A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.     

Exploring protein fold space by secondary structure prediction using data distribution method on Grid platform

MOTIVATION: Since the newly developed Grid platform has been considered as a powerful tool to share resources in the Internet environment, it is of interest to demonstrate an efficient methodology to process massive biological data on the Grid environments at a low cost. This paper presents an efficient and economical method based on a Grid platform to predict secondary structures of all proteins in a given organism, which normally requires a long computation time through sequential execution, by means of processing a large amount of protein sequence data simultaneously. From the prediction results, a genome scale protein fold space can be pursued. RESULTS: Using the improved Grid platform, the secondary structure prediction on genomic scale and protein topology derived from the new scoring scheme for four different model proteomes was presented. This protein fold space was compared with structures from the Protein Data Bank, database and it showed similarly aligned distribution. Therefore, the fold space approach based on this new scoring scheme could be a guideline for predicting a folding family in a given organism.     

Connecting the protein structure universe by using sparse recurring fragments

The quest to order and classify protein structures has lead to various classification schemes, focusing mostly on hierarchical relationships between structural domains. At the coarsest classification level, such schemes typically identify hundreds of types of fundamental units called folds. As a result, we picture protein structure space as a collection of isolated fold islands. It is obvious, however, that many protein folds share structural and functional commonalities. Locating those commonalities is important for our understanding of protein structure, function, and evolution. Here, we present an alternative view of the protein fold space, based on an interfold similarity measure that is related to the frequency of fragments shared between folds. In this view, protein structures form a complicated, crossconnected network with very interesting topology. We show that interfold similarity based on sequence/structure fragments correlates well with similarities of functions between protein populations in different folds.     

Helix-bundle membrane protein fold templates

In the fold recognition approach to structure prediction, a sequence is tested for compatibility with an already known fold. For membrane proteins, however, few folds have been determined experimentally. Here the feasibility of computing the vast majority of likely membrane protein folds is tested. The results indicate that conformation space can be effectively sampled for small numbers of helices. The vast majority of potential monomeric membrane protein structures can be represented by about 30-folds for three helices, but increases exponentially to about 1,500,000 folds for seven helices. The generated folds could serve as templates for fold recognition or as starting points for conformational searches that are well distributed throughout conformation space.     

Structural Bridges through Fold Space

Several protein structure classification schemes exist that partition the protein universe into structural units called folds. Yet these schemes do not discuss how these units sit relative to each other in a global structure space. In this paper we construct networks that describe such global relationships between folds in the form of structural bridges. We generate these networks using four different structural alignment methods across multiple score thresholds. The networks constructed using the different methods remain a similar distance apart regardless of the probability threshold defining a structural bridge. This suggests that at least some structural bridges are method specific and that any attempt to build a picture of structural space should not be reliant on a single structural superposition method. Despite these differences all representations agree on an organisation of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β. We project estimated fold ages onto the networks and find that not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying an edge. We also examine different centrality measures for folds within the networks and how these relate to fold age. While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and that of the more recently evolved structures to provide the peripheral landscape. These findings suggest that evolutionary information is encoded along these structural bridges. Finally, we identify four highly central pivotal folds representing dominant topological features which act as key attractors within our landscapes.     

A comparison of genotype-phenotype maps for RNA and proteins

The relationship between the genotype (sequence) and the phenotype (structure) of macromolecules affects their ability to evolve new structures and functions. We here compare the genotype space organization of proteins and RNA molecules to identify differences that may affect this ability. To this end, we computationally study the genotype-phenotype relationship for short RNA and lattice proteins of a reduced monomer alphabet size, to make exhaustive analysis and direct comparison of their genotype spaces feasible. We find that many fewer protein molecules than RNA molecules fold, but they fold into many more structures than RNA. In consequence, protein phenotypes have smaller genotype networks whose member genotypes tend to be more similar than for RNA phenotypes. Neighborhoods in sequence space of a given radius around an RNA molecule contain more novel structures than for protein molecules. We compare this property to evidence from natural RNA and protein molecules, and conclude that RNA genotype space may be more conducive to the evolution of new structure phenotypes.     

