Deposition diseases and 3D domain swapping

Protein aggregation is a feature of both normal cellular assemblies and pathological protein depositions. Although the limited order of aggregates has often impeded their structural characterization, 3D domain swapping has been implicated in the formation of several protein aggregates. Here, we review known structures displaying 3D domain swapping in the context of amyloid and related fibrils, prion proteins, and macroscopic aggregates, and we discuss the possible involvement of domain swapping in protein deposition diseases.     

3D domain swapping: as domains continue to swap

Three-dimensional (3D) domain swapping creates a bond between two or more protein molecules as they exchange their identical domains. Since the term '3D domain swapping' was first used to describe the dimeric structure of diphtheria toxin, the database of domain-swapped proteins has greatly expanded. Analyses of the now about 40 structurally characterized cases of domain-swapped proteins reveal that most swapped domains are at either the N or C terminus and that the swapped domains are diverse in their primary and secondary structures. In addition to tabulating domain-swapped proteins, we describe in detail several examples of 3D domain swapping which show the swapping of more than one domain in a protein, the structural evidence for 3D domain swapping in amyloid proteins, and the flexibility of hinge loops. We also discuss the physiological relevance of 3D domain swapping and a possible mechanism for 3D domain swapping. The present state of knowledge leads us to suggest that 3D domain swapping can occur under appropriate conditions in any protein with an unconstrained terminus. As domains continue to swap, this review attempts not only a summary of the known domain-swapped proteins, but also a framework for understanding future findings of 3D domain swapping.     

Three-dimensional domain swapping in the protein structure space

Since the proposal of three-dimensional (3D) domain swapping, many 3D domain-swapped structures have been reported. However, when compared with the vast protein structure space, it is still unclear whether 3D domain swapping is a general mechanism for protein assembly. Here, we investigated this possibility by constructing a dataset consisting of more than 500 domain-swapped structures. The domain-swapped structures were mapped into the protein structure space. We found that about 10% of protein folds and 5% of protein families contain domain-swapped structures. When comparing the domain-swapped structures in a family/superfamily, we found that proteins within a family/superfamily can swap in different ways. Interface analysis revealed that the hinge loops contributed more than half of the open interface in 70% of bona fide domain-swapped dimers, indicating that the hinge loops play an important role in stabilizing the domain-swapped conformations. Our study supports the suggestion that domain swapping is a general property of all proteins and will facilitate further understanding the mechanism of 3D domain swapping.     

3D domain swapping, protein oligomerization, and amyloid formation

In 3D domain swapping, first described by Eisenberg, a structural element of a monomeric protein is replaced by the same element from another subunit. This process requires partial unfolding of the closed monomers that is then followed by adhesion and reconstruction of the original fold but from elements contributed by different subunits. If the interactions are reciprocal, a closed-ended dimer will be formed, but the same phenomenon has been suggested as a mechanism for the formation of open-ended polymers as well, such as those believed to exist in amyloid fibrils. There has been a rapid progress in the study of 3D domain swapping. Oligomers higher than dimers have been found, the monomer-dimer equilibrium could be controlled by mutations in the hinge element of the chain, a single protein has been shown to form more than one domain-swapped structure, and recently, the possibility of simultaneous exchange of two structural domains by a single molecule has been demonstrated. This last discovery has an important bearing on the possibility that 3D domain swapping might be indeed an amyloidogenic mechanism. Along the same lines is the discovery that a protein of proven amyloidogenic properties, human cystatin C, is capable of 3D domain swapping that leads to oligomerization. The structure of domain-swapped human cystatin C dimers explains why a naturally occurring mutant of this protein has a much higher propensity for aggregation, and also suggests how this same mechanism of 3D domain swapping could lead to an open-ended polymer that would be consistent with the cross-beta structure, which is believed to be at the heart of the molecular architecture of amyloid fibrils.     

3D domain swapping: a mechanism for oligomer assembly.

3D domain swapping is a mechanism for forming oligomeric proteins from their monomers. In 3D domain swapping, one domain of a monomeric protein is replaced by the same domain from an identical protein chain. The result is an intertwined dimer or higher oligomer, with one domain of each subunit replaced by the identical domain from another subunit. The swapped "domain" can be as large as an entire tertiary globular domain, or as small as an alpha-helix or a strand of a beta-sheet. Examples of 3D domain swapping are reviewed that suggest domain swapping can serve as a mechanism for functional interconversion between monomers and oligomers, and that domain swapping may serve as a mechanism for evolution of some oligomeric proteins. Domain-swapped proteins present examples of a single protein chain folding into two distinct structures.     

Topological determinants of protein domain swapping

Protein domain swapping has been repeatedly observed in a variety of proteins and is believed to result from destabilization due to mutations or changes in environment. Based on results from our studies and others, we propose that structures of the domain-swapped proteins are mainly determined by their native topologies. We performed molecular dynamics simulations of seven different proteins, known to undergo domain swapping experimentally, under mildly denaturing conditions and found in all cases that the domain-swapped structures can be recapitulated by using protein topology in a simple protein model. Our studies further indicated that, in many cases, domain swapping occurs at positions around which the protein tends to unfold prior to complete unfolding. This, in turn, enabled prediction of protein structural elements that are responsible for domain swapping. In particular, two distinct domain-swapped dimer conformations of the focal adhesion targeting domain of focal adhesion kinase were predicted computationally and were supported experimentally by data obtained from NMR analyses.     

Identification and distribution of protein families in 120 completed genomes using Gene3D

Using a new protocol, PFscape, we undertake a systematic identification of protein families and domain architectures in 120 complete genomes. PFscape clusters sequences into protein families using a Markov clustering algorithm (Enright et al., Nucleic Acids Res 2002;30:1575-1584) followed by complete linkage clustering according to sequence identity. Within each protein family, domains are recognized using a library of hidden Markov models comprising CATH structural and Pfam functional domains. Domain architectures are then determined using DomainFinder (Pearl et al., Protein Sci 2002;11:233-244) and the protein family and domain architecture data are amalgamated in the Gene3D database (Buchan et al., Genome Res 2002;12:503-514). Using Gene3D, we have investigated protein sequence space, the extent of structural annotation, and the distribution of different domain architectures in completed genomes from all kingdoms of life. As with earlier studies by other researchers, the distribution of domain families shows power-law behavior such that the largest 2,000 domain families can be mapped to approximately 70% of nonsingleton genome sequences; the remaining sequences are assigned to much smaller families. While approximately 50% of domain annotations within a genome are assigned to 219 universal domain families, a much smaller proportion (< 10%) of protein sequences are assigned to universal protein families. This supports the mosaic theory of evolution whereby domain duplication followed by domain shuffling gives rise to novel domain architectures that can expand the protein functional repertoire of an organism. Functional data (e.g. COG/KEGG/GO) integrated within Gene3D result in a comprehensive resource that is currently being used in structure genomics initiatives and can be accessed via http://www.biochem.ucl.ac.uk/bsm/cath/Gene3D/.     

Crystal structure of human glyoxalase I--evidence for gene duplication and 3D domain swapping.

The zinc metalloenzyme glyoxalase I catalyses the glutathione-dependent inactivation of toxic methylglyoxal. The structure of the dimeric human enzyme in complex with S-benzyl-glutathione has been determined by multiple isomorphous replacement (MIR) and refined at 2.2 A resolution. Each monomer consists of two domains. Despite only low sequence homology between them, these domains are structurally equivalent and appear to have arisen by a gene duplication. On the other hand, there is no structural homology to the 'glutathione binding domain' found in other glutathione-linked proteins. 3D domain swapping of the N- and C-terminal domains has resulted in the active site being situated in the dimer interface, with the inhibitor and essential zinc ion interacting with side chains from both subunits. Two structurally equivalent residues from each domain contribute to a square pyramidal coordination of the zinc ion, rarely seen in zinc enzymes. Comparison of glyoxalase I with other known structures shows the enzyme to belong to a new structural family which includes the Fe2+-dependent dihydroxybiphenyl dioxygenase and the bleomycin resistance protein. This structural family appears to allow members to form with or without domain swapping.     

Sequence-structure signals of 3D domain swapping in proteins

Three-dimensional domain swapping occurs when two or more identical proteins exchange identical parts of their structure to generate an oligomeric unit. It affects proteins with diverse sequences and structures, and is expected to play important roles in evolution, functional regulation and even conformational diseases. Here, we search for traces of domain swapping in the protein sequence, by means of algorithms that predict the structure and stability of proteins using database-derived potentials. Regions whose sequences are not optimal with regard to the stability of the native structure, or showing marked intrinsic preferences for non-native conformations in absence of tertiary interactions are detected in most domain-swapping proteins. These regions are often located in areas crucial in the swapping process and are likely to influence it on a kinetic or thermodynamic level. In addition, cation-pi interactions are frequently observed to zip up the edges of the interface between intertwined chains or to involve hinge loop residues, thereby modulating stability. We end by proposing a set of mutations altering the swapping propensities, whose experimental characterization would contribute to refine our in silico derived hypotheses.     

3DCoffee: combining protein sequences and structures within multiple sequence alignments

Most bioinformatics analyses require the assembly of a multiple sequence alignment. It has long been suspected that structural information can help to improve the quality of these alignments, yet the effect of combining sequences and structures has not been evaluated systematically. We developed 3DCoffee, a novel method for combining protein sequences and structures in order to generate high-quality multiple sequence alignments. 3DCoffee is based on TCoffee version 2.00, and uses a mixture of pairwise sequence alignments and pairwise structure comparison methods to generate multiple sequence alignments. We benchmarked 3DCoffee using a subset of HOMSTRAD, the collection of reference structural alignments. We found that combining TCoffee with the threading program Fugue makes it possible to improve the accuracy of our HOMSTRAD dataset by four percentage points when using one structure only per dataset. Using two structures yields an improvement of ten percentage points. The measures carried out on HOM39, a HOMSTRAD subset composed of distantly related sequences, show a linear correlation between multiple sequence alignment accuracy and the ratio of number of provided structure to total number of sequences. Our results suggest that in the case of distantly related sequences, a single structure may not be enough for computing an accurate multiple sequence alignment.     

Use of a structural alphabet to find compatible folds for amino acid sequences

The structural annotation of proteins with no detectable homologs of known 3D structure identified using sequence‐search methods is a major challenge today. We propose an original method that computes the conditional probabilities for the amino‐acid sequence of a protein to fit to known protein 3D structures using a structural alphabet, known as “Protein Blocks” (PBs). PBs constitute a library of 16 local structural prototypes that approximate every part of protein backbone structures. It is used to encode 3D protein structures into 1D PB sequences and to capture sequence to structure relationships. Our method relies on amino acid occurrence matrices, one for each PB, to score global and local threading of query amino acid sequences to protein folds encoded into PB sequences. It does not use any information from residue contacts or sequence‐search methods or explicit incorporation of hydrophobic effect. The performance of the method was assessed with independent test datasets derived from SCOP 1.75A. With a Z‐score cutoff that achieved 95% specificity (i.e., less than 5% false positives), global and local threading showed sensitivity of 64.1% and 34.2%, respectively. We further tested its performance on 57 difficult CASP10 targets that had no known homologs in PDB: 38 compatible templates were identified by our approach and 66% of these hits yielded correctly predicted structures. This method scales‐up well and offers promising perspectives for structural annotations at genomic level. It has been implemented in the form of a web‐server that is freely available at http://www.bo‐protscience.fr/forsa .     

Protein structure search and local structure characterization

Background  

Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.     

Exploring the Roles of Proline in Three-Dimensional Domain Swapping from Structure Analysis and Molecular Dynamics Simulations

Three-dimensional (3D) domain swapping is a mechanism to form protein oligomers. It has been proposed that several factors, including proline residues in the hinge region, may affect the occurrence of 3D domain swapping. Although introducing prolines into the hinge region has been found to promote domain swapping for some proteins, the opposite effect has also been observed in several studies. So far, how proline affects 3D domain swapping remains elusive. In this work, based on a large set of 3D domain-swapped structures, we performed a systematic analysis to explore the correlation between the presence of proline in the hinge region and the occurrence of 3D domain swapping. We further analyzed the conformations of proline and pre-proline residues to investigate the roles of proline in 3D domain swapping. We found that more than 40% of the domain-swapped structures contained proline residues in the hinge region. Unexpectedly, conformational transitions of proline residues were rarely observed upon domain swapping. Our analyses showed that hinge regions containing proline residues preferred more extended conformations, which may be beneficial for the occurrence of domain swapping by facilitating opening of the exchanged segments.     

PROMALS3D: a tool for multiple protein sequence and structure alignments

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods.     

Crystal structure of cyanovirin-N, a potent HIV-inactivating protein, shows unexpected domain swapping.

The crystal structure of cyanovirin-N (CV-N), a protein with potent antiviral activity, was solved at 1.5 A resolution by molecular replacement using as the search model the solution structure previously determined by NMR. The crystals belong to the space group P3221 with one monomer of CV-N in each asymmetric unit. The primary structure of CV-N contains 101 residues organized in two domains, A (residues 1 to 50) and B (residues 51 to 101), with a high degree of internal sequence and structural similarity. We found that under the conditions of the crystallographic experiments (low pH and 26 % isopropanol), two symmetrically related monomers form a dimer by domain swapping, such that domain A of one monomer interacts with domain B' of its crystallographic symmetry mate and vice versa. Because the two swapped domains are distant from each other, domain swapping does not result in additional intramolecular interactions. Even though one of the protein sample solutions that was used for crystallization clearly contained 100 % monomeric CV-N molecules, as judged by various methods, we were only able to obtain crystals containing domain-swapped dimers. With the exception of the unexpected phenomenon of domain swapping, the crystal structure of CV-N is very similar to the NMR structure, with a root-mean-square deviation of 0.55 A for the main-chain atoms, the best agreement reported to date for structures solved using both techniques.     

