AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
MBPBERT provides a scalable and efficient in silico solution for high-throughput discovery of novel MBPs and screening of peptides with metal-specific binding preferences, potentially reducing the reliance on resource-intensive experimental validation.
BiteNetI is a structure-based deep learning model that uses 3D convolutional neural networks to simultaneously localize ion-binding centers and predict binding residues for 14 biologically relevant ions, supporting comprehensive and large-scale annotation of protein-ion interactions.
A Grouped Multi-Task Learning (GMTL) strategy is implemented, allowing the model to capture shared binding patterns among ligands with similar biological significance, allowing the model to capture shared binding patterns among ligands with similar biological significance.
Abstract Motivation Accurate identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) is critical for elucidating transcriptional and post-transcriptional regulatory mechanisms. However, existing computational approaches often rely on inferred labels or domain-specific annotations, which limit the...
Testing the ability of common large language models to consider design principles to generate de novo proteins that bind metals and lipophilic small molecules without copying existing sequences highlights the utility of LLMs in making protein design more comprehensible and accessible to users without sophisticated desi...
Results demonstrate that MolFormer-XL, which combines pre-trained molecular representations with a Transformer-based architecture and learned SMILES embeddings, provides a promising approach for transfer under severe domain-specific data scarcity in environmental mass spectrometry.
Peptide-protein affinity benchmarks should align data partitions with the intended use and jointly assess the effects of data scale, molecular representation, and downstream learner.
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