Protein Functional Annotation with DeepFRI is Now Available on Vecura

What is DeepFRI?

DeepFRI (Deep Functional Residue Identification) is a sophisticated computational tool that utilizes graph convolutional networks (GCNs) and convolutional neural networks (CNNs) to predict protein function. Given either an amino acid sequence or a 3D structure, the model identifies relevant Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. It is especially useful for researchers seeking to rapidly annotate newly sequenced proteins or gain deeper insights into the structure-function relationships of complex proteomes.

What can users do with DeepFRI on Vecura?

With DeepFRI on Vecura, users can:

• Predict Gene Ontology (Molecular Function, Biological Process, Cellular Component) and Enzyme Commission numbers for proteins.
• Utilize either primary sequence data (via CNN) or 3D structural data (via GCN) to achieve high-accuracy functional annotations.
• Generate per-residue saliency maps using grad-CAM or guided backpropagation when structural data is provided, highlighting key residues responsible for specific functions.
• Streamline large-scale functional analysis through batch processing of FASTA files without managing underlying computational infrastructure.

What the output means

The output provides a ranked list of predicted GO terms or EC numbers, accompanied by confidence scores indicating the model's certainty. For structure-based inputs, users can also receive saliency maps, which serve as visual indicators of which amino acid residues drive the predicted functional label.

This output should be used to support scientific decision making. It does not replace experimental validation.

Why this matters

The biological function of a vast number of proteins remains unknown, and experimental characterization is often a slow, resource-intensive process. By leveraging deep learning to bridge this gap, DeepFRI enables high-throughput functional annotation, allowing researchers to prioritize candidates for experimental validation and gain a clearer understanding of protein behavior within cellular processes.

The ability to highlight specific residues—the "active sites" or critical functional areas—is a game-changer for site-directed mutagenesis. It turns a black-box prediction into a actionable guide for laboratory experiments, significantly accelerating the pace of discovery in structural biology and protein engineering.

• Developed by: Flatiron Institute
• Source: Official GitHub Repository
• Reference: DeepFRI Original Paper (bioRxiv)