PredIDR3: A new output-encoding scheme and abundant negative source provide more information for deep learning-based protein intrinsic disorder prediction
PredIDR3 series, an updated version of PredIDR2 tested in CAID3 to accurately predict IDRs from protein sequences, includes two methods depending on ensemble way, achieving comparable performance to the top-ranking methods of CAID3 in all criteria measured.
PMPNN-DDG, a Random Forest-based DDG prediction model, trained on a novel set of interpretable features extracted from the recently proposed message-passing neural network-based fixed backbone protein design model, ProteinMPNN, is proposed.
Results demonstrate that BFPSM outperforms state-of-the-art methods at the sequence level, highlighting its utility for clinical and functional variant prioritization.
Hai-Zhou-Rong Chen, Si-Yuan Yang, Chen Ye et al.· Computational biology and ch...· 0 citations
It is demonstrated that modern EC predictors largely fail to distinguish catalytically incompetent variants from functional enzymes, and it is proposed that integrating structure-aware negative examples into both training and benchmarking is critical for developing functionally robust models in computational enzymology...
João Sartori, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado· bioRxiv· 0 citations
Interleukin-2 (IL-2) is a pleiotropic cytokine central to T-cell activation, proliferation, and immunological tolerance, yet existing computational tools for predicting IL-2-inducing peptides suffer from near-zero sensitivity under realistic class-imbalanced conditions, rendering them impractical for positive-class dis...
Juan Peter Timothy Yuune, Van-The Le, Yu-Yen Ou· Journal of Biomolecular Stru...· 0 citations
MT-ProtBERT consistently outperforms PARROT, an RNN-based IDP-specific model, across all tasks, and demonstrates that combining self-supervised and biochemistry-informed tasks, and multi-scale learning enables robust modeling of unstructured proteins under data scarcity.
Jian Sun, Kingshuk Ghosh, Lilianna Houston et al.· 0 citations
This study evaluates four encoding schemes—one-hot, k-mer (substring-based encoding), embeddings, and Position-Specific Scoring Matrix (PSSM) using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) and shows that k-mer encoding achieved the highest accuracy.
T. Kurniawan, Deshinta Arova Dewi, Randy Joy Magno Ventayen· Journal of Advances in Infor...· 0 citations
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