Parameterization and classification of the protein universe via geometric techniques

We present a scheme for the classification of 3487 non-redundant protein structures into 1207 non-hierarchical clusters by using recurring structural patterns of three to six amino acids as keys of classification. This results in several signature patterns, which seem to decide membership of a protein in a functional category. The patterns provide clues to the key residues involved in functional sites as well as in protein-protein interaction. The discovered patterns include a "glutamate double bridge" of superoxide dismutase, the functional interface of the serine protease and inhibitor, interface of homo/hetero dimers, and functional sites of several enzyme families. We use geometric invariants to decide superimposability of structural patterns. This allows the parameterization of patterns and discovery of recurring patterns via clustering. The geometric invariant-based approach eliminates the computationally explosive step of pair-wise comparison of structures. The results provide a vast resource for the biologists for experimental validation of the proposed functional sites, and for the design of synthetic enzymes, inhibitors and drugs.     

Determining and visualizing flexibility in protein structures

How to compare the structures of an ensemble of protein conformations is a fundamental problem in structural biology. As has been previously observed, the widely used RMSD measure due to Kabsch, in which a rigid‐body superposition minimizing the least‐squares positional deviations is performed, has its drawbacks when comparing and visualizing a set of flexible protein structures. Here, we develop a method, fleximatch, of protein structure comparison that takes flexibility into account. Based on a distance matrix measure of flexibility, a weighted superposition of distance matrices rather than of atomic coordinates is performed. Subsequently, this allows a consistent determination of (a) a superposition of structures for visualization, (b) a partitioning of the protein structure into rigid molecular components (core atoms), and (c) an atomic mobility measure. The method is suitable for highlighting both particularly flexible and rigid parts of a protein from structures derived from NMR, X‐ray diffraction or molecular simulation. Proteins 2015; 83:820–826. © 2015 Wiley Periodicals, Inc.     

Patterns,structures, and amino acid frequencies in structural building blocks,a protein secondary structure classification scheme

To study local structures in proteins, we previously developed an autoassociative artificial neural network (autoANN) and clustering tool to discover intrinsic features of macromolecular structures. The hidden unit activations computed by the trained autoANN are a convenient low-dimensional encoding of the local protein backbone structure. Clustering these activation vectors results in a unique classification of protein local structural features called Structural Building Blocks (SBBs). Here we describe application of this method to a larger database of proteins, verification of the applicability of this method to structure classification, and subsequent analysis of amino acid frequencies and several commonly occurring patterns of SBBs. The SBB classification method has several interesting properties: 1) it identifies the regular secondary structures, α helix and β strand; 2) it consistently identifies other local structure features (e.g., helix caps and strand caps); 3) strong amino acid preferences are revealed at some positions in some SBBs; and 4) distinct patterns of SBBs occur in the “random coil” regions of proteins. Analysis of these patterns identifies interesting structural motifs in the protein backbone structure, indicating that SBBs can be used as “building blocks” in the analysis of protein structure. This type of pattern analysis should increase our understanding of the relationship between protein sequence and local structure, especially in the prediction of protein structures. © 1997 Wiley-Liss, Inc.     

Comprehensive evaluation of protein structure alignment methods: scoring by geometric measures

We report the largest and most comprehensive comparison of protein structural alignment methods. Specifically, we evaluate six publicly available structure alignment programs: SSAP, STRUCTAL, DALI, LSQMAN, CE and SSM by aligning all 8,581,970 protein structure pairs in a test set of 2930 protein domains specially selected from CATH v.2.4 to ensure sequence diversity. We consider an alignment good if it matches many residues, and the two substructures are geometrically similar. Even with this definition, evaluating structural alignment methods is not straightforward. At first, we compared the rates of true and false positives using receiver operating characteristic (ROC) curves with the CATH classification taken as a gold standard. This proved unsatisfactory in that the quality of the alignments is not taken into account: sometimes a method that finds less good alignments scores better than a method that finds better alignments. We correct this intrinsic limitation by using four different geometric match measures (SI, MI, SAS, and GSAS) to evaluate the quality of each structural alignment. With this improved analysis we show that there is a wide variation in the performance of different methods; the main reason for this is that it can be difficult to find a good structural alignment between two proteins even when such an alignment exists. We find that STRUCTAL and SSM perform best, followed by LSQMAN and CE. Our focus on the intrinsic quality of each alignment allows us to propose a new method, called "Best-of-All" that combines the best results of all methods. Many commonly used methods miss 10-50% of the good Best-of-All alignments. By putting existing structural alignments into proper perspective, our study allows better comparison of protein structures. By highlighting limitations of existing methods, it will spur the further development of better structural alignment methods. This will have significant biological implications now that structural comparison has come to play a central role in the analysis of experimental work on protein structure, protein function and protein evolution.     

