Funf Gedich in Sijo­Form

Zaborowski, U@bersetzt von Hans-Tu@rgen 한국외국어대학 학도호국단 1976 외대 Vol.11 No.1

A Maximum-Likelihood Approach to Force-Field Calibration

Zaborowski, Bartłomiej,Jagieła, Dawid,Czaplewski, Cezary,Hałabis, Anna,Lewandowska, Agnieszka,Ż,mudziń,ska, Wioletta,Ołdziej, Stanisław,Karczyń,ska, Agnieszka,Omieczynski, Christian American Chemical Society 2015 JOURNAL OF CHEMICAL INFORMATION AND MODELING Vol.55 No.9

<P>A new approach to the calibration of the force fields is proposed, in which the force-field parameters are obtained by maximum-likelihood fitting of the calculated conformational ensembles to the experimental ensembles of training system(s). The maximum-likelihood function is composed of logarithms of the Boltzmann probabilities of the experimental conformations, calculated with the current energy function. Because the theoretical distribution is given in the form of the simulated conformations only, the contributions from all of the simulated conformations, with Gaussian weights in the distances from a given experimental conformation, are added to give the contribution to the target function from this conformation. In contrast to earlier methods for force-field calibration, the approach does not suffer from the arbitrariness of dividing the decoy set into native-like and non-native structures; however, if such a division is made instead of using Gaussian weights, application of the maximum-likelihood method results in the well-known energy-gap maximization. The computational procedure consists of cycles of decoy generation and maximum-likelihood-function optimization, which are iterated until convergence is reached. The method was tested with Gaussian distributions and then applied to the physics-based coarse-grained UNRES force field for proteins. The NMR structures of the tryptophan cage, a small α-helical protein, determined at three temperatures (<I>T</I> = 280, 305, and 313 K) by Hałabis et al. (J. Phys. Chem. B<x> </x>2012<x>, </x>116<x>, </x>6898−<lpage>6907</lpage>), were used. Multiplexed replica-exchange molecular dynamics was used to generate the decoys. The iterative procedure exhibited steady convergence. Three variants of optimization were tried: optimization of the energy-term weights alone and use of the experimental ensemble of the folded protein only at <I>T</I> = 280 K (run 1); optimization of the energy-term weights and use of experimental ensembles at all three temperatures (run 2); and optimization of the energy-term weights and the coefficients of the torsional and multibody energy terms and use of experimental ensembles at all three temperatures (run 3). The force fields were subsequently tested with a set of 14 α-helical and two α + β proteins. Optimization run 1 resulted in better agreement with the experimental ensemble at <I>T</I> = 280 K compared with optimization run 2 and in comparable performance on the test set but poorer agreement of the calculated folding temperature with the experimental folding temperature. Optimization run 3 resulted in the best fit of the calculated ensembles to the experimental ones for the tryptophan cage but in much poorer performance on the training set, suggesting that use of a small α-helical protein for extensive force-field calibration resulted in overfitting of the data for this protein at the expense of transferability. The optimized force field resulting from run 2 was found to fold 13 of the 14 tested α-helical proteins and one small α + β protein with the correct topologies; the average structures of 10 of them were predicted with accuracies of about 5 Å C<SUP>α</SUP> root-mean-square deviation or better. Test simulations with an additional set of 12 α-helical proteins demonstrated that this force field performed better on α-helical proteins than the previous parametrizations of UNRES. The proposed approach is applicable to any problem of maximum-likelihood parameter estimation when the contributions to the maximum-likelihood function cannot be evaluated at the experimental points and the dimension of the configurational space is too high to construct histograms of the experimental distributions.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jcisd8/2015/jcisd8.2015.55.issue-9/acs.jcim.5b00395/production/images/me

Use of Restraints from Consensus Fragments of Multiple Server Models To Enhance Protein-Structure Prediction Capability of the UNRES Force Field

