstable proteasome targets

Catechol-O-methyltransferase (COMT) is a key enzyme in the metabolism of catecholamines. Substrates of the enzyme include neurotransmitters such as dopamine and epinephrine, and therefore, COMT plays a central role in neurobiology. Since COMT also metabolises catecholamine drugs such as L-DOPA, variation in COMT activity could affect pharmacokinetics and drug availability. Certain COMT missense variants have been shown to display decreased enzymatic activity. Additionally, studies have shown that such missense variants may lead to loss-of-function induced by impaired structural stability, which results in activation of the protein quality control system and degradation by the ubiquitin-proteasome system. Here, we demonstrate that two rare missense variants of COMT are ubiquitylated and targeted for proteasomal degradation as a result of structural destabilisation and misfolding. This results in strongly reduced intracellular steady-state levels of the enzyme, which for the L135P variant is rescued upon binding to the COMT inhibitors entacapone and tolcapone. Our results reveal that the degradation is independent of the COMT isoform, as both soluble (S-COMT) and ER membrane-bound (MB-COMT) variants are degraded. In silico structural stability predictions identify regions within the protein that are critical for stability overlapping with evolutionarily conserved residues, pointing towards other variants that are likely destabilised and degraded.

biochemistry↗

bioRxiv2022

Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components

In terms of its relative frequency, lysine is a common amino acid in the human proteome. However, by bioinformatics we find hundreds of proteins that contain long and evolutionarily conserved stretches completely devoid of lysine residues. These so-called lysine deserts show a high prevalence in intrinsically disordered proteins with known or predicted functions within the ubiquitin-proteasome system (UPS), including many E3 ubiquitin-protein ligases and UBL domain proteasome substrate shuttles, such as BAG6, RAD23A, UBQLN1 and UBQLN2. We show that introduction of lysine residues into the deserts leads to a striking increase in ubiquitylation of some of these proteins. In case of BAG6, we show that ubiquitylation is catalyzed by the E3 RNF126, while RAD23A is ubiquitylated by E6AP. Despite the elevated ubiquitylation, mutant RAD23A appears stable, but displays a partial loss of function phenotype in fission yeast. In case of UBQLN1 and BAG6, introducing lysine leads to a reduced abundance due to proteasomal degradation of the proteins. For UBQLN1 we show that arginine residues within the lysine depleted region are critical for its ability to form cytosolic inclusions. We propose that selective pressure to avoid lysine residues may be a common evolutionary mechanism to prevent unwarranted ubiquitylation and/or perhaps other lysine post-translational modifications. This may be particularly relevant for UPS components as they closely and frequently encounter the ubiquitylation machinery and are thus more susceptible to non-specific ubiquitylation.

biochemistry↗

bioRxiv2022

Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: comparison of experiments and computation.

Recent advances in DNA sequencing technologies are revealing a large variability in the human genome. Our capacity to establish genotype-phenotype correlations in such large-scale is, however, limited. This task is particularly challenging due to the multifunctional nature of proteins. Here we describe an extensive analysis of the stability and function of naturally-occurring variants (found in the COSMIC and gnomAD databases) of the cancer-associated human NAD(P)H: quinone oxidoreductase 1 (NQO1). First, we performed in silico saturation mutagenesis studies (>5000 substitutions) aimed to identify regions in NQO1 important for stability and function. We then experimentally characterized twenty-two naturally-occurring variants in terms of protein levels during bacterial expression, solubility, thermal stability and coenzyme binding. These studies showed a good overall correlation between experimental analysis and computational predictions; also the magnitude of the effects of the substitutions are similarly distributed in variants from the COSMIC and gnomAD databases. Outliers in these experimental-computational genotype-phenotype correlations remain, and we discuss these on the grounds and limitations of our approaches. Our work represents a further step to characterize the mutational landscape of NQO1 in the human genome and may help to improve high-throughput in silico tools for genotype-phenotype correlations in multifunctional proteins associated with disease.

biophysics↗

bioRxiv2022

Discovering functionally important sites in proteins

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

biophysics↗

bioRxiv2022

Rapid protein stability prediction using deep learning representations

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate [~] 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available--including via a Web interface--and enables large-scale analyses of stability in experimental and predicted protein structures.

biophysics↗