Amplitude spectrum distance: measuring the global shape divergence of protein fragments
 Clovis Galiez^{1}Email author and
 François Coste^{1}
Received: 5 March 2015
Accepted: 31 July 2015
Published: 14 August 2015
Abstract
Background
In structural bioinformatics, there is an increasing interest in identifying and understanding the evolution of local protein structures regarded as key structural or functional protein building blocks. A central need is then to compare these, possibly short, fragments by measuring efficiently and accurately their (dis)similarity. Progress towards this goal has given rise to scores enabling to assess the strong similarity of fragments. Yet, there is still a lack of more progressive scores, with meaningful intermediate values, for the comparison, retrieval or clustering of distantly related fragments.
Results
We introduce here the Amplitude Spectrum Distance (ASD), a novel way of comparing protein fragments based on the discrete Fourier transform of their C _{ α } distance matrix. Defined as the distance between their amplitude spectra, ASD can be computed efficiently and provides a parameterfree measure of the global shape dissimilarity of two fragments. ASD inherits from nice theoretical properties, making it tolerant to shifts, insertions, deletions, circular permutations or sequence reversals while satisfying the triangle inequality. The practical interest of ASD with respect to RMSD, RMSD_{d}, BC and TM scores is illustrated through zinc finger retrieval experiments and concrete structure examples. The benefits of ASD are also illustrated by two additional clustering experiments: domain linkers fragments and complementaritydetermining regions of antibodies.
Conclusions
Taking advantage of the Fourier transform to compare fragments at a global shape level, ASD is an objective and progressive measure taking into account the whole fragments. Its practical computation time and its properties make ASD particularly relevant for applications requiring meaningful measures on distantly related protein fragments, such as similar fragments retrieval asking for high recalls as shown in the experiments, or for any application taking also advantage of triangle inequality, such as fragments clustering.
ASD program and source code are freely available at: http://www.irisa.fr/dyliss/public/ASD/.
Keywords
Protein Structural comparison Fourier transform Pseudometric Insertions and deletionsBackground
Evaluation of the structural similarity of two proteins is an important task in bioinformatics that is mainly performed at three levels: global protein comparison, structural motif comparison (for spatially contiguous pieces of structure) and fragment comparison (for sequentially contiguous pieces of structures).

Mining fragments related to a particular protein function [1];

Building global structural alignment by combinatorial extension [2];

Representing globally a structure [1] and comparing globally two proteins as a bagoffragment of variable length [3], or fixed length [4];

Comparing/clustering fragments in order to feed learning algorithms to infer structural alphabets/building blocks for protein structure prediction [5–8];

Assessing the structure prediction from sequence by comparing locally predicted fragments with their native conformation [9].
The classical score used to measure the dissimilarity of two protein structures is the coordinate rootmeansquare deviation (RMSD) defined as the minimum average distance between superimposed atoms (usually the C _{ α }) of the proteins by optimal rigidbody rotation and translation. Drawbacks of RMSD are well known: it necessitates computing the optimal superimposition of the atoms, it tends to increase with proteins’ length and it is more sensitive to local than global structural deviations. Many other measures have been proposed [10], and among those, one has to cite the distance variant of RMSD, the RMSD _{ d } [11]. Rather than comparing the 3D coordinates of the atoms, it performs a more global comparison of the internal distance matrices of each protein, alleviating this way the need of superimposing the structures thanks to the invariance of internal distances by rotation and translation (at the price of not distinguishing mirrored structures). More recently, an interesting advance in measuring the similarity of protein fragments has come up with the BinetCauchy (BC) score putting forward several advantages over RMSD: it avoids explicit structure superimposition, enables mining mirror image fragments, is less sensitive to fragment lengths and provides better discrimination of medium range RMSD values [12]. BC score, RMSD and RMSD _{ d }, are computable by tractable exact algorithms. Moreover, they do not rely on expertchosen parameters, so that they universally apply for protein fragments. The limitation of these scores is that they measure the distance between two ordered sets of residues already aligned onetoone (the i ^{ t h } residue of the first set is aligned with the i ^{ t h } residue of the second set, typically in the same order than in the fragments’ sequences), making them less suited for the comparison of homologous fragments with mismatches resulting, for instance, from insertions or deletions.
