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S8kPred — Protein Secondary Structure Prediction

PyPI Python License: MIT

S8kPred predicts protein secondary structure directly from amino acid sequence using XGBoost models trained on PSSM (Position-Specific Scoring Matrix) and tripeptide propensity features.

  • 3-state: Helix (H), Beta-strand (E), Coil/Loop (L)
  • 8-state: H, G, I, E, B, T, S, L (full DSSP alphabet)

Requirements

Dependency Purpose
Python ≥ 3.9 Runtime
numpy, pandas, xgboost, scikit-learn Core ML pipeline
biopython FASTA I/O for PSI-BLAST
NCBI PSI-BLAST PSSM generation (external binary)
UniRef50 (or similar) BLAST database PSI-BLAST database
biotite, matplotlib (optional) Cartoon structure plots

Installation

From PyPI (recommended)

pip install s8kpred

With cartoon plot support

pip install s8kpred[plot]

From GitHub (latest development version)

pip install git+https://github.com/mayank2801/s8kpred.git

From source

git clone [https://github.com/mayank2801/s8kpred.git](https://github.com/mayank2801/s8kpred.git)
cd s8kpred
pip install -e .          # editable install
pip install -e .[plot]    # with plotting extras

Setting up PSI-BLAST

S8kPred requires NCBI PSI-BLAST to generate evolutionary features. You have two options:

Option A — System install

# Ubuntu / Debian
sudo apt install ncbi-blast+

# macOS (Homebrew)
brew install blast

# Conda
conda install -c bioconda -c conda-forge "blast>=2.14"

Option B — Manual download

Download the NCBI BLAST+ toolkit from: https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/

Then either add the bin/ folder to your PATH or pass the full path via --psiblast.

Setting up a BLAST database

S8kPred works best with UniRef50. Download and format it:

# Download
wget [https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50/uniref50.fasta.gz](https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50/uniref50.fasta.gz)
gunzip uniref50.fasta.gz

# Build BLAST database
mkdir -p ~/blast_dbs/uniref50
makeblastdb -in uniref50.fasta \\
            -dbtype prot \\
            -out ~/blast_dbs/uniref50/uniref50 \\
            -title "UniRef50"

Then point s8kpred at it:

export S8KPRED_BLASTDB=~/blast_dbs/uniref50/uniref50

or pass --blastdb ~/blast_dbs/uniref50/uniref50 on every invocation.

Model data files

The trained XGBoost models and lookup tables are not bundled in the PyPI wheel because of their size. Download them from the Releases page and place them in the s8kpred/data/ directory inside your Python environment:

s8kpred/data/
  TriPeptidePropensityThreeStateSecStructure2AND.csv
  TriPeptidePropensityEightStateSecStructure.csv
  TripeptideBinaryTable_60.csv
  model_3state.json
  model_8state.ubj

Or override paths at runtime:

s8kpred predict -i input.fasta \\
  --blastdb ~/blast_dbs/uniref50/uniref50 \\
  --model-3state /path/to/model_3state.json \\
  --model-8state /path/to/model_8state.ubj

Quick start

Command line

# Single FASTA file
s8kpred predict -i protein.fasta --blastdb ~/blast_dbs/uniref50/uniref50

# Multi-sequence FASTA
s8kpred predict -i multi_seq.fasta --blastdb ~/blast_dbs/uniref50/uniref50

# Multiple separate FASTA files in one run
s8kpred predict -i seq1.fasta seq2.fasta seq3.fasta \\
                --blastdb ~/blast_dbs/uniref50/uniref50

# Inline sequence (no file needed)
s8kpred predict \\
  --sequence MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGD \\
  --id my_protein \\
  --blastdb ~/blast_dbs/uniref50/uniref50

# Custom output folder and job name
s8kpred predict -i input.fasta \\
  --blastdb ~/blast_dbs/uniref50/uniref50 \\
  --output-dir ./results \\
  --job experiment_01

# Skip 8-state prediction
s8kpred predict -i input.fasta --blastdb ... --no-8state

# Skip cartoon plots
s8kpred predict -i input.fasta --blastdb ... --no-plot

# Quiet mode (suppress progress output)
s8kpred predict -i input.fasta --blastdb ... --quiet

# Use more PSI-BLAST threads
s8kpred predict -i input.fasta --blastdb ... --threads 16

# Override PSI-BLAST location
s8kpred predict -i input.fasta \\
  --psiblast /opt/ncbi-blast/bin/psiblast \\
  --blastdb ~/blast_dbs/uniref50/uniref50

