A study of the occurrence of regulator secondary structures in globular proteins

The distribution of regular secondary structures, viz. α-helices and β-strands, along the length of over 70 properties whose secondary structural details have been reported, has been analysed. The occurrence of these regular structures tends to be a maximum at the N- and C-termini. Our analysis suggests that both these free ends could possibly serve as nucleating centers for secondary structures and could play an important role in the folding of proteins.     

Prediction of protein secondary structure based on residue pair types and conformational states using dynamic programming algorithm

We have used a statistical approach for protein secondary structure prediction based on information theory and simultaneously taking into consideration pairwise residue types and conformational states. Since the prediction of residue secondary structure by one residue window sliding make ambiguity in state prediction, we used a dynamic programming algorithm to find the path with maximum score. A score system for residue pairs in particular conformations is derived for adjacent neighbors up to ten residue apart in sequence. The three state overall per-residue accuracy, Q3, of this method in a jackknife test with dataset created from PDBSELECT is more than 70%.     

蛋白质二级结构预测服务器PSRSM

蛋白质二级结构预测是蛋白质结构研究的一个重要环节,大量的新预测方法被提出的同时,也不断有新的蛋白质二级结构预测服务器出现。试验选取7种目前常用的蛋白质二级结构预测服务器:PSRSM、SPOT-1D、MUFOLD、Spider3、RaptorX,Psipred和Jpred4,对它们进行了使用方法的介绍和预测效果的评估。随机选取了PDB在2018年8月至11月份发布的180条蛋白质作为测试集,评估角度为:Q3、Sov、边界识别率、内部识别率、转角C识别率,折叠E识别率和螺旋H识别率七种角度。上述服务器180条测试数据的Q3结果分别为:89.96%、88.18%、86.74%、85.77%、83.61%,79.72%和78.29%。结果表明PSRSM的预测结果最好。180条测试集中,以同源性30%,40%,70%分类的实验结果中,PSRSM的Q3结果分别为:89.49%、90.53%、89.87%,均优于其他服务器。实验结果表明,蛋白质二级结构预测可从结合多种深度学习方法以及使用大数据训练模型方向做进一步的研究。     

蛋白质二级结构指定

蛋白质二级结构是指蛋白质骨架结构中有规律重复的构象。由蛋白质原子坐标正确地指定蛋白质二级结构是分析蛋白质结构与功能的基础,二级结构的指定对于蛋白质分类、蛋白质功能模体的发现以及理解蛋白质折叠机制有着重要的作用。并且蛋白质二级结构信息广泛应用到蛋白质分子可视化、蛋白质比对以及蛋白质结构预测中。目前有超过20种蛋白质二级结构指定方法,这些方法大体可以分为两大类:基于氢键和基于几何,不同方法指定结果之间的差异较大。由于尚没有蛋白质二级结构指定方法的综述文献,因此,本文主要介绍和总结已有蛋白质二级结构指定方法。     

基于CNN与LSTM模型的蛋白质二级结构预测

蛋白质结构的预测在理解蛋白质结构组成和蛋白质的生物学功能有重要意义,而蛋白质二级结构预测是蛋白质结构预测的重要环节。当PSSM位置特异性进化矩阵被广泛应用于将蛋白质初级结构序列编码作为输入样本后,每个残基可以被表示成二维空间的数据平面,由此文中尝试利用卷积神经网络对其进行训练。文中还设计了另一种卷积神经网络,利用长短记忆网络感知了CNN最后卷积特征面的横向特征和纵向特征后连同卷积神经网络的全连接共同完成分类,最后用ensemble方法对两类卷积神经网络模型进行了整合,最终ensemble方法中包含两类卷积神经网络的六个模型,在CB513蛋白质数据集测得的Q3结果为77.2。     