In order to cope with mismatches, an approach is to search for the best (sub)alignment between the residues of both fragments. The problem is then to conciliate two conflicting goals: maximize the number of aligned residues and minimize their structural deviation. A way to quantify the best practical tradeoff has been designing scores normalized with respect to the alignment length relatively to their expectation between random proteins. This includes wellknown scores developed for the comparison of whole protein structures such as the TMscore used in TMalign, and its successor FrTMalign, weighting the close atom pairs stronger than the distant matches to focus more on global fold than local variations [13, 14], or the Zscore of DALI based on a measure of the relative dissimilarity of the distance matrices, weighting down the contribution of pairs in the long distance range by an exponential envelop function [15]. While useful in practice, these scores rely on underlying models of typical random structures and are thus biased by construction towards particular application domains, as witnessed by the presence of “magic numbers” in their formula. Another issue is that the problem of finding the best alignment optimizing these scores is usually difficult and programs such as TMalign, FrTMalign and DALI rely thus on heuristic methods that do not guarantee that the optimal score has been found. A remarkable exception is DALIX [16] which introduces an exact and worsecase exponential algorithm that can already be used to align optimally some protein domains in reasonable time with respect to DALI’s objective function. Finally, let us remark that in the best (sub)alignment approach, unmatched residues do not contribute to the overall score. Scoring of the alignment deals with the aligned parts of the structure but no matter how the structures look like over the non aligned part, the score will remain the same. This can be a critical issue for many tasks. For example, when clustering protein fragments, if the similarity of fragments is assessed only over the aligned part then it will lead to inconsistent clusters: a fragment A may be identical to a fragment B over its first 70 % of structure, a fragment C can be identical to the same fragment B over its last 70 % of the structure, but A can be very different from C because they share only 40 % of structure, and in this case, any clustering of A, B and C will be unsatisfactory: one has to look at the whole dissimilarity of the A, B and C fragments.
None of the approaches seen so far are then completely satisfactory: by presupposing a onetoone total alignment, we miss the tolerance to indels and by creating a partial alignment between residues we miss the measure of the non aligned part of the structure while introducing arbitrary parameters.
We propose here a novel dissimilarity, named ASD (for Amplitude Spectrum Distance), that overcomes these issues by using the Fourier transform to compare the fragments at a global shape level without explicit structure superimposition. More precisely, ASD measures the whole dissimilarity between two fragments as the distance between the amplitude spectra of the discrete Fourier transform of their C _{ α } distance matrix. ASD is computable with a tractable exact algorithm (complexity in O N ^{2} log N). Moreover, ASD is a pseudometric: it respects the triangle inequality (TI) what provides two main advantages for applications. A computational one, since TI enables to design efficient nearest neighbor retrieval and classification algorithms (see [17]). And a qualitative one, since as pointed out by [18], interfragments scores that respect TI provide more meaningful intermediate comparisons and permits a better classification when clustering protein structures (see [19]). Indeed, in order to cluster protein fragments, if a fragment A is similar to a fragment B (i.e. they are in the same cluster X), then for a third protein fragment C, say very close to A, should also belong to cluster X, so that the dissimilarity between A and C should also be low, what is ensured by TI.
In this paper, we first introduce ASD formal definition and present its properties that makes it suitable for protein fragment comparison. We present then some variants of ASD: a padded version to compare shifted fragments, a normalized version with respect to the length of the fragments and a family of truncated versions enabling to decrease slightly the precision for faster computation. We finally present experiments in which we compare ASD to reference scores: RMSD, RMSD_{d}, BC and TM. Let us note that neither DaliLite [20] nor DALIX could have been used for experimental comparison since the first one cannot handle so small fragments and, as shown by preliminary experiments, the second one was too slow for so many pairwise comparisons.
Methods
We introduce here the formal definition of ASD and present its main properties before introducing some variants of this measure.
Definition of ASD
We limit ourselves here to backbone structure comparison of two protein fragments. Formally, we identify a protein P of N residues with a sequence p _{1},...,p _{ N } of points in the threedimensional Euclidean space \(\mathbb {R}^ 3\) representing coordinates of the backbone alpha carbons.
where d is the usual Euclidean distance of \(\mathbb {R}^{3}\).
