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DL-QSARES Antifungal Peptide Prediction

DL-QSARES is designed to identify antifungal peptides (AFPs) from peptide sequences. This tool employs the ESM2 protein language model to extract deep contextual sequence representations, which are subsequently processed by a Multi-Layer Perceptron (MLP) classifier for prediction. The ESM2-based framework enables accurate identification of antifungal peptides by capturing evolutionary and contextual information embedded within peptide sequences.

Expected Output
  • Antifungal Peptide (AFP) / Non-Antifungal Peptide (Non-AFP)
  • Prediction probability score
DeepACPred Anticancer Peptide & Cancer Type Prediction

DeepACPred is developed for the identification and characterization of anticancer peptides (ACPs). The model integrates deep sequence embeddings generated by ESM2 with physicochemical properties extracted from the AAindex database. By leveraging feature fusion and deep learning strategies, DeepACPred not only distinguishes ACPs from non-ACPs but also predicts the potential cancer types targeted by ACPs through a multi-label classification framework.

Expected Output
  • Anticancer Peptide (ACP) or Non-Anticancer Peptide (Non-ACP)
  • Predicted cancer type(s) associated with the peptide
Multi-Hemo Peptide Hemolytic Activity Prediction

Multi-Hemo predicts the hemolytic potential of peptides based on both sequence and structural characteristics. The model combines ESM2-derived embeddings with physicochemical and structural descriptors extracted from the AAindex database. Feature fusion is subsequently performed to capture multiple aspects of peptide properties associated with hemolytic activity.

Expected Output
  • Hemolytic Peptide / Non-Hemolytic Peptide
  • Prediction probability score
AMP-SpectraMIC Antimicrobial Spectrum Prediction

AMP-SpectraMIC is designed to predict the antimicrobial spectrum of antimicrobial peptides (AMPs). The model integrates ESM2 embeddings with AAindex-derived physicochemical properties to characterize peptide functionality and infer potential antimicrobial activities against different microbial groups.

Expected Output
  • Predicted antimicrobial spectrum
Physicochemical Properties Sequence Property Computation

Given an amino acid sequence, this tool computes key physicochemical properties including molecular mass, isoelectric point (pI), net charge at pH 7, average hydrophobicity, GRAVY score, molecular formula and amino acid composition.

Expected Output
  • Molecular mass (Da) & isoelectric point (pI)
  • Net charge, hydrophobicity & GRAVY score
  • Molecular formula & amino acid composition
Input Requirements: Peptide sequences should be provided in FASTA format or entered directly into the sequence input box. Multiple sequences can be submitted simultaneously. For large-scale analyses, users are encouraged to submit FASTA files containing multiple peptide sequences.

Input Sequences

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Drag & Drop files here or Click to upload (Single or Bulk FASTA)

Analysis Results

Note: Prediction results are generated based on trained deep learning models and should be considered computational estimates.