Biology subjects

Diaz-Rovira, A. M.

Publications and source records attributed to Diaz-Rovira, A. M..

6 recordsLinked to original sources

bioRxiv2026

The Human Bindome: A Proteome-scale Atlas of Designed Binder Candidates

Affinity reagents such as antibodies are indispensable for interrogating proteins biological function. Yet they are costly and frequently unreliable, with unknown sequences, posing challenges to reproducible experimental research. Deep learning-based protein design can now in silico generate affinity reagents achieving reliable experimental success rates, but has remained largely confined to specialist laboratories. Here we present the Human Bindome, a proteome-scale atlas of high-confidence in silico protein binder candidates. By embedding the experimentally benchmarked BindCraft method in an accelerated, parallelized framework with automated domain-level target selection, we generated 306,146 binder candidates covering 8,296 human proteins (40.9% of the full proteome). Every candidate carries a defined sequence, a predicted binder-target structure model, and in silico confidence metrics. We characterize proteome-wide coverage and show that binder epitopes frequently overlap functional sites. This positions the Bindome as a resource of genetically encodable perturbagens for site-specific, modular control of protein function. The Bindome is freely available through a web interface (https://bindome.epfl.ch), with agentic, natural-language querying and as data splits for machine-learning model development. We anticipate that the Bindome will be valuable for the scientific community by providing affinity and perturbation reagents with broad applications in dissecting biological mechanisms as well as in drug and target discovery.

synthetic biology↗

bioRxiv2025

Mapping the latent CRBN-molecular glue degrader interactome

Molecular glue degraders (MGDs) are a transformative modality in drug discovery. MGDs that work in concert with the E3 ligase CRL4CRBN can degrade a range of substrates through tailored MGDs. To explore CRL4CRBN reprogrammability, we tested whether reported CRBN-MGD substrates are part of a network of latent CRBN interactors, detectable with generic CRBN-MGDs. Leveraging highly parallel interaction measurement (GluePCA) between CRL4CRBN and human zinc-fingers (ZFs), we identified [~]210 ZFs bound to CRBN-pomalidomide, where top binders are already reported as degraded by dedicated MGDs. To map latent CRBN-MGDs interactions proteome-wide, and thus define the immediately accessible CRBN target space, we combined AI-derived protein surface queries (MaSIF-mimicry) with GluePCA. This pipeline identified 6 known and 43 novel CRBN-pomalidomide binders, thereby providing privileged starting points for MGD development. We expect this binding-focused, highly parallel workflow to be readily applicable to other MGD/E3 ligase systems, extending the target landscape of this emerging drug class.

molecular biology↗

bioRxiv2025

Characterization of PROTACs by Ternary Complex Landscape Exploration using Monte Carlo simulations

PROTACs (Proteolysis-Targeting Chimeras) have emerged as a powerful modality for targeted protein degradation, yet their optimization still relies heavily on trial-and-error methods. A key factor in PROTAC degradation efficiency is the formation of the ternary complex (TC) between the PROTAC and its target proteins. However, due to their dynamic nature, PROTAC-mediated TCs can adopt multiple conformations, making their characterization challenging. Computational methods that account for this flexibility can provide more accurate predictions aligned with experimental results. Here, we explore the dynamic nature of TCs by analyzing their energy landscapes using protein-protein docking coupled with Monte Carlo sampling. This approach enables the identification of energetically relevant TC conformations, including those observed in experimental crystal structures, and allows estimation of thermodynamic and kinetic stability, as shown for a set of VHL-WDR5 PROTACs. Insights from these landscapes could support the screening and optimization of tens of similar PROTACs based on TC stability.

biophysics↗

bioRxiv2025

Efficient Design of Affilin(R) Protein Binders for HER3

Engineered scaffold-based proteins that bind to concrete targets with high affinity offer significant advantages over traditional antibodies in theranostic applications. Their development often relies on display methods, where large libraries of variants are physically contacted with the desired target protein and pools of binding variants can be selected. Herein, we use a combined artificial intelligence/physics-based computational framework and phage display approach to obtain ubiquitin based Affilin(R) proteins targeting the HER3 extracellular domain, a relevant tumor target. We demonstrate that the developed in silico pipeline can generate de novo Affilin(R) proteins with high experimental success rate using a small training set of sequences (<1000 sequences). The classical phage display yielded primary candidates with low nanomolar affinities. These binders could be further optimized by phage display and computational maturation alike. These combined efforts resulted in four HER3 ligands with high affinity, cell binding, and serum stability that have theranostic potential.

biochemistry↗

bioRxiv2022

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures

Machine learning protein structure prediction, such as RosettaFold and AlphaFold2, have impacted the structural biology field, raising a fair amount of discussion around its potential role in drug discovery. While we find some preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a target with low sequence identity. In order to address this, we have developed an AlphaFiold2 version where we exclude all structural templates with more than 30% sequence identity. In a previous study, we used those models in conjunction with state of the art free energy perturbation methods. In this work we focus on using them in rigid receptor ligand docking. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario; one might think in including some post processing modeling to drive the binding site into a more realistic holo target model.

biochemistry↗

bioRxiv2022

Deep Learning Structural Models Sufficiently Accurate for Free Energy Calculations? Application of FEP+ to AlphaFold2 Predicted Structures

The availability of AlphaFold2 has led to great excitement in the scientific community - particularly among drug hunters - due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the {Delta}{Delta}G values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using X-ray structures. We conclude that under the right circumstances, AlphaFold2 modeled structures are accurate enough to be used by physics-based methods such as FEP, in typical lead optimization stages of a drug discovery program.

biochemistry↗