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。     

Analysis of loop boundaries using different local structure assignment methods

Loops connect regular secondary structures. In many instances, they are known to play important biological roles. Analysis and prediction of loop conformations depend directly on the definition of repetitive structures. Nonetheless, the secondary structure assignment methods (SSAMs) often lead to divergent assignments. In this study, we analyzed, both structure and sequence point of views, how the divergence between different SSAMs affect boundary definitions of loops connecting regular secondary structures. The analysis of SSAMs underlines that no clear consensus between the different SSAMs can be easily found. Because these latter greatly influence the loop boundary definitions, important variations are indeed observed, that is, capping positions are shifted between different SSAMs. On the other hand, our results show that the sequence information in these capping regions are more stable than expected, and, classical and equivalent sequence patterns were found for most of the SSAMs. This is, to our knowledge, the most exhaustive survey in this field as (i) various databank have been used leading to similar results without implication of protein redundancy and (ii) the first time various SSAMs have been used. This work hence gives new insights into the difficult question of assignment of repetitive structures and addresses the issue of loop boundaries definition. Although SSAMs give very different local structure assignments capping sequence patterns remain efficiently stable.     

Knowledge-based protein secondary structure assignment

We have developed an automatic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone torsional angle information. Parameters of the pattern recognition procedure were optimized using designations provided by the crystallographers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 22:2577-2637, 1983) shows that STRIDE and DSSP assign secondary structural states in 58 and 31% of 226 protein chains in our data sample, respectively, in greater agreement with the specific residue-by-residue definitions provided by the discoverers of the structures while in 11% of the chains, the assignments are the same. STRIDE delineates every 11th helix and every 32nd strand more in accord with published assignments. © 1995 Wiley-Liss, Inc.     

Protein secondary structure from Fourier transform infrared spectroscopy: a data base analysis

An infrared (ir) method to determine the secondary structure of proteins in solution using the amide I region of the spectrum has been devised. The method is based on the circular dichroism (CD) matrix method for secondary structure analysis given by Compton and Johnson (L. A. Compton and W. C. Johnson, 1986, Anal. Biochem. 155, 155-167). The infrared data matrix was constructed from the normalized Fourier transform infrared spectra from 1700 to 1600 cm-1 of 17 commercially available proteins. The secondary structure matrix was constructed from the X-ray data of the seventeen proteins with secondary structure elements of helix, beta-sheet, beta-turn, and other (random). The CD and ir methods were compared by analyzing the proteins of the CD and ir databases as unknowns. Both methods produce similar results compared to structures obtained by X-ray crystallographic means with the CD slightly better for helix conformation, and the ir slightly better for beta-sheet. The relatively good ir analysis for concanavalin A and alpha-chymotrypsin indicate that the ir method is less affected by the presence of aromatic groups. The concentration of the protein and the cell path length need not be known for the ir analysis since the spectra can be normalized to the total ir intensity in the amide I region. The ir spectra for helix, beta-sheet, beta-turn, and other, as extracted from the data-base, agree with the literature band assignments. The ir data matrix and the inverse matrix necessary to analyze unknown proteins are presented.     

Spectroscopic methods for analysis of protein secondary structure

Several methods for determination of the secondary structure of proteins by spectroscopic measurements are reviewed. Circular dichroism (CD) spectroscopy provides rapid determinations of protein secondary structure with dilute solutions and a way to rapidly assess conformational changes resulting from addition of ligands. Both CD and Raman spectroscopies are particularly useful for measurements over a range of temperatures. Infrared (IR) and Raman spectroscopy require only small volumes of protein solution. The frequencies of amide bands are analyzed to determine the distribution of secondary structures in proteins. NMR chemical shifts may also be used to determine the positions of secondary structure within the primary sequence of a protein. However, the chemical shifts must first be assigned to particular residues, making the technique considerably slower than the optical methods. These data, together with sophisticated molecular modeling techniques, allow for refinement of protein structural models as well as rapid assessment of conformational changes resulting from ligand binding or macromolecular interactions. A selected number of examples are given to illustrate the power of the techniques in applications of biological interest.     

