Identification of non random motifs in loops using a structural alphabet

Regad, Leslie; Martin, Juliette; Camproux, Anne‐Claude

doi:10.1109/cibcb.2006.331017

Cited by 11 publications

(17 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found an average intra-cluster RMSd w equal to 0.85 Å versus 2.72 Å for the inter-cluster RMSd dev , which confirms our previous results [40]. In the loop classification [65] clusters grouping seven-residue loops present an average RMSd close to 1 Å.…”

Section: Discussionsupporting

confidence: 89%

See 1 more Smart Citation

Mining protein loops using a structural alphabet and statistical exceptionality

et al. 2010

View full text Add to dashboard Cite

BackgroundProtein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied.ResultsWe developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 Å). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints.ConclusionsWe developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of ...

show abstract

Section: Discussionsupporting

confidence: 89%

“…This alphabet is a collection of 27 prototypes of four residues, denoted [A-Z, a], based on a hidden Markov model [40,42]. It permits the encoding of a protein structure of n residues into a sequence of ( n - 3) structural letters.…”

Section: Resultsmentioning

confidence: 99%

Mining protein loops using a structural alphabet and statistical exceptionality

et al. 2010

View full text Add to dashboard Cite

show abstract

“…HAL author manuscript inserm-00175058, version 1 [285,352] have proved their efficiency both in the description and the prediction of small loops [99,283,362,363] or long fragments [349,[364][365][366][367][368]. In the following, we will detail some interesting developments based on the application of these local protein structures.…”

Section: Hal Author Manuscript Inserm-00175058 Versionmentioning

confidence: 99%

Local Protein Structures

Offmann¹,

Tyagi²,

Brevern

2007

CBIO

View full text Add to dashboard Cite

Protein structures are classically described as composed of two regular states, the α-helices and the β-strands and one non-regular and variable state, the coil. Nonetheless, this simple definition of secondary structures hides numerous limitations. In fact, the rules for secondary structure assignment are complex. Thus, numerous assignment methods based on different criteria have emerged leading to heterogeneous and diverging results. In the same way, 3 states may over-simplify the description of protein structure; 50% of all residues, i.e., the coil, are not genuinely described even when it encompass precise local protein structures.Description of local protein structures have hence focused on the elaboration of complete sets of small prototypes or "structural alphabets", able to analyze local protein structures and to approximate every part of the protein backbone. They have also been used to predict the protein backbone conformation and in ab initio / de novo methods. In this paper, we review different approaches towards the description of local structures, mainly through their description in terms of secondary structures and in terms of structural alphabets. We provide some insights into their potential applications.

show abstract

“…• in addition, HMM-SA was shown to be a powerful tool for the analysis of protein loops, the most variable and flexible regions in proteins (Camproux et al, 2001;Regad et al, 2006). They are, however, often known to play an important role in protein function and stability…”

Section: Different Sucessful Applications Of Hmm-samentioning

confidence: 99%

“…Those patterns will be defined here as four SL words encoding seven AA residues. This length has been chosen to obtain satisfying representativities (Regad et al, 2006). However, the prediction method is independent on the pattern length and could be applied to any identified pattern.…”

Section: Using Hmm To Detect Interesting Hmm-sa Patternsmentioning

confidence: 99%