The ups and downs of protein topology; rapid comparison of protein structure

Protein topology can be described at different levels. At the most fundamental level, it is a sequence of secondary structure elements (a "primary topology string"). Searching predicted primary topology strings against a library of strings from known protein structures is the basis of some protein fold recognition methods. Here a method known as TOPSCAN is presented for rapid comparison of protein structures. Rather than a simple two-letter alphabet (encoding strand and helix), more complex alphabets are used encoding direction, proximity, accessibility and length of secondary elements and loops in addition to secondary structure. Comparisons are made between the structural information content of primary topology strings and encodings which contain additional information ("secondary topology strings"). The algorithm is extremely fast, with a scan of a large domain against a library of more than 2000 secondary structure strings completing in approximately 30 s. Analysis of protein fold similarity using TOPSCAN at primary and secondary topology levels is presented.     

Protein structure mining using a structural alphabet

We present a comprehensive evaluation of a new structure mining method called PB-ALIGN. It is based on the encoding of protein structure as 1D sequence of a combination of 16 short structural motifs or protein blocks (PBs). PBs are short motifs capable of representing most of the local structural features of a protein backbone. Using derived PB substitution matrix and simple dynamic programming algorithm, PB sequences are aligned the same way amino acid sequences to yield structure alignment. PBs are short motifs capable of representing most of the local structural features of a protein backbone. Alignment of these local features as sequence of symbols enables fast detection of structural similarities between two proteins. Ability of the method to characterize and align regions beyond regular secondary structures, for example, N and C caps of helix and loops connecting regular structures, puts it a step ahead of existing methods, which strongly rely on secondary structure elements. PB-ALIGN achieved efficiency of 85% in extracting true fold from a large database of 7259 SCOP domains and was successful in 82% cases to identify true super-family members. On comparison to 13 existing structure comparison/mining methods, PB-ALIGN emerged as the best on general ability test dataset and was at par with methods like YAKUSA and CE on nontrivial test dataset. Furthermore, the proposed method performed well when compared to flexible structure alignment method like FATCAT and outperforms in processing speed (less than 45 s per database scan). This work also establishes a reliable cut-off value for the demarcation of similar folds. It finally shows that global alignment scores of unrelated structures using PBs follow an extreme value distribution. PB-ALIGN is freely available on web server called Protein Block Expert (PBE) at http://bioinformatics.univ-reunion.fr/PBE/.     

Fast structure alignment for protein databank searching.

A fast method is described for searching and analyzing the protein structure databank. It uses secondary structure followed by residue matching to compare protein structures and is developed from a previous structural alignment method based on dynamic programming. Linear representations of secondary structures are derived and their features compared to identify equivalent elements in two proteins. The secondary structure alignment then constrains the residue alignment, which compares only residues within aligned secondary structures and with similar buried areas and torsional angles. The initial secondary structure alignment improves accuracy and provides a means of filtering out unrelated proteins before the slower residue alignment stage. It is possible to search or sort the protein structure databank very quickly using just secondary structure comparisons. A search through 720 structures with a probe protein of 10 secondary structures required 1.7 CPU hours on a Sun 4/280. Alternatively, combined secondary structure and residue alignments, with a cutoff on the secondary structure score to remove pairs of unrelated proteins from further analysis, took 10.1 CPU hours. The method was applied in searches on different classes of proteins and to cluster a subset of the databank into structurally related groups. Relationships were consistent with known families of protein structure.     

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     

CONFOLD: Residue‐residue contact‐guided ab initio protein folding

Predicted protein residue–residue contacts can be used to build three‐dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three‐dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two‐stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β‐sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM‐score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM‐score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/ . Proteins 2015; 83:1436–1449. © 2015 Wiley Periodicals, Inc.     

Fold recognition without folds

Fold recognition predicts protein three-dimensional structure by establishing relationships between a protein sequence and known protein structures. Most methods explicitly use information derived from the secondary and tertiary structure of the templates. Here we show that rigorous application of a sequence search method (PSI-BLAST) with no reference to secondary or tertiary structure information is able to perform as well as traditional fold recognition methods. Since the method, SENSER, does not require knowledge of the three-dimensional structure, it can be used to infer relationships that are not tractable by methods dependent on structural templates.     

同义密码子携带多少蛋白质二级结构信息

应用信息论方法考察了大肠杆菌人两种生物的同义密码子用语和蛋白质二级结构的关联情况。研究结果表明：大肠杆菌和人的基因组中都存在着一些同义密码子明显携带有蛋白质二级结构信息,尽管这些信息量都很小;同义密码子与蛋白质二级结构的关联是种属特异性。     

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.     

Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines

In principle, structural information of protein sequences with no detectable homology to a protein of known structure could be obtained by predicting the arrangement of their secondary structural elements. Although some ab initio methods for protein structure prediction have been reported, the long-range interactions required to accurately predict tertiary structures of β-sheet containing proteins are still difficult to simulate. To remedy this problem and facilitate de novo prediction of β-sheet containing protein structures, we developed a support vector machine (SVM) approach that classified parallel and antiparallel orientation of β-strands by using the information of interstrand amino acid pairing preferences. Based on a second-order statistics on the relative frequencies of each possible interstrand amino acid pair, we defined an average amino acid pairing encoding matrix (APEM) for encoding β-strands as input in the prediction model. As a result, a prediction accuracy of 86.89% and a Matthew's correlation coefficient value of 0.71 have been achieved through 7-fold cross-validation on a non-redundant protein dataset from PISCES. Although several issues still remain to be studied, the method presented here to some extent could indicate the important contribution of the amino acid pairs to the β-strand orientation, and provide a possible way to further be combined with other algorithms making a full ‘identification’ of β-strands.     

Angle-distance image matching techniques for protein structure comparison

Proteins that contain similar structural elements often have analogous functions regardless of the degree of sequence similarity or structure connectivity in space. In general, protein structure comparison (PSC) provides a straightforward methodology for biologists to determine critical aspects of structure and function. Here, we developed a novel PSC technique based on angle-distance image (A-D image) transformation and matching, which is independent of sequence similarity and connectivity of secondary structure elements (SSEs). An A-D image is constructed by utilizing protein secondary structure information. According to various types of SSEs, the mutual SSE pairs of the query protein are classified into three different types of sub-images. Subsequently, corresponding sub-images between query and target protein structures are compared using modified cross-correlation approaches to identify the similarity of various patterns. Structural relationships among proteins are displayed by hierarchical clustering trees, which facilitate the establishment of the evolutionary relationships between structure and function of various proteins.Four standard testing datasets and one newly created dataset were used to evaluate the proposed method. The results demonstrate that proteins from these five datasets can be categorized in conformity with their spatial distribution of SSEs. Moreover, for proteins with low sequence identity that share high structure similarity, the proposed algorithms are an efficient and effective method for structural comparison.     

Amino acid propensities for secondary structures are influenced by the protein structural class

Amino acid propensities for secondary structures were used since the 1970s, when Chou and Fasman evaluated them within datasets of few tens of proteins and developed a method to predict secondary structure of proteins, still in use despite prediction methods having evolved to very different approaches and higher reliability. Propensity for secondary structures represents an intrinsic property of amino acid, and it is used for generating new algorithms and prediction methods, therefore our work has been aimed to investigate what is the best protein dataset to evaluate the amino acid propensities, either larger but not homogeneous or smaller but homogeneous sets, i.e., all-alpha, all-beta, alpha-beta proteins. As a first analysis, we evaluated amino acid propensities for helix, beta-strand, and coil in more than 2000 proteins from the PDBselect dataset. With these propensities, secondary structure predictions performed with a method very similar to that of Chou and Fasman gave us results better than the original one, based on propensities derived from the few tens of X-ray protein structures available in the 1970s. In a refined analysis, we subdivided the PDBselect dataset of proteins in three secondary structural classes, i.e., all-alpha, all-beta, and alpha-beta proteins. For each class, the amino acid propensities for helix, beta-strand, and coil have been calculated and used to predict secondary structure elements for proteins belonging to the same class by using resubstitution and jackknife tests. This second round of predictions further improved the results of the first round. Therefore, amino acid propensities for secondary structures became more reliable depending on the degree of homogeneity of the protein dataset used to evaluate them. Indeed, our results indicate also that all algorithms using propensities for secondary structure can be still improved to obtain better predictive results.     

Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods

The predictive limits of the amino acid composition for the secondary structural content (percentage of residues in the secondary structural states helix, sheet, and coil) in proteins are assessed quantitatively. For the first time, techniques for prediction of secondary structural content are presented which rely on the amino acid composition as the only information on the query protein. In our first method, the amino acid composition of an unknown protein is represented by the best (in a least square sense) linear combination of the characteristic amino acid compositions of the three secondary structural types computed from a learning set of tertiary structures. The second technique is a generalization of the first one and takes into account also possible compositional couplings between any two sorts of amino acids. Its mathematical formulation results in an eigenvalue/eigenvector problem of the second moment matrix describing the amino acid compositional fluctuations of secondary structural types in various proteins of a learning set. Possible correlations of the principal directions of the eigenspaces with physical properties of the amino acids were also checked. For example, the first two eigenvectors of the helical eigenspace correlate with the size and hydrophobicity of the residue types respectively. As learning and test sets of tertiary structures, we utilized representative, automatically generated subsets of Protein Data Bank (PDB) consisting of non-homologous protein structures at the resolution thresholds ≤1.8Å, ≤2.0Å, ≤2.5Å, and ≤3.0Å. We show that the consideration of compositional couplings improves prediction accuracy, albeit not dramatically. Whereas in the self-consistency test (learning with the protein to be predicted), a clear decrease of prediction accuracy with worsening resolution is observed, the jackknife test (leave the predicted protein out) yielded best results for the largest dataset (≤3.0 Å, almost no difference to the self-consistency test!), i.e., only this set, with more than 400 proteins, is sufficient for stable computation of the parameters in the prediction function of the second method. The average absolute error in predicting the fraction of helix, sheet, and coil from amino acid composition of the query protein are 13.7, 12.6, and 11.4%, respectively with r.m.s. deviations in the range of 8.6 ÷ 11.8% for the 3.0 Å dataset in a jackknife test. The absolute precision of the average absolute errors is in the range of 1 ÷ 3% as measured for other representative subsets of the PDB. Secondary structural content prediction methods found in the literature have been clustered in accordance with their prediction accuracies. To our surprise, much more complex secondary structure prediction methods utilized for the same purpose of secondary structural content prediction achieve prediction accuracies very similar to those of the present analytic techniques, implying that all the information beyond the amino acid composition is, in fact, mainly utilized for positioning the secondary structural state in the sequence but not for determination of the overall number of residues in a secondary structural type. This result implies that higher prediction accuracies cannot be achieved relying solely on the amino acid composition of an unknown query protein as prediction input. Our prediction program SSCP has been made available as a World Wide Web and E-mail service. © 1996 Wiley-Liss, Inc.     

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.     

PRI-Modeler: extracting RNA structural elements from PDB files of protein-RNA complexes

A complete understanding of protein and RNA structures and their interactions is important for determining the binding sites in protein-RNA complexes. Computational approaches exist for identifying secondary structural elements in proteins from atomic coordinates. However, similar methods have not been developed for RNA, due in part to the very limited structural data so far available. We have developed a set of algorithms for extracting and visualizing secondary and tertiary structures of RNA and for analyzing protein-RNA complexes. These algorithms have been implemented in a web-based program called PRI-Modeler (protein-RNA interaction modeler). Given one or more protein data bank files of protein-RNA complexes, PRI-Modeler analyzes the conformation of the RNA, calculates the hydrogen bond (H bond) and van der Waals interactions between amino acids and nucleotides, extracts secondary and tertiary RNA structure elements, and identifies the patterns of interactions between the proteins and RNAs. This paper presents PRI-Modeler and its application to the hydrogen bond and van der Waals interactions in the most representative set of protein-RNA complexes. The analysis reveals several interesting interaction patterns at various levels. The information provided by PRI-Modeler should prove useful for determining the binding sites in protein-RNA complexes. PRI-Modeler is accessible at http://wilab.inha.ac.kr/primodeler/, and supplementary materials are available in the analysis results section at http://wilab.inha.ac.kr/primodeler/.     

Targeting novel folds for structural genomics

The ultimate goal of structural genomics is to obtain the structure of each protein coded by each gene within a genome to determine gene function. Because of cost and time limitations, it remains impractical to solve the structure for every gene product experimentally. Up to a point, reasonably accurate three‐dimensional structures can be deduced for proteins with homologous sequences by using comparative modeling. Beyond this, fold recognition or threading methods can be used for proteins showing little homology to any known fold, although this is relatively time‐consuming and limited by the library of template folds currently available. Therefore, it is appropriate to develop methods that can increase our knowledge base, expanding our fold libraries by earmarking potentially “novel” folds for experimental structure determination. How can we sift through proteomic data rapidly and yet reliably identify novel folds as targets for structural genomics? We have analyzed a number of simple methods that discriminate between “novel” and “known” folds. We propose that simple alignments of secondary structure elements using predicted secondary structure could potentially be a more selective method than both a simple fold recognition method (GenTHREADER) and standard sequence alignment at finding novel folds when sequences show no detectable homology to proteins with known structures. Proteins 2002;48:44–52. © 2002 Wiley‐Liss, Inc.     