Protein structure comparison: implications for the nature of 'fold space', and structure and function prediction

The identification of geometric relationships between protein structures offers a powerful approach to predicting the structure and function of proteins. Methods to detect such relationships range from human pattern recognition to a variety of mathematical algorithms. A number of schemes for the classification of protein structure have found widespread use and these implicitly assume the organization of protein structure space into discrete categories. Recently, an alternative view has emerged in which protein fold space is seen as continuous and multidimensional. Significant relationships have been observed between proteins that belong to what have been termed different 'folds'. There has been progress in the use of these relationships in the prediction of protein structure and function.     

Cradle-loop barrels and the concept of metafolds in protein classification by natural descent

Current classification systems for protein structure show many inconsistencies both within and between systems. The metafold concept was introduced to identify fold similarities by consensus and thus provide a more unified view of fold space. Using cradle-loop barrels as an example, we propose to use the metafold as the next hierarchical level above the fold, encompassing a group of topologically related folds for which a homologous relationship has been substantiated. We see this as an important step on the way to a classification of proteins by natural descent.     

Exploring protein sequence space using knowledge-based potentials

Knowledge-based potentials can be used to decide whether an amino acid sequence is likely to fold into a prescribed native protein structure. We use this idea to survey the sequence-structure relations in protein space. In particular, we test the following two propositions which were found to be important for efficient evolution: the sequences folding into a particular native fold form extensive neutral networks that percolate through sequence space. The neutral networks of any two native folds approach each other to within a few point mutations. Computer simulations using two very different potential functions, M. Sippl's PROSA pair potential and a neural network based potential, are used to verify these claims.     

Globin-like蛋白质折叠类型识别

蛋白质折叠类型识别是蛋白质结构研究的重要内容.以SCOP中的Globin-like折叠为研究对象,选择其中序列同一性小于25%的17个代表性蛋白质为训练集,采用机器和人工结合的办法进行结构比对,产生序列排比,经过训练得到了适合Globin-like折叠的概形隐马尔科夫模型(profile HMM)用于该折叠类型的识别.以Astrall.65中的68057个结构域样本进行检验,识别敏感度为99.64%,特异性100%.在折叠类型水平上,与Pfam和SUPERFAMILY单纯使用序列比对构建的HMM相比,所用模型由多于100个归为一个,仍然保持了很高的识别效果.结果表明:对序列相似度很低但具有相同折叠类型的蛋白质,可以通过引入结构比对的方法建立统一的HMM模型,实现高准确率的折叠类型识别.     

De novo protein design. II. Plasticity in sequence space.

It is generally accepted that many different protein sequences have similar folded structures, and that there is a relatively high probability that a new sequence possesses a previously observed fold. An indirect consequence of this is that protein design should define the sequence space accessible to a given structure, rather than providing a single optimized sequence. We have recently developed a new approach for protein sequence design, which optimizes the complete sequence of a protein based on the knowledge of its backbone structure, its amino acid composition and a physical energy function including van der Waals interactions, electrostatics, and environment free energy. The specificity of the designed sequence for its template backbone is imposed by keeping the amino acid composition fixed. Here, we show that our procedure converges in sequence space, albeit not to the native sequence of the protein. We observe that while polar residues are well conserved in our designed sequences, non-polar amino acids at the surface of a protein are often replaced by polar residues. The designed sequences provide a multiple alignment of sequences that all adopt the same three-dimensional fold. This alignment is used to derive a profile matrix for chicken triose phosphate isomerase, TIM. The matrix is found to recognize significantly the native sequence for TIM, as well as closely related sequences. Possible application of this approach to protein fold recognition is discussed.     

