engleski

New structural features that significantly improve protein folding rate models

The study of protein folding is attractive to scientists from various natural sciences, including physicists/biophysicists [1]. One of them is M. Karplus, Nobel laureate 2013, who modelled protein folding and unfolding of 33 proteins using models selected from 24 generated sequence-based features/descriptors [2]. Protein folding rates correlate with protein length (the total number of amino acids in a protein), the content of secondary structures in the protein, the absolute and relative mean distance of contact order between amino acids, etc. [3]. We have defined and introduced several new sequence-based attributes/features suitable for modelling the folding rates of proteins, such as the features related to the secondary structure of proteins (segments of alpha or beta secondary structure) and the average distance of amino acids. Models were also developed with different combinations of the above features. Improved models have been developed to predict the folding rates of proteins and all features are not dependent on the 3D structure of the protein. References: [1] D.N. Ivankov, A.V. Finkelstein, Biomolecules 10, 250 (2020) [2] A. R. Dinner, S. S. So, M. Karplus, Advan. Chem. Phys. 120, 1 (2002) [3] H. Zhou, Y. Zhou, Biophys. J. 82, 458 (2002)

protein folding rates ; prediction ; sequence-based attributes, secondary structure segments ; secondary structure entropy ; distance

Basic grant of MZO/RBI to Bono Lučić Participant-lecturer: Bono Lučić