We define the following dissimilarity between two protein fragments P and Q by considering the distance between the amplitude spectra of the associated distance matrices:
Definition 1 (Amplitude Spectrum Distance).
Exact value of ASD can be computed efficiently by \(\ensuremath {O\left (N^{2} \log N\right)}\) algorithm [22].
The idea behind this definition is that we do not compare onetoone C _{ α } distances of proteins, but rather global features (namely the components of the spectra) to assess protein similarity. By focusing on their amplitude and forgetting the phase of the signal, this comparison is more tolerant to insertions/deletions/shifts and enables this way to score more meaningfully intermediates values as shown in the experiments part.
Properties of ASD
We present here theoretical properties of ASD. All the demonstrations are given in Additional file 1 and Additional file 2.
Properties for structural comparison
Invariance by isometric transformation
By translating and/or rotating a protein fragment, one would like to keep its similarity with other fragments unchanged. Actually, like any score based on internal distances such as RMSD _{ d } or DALI’s score, ASD is unchanged by any isometric transformation and thus by fragment translation, rotation or symmetry.
These scores being invariant by mirroring, it may be critical for some applications to distinguish mirrored matches from the classical ones. For any pair of fragments P and Q assessed to be similar by such a score, this can be done simply by computing the sign of the determinant d e t(P ^{⊤} Q) where P and Q are the N×3 matrices of the C _{ α } coordinates: a positive determinant shows that it is not a mirror, while a negative one indicates a better superimposition by mirroring one of the two structures [23].
Small sensitivity to small changes
ASD can qualified as a gradual dissimilarity since applying small deformations over a protein structure will result at most into a proportional change of ASD.
Euclidean bound and coherence with RMSD_{d}
Besides this bound relative to RMSD _{ d }, in the section, experimental support of the nice correlation between classical RMSD and ASD in case of totally aligned (onetoone) fragments.
Specific properties of ASD
Invariance by circular permutation
This property will show its importance when dealing with the padded extension of ASD in the next section.
Invariance by sequential inversion
As ASD compares, literally speaking, sequences of points in a 3dimensional space, no matter the direction of the sequence, if they are superimposable they are considered as similar.
This property enables to retrieve protein fragments that have the same conformation without taking into account the direction of the sequence. That property, for the best of our knowledge, only appears in nonsequential aligners (such as MICAN [24]) and that are thus very expensive to compute. See in the section for an example of a structural match with one reversed sequence.
ASD is a pseudometric
Being a pseudometric can be of great interest for designing efficient algorithms since this property, especially the triangle inequality, is often mandatory for pruning the search space of a nearest neighbor algorithm like in [17].

∀P,ASD(P,P)=0

∀P,Q,ASD(P,Q)=ASD(Q,P)

∀P,Q,R,
ASD(P,R)≤ASD(P,Q)+ASD(Q,R)
Thus, ASD is a pseudometric.
Yet, ASD is not a metric over the fragments since we can have A S D(P,Q)=0 for two differentproteins P and Q (taking for example Q to be the mirror of P, one gets A S D(P,Q)=0, but P≠Q).
ASD variants
Padded ASD
To gain flexibility with respect to the fragments alignment, padded matrices can be used to return the best ASD with respect to shifting them.
where \(\widetilde {D_{P}}\) and \(\widetilde {D_{Q}}\) are “padded” versions (both of dimension N=N _{ P }+N _{ Q }, padded with zeros) of the matrices D _{ P } and D _{ Q } (of dimensions N _{ P } and N _{ Q } respectively).
where D _{ P∖Q } is the difference of the distance matrices in the optimal alignment as illustrated in Fig. 1 c, meaning that, at most, \(\widetilde {\text {ASD}}\) measures only where P and Q differ.
Since \(\widetilde {\text {ASD}}\) is more practical than the original ASD while sharing the same properties, we will use it hereafter and ASD will denote this padded variant in the sequel of this document.
Normalized ASD
As shown in Fig. 6 a, the distribution of ASD values is dependent of the fragments’ length. We introduce here a new normalization of ASD named NASD (for “Normalized ASD”) to overcome this issue.
NASD performs well to normalize the scores with respect to the length of the fragments (see Fig. 6 b). This comes at the price of a small information loss caused by the a priori distance matrices normalization, as it may be seen in the experiment on zinc finger retrieval presented in the section which does not require length normalization and shows better results for ASD than NASD. We observed moreover on fragments of length 20 that the Pearson correlation coefficient of NASD with ASD was only 0.53, but that they were nevertheless well correlated for small values, with a Pearson correlation coefficient of 0.9 for values of ASD below 1000 (see Fig. 7 a).
Truncated ASD
When computing ASD, we use the 2norm over the module of each Fourier coefficients of the distance matrix. That is to say that computing ASD requires to compare all the Fourier coefficients.
When computational cost matters, it is possible to compare only a small part of them. As Fig. 7 b suggests, we can significantly reduce the computational cost by slightly reducing the precision of ASD. Indeed, Fig. 7 b shows the difference obtained by computing ASD over 40×40=1600 coefficients versus 5×5=25 coefficients. The Pearson correlation coefficient is as high as 0.95.
Results and discussion
To better understand how ASD compares empirically to classical scores, we have carried out several experiments that we present here.
We first study the distribution and the significance of the scores and observe a good correlation between ASD and RMSD for similar fragments (i.e. that are found totally superimposable by the structural alignment tool FrTMalign). We exhibit then explicit examples of divergence between ASD and RMSD leading us to identify 4 causes of disagreement between them. The ability of ASD to retrieve structures with very similar backbones, but in a mainchain reverse order, is illustrated on concrete examples. This property is rare among scores but may be structurally meaningful as pointed out by [25].
We compare then ASD, NASD, RMSD, BC and TM score on a realistic task of related fragment retrieval experiment. We mimic a classical scenario where a first fragment of interest is known and the goal is to find all the other structurally related fragments. The experiment is based on a zinc finger (ZF) family which has been been well studied and annotated and is thus a good basis for comparing safely the different methods: from a given ZF structure, that we call a seed, we want to retrieve all the other ZF fragments contained in a dataset mixing true ZF fragments and random fragments from a representative dataset of proteins. The ZF pattern presenting several insertion sites, this is a good test to compare how these scores can deal with insertions or deletions: in this experiment, ASD achieves a significantly better precision for high recalls, showing that it is wellsuited for the retrieval of distant related fragments even in the presence of indels.
Finally, we show the relevance of ASD for fragment clustering tasks. First, we consider the set of complementaritydetermining regions of immunoglobin (CDR) fragments, that are well known for their very divergent sequences, and show how ASD is able to detect structurally related CDR fragments which target potentially related antigenes. We then give another example of unsupervised classification of domain linkers, and show how the hierarchical clustering using ASD directly catches the manual classification done by [26].
Before presenting the results in details, we introduce first the datasets used.
Datasets
To perform allagainstall fragment comparisons in reasonable amounts of time for each score on small but representative sets of fragments, we built the SkF _{ N } datasets for N equal to 20,30,40,50 and 60 by extracting respectively all (overlapping) fragments of length N from the 40 protein domains from the classical "Skolnick data set" described in [27].
For the ZF fragment retrieval experiments, we used the PDB files listed as 3D crossreferences in PS00028 file from Prosite’s Release 20.99 [28] for C2H2 zinc finger motif Cx(2,4)Cx(3)[LIVMFYWC]x(8)Hx(3,5)H. Let us remark that C2H2 motif can match regions of different lengths due to the flexible size of the gaps. To enable fixedlength comparison and retrieval of the fragments by the different methods, we extracted all the fragments of 23 residues (ensuring to cover extensively all the ZF sites) starting at the beginning of each pattern match (at the first C). When several models were present in the PDB file, we used only the first model of the structure. By visual inspection we discarded the fragment from residue 18 to 41 in the PDB structure 2MA7 that exhibits a linear shape unlikely to be a ZF. The resulting set of ZF fragments is named ZF. To build a representative control set, we extracted 64 Astral protein domains by sampling randomly 16 protein domains in each of the 4 SCOP classes (all alpha, all beta, alpha/beta,alpha+beta) from the Astral 2.03 database [29]. From these domains, we extracted all (overlapping) fragments of 23 residues (the length of the fragments in ZF). Finally, we removed PDB files of fragments that have alternative C _{ α } atoms coordinate for one residue position. We denote by Astral64 the resulting 10,587 protein fragments dataset and we denote by Astral64+ZF the dataset consisting in merging Astral64 and ZF.
