New methods to improve modeling and prediction of protein structure, dynamics and function
We have developed and combined several novel methods to improve protein structure prediction from the amino acid sequence, and modeling of protein dynamics. One of the most promising developments in protein structure prediction are many-body potentials that take into account dense packing, and cooperativity of interactions in protein cores. We developed a method that uses whole protein information filtered through machine learners to score protein models based on their likeness to native structures. These results were published by us , and tested successfully in CASP 9, where our prediction group 4_BODY_POTENTIALS was among top three predictors in the category of template-free modeling for the most difficult targets. Recently we have significantly improved our potentials by considering electrostatic interactions and residue depth and used them for the prediction of protein structure and blind tested them in CASP 10. Our prediction group Kloczkowski_Lab was ranked as the third one in prediction of structure (based on the single model) for all targets, and ranked also as the second one for template free-modeling (see: http://www.predictioncenter.org/casp10/groups_analysis.cgi ) . By combing statistical contact potentials with entropies from the elastic network models of proteins we can compute free energy and improve coarse-grained modeling of protein structure and dynamics . The consideration of protein flexibility and its fluctuational dynamics improves protein structure prediction, leads to a better refinement of computational models of proteins, and significantly improves protein docking [4,5]. We studied also the self-assembly of FVFLM peptide and its influence on the kinetics of Aβ16-20 oligomerization.
1. P. Gniewek, A. Kolinski, R.L. Jernigan, and A. Kloczkowski, Proteins 79, 1923 (2011)
2. E. Faraggi and A. Kloczkowski, Proteins 82, 3170-6, (2014)
3. M.T. Zimmermann, S.P. Leelananda, A. Kloczkowski, and R.L. Jernigan, JPC B116, 6725 (2012)
4. P. Gniewek, A. Kolinski, R.L. Jernigan, and A. Kloczkowski, JCP 136, 195101 (2012)
5. P. Gniewek, A. Kolinski, R.L. Jernigan, and A. Kloczkowski, Proteins 80, 335 (2012)