Python API

from s8kpred import predict, predict_file

# ── Single sequence ──────────────────────────────────────────────────
result = predict(
    sequence="MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGD",
    seq_id="my_protein",
    blastdb="/data/blast/uniref50/uniref50",
)

print(result.results_3state["my_protein"])   # e.g. "CCCHHHHHHCCEEEEC..."
print(result.results_8state["my_protein"])   # e.g. "LLLHHHHHHLLEEEELL..."
print(result.job_dir)                         # Path to all output files

# ── Single FASTA file ────────────────────────────────────────────────
result = predict_file(
    fasta_file="proteins.fasta",
    blastdb="/data/blast/uniref50/uniref50",
    output_dir="./results",
)
print(result.summary())

# ── Multi-sequence FASTA ─────────────────────────────────────────────
result = predict_file("multi_seq.fasta", blastdb="...")
for seq_id, ss in result.results_3state.items():
    print(f"{seq_id}: {ss}")

# ── Custom model paths ────────────────────────────────────────────────
from pathlib import Path
result = predict_file(
    "proteins.fasta",
    blastdb="...",
    model_3state=Path("/models/model_3state.json"),
    model_8state=Path("/models/model_8state.ubj"),
)

# ── Skip 8-state to save time ─────────────────────────────────────────
result = predict("MKTAYI...", blastdb="...", run_8state=False)

Output files

All outputs are written to a timestamped job directory under --output-dir:

s8kpred_jobs/
└── 20250210_153042_a1b2c3/
    ├── FASTA/
    │   └── input_sequence.fasta        # combined input
    ├── pssm_outputs/
    │   ├── Seq_1.pssm                  # raw PSI-BLAST PSSM
    │   └── ...
    ├── PSSM_Features_ML_17W.csv        # sliding-window PSSM features
    ├── ResultThreeState.ss2            # PSIPRED-style vertical format
    ├── ResultThreeState.horiz           ├── ResultThreeState.csv            # per-residue probabilities   # PSIPRED-style horizontal format
    ├── ResultThreeState.fas            # pseudo-FASTA format
    ├── ResultThreeState.csv            # per-residue probabilities
    ├── ResultEightState.ss2
    ├── ResultEightState.horiz
    ├── ResultEightState.fas
    ├── ResultEightState.csv
    ├── Seq_1_cartoon.png               # helix/sheet cartoon (requires biotite)
    └── log.dat                         # timing and status log

Secondary structure codes

Code State
3-state
H α-Helix
E β-Strand
L Loop / Coil
8-state
H α-Helix
G 3₁₀-Helix
I π-Helix
E β-Strand
B β-Bridge
T Turn
S Bend
L Loop / Coil

Environment variables

Variable Default Description
S8KPRED_BLASTDB (empty) BLAST database path prefix
S8KPRED_PSIBLAST psiblast PSI-BLAST binary path
S8KPRED_ITERATIONS 3 PSI-BLAST iterations

CLI reference

s8kpred predict --help

usage: s8kpred predict [-h] (-i FASTA [FASTA ...] | -s SEQ)
                       [--blastdb DB] [--psiblast BIN] [--iterations N]
                       [--threads N] [-o DIR] [--job ID]
                       [--model-3state PATH] [--model-8state PATH]
                       [--no-3state] [--no-8state] [--no-plot] [-q]
                       [--id ID]

Citation

If you use S8kPred in your research, please cite:

[Your citation here]

License

MIT — see LICENSE for details.

Here is a combined, cleaned, publication‑ready README saved as a block of plain text that you can copy into a file named README.txt (or README.md) for your GitHub repo.

You can paste this directly into README.txt and commit it.

# S8kPred — Protein Secondary Structure Prediction

[![PyPI](https://img.shields.io/pypi/v/s8kpred)](https://pypi.org/project/s8kpred/)
[![Python](https://img.shields.io/pypi/pyversions/s8kpred)](https://pypi.org/project/s8kpred/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

**S8kPred** predicts protein secondary structure directly from amino acid sequence using XGBoost models trained on PSSM (Position‑Specific Scoring Matrix) and tripeptide propensity features. It supports both fast coarse‑grained 3‑state labels and fine‑grained 8‑state DSSP‑style annotations.