Protein secondary structure analyses from circular dichroism spectroscopy: methods and reference databases

Circular dichroism (CD) spectroscopy has been a valuable method for the analysis of protein secondary structures for many years. With the advent of synchrotron radiation circular dichroism (SRCD) and improvements in instrumentation for conventional CD, lower wavelength data are obtainable and the information content of the spectra increased. In addition, new computation and bioinformatics methods have been developed and new reference databases have been created, which greatly improve and facilitate the analyses of CD spectra. This article discusses recent developments in the analysis of protein secondary structures, including features of the DICHROWEB analysis webserver.     

Backbone assignment,secondary structure and Protein A binding of an isolated,human antibody VH domain

Summary Antibody heavy chain variable domains (VH) lacking their light chain domain (VL) partner are prime candidates for the design of minimum-size immunoreagents. To obtain structural information about isolated VH domains, a human VH was labelled with ¹⁵N or doubly labelled with both ¹⁵N and ¹³C and was studied by heteronuclear nuclear magnetic resonance spectroscopy. Most (90%) of the ¹H and ¹⁵N main-chain signals were assigned through two-dimensional TOCSY and NOESY experiments on the unlabelled VH and three-dimensional heteronuclear multiple quantum correlation TOCSY and NOESY experiments on the ¹⁵N-labelled VH. Four short stretches of the polypeptide chain could only be assigned on the basis of three-dimensional HNCA and HN(CO)CA experiments on the ¹³C-/¹⁵N-labelled protein. Long-range interstrand backbone NOEs suggest the presence of two adjacent -sheets formed by altogether nine antiparallel -strands. ³J_H ^NHC coupling constants and the location of slowly exchanging backbone amides support this interpretation. The secondary structure of the isolated VH is identical to that of heavy chain variable domains in intact antibodies, where VH domains are packed against a VL domain. The backbone assignments of the VH made it possible to locate its Protein A binding site. Chemical shift movements after complexing with the IgG binding fragment of Protein A indicate binding through one of the two -sheets of the VH. This -sheet is solvent exposed in intact antibodies. The Protein A binding site obviously differs from that on the Fc portion of immunoglobulins and is unique to members of the human VH_III gene subgroup.Abbreviations CDR complementarity determining region - CHAPS [(cholamidopropyl)-dimethylammonio]-1-propanesulfonate - DQF-COSY double-quantum-filtered correlated spectroscopy - Fab antigen binding antibody fragment - Fc crystallisable antibody fragment - Fv heterodimer of VH and VL - H1 (2, 3) hypervariable loop 1 (2, 3) - IgG immunoglobulin G - NOE nuclear Overhauser effect - NOESY nuclear Overhauser enhancement spectroscopy - HMQC heteronuclear multiple quantum correlation spectroscopy - HSQC heteronuclear single quantum correlation spectroscopy - scFv single chain Fv - TOCSY total correlation spectroscopy - TPPI time-proportional phase incrementation - VH antibody heavy chain variable region - VL antibody light chain variable region. Mutants are denoted by the wild-type amino acid (one-letter code), follwed by the residue number and the new amino acid     

Third type of secondary structure: noncooperative mobile conformation. Protein Data Bank analysis

Analysis of 68 proteins from Protein Data Bank disclosed a new widely spread type of the secondary structure that is designated as mobile (M-) conformation. Helical parameters of M-conformation are close to the poly-L-proline II type helix. Its occurrence in globular proteins approximates that of the beta-sheet. The angles corresponding to the position of the M-conformation maximum in distribution of amino acid residues on a conformational map are phi: -65 degrees, psi: 140 degrees. Unique features and high occurrence in proteins make it possible to distinguish the M-conformation as an independent third type of the secondary structure in globular proteins, that should be included in the present classification.     

SABA (secondary structure assignment program based on only alpha carbons): a novel pseudo center geometrical criterion for accurate assignment of protein secondary structures

Most widely used secondary structure assignment methods such as DSSP identify structural elements based on N-H and C=O hydrogen bonding patterns from X-ray or NMR-determined coordinates. Secondary structure assignment algorithms using limited Cα information have been under development as well, but their accuracy is only ~80% compared to DSSP. We have hereby developed SABA (Secondary Structure Assignment Program Based on only Alpha Carbons) with~90% accuracy. SABA defines a novel geometrical parameter, termed a pseudo center, which is the midpoint of two continuous Cαs. SABA is capable of identifying α-helices, 3(10)-helices, and β-strands with high accuracy by using cut-off criteria on distances and dihedral angles between two or more pseudo centers. In addition to assigning secondary structures to Cα-only structures, algorithms using limited Cα information with high accuracy have the potential to enhance the speed of calculations for high capacity structure comparison.     

