Research

Protein design

Achievement of rational control of protein structure and function has been a longstanding goal in protein science. Realization of this goal would have transformative implications on several disciplines of science: biochemistry, molecular biology, biomedicine, biotechnology and nanotechnology. In addition to paving the way for the development of a range of functional biomolecules - from protein drugs to enzymes and biomaterials - it would also refine and extend the understanding of the fundamental determinants of protein structure formation. We develop computational methods to design novel protein assemblies and to control the assembly structure formation at high precision. We use the designed assemblies as model system to understand protein assembly formation and the evolution of protein complexes as well as to design functional proteins for use in biotechnology and nanotechnology.

Lizatović R, Assent M, Barendregt A, Dahlin J, Bille A, Satzinger K, Tupina D, Heck AJR, Wennmalm S, André I. A Protein-Based Encapsulation System with Calcium-Controlled Cargo Loading and Detachment. Angew Chem Int Ed Engl. 2018 Aug 27;57(35):11334-11338.

Lizatović R, Aurelius O, Stenstrom O, Drakenberg T, Akke M, Logan DT and André I. A De Novo Designed Coiled-Coil Peptide with a Reversible pH-Induced Oligomerization Switch. Structure 2016.

Norn CN and André I. Computational design of protein self-assembly. Current Opinion in Structural Biology 2016; pp 39-45.

Kaltofen S, Li C, Huang PS, Serpell LC, Barth A, André I. Computational de novo design of a self-assembling Peptide with predefined structure. Journal of molecular biology. 2015;427:550-62.

Rämisch S, Weiniger U, Martinsson J, Akke M and Andre I "Computational design of Leucine-Rich Repeat proteins with a defined geometry" Proc Natl Acad Sci, 2014, pii: 201413638.

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Guiding structural modeling with experimental data

Molecular simulation is a powerful tool for interpreting experimental data but it can be challenging to properly balance information encoded in force fields and energy functions with information from experimental data. We develop probability methods to guide structure prediction methods with experimental data from X-ray scattering experiments in order to characterize structure, conformational ensembles and assembly pathways of proteins.

Potrzebowski W, Trewhella J, André I. Bayesian inference of protein conformational ensembles from limited structural data. PLoS Computational Biology. 2018. 14, 12, e1006641.

André I. Modeling the Structure of Helical Assemblies with Experimental Constraints in Rosetta. Methods Mol Biol. 2018;1764:475-489.

Boelt SG, Norn C, Rasmussen MI, André I, Čiplys E, Slibinskas R, Houen G, Højrup P. Mapping the Ca(2+) induced structural change in calreticulin. J Proteomics. 2016 Jun 16;142:138-48.

Potrzebowski W, André I. "Automated determination of fibrillar structures by simultaneous model building and fiber diffraction refinement". Nat Methods. 2015; doi: 10.1038/nmeth.3399

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Evolution

Natural proteins have emerged through the process of evolution. A diverse set of fitness pressures act upon each proteins directing the evolution of protein sequence and structure. We are interesting in understanding how much of the sequence variability of natural proteins can be explained by fundamental biophysical constraints such as thermodynamic stability. We have developed a detailed structure-based model to simulate evolution of protein sequences to analyze amino acid substitution patterns, epistatic interactions and co-evolution

André I, Strauss CEM, Kaplan DB, Bradley P, and Baker D. "Emergence of symmetry in homooligomeric biological assemblies." PNAS 2008 105 (42) 16148-16152.

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Protein structure prediction

Many biological processes are controlled by large multi-component protein assemblies, the size and complexity of which typically precludes the determination of their high-resolution structures. Yet, for these systems ample non-structural data is typically available providing a wealth of lower resolution information. Our goal is to use high-resolution structural modeling techniques guided by constraints taken from lower resolution experimental data to generate structural models of important biological protein assemblies for which high resolution structural determination is unfeasible. In particular, we are interested in a subclass of protein assemblies, homomeric assemblies, which are produced by the repetition of single type of subunit. Homomeric assemblies typically adopt internal symmetry, which provides a crucial constraint in the molecular modeling.

Potrzebowski W, Trewhella J, André I. Bayesian inference of protein conformational ensembles from limited structural data. PLoS Computational Biology. 2018. 14, 12, e1006641.

André I. Modeling the Structure of Helical Assemblies with Experimental Constraints in Rosetta. Methods Mol Biol. 2018;1764:475-489.

Potrzebowski W, André I. "Automated determination of fibrillar structures by simultaneous model building and fiber diffraction refinement". Nat Methods. 2015; doi: 10.1038/nmeth.3399

Rämisch S, Lizatović R, André I. Automated de novo phasing and model building of coiled-coil proteins. Acta Cryst D. 2015;D71.

Rämisch S, Lizatovic and Andre I. "Exploring alternate states and oligomerization preferences of coiled-coils by de novo structure modeling" Proteins, 2014. doi: 10.1002/prot.24729

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