For the CDR clustering experiment, we used the 559 L1CDR fragments of the database SAbDab described in [30]. 207 of these 559 fragments had an attributed cluster in the database.
For the domain linker clustering experiment, we used the database described in [26] that contains 1279 fragments, whose length ranges from 2 to 58 residues, 50 % of them having less than 9 residues.
All the datasets used in the experiments can be accessed at http://www.irisa.fr/dyliss/public/ASD/.
Scores distributions
Significance of ASD values
Correlations between scores
We ran over the SkF _{ 20 } dataset an “allagainstall” comparison using RMSD, TMscore, NASD and ASD, corresponding to a significant set of 15,026,162 fragment comparisons for each score.

The backbone ordering is reversed, so that the RMSD becomes good when we reverse the ordering of the backbone residues (see “Reverse ordered structure” section below and Fig. 8 for concrete examples),

The structures are mirrored, it is a consequence of taking internal distances as input data,

The structures have a very good fit over a subpart which is shifted in sequence, and thus, RMSD makes a meaningless comparison. See Fig. 4 a for a concrete example of common subpart superimposition in two different proteins. Figure 4 b shows out an example of sequence shift impact for the same hairpin when comparing fragments corresponding to a shift by 4 residues of the sequence window.
Finally, as already discussed before, we can also see on this dataset the good normalization of NASD with respect to length compared to ASD (Fig. 6) even if some information is lost (7 a) and the good correlation of ASD with its truncated variant in Fig. 7 b, so that the computing time can be drastically reduced when speed is more important than high accuracy.
Reverse ordered structure
As shown in [25], structural similarity of convergent enzymes may occur in a “nonsequential way”. Indeed, one can almost superimpose two structures but the secondary structure will not be sequentially linked in the same way and may be mainchain reversed in the sequence. Thus, comparing two structures up to the sequential direction may be useful in this kind of enzymes.
ASD is able to mine protein fragments regardless to the sequential order. To illustrate this property, Fig. 8 a presents some concrete instances of fragments from SkF _{ 20 } found similar by ASD that superimpose well when aligning residues in reverse order of each other.
Zinc finger retrieval
We present here how the different structural scores and measures compare on a realistic task of ZF fragment retrieval: using an arbitrary ZF fragment as a seed, we ran a nearest neighbor retrieval experiment over Astral64+ZF, considering the fragments of Astral64 as false hits and the fragments of the ZF dataset as true hits. There are 10,587 fragments in Astral64 (which are considered as the false hits), and 321 fragments in the ZF dataset (which are considered as true hits).
Computing the area under each precisionrecall curve (PR AUC) [31] enables to compare the performance of the scores, the bigger being the better and the optimal value of PR AUC being 1.0 (perfect precision for perfect recall). Fig. 9, shows the average PR AUC on all the seeds for each score. We see that ASD has a better PR AUC than any other of the tested scores. At the second place, RMSD and RMSDd perform quite well in these experiments. The improvement brought by ASD with respect to RMSD is significant (the Welch ttest between ASD and RMSD values has a pvalue of 1.5.10^{−10}) and is mainly explained by the good precision obtained for high recalls as shown in Fig. 10 (ASD has a mean precision 26 % higher than the RMSD for 90 % of recall). This excellent recall contrasts with BC score which is very specific and retrieves only close fragments without indels, showing a complementary ability to discriminate better at finer scales.
TMscore is ranked last for this fragment retrieval task (see Fig. 11 for an example providing a more detailed view on its distribution with respect to ASD and respective instances of false positive hits). We tested also FrTMalign to see how a tool searching for (sub)alignment compares with the other scores. FrTMalign was too slow to perform the complete experimentation but we were able to run it on a few seeds (two precision recall curves are shown in Fig. 12) and we observed very irregular performances, ranging from worse (PR AUC of 0.62 for 1BBO residues 32:54 as ZF seed while of 0.98 for ASD and 0.83 for BC) to better (PR AUC of 0.86 for 1A1F residues 107:130 as ZF seed while of 0.83 for ASD and 0.85 for ASDasym, introduced below, but dropping rapidly for 90 % of recall to a precision of 0.58 compared to 0.72 and 0.78 for ASD and ASDasym respectively).
