Structural fragment clustering reveals novel structural and functional motifs in α-helical transmembrane proteins

Marsico, Annalisa; Henschel, Andreas; Winter, Christof; Tuukkanen, Anne; Vassilev, Boris; Scheubert, Kerstin; Schroeder, Michael

doi:10.1186/1471-2105-11-204

Cited by 14 publications

(14 citation statements)

References 54 publications

(75 reference statements)

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“…), while others have been identified from conserved sequence patterns (e.g., Refs. ). The occurrence of these motifs, and others we have identified, in both kinked and straight helices are shown in Table .…”

Section: Resultsmentioning

confidence: 97%

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Helix kinks are equally prevalent in soluble and membrane proteins

2014

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Helix kinks are a common feature of α-helical membrane proteins, but are thought to be rare in soluble proteins. In this study we find that kinks are a feature of long α-helices in both soluble and membrane proteins, rather than just transmembrane α-helices. The apparent rarity of kinks in soluble proteins is due to the relative infrequency of long helices (≥20 residues) in these proteins. We compare length-matched sets of soluble and membrane helices, and find that the frequency of kinks, the role of Proline, the patterns of other amino acid around kinks (allowing for the expected differences in amino acid distributions between the two types of protein), and the effects of hydrogen bonds are the same for the two types of helices. In both types of protein, helices that contain Proline in the second and subsequent turns are very frequently kinked. However, there are a sizeable proportion of kinked helices that do not contain a Proline in either their sequence or sequence homolog. Moreover, we observe that in soluble proteins, kinked helices have a structural preference in that they typically point into the solvent.

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Section: Resultsmentioning

confidence: 97%

“…19,24 A number of articles have suggested more complex sequence motifs that may be important in kinks (e.g., Refs. 19,[30][31][32], but once again none of these motifs are consistently observed across studies. These studies have a similar approach to their choice of data set.…”

Section: Introductionmentioning

confidence: 95%

Helix kinks are equally prevalent in soluble and membrane proteins

2014

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“…In the all-α class, the proteins fold as α-helices bundles crossing the membrane (Fuchs et al 2009;Lo et al 2009;Nugent and Jones 2010;Wang et al 2011), the structural stability being governed by tight and specific interactions between residues (Marsico et al 2010a, b;Walters and DeGrado 2006;Nagarathnam et al 2011). In the all-α class, the proteins fold as α-helices bundles crossing the membrane (Fuchs et al 2009;Lo et al 2009;Nugent and Jones 2010;Wang et al 2011), the structural stability being governed by tight and specific interactions between residues (Marsico et al 2010a, b;Walters and DeGrado 2006;Nagarathnam et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Sequence–structure relationship study in all-α transmembrane proteins using an unsupervised learning approach

Esque¹,

Urbain²,

Etchebest³

et al. 2015

Amino Acids

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Transmembrane proteins (TMPs) are major drug targets, but the knowledge of their precise topology structure remains highly limited compared with globular proteins. In spite of the difficulties in obtaining their structures, an important effort has been made these last years to increase their number from an experimental and computational point of view. In view of this emerging challenge, the development of computational methods to extract knowledge from these data is crucial for the better understanding of their functions and in improving the quality of structural models. Here, we revisit an efficient unsupervised learning procedure, called Hybrid Protein Model (HPM), which is applied to the analysis of transmembrane proteins belonging to the all-α structural class. HPM method is an original classification procedure that efficiently combines sequence and structure learning. The procedure was initially applied to the analysis of globular proteins. In the present case, HPM classifies a set of overlapping protein fragments, extracted from a non-redundant databank of TMP 3D structure. After fine-tuning of the learning parameters, the optimal classification results in 65 clusters. They represent at best similar relationships between sequence and local structure properties of TMPs. Interestingly, HPM distinguishes among the resulting clusters two helical regions with distinct hydrophobic patterns. This underlines the complexity of the topology of these proteins. The HPM classification enlightens unusual relationship between amino acids in TMP fragments, which can be useful to elaborate new amino acids substitution matrices. Finally, two challenging applications are described: the first one aims at annotating protein functions (channel or not), the second one intends to assess the quality of the structures (X-ray or models) via a new scoring function deduced from the HPM classification.

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“…Moreover, despite recent interest in helix packing in transmembrane proteins, their approach has not been superceded by up‐to‐date works [90,145]‐ [147]. Therefore, it was natural do adopt the WDG classification as the basis for our research.…”

Section: Resultsmentioning

confidence: 99%

Probabilistic grammatical model for helix‐helix contact site classification

Dyrka

Nebel

Kotulska

2013

Algorithms Mol Biol

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BackgroundHidden Markov Models power many state‐of‐the‐art tools in the field of protein bioinformatics. While excelling in their tasks, these methods of protein analysis do not convey directly information on medium‐ and long‐range residue‐residue interactions. This requires an expressive power of at least context‐free grammars. However, application of more powerful grammar formalisms to protein analysis has been surprisingly limited.ResultsIn this work, we present a probabilistic grammatical framework for problem‐specific protein languages and apply it to classification of transmembrane helix‐helix pairs configurations. The core of the model consists of a probabilistic context‐free grammar, automatically inferred by a genetic algorithm from only a generic set of expert‐based rules and positive training samples. The model was applied to produce sequence based descriptors of four classes of transmembrane helix‐helix contact site configurations. The highest performance of the classifiers reached AUCROC of 0.70. The analysis of grammar parse trees revealed the ability of representing structural features of helix‐helix contact sites.ConclusionsWe demonstrated that our probabilistic context‐free framework for analysis of protein sequences outperforms the state of the art in the task of helix‐helix contact site classification. However, this is achieved without necessarily requiring modeling long range dependencies between interacting residues. A significant feature of our approach is that grammar rules and parse trees are human‐readable. Thus they could provide biologically meaningful information for molecular biologists.

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Structural fragment clustering reveals novel structural and functional motifs in α-helical transmembrane proteins

Cited by 14 publications

References 54 publications

Helix kinks are equally prevalent in soluble and membrane proteins

Helix kinks are equally prevalent in soluble and membrane proteins

Sequence–structure relationship study in all-α transmembrane proteins using an unsupervised learning approach

Probabilistic grammatical model for helix‐helix contact site classification

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