Mozolewska, Magdalena A.,Krupa, Paweł,Zaborowski, Bartłomiej,Liwo, Adam,Lee, Jooyoung,Joo, Keehyoung,Czaplewski, Cezary American Chemical Society 2016 JOURNAL OF CHEMICAL INFORMATION AND MODELING Vol.56 No.11

<P>Recently, we developed a new approach to protein-structure prediction, which combines template-based modeling with the physics-based coarse-grained UNited RESidue (UNRES) force field. In this approach, restrained multiplexed replica exchange molecular dynamics simulations with UNRES, with the C-alpha-distance and virtual-bond-dihedral angle restraints derived from knowledge-based models are carried out. In this work, we report a test of this approach in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11), in which we used the template-based models from early-stage predictions by the LEE group CASP11 server (group 038, called 'nns'), and further improvement of the method. The quality of the models obtained in CASP11 was better than that resulting from unrestrained UNRES simulations; however, the obtained models were generally worse than the final nns models. Calculations with the final nns models, performed after CASP11, resulted in substantial improvement, especially for multi-domain proteins. Based on these results, we modified the procedure by deriving restraints from models from multiple servers, in this study the four top-performing servers in CASP11 (nns, BAKER-ROSETTASERVER, Zhang-server, and QUARK), and implementing either all restraints or only the restraints on the fragments that appear similar in the majority of models (the consensus fragments), outlier models discarded. Tests with 29 CASP11 human-prediction targets with length less than 400 amino-acid residues demonstrated that the consensus-fragment approach gave better results, i.e., lower a-carbon root-mean-square deviation from the experimental structures, higher template modeling score, and global distance test total score values than the best of the parent server models. Apart from global improvement (repacking and improving the orientation of domains and other substructures), improvement was also reached for template-based modeling targets, indicating that the approach has refinement capacity. Therefore, the consensus-fragment analysis is able to remove lower-quality models and poor-quality parts of the models without knowing the experimental structure.</P>

Use of the UNRES force field in template-assisted prediction of protein structures and the refinement of server models: Test with CASP12 targets

Karczyń,ska, Agnieszka,Mozolewska, Magdalena A.,Krupa, Paweł,Giełdoń,, Artur,Bojarski, Krzysztof K.,Zaborowski, Bartłomiej,Liwo, Adam,Ś,lusarz, Rafał,Ś,lusarz, Magdalena,Lee, Jooyo Elsevier 2018 Journal of molecular graphics & modelling Vol.83 No.-

<P><B>Abstract</B></P> <P>Knowledge-based methods are, at present, the most effective ones for the prediction of protein structures; however, their results heavily depend on the similarity of a target sequence to those of proteins with known structures. On the other hand, the physics-based methods, although still less accurate and more expensive to execute, are independent of databases and give reasonable results where the knowledge-based methods fail because of weak sequence similarity. Therefore, a plausible approach seems to be the use of knowledge-based methods to determine the sections of the structures that correspond to sufficient sequence similarity and physics-based methods to determine the remaining structure. By participating in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) as the KIAS-Gdansk group, we tested our recently developed hybrid approach, in which protein-structure prediction is carried out by using the physics-based UNRES coarse-grained energy function, with restraints derived from the server models. Best predictions among all groups were obtained for 2 targets and 80% of our models were in the upper 50% of the models submitted to CASP. Our method was also able to exclude, with about 70% confidence, the information from the servers that performed poorly on a given target. Moreover, the method resulted in the best models of 2 refinement targets and performed remarkably well on oligomeric targets.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Hybrid (physics- and template-based) approach to protein structure prediction. </LI> <LI> Molecular dynamics with coarse-grained UNRES model, restraints from templates. </LI> <LI> Restraints derived from similar fragments of multiple templates. </LI> <LI> Method able filter out information from poor templates (70% confidence). </LI> <LI> Outstanding prediction obtained for some targets. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>