The SSEA server for protein secondary structure alignment

SUMMARY: We present a web server that computes alignments of protein secondary structures. The server supports both performing pairwise alignments and searching a secondary structure against a library of domain folds. It can calculate global and local secondary structure element alignments. A combination of local and global alignment steps can be used to search for domains inside the query sequence or help in the discrimination of novel folds. Both the SCOP and PDB fold libraries, clustered at 95 and 40% sequence identity, are available for alignment. AVAILABILITY: The web server interface is freely accessible to academic users at http://protein.cribi.unipd.it/ssea/. The executable version and benchmarking data are available from the same web page.     

NdPASA: a pairwise sequence alignment server for distantly related proteins

SUMMARY: NdPASA is a web server specifically designed to optimize sequence alignment between distantly related proteins. The program integrates structure information of the template sequence into a global alignment algorithm by employing neighbor-dependent propensities of amino acids as a unique parameter for alignment. NdPASA optimizes alignment by evaluating the likelihood of a residue pair in the query sequence matching against a corresponding residue pair adopting a particular secondary structure in the template sequence. NdPASA is most effective in aligning homologous proteins sharing low percentage of sequence identity. The server is designed to aid homologous protein structure modeling. A PSI-BLAST search engine was implemented to help users identify template candidates that are most appropriate for modeling the query sequences.     

Analysis of proton chemical shifts in regular secondary structure of proteins

Summary The contribution of peptide groups to H and H proton chemical shifts can be modeled with empirical equations that represent magnetic anisotropy and electrostatic interactions [Ösapay, K. and Case, D.A. (1991) J. Am. Chem. Soc., 113, 9436–9444]. Using these, a model for the random coil reference state can be generated by averaging a dipeptide over energetically allowed regions of torsion-angle space. Such calculations support the notion that the empirical constant used in earlier studies arises from neighboring peptide contributions in the reference state, and suggest that special values be used for glycine and proline residues, which differ significantly from other residues in their allowed ,-ranges. New constants for these residues are reported that provide significant improvements in predicted backbone shifts. To illustrate how secondary structure affects backbone chemical shifts we report calculations on oligopeptide models for helices, sheets and turns. In addition to suggesting a physical mechanism for the widely recognized average difference between and secondary structures, these models suggest several additional regularities that should be expected: (a) H protons at the edges of -sheets will have a two-residue periodicity; (b) the H² and H³ protons of glycine residues will exhibit different shifts, particularly in sheets; (c) H protons will also be sensitive to local secondary structure, but in different directions and to a smaller extent than H protons; (d) H protons in turns will generally be shifted upfield, except those in position 3 of type I turns. Examples of observed shift patterns in several proteins illustrate the application of these ideas.     

IWS: integrated web server for protein sequence and structure analysis

Rapid increase in protein sequence information from genome sequencing projects demand the intervention of bioinformatics tools to recognize interesting gene-products and associated function. Often, multiple algorithms need to be employed to improve accuracy in predictions and several structure prediction algorithms are on the public domain. Here, we report the availability of an Integrated Web-server as a bioinformatics online package dedicated for in-silico analysis of protein sequence and structure data (IWS). IWS provides web interface to both in-house and widely accepted programs from major bioinformatics groups, organized as 10 different modules. IWS also provides interactive images for Analysis Work Flow, which will provide transparency to the user to carry out analysis by moving across modules seamlessly and to perform their predictions in a rapid manner. AVAILABILITY: IWS IS AVAILABLE FROM THE URL: http://caps.ncbs.res.in/iws.     

Protein structure refinement by optimization

Knowledge‐based protein potentials are simplified potentials designed to improve the quality of protein models, which is important as more accurate models are more useful for biological and pharmaceutical studies. Consequently, knowledge‐based potentials often are designed to be efficient in ordering a given set of deformed structures denoted decoys according to how close they are to the relevant native protein structure. This, however, does not necessarily imply that energy minimization of this potential will bring the decoys closer to the native structure. In this study, we introduce an iterative strategy to improve the convergence of decoy structures. It works by adding energy optimized decoys to the pool of decoys used to construct the next and improved knowledge‐based potential. We demonstrate that this strategy results in significantly improved decoy convergence on Titan high resolution decoys and refinement targets from Critical Assessment of protein Structure Prediction competitions. Our potential is formulated in Cartesian coordinates and has a fixed backbone potential to restricts motions to be close to those of a dihedral model, a fixed hydrogen‐bonding potential and a variable coarse grained carbon alpha potential consisting of a pair potential and a novel solvent potential that are b‐spline based as we use explicit gradient and Hessian for efficient energy optimization. Proteins 2015; 83:1616–1624. © 2015 Wiley Periodicals, Inc.     

