Role Summary:

As a Senior Computational Biologist, you will lead the in silico discovery, modeling, and optimization of antibodies and novel binding proteins (e.g., nanobodies, affibodies, aptamers) tailored for rapid diagnostic test formats such as lateral flow assays and paper-based nucleic acid amplification systems. You will integrate structural bioinformatics, machine learning, and molecular simulation techniques to design high-affinity, high-specificity binders suited to point-of-care use.

Key Responsibilities:

1. Binder Design & Optimization:

- Perform in silico screening and affinity maturation of antibody candidates and alternative scaffolds for diagnostic use.

- Design and optimize CDR regions using structure-guided and AI-based techniques.

- Predict binding affinities and cross-reactivity using docking and molecular dynamics

simulations.

2. Computational Pipeline Development:

- Develop and manage scalable pipelines for antibody-antigen modeling, docking, and binder optimization.

- Automate sequence-to-structure modeling and ML-based ranking of candidates.

3. Diagnostic Relevance:

- Tailor designs for rapid test constraints such as surface immobilization, lateral flow compatibility, and low-resource settings.

- Collaborate with wet-lab teams to iteratively test and refine computational designs.

4. Cross-functional Collaboration:

- Work closely with protein engineering, assay development, and molecular biology teams.

- Contribute to strategic decisions on antigen targets and binder formats.

Qualifications:

PhD or Post Doc in Computational Biology, Structural Bioinformatics, Immunology, or related field with relevant industrial experience

Proven experience in antibody modeling, protein structure prediction, and binder design.

Proficiency in tools such as Rosetta, PyRosetta, AlphaFold, Schrödinger, MOE,

HADDOCK, or AutoDock.

Hands-on with Python, bioinformatics libraries, and molecular modeling packages.

Familiarity with next-gen antibody formats (scFv, VHH, DARPin, aptamer, etc.).

Strong understanding of the biophysics of protein-protein interactions.

Nice-to-Have:

Experience integrating machine learning for binder discovery (e.g., deep learning on sequence or structure data).

Background in omics data analysis, especially for target identification.

Exposure to diagnostics workflows, particularly lateral flow assays or biosensors.

Experience in cloud-based or HPC environments for large-scale simulations.