An alternative view of protein fold space

Shindyalov, Ilya N.; Bourne, Philip E.

doi:10.1002/(sici)1097-0134(20000215)38:3<247::aid-prot2>3.0.co;2-t

Cited by 87 publications

(62 citation statements)

References 32 publications

(47 reference statements)

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“…4,38 These studies have emphasized the discreteness of space on the domain level of protein structures. On the other hand, recently Shindyalov & Bourne 39 pointed out that substructures obtained from an all-against-all structure comparison using their CE method sometimes distribute among protein domains transgressing their respective fold types. Their substructures are around 130 residue long continuous chains, longer than the conventional concept of supersecondary structure.…”

Section: Discussionmentioning

confidence: 99%

“…For comparison, structure alignments of the same protein pairs are calculated using CE. 36 The plot clearly shows that alignments by SAL tend to have a larger coverage with a smaller RMSD compared to those generated by the CE method. An extreme case is the alignment of 1aonO (97 residues long, b roll) to 1di1A (290 residues long, orthogonal a bundle), where CE gives a RMSD of 15.4 Å with 89.7% coverage, while SAL gives a RMSD of 3.7 Å and 60.8% coverage, inserting 22 gaps.…”

Section: Identification Of the Native Foldmentioning

confidence: 99%

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The PDB is a Covering Set of Small Protein Structures

Kihara

Skolnick

2003

Journal of Molecular Biology

109

115

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Structure comparisons of all representative proteins have been done. Employing the relative root mean square deviation (RMSD) from native enables the assessment of the statistical significance of structure alignments of different lengths in terms of a Z-score. Two conclusions emerge: first, proteins with their native fold can be distinguished by their Z-score. Second and somewhat surprising, all small proteins up to 100 residues in length have significant structure alignments to other proteins in a different secondary structure and fold class; i.e. 24.0% of them have 60% coverage by a template protein with a RMSD below 3.5 Å and 6.0% have 70% coverage. If the restriction that we align proteins only having different secondary structure types is removed, then in a representative benchmark set of proteins of 200 residues or smaller, 93% can be aligned to a single template structure (with average sequence identity of 9.8%), with a RMSD less than 4 Å , and 79% average coverage. In this sense, the current Protein Data Bank (PDB) is almost a covering set of small protein structures. The length of the aligned region (relative to the whole protein length) does not differ among the top hit proteins, indicating that protein structure space is highly dense. For larger proteins, non-related proteins can cover a significant portion of the structure. Moreover, these top hit proteins are aligned to different parts of the target protein, so that almost the entire molecule can be covered when combined. The number of proteins required to cover a target protein is very small, e.g. the top ten hit proteins can give 90% coverage below a RMSD of 3.5 Å for proteins up to 320 residues long. These results give a new view of the nature of protein structure space, and its implications for protein structure prediction are discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Identification Of the Native Foldmentioning

confidence: 99%

The PDB is a Covering Set of Small Protein Structures

Kihara

Skolnick

2003

Journal of Molecular Biology

109

115

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“…This set of structure pairs is partitioned into four sets: (1) pairs that agree on the three CATH classifiers: Classification/Architecture/Topology (the CAT set), (2) pairs that agree only on the first two classifiers (the CA set), (3) pairs that agree only on the first classifier (the C set) and (4) others. In Figure 3 we plot, for several threshold values of the geometric similarity measures (less than 2.5 Å, 3 Å, …, 5 Å), the number of alignments found at that level of similarity (upper panel) as well as the percentage of each of the four sets found at that level (lower panel).…”

Section: Analysis Of the Good Alignments Found By The Best-of-all Methodsmentioning

confidence: 99%

Comprehensive Evaluation of Protein Structure Alignment Methods: Scoring by Geometric Measures

Kolodny

Koehl

Levitt

2005

Journal of Molecular Biology

268

293

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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.

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“…Alternatively, "continuous" implies that the space between these entities is generally populated by crossfold similarities (e.g., refs. 2,5,6,9,[13][14][15]. If such similarities are abundant, then one must account for them when organizing and searching proteins (5,8,16).…”

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confidence: 99%

Global view of the protein universe

Nepomnyachiy

Ben‐Tal

Kolodny

2014

Proc. Natl. Acad. Sci. U.S.A.

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To explore protein space from a global perspective, we consider 9,710 SCOP (Structural Classification of Proteins) domains with up to 70% sequence identity and present all similarities among them as networks: In the "domain network," nodes represent domains, and edges connect domains that share "motifs," i.e., significantly sized segments of similar sequence and structure. We explore the dependence of the network on the thresholds that define the evolutionary relatedness of the domains. At excessively strict thresholds the network falls apart completely; for very lax thresholds, there are network paths between virtually all domains. Interestingly, at intermediate thresholds the network constitutes two regions that can be described as "continuous" versus "discrete." The continuous region comprises a large connected component, dominated by domains with alternating alpha and beta elements, and the discrete region includes the rest of the domains in isolated islands, each generally corresponding to a fold. We also construct the "motif network," in which nodes represent recurring motifs, and edges connect motifs that appear in the same domain. This network also features a large and highly connected component of motifs that originate from domains with alternating alpha/ beta elements (and some all-alpha domains), and smaller isolated islands. Indeed, the motif network suggests that nature reuses such motifs extensively. The networks suggest evolutionary paths between domains and give hints about protein evolution and the underlying biophysics. They provide natural means of organizing protein space, and could be useful for the development of strategies for protein search and design.protein cooccurrence networks | protein similarity networks H ow are proteins related to each other? Which physicochemical considerations affect protein evolution and how? A global view of the protein universe may shed light on these fundamental questions. It could also suggest new strategies for protein search and design (1-3). However, forming a global picture of the protein universe is difficult because we have to piece it together from the many local glimpses that our empirical data and computational tools provide. In other words, a global picture needs to portray the relationships among all proteins, yet we only have evidence of such relationships among several proteins, based on the similarity between their sequences, structures, and functions. The considerable size of the Protein Data Bank (4) also complicates this task.In particular, an intensely debated question is whether protein space is "discrete" or "continuous" (2, 3, 5-10). These terms are loosely defined. Discrete implies that the global picture consists of separate, island-like, structural entities. In the hierarchical protein domains Structural Classification of Proteins (SCOP) (11) these entities are termed "folds," and in the CATH database (12) they are called "topologies." Alternatively, "continuous" implies that the space between these entities is generally populated by...

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An alternative view of protein fold space

Cited by 87 publications

References 32 publications

The PDB is a Covering Set of Small Protein Structures

The PDB is a Covering Set of Small Protein Structures

Comprehensive Evaluation of Protein Structure Alignment Methods: Scoring by Geometric Measures

Global view of the protein universe

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