- **3-state**: Helix (H), Beta‑strand (E), Coil/Loop (L)  
- **8-state**: H, G, I, E, B, T, S, L (full DSSP alphabet)

Protein secondary structure prediction is a fundamental problem in structural bioinformatics, providing insights into folding, function, and interaction landscapes. S8kPred combines evolutionary information from PSI‑BLAST with local sequence‑motif features to deliver robust predictions from sequence alone.

---

## 🌐 Web Server

A freely accessible web server is available at:  
👉 **http://s8kpred.in**

- No installation required  
- Accepts FASTA input  
- Returns interactive and downloadable results

---

## 📦 Model & Data Availability

Pretrained XGBoost models and lookup tables are hosted on:

- GitHub **Releases**: https://github.com/mayank2801/s8kpred/releases  
- Zenodo (search for `"S8kPred"`)

These files are excluded from the PyPI wheel due to size and must be downloaded separately.

---

## Requirements

| Dependency | Purpose |
|-----------|---------|
| Python ≥ 3.9 | Runtime environment |
| `numpy`, `pandas`, `xgboost`, `scikit‑learn` | Core machine learning pipeline |
| `biopython` | FASTA input handling |
| **NCBI PSI‑BLAST** | PSSM generation (external binary) |
| **UniRef50 (or similar) BLAST database** | Sequence database for PSI‑BLAST |
| `biotite`, `matplotlib` *(optional)* | Structure cartoons and plotting |