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.     

Refinement by shifting secondary structure elements improves sequence alignments

Constructing a model of a query protein based on its alignment to a homolog with experimentally determined spatial structure (the template) is still the most reliable approach to structure prediction. Alignment errors are the main bottleneck for homology modeling when the query is distantly related to the template. Alignment methods often misalign secondary structural elements by a few residues. Therefore, better alignment solutions can be found within a limited set of local shifts of secondary structures. We present a refinement method to improve pairwise sequence alignments by evaluating alignment variants generated by local shifts of template‐defined secondary structures. Our method SFESA is based on a novel scoring function that combines the profile‐based sequence score and the structure score derived from residue contacts in a template. Such a combined score frequently selects a better alignment variant among a set of candidate alignments generated by local shifts and leads to overall increase in alignment accuracy. Evaluation of several benchmarks shows that our refinement method significantly improves alignments made by automatic methods such as PROMALS, HHpred and CNFpred. The web server is available at http://prodata.swmed.edu/sfesa . Proteins 2015; 83:411–427. © 2014 Wiley Periodicals, Inc.     

Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge-based potentials, and clustering

In this article, we present an iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing secondary structure elements (SSEs). The protocol is based on three modules: a torsion-space local sampling algorithm, a knowledge-based potential, and a conformational clustering algorithm. Alternative methods are tested for each module in the protocol. For each segment, random initial conformations were constructed by perturbing the native dihedral angles of loops (and SSEs) of the segment to be refined while keeping the protein body fixed. Two refinement procedures based on molecular mechanics force fields - using either energy minimization or molecular dynamics - were also tested but were found to be less successful than the IMO protocol. We found that DFIRE is a particularly effective knowledge-based potential and that clustering algorithms that are biased by the DFIRE energies improve the overall results. Results were further improved by adding an energy minimization step to the conformations generated with the IMO procedure, suggesting that hybrid strategies that combine both knowledge-based and physical effective energy functions may prove to be particularly effective in future applications.     

R-CHIE: a web server and R package for visualizing RNA secondary structures

Visually examining RNA structures can greatly aid in understanding their potential functional roles and in evaluating the performance of structure prediction algorithms. As many functional roles of RNA structures can already be studied given the secondary structure of the RNA, various methods have been devised for visualizing RNA secondary structures. Most of these methods depict a given RNA secondary structure as a planar graph consisting of base-paired stems interconnected by roundish loops. In this article, we present an alternative method of depicting RNA secondary structure as arc diagrams. This is well suited for structures that are difficult or impossible to represent as planar stem-loop diagrams. Arc diagrams can intuitively display pseudo-knotted structures, as well as transient and alternative structural features. In addition, they facilitate the comparison of known and predicted RNA secondary structures. An added benefit is that structure information can be displayed in conjunction with a corresponding multiple sequence alignments, thereby highlighting structure and primary sequence conservation and variation. We have implemented the visualization algorithm as a web server R-chie as well as a corresponding R package called R4RNA, which allows users to run the software locally and across a range of common operating systems.     

Top-down approach in protein RDC data analysis: <Emphasis Type="Italic">de novo</Emphasis> estimation of the alignment tensor

In solution NMR spectroscopy the residual dipolar coupling (RDC) is invaluable in improving both the precision and accuracy of NMR structures during their structural refinement. The RDC also provides a potential to determine protein structure de novo. These procedures are only effective when an accurate estimate of the alignment tensor has already been made. Here we present a top–down approach, starting from the secondary structure elements and finishing at the residue level, for RDC data analysis in order to obtain a better estimate of the alignment tensor. Using only the RDCs from N–H bonds of residues in α-helices and CA–CO bonds in β-strands, we are able to determine the offset and the approximate amplitude of the RDC modulation-curve for each secondary structure element, which are subsequently used as targets for global minimization. The alignment order parameters and the orientation of the major principal axis of individual helix or strand, with respect to the alignment frame, can be determined in each of the eight quadrants of a sphere. The following minimization against RDC of all residues within the helix or strand segment can be carried out with fixed alignment order parameters to improve the accuracy of the orientation. For a helical protein Bax, the three components A _xx , A _yy and A _zz , of the alignment order can be determined with this method in average to within 2.3% deviation from the values calculated with the available atomic coordinates. Similarly for β-sheet protein Ubiquitin they agree in average to within 8.5%. The larger discrepancy in β-strand parameters comes from both the diversity of the β-sheet structure and the lower precision of CA–CO RDCs. This top-down approach is a robust method for alignment tensor estimation and also holds a promise for providing a protein topological fold using limited sets of RDCs.     