The Protein Coil Library: a structural database of nonhelix, nonstrand fragments derived from the PDB

Approximately half the structure of folded proteins is either alpha-helix or beta-strand. We have developed a convenient repository of all remaining structure after these two regular secondary structure elements are removed. The Protein Coil Library (http://roselab.jhu.edu/coil/) allows rapid and comprehensive access to non-alpha-helix and non-beta-strand fragments contained in the Protein Data Bank (PDB). The library contains both sequence and structure information together with calculated torsion angles for both the backbone and side chains. Several search options are implemented, including a query function that uses output from popular PDB-culling servers directly. Additionally, several popular searches are stored and updated for immediate access. The library is a useful tool for exploring conformational propensities, turn motifs, and a recent model of the unfolded state.     

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

A novel method for predicting the secondary structures of proteins from amino acid sequence has been presented. The protein secondary structure seqlets that are analogous to the words in natural language have been extracted. These seqlets will capture the relationship between amino acid sequence and the secondary structures of proteins and further form the protein secondary structure dictionary. To be elaborate, the dictionary is organism-specific. Protein secondary structure prediction is formulated as an integrated word segmentation and part of speech tagging problem. The word-lattice is used to represent the results of the word segmentation and the maximum entropy model is used to calculate the probability of a seqlet tagged as a certain secondary structure type. The method is markovian in the seqlets, permitting efficient exact calculation of the posterior probability distribution over all possible word segmentations and their tags by viterbi algorithm. The optimal segmentations and their tags are computed as the results of protein secondary structure prediction. The method is applied to predict the secondary structures of proteins of four organisms respectively and compared with the PHD method. The results show that the performance of this method is higher than that of PHD by about 3.9% Q3 accuracy and 4.6% SOV accuracy. Combining with the local similarity protein sequences that are obtained by BLAST can give better prediction. The method is also tested on the 50 CASP5 target proteins with Q₃ accuracy 78.9% and SOV accuracy 77.1%. A web server for protein secondary structure prediction has been constructed which is available at http://www.insun.hit.edu.cn:81/demos/biology/index.html.     

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

1 Introduction The prediction of protein structure and function from amino acid sequences is one of the most impor-tant problems in molecular biology. This problem is becoming more pressing as the number of known pro-tein sequences is explored as a result of genome and other sequencing projects, and the protein sequence- structure gap is widening rapidly[1]. Therefore, com-putational tools to predict protein structures are needed to narrow the widening gap. Although the prediction of three dim…     

A 3D building blocks approach to analyzing and predicting structure of proteins

A new approach is introduced for analyzing and ultimately predicting protein structures, defined at the level of C alpha coordinates. We analyze hexamers (oligopeptides of six amino acid residues) and show that their structure tends to concentrate in specific clusters rather than vary continuously. Thus, we can use a limited set of standard structural building blocks taken from these clusters as representatives of the repertoire of observed hexamers. We demonstrate that protein structures can be approximated by concatenating such building blocks. We have identified about 100 building blocks by applying clustering algorithms, and have shown that they can "replace" about 76% of all hexamers in well-refined known proteins with an error of less than 1 A, and can be joined together to cover 99% of the residues. After replacing each hexamer by a standard building block with similar conformation, we can approximately reconstruct the actual structure by smoothly joining the overlapping building blocks into a full protein. The reconstructed structures show, in most cases, high resemblance to the original structure, although using a limited number of building blocks and local criteria of concatenating them is not likely to produce a very precise global match. Since these building blocks reflect, in many cases, some sequence dependency, it may be possible to use the results of this study as a basis for a protein structure prediction procedure.     

