Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

Влияние молекулярной массы на процесс электроформования волокнистых материалов, полученных из растворов полиакрилонитрила

In the article influence of the molecular weight of polyacrylonitrile on electrospinning process and also on the diameter and mechanical properties of the obtained fibers and fiber materials are considered. Polymers with molecular weight from 280 000 to 700 000 were investigated. Fibers with a diameter from 200 to 1100 nm were obtained.В статье рассматривается влияние молекулярной массы полиакрилонитрила на диаметр и механические свойства полученных волокон и волокнистых материалов. Исследованы полимеры с молекулярной массой от 280 000 до 700 000. Получены волокна с диаметром от 200-1100 нм

New statistical potential for quality assessment of protein models and a survey of energy functions

Abstract Background Scoring functions, such as molecular mechanic forcefields and statistical potentials are fundamentally important tools in protein structure modeling and quality assessment. Results The performances of a number of publicly available scoring functions are compared with a statistical rigor, with an emphasis on knowledge-based potentials. We explored the effect on accuracy of alternative choices for representing interaction center types and other features of scoring functions, such as using information on solvent accessibility, on torsion angles, accounting for secondary structure preferences and side chain orientation. Partially based on the observations made, we present a novel residue based statistical potential, which employs a shuffled reference state definition and takes into account the mutual orientation of residue side chains. Atom- and residue-level statistical potentials and Linux executables to calculate the energy of a given protein proposed in this work can be downloaded from http://www.fiserlab.org/potentials. Conclusions Among the most influential terms we observed a critical role of a proper reference state definition and the benefits of including information about the microenvironment of interaction centers. Molecular mechanical potentials were also tested and found to be over-sensitive to small local imperfections in a structure, requiring unfeasible long energy relaxation before energy scores started to correlate with model quality.</p

Исследование процесса растяжения жидкой полимерной струи в электрическом поле из растворов полиакрилонитрила

The article considers stretching of liquid polyacrylonitrile jet in electrostatic field. Polymers with molecular weight from 130 000 to 700 000 were investigated. Regularities of stretching of the jet based on polyacrylonitrile solutions were determined.В статье рассматривается процесс растяжения жидкой полимерной струи растворов полиакрилонитрила (ПАН) находящейся в электрическом поле. Исследованы полимеры с молекулярной массой от 130 000 до 700 000. Установлены основные закономерности процесса растяжения струи на основе растворов ПАН

A Novel Side-Chain Orientation Dependent Potential Derived from Random-Walk Reference State for Protein Fold Selection and Structure Prediction

An accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, however, which ignored the inherent sequence connectivity and entropic elasticity of proteins.We developed a new pair-wise distance-dependent, atomic statistical potential function (RW), using an ideal random-walk chain as reference state, which was optimized on CASP models and then benchmarked on nine structural decoy sets. Second, we incorporated a new side-chain orientation-dependent energy term into RW (RWplus) and found that the side-chain packing orientation specificity can further improve the decoy recognition ability of the statistical potential.RW and RWplus demonstrate a significantly better ability than the best performing pair-wise distance-dependent atomic potential functions in both native and near-native model selections. It has higher energy-RMSD and energy-TM-score correlations compared with other potentials of the same type in real-life structure assembly decoys. When benchmarked with a comprehensive list of publicly available potentials, RW and RWplus shows comparable performance to the state-of-the-art scoring functions, including those combining terms from multiple resources. These data demonstrate the usefulness of random-walk chain as reference states which correctly account for sequence connectivity and entropic elasticity of proteins. It shows potential usefulness in structure recognition and protein folding simulations. The RW and RWplus potentials, as well as the newly generated I-TASSER decoys, are freely available in http://zhanglab.ccmb.med.umich.edu/RW

Computational Analysis and Experimental Validation of Gene Predictions in Toxoplasma gondii

Toxoplasma gondii is an obligate intracellular protozoan that infects 20 to 90% of the population. It can cause both acute and chronic infections, many of which are asymptomatic, and, in immunocompromised hosts, can cause fatal infection due to reactivation from an asymptomatic chronic infection. An essential step towards understanding molecular mechanisms controlling transitions between the various life stages and identifying candidate drug targets is to accurately characterize the T. gondii proteome.We have explored the proteome of T. gondii tachyzoites with high throughput proteomics experiments and by comparison to publicly available cDNA sequence data. Mass spectrometry analysis validated 2,477 gene coding regions with 6,438 possible alternative gene predictions; approximately one third of the T. gondii proteome. The proteomics survey identified 609 proteins that are unique to Toxoplasma as compared to any known species including other Apicomplexan. Computational analysis identified 787 cases of possible gene duplication events and located at least 6,089 gene coding regions. Commonly used gene prediction algorithms produce very disparate sets of protein sequences, with pairwise overlaps ranging from 1.4% to 12%. Through this experimental and computational exercise we benchmarked gene prediction methods and observed false negative rates of 31 to 43%.This study not only provides the largest proteomics exploration of the T. gondii proteome, but illustrates how high throughput proteomics experiments can elucidate correct gene structures in genomes

Four Distances between Pairs of Amino Acids Provide a Precise Description of their Interaction

The three-dimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a four-dimensional grid and compared to a random model, from which the preference for specific four-distances was calculated. A clear relation between the quality of the experimental data and the tightness of the distance distribution was observed, with crystal structure data providing far tighter distance distributions than NMR data. Since the four-distance data have higher information content than classical bond descriptions, we were able to identify many unique inter-residue features not found previously in proteins. For example, we found that the side chains of Arg, Glu, Val and Leu are not symmetrical in respect to the interactions of their head groups. The described method may be developed into a function, which computationally models accurately protein structures

Модельные характеристики кардиореспираторной системы высококвалифицированных гребцов на байдарках и каноэ

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the "modeling by satisfaction of spatial restraints" strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program's predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER's objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance