A Physical Approach to Protein Structure Prediction

We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4^th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.     

Prediction of Protein Secondary Structure Using Feature Selection and Analysis Approach

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80 % currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25 % was used to train and test the proposed method. The results indicate that overall accuracy of 87.8 % was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89 % at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.     

A Kinetic Approach to the Prediction of RNA Secondary Structures

Abstract

A new approach to the prediction of secondary RNA structures based on the analysis of the kinetics of molecular self-organisation is proposed herein. The Markov process is used to describe structural reconstructions during secondary structure formation. This process is modelled by a Monte-Carlo method. Examples of the calculation by this method of the secondary structures kinetic ensemble are given. Distribution of time-dependent probabilities within the ensembles is obtained.

An effective method for search for the equilibrium ensemble is also suggested. This method is based on the construction of a tree of all possible secondary structures of RNA. By ascribing a probability for each structure (according to its free energy) the Boltzmann equilibrium ensemble can be obtained.     

Drug Repurposing: A Systematic Approach to Evaluate Candidate Oral Neuroprotective Interventions for Secondary Progressive Multiple Sclerosis

Objective

To develop and implement an evidence based framework to select, from drugs already licenced, candidate oral neuroprotective drugs to be tested in secondary progressive multiple sclerosis.

Design

Systematic review of clinical studies of oral putative neuroprotective therapies in MS and four other neurodegenerative diseases with shared pathological features, followed by systematic review and meta-analyses of the in vivo experimental data for those interventions. We presented summary data to an international multi-disciplinary committee, which assessed each drug in turn using pre-specified criteria including consideration of mechanism of action.

Results

We identified a short list of fifty-two candidate interventions. After review of all clinical and pre-clinical evidence we identified ibudilast, riluzole, amiloride, pirfenidone, fluoxetine, oxcarbazepine, and the polyunsaturated fatty-acid class (Linoleic Acid, Lipoic acid; Omega-3 fatty acid, Max EPA oil) as lead candidates for clinical evaluation.

Conclusions

We demonstrate a standardised and systematic approach to candidate identification for drug rescue and repurposing trials that can be applied widely to neurodegenerative disorders.     

RNA-SSPT: RNA Secondary Structure Prediction Tools

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.     

A Method for WD40 Repeat Detection and Secondary Structure Prediction

WD40-repeat proteins (WD40s), as one of the largest protein families in eukaryotes, play vital roles in assembling protein-protein/DNA/RNA complexes. WD40s fold into similar β-propeller structures despite diversified sequences. A program WDSP (WD40 repeat protein Structure Predictor) has been developed to accurately identify WD40 repeats and predict their secondary structures. The method is designed specifically for WD40 proteins by incorporating both local residue information and non-local family-specific structural features. It overcomes the problem of highly diversified protein sequences and variable loops. In addition, WDSP achieves a better prediction in identifying multiple WD40-domain proteins by taking the global combination of repeats into consideration. In secondary structure prediction, the average Q3 accuracy of WDSP in jack-knife test reaches 93.7%. A disease related protein LRRK2 was used as a representive example to demonstrate the structure prediction.     

简单的一致性方法预测蛋白质二级结构

蛋白质二级结构预测对于我们了解蛋白质空间结构是至关重要的一步。文章提出了一种简单的二级结构预测方法,该方法采用多数投票法将现有的3种较好的二级结构预测方法的预测结果汇集形成一致性预测结果。从PDB数据库中随机选取近两年新测定结构的57条相似性小于30%的蛋白质,对该方法的预测结果进行测试,其Q3准确率比3种独立的方法提高了1.12—2.29%,相关系数及SOV准确率也有相应的提高。并且各项准确率均比同样采用一致性方法的Jpred二级结构预测程序准确率要高。这种预测方法虽然原理简单,但无须使用额外的参数,计算量小,易于实现,最重要的前提就是必须选用目前准确性比较出色的蛋白质二级结构预测方法。     

Outline of A New Approach to Evaluate Ecological Integrity of Salt Marshes

The integrity of coastal salt marshes can be determined from the extent to which they provide key ecosystem services: food and habitat for fish and wildlife, good water quality, erosion and flood control, and recreation and cultural use. An outline of a new approach for linking ecosystem services with metrics of structure and function to evaluate the ecological integrity of salt marshes is described. One main objective of the approach is to determine whether differences in structure and function can be detected among salt marshes with similar geomorphology and hydrology but different degrees of anthropogenic stress. The approach is currently being applied to salt marshes of Narragansett Bay, RI, USA. Stable nitrogen isotopic ratios of the marsh biota reflected the nitrogen sources from the adjacent watersheds and were significantly correlated with percent residential land use. Results show that plant zonation significantly ( r = —0.82; p < 0.05) relates with percent residential land use and is potentially a sensitive indicator of anthropogenic disturbance of New England salt marshes. We are currently examining species diversity, denitrification rates, and susceptibility to erosion among the sites for additional indicators of salt marsh condition. Our results to date suggest that this approach will provide the methods needed for managers to systematically monitor and evaluate the integrity of salt marshes     

Outline of A New Approach to Evaluate Ecological Integrity of Salt Marshes

The integrity of coastal salt marshes can be determined from the extent to which they provide key ecosystem services: food and habitat for fish and wildlife, good water quality, erosion and flood control, and recreation and cultural use. An outline of a new approach for linking ecosystem services with metrics of structure and function to evaluate the ecological integrity of salt marshes is described. One main objective of the approach is to determine whether differences in structure and function can be detected among salt marshes with similar geomorphology and hydrology but different degrees of anthropogenic stress. The approach is currently being applied to salt marshes of Narragansett Bay, RI, USA. Stable nitrogen isotopic ratios of the marsh biota reflected the nitrogen sources from the adjacent watersheds and were significantly correlated with percent residential land use. Results show that plant zonation significantly ( r = —0.82; p < 0.05) relates with percent residential land use and is potentially a sensitive indicator of anthropogenic disturbance of New England salt marshes. We are currently examining species diversity, denitrification rates, and susceptibility to erosion among the sites for additional indicators of salt marsh condition. Our results to date suggest that this approach will provide the methods needed for managers to systematically monitor and evaluate the integrity of salt marshes     

A Consumer-Resource Approach to Community Structure

Because all species are consumers and all, eventually, are consumedby other species, consumer-resource interaction is one of themost fundamental processes of ecology. Simple models that includethe direct mechanisms of consumer-resource interactions maythus be the fundamental building-block for models of communitystructure. These models are easily extended to include suchcomplexity as the effects of physical limiting factors, spatialheterogeneity in resource supply, fluctuating resource supply,and multiple trophic levels. Each such modification places constraintson the traits of species that can persist. Consumer-resourcemodels make predictions about many aspects of community structure,including species richness, species composition, species dominance,population dynamics, morphological or physiological traits ofspecies, and patterns of phenotypic variation within species.Thus, each model affords numerous opportunities to test andmodify or reject it. A review of a variety of communities suggeststhat much of the structure of each community can be explainedby a relatively simple consumer-resource model, but that differentelements of complexity may be important in different communities.     

Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction

Chemical and enzymatic footprinting experiments, such as shape (selective 2′-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the -hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be ‘correct’, in as much as the shape data is ‘correct’. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.     

猪SLA-DR二级结构预测及同源模建

应用EXPASY服务器（http：／／www．expasy．oh／tools）上的SOPMA法对SLA—DR的α链和口链进行二级结构的预测,并与人的HLA—DR相应的α链和β链的氨基酸和二级结构成分比较,在此基础上同源模建SLA—DRα链、β链及复合体SLA—DR的三级结构。结果显示,SLA—DR的α链各二级结构成分α螺旋、β折叠、转角和无规则卷曲的数目分别为36、61、4和69,β链中分别为42、56、16和74。α链和β链各二级结构成分与复合体SLA—DR具有高度的符合率,分别达到98．7％（α螺旋）、99．1％（β折叠）、83．3％（转角）和97．2％（无规则卷曲）。各功能区分析,SLA—DR的α1和β1区为结合抗原的高变化区。同源模建和三级结构分析表明SLA—DR的α链和口链具有独立的三级结构,并可以组成复合体SLA—DR。     

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.     

Hybrid Global Optimization Algorithms for Protein Structure Prediction: Alternating Hybrids

Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the αBB, and a stochastically based method, conformational space annealing (CSA). The αBB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the αBB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods.     

Reconstruction and Stability of Secondary Structure Elements in the Context of Protein Structure Prediction

Efficient and accurate reconstruction of secondary structure elements in the context of protein structure prediction is the major focus of this work. We present a novel approach capable of reconstructing α-helices and β-sheets in atomic detail. The method is based on Metropolis Monte Carlo simulations in a force field of empirical potentials that are designed to stabilize secondary structure elements in room-temperature simulations. Particular attention is paid to lateral side-chain interactions in β-sheets and between the turns of α-helices, as well as backbone hydrogen bonding. The force constants are optimized using contrastive divergence, a novel machine learning technique, from a data set of known structures. Using this approach, we demonstrate the applicability of the framework to the problem of reconstructing the overall protein fold for a number of commonly studied small proteins, based on only predicted secondary structure and contact map. For protein G and chymotrypsin inhibitor 2, we are able to reconstruct the secondary structure elements in atomic detail and the overall protein folds with a root mean-square deviation of <10 Å. For cold-shock protein and the SH3 domain, we accurately reproduce the secondary structure elements and the topology of the 5-stranded β-sheets, but not the barrel structure. The importance of high-quality secondary structure and contact map prediction is discussed.     

DAIRRy-BLUP: A High-Performance Computing Approach to Genomic Prediction

In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression–best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR-BLUP implementation, based on single-trait observations (y), that uses the Average Information algorithm for restricted maximum-likelihood estimation of the variance components. The goal of DAIRRy-BLUP is to enable the analysis of large-scale data sets to provide more accurate estimates of marker effects and breeding values. A distributed-memory framework is required since the dimensionality of the problem, determined by the number of SNP markers, can become too large to be analyzed by a single computing node. Initial results show that DAIRRy-BLUP enables the analysis of very large-scale data sets (up to 1,000,000 individuals and 360,000 SNPs) and indicate that increasing the number of phenotypic and genotypic records has a more significant effect on the prediction accuracy than increasing the density of SNP arrays.     

Contribution of Genome-Wide Association Studies to Scientific Research: A Pragmatic Approach to Evaluate Their Impact

The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.