A Modular Perspective of Protein Structures: Application to Fragment Based Loop Modeling

Fernández-Fuentes, Narcís; Fiser, András

doi:10.1007/978-1-62703-065-6_9

Cited by 10 publications

(11 citation statements)

References 56 publications

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“…Degeneracy at the secondary and supersecondary structural levels has been well studied (2,3,16), with emergent insights greatly benefiting protein design and structure prediction applications (4,5,(25)(26)(27)(28)(29)(30). Much work has focused on clustering short contiguous backbone fragments of fixed length (5,(31)(32)(33)(34).…”

supporting

confidence: 39%

“…A more recent analysis by Fiser and coworkers (16) classified all instances of two consecutive regular secondary-structural elements (SSEs) connected by a loop based on four parameters defining the relative orientation of the two SSEs, showing considerable degeneracy and saturation of the Protein Data Bank (PDB). The library of these motifs (Smotifs) has been used in loop and structure prediction (28,29). The modularity of Smotifs is consistent with emerging experimental evidence to suggest that supersecondary motifs can serve as standard building blocks of structure.…”

supporting

confidence: 39%

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Tertiary alphabet for the observable protein structural universe

Mackenzie

Zhou

Grigoryan

2016

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

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Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.tertiary motif | structural degeneracy | protein structural universe | sequence-structure relationships | structural modularity I n this work, we aim to decompose the protein structure space into its basic elements as a way of understanding its design principles and describing its limits. Reductionist representations of protein structure have been of long-standing interest (1), with many studies having shown degeneracy at various structural levels (2-5). Features ranging from backbone or side-chain dihedral angles (6, 7) to domains and folds (8, 9) have been classified, and structural basins of attraction have been found in select motifs (10-17), offering a glimpse of a modular space with frequently repeating elements. The reason behind this modularity appears to be a combination of evolutionary history and the fundamental physics of structure. In particular, degeneracy at the level of domains, folds, and functional modules is likely strongly influenced by evolution, whereby such elements recur in different proteins often because of a common ancestor (18,19). On the other hand, statistics of more detailed structural features are better explained from the thermodynamic perspective. For example, observed Ramachandran backbone dihedral angle preferences are largely determined...

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supporting

confidence: 39%

supporting

confidence: 39%

Tertiary alphabet for the observable protein structural universe

Mackenzie

Zhou

Grigoryan

2016

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

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“…In addition to being of fundamental interest, such libraries have enabled advancements in modeling, prediction, and design applications (see Figure 1). For example, Fernandez-Fuentes et al generated the Smotif library of super-secondary motifs, which they defined as two sequence-adjacent SSEs connected by a loop [10,11]. The authors used four geometric parameters that describe the relative orientation of adjacent SSEs to group all Smotifs instances into just 324 types [12].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, novel folds did show different patterns of Smotif utilization, including higher usage of rare motifs. The group has shown the utility of Smotifs in loop modeling [10,11], NMR structure determination [13], and structure prediction [14]. Other attempts to discern motifs that make up the structural universe have focused to classifying contigious backbone segments by root-mean-square-deviation (RMSD) upon optimal superposition.…”

Section: Introductionmentioning

confidence: 99%

Protein structural motifs in prediction and design

Mackenzie

Grigoryan

2017

Current Opinion in Structural Biology

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The Protein Data Bank (PDB) has been an integral resource for shaping our fundamental understanding of protein structure and for the advancement of such applications as protein design and structure prediction. Over the years, information from the PDB has been used to generate models ranging from specific structural mechanisms to general statistical potentials. With accumulating structural data, it has become possible to mine for more complete and complex structural observations, deducing more accurate generalizations. Motif libraries, which capture recurring structural features along with their sequence preferences, have exposed modularity in the structural universe and found successful application in various problems of structural biology. Here we summarize recent achievements in this arena, focusing on sub-domain level structural patterns and their applications to protein design and structure prediction, and suggest promising future directions as the structural database continues to grow.

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“…Template-based methods build loops by using loop fragments extracted from known protein structures in the Protein Data Bank [11], [19], [27]. Recent advances in template-free loop modeling have enabled prediction of structures of long loops with impressive accuracy when crystal contacts or protein family specific information such as that of GPCR family is taken into account [14], [23], [25].…”

Section: Introductionmentioning

confidence: 99%

Fast Protein Loop Sampling and Structure Prediction Using Distance-Guided Sequential Chain-Growth Monte Carlo Method

2014

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Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DiSGro). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is Å, with a lowest energy RMSD of Å, and an average ensemble RMSD of Å. A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about cpu minutes for 12-residue loops, compared to ca cpu minutes using the FALCm method. Test results on benchmark datasets show that DiSGro performs comparably or better than previous successful methods, while requiring far less computing time. DiSGro is especially effective in modeling longer loops (– residues).

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A Modular Perspective of Protein Structures: Application to Fragment Based Loop Modeling

Cited by 10 publications

References 56 publications

Tertiary alphabet for the observable protein structural universe

Tertiary alphabet for the observable protein structural universe

Protein structural motifs in prediction and design

Fast Protein Loop Sampling and Structure Prediction Using Distance-Guided Sequential Chain-Growth Monte Carlo Method

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