Inter-helical Interactions in Membrane Proteins: Analysis Based on the Local Backbone Geometry and the Side Chain Interactions

Jha, Anupam Nath; Vishveshwara, Saraswathi

doi:10.1080/07391102.2009.10507284

Cited by 5 publications

(4 citation statements)

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“…In other words, it does not suffice to merely specify the distance between pairs of amino acids. Addition relevant information includes the sequence separation between the amino acids in contact and the relative orientations of the Frenet coordinate systems5 of the tangent‐normal–binormal at each of the two amino acid locations 6…”

Section: Introductionmentioning

confidence: 99%

Amino acid interaction preferences in proteins

2010

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Understanding the key factors that influence the interaction preferences of amino acids in the folding of proteins have remained a challenge. Here we present a knowledge-based approach for determining the effective interactions between amino acids based on amino acid type, their secondary structure, and the contact based environment that they find themselves in the native state structure as measured by their number of neighbors. We find that the optimal information is approximately encoded in a 60 3 60 matrix describing the 20 types of amino acids in three distinct secondary structures (helix, beta strand, and loop). We carry out a clustering scheme to understand the similarity between these interactions and to elucidate a nonredundant set. We demonstrate that the inferred energy parameters can be used for assessing the fit of a given sequence into a putative native state structure.

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Section: Introductionmentioning

confidence: 99%

Amino acid interaction preferences in proteins

2010

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“…The helix‐helix packing mode in transmembrane proteins was quantitatively studied by [83]. They represented the mutual orientations of local axes by a single parameter.…”

Section: Introductionmentioning

confidence: 99%

“…TM helices are typically longer (on average: 26 amino acids) and more tightly packed than helices in soluble proteins [ 81 , 82 ]. A stable interaction between two helices requires that several residues from each helix are involved in the helix‐helix contact [ 83 ]. We call this structure a helix‐helix interface in this manuscript.…”

Section: Introductionmentioning

confidence: 99%

“…Helices have smaller crossing angles in membrane proteins than in water soluble proteins, which is consistent with the requirements of structural compactness and membrane spanning [ 88 ]. The helix‐helix packing mode in transmembrane proteins was quantitatively studied by [ 83 ]. They represented the mutual orientations of local axes by a single parameter.…”

Section: Introductionmentioning

confidence: 99%

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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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Intrinsic protein geometry with application to non-proline cis peptide planes

Hou

Dai

et al. 2018

J Math Chem

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A protein is traditionally visualised as a piecewise linear discrete curve, and its geometry is conventionally characterised by the extrinsically determined Ramachandran angles. However, a protein backbone has also two independent intrinsic geometric structures, due to the peptide planes and the side chains. Here we adapt and develop modern 3D virtual reality techniques to scrutinize the atomic geometry along a protein backbone, in the vicinity of a peptide plane. For this we compare backbone geometry-based (extrinsic) and structure-based (intrinsic) coordinate systems, and as an example we inspect the trans and cis peptide planes. We reveal systematics in the way how a cis peptide plane deforms the neighbouring atomic geometry, and we develop a virtual reality based visual methodology that can identify the presence of a cis peptide plane from the arrangement of atoms in its vicinity. Our approach can easily detect exceptionally placed atoms in crystallographic structures. Thus it can be employed as a powerful visual refinement tool which is applicable also in the case when resolution of the protein structure is limited and whenever refinement is needed. As concrete examples we identify a number of crystallographic protein structures in Protein Data Bank (PDB) that display exceptional atomic positions around their cis peptide planes.

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Inter-helical Interactions in Membrane Proteins: Analysis Based on the Local Backbone Geometry and the Side Chain Interactions

Cited by 5 publications

References 53 publications

Amino acid interaction preferences in proteins

Amino acid interaction preferences in proteins

Probabilistic grammatical model for helix‐helix contact site classification

Intrinsic protein geometry with application to non-proline cis peptide planes

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