The relationship between amide proton chemical shifts and secondary structure in proteins

Summary The parameters for HN chemical shift calculations of proteins have been determined using data from high-resolution crystal structures of 15 proteins. Employing these chemical shift calculations for HN protons, the observed secondary structure chemical shift trends of HN protons, i.e., upfield shifts on helix formation and downfield shifts on -sheet formation, are discussed. Our calculations suggest that the main reason for the difference in NH chemical shifts in helices and sheets is not an effect from the directly hydrogen-bonded carbonyl, which gives rise to downfield shifts in both cases, but arises from an additional upfield shift predicted in helices and originating in residues i-2 and i-3. The calculations also explain the well-known relationship between amide proton shifts and hydrogen-bond lengths. In addition, the HN chemical shifts of the distorted amphipathic helices of the GCN4 leucine zipper are calculated and used to characterise the solution structure of the helices. By comparing the calculated and experimental shifts, it is shown that in general the agreement is good between residues 15 and 28. The most interesting observation is that in the N-terminal half of the zipper, although both calculated and experimental shifts show clear periodicity, they are no longer in phase. This suggests that for the N-terminal half, in the true average solution structure the period of the helix coil is longer by roughly one residue compared to the NMR structures.     

Relationship between1H and13C NMR chemical shifts and the secondary and tertiary structure of a zinc finger peptide

Summary Essentially complete assignments have been obtained for the¹H and protonated¹³C NMR spectra of the zinc finger peptide Xfin-31 in the presence and absence of zinc. The patterns observed for the¹H and¹³C chemical shifts of the peptide in the presence of zinc, relative to the shifts in the absence of zinc, reflect the zinc-mediated folding of the unstructured peptide into a compact globular structure with distinct elements of secondary structure. Chemical shifts calculated from the 3D solution structure of the peptide in the presence of zinc and the observed shifts agree to within ca. 0.2 and 0.6 ppm for the backbone C^aH and NH protons, respectively. In addition, homologous zinc finger proteins exhibit similar correlations between their¹H chemical shifts and secondary structure.     

蛋白质二级结构在线服务器预测评估

蛋白质二级结构的预测,对于研究蛋白质的功能和人类生命科学意义非凡。1951年开始提出预测蛋白质二级结构,1983年对于二级结构的预测只有50%的准确率。经过多年的发展,预测方式不断的改进和完善,到如今准确率已经超过80%。但目前预测在线服务器繁多,连续自动模型评估(CAMEO)也只给出服务器三级结构的预测评估,二级结构评估还未实现。针对上述问题,选取了以下6个服务器:PSRSM、MUFOLD、SPIDER、RAPTORX、JPRED和PSIPRED,对其预测的二级结构进行评估。并且为保证测试集不在训练集内,实验数据选取蛋白质结构数据库(Protein Data Bank,PDB)最新发布的蛋白质。在基于蛋白质同源性30%、50%和70%的实验中,PSRSM取得Q3的准确率分别为91.44%、88.12%和90.17%,比其他预测服务器中最高的MUFOLD分别高出3.19%、1.33%和2.19%,证明在同一类同源性数据中PSRSM比其他服务器有更好的预测效果。除此之外实验也得到其预测的Sov准确度也比其他服务器要高。比较各类服务器的方法与结果,得出今后蛋白质二级结构预测应当重点从大数据、模板和深度学习的角度进行研究。     

VADAR: a web server for quantitative evaluation of protein structure quality

VADAR (Volume Area Dihedral Angle Reporter) is a comprehensive web server for quantitative protein structure evaluation. It accepts Protein Data Bank (PDB) formatted files or PDB accession numbers as input and calculates, identifies, graphs, reports and/or evaluates a large number (>30) of key structural parameters both for individual residues and for the entire protein. These include excluded volume, accessible surface area, backbone and side chain dihedral angles, secondary structure, hydrogen bonding partners, hydrogen bond energies, steric quality, solvation free energy as well as local and overall fold quality. These derived parameters can be used to rapidly identify both general and residue-specific problems within newly determined protein structures. The VADAR web server is freely accessible at http://redpoll.pharmacy.ualberta.ca/vadar.     

