Biology subjects

Angioletti-Uberti, S.

Publications and source records attributed to Angioletti-Uberti, S..

8 recordsLinked to original sources

bioRxiv2026

BAGEL-CAR: Reflections on the Bits to Binders Competition

The antigen-binding segment of chimeric antigen receptors (CARs) in CAR-T therapy has emerged as a compelling application of de novo AI protein design. In the Bits to Binders competition, our group submitted 414 BAGEL-CAR designs for CD20-directed CAR binding segments, 38.4% of which were statistically enriched in a pooled CAR-T proliferation screen, the highest among all teams and more than double the next-best. Strikingly, after detailing the three design strategies employed, we show that two of them carry the entire success of our campaign, with both of them outcompeting all the other teams' aggregate results. Crucially, during the competition, we did not refold any of the binders after backbone painting. Had we applied this typical validation step with ESMFold, all experimentally enriched designs would have been filtered out. Our findings show that each filtering step should be stress-tested per biomolecular system and intermediate outputs of design pipelines should be assayed regardless of 'common practice'. Additionally, we appear to be the only team that designed against ESMFold's predicted CD20 dimer geometry, which diverges substantially from the experimentally resolved 6Y97 structure, raising, but not resolving, the possibility that this alternative target geometry contributed to our high hit rate. We hope these reflections help inform the future of effective CAR-T and binder design campaigns.

bioinformatics↗

bioRxiv2026

An Energy Landscape Approach to Miniaturizing Enzymes using Protein Language Model Embeddings

AO_SCPLOWBSTRACTC_SCPLOWWe present a general approach to find amino acid sequences corresponding to the most compact enzyme likely to retain the structure of a given catalytic site. Our approach is based on using Monte Carlo (MC) simulations to sample an energy landscape where minima correspond, by construction, to sequences with the aforementioned properties. Building on previous work (Wu et al., 2025) and with the BAGEL package (Lala et al., 2025), we implement a route to achieve this goal using only the information extracted from a protein language model (PLM), without structural information. After generating a set of candidate sequences with this PLM-guided BAGEL optimization, we further filter potential candidates for downstream experimental validation using a two-stage protocol. First, deep-learning-based structure prediction models (ESMFold, Chai-1, Boltz-2) are used to identify a structural consensus among designs with highly conserved active-site geometries, yielding many candidates with active-site RMSD below a few angstroms relative to the wild-type and pLDDT scores above 80. Second, molecular dynamics simulations are performed on a filtered subset of sequences (based on active-site RMSD and SolubleMPNN log-likelihoods) to evaluate active-site stability when including thermal fluctuations. For the most promising enzymes, these yield RMSF values in the active site below 1.0 [A] and an active-site RMSD drift between 0.5 and 1.5 [A], making these mini-variants comparable to the wild type, though outcomes vary across enzymes. Given the protocols generality, we believe these results represent a step forward in AI-guided enzyme design. To facilitate rapid experimental validation by the broader community, we open-source all sequences generated by our computational pipeline. These include designs for four representative enzymes of this study: PETase, subtilisin Carlsberg (serine protease), Taq DNA polymerase, and VioA.

bioinformatics↗

bioRxiv2025

Harnessing protein-folding algorithms to drug intrinsically disordered epitopes

Due to their lack of a specific structure and dynamical nature, targeting of epitopes that are part of an intrinsically disordered region of a protein is a notoriously difficult task. Here, we describe a computational approach to overcome this problem, based on the use of a protein-folding algorithm and its confidence metrics within a Monte Carlo optimization pipeline to generate peptide-based binders. For different protein targets, we show by accurate free energy calculations that our approach is able to design peptides with binding free energies on the order of tens of kBT, i.e., with strengths comparable to covalent interactions. Direct observation of the bound complex through molecular simulations shows that the targeted epitope folds into structured domains with lowered thermal fluctuations upon binding, while remaining unstructured and dynamic in the unbound state, suggesting that the protein-folding algorithm must have learned the principles of induced (co-)folding. Given the ubiquitous presence of unstructured regions in proteins, our results suggest a potential pathway to design drugs targeting a large variety of previously untargetable epitopes, and open new possibilities for therapeutic intervention in diseases where disordered proteins play a key role. I SIGNIFICANCESmall-molecule drugs that bind to a protein via a lock-and-key mechanism require the targeted epitope to form a well-structured, stable binding pocket, thereby preventing binding to unstructured regions. To overcome this limit, we present a general approach, based on a protein-folding algorithm, to find peptide sequences that induce the formation of such a binding interface when no pocket is normally present. In other words, we show how a protein-folding algorithm can be used to program epitope recognition by induced folding instead of rigid lock-and-key matching. In this way, we show that we can extend druggable epitopes to intrinsically disordered, dynamical regions of proteins.

bioinformatics↗

bioRxiv2025

BAGEL: Protein Engineering via Exploration of an Energy Landscape

Despite recent breakthroughs in deep learning methods for protein design, existing computational pipelines remain rigid, highly specific, and ill-suited for tasks requiring non-differentiable or multi-objective design goals. In this report, we introduce BAGEL, a modular, open-source framework for programmable protein engineering, enabling flexible exploration of sequence space through model-agnostic and gradient-free exploration of an energy landscape. BAGEL formalizes protein design as the sampling of an energy function, either to optimize (find a global optimum) or to explore a basin of interest (generate diverse candidates). This energy function is composed of user-defined terms capturing geometric constraints, sequence embedding similarities, or structural confidence metrics. BAGEL also natively supports multi-state optimization and advanced Monte Carlo techniques, providing researchers with a flexible alternative to fixed-backbone and inverse-folding paradigms common in current design workflows. Moreover, the package seamlessly integrates a wide range of publicly available deep learning protein models, allowing users to rapidly take full advantage of any future improvements in model accuracy and speed. We illustrate the versatility of BAGEL on four archetypal applications: designing de novo peptide binders, targeting intrinsically disordered epitopes, selectively binding to species-specific variants, and generating enzyme variants with conserved catalytic sites. By offering a modular, easy-to-use platform to define custom protein design objectives and optimization strategies, BAGEL aims to speed up the design of new proteins. Our goal with its release is to democratize protein design, abstracting the process as much as possible from technical implementation details and thereby making it more accessible to the broader scientific community, unlocking untapped potential for innovation in biotechnology and therapeutics.