ExSer: A standalone tool to mine protein data bank (PDB) for secondary structural elements

Detailed structural analysis of protein necessitates investigation at primary, secondary and tertiary levels, respectively. Insight into protein secondary structures pave way for understanding the type of secondary structural elements involved (α-helices, β-strands etc.), the amino acid sequence that encode the secondary structural elements, number of residues, length and, percentage composition of the respective elements in the protein. Here we present a standalone tool entitled "ExSer" which facilitate an automated extraction of the amino acid sequence that encode for the secondary structural regions of a protein from the protein data bank (PDB) file. AVAILABILITY: ExSer is freely downloadable from http://code.google.com/p/tool-exser/     

What are the baselines for protein fold recognition?

MOTIVATION: What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein-ranging from the length of the sequence to a knowledge of its secondary structure-to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications? EXPERIMENTS PERFORMED: A number of rapid automatic methods which score similarities between protein domains were devised and tested. These methods ranged from those that incorporated no secondary structure information, such as measuring absolute differences in sequence lengths, to more complex alignments of secondary structure elements. Each method was assessed for accuracy by comparison with the Class Architecture Topology Homology (CATH) classification. Methods were rated against both a random baseline fold assignment method as a lower control and FSSP as an upper control. Similarity trees were constructed in order to evaluate the accuracy of optimum methods at producing a classification of structure. RESULTS: Using a rigorous comparison of methods with CATH, the random fold assignment method set a lower baseline of 11% true positives allowing for 3% false positives and FSSP set an upper benchmark of 47% true positives at 3% false positives. The optimum secondary structure alignment method used here achieved 27% true positives at 3% false positives. Using a less rigorous Critical Assessment of Structure Prediction (CASP)-like sensitivity measurement the random assignment achieved 6%, FSSP-59% and the optimum secondary structure alignment method-32%. Similarity trees produced by the optimum method illustrate that these methods cannot be used alone to produce a viable protein structural classification system. CONCLUSIONS: Simple methods that use perfect secondary structure information to assign folds cannot produce an accurate protein taxonomy, however they do provide useful baselines for fold recognition. In terms of a typical CASP assessment our results suggest that approximately 6% of targets with folds in the databases could be assigned correctly by randomly guessing, and as many as 32% could be recognised by trivial secondary structure comparison methods, given knowledge of their correct secondary structures.     

Simultaneous modeling of multiple loops in proteins.

The most reliable methods for predicting protein structure are by way of homologous extension, using structural information from a closely related protein, or by "threading" through a set of predefined protein folds ("inverse folding"). Both sets of methods provide a model for the core of the protein--the structurally conserved secondary structures. Due to the large variability both in sequence and size of the loops that connect these secondary structures, they generally cannot be modeled using these techniques. Loop-closure algorithms are aimed at predicting loop structures, given their end-to-end distance. Various such algorithms have been described, and all have been tested by predicting the structure of a single loop in a known protein. In this paper we propose a method, which is based on the bond-scaling-relaxation loop-closure algorithm, for simultaneously predicting the structures of multiple loops, and demonstrate that, for two spatially close loops, simultaneous closure invariably leads to more accurate predictions than sequential closure. The accuracy of the predictions obtained for pairs of loops in the size range of 5-7 residues each is comparable to that obtained by other methods, when predicting the structures of single loops: the RMS deviations from the native conformations of various test cases modeled are approximately 0.6-1.7 A for backbone atoms and 1.1-3.3 A for all-atoms.     

Structural analysis based on state-space modeling.

A new method has been developed to compute the probability that each amino acid in a protein sequence is in a particular secondary structural element. Each of these probabilities is computed using the entire sequence and a set of predefined structural class models. This set of structural classes is patterned after Jane Richardson''s taxonomy for the domains of globular proteins. For each structural class considered, a mathematical model is constructed to represent constraints on the pattern of secondary structural elements characteristic of that class. These are stochastic models having discrete state spaces (referred to as hidden Markov models by researchers in signal processing and automatic speech recognition). Each model is a mathematical generator of amino acid sequences; the sequence under consideration is modeled as having been generated by one model in the set of candidates. The probability that each model generated the given sequence is computed using a filtering algorithm. The protein is then classified as belonging to the structural class having the most probable model. The secondary structure of the sequence is then analyzed using a "smoothing" algorithm that is optimal for that structural class model. For each residue position in the sequence, the smoother computes the probability that the residue is contained within each of the defined secondary structural elements of the model. This method has two important advantages: (1) the probability of each residue being in each of the modeled secondary structural elements is computed using the totality of the amino acid sequence, and (2) these probabilities are consistent with prior knowledge of realizable domain folds as encoded in each model. As an example of the method''s utility, we present its application to flavodoxin, a prototypical alpha/beta protein having a central beta-sheet, and to thioredoxin, which belongs to a similar structural class but shares no significant sequence similarity.