I provide independent scientific consulting in structural biology, spanning experimental model building in cryo-EM and macromolecular crystallography (MX) and modern AI-based structure prediction. I support academic and biotech projects that need careful model building and interpretation, method development, or scalable computational workflows.

With 20+ structures deposited in the PDB, and as a developer of CCP4 and CCP-EM — the leading crystallography and cryo-EM software suites — I bring hands-on experimental expertise alongside large-scale AI-based prediction and validation.


Scope of services

Typical areas of support include:

  • Cryo-EM and crystallographic (MX) model building, rebuilding, and refinement
  • Validation and interpretation of experimental and predicted structural models
  • AI-based protein structure and protein–protein complex prediction
  • Structural analysis to support experimental target selection and prioritisation
  • Large-scale inference campaigns and benchmarking studies
  • Design and deployment of reproducible structure-prediction workflows for HPC and cloud environments
  • Method development in structural bioinformatics and model validation
  • Hands-on workshops and training in cryo-EM, crystallography (MX), and AI-based structure prediction


Training and workshops

With over 10 years of teaching at CCP4 and CCP-EM workshops, I organise and teach hands-on training covering cryo-EM, macromolecular crystallography (MX), and AI-based structure prediction — for research groups, facilities, and training schools. Sessions can be tailored to a team’s experience level and tools, from introductory model interpretation to advanced model building and prediction workflows.


Example deliverables

Depending on project scope, deliverables may include:

  • Built, rebuilt, or refined experimental models with validation assessments
  • Annotated structural model sets with confidence and validation metrics
  • Concise technical reports supporting experimental decision-making
  • Comparative analyses of alternative structural hypotheses
  • Reproducible computational workflows or pipelines
  • Feasibility or benchmarking assessments for AI-based modelling approaches
  • Accessible protein structure prediction pipelines for HPC and cloud environments, suitable for non-specialist users

Examples are drawn from recent academic collaborations and early-stage biotech projects.


Engagement models

Engagements are typically structured as:

  • Short advisory engagements Focused discussions, data or model review, and strategic input

  • Project-based work Defined scope and deliverables, with fixed or capped budgets

  • Retainer arrangements Reserved consulting capacity for ongoing collaboration across project phases

Details are agreed in advance based on scope and computational requirements.


Practicalities

  • Location: Hamburg, Germany (remote work by default)
  • Availability: project-based and advisory engagements
  • Cloud compute costs are handled separately from consulting fees