基于PDB数据库的三个RNA二级结构预测软件评估

随着21世纪分子生物学研究的蓬勃发展,RNA二级结构预测成为其中一项重要内容。由于RNA二级结构预测的准确性最为关键,因此寻找高精度且易操作的二级结构预测工具显得非常重要。本文选取三种简单且易操作的二级结构预测软件,先基于PDB数据库收录的318个RNA发夹序列进行二级结构预测,进而通过比较预测结果与实验测定结果进行软件预测性能评估。比较结果显示,RNAstructure为三个软件中性能最优的RNA二级结构预测软件。     

P53蛋白质N末端的二级结构预测及其三维构象

以编码Ｐ５３Ｎ末端１２０个残基的ｍＲＮＡ二级结构为基础,结合Ｃｈｏｕ－Ｆａｓｍａｎ蛋白质二级结构预测原则,预测出Ｐ５３蛋白质Ｎ端的９３个残基包含四段α螺旋结构（１４－２６;３８－４６;５１－５６;６８－７０）,没有发现β片层。与四种以多重序列联配为基础的蛋白质二级结构预测方法（准确率均为７３．２０％左右）相对照,结果十分相近。在ＳＧＩ工作站上以此为初始结构建立的三维构象提示,Ｐ５３Ｎ末端前８０个氨基酸肽段呈弧型板块结构,其转录激活区由两段主要螺旋组成,呈上下构形,占据弧型板块的顶部及底部外侧缘。Ｃ端１３个富含脯氨酸肽段则呈弯曲松散状。这些构象与Ｐ５３Ｎ末端的生物功能是相吻合的     

Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets.     

Why is the biological hydrophobicity scale more accurate than earlier experimental hydrophobicity scales?

The recognition of transmembrane helices by the translocon is primarily guided by the average hydrophobicity of the potential transmembrane helix. However, the exact hydrophobicity of each amino acid can be identified in several different ways. The free energy of transfer for amino acid analogues between a hydrophobic media, for example, octanol and water can be measured or obtained from simulations, the hydrophobicity can also be estimated by statistical properties from known transmembrane segments and finally the contribution of each amino acid type for the probability of translocon recognition has recently been measured directly. Although these scales correlate quite well, there are clear differences between them and it is not well understood which scale represents neither the biology best nor what the differences are. Here, we try to provide some answers to this by studying the ability of different scales to recognize transmembrane helices and predict the topology of transmembrane proteins. From this analysis it is clear that the biological hydrophobicity scale as well scales created from statistical analysis of membrane helices perform better than earlier experimental scales that are mainly based on measurements of amino acid analogs and not directly on transmembrane helix recognition. Using these results we identified the properties of the scales that perform better than other scales. We find, for instance, that the better performing scales consider proline more hydrophilic. This shows that transmembrane recognition is not only governed by pure hydrophobicity but also by the helix preferences for amino acids, as proline is a strong helix breaker. Proteins 2014; 82:2190–2198. © 2014 Wiley Periodicals, Inc.     

The Effect of Maillard Conjugation of Deamidated Wheat Proteins with Low Molecular Weight Carbohydrates on the Secondary Structure of the Protein

A soluble isolated wheat protein fraction (sIWP) prepared from isolated wheat protein (30–35% deamidation) was incubated alone or in the presence of glucose or maltodextrins of various molecular weights (MW 1, 1.9 and 4.3 kDa) at 60 °C and 75% relative humidity to promote the formation of Maillard conjugates. The formation of Maillard conjugates was confirmed by the loss of available -NH₂ groups on incubation. Approximately 3–4 carbohydrate moieties (glucose or low molecular weight carbohydrates in the commercial maltodextrin) were attached per mole of sIWP after 24 h incubation. Principal component analysis of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectra measured in the dry state showed that there were no major structural changes among non-incubated sIWP, sIWP incubated alone, sIWP–glucose conjugate and sIWP–maltodextrin (MW 1 kDa) conjugate. Structural changes were observed when the protein was incubated with larger molecular weight maltodextrin (MW 1.9 kDa or 4.3 kDa). However, there were no detectable differences in their circular dichroism (CD) spectra suggesting the absence of conformational changes in proteins with or without attached carbohydrates in solution state. The differences between the FTIR and CD results are possibly due to differences in water content of the samples although pressure-induced changes to protein structure induced in the ATR cell and the influence of unreacted maltodextrins cannot be discounted. Attachment of low molecular weight carbohydrate moieties on a relatively large molecular weight protein (i.e. sIWP with average MW of 40.4 kDa) with low lysine content (average three per mole of protein) is not sufficient to have an impact on the secondary structure of the protein.     