Fishing with (Proto)Net-a principled approach to protein target selection

Structural genomics strives to represent the entire protein space. The first step towards achieving this goal is by rationally selecting proteins whose structures have not been determined, but that represent an as yet unknown structural superfamily or fold. Once such a structure is solved, it can be used as a template for modelling homologous proteins. This will aid in unveiling the structural diversity of the protein space. Currently, no reliable method for accurate 3D structural prediction is available when a sequence or a structure homologue is not available. Here we present a systematic methodology for selecting target proteins whose structure is likely to adopt a new, as yet unknown superfamily or fold. Our method takes advantage of a global classification of the sequence space as presented by ProtoNet-3D, which is a hierarchical agglomerative clustering of the proteins of interest (the proteins in Swiss-Prot) along with all solved structures (taken from the PDB). By navigating in the scaffold of ProtoNet-3D, we yield a prioritized list of proteins that are not yet structurally solved, along with the probability of each of the proteins belonging to a new superfamily or fold. The sorted list has been self-validated against real structural data that was not available when the predictions were made. The practical application of using our computational-statistical method to determine novel superfamilies for structural genomics projects is also discussed.     

蛋白质折叠类型分类方法及分类数据库

蛋白质折叠规律研究是生命科学重大前沿课题,折叠分类是蛋白质折叠研究的基础。目前的蛋白质折叠类型分类基本上靠专家完成,不同的库分类并不相同,迫切需要一个建立在统一原理基础上的蛋白质折叠类型数据库。本文以ASTRAL-1.65数据库中序列同源性在25%以下、分辨率小于2.5的蛋白为基础,通过对蛋白质空间结构的观察及折叠类型特征的分析,提出以蛋白质折叠核心为中心、以蛋白质结构拓扑不变性为原则、以蛋白质折叠核心的规则结构片段组成、连接和空间排布为依据的蛋白质折叠类型分类方法,建立了低相似度蛋白质折叠分类数据库——LIFCA,包含259种蛋白质折叠类型。数据库的建立,将为进一步的蛋白质折叠建模及数据挖掘、蛋白质折叠识别、蛋白质折叠结构进化研究奠定基础。     

Characterization of protein fold by wide-angle X-ray solution scattering

Wide-angle X-ray solution scattering (WAXS) patterns contain substantial information about the three-dimensional structure of a protein. Although WAXS data have far less information than is required for determination of a full three-dimensional structure, the actual amount of information contained in a WAXS pattern has not been carefully quantified. Here we carry out an analysis of the amount of information that can be extracted from a WAXS pattern and demonstrate that it is adequate to estimate the secondary-structure content of a protein and to strongly limit its possible tertiary structures. WAXS patterns computed from the atomic coordinates of a set of 498 protein domains representing all of known fold space were used as the basis for constructing a multidimensional space of all corresponding WAXS patterns (‘WAXS space’). Within WAXS space, each scattering pattern is represented by a single vector. A principal components analysis was carried out to identify those directions in WAXS space that provide the greatest discrimination among patterns. The number of dimensions that provide significant discrimination among protein folds agrees well with the number of independent parameters estimated from a naïve Shannon sampling theorem approach. Estimates of the relative abundances of secondary structures were made using training/test sets derived from this data set. The average error in the estimate of α-helical content was 11%, and of β-sheet content was 9%. The distribution of proteins that are members of the four structure classes, α, β, α/β and α+β, are well separated in WAXS space when data extending to a spacing of 2.2 Å are used. Quantification of the information embedded within a WAXS pattern indicates that these data can be used as a powerful constraint in homology modeling of protein structures.     

An in silico exploration of the neutral network in protein sequence space

Designating amino-acid sequences that fold into a common main-chain structure as "neutral sequences" for the structure, regardless of their function or stability, we investigated the distribution of neutral sequences in protein sequence space. For four distinct target structures (alpha, beta,alpha/beta and alpha+beta types) with the same chain length of 108, we generated the respective neutral sequences by using the inverse folding technique with a knowledge-based potential function. We assumed that neutral sequences for a protein structure have Z scores higher than or equal to fixed thresholds, where thresholds are defined as the Z score for the corresponding native sequence (case 1) or much greater Z score (case 2). An exploring walk simulation suggested that the neutral sequences mapped into the sequence space were connected with each other through straight neutral paths and formed an inherent neutral network over the sequence space. Through another exploring walk simulation, we investigated contiguous regions between or among the neutral networks for the distinct protein structures and obtained the following results. The closest approach distance between the two neutral networks ranged from 5 to 29 on the Hamming distance scale, showing a linear increase against the threshold values. The sequences located at the "interchange" regions between the two neutral networks have intermediate sequence-profile-scores for both corresponding structures. Introducing a "ball" in the sequence space that contains at least one neutral sequence for each of the four structures, we found that the minimal radius of the ball that is centered at an arbitrary position ranged from 35 to 50, while the minimal radius of the ball that is centered at a certain special position ranged from 20 to 30, in the Hamming distance scale. The relatively small Hamming distances (5-30) may support an evolution mechanism by transferring from a network for a structure to another network for a more beneficial structure via the interchange regions.     

Development of a motif‐based topology‐independent structure comparison method to identify evolutionarily related folds

Structure conservation, functional similarities, and homologous relationships that exist across diverse protein topologies suggest that some regions of the protein fold universe are continuous. However, the current structure classification systems are based on hierarchical organizations, which cannot accommodate structural relationships that span fold definitions. Here, we describe a novel, super‐secondary‐structure motif‐based, topology‐independent structure comparison method (SmotifCOMP) that is able to quantitatively identify structural relationships between disparate topologies. The basis of SmotifCOMP is a systematically defined super‐secondary‐structure motif library whose representative geometries are shown to be saturated in the Protein Data Bank and exhibit a unique distribution within the known folds. SmotifCOMP offers a robust and quantitative technique to compare domains that adopt different topologies since the method does not rely on a global superposition. SmotifCOMP is used to perform an exhaustive comparison of the known folds and the identified relationships are used to produce a nonhierarchical representation of the fold space that reflects the notion of a continuous and connected fold universe. The current work offers insight into previously hypothesized evolutionary relationships between disparate folds and provides a resource for exploring novel ones. Proteins 2016; 84:1859–1874. © 2016 Wiley Periodicals, Inc.     

Evolution of protein sequences and structures.

The relationship between sequence similarity and structural similarity has been examined in 36 protein families with five or more diverse members whose structures are known. The structural similarity within a family (as determined with the DALI structure comparison program) is linearly related to sequence similarity (as determined by a Smith-Waterman search of the protein sequences in the structure database). The correlation between structural similarity and sequence similarity is very high; 18 of the 36 families had linear correlation coefficients r>/=0.878, and only nine had correlation coefficients r相似文献   

Sequence annotation of nuclear receptor ligand-binding domains by automated homology modeling

The quality of three-dimensional homology models derived from protein sequences provides an independent measure of the suitability of a protein sequence for a certain fold. We have used automated homology modeling and model assessment tools to identify putative nuclear hormone receptor ligand-binding domains in the genome of Caenorhabditis elegans. Our results indicate that the availability of multiple crystal structures is crucial to obtaining useful models in this receptor family. The majority of annotated mammalian nuclear hormone receptors could be assigned to a ligand-binding domain fold by using the best model derived from any of four template structures. This strategy also assigned the ligand-binding domain fold to a number of C.elegans. sequences without prior annotation. Interestingly, the retinoic acid receptor crystal structure contributed most to the number of sequences that could be assigned to a ligand-binding domain fold. Several causes for this can be suggested, including the high quality of this protein structure in terms of our assessment tools, similarity between the biological function or ligand of this receptor and the modeled genes and gene duplication in C.elegans.     

Protein domain assignment from the recurrence of locally similar structures

Domains are basic units of protein structure and essential for exploring protein fold space and structure evolution. With the structural genomics initiative, the number of protein structures in the Protein Databank (PDB) is increasing dramatically and domain assignments need to be done automatically. Most existing structural domain assignment programs define domains using the compactness of the domains and/or the number and strength of intra-domain versus inter-domain contacts. Here we present a different approach based on the recurrence of locally similar structural pieces (LSSPs) found by one-against-all structure comparisons with a dataset of 6373 protein chains from the PDB. Residues of the query protein are clustered using LSSPs via three different procedures to define domains. This approach gives results that are comparable to several existing programs that use geometrical and other structural information explicitly. Remarkably, most of the proteins that contribute the LSSPs defining a domain do not themselves contain the domain of interest. This study shows that domains can be defined by a collection of relatively small locally similar structural pieces containing, on average, four secondary structure elements. In addition, it indicates that domains are indeed made of recurrent small structural pieces that are used to build protein structures of many different folds as suggested by recent studies.