THE THEORETICAL MODELING OF SELF-ORGANISATION OF THE HETEROPOLYMER GLOBULS SUCH AS PROTEIN AND RNA

Considered have been the simple models of the proteins and RNA and the time of its coming in the global power minimum, depending on the primary structure and on the outside conditions. The numerical modeling has been performed, using the method of Monte-Karlo by the diagram of Metropolis. For the first time it has been shown, that the optimum conditions exist, where the obtaining of the native structure occurs "quickly", considerable quicker than with complete sorting of all conformations of the chain, even for the random consequences; that the overlapping of the field of the native structure quick obtaining with the field of its thermodynamic stability it is the property of the just edited heteropolymers, which does not possess the random chains. The results of the work can be used in the further theoretical analysis of the kinetic and thermodynamic properties of the polymers and in construction of the new artificial proteinsAvailable from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Critical assessment of protein intrinsic disorder prediction

International audienceIntrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has F max = 0.483 on the full dataset and F max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude