The Evolution of Protein FoldsSpencer Bliven

Department of BioinformaticsUniversity of California San Diego

Andreas PrlićSan Diego Supercomputer CenterUniversity of California San Diego

Philip BourneSkaggs School of Pharmacy and Pharmaceutical Sciences

University of California San Diego

Methods1.Identification of domain and subdomain architecture

a.Use SCOP domains where available, or calculate using the Protein Domain Parser (PDP) algorithm

b.Detect symmetry using CE-Symm (manuscript in preparation)

c.Detect circular permutations using CE-CP (Shindyalov, 2000; Prlic, 2010)

2.All-vs-all structural similarity calculationa.Filter representatives to 40% sequence identity

b.Use FATCAT algorithm (Ye and Godzik, 2003) to compare all protein domains from the Protein Data Bank (nearly 1 billion alignments, accessible from http://www.pdb.org)

3.Network analysisa.Filter out low-scoring structural alignments (TMScore≥0.5, Length≥25,

Coverage≥60%)

b.Map functional properties onto nodes: SCOP/CATH classification, GO terms, symmetry order, ligand information, etc.

4.Phylogenetic analysis (planned)Future work will center on quantifying the prevalence of subdomain rearrangement by integrating evolutionary data with the structural comparison data.

The nature of protein fold space is hotly debated. Do the protein folds observed in nature fall into clean, discrete clusters, or is fold space more accurately modeled as a vast continuum, of which only a small sample of proteins has yet been observed? Previous efforts to answer this question have focused on geometric spaces (PCA, multidimensional scaling, locally linear embedding) or network models (conformational space networks) (Chodera, 2011). While such schemes may facilitate protein comparison and classification, the choice of a mathematical framework for fold space is arbitrary without a connection to concrete biological processes. To accurately capture the true relationships between protein folds, a model must consider the evolutionary history of those folds.

Here we present a high-level model of protein evolution, which focuses on mutations that preserve the global 3D structure of proteins. We hypothesize that the combination of subtle local changes (e.g. PTMs) and large, but structure-preserving, rearrangements (e.g. duplications) can account for both the continuity of intermediate structures within protein folds and the evolution of seemingly novel folds. Our model categorizes known biological mutation processes, such as DNA replication errors and crossover errors, and places them in a simple theoretical framework.

To test this model, we present evidence from the analysis of a recent systematic comparison of all protein domains from the Protein Data Bank (PDB). We also show that the model is consistent with existing evolutionary models for gene duplication, circular permuted proteins, and proteins with internal symmetry. Future work will focus on explicitly determining evolutionary events relating distantly homologous folds.

Abstract

Poster first presented at the 20th Annual International Conference on Intelligent Systems for Molecular Biology (2012). Funded in association with the Protein Data Bank. The presentation of this work was made possible by a Travel Fellowship awarded by ISCB with grant funds obtained from the NIH NIMGS-National Institute of General Medical Sciences.

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/3.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.

Model for the Evolution of New FoldsGiven these observations, we hypothesize that structure-preserving rearrangements are a major mechanism for the evolution of new protein folds. To test this, we classify mutations into the following categories:

1.Gene fusionA gene fusion occurs when two previously independent protein sequences are joined to create a single peptide product. This includes duplications, where the source sequence are identical, as well as the creation of chimeric proteins. To preserve function, the source proteins should either interact or form independently folding domains; other fusions are likely to be selected against.

2.Gene fissionA single peptide may split into two independently transcribed peptides. While a fission may be accompanied by the loss of the protein-protein interface, fissions which maintain the global structure of the protein are likely to be selected for.

3.Gain of protein-protein interfaceThe evolution of a new protein-protein interface results in a new quaternary structure. This may be either a heteromer, as shown in the diagram, or a homomer.

4.Loss of protein-protein interfaceProtein-protein interfaces may also be lost. This may occur in conjunction with fission.

5.Non-architectural mutationsAny mutation which does not alter the number of domains, binding, or connectivity is termed a non-architectural mutation. This includes insertions, deletions, and substitutions of amino acids or even whole secondary structures.

6.Order-disorder transitionsFor completeness, order-disorder transitions are included as a possible source of novel folds. However, such transitions are extremely difficult to detect, and are indistinguishable from primordial folds by the methods used in this study.

Principles of Protein Evolution1. Global structure is conserved in many significant rearrangements

2. Domains and subdomains are reused for many functions

(a) The TorD chaperon from Shewanella massilia (1N1C) is a dimer, but instead of each chain folding into a compact domain, each chain contributes to the structure or each domain. (b) The DmsD protein from Salmonella typhimurium (1S9U) has a very similar structure and has 20% sequence identity, but forms a monomer (Sippl, 2009).

(a) (b)

Domain Swapping Circular Permutation Symmetry(a)

(b) (c)

(a) Alignment of Regulatory Protein A from Human papillomavirus (1A7G) with AppA BLUF domain from Rhodobacter sphaeroides (2IYG). The two structures are related by a circular permutation, as can be seen from the rainbow coloring in (b) and (c)

The symmetric protein FGF-1 (3JUT), colored to highlight the 3-fold symmetry. Evidence suggests that ß-trefoils may have evolved from a trimeric precursor (Lee, 2011).