---

## Installation

### From PyPI (recommended)

```bash
pip install s8kpred

With cartoon plot support

pip install s8kpred[plot]

From GitHub (latest development version)

pip install git+https://github.com/mayank2801/s8kpred.git

From source (editable)

git clone https://github.com/mayank2801/s8kpred.git
cd s8kpred
pip install -e .          # editable install
pip install -e .[plot]    # with plotting extras

Setting up PSI‑BLAST

S8kPred requires NCBI PSI‑BLAST to generate PSSM‑based evolutionary features.

Option A — System install

# Ubuntu / Debian
sudo apt install ncbi‑blast+

# macOS (Homebrew)
brew install blast

# Conda
conda install -c bioconda -c conda‑forge "blast>=2.14"

Option B — Manual download

Download from the NCBI BLAST+ site: https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/

Then either add the bin/ folder to your PATH, or pass the full path via the --psiblast option.

Setting up a BLAST database

For best results, use UniRef50.

# Download
wget https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50/uniref50.fasta.gz
gunzip uniref50.fasta.gz

# Build BLAST database
mkdir -p ~/blast_dbs/uniref50
makeblastdb -in uniref50.fasta \
            -dbtype prot \
            -out ~/blast_dbs/uniref50/uniref50 \
            -title "UniRef50"

Point S8kPred at the database:

export S8KPRED_BLASTDB=~/blast_dbs/uniref50/uniref50

or pass it at runtime:

s8kpred predict -i input.fasta --blastdb ~/blast_dbs/uniref50/uniref50

Model data files

Download the model artifacts from the Releases page and place them in:

s8kpred/data/

Required files:

  • TriPeptidePropensityThreeStateSecStructure2AND.csv
  • TriPeptidePropensityEightStateSecStructure.csv
  • TripeptideBinaryTable_60.csv
  • model_3state.json
  • model_8state.ubj

Alternatively, override paths at runtime:

s8kpred predict -i input.fasta \
  --blastdb ~/blast_dbs/uniref50/uniref50 \
  --model-3state /path/to/model_3state.json \
  --model-8state /path/to/model_8state.ubj

Quick start

Command line

Single FASTA file:

s8kpred predict -i protein.fasta --blastdb ~/blast_dbs/uniref50/uniref50

Multiple sequences in one file:

s8kpred predict -i multi_seq.fasta --blastdb ~/blast_dbs/uniref50/uniref50

Multiple separate files:

s8kpred predict -i seq1.fasta seq2.fasta seq3.fasta \
                --blastdb ~/blast_dbs/uniref50/uniref50

Inline sequence (no file):

s8kpred predict \
  --sequence MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGD \
  --id my_protein \
  --blastdb ~/blast_dbs/uniref50/uniref50

Custom output folder and job name:

s8kpred predict -i input.fasta \
  --blastdb ~/blast_dbs/uniref50/uniref50 \
  --output-dir ./results \
  --job experiment_01

Skip 8‑state prediction:

s8kpred predict -i input.fasta --blastdb ... --no-8state

Skip cartoon plots:

s8kpred predict -i input.fasta --blastdb ... --no-plot

Quiet mode:

s8kpred predict -i input.fasta --blastdb ... --quiet

Use more threads:

s8kpred predict -i input.fasta --blastdb ... --threads 16

Override PSI‑BLAST binary:

s8kpred predict -i input.fasta \
  --psiblast /opt/ncbi‑blast/bin/psiblast \
  --blastdb ~/blast_dbs/uniref50/uniref50

Python API

Single sequence:

from s8kpred import predict

result = predict(
    sequence="MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGD",
    seq_id="my_protein",
    blastdb="/data/blast/uniref50/uniref50",
)

print(result.results_3state["my_protein"])   # e.g. "CCCHHHHHHCCEEEEC..."
print(result.results_8state["my_protein"])   # e.g. "LLLHHHHHHLLEEEELL..."
print(result.job_dir)                         # Path to all output files

Single FASTA file:

from s8kpred import predict_file

result = predict_file(
    fasta_file="proteins.fasta",
    blastdb="/data/blast/uniref50/uniref50",
    output_dir="./results",
)
print(result.summary())

Multi‑sequence FASTA:

result = predict_file("multi_seq.fasta", blastdb="...")
for seq_id, ss in result.results_3state.items():
    print(f"{seq_id}: {ss}")

Custom model paths:

from pathlib import Path
result = predict_file(
    "proteins.fasta",
    blastdb="...",
    model_3state=Path("/models/model_3state.json"),
    model_8state=Path("/models/model_8state.ubj"),
)

Skip 8‑state:

result = predict("MKTAYI...", blastdb="...", run_8state=False)

Output files

All outputs are written to a timestamped job directory under --output-dir:

s8kpred_jobs/
└── 20250210_153042_a1b2c3/
    ├── FASTA/
    │   └── input_sequence.fasta        # combined input
    ├── pssm_outputs/
    │   ├── Seq_1.pssm                  # raw PSI‑BLAST PSSM
    │   └── ...
    ├── PSSM_Features_ML_17W.csv        # sliding‑window PSSM features
    ├── ResultThreeState.ss2            # 3‑state, PSIPRED‑style vertical
    ├── ResultThreeState.horiz          # 3‑state, PSIPRED‑style horizontal
    ├── ResultThreeState.fas            # pseudo‑FASTA format
    ├── ResultThreeState.csv            # per‑residue 3‑state probabilities
    ├── ResultEightState.ss2            # 8‑state, vertical
    ├── ResultEightState.horiz          # 8‑state, horizontal
    ├── ResultEightState.fas            # pseudo‑FASTA format
    ├── ResultEightState.csv            # per‑residue 8‑state probabilities
    ├── Seq_1_cartoon.png               # helix/sheet cartoon (requires biotite)
    └── log.dat                         # timing and status log

Secondary structure codes

3‑state:

Code State
H α‑Helix
E β‑Strand
L Loop / Coil

8‑state (DSSP):

Code State
H α‑Helix
G 3₁₀‑Helix
I π‑Helix
E β‑Strand
B β‑Bridge
T Turn
S Bend
L Loop / Coil

Environment variables

Variable Default Description
S8KPRED_BLASTDB (empty) BLAST database path prefix
S8KPRED_PSIBLAST psiblast PSI‑BLAST binary path
S8KPRED_ITERATIONS 3 PSI‑BLAST iterations

CLI reference

s8kpred predict --help

Typical usage:

usage: s8kpred predict [-h] (-i FASTA [FASTA ...] | -s SEQ)
                       [--blastdb DB] [--psiblast BIN] [--iterations N]
                       [--threads N] [-o DIR] [--job ID]
                       [--model-3state PATH] [--model-8state PATH]
                       [--no-3state] [--no-8state] [--no-plot] [-q]
                       [--id ID]

📖 Citation

If you use S8kPred in your research, please cite:

Kumar, M., & Rathore, R.S. (2026). S8kPred : A Novel Approach for Protein Secondary Structure Prediction Using 8000 Tripeptide Propensities. Peptide Science. DOI: 10.1002/pep2.70029

(You can expand this line with bibtex or full journal details as needed.)

🔬 Reproducibility

📄 License

This project is licensed under the MIT License – see the LICENSE file for details.

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