Increasing protein stability using a rational approach combining sequence homology and structural alignment: Stabilizing the WW domain

This study shows that a combination of sequence homology and structural information can be used to increase the stability of the WW domain by 2.5 kcal mol(-1) and increase the T(m) by 28 degrees C. Previous homology-based protein design efforts typically investigate positions with low sequence identity, whereas this study focuses on semi-conserved core residues and proximal residues, exploring their role(s) in mediating stabilizing interactions on the basis of structural considerations. The A20R and L30Y mutations allow increased hydrophobic interactions because of complimentary surfaces and an electrostatic interaction with a third residue adjacent to the ligand-binding hydrophobic cluster, increasing stability significantly beyond what additivity would predict for the single mutations. The D34T mutation situated in a pi-turn possibly disengages Asn31, allowing it to make up to three hydrogen bonds with the backbone in strand 1 and loop 2. The synergistic mutations A20R/L30Y in combination with the remotely located mutation D34T add together to create a hYap WW domain that is significantly more stable than any of the protein structures on which the design was based (Pin and FBP28 WW domains).     

CHOP proteins into structural domain-like fragments

We developed a method CHOP dissecting proteins into domain-like fragments. The basic idea was to cut proteins beginning from very reliable experimental information (PDB), proceeding to expert annotations of domain-like regions (Pfam-A), and completing through cuts based on termini of known proteins. In this way, CHOP dissected more than two thirds of all proteins from 62 proteomes. Analysis of our structural domain-like fragments revealed four surprising results. First, >70% of all dissected proteins contained more than one fragment. Second, most domains spanned on average over approximately 100 residues. This average was similar for eukaryotic and prokaryotic proteins, and it is also valid-although previously not described-for all proteins in the PDB. Third, single-domain proteins were significant longer than most domains in multidomain proteins. Fourth, three fourths of all domains appeared shorter than 210 residues. We believe that our CHOP fragments constituted an important resource for functional and structural genomics. Nevertheless, our main motivation to develop CHOP was that the single-linkage clustering method failed to adequately group full-length proteins. In contrast, CLUP-the simple clustering scheme CLUP introduced here-succeeded largely to group the CHOP fragments from 62 proteomes such that all members of one cluster shared a basic structural core. CLUP found >63,000 multi- and >118,000 single-member clusters. Although most fragments were restricted to a particular cluster, approximately 24% of the fragments were duplicated in at least two clusters. Our thresholds for grouping two fragments into the same cluster were rather conservative. Nevertheless, our results suggested that structural genomics initiatives have to target >30,000 fragments to at least cover the multimember clusters in 62 proteomes.     

Architecture of the herpes simplex virus major capsid protein derived from structural bioinformatics

The dispositions of 39 alpha helices of greater than 2.5 turns and four beta sheets in the major capsid protein (VP5, 149 kDa) of herpes simplex virus type 1 were identified by computational and visualization analysis from the 8.5A electron cryomicroscopy structure of the whole capsid. The assignment of helices in the VP5 upper domain was validated by comparison with the recently determined crystal structure of this region. Analysis of the spatial arrangement of helices in the middle domain of VP5 revealed that the organization of a tightly associated bundle of ten helices closely resembled that of a domain fold found in the annexin family of proteins. Structure-based sequence searches suggested that sequences in both the N and C-terminal portions of the VP5 sequence contribute to this domain. The long helices seen in the floor domain of VP5 form an interconnected network within and across capsomeres. The combined structural and sequence-based informatics has led to an architectural model of VP5. This model placed in the context of the capsid provides insights into the strategies used to achieve viral capsid stability.     

A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality

Background  

Identifying all protein complexes in an organism is a major goal of systems biology. In the past 18 months, the results of two genome-scale tandem affinity purification-mass spectrometry (TAP-MS) assays in yeast have been published, along with corresponding complex maps. For most complexes, the published data sets were surprisingly uncorrelated. It is therefore useful to consider the raw data from each study and generate an accurate complex map from a high-confidence data set that integrates the results of these and earlier assays.     

Conformational contagion in a protein: structural properties of a chameleon sequence

Certain sequences, known as chameleon sequences, take both alpha- and beta-conformations in natural proteins. We demonstrate that a wild chameleon sequence fused to the C-terminal alpha-helix or beta-sheet in foreign stable proteins from hyperthermophiles forms the same conformation as the host secondary structure. However, no secondary structural formation is observed when the sequence is attached to the outside of the secondary structure. These results indicate that this sequence inherently possesses an ability to make either alpha- or beta-conformation, depending on the sequentially neighboring secondary structure if little other nonlocal interaction occurs. Thus, chameleon sequences take on a satellite state through contagion by the power of a secondary structure. We propose this "conformational contagion" as a new nonlocal determinant factor in protein structure and misfolding related to protein conformational diseases.     

Customised fragments libraries for protein structure prediction based on structural class annotations

Background

Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets’ lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process.

Results

Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (−0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area.