bioinformatics↗

bioRxiv2025

Mind the Gap: An Embedding Guide to Safely Travel in Sequence Space

We present a hybrid approach combining a protein language model (pLM) with Monte Carlo (MC) sampling for generating enzyme mutants free of mutations deleterious for structural preservation. Given the amino acid sequence of the original enzyme and a set of residues for which the local environment should be conserved, i.e., the catalytic site, our approach generates mutants that differ vastly in the overall sequence while retaining the geometry of the conserved region, thereby representing promising candidates for further experimental screening. Unlike end-to-end deep learning approaches, whose results are harder to interpret and control, the use of a well-established, classic technique such as MC sampling allows us to easily interpret the generative process as the sampling of an energy landscape determined by the pLM. In turn, such an interpretation enables us to steer this generative process and control its outcome by making use of robust statistical mechanics concepts, e.g., temperature, thereby explicitly guaranteeing certain properties of the generated mutants. Given the increasing relevance of generative algorithms in the design and search for novel, optimised enzymes, we believe that our results constitute an important step for the future development of this class of techniques. To facilitate experimental verification, we finally provide hundreds of sequences for 13 different enzymes involved in catalytic processes ranging from carbon dioxide conversion to DNA replication.

synthetic biology↗

bioRxiv2024

Self-reactive B cells traverse a perfect storm of somatic mutagenesis to cause a virus-induced autoimmune disease

The unexplained association between infection and autoimmune disease is strongest for hepatitis C virus-induced cryoglobulinemic vasculitis (HCV-CV). We traced the evolution of the pathogenic rheumatoid factor (RhF) autoantibodies in four HCV-CV patients by deep single cell multi-omic analysis, revealing three sources of B cell somatic mutation converged to drive accumulation of a large disease causing clone. A sensitive method for quantifying low affinity binding revealed three recurring heavy/light chain combinations created by V(D)J recombination bound self IgG but not viral E2 antigen. Whole genome sequencing revealed accumulation of thousands of somatic mutations, at levels comparable to CLL and normal memory B cells, but with 1-2 corresponding to driver mutations found recurrently in B cell leukemia/lymphoma. V(D)J hypermutation created autoantibodies with compromised solubility. In this virus-induced autoimmune disease, infection promotes a perfect storm of somatic mutagenesis in the descendants of a single B cell.

immunology↗

bioRxiv2023

Valency of Ligand-Receptor Binding Ignored by Pair Potentials

Molecular dynamics simulations have been crucial for investigating the dynamics of nanoparticle uptake by cell membranes via ligand-receptor interactions. Use of coarsegrained models has enabled evaluation of the effects of nanoparticle size, shape, ligand distribution on nanoparticles surface, or used thoroughly in the past decade, where a percentage of lipid heads, receptors, are attracted to sites on the nanoparticle surface, ligands. However, when pair-potentials are used to represent ligand-receptor interactions, the number of receptors interacting with one ligand, valency, may vary. We demonstrate that the curvature of a nanoparticle, strength of ligand-receptor interactions, and ligand or receptor concentration change the valency - ranging from 3.4 to 5.1 in this study. Such change in valency can create inaccurate comparisons between nanoparticles, or even result in the uptake of smaller nanoparticles than would be expected. To rectify this inconsistency we propose the adoption of a model based on bond-formation and use it to determine the extent to which previous studies may have been effected. This work recommends avoiding pair-potentials for modeling ligandreceptor interactions to ensure methodological consistency in nanoparticle studies. TOC GraphicA rendering of a ligand coated nanoparticle coming into contact with a lipid bilayer membrane. The receptor in the membrane is highlighted for clarity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/557452v3_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@10730a2org.highwire.dtl.DTLVardef@109f0b9org.highwire.dtl.DTLVardef@19b0d80org.highwire.dtl.DTLVardef@93557a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Nanoscale Friction of Model Hair Surfaces

We investigate the nanoscale friction between biomimetic hair surfaces using chemical colloidal probe atomic force microscopy experiments and nonequilibrium molecular dynamics simulations. In the experiments, friction is measured between water-lubricated silica surfaces functionalised with monolayers of either octadecyl or sulfonate groups, which are representative of the surfaces of virgin and ultimately bleached hair, respectively. In the simulations, friction is monitored between coarse-grained model hair surfaces with different levels of chemical damage, where different fractions of grafted lipid molecules are randomly replaced with sulfonate groups. The sliding velocity dependence of friction can be described using an extended stress-augmented thermally activation model. As the damage level increases, the friction generally increases, but its sliding velocity-dependence decreases. At low sliding speeds, which are closer to those encountered physiologically and experimentally, we observe a monotonic increase of friction with the damage ratio, which is consistent with our new experiments using biomimetic surfaces and previous ones using real hair. This observation demonstrates that modified surface chemistry, rather than roughness changes or subsurface damage, control the increase in nanoscale friction of damaged hair. We expect the experimental and computational model surfaces proposed here to be useful to screen the tribological performance of hair care formulations.

biochemistry↗