Previously, we discussed how to translate BioLM protocols into Kedro workflows, demonstrating how to move from exploratory notebooks to structured, reproducible pipelines. Bioinformatics workflows often require specialized approaches that handle the unique challenges of biological data processing, cloud execution, and workflow orchestration.

Nextflow emerges as a compelling option for BioLM workflows, particularly when you need to process large datasets, run on cloud infrastructure, or integrate with existing bioinformatics ecosystems. Nextflow is purpose-built for scientific workflows, offering process-based parallelism, built-in containerization, and seamless cloud deployment.

Our antibody engineering protocol again provides an excellent case study for this transition. Starting from the same Jupyter notebook that processed four BenchBB targets (IL7Rα, PDL-1, EGFR, and MBP), we can explore how Nextflow’s workflow engine handles the unique demands of biological data processing and AI model integration.

Starting From the Protocol

The antibody engineering protocol demonstrated in the notebook focuses on variant binder design—modifying existing antibodies that already possess desirable properties to improve certain capabilities. This approach leverages stable, non-immunogenic frameworks and existing binding capabilities while exploring the design space around the Complementarity-Determining Regions (CDRs).

The workflow processes four key targets from the BenchBB dataset:

• EGFR (PDB: 3c09) – Epidermal growth factor receptor
• PDL1 (PDB: 5x8m) – Programmed death-ligand 1
• MBP (PDB: 5bjz) – Myelin basic protein
• IL-7Rα (PDB: 6p67) – Interleukin-7 receptor alpha

For each target, the protocol extracts heavy chain, light chain, and antigen sequences from the PDB structure, then uses BioLM’s AntiFold model to generate 100 variant sequences. AntiFold is an inverse folding method that conditions on existing antibody structure and antigen complexes, making it particularly well-suited for variant design that maintains binding specificity while exploring sequence diversity.

The generated variants focus on the three CDR regions of both heavy and light chains, with the model providing scores for each variant including global_score, score, mutations, and sequence recovery metrics. Analysis of the results shows that CDR H3 exhibits the most diversity—consistent with its role as the longest CDR and a region often highly responsible for antibody binding strength.

Why Nextflow for Bioinformatics Workflows?

Bioinformatics workflows present unique challenges that Nextflow is specifically designed to address. The framework’s process-based architecture treats each computational step as an independent process that can be parallelized, containerized, and distributed across compute resources. This is particularly valuable for bioinformatics workflows where you might need to:

• Process hundreds of protein sequences in parallel
• Run computationally intensive AI models on different targets simultaneously
• Handle large PDB files and genomic datasets efficiently
• Scale up to cloud infrastructure when local resources are insufficient

The framework’s built-in support for containers (Docker and Singularity) ensures reproducibility across different computing environments—a critical requirement when working with complex biological software dependencies. Nextflow can orchestrate processes written in any language, making it easier to integrate specialized bioinformatics tools alongside BioLM’s Python SDK.

From Notebook to Nextflow

Translating our antibody engineering notebook to Nextflow required thinking in terms of processes, channels, and workflow orchestration. The notebook’s linear structure naturally mapped to Nextflow processes, with each major operation—PDB downloading, sequence extraction, variant generation, and analysis—becoming a distinct process with explicit input and output channels.

One key advantage of Nextflow’s approach is its handling of parallel execution. While the original notebook processed targets sequentially, the Nextflow version can process all four BenchBB targets simultaneously, significantly reducing total runtime. The framework automatically manages process dependencies, ensuring that variant generation only begins after sequence extraction is complete for each target.

Nextflow uses command-line parameters and configuration files that are familiar to bioinformatics users, making it easier to integrate with existing bioinformatics pipelines and cloud execution platforms. This approach allows researchers to experiment with different parameters without modifying code, and the framework’s checkpoint functionality enables resuming from specific points in the workflow.

Running the Pipeline and Customizing Experiments

Once the Nextflow pipeline is set up, running the complete antibody engineering workflow is straightforward. The pipeline can be executed with a single command: nextflow run antibody_engineering.nf --num_variants 100. This processes all four protein targets (EGFR, PDL1, MBP, and IL-7Rα) through the complete workflow, from raw PDB structures to final variant analysis.

