New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures

Sillitoe, Ian; Cuff, Alison L.; Dessailly, Benoît H.; Dawson, Natalie L.; Furnham, Nicholas; Lee, David A.; Lees, Jonathan G.; Lewis, Tony E.; Studer, Romain A.; Rentzsch, Robert; Yeats, Corin; Thornton, Janet M.; Orengo, Christine A.

doi:10.1093/nar/gks1211

Cited by 203 publications

(211 citation statements)

References 27 publications

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“…Protein structure superpositioning has been used for many years for making structure comparisons that have led to well-established structural ontologies of the many protein folds, including CATH (Sillitoe et al 2013), SCOP (Murzin et al 1995), and Pfam (Punta et al 2012). Because of the comparatively small number of available 3D atomic structures for RNA, fewer resources exist for their curation and comparison.…”

Section: Data Setmentioning

confidence: 99%

Elastic network models capture the motions apparent within ensembles of RNA structures

Zimmermann

Jernigan

2014

RNA

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The role of structure and dynamics in mechanisms for RNA becomes increasingly important. Computational approaches using simple dynamics models have been successful at predicting the motions of proteins and are often applied to ribonucleoprotein complexes but have not been thoroughly tested for well-packed nucleic acid structures. In order to characterize a true set of motions, we investigate the apparent motions from 16 ensembles of experimentally determined RNA structures. These indicate a relatively limited set of motions that are captured by a small set of principal components (PCs). These limited motions closely resemble the motions computed from low frequency normal modes from elastic network models (ENMs), either at atomic or coarse-grained resolution. Various ENM model types, parameters, and structure representations are tested here against the experimental RNA structural ensembles, exposing differences between models for proteins and for folded RNAs. Differences in performance are seen, depending on the structure alignment algorithm used to generate PCs, modulating the apparent utility of ENMs but not significantly impacting their ability to generate functional motions. The loss of dynamical information upon coarse-graining is somewhat larger for RNAs than for globular proteins, indicating, perhaps, the lower cooperativity of the less densely packed RNA. However, the RNA structures show less sensitivity to the elastic network model parameters than do proteins. These findings further demonstrate the utility of ENMs and the appropriateness of their application to well-packed RNA-only structures, justifying their use for studying the dynamics of ribonucleo-proteins, such as the ribosome and regulatory RNAs.

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Section: Data Setmentioning

confidence: 99%

Elastic network models capture the motions apparent within ensembles of RNA structures

Zimmermann

Jernigan

2014

RNA

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“…We used a previously reported method of domain identification 27 based on the Class Architecture Topology Homology (CATH) and Domain Parser databases. CATH domains are identified on the basis of sequence homology 53 and thus do not always represent autonomous folding units. Some CATH domains are composed of non-contiguous segments of the protein.…”

Section: Methodsmentioning

confidence: 99%

Accurate Prediction of Cellular Co-Translational Folding Indicates Proteins can Switch from Post- to Co-Translational Folding

Nissley

2017

Biophysical Journal

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The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally-a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.

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“…The fragments of chains are defined by their secondary structure. Identification of secondary structural folds and the composition of protein domains follow the CATH [18] and PDBsum [28] classifications. Likewise, interdomain/inter-chain contacts have been identified on the basis of the PDBsum distance criteria [28].…”

Section: Introduction To the Fuzzy Oil Drop (Fod) Modelmentioning

confidence: 99%

“…This result has implications for understanding not only the folding code but also the evolution of new folds. The CATH [18] classification for these two proteins is as follows: 1.10.8.40-Mainly Alpha Orthogonal Bundle for 2JWS and 3.10.20.10.-R-Alpha Beta Roll for 2JWU. …”

mentioning

confidence: 99%

Structural Role of Hydrophobic Core in Proteins-Selected Examples

Banach¹,

Kalinowska²,

Konieczny³

et al. 2016

J Proteomics Bioinform

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This paper discusses the sequence/structure relation. The core question concerns the degree to which similar sequences produce similar structures and vice versa. A mechanism by which similar sequences may result in dissimilar structures is proposed, based on the Fuzzy Oil Drop (FOD) model in which structural similarity is estimated by analyzing the protein's hydrophobic core. We show that local changes in amino acid sequences, in addition to producing local structural alterations at the substitution site, may also change the shape of the hydrophobic core, significantly affecting the overall tertiary conformation of the protein. Our analysis focuses on four sets of proteins: 1) Pair of designer proteins with specially prepared sequences; 2) Pair of natural proteins modified (mutated) to converge to a point of high-level sequence identity while retaining their respective wild-type tertiary folds; 3) Pair of natural proteins with common ancestry but with differing structures and biological profiles shaped by divergent evolution; and 4) Pair of natural proteins of high structural similarity with no sequence similarity and different biological function. with even higher levels of sequence identity (95%) and differing folds. Thus, conformational switching to an alternative monomeric fold of comparable stability can be effected with just a handful of mutations in a small protein. This result has implications for understanding not only the folding code but also the evolution of new folds. The CATH [18] classification for these two proteins is as follows: 1.10.8.40-Mainly Alpha Orthogonal Bundle for 2JWS and 3.10.20.10.-R-Alpha Beta Roll for 2JWU. Wild type proteins with aim-oriented mutationsTwo proteins: G311 (1ZXG) and A219 (1ZXH) are modified versions of wild-type proteins designated G and A (IgG binding domains, source: Staphylococcus aureus for 1ZXG and Streptococcus sp. for 1ZXH) respectively [19]. The series of mutations aimed at achieving a high level of sequence identity while preserving wild-type 3D structures. Both proteins (G311 vs. G and A219 vs. A) represent backbone RMS-D 1.4 Å, maintaining wild-type secondary structures: α/β for G311 and helical for A219. The final sequence identity of both modified proteins is on the level of 59%. All relevant data was taken from a paper describing experimental results of protein modifications [19]. Homologous proteinsThe next pair comprises two proteins with common ancestry but different folds and functions: 2PIJ (Pfl 6 BETA) (source: Pseudomonas fluorescens) Cro protein from prophage pfl 6 in P. fluorescens pf-5 [20] and 3BD1 (Xfaso 1) Cro protein from putative prophage element X. fastidiosa strain ann-1 [20] (source: Xylella fastidiosa). Both proteins share an identical CATH classification: 1.10.260.40-Mainly Alpha Orthogonal Bundle.The differences between homologous proteins are due to evolutionary pressure. In the presented case the sequence identity is 40%, yet the α-helix is replaced by a β-sheet in the C-terminal region spanning approximately 25 residu...

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New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures

Cited by 203 publications

References 27 publications

Elastic network models capture the motions apparent within ensembles of RNA structures

Elastic network models capture the motions apparent within ensembles of RNA structures

Accurate Prediction of Cellular Co-Translational Folding Indicates Proteins can Switch from Post- to Co-Translational Folding

Structural Role of Hydrophobic Core in Proteins-Selected Examples

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