Biology subjects

Fernandez-Diaz, R.

Publications and source records attributed to Fernandez-Diaz, R..

4 recordsLinked to original sources

bioRxiv2026

Peptidomics Mapping of Proteolysis Highlights Triple Activation of Sprouted Seeds by Germination, Homogenisation and Species Mixture.

We investigated how seed proteolysis was enhanced by germination, by subsequent homogenisation (disrupting sprout compartments), and by co-incubation of homogenates from different species. Mass spectrometry of released peptides tracked proteolytic signatures from chickpea, lentil, mung and broccoli proteins, in soaked seeds, in sprouted seeds, and after sprout homogenisation followed by incubation alone or in mixture with other sprouts. The proteolytic signatures differed markedly among the four species, and in the different treatment conditions. After homogenisation, legumain-like cleavage (after asparagine) increased in lentil, and proline-rich peptides increased in broccoli. For co-incubated homogenised sprouts, each species homogenate significantly contributed 6 to 57% of proteolytic patterns in peptides of other species, with chickpea and broccoli homogenates notably releasing metabolic protein peptides from mung and lentil. Thus, germination, homogenisation and homogenate species mixtures can each contribute to proteolysis of seed peptides, potentially increasing digestibility and reducing allergenicity. HIGHLIGHTSO_LIProteolysis motifs in soaked seeds and sprouts very diverse among species C_LIO_LISeed germination proteolysis altered by homogenisation C_LIO_LISeed germination proteolysis altered by co-incubation of different species C_LIO_LIFoods based on homogenised sprout mixtures may release more digestible peptides C_LI

bioinformatics↗

bioRxiv2025

HemeFinder: a Computational Predictor for Heme-Binding Sites in Proteins

HemeFinder has been developed to predict heme-binding sites in natural and heme-dependent de novo enzymes. This tool relies on the structural and physicochemical characteristics of heme-binding sites, including shape, residue composition, and geometric descriptors. HemeFinder benchmarks more than 94% accuracy in identifying the correct heme location, considering the complete set of solutions, and 72% accuracy for the proper location with the correct iron-coordinating residues among the three best-ranked solutions. HemeFinder performs within seconds for monomeric systems and takes minutes for larger multimeric ones, demonstrating that its speed does not compromise its performance. An illustrative case of its potential is provided. HemeFinder is applied to the heme carrier protein 1 (HCP1), a transmembrane protein involved in heme recruitment in evolved organisms, for which no ligand-bound structures have been revealed. HemeFinder provides a relevant prediction of the binding of porphyrin and, when combined with protein-ligand docking, offers the first evidence of low-energy Heme-HCP1 complexes and unveils possible heme pathways. HemeFinder is an interesting, fast, and accurate tool for identifying heme-binding sites in proteins. Source code, documentation, and data are available at https://github.com/laura-tiessler/hemefinder and ESI.

bioinformatics↗

bioRxiv2024

Effect of dataset partitioning strategies for evaluating out-of-distribution generalisation for predictive models in biochemistry

AO_SCPLOWBSTRACTC_SCPLOWQuantifying model generalization to out-of-distribution data has been a longstanding challenge in machine learning. Addressing this issue is crucial for leveraging machine learning in scientific discovery, where models must generalize to new molecules or materials. Current methods typically split data into train and test sets using various criteria -- temporal, sequence identity, scaffold, or random cross-validation -- before evaluating model performance. However, with so many splitting criteria available, existing approaches offer limited guidance on selecting the most appropriate one, and they do not provide mechanisms for incorporating prior knowledge about the target deployment distribution(s). To tackle this problem, we have developed a novel metric, AU-GOOD, which quantifies expected model performance under conditions of increasing dissimilarity between train and test sets, while also accounting for prior knowledge about the target deployment distribution(s), when available. This metric is broadly applicable to biochemical entities, including proteins, small molecules, nucleic acids, or cells; as long as a relevant similarity function is defined for them. Recognizing the wide range of similarity functions used in biochemistry, we propose criteria to guide the selection of the most appropriate metric for partitioning. We also introduce a new partitioning algorithm that generates more challenging test sets, and we propose statistical methods for comparing models based on AU-GOOD. Finally, we demonstrate the insights that can be gained from this framework by applying it to two different use cases: developing predictors for pharmaceutical properties of small molecules, and using protein language models as embeddings to build biophysical property predictors.

bioinformatics↗

bioRxiv2023

AutoPeptideML: Automated Machine Learning for Building Trustworthy Peptide Bioactivity Predictors

MotivationAutomated machine learning (AutoML) solutions can bridge the gap between new computational advances and their real-world applications by enabling experimental scientists to build their own custom models. We examine different steps in the development life-cycle of peptide bioactivity binary predictors and identify key steps where automation can not only result in a more accessible method, but also more robust and interpretable evaluation leading to more trustworthy models. ResultsWe present a new automated method for drawing negative peptides that achieves better balance between specificity and generalisation than current alternatives. We study the effect of homology-based partitioning for generating the training and testing data subsets and demonstrate that model performance is overestimated when no such homology correction is used, which indicates that prior studies may have overestimated their performance when applied to new peptide sequences. We also conduct a systematic analysis of different protein language models as peptide representation methods and find that they can serve as better descriptors than a naive alternative, but that there is no significant difference across models with different sizes or algorithms. Finally, we demonstrate that an ensemble of optimised traditional machine learning algorithms can compete with more complex neural network models, while being more computationally efficient. We integrate these findings into AutoPeptideML, an easy-to-use AutoML tool to allow researchers without a computational background to build new predictive models for peptide bioactivity in a matter of minutes. AvailabilitySource code, documentation, and data are available at https://github.com/IBM/AutoPeptideML and a dedicated webserver at http://peptide.ucd.ie/AutoPeptideML.

bioinformatics↗