Identifying novel sequence variants of RNA 3D motifs

Predicting RNA 3D structure from sequence is a major challenge in biophysics. An important sub-goal is accurately identifying recurrent 3D motifs from RNA internal and hairpin loop sequences extracted from secondary structure (2D) diagrams. We have developed and validated new probabilistic models for 3D motif sequences based on hybrid Stochastic Context-Free Grammars and Markov Random Fields (SCFG/MRF). The SCFG/MRF models are constructed using atomic-resolution RNA 3D structures. To parameterize each model, we use all instances of each motif found in the RNA 3D Motif Atlas and annotations of pairwise nucleotide interactions generated by the FR3D software. Isostericity relations between non-Watson–Crick basepairs are used in scoring sequence variants. SCFG techniques model nested pairs and insertions, while MRF ideas handle crossing interactions and base triples. We use test sets of randomly-generated sequences to set acceptance and rejection thresholds for each motif group and thus control the false positive rate. Validation was carried out by comparing results for four motif groups to RMDetect. The software developed for sequence scoring (JAR3D) is structured to automatically incorporate new motifs as they accumulate in the RNA 3D Motif Atlas when new structures are solved and is available free for download.     

Delineation of modular proteins: domain boundary prediction from sequence information

The delineation of domain boundaries of a given sequence in the absence of known 3D structures or detectable sequence homology to known domains benefits many areas in protein science, such as protein engineering, protein 3D structure determination and protein structure prediction. With the exponential growth of newly determined sequences, our ability to predict domain boundaries rapidly and accurately from sequence information alone is both essential and critical from the viewpoint of gene function annotation. Anyone attempting to predict domain boundaries for a single protein sequence is invariably confronted with a plethora of databases that contain boundary information available from the internet and a variety of methods for domain boundary prediction. How are these derived and how well do they work? What definition of 'domain' do they use? We will first clarify the different definitions of protein domains, and then describe the available public databases with domain boundary information. Finally, we will review existing domain boundary prediction methods and discuss their strengths and weaknesses.     

Use of covariance analysis for the prediction of structural domain boundaries from multiple protein sequence alignments

Current methods for identification of domains within protein sequences require either structural information or the identification of homologous domain sequences in different sequence contexts. Knowledge of structural domain boundaries is important for fold recognition experiments and structural determination by X-ray crystallography or nuclear magnetic resonance spectroscopy using the divide-and-conquer approach. Here, a new and conceptually simple method for the identification of structural domain boundaries in multiple protein sequence alignments is presented. Analysis of covariance at positions within the alignment is first used to predict 3D contacts. By the nature of the domain as an independent folding unit, inter-domain predicted contacts are fewer than intra-domain predicted contacts. By analysing all possible domain boundaries and constructing a smoothed profile of predicted contact density (PCD), true structural domain boundaries are predicted as local profile minima associated with low PCD. A training data set is constructed from 52 non-homologous two-domain protein sequences of known 3D structure and used to determine optimal parameters for the profile analysis. The alignments in the training data set contained 48 +/- 17 (mean +/- SD) sequences and lengths of 257 +/- 121 residues. Of the 47 alignments yielding predictions, 35% of true domain boundaries are predicted to within 15 amino acids by the local profile minimum with the lowest profile value. Including predictions from the second- and third-lowest local minima increases the correct domain boundary coverage to 60%, whereas the lowest five local minima cover 79% of correct domain boundaries. Through further profile analysis, criteria are presented which reliably identify subsets of more accurate predictions. Retrospective analysis of CASP3 targets shows predictions of sufficient accuracy to enable dramatically improved fold recognition results. Finally, a prediction is made for geminivirus AL1 protein which is in full agreement with biochemical data, yielding a plausible, novel threading result.     

SnapDRAGON: a method to delineate protein structural domains from sequence data

We describe a method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space. SnapDRAGON is a suite of programs developed to predict domain boundaries based on the consistency observed in a set of alternative ab initio three-dimensional (3D) models generated for a given protein multiple sequence alignment. This is achieved by running a distance geometry-based folding technique in conjunction with a 3D-domain assignment algorithm. The overall accuracy of our method in predicting the number of domains for a non-redundant data set of 414 multiple alignments, representing 185 single and 231 multiple-domain proteins, is 72.4 %. Using domain linker regions observed in the tertiary structures associated with each query alignment as the standard of truth, inter-domain boundary positions are delineated with an accuracy of 63.9 % for proteins comprising continuous domains only, and 35.4 % for proteins with discontinuous domains. Overall, domain boundaries are delineated with an accuracy of 51.8 %. The prediction accuracy values are independent of the pair-wise sequence similarities within each of the alignments. These results demonstrate the capability of our method to delineate domains in protein sequences associated with a wide variety of structural domain organisation.     

Predicting protein crystallization propensity from protein sequence

The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein’s propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1,300 proteins that expressed well but were insoluble, and for ~720 unique proteins that resulted in X-ray structures. The correlation of the protein’s iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes correlating with a protein’s propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize the data. These tools are available via the web site .