Finally, since ASD is invariant by mirroring, we ran a complementary experiment extending ASD with an additional test to discard antisymmetric false positives. To this end, we used the determinant introduced in the section describing ASD properties. We ranked first according to ASD the instances that have a positive determinant with the seed, and behind (once again according to ASD), the instances whose determinant was negative. The results of this ranking are labeled "ASDasym" on the Figs. 9 and 10. We see an additional improvement with respect to ASD: ASDasym has a mean precision 44 % higher than the RMSD for 90 % of recall.
Classification of CDR L1
Antibodies are proteins that play a key role in immunitarian system by binding a specific antigen. Actually, only a small part of their threedimensional structure, called complementaritydetermining regions of antibodies (CDR), determines the antigene they bind. Each CDR is composed of six protein fragments named L1,L2,L3– fragments on the light chain of the antibody – and H1,H2,H3– fragments on the heavy chain of the antibody. The paper [30] presents the SAbDab database of antibodies that includes the most recent classification of CDR established by [32].
We present here, as an example, an automatic clustering of L1 CDR fragments relying on ASD score and we compare it to the results of [32]. Since these fragments are of different lengths, usual scores are not able to perform the structural comparison of the different fragments. To give a way of comparison we compared our results to what can be obtained with the structural aligner TMalign [14].
Figure 13 c and d show a standard completelinkage hierarchical classification using ASD and TMalign respectively. We can see that the clusters are more scattered with TMAlign than with ASD. This means that clusters are more robust and that the association of a new structure to a cluster is easier using ASD.
DaviesBouldin index [33] measures the quality of a clustering and can be used to cut the dendrogram of a hierarchical clustering by looking at its local minimum value. We found local minimum values of 0.6 and 0.2 for TMAlign and ASD respectively. As the DBIndex is lower for ASD, the clustering quality is better using ASD. The corresponding cuts lead to 7 clusters in the case of TMAlign with 71.3 % of agreement with the reference classification, while using ASD, it results in 10 clusters having an agreement of 84.0 % with the reference.
Last, the computation time was about 10 times faster with ASD than with TMalign, and could have been sped up furthermore using triangle inequality property.
Classification of domain linkers
Conclusion
Taking advantage of the power of discrete Fourier transform, we have introduced ASD, an efficiently computable pseudometric measuring the global shape dissimilarity of protein fragments. By comparing the amplitude spectra of the internal distance matrices, ASD performs a more comprehensive comparison than by onetoone distances between the residues, that makes it tolerant to indels while 1) requiring neither to search for best (sub)alignment nor to introduce adhoc parameters (avoiding thus the consequent empirical tuning of quality/length tradeoff) and 2) preserving the triangle inequalityproperty.
Several experiments have been presented to assess the relevance of ASD on real fragment comparison tasks. First, through a large set of fragments comparisons, we have seen that ASD is well correlated with classical scores for easy alignment cases and that main disagreements are due either to its flexible comparison of fragments (e.g. tolerant indel and shifts, providing ASD an advantage over the other scores) or to the invariance of ASD by mirroring and reversal (that can be easily bypassed if needed, as in the ZF experiment with the ASDasym ariant).
Second, we have estimated the benefit of ASD with respect to other scores for more difficult cases involving the comparison of distantly related fragments. In the lack of a Gold standard, we have set up an indirect experimental assessment to evaluate the scores on a realistic task: from one instance of a structure of a zinc finger (ZF) fragment, we evaluate how well ASD retrieves all the fragments – including those that carry indels– belonging to the same structural ZF family among nonZF fragments. This experiment has witnessed a good tolerance of ASD to indels compared to BC score, TMScore and RMSD, and illustrated its usefulness for retrieval applications requiring a high recall on distantly related fragments.