Physical principles of protein structure and protein folding

Physical principles determining the protein structure and protein folding are reviewed: (i) the molecular theory of protein secondary structure and the method of its prediction based on this theory; (ii) the existence of a limited set of thermodynamically favourable folding patterns of α- and β-regions in a compact globule which does not depend on the details of the amino acid sequence; (iii) the moderns approaches to the prediction of the folding patterns of α- and β-regions in concrete proteins; (iv) experimental approaches to the mechanism of protein folding. The review reflects theoretical and experimental works of the author and his collaborators as well as those of other groups.     

Secondary structure prediction and folding of globular protein: refolding of ferredoxin

The physicochemical mechanism of protein folding has been elucidated by the island model, describing a growth type of folding. The folding pathway is closely related with nucleation on the polypeptide chain and thus the formation of small local structures or secondary structures at the earliest stage of folding is essential to all following steps. The island model is applicable to any protein, but a high precision of secondary structure prediction is indispensable to folding simulation. The secondary structures formed at the earliest stage of folding are supposed to be of standard form, but they are usually deformed during the folding process, especially at the last stage, although the degree of deformation is different for each protein. Ferredoxin is an example of a protein having this property. According to X-ray investigation (1FDX), ferredoxin is not supposed to have secondary structures. However, if we assumed that in ferredoxin all the residues are in a coil state, we could not attain the correct structure similar to the native one. Further, we found that some parts of the chain are not flexible, suggesting the presence of secondary structures, in agreement with the recent PDB data (1DUR). Assuming standard secondary structures (-helices and -strands) at the nonflexible parts at the early stage of folding, and deforming these at the final stage, a structure similar to the native one was obtained. Another peculiarity of ferredoxin is the absence of disulfide bonds, in spite of its having eight cysteines. The reason cysteines do not form disulfide bonds became clear by applying the lampshade criterion, but more importantly, the two groups of cysteines are ready to make iron complexes, respectively, at a rather later stage of folding. The reason for poor prediction accuracy of secondary structure with conventional methods is discussed.     

Combining evolutionary information and neural networks to predict protein secondary structure

Using evolutionary information contained in multiple sequence alignments as input to neural networks, secondary structure can be predicted at significantly increased accuracy. Here, we extend our previous three-level system of neural networks by using additional input information derived from multiple alignments. Using a position-specific conservation weight as part of the input increases performance. Using the number of insertions and deletions reduces the tendency for overprediction and increases overall accuracy. Addition of the global amino acid content yields a further improvement, mainly in predicting structural class. The final network system has a sustained overall accuracy of 71.6% in a multiple cross-validation test on 126 unique protein chains. A test on a new set of 124 recently solved protein structures that have no significant sequence similarity to the learning set confirms the high level of accuracy. The average cross-validated accuracy for all 250 sequence-unique chains is above 72%. Using various data sets, the method is compared to alternative prediction methods, some of which also use multiple alignments: the performance advantage of the network system is at least 6 percentage points in three-state accuracy. In addition, the network estimates secondary structure content from multiple sequence alignments about as well as circular dichroism spectroscopy on a single protein and classifies 75% of the 250 proteins correctly into one of four protein structural classes. Of particular practical importance is the definition of a position-specific reliability index. For 40% of all residues the method has a sustained three-state accuracy of 88%, as high as the overall average for homology modelling. A further strength of the method is greatly increased accuracy in predicting the placement of secondary structure segments. © 1994 Wiley-Liss, Inc.