Assessing secondary structure assignment of protein structures by using pairwise sequence-alignment benchmarks

How to make an objective assignment of secondary structures based on a protein structure is an unsolved problem. Defining the boundaries between helix, sheet, and coil structures is arbitrary, and commonly accepted standard assignments do not exist. Here, we propose a criterion that assesses secondary structure assignment based on the similarity of the secondary structures assigned to pairwise sequence-alignment benchmarks, where these benchmarks are determined by prior structural alignments of the protein pairs. This criterion is used to rank six secondary structure assignment methods: STRIDE, DSSP, SECSTR, KAKSI, P-SEA, and SEGNO with three established sequence-alignment benchmarks (PREFAB, SABmark, and SALIGN). STRIDE and KAKSI achieve comparable success rates in assigning the same secondary structure elements to structurally aligned residues in the three benchmarks. Their success rates are between 1-4% higher than those of the other four methods. The consensus of STRIDE, KAKSI, SECSTR, and P-SEA, called SKSP, improves assignments over the best single method in each benchmark by an additional 1%. These results support the usefulness of the sequence-alignment benchmarks as a means to evaluate secondary structure assignment. The SKSP server and the benchmarks can be accessed at http://sparks.informatics.iupui.edu     

Correlation between 15N NMR chemical shifts in proteins and secondary structure

Summary An empirical correlation between the peptide ¹⁵N chemical shift, ¹⁵N_i, and the backbone torsion angles _i, _i–1 is reported. By using two-dimensional shielding surfaces (_i1–1), it is possible in many cases to make reasonably accurate predictions of ¹⁵N chemical shifts for a given structure. On average, the rms error between experiment and prediction is about 3.5 ppm. Results for threonine, valine and isoleucine are worse (4.8 ppm), due presumably to ₁-distribution/-gauche effects. The rms errors for the other amino acids are 3 ppm, for a typical maximal chemical shift range of 15–20 ppm. Thus, there is a significant correlation between ¹⁵N chemical shift and secondary structure.     

SymmRef: a flexible refinement method for symmetric multimers

Symmetric protein complexes are abundant in the living cell. Predicting their atomic structure can shed light on the mechanism of many important biological processes. Symmetric docking methods aim to predict the structure of these complexes given the unbound structure of a single monomer, or its model. Symmetry constraints reduce the search-space of these methods and make the prediction easier compared to asymmetric protein-protein docking. However, the challenge of modeling the conformational changes that the monomer might undergo is a major obstacle. In this article, we present SymmRef, a novel method for refinement and reranking of symmetric docking solutions. The method models backbone and side-chain movements and optimizes the rigid-body orientations of the monomers. The backbone movements are modeled by normal modes minimization and the conformations of the side-chains are modeled by selecting optimal rotamers. Since solved structures of symmetric multimers show asymmetric side-chain conformations, we do not use symmetry constraints in the side-chain optimization procedure. The refined models are re-ranked according to an energy score. We tested the method on a benchmark of unbound docking challenges. The results show that the method significantly improves the accuracy and the ranking of symmetric rigid docking solutions. SymmRef is available for download at http:// bioinfo3d.cs.tau.ac.il/SymmRef/download.html.     

Protein solution structure calculations in solution: Solvated molecular dynamics refinement of calbindin D9k

The three-dimensional solution structures of proteins determinedwith NMR-derived constraints are almost always calculated in vacuo. Thesolution structure of (Ca²⁺)_{_2}-calbindinD_9k has been redetermined by new restrained molecular dynamics(MD) calculations that include Ca²⁺ ions and explicit solventmolecules. Four parallel sets of MD refinements were run to provide accuratecomparisons of structures produced in vacuo, in vacuo withCa²⁺ ions, and with two different protocols in a solvent bathwith Ca²⁺ ions. The structural ensembles were analyzed interms of structural definition, molecular energies, packing density,solvent-accessible surface, hydrogen bonds, and the coordination of calciumions in the two binding loops. Refinement including Ca²⁺ ionsand explicit solvent results in significant improvements in the precisionand accuracy of the structure, particularly in the binding loops. Theseresults are consistent with results previously obtained in free MDsimulations of proteins in solution and show that the rMD refinedNMR-derived solution structures of proteins, especially metalloproteins, canbe significantly improved by these strategies.