Research goals

We combine computational and experimental methods to understand the structure, interactions and evolution of proteins. ​ A fundamental goal is to encode our understanding of proteins into computational and experimental methods that enables the design self-assembling protein systems. We also want to understand the process of self-assembly by inferring self-assembly pathways from simulated protein structures and time-resolved experimental data.

Research areas

Protein design

Protein design

We use computational protein design and directed evolution to engineer self-assembling proteins.







Guiding structural modeling with experimental data

Guiding structural modeling with experimental data

We develop probabilistic methods for combining experimental data with structure modeling simulations. 


Protein structure prediction

Protein structure prediction

We develop methods to predict the structure of larger protein assemblies.





Protein evolution

Protein evolution

We develop structure-based simulations of protein evolution and carry out ancestral sequence reconstruction to understand the evolution of homomeric assemblies.

Our Projects

A De Novo Designed Coiled-Coil Peptide with a Reversible pH-Induced Oligomerization Switch

A De Novo Designed Coiled-Coil Peptide with a Reversible pH-Induced Oligomerization Switch

Computational Protein Design
Computational de novo design of a self-assembling Peptide with predefined structure

Computational de novo design of a self-assembling Peptide with predefined structure

Computational Protein Design
Computational assessment of folding energy landscapes in heterodimeric coiled coils

Computational assessment of folding energy landscapes in heterodimeric coiled coils

Protein structure prediction
Automated determination of fibrillar structures by simultaneous model building and fiber diffraction refinement

Automated determination of fibrillar structures by simultaneous model building and fiber diffraction refinement

Guiding structural modeling with experimental data
Exploring alternate states and oligomerization preferences of coiled-coils by de novo structure modeling

Exploring alternate states and oligomerization preferences of coiled-coils by de novo structure modeling

Protein structure prediction
Automated de novo phasing and model building of coiled-coil proteins

Automated de novo phasing and model building of coiled-coil proteins

Protein structure prediction
A Protein‐Based Encapsulation System with Calcium‐Controlled Cargo Loading and Detachment

A Protein‐Based Encapsulation System with Calcium‐Controlled Cargo Loading and Detachment

Computational Protein Design
Computational design of a leucine-rich repeat protein with a predefined geometry

Computational design of a leucine-rich repeat protein with a predefined geometry

Computational Protein Design
Bayesian inference of protein conformational ensembles from limited structural data

Bayesian inference of protein conformational ensembles from limited structural data

Guiding structural modeling with experimental data
Assembly of Capsids from Hepatitis B Virus Core Protein Progresses through Highly Populated Intermediates in the Presence and Absence of RNA

Assembly of Capsids from Hepatitis B Virus Core Protein Progresses through Highly Populated Intermediates in the Presence and Absence of RNA

self-assembly

Support from

European Research Council
Novo Nordisk Foundation
Swedish Research Concil

Andrelab

We combine computational and experimental methods to understand the structure, interactions and evolution of proteins.
copyright © andrelab.lu.se {2023}, Lund University

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