Rice proteome annotation

Protein localization,resolved for rice.

RSLpred-2.0 uses a rice-specific convolutional neural network to predict where proteins act within the cell—from a single FASTA sequence to a four-level localization report.

Run a prediction Standalone package

Prediction hierarchy

One sequence, four decisions

Level I

Single vs dual

Primary routing

Level II

10 classes

Single localization

Level III

6 pairs

Dual localization

Level IV

Membrane type

Single- or multi-pass

From sequence to compartment

The complete prediction workflow

TPC · 8,000 features · CNN

RSLpred-2.0 workflow from rice protein sequence and tripeptide composition through a convolutional neural network and four-level localization hierarchy — RSLpred-2.0 four-level protein subcellular localization workflow.Generic query rows illustrate the report format.

Why species-specific?

Built around the rice proteome

Oryza sativa is a central model for cereal biology. Knowing where its proteins localize helps researchers interpret trafficking, interactions, regulation, and organelle-specific function.

RSLpred-2.0 converts each sequence into an 8,000-dimensional tripeptide-composition vector and applies CNN models across four linked localization levels.

Publication

Please cite RSLpred2

Duhan, N., & Kaundal, R. (2025). RSLpred2: An Integrated Web Server for the Annotation of Rice Proteome Subcellular Localization Using Deep Learning. Rice, 18, 58.DOI: 10.1186/s12284-025-00767-7