Biology subjects

Villegas-Morcillo, A.

Publications and source records attributed to Villegas-Morcillo, A..

6 recordsLinked to original sources

bioRxiv2026

The Quagmire of Immunogenicity-Guided Antibody Design: Labels Limit Progress

Generative sequence-based antibody design is increasingly steered by learned property predictors, so a design objective is only as well specified as the labels behind its oracle. Immunogenicity is among the most clinically consequential properties to condition on, and among the least well specified: the supervisory signal is a single anti-drug-antibody percentage (ADA%) per molecule, aggregated from published trials. To analyze that signal, we first extended and corrected the widely used 217-entry dataset to 231 therapeutics with paired sequences, spanning formats from mouse to fully human. Then, we manually reconstructed the clinical evidence for 110 of them from primary sources. We find that an antibody format lookup outperforms every sequence-based learned predictor on the 231-antibody dataset. Apparent accuracy tracks the format composition of the evaluation split rather than sequence signal. Furthermore, the labels understate their own evidence: molecules labeled at [≤]6 ADA% span up to 86% across trials. We recomputed labels from the trial-level measurements and found that the advantage of the format-based approach disappears, while the sequence-based predictors degrade less under the cohort-weighted mean. We conclude that progress is limited by the label itself, and recommend reporting ADA% at the trial level, treating the target as a distribution rather than a point value, and evaluating against a format-based baseline.

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bioRxiv2025

All-Atom Protein Sequence Design using Discrete Diffusion Models

Advancing protein design is crucial for breakthroughs in medicine and biotechnology. Traditional approaches for protein sequence representation often rely solely on the 20 canonical amino acids, limiting the representation of non-canonical amino acids and residues that undergo post-translational modifications. This work explores discrete diffusion models for generating novel protein sequences using the all-atom chemical representation SELFIES. By encoding the atomic composition of each amino acid in the protein, this approach expands the design possibilities beyond standard sequence representations. Using a modified ByteNet architecture within the discrete diffusion D3PM framework, we evaluate the impact of this all-atom representation on protein quality, diversity, and novelty, compared to conventional amino acid-based models. To this end, we develop a comprehensive assessment pipeline to determine whether generated SELFIES sequences translate into valid proteins containing both canonical and non-canonical amino acids. Additionally, we examine the influence of two noise schedules within the diffusion process--uniform (random replacement of tokens) and absorbing (progressive masking)--on generation performance. While models trained on the all-atom representation struggle to consistently generate fully valid proteins, the successfully generated proteins show improved novelty and diversity compared to their amino acid-based model counterparts. Furthermore, the all-atom representation achieves structural foldability results comparable to those of amino acid-based models. Lastly, our results highlight the absorbing noise schedule as the most effective for both representations. Data and code are available at https://github.com/Intelligent-molecular-systems/All-Atom-Protein-Sequence-Generation. Scientific ContributionThis work introduces a discrete diffusion-based framework for protein sequence generation using an all-atom representation, enabling the incorporation of non-canonical amino acids and post-translational modifications. Additionally, it provides a comprehensive evaluation pipeline to assess the validity of generated proteins, demonstrating how noise schedules within the diffusion process impact sequence novelty, diversity, and structural foldability.

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bioRxiv2023

Guiding diffusion models for antibody sequence and structure co-design with developability properties

Recent advances in deep generative methods have allowed antibody sequence and structure co-design. This study addresses the challenge of tailoring the highly variable complementarity-determining regions (CDRs) in antibodies to fulfill developability requirements. We introduce a novel approach that integrates property guidance into the antibody design process using diffusion probabilistic models. This approach allows us to simultaneously design CDRs conditioned on antigen structures while considering critical properties like solubility and folding stability. Our property-conditioned diffusion model offers versatility by accommodating diverse property constraints, presenting a promising avenue for computational antibody design in therapeutic applications. Code is available at https://github.com/amelvim/antibody-diffusion-properties.

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bioRxiv2022

So ManyFolds, So Little Time: Efficient Protein Structure Prediction With pLMs and MSAs

In recent years, machine learning approaches for de novo protein structure prediction have made significant progress, culminating in AlphaFold which approaches experimental accuracies in certain settings and heralds the possibility of rapid in silico protein modelling and design. However, such applications can be challenging in practice due to the significant compute required for training and inference of such models, and their strong reliance on the evolutionary information contained in multiple sequence alignments (MSAs), which may not be available for certain targets of interest. Here, we first present a streamlined AlphaFold architecture and training pipeline that still provides good performance with significantly reduced computational burden. Aligned with recent approaches such as OmegaFold and ESMFold, our model is initially trained to predict structure from sequences alone by leveraging embeddings from the pretrained ESM-2 protein language model (pLM). We then compare this approach to an equivalent model trained on MSA-profile information only, and find that the latter still provides a performance boost - suggesting that even state-of-the-art pLMs cannot yet easily replace the evolutionary information of homologous sequences. Finally, we train a model that can make predictions from either the combination, or only one, of pLM and MSA inputs. Ultimately, we obtain accuracies in any of these three input modes similar to models trained uniquely in that setting, whilst also demonstrating that these modalities are complimentary, each regularly outperforming the other.

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bioRxiv2022

An Analysis of Protein Language Model Embeddings for Fold Prediction

The identification of the protein fold class is a challenging problem in structural biology. Recent computational methods for fold prediction leverage deep learning techniques to extract protein fold-representative embeddings mainly using evolutionary information in the form of multiple sequence alignment (MSA) as input source. In contrast, protein language models (LM) have reshaped the field thanks to their ability to learn efficient protein representations (protein-LM embeddings) from purely sequential information in a self-supervised manner. In this paper, we analyze a framework for protein fold prediction using pre-trained protein-LM embeddings as input to several fine-tuning neural network models which are supervisedly trained with fold labels. In particular, we compare the performance of six protein-LM embeddings: the LSTM-based UniRep and SeqVec, and the transformer-based ESM-1b, ESM-MSA, ProtBERT, and ProtT5; as well as three neural networks: Multi-Layer Perceptron (MLP), ResCNN-BGRU (RBG), and Light-Attention (LAT). We separately evaluated the pairwise fold recognition (PFR) and direct fold classification (DFC) tasks on well-known benchmark datasets. The results indicate that the combination of transformer-based embeddings, particularly those obtained at amino acid-level, with the RBG and LAT fine-tuning models performs remarkably well in both tasks. To further increase prediction accuracy, we propose several ensemble strategies for PFR and DFC, which provide a significant performance boost over the current state-of-the-art results. All this suggests that moving from traditional protein representations to protein-LM embeddings is a very promising approach to protein fold-related tasks.

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bioRxiv2020

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

MotivationProtein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. ResultsWe applied an existing deep sequence model that had been pre-trained in an unsupervised setting on the supervised task of protein function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for deep prediction models, as a two-layer perceptron was enough to achieve state-of-the-art performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that three-dimensional structure is also potentially learned during the unsupervised pre-training. AvailabilityImplementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. Contactameliavm@ugr.es Supplementary informationSupplementary data are available online.

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