An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes

Hydrogen bonding is a key contributor to the specificity of intramolecular and intermolecular interactions in biological systems. Here, we develop an orientation-dependent hydrogen bonding potential based on the geometric characteristics of hydrogen bonds in high-resolution protein crystal structures, and evaluate it using four tests related to the prediction and design of protein structures and protein-protein complexes. The new potential is superior to the widely used Coulomb model of hydrogen bonding in prediction of the sequences of proteins and protein-protein interfaces from their structures, and improves discrimination of correctly docked protein-protein complexes from large sets of alternative structures.     

Life prediction for a vacuum fluorescent display based on two improved models using the three‐parameter Weibull right approximation method

To obtain precise life information for vacuum fluorescent displays (VFDs), luminance degradation data for VFDs were collected from a group of normal life tests. Instead of exponential function, the three‐parameter Weibull right approximation method (TPWRAM) was applied to describe the luminance degradation path of optoelectronic products, and two improved models were established. One of these models calculated the average life by fitting average luminance degradation data, and the other model obtained VFD life by combining the approximation method with luminance degradation test data from each individual sample. The results indicated that the test design under normal working stress was appropriate, and the selection of censored test data was simple. The two models improved by TPWRAM both revealed the luminance decaying law for VFD, and the pseudo failure time was accurately extrapolated. It was further confirmed by comparing relative error that using the second model gave a more accurate prediction of VFD life. The improved models in this study can provide technical references for researchers and manufacturers in aspects of life prediction methodology for its development.     

Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes

Classification tree models are flexible analysis tools which have the ability to evaluate interactions among predictors as well as generate predictions for responses of interest. We describe Bayesian analysis of a specific class of tree models in which binary response data arise from a retrospective case-control design. We are also particularly interested in problems with potentially very many candidate predictors. This scenario is common in studies concerning gene expression data, which is a key motivating example context. Innovations here include the introduction of tree models that explicitly address and incorporate the retrospective design, and the use of nonparametric Bayesian models involving Dirichlet process priors on the distributions of predictor variables. The model specification influences the generation of trees through Bayes' factor based tests of association that determine significant binary partitions of nodes during a process of forward generation of trees. We describe this constructive process and discuss questions of generating and combining multiple trees via Bayesian model averaging for prediction. Additional discussion of parameter selection and sensitivity is given in the context of an example which concerns prediction of breast tumour status utilizing high-dimensional gene expression data; the example demonstrates the exploratory/explanatory uses of such models as well as their primary utility in prediction. Shortcomings of the approach and comparison with alternative tree modelling algorithms are also discussed, as are issues of modelling and computational extensions.     

Progress in designing nucleic acid structures and fine-tuning their interactions

The prediction of the structure of biological macromolecules at the atomic level and the design of new meta-stable structures and secondary interactions are critical tests of our understanding of the structures and the inter-atomic forces that underlie molecular biology. The capacity to accurately predict and design new structures and interactions will allow us to create nucleic acid sequences that will fold in new and useful ways. Here, we present some results to demonstrate the progress we have made in designing and assembling new nucleic acid structures that will make an increasingly important contribution to biology and medicine. We call the reaction cycle that exemplifies our approach 'A handshake from a hairpin on the way to a double helix.'     

基于遗传算法预测2D三向的蛋白质结构

本文基于范德华力势能预测2D三向的蛋白质结构。首先,将蛋白质结构预测这一生物问题转化为数学问题,并建立基于范德华力势能函数的数学模型。其次,使用遗传算法对数学模型进行求解,为了提高蛋白质结构预测效率,我们在标准遗传算法的基础上引入了调整算子这一概念,改进了遗传算法。最后,进行数值模拟实验。实验的结果表明范德华力势能函数模型是可行的,同时,和规范遗传算法相比,改进后的遗传算法能够较大幅度提高算法的搜索效率,并且遗传算法在蛋白质结构预测问题上有巨大潜力。     

Epistatic models and pre-selection of markers improve prediction of performance in corn

In our previous papers, we demonstrated that the inclusion of epistatic interactions in marker models improved prediction for corn (Zea mays L.) grain quality traits. The utility of pre-selecting markers for epistatic models was not reported. In papers by other researchers, including epistatic effects in a model did not improve prediction efficacy for whole genome selection. The objectives of this study were therefore to evaluate the value of: (1) pre-selecting markers and interactions at different type 1 error levels to predict performance; (2) adding epistatic interactions to models including all markers, and (3) using marker-based models to predict performance of kernel weight (KWT), flowering date (FDT), and plant height (PHT). Data for KWT, FDT, PHT, and oil and protein concentrations were obtained for 500 S₂ lines and their testcrosses from the crosses of Illinois high oil × Illinois low oil and Illinois high protein × Illinois low protein corn strains. Pre-selection using an epistatic model including both single-locus and two-locus interaction effects significant at the P = 0.05 level significantly increased prediction efficacy over selection including all markers and epistatic interactions. Adding all epistatic interactions to a model including all markers did not improve prediction. For most traits, prediction based on the P = 0.05 epistatic pre-selection model was nearly as effective as prediction based on phenotype, suggesting subsequent marker-based selection would be effective.     

Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.     

Predicting absolute ligand binding free energies to a simple model site

A central challenge in structure-based ligand design is the accurate prediction of binding free energies. Here we apply alchemical free energy calculations in explicit solvent to predict ligand binding in a model cavity in T4 lysozyme. Even in this simple site, there are challenges. We made systematic improvements, beginning with single poses from docking, then including multiple poses, additional protein conformational changes, and using an improved charge model. Computed absolute binding free energies had an RMS error of 1.9 kcal/mol relative to previously determined experimental values. In blind prospective tests, the methods correctly discriminated between several true ligands and decoys in a set of putative binders identified by docking. In these prospective tests, the RMS error in predicted binding free energies relative to those subsequently determined experimentally was only 0.6 kcal/mol. X-ray crystal structures of the new ligands bound in the cavity corresponded closely to predictions from the free energy calculations, but sometimes differed from those predicted by docking. Finally, we examined the impact of holding the protein rigid, as in docking, with a view to learning how approximations made in docking affect accuracy and how they may be improved.     

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC₅₀ values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.     

A simple model of backbone flexibility improves modeling of side-chain conformational variability

The considerable flexibility of side-chains in folded proteins is important for protein stability and function, and may have a role in mediating allosteric interactions. While sampling side-chain degrees of freedom has been an integral part of several successful computational protein design methods, the predictions of these approaches have not been directly compared to experimental measurements of side-chain motional amplitudes. In addition, protein design methods frequently keep the backbone fixed, an approximation that may substantially limit the ability to accurately model side-chain flexibility. Here, we describe a Monte Carlo approach to modeling side-chain conformational variability and validate our method against a large dataset of methyl relaxation order parameters derived from nuclear magnetic resonance (NMR) experiments (17 proteins and a total of 530 data points). We also evaluate a model of backbone flexibility based on Backrub motions, a type of conformational change frequently observed in ultra-high-resolution X-ray structures that accounts for correlated side-chain backbone movements. The fixed-backbone model performs reasonably well with an overall rmsd between computed and predicted side-chain order parameters of 0.26. Notably, including backbone flexibility leads to significant improvements in modeling side-chain order parameters for ten of the 17 proteins in the set. Greater accuracy of the flexible backbone model results from both increases and decreases in side-chain flexibility relative to the fixed-backbone model. This simple flexible-backbone model should be useful for a variety of protein design applications, including improved modeling of protein-protein interactions, design of proteins with desired flexibility or rigidity, and prediction of correlated motions within proteins.     

An extension to the compartmental model of type 1 diabetic patients to reproduce exercise periods with glycogen depletion and replenishment

The purpose of this work is to present the main interactions promoted by exercise and synthesize them into mathematical equations. It is intended to extend the ability of the compartmental glucose-insulin model introduced by Sorensen [1985. A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes. Ph.D. Dissertation, Chemical Engineering Department, MIT, Cambridge] to reproduce variations in the blood glucose concentration induced by exercise in diabetic patients and to complement the previous work by Lenart and Parker [2002. Modeling exercise effects in type I diabetic patients. In: Proceedings of the 15th Triennial World Congress, Barcelona, Spain] and Lenart, DiMascio and Parker [2002. Modeling glycogen-exercise interactions in type I diabetic patients. In: Proceedings of the A.I.Ch.E. Annual Meeting, Indianapolis, IN]. The immediate consequences of exercise are incorporated in this research: redistribution of blood flows, increments in peripheral glucose and insulin uptakes, and increment in hepatic glucose production. The extended model was verified with experimental data for light and moderate intensity exercise. In addition, data extrapolation was introduced to simulate heavy intensity exercise. The hepatic glycogen reservoir limits the peripheral glucose uptake for prolonged exercise. Therefore, the depletion and replenishment of hepatic glycogen were modeled, looking to reproduce the blood glucose levels for a type 1 diabetic patient during a normal day, with meal intakes, insulin infusions and/or boluses, and a predefined exercise regime. From the extensive simulation evaluation, it is found that the new exercise model provides a good approximation to the available experimental data from literature.     

Measurement for Solitariness and Gregarism: Analysing Spacing, Attraction and Interactions in Four Species of Zetoborinae (Blattaria)

Abstract Distinguishing gregarism based on attraction between conspecifics from aggregation based on environmental factors is not a trivial task. In this paper, we propose a laboratory experimental design for characterizing gregarism, using Zetoborine cockroaches as a model. Three complementary tests were used: the classical spacing and attraction tests, complemented by analyses of social interactions with flow charts on factorial maps. Convergent results from the three tests allowed a tentative characterization of solitariness and different degrees of gregarism. These elements were used to improve the interpretation of spacing patterns of four species of cockroaches observed in the field.     