Results are organized in the results/ directory structure, with intermediate outputs in subdirectories, processed variants in JSON format, feature engineering results in CSV files, and final reports and visualizations in HTML format. The pipeline produces the same outputs as the original notebook but in a more structured format: individual variant files for each protein target, a comprehensive dataset combining all variants with CDR annotations, and analysis visualizations including CDR sequence distribution plots and variant pairplots that show the relationships between different scoring metrics.

One of the key advantages of the Nextflow approach is the ability to easily customize experiments through command-line parameters. The workflow accepts parameters including the number of variants to generate, sampling temperature, and output directory. To run the pipeline on different protein targets or with different parameters, simply modify the command-line arguments without touching any code. This makes it easy to experiment with different molecular targets, design parameters, and AI model configurations.

The pipeline also supports running from different checkpoints, which is particularly useful for long-running analyses or when you want to focus on specific parts of the workflow. For example, you can resume from a specific process or run only certain parts of the analysis. This checkpoint functionality makes it easy to iterate on specific parts of the analysis without rerunning the entire pipeline.

Cloud-Native Execution with Seqera Platform

One of Nextflow’s greatest strengths is its seamless integration with several cloud platforms, particularly the Seqera Platform. Nextflow workflows are designed from the ground up for distributed execution, making them ideal for bioinformatics applications that require scalable compute resources.

The Seqera Platform provides a comprehensive cloud-native environment for running Nextflow workflows across diverse bioinformatics applications. The platform handles infrastructure complexity—provisioning compute resources, managing data storage, and orchestrating workflow execution—while offering an intuitive web interface for parameter configuration and result visualization.

This cloud-native approach offers several advantages for bioinformatics teams:

• Elastic scaling – Automatically provision more compute when processing large datasets
• Cost optimization – Pay only for the resources you actually use
• Collaborative access – Share workflows and results with team members easily
• Reproducible environments – Container-based execution ensures consistent results across different systems
• Community workflows – Access to a large library of pre-built bioinformatics workflows

The platform’s extensive ecosystem includes workflows for genomics, proteomics, structural biology, and other computational biology applications. This makes it an ideal environment for organizations looking to integrate BioLM’s AI capabilities alongside their existing bioinformatics pipelines, leveraging the platform’s robust infrastructure and community resources.

Key Takeaways and Framework Benefits

This example demonstrates Nextflow as a powerful companion framework for implementing BioLM protocols, showing how organizations can integrate BioLM’s AI capabilities into their existing Nextflow-based bioinformatics infrastructure. For teams already using Nextflow for genomic analysis, structural biology, or other computational workflows, adding BioLM models becomes a natural extension rather than a complete platform migration.

The antibody engineering workflow shows how BioLM’s API calls can be seamlessly integrated into existing Nextflow processes. Organizations can leverage their current workflow patterns, parameter management systems, and cloud infrastructure while gaining access to cutting-edge AI models for protein design and analysis. This approach minimizes disruption to established pipelines while adding powerful new capabilities.

For bioinformatics teams, this integration pattern offers several practical advantages. Existing Nextflow expertise can be directly applied to BioLM workflows, reducing the learning curve for new team members. Current infrastructure investments—whether in cloud platforms, container registries, or monitoring systems—can be extended to support AI-enhanced analyses without requiring separate tooling or processes.

The workflow also demonstrates how BioLM can complement existing bioinformatics tools. By treating BioLM API calls as standard Nextflow processes, organizations can easily combine AI-generated insights with traditional analysis methods, creating hybrid workflows that leverage both computational biology expertise and machine learning capabilities.

Here at BioLM, we’re ready to help you implement these workflows in your own research. For our users with our premium support plan, we can provide hands-on support to integrate your BioLM protocols with workflows like Nextflow, helping you scale up existing pipelines or build new ones from scratch.

For more information about this example, visit the nf-biolm repository.