v2.10.0

CHANGELOG.md

@@ -7,6 +7,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ---
 
+## [2.10.0] - 2024-12-13
+
+### 2.10.0 Added
+
+- Added TemStaPro prediction workflow.
+
+---
+
 ## [2.9.0] - 2024-12-13
 
 ### 2.9.0 Added

README.md

@@ -22,6 +22,7 @@ A collection of AWS HealthOmics workflows to accelerate drug discovery.
 - [Generate Protein Sequence Embeddings](https://github.com/aws-samples/drug-discovery-workflows/tree/main/assets/workflows/generate-protein-seq-embeddings):  From Meta. Generate ESM-2 vector embeddings for one or more protein amino acid sequences.
 - [NanobodyBuilder2](https://github.com/oxpig/ImmuneBuilder): From Oxford Protein Informatics Group. Predict the 3D structure of single-chain nanobodies.
 - [RFDiffusion-ProteinMPNN](https://github.com/aws-samples/drug-discovery-workflows/tree/main/assets/workflows/rfdiffusion-proteinmpnn): From the Institute for Protein Design at the University of Washington. Generate protein backbone structures and sequences given a binding target or other structural context.
+- [TemStaPro](https://github.com/ievapudz/TemStaPro): From Institute of Biotechnology, Life Sciences Center, Vilnius University. Predict protein thermostability using sequence representations from a protein language model.
 - [ThermoMPNN](https://github.com/Kuhlman-Lab/ThermoMPNN): From the University of North Carolina School of Medicine. Predict changes in thermodynamic stability for protein point mutants.
 
 ### E2E Workflows

assets/containers/temstapro/Dockerfile

@@ -0,0 +1,29 @@
+ARG AWS_DEFAULT_REGION=us-east-1
+
+FROM 763104351884.dkr.ecr.${AWS_DEFAULT_REGION}.amazonaws.com/pytorch-inference:2.2.0-gpu-py310-cu118-ubuntu20.04-ec2
+ARG COMMIT=db001d2b2479131bad2d0ee170b75001e9fad076
+
+RUN apt-get update \
+    && apt-get upgrade -y \
+    && apt-get install nano -y \
+    && apt-get autoremove -y \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Download ThermoMPNN Code
+RUN wget -q -P /tmp "https://github.com/ievapudz/TemStaPro/archive/${COMMIT}.zip" \
+    && mkdir -p /home/TemStaPro \
+    && unzip /tmp/${COMMIT}.zip -d /tmp \
+    && mv /tmp/TemStaPro-${COMMIT}/* /home/TemStaPro \
+    && rm -rf /tmp/TemStaPro-${COMMIT}
+
+WORKDIR /home/TemStaPro
+
+COPY requirements.txt /tmp/requirements.txt
+RUN pip install --no-cache-dir -U -r /tmp/requirements.txt \
+    && rm /tmp/requirements.txt
+
+ENV TMPDIR="/tmp"
+ENV PYTHONPATH="/home/TemStaPro:${PYTHONPATH}"
+
+ENTRYPOINT []

assets/containers/temstapro/requirements.txt

@@ -0,0 +1,2 @@
+transformers==4.24.0
+sentencepiece==0.1.96

assets/data/temstapro

@@ -0,0 +1 @@
+Rostlab/prot_t5_xl_half_uniref50-enc

assets/workflows/temstapro/README.md

@@ -0,0 +1,5 @@
+# Predict protein thermostability using sequence representations from a protein language model
+
+## Summary
+
+Predict protein thermostability using sequence representations from the [TemStaPro](https://github.com/ievapudz/TemStaPro) protein language model. From Institute of Biotechnology, Life Sciences Center, Vilnius University.

assets/workflows/temstapro/config.yaml

@@ -0,0 +1,18 @@
+name: TemStaPro
+description: "Predict protein thermostability using sequence representations from a protein language model. From Institute of Biotechnology, Life Sciences Center, Vilnius University."
+engine: NEXTFLOW
+main: main.nf
+parameterTemplate:
+  fasta_path:
+    description: "Input file in FASTA format."
+    optional: false
+  window_size_predictions:
+    description: "set the window size for average smoothing of per residue predictions for plotting. Defaults to 81."
+    optional: true    
+  portion_size:
+    description: "Maximum size of input sequence divisions. Set to 0 for no division. Defaults to 1000."
+    optional: true      
+storageCapacity: 1200
+tags:
+  Name: "TemStaPro"
+accelerators: GPU

assets/workflows/temstapro/main.nf

@@ -0,0 +1,69 @@
+#!/usr/bin/env nextflow
+
+nextflow.enable.dsl = 2
+
+workflow TemStaPro {
+    take:
+        fasta_path
+        window_size_predictions
+        portion_size
+        prot_t5_params
+
+    main:
+    TemStaProTask(
+        fasta_path,
+        window_size_predictions,
+        portion_size,
+        prot_t5_params
+    )
+
+    TemStaProTask.out.set { results }
+
+    emit:
+    results
+}
+
+process TemStaProTask {
+    label 'temstapro'
+    cpus 4
+    memory '16 GB'
+    maxRetries 1
+    accelerator 1, type: 'nvidia-tesla-a10g'
+    publishDir "/mnt/workflow/pubdir/${workflow.sessionId}/${task.process.replace(':', '/')}/${task.index}/${task.attempt}"
+
+    input:
+        path fasta_path
+        val window_size_predictions
+        val portion_size
+        path prot_t5_params
+
+    output:
+    path 'output/*'
+
+    script:
+    """
+    set -euxo pipefail
+    mkdir output
+    /opt/conda/bin/python /home/TemStaPro/temstapro \
+        --input-fasta $fasta_path \
+        --PT-directory $prot_t5_params \
+        --temstapro-directory '/home/TemStaPro' \
+        --more-thresholds \
+        --mean-output 'output/mean_output.tsv' \
+        --per-res-output 'output/per_res_output.tsv' \
+        --window-size-predictions $window_size_predictions \
+        --per-residue-plot-dir output \
+        --portion-size $portion_size
+        
+    """
+}
+
+workflow {
+    TemStaPro(
+        Channel.fromPath(params.fasta_path),
+        params.window_size_predictions,
+        params.portion_size,
+        params.prot_t5_params
+    )
+}
+

assets/workflows/temstapro/nextflow.config

@@ -0,0 +1,15 @@
+params {
+    window_size_predictions = 81
+    portion_size = 1000
+
+    prot_t5_params = "s3://{{S3_BUCKET_NAME}}/ref-data/temstapro/Rostlab/prot_t5_xl_half_uniref50-enc/"
+}
+
+process {
+    withLabel: temstapro { container = "{{temstapro:latest}}" }
+}
+
+docker {
+    enabled = true
+    runOptions = "--gpus all"
+}