Prediction of protein secondary structure by an enhanced neural network.

Computational model of neural network is used for prediction of secondary structure of globular proteins of known sequence. In contrast to earlier works some information about expected tertiary interactions were built in into the neural network. As a result the prediction accuracy was improved by 3% to 5%. Possible applications of this new approach are briefly discussed.     

Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction.

Analysis of biopolymer sequences and structures generally adopts one of two approaches: use of detailed biophysical theoretical models of the system with experimentally-determined parameters, or largely empirical statistical models obtained by extracting parameters from large datasets. In this work, we demonstrate a merger of these two approaches using Bayesian statistics. We adopt a common biophysical model for local protein folding and peptide configuration, the helix-coil model. The parameters of this model are estimated by statistical fitting to a large dataset, using prior distributions based on experimental data. L(1)-norm shrinkage priors are applied to induce sparsity among the estimated parameters, resulting in a significantly simplified model. Formal statistical procedures for evaluating support in the data for previously proposed model extensions are presented. We demonstrate the advantages of this approach including improved prediction accuracy and quantification of prediction uncertainty, and discuss opportunities for statistical design of experiments. Our approach yields a 39% improvement in mean-squared predictive error over the current best algorithm for this problem. In the process we also provide an efficient recursive algorithm for exact calculation of ensemble helicity including sidechain interactions, and derive an explicit relation between homo- and heteropolymer helix-coil theories and Markov chains and (non-standard) hidden Markov models respectively, which has not appeared in the literature previously.     

A simple probabilistic model of multibody interactions in proteins

Protein structure prediction methods typically use statistical potentials, which rely on statistics derived from a database of know protein structures. In the vast majority of cases, these potentials involve pairwise distances or contacts between amino acids or atoms. Although some potentials beyond pairwise interactions have been described, the formulation of a general multibody potential is seen as intractable due to the perceived limited amount of data. In this article, we show that it is possible to formulate a probabilistic model of higher order interactions in proteins, without arbitrarily limiting the number of contacts. The success of this approach is based on replacing a naive table‐based approach with a simple hierarchical model involving suitable probability distributions and conditional independence assumptions. The model captures the joint probability distribution of an amino acid and its neighbors, local structure and solvent exposure. We show that this model can be used to approximate the conditional probability distribution of an amino acid sequence given a structure using a pseudo‐likelihood approach. We verify the model by decoy recognition and site‐specific amino acid predictions. Our coarse‐grained model is compared to state‐of‐art methods that use full atomic detail. This article illustrates how the use of simple probabilistic models can lead to new opportunities in the treatment of nonlocal interactions in knowledge‐based protein structure prediction and design. Proteins 2013; 81:1340–1350. © 2013 Wiley Periodicals, Inc.     

SARNA-Predict: accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing

Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot (pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).     

Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.     

Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules

MOTIVATION: Various methods have been proposed to predict the binding affinities of peptides to Major Histocompatibility Complex class I (MHC-I) molecules based on experimental binding data. They can be classified into two groups: (1) AIB methods that assume independent contributions of all peptide positions to the binding to MHC-I molecule (e.g. scoring matrices) and (2) general methods which can take into account interactions between different positions (e.g. artificial neural networks). We aim to compare the prediction accuracies of these methods, and quantify the impact of interactions between peptide positions. RESULTS: We compared several previously published and widely used methods and discovered that the best AIB methods gave significantly better predictions than three previously published general methods, possibly due to the lack of a sufficient training data for the general methods. The best results, however, were achieved with our newly developed general method, which combined a matrix describing independent binding with pair coefficients describing pair-wise interactions between peptide positions. The pair coefficients consistently but only slightly improved prediction accuracy, and were much smaller than the matrix entries. This explains why neglecting them-as is done in AIB methods-can still lead to good predictions. AVAILABILITY: The new prediction model is implemented at http://zlab.bu.edu/SMM. The underlying matrix and pair coefficients are also available as supplementary materials.     

Experimental study and constitutive modeling of the viscoelastic mechanical properties of the human prolapsed vaginal tissue

In this paper, the viscoelastic mechanical properties of vaginal tissue are investigated. Using previous results of the authors on the mechanical properties of biological soft tissues and newly experimental data from uniaxial tension tests, a new model for the viscoelastic mechanical properties of the human vaginal tissue is proposed. The structural model seems to be sufficiently accurate to guarantee its application to prediction of reliable stress distributions, and is suitable for finite element computations. The obtained results may be helpful in the design of surgical procedures with autologous tissue or prostheses.