GTP binding regulator from Thermotoga maritima [1VR8] consists of two αβββ motifs, colored orange and yellow. The half-glyoxalase query motif (cyan) is aligned for comparison.

Structure of glyoxalase I from E. coli [3HDP]. Colors show the two-fold symmetry of the protein around its nickel-binding site.

Dimer form of glyoxalase I [1F9Z], colored by chain. Relative to 3HDP, the symmetric motifs are swapped between chains.

1,2‑dihydroxynaphthalene dioxygenase [2EHZ] from Pseudomonas (orange/grey) consists of four copies of the half-glyoxalase motif (cyan). Iron is bound at the active site.

FusionFission

Loss of interface

Gain of interface

ConclusionWe aim to provide a model for analyzing protein fold space based not on geometric properties, but on evolutionary relationships. This approach avoids the dichotomy of "continuous" and "discrete". Our model is derived from the observation that many proteins which are currently classified as belonging to separate folds can be decomposed into subdomains which appear in many different subdomain architectures.

The evolution of protein symmetry is a prime example of this phenomenon. Proteins may be symmetric at the level of quaternary structure, tertiary structure, or both. Significant homology can often be detected between the repeated subunits of symmetric proteins, suggesting that symmetry evolves by duplication and fusion events. These events preserve the protein-protein interaction interfaces and the global structure of the protein, providing a plausible pathway for radical fold change without non-functional intermediates. Likewise, the currently accepted models for the evolution of circular permutations can be couched as a special case of the model presented here (Weiner, 2006).

To investigate the evolution of protein folds as broadly as possible, an extensive all-vs-all comparison of domains from the Protein Databank was conducted. This consisted of nearly one billion pairwise alignments, and was performed on the Open Science Grid distributed cluster. This wealth of data allows the clustering of potentially homologous domains. Research is ongoing to incorporate evolutionary information into this network, with the ultimate goal of quantifying the prevalence of each mechanism for fold evolution.

Chodera & Pande (2011) PNAS. vol.108(32) pp. 12969–12970

Lee & Blaber (2011) PNAS, vol.108 (1) pp. 126–130Prlic et al. (2010) Bioinformatics. vol. 26 (23) pp.

2983-2985

Shindyalov and Bourne (2000) Proteins. vol. 38 (3) pp. 247-60

Sippl (2009) Curr Opin Struct Biol. vol.19 (3) pp. 312-20

Thornalley (1993) Mol. Aspects Med. 14 (4) pp. 287–371Ye and Godzik (2003). Bioinformatics. vol. 19(Suppl 2),

pp. ii246–55.Weiner & Bornberg-Bauer (2006) Mol. Biol. Evol. vol.

23(4) pp. 734–743

Case Study: Transmembrane Proteins

(Left) Structural similarities between domains of transmembrane proteins, as classified by the Transporter Classification Database (TCDB). A moderate threshold of TM‑Score≥0.5 was used, leaving the network fairly sparse. The nodes are colored according to their TCDB classification.

(Right) Enlargement of the boxed section of the graph, with 3D structures overlaid. All structures are similar to the Rossmann fold, but several are classified in different SCOP folds (e.g. PTS system IIB component-like (d1iiba_)). A wide diversity of TCDB functions are annotated to the structurally similar cluster.

PDP:2AEFAa PDP:3IPRAa

PDP:2FN9Ab

d1iiba_

d2r4qa1

PDP:3L9WAa

d1pdoa_

PDP:3A1DAa

d1djla_d1shux_

d1d4oa_

d1nrjb_

PDP:2IYEAa

PDP:2FN9Aa

PDP:2ZXEAa

PDP:2VOYIa

d1wa5a_

PDP:3QELBa

PDP:3QELBb

PDP:2O1EAb

d1nrza_

PDP:2OSVAa

PDP:2OSVAb

PDP:3HH8Aa

PDP:2O1EAa

PDP:3HH8Ab

PDP:3IPCAb

PDP:2Q8PAa

d1id1a_

d2hmva1

PDP:2WI8Aa

PDP:3QJGAa

PDP:3P2YAb

d1ls1a2

PDP:2FEWBa

PDP:3RBZAb

d1vkra_PDP:2VQ3Aa

PDP:3IPCAa

1.0

0.90.8

0.7

Transmembrane Electron Carriers

Channels/Pores

Incompletely Characterized

Transport Systems

Group Translocators

Accessory Factors Involved

in Transport

Primary Active

Transporters

Electrochemical Potential-driven

Transporters

Legend:Color indicates TCDB ClassEdge weight indicates TM-Score

0.5

0.64

0.52

0.57

0.54

0.53 0.530.58 0.65

0.66

0.75

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0.52

0.53

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0.55

0.75

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0.64

0.73

0.730.61

0.66

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0.59

0.58

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0.52

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0.6

0.650.52

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0.7

0.520.69

0.77

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0.76

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0.62

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0.61

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