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Treatment of disulfide bonds in coarse-grained UNRES force field

Krupa P.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2016

Vol. 20, No 4

393--398

Disulfide bonds, despite the advances of the computational methods, are underrepresented in theoretical chemistry and the role of disulfide bonds is of ten diminished in bioinformatical studies. Most of the molecular modeling tools do not allow studying the process of disulfide bond formation and breaking, which is equally important as the sole presence of disulfide bonds in proteins and peptides. The UNRES (UNited RESidue) coarse-grained force field allows treating disulfide bonds in two ways: as static (formed or broken in the simulation) or dynamic (all specified cysteine residues can form and break disulfide bonds during simulation). The comparison between those two approaches of disulfide-bond treatment is presented for protein folding on the example of four small β - and α + β proteins with one, two, three and four disulfide bonds. The results clearly show that proper disulfide bond treatment is important in simulations and significantly enhances the quality of folded structures.

A new approach to homology modeling

He Y., Scheraga H. A., Rackovsky S.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2014

Vol. 18, No 3

211--128

The need to interpret experimental results led to, first, an all-atom f orce field, followed by a coarse-grained one. As an aid to these force fields, a new approac h is introduced here to predict protein structure based on the physical properties of th e amino acids. This approach includes three key components: Kidera factors describing the ph ysical properties, Fourier transformation and UNRES coarse-grained force field simulations. Different from traditional homology modeling methods which are based on evolution, this approach is phys ics-based, and does not have the same weaknesses as the traditional homology modeling method s. Our results show that this approach can produce above average prediction results, and can be used as a useful tool for protein structure prediction.