Biology subjects

Fenoy, E.

Publications and source records attributed to Fenoy, E..

3 recordsLinked to original sources

bioRxiv2025

GNN2Pfam: Integrating protein sequence and structure with graph neural networks for Pfam domain annotation

The challenge of establishing the relationship between protein sequences and their function cannot yet be considered completely solved. State-of-the-art annotation of Pfam domains is based on hidden Markov models (HMMs) built from hand-crafted sequence alignments. However, while this approach has been highly successful during the last decades since its proposal, there is yet a very large number of proteins that remain unannotated because there is no possible alignment to already known and functionally characterized sequences, or HMM fails to discriminate between similar domains. Adding structural information using deep and graph neural networks (GNNs) presents an opportunity to build upon existing models in those more challenging cases. GNNs excel at capturing complex relationships in data and can learn a model that shares information across all existing families, thus being able to generalize Pfam domain predictions to novel sequences. In this work we propose GNN2Pfam, an end-to-end GNN-based method for Pfam family domain annotation. Our strategy allows one single model to be trained for all species and families. This novel proposal uses the protein 3D structure together with a sequence representation obtained from a large pre-trained model. The GNN2Pfam model is based on a graph derived from amino acid interactions in the 3D structure, learning both sequential and structural features from this representation. Experiments show that the proposed GNN-based model can clearly outperform the HMM state-of-the-art predictive performance in Pfam domains annotations. These results suggest that GNN models can be the key component of future protein annotation tools. Data and source code are available at https://github.com/efenoy/GNN2Pfam.

bioinformatics↗

bioRxiv2025

ET-Pfam: Ensemble transfer learning for protein family prediction

MotivationDue to the rapid growth of sequence generation, which has surpassed the expert curators ability to manually review and annotate them, the computational annotation of proteins remains a significant challenge in bioinformatics nowadays. The Pfam database contains a large collection of proteins that are nowadays annotated with domain families through multiple sequence alignments and profile Hidden Markov models (pHMMs). However, such computational annotation methods have some limitations such as problems for handling large datasets and the fact that multiple sequence alignments are computationally challenging to compute with high accuracy due to the increase in complexity as the number of sequences and lengths grow. Additionally, each HMM is independently obtained for each family missing the opportunity of learning patterns across families, that is from a complete view of all the dataset. As an alternative, some deep learning (DL) models have been recently proposed, nevertheless with simple representations of the inputs and moderate improvements in performance. ResultsIn this work we present ET-Pfam, a novel approach based on transfer learning and ensembles of multiple DL classifiers to predict functional families in the Pfam database. Several base DL models are first trained using learned representations from a protein large language model, with different hyperparameters to increase diversity. Then, the base models are integrated using classical ensemble strategies and novel voting approaches by learning weights for each model and for each Pfam family. Results demonstrate that the proposed ET-Pfam method can consistently diminish classification error rates compared to individual DL models, boosting prediction performance. Among the novel ensemble strategies presented here, the learned weights by family voting achieved the best performance, with the lowest error rate (7.00%), significantly surpassing the best individual base model error (12.91%) and three competitors of the state- of-the-art on the same Pfam dataset. AvailabilityData and source code are available at https://github.com/sinc-lab/ET-Pfam.

bioinformatics↗

bioRxiv2023

sincFold: end-to-end learning of short- and long-range interactions for RNA folding

MotivationCoding and non-coding RNA molecules participate in many important biological processes. Non-coding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged thanks to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but still leaving a wide margin for improvement. ResultsIn this work we present sincFold an end-to-end deep learning approach that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared against classical methods and recent deep learning models, showing that it can outperform state-of-the-art methods. AvailabilityThe source code is available at https://github.com/sinc-lab/sincFold (v0.16) and the web access is provided at https://sinc.unl.edu.ar/web-demo/sincFold Contactlbugnon@sinc.unl.edu.ar

bioinformatics↗