And then, the benefits of ASD when dealing with classification tasks were illustrated by the CDR and domain linker clustering experiments. In both cases ASD is the only score, to the best of our knowledge, which is capable of comparing fragments of different lengths without relying on structural alignment. On these experiments, ASD performs better and faster than the common TMalign aligner and mostly agree with existing classifications built by experts. Moreover, thanks to the sharpness of the clusters derived from ASD, one gets an accurate insight for attributing a cluster to nonclassified fragments.
The definition and properties of ASD coupled to these first experiments make ASD a good candidate to fill the current gap in measuring the structural divergence of fragments.
To go further in the study of ASD, carrying out additional practical experiments would help to appreciate the impact and interest of ASD invariance with respect to mirroring and main chain reversal. It would also be interesting to investigate the relevance of ASD for the comparison of whole protein domains.Concerning the possible developments of ASD, the computation time could be sped up for massive comparisons by considering less Fourier coefficients as proposed in the ASD variants section and eventually by weighting them adequately ; but this would require a careful study of the speed gain versus the precision loss.Finally, from a general perspective, we have shown here that the spectra of the distance matrix of a protein fragment contains information for the comparison of fragments. One further direction of research would be to use this information to determine the key elements of the spectra that make some related fragments similar.
An application would be for instance to determine the characteristic spectra of a family of protein fragments to build a dedicated dissimilarity measure enabling a finer retrieval of new members.
Declarations
Acknowledgements
We gratefully acknowledge Inria for the 3years PhD grant "Inria CordiS" attributed to CG.
Authors’ Affiliations
References
 Friedberg I, Godzik A. Connecting the protein structure universe by using sparse recurring fragments. Structure. 2005; 13(8):1213–24.View ArticlePubMedGoogle Scholar
 Shindyalov IN, Bourne PE. Protein structure alignment by incremental combinatorial extension (ce) of the optimal path.Protein Eng. 1998; 11(9):739–47.View ArticlePubMedGoogle Scholar
 Zhi D, Shatsky M, Brenner SE. Alignmentfree local structural search by writhe decomposition. Bioinformatics. 2010; 26(9):1176–84.View ArticlePubMedPubMed CentralGoogle Scholar
 Xuefeng C, Cheng LS, Lin H, Ming L. Fingerprinting protein structures effectively and efficiently. Bioinformatics. 2013. doi:10.1093/bioinformatics/btt659.
 Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J Mol Biol. 1997; 268(1):209–25.View ArticlePubMedGoogle Scholar
 Camproux AC, Gautier R, Tufféry P. A hidden Markov model derived structural alphabet for proteins. J Mol Biol. 2004; 339(3):591–605.View ArticlePubMedGoogle Scholar
 Etchebest C, Benros C, Hazout S, de Brevern AG. A structural alphabet for local protein structures: Improved prediction methods. Proteins: Struct Funct Bioinform. 2005; 59(4):810–27.View ArticleGoogle Scholar
 Li SC, Bu D, Gao X, Xu J, Li M. Designing succinct structural alphabets. Bioinformatics. 2008; 24(13):182–9.View ArticleGoogle Scholar
 Zhou H, Skolnick J. Protein model quality assessment prediction by combining fragment comparisons and a consensus c(alpha) contact potential. Proteins. 2008; 71:1211–8.View ArticlePubMedPubMed CentralGoogle Scholar
 Hasegawa H, Holm L. Advances and pitfalls of protein structural alignment. Current Opinion Struct Biol. 2009; 19(3):341–8.View ArticleGoogle Scholar
 Eidhammer I, Jonassen I, Taylor WR. Structure comparison and structure patterns. J Comput Biol. 1999; 7:685–716.View ArticleGoogle Scholar
 Guyon F, Tufféry P. Fast protein fragment similarity scoring using a binet–cauchy kernel. Bioinformatics. 2013. doi:10.1093/bioinformatics/btt618.
 Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins: Struct Funct Bioinform. 2004; 57(4):702–10.View ArticleGoogle Scholar
 Zhang Y, Skolnick J. Tmalign: A protein structure alignment algorithm based on tmscore. Nucleic Acids Res. 2005; 33:2302–9.View ArticlePubMedPubMed CentralGoogle Scholar
 Holm L, Park J. Dalilite workbench for protein structure comparison. Bioinformatics. 2000; 16(6):566–7.View ArticlePubMedGoogle Scholar
 Wohlers I, Andonov R, Klau GW. Optimal DALI protein structure alignment. IEEE/ACM Trans Comput Biol Bioinform. 2012;20. RR7915 RR7915.Google Scholar
 Ciaccia P, Patella M, Zezula P. Mtree: An efficient access method for similarity search in metric spaces. In: Proceedings of the 23^{rd} International Conference on Very Large Data Bases. VLDB ’97. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.: 1997. p. 426–35. http://dl.acm.org/citation.cfm?id=645923.671005.Google Scholar
 Røgen P, Fain B. Automatic classification of protein structure by using Gauss integrals. Proc Nat Acad Sci. 2003; 100(1):119–24.View ArticlePubMedGoogle Scholar
 Koehl P. Protein structure similarities. Current Opinion Struct Biol. 2001; 11(3):348–53.View ArticleGoogle Scholar
 Holm L, Park J. Dalilite workbench for protein structure comparison. Bioinformatics (Oxford, England). 2000; 16(6):566–7.View ArticleGoogle Scholar
 Jain AK. Fundamentals of Digital Image Processing. Upper Saddle River, NJ, USA: PrenticeHall, Inc.; 1989.Google Scholar
 Cooley J, Tukey J. An algorithm for the machine calculation of complex Fourier series. Math Comput. 1965; 19(90):297–301.View ArticleGoogle Scholar
 Guyon F, Tufféry P. Assessing 3D scores for protein structure fragment mining. Open Access Bioinforma. 2010; 2:67–77.Google Scholar
 Minami S, Sawada K, Chikenji G. Mican : a protein structure alignment algorithm that can handle multiplechains, inverse alignments, calpha only models, alternative alignments, and nonsequential alignments. BMC Bioinformatics. 2013; 14(1):24.View ArticlePubMedPubMed CentralGoogle Scholar
 Makarova KS, Grishin NV. Thermolysin and mitochondrial processing peptidase: how far structurefunctional convergence goes.Protein Sci. 1999; 8(11):2537–40.PubMedPubMed CentralGoogle Scholar
 George RA, Heringa J. An analysis of protein domain linkers: their classification and role in protein folding. Protein Eng. 2002; 15(11):871–9. doi:10.1093/protein/15.11.871. http://peds.oxfordjournals.org/content/15/11/871.full.pdf+html.
 Lancia G, Carr R, Walenz B, Istrail S. 101 optimal pdb structure alignments: A branchandcut algorithm for the maximum contact map overlap problem. Proceedings of the Fifth Annual International Conference on Computational Biology. 2001:193–202.Google Scholar
 Sigrist CJA, Castro ED, Cerutti L, Cuche BA, Hulo N, Bridge A, Bougueleret L, Xenarios I. New and continuing developments at prosite. Nucleic Acids Res. 2013; 41(DatabaseIssue):344–7.View ArticleGoogle Scholar
 Chandonia JMM, Hon G, Walker NS, Lo Conte L, Koehl P, Levitt M, Brenner SE. The ASTRAL Compendium in 2004,. Nucleic Acids Res. 2004; 32(Database issue):189–92.View ArticleGoogle Scholar
 Dunbar J, Krawczyk K, Leem J, Baker T, Fuchs A, Georges G, Shi J, Deane CM. Sabdab: the structural antibody database. Nucleic Acids Res. 2014; 42(D1):1140–1146. doi:10.1093/nar/gkt1043. http://nar.oxfordjournals.org/content/42/D1/D1140.full.pdf+html.
 Davis J, Goadrich M. The relationship between precisionrecall and roc curves. In: Proceedings of the 23^{rd} International Conference on Machine Learning. NY, USA,: ACM New York: 2006. p. 233–40.Google Scholar
 North B, Lehmann A, Jr RLD. A new clustering of antibody {CDR} loop conformations. J Mol Biol. 2011; 406(2):228–56. doi:10.1016/j.jmb.2010.10.030.View ArticlePubMedGoogle Scholar
 Davies DL, Bouldin DW. A cluster separation measure. IEEE Trans Pattern Anal Mach Intell. 1979; 1(2):224–7. doi:10.1109/TPAMI.1979.4766909.View ArticlePubMedGoogle Scholar
Copyright
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.