Predictions of protein segments with the same aminoacid sequence and different secondary structure: a benchmark for predictive methods

The most stringent test for predictive methods of protein secondary structure is whether identical short sequences that are known to be present with different conformations in different proteins known at atomic resolution can be correctly discriminated. In this study, we show that the prediction efficiency of this type of segments in unrelated proteins reaches an average accuracy per residue ranging from about 72 to 75% (depending on the alignment method used to generate the input sequence profile) only when methods of the third generation are used. A comparison of different methods based on segment statistics (2nd generation methods) and/or including also evolutionary information (3rd generation methods) indicate that the discrimination of the different conformations of identical segments is dependent on the method used for the prediction. Accuracy is similar when methods similarly performing on the secondary structure prediction are tested. When evolutionary information is taken into account as compared to single sequence input, the number of correctly discriminated pairs is increased twofold. The results also highlight the predictive capability of neural networks for identical segments whose conformation differs in different proteins.     

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。     

Protein secondary structure: entropy, correlations and prediction

MOTIVATION: Is protein secondary structure primarily determined by local interactions between residues closely spaced along the amino acid backbone or by non-local tertiary interactions? To answer this question, we measure the entropy densities of primary and secondary structure sequences, and the local inter-sequence mutual information density. RESULTS: We find that the important inter-sequence interactions are short ranged, that correlations between neighboring amino acids are essentially uninformative and that only one-fourth of the total information needed to determine the secondary structure is available from local inter-sequence correlations. These observations support the view that the majority of most proteins fold via a cooperative process where secondary and tertiary structure form concurrently. Moreover, existing single-sequence secondary structure prediction algorithms are almost optimal, and we should not expect a dramatic improvement in prediction accuracy. AVAILABILITY: Both the data sets and analysis code are freely available from our Web site at http://compbio.berkeley.edu/     

Advanced NMR approaches for a detailed structure analysis of natural products

Some new nuclear magnetic resonance (NMR) approaches to elucidate chemical structures, which have not been determined by routine NMR methods, are presented. Selective detection of methine (CH), methylene (CH(2)), or methyl (CH(3)) signals in each subspectrum by editing NMR methods was utilized to reduce the complexity in crowded spectra. It also increased the peak separation and enhanced the sensitivity by limiting the measuring area of the 2D spectra. Several 2D methods to measure (2,3)J(CH) values, which are useful for stereochemical assignment are then introduced. To determine the structure of a highly hydrogen-deficient molecule, efficient correlation methods for long-range (13)C-(13)C coupling and (1)H-(15)N HMBC are also described.     

Protein structure comparison by probability-based matching of secondary structure elements

MOTIVATION: Protein structure comparison (PSC) has been used widely in studies of structural and functional genomics. However, PSC is computationally expensive and as a result almost all of the PSC methods currently in use look only for the optimal alignment and ignore many alternative alignments that are statistically significant and that may provide insight into protein evolution or folding. RESULTS: We have developed a new PSC method with efficiency to detect potentially viable alternative alignments in all-against-all database comparisons. The efficiency of the new PSC method derives from the ability to directly home in on a limited number of viable and ranked alignment solutions based on intuitively derived SSE (secondary structure element)-matching probabilities.     

Protein secondary structure prediction with dihedral angles

We present DESTRUCT, a new method of protein secondary structure prediction, which achieves a three-state accuracy (Q3) of 79.4% in a cross-validated trial on a nonredundant set of 513 proteins. An iterative set of cascade-correlation neural networks is used to predict both secondary structure and psi dihedral angles, with predicted values enhancing the subsequent iteration. Predictive accuracies of 80.7% and 81.7% are achieved on the CASP4 and CASP5 targets, respectively. Our approach is significantly more accurate than other contemporary methods, due to feedback and a novel combination of structural representations.