Conclusions

Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0576-2) contains supplementary material, which is available to authorized users.     

Automated search of natively folded protein fragments for high-throughput structure determination in structural genomics

Structural genomic projects envision almost routine protein structure determinations, which are currently imaginable only for small proteins with molecular weights below 25,000 Da. For larger proteins, structural insight can be obtained by breaking them into small segments of amino acid sequences that can fold into native structures, even when isolated from the rest of the protein. Such segments are autonomously folding units (AFU) and have sizes suitable for fast structural analyses. Here, we propose to expand an intuitive procedure often employed for identifying biologically important domains to an automatic method for detecting putative folded protein fragments. The procedure is based on the recognition that large proteins can be regarded as a combination of independent domains conserved among diverse organisms. We thus have developed a program that reorganizes the output of BLAST searches and detects regions with a large number of similar sequences. To automate the detection process, it is reduced to a simple geometrical problem of recognizing rectangular shaped elevations in a graph that plots the number of similar sequences at each residue of a query sequence. We used our program to quantitatively corroborate the premise that segments with conserved sequences correspond to domains that fold into native structures. We applied our program to a test data set composed of 99 amino acid sequences containing 150 segments with structures listed in the Protein Data Bank, and thus known to fold into native structures. Overall, the fragments identified by our program have an almost 50% probability of forming a native structure, and comparable results are observed with sequences containing domain linkers classified in SCOP. Furthermore, we verified that our program identifies AFU in libraries from various organisms, and we found a significant number of AFU candidates for structural analysis, covering an estimated 5 to 20% of the genomic databases. Altogether, these results argue that methods based on sequence similarity can be useful for dissecting large proteins into small autonomously folding domains, and such methods may provide an efficient support to structural genomics projects.     

Threading structural model of the manganese-stabilizing protein PsbO reveals presence of two possible beta-sandwich domains.

The manganese-stabilizing protein (PsbO) is an essential component of photosystem II (PSII) and is present in all oxyphotosynthetic organisms. PsbO allows correct water splitting and oxygen evolution by stabilizing the reactions driven by the manganese cluster. Despite its important role, its structure and detailed functional mechanism are still unknown. In this article we propose a structural model based on fold recognition and molecular modeling. This model has additional support from a study of the distribution of characteristics of the PsbO sequence family, such as the distribution of conserved, apolar, tree-determinants, and correlated positions. Our threading results consistently showed PsbO as an all-beta (beta) protein, with two homologous beta domains of approximately 120 amino acids linked by a flexible Proline-Glycine-Glycine (PGG) motif. These features are compatible with a general elongated and flexible architecture, in which the two domains form a sandwich-type structure with Greek key topology. The first domain is predicted to include 8 to 9 beta-strands, the second domain 6 to 7 beta-strands. An Ig-like beta-sandwich structure was selected as a template to build the 3-D model. The second domain has, between the strands, long-loops rich in Pro and Gly that are difficult to model. One of these long loops includes a highly conserved region (between P148 and P174) and a short alpha-helix (between E181 and N188)). These regions are characteristic parts of PsbO and show that the second domain is not so similar to the template. Overall, the model was able to account for much of the experimental data reported by several authors, and it would allow the detection of key residues and regions that are proposed in this article as essential for the structure and function of PsbO.     

Secondary structure-based profiles: use of structure-conserving scoring tables in searching protein sequence databases for structural similarities

The profile method, for detecting distantly related proteins by sequence comparison, has been extended to incorporate secondary structure information from known X-ray structures. The sequence of a known structure is aligned to sequences of other members of a given folding class. From the known structure, the secondary structure (alpha-helix, beta-strand or "other") is assigned to each position of the aligned sequences. As in the standard profile method, a position-dependent scoring table, termed a profile, is calculated from the aligned sequences. However, rather than using the standard Dayhoff mutation table in calculating the profile, we use distinct amino acid mutation tables for residues in alpha-helices, beta-strands or other secondary structures to calculate the profile. In addition, we also distinguish between internal and external residues. With this new secondary structure-based profile method, we created a profile for eight-stranded, antiparallel beta barrels of the insecticyanin folding class. It is based on the sequences of retinol-binding protein, insecticyanin and beta-lactoglobulin. Scanning the sequence database with this profile, it was possible to detect the sequence of avidin. The structure of streptavidin is known, and it appears to be distantly related to the antiparallel beta barrels. Also detected is the sequence of complement component C8, which we therefore predict to be a member of this folding class.     

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.