Dihedral-Based Segment Identification and Classification of Biopolymers I: Proteins

Nagy, Gábor; Oostenbrink, Chris

doi:10.1021/ci400541d

Cited by 36 publications

(47 citation statements)

References 26 publications

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“…3 In short, DISICL is based on the classification of segments of biopolymers. First, relevant (backbone) dihedral angles are calculated and attributed to regions in the dihedral angle space.…”

Section: Methodsmentioning

confidence: 99%

Dihedral-Based Segment Identification and Classification of Biopolymers II: Polynucleotides

Nagy

Oostenbrink

2014

J. Chem. Inf. Model.

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In an accompanying paper (Nagy, G.; Oostenbrink, C. Dihedral-based segment identification and classification of biopolymers I: Proteins. J. Chem. Inf. Model. 2013, DOI: 10.1021/ci400541d), we introduce a new algorithm for structure classification of biopolymeric structures based on main-chain dihedral angles. The DISICL algorithm (short for DIhedral-based Segment Identification and CLassification) classifies segments of structures containing two central residues. Here, we introduce the DISICL library for polynucleotides, which is based on the dihedral angles ε, ζ, and χ for the two central residues of a three-nucleotide segment of a single strand. Seventeen distinct structural classes are defined for nucleotide structures, some of which—to our knowledge—were not described previously in other structure classification algorithms. In particular, DISICL also classifies noncanonical single-stranded structural elements. DISICL is applied to databases of DNA and RNA structures containing 80,000 and 180,000 segments, respectively. The classifications according to DISICL are compared to those of another popular classification scheme in terms of the amount of classified nucleotides, average occurrence and length of structural elements, and pairwise matches of the classifications. While the detailed classification of DISICL adds sensitivity to a structure analysis, it can be readily reduced to eight simplified classes providing a more general overview of the secondary structure in polynucleotides.

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Section: Methodsmentioning

confidence: 99%

Dihedral-Based Segment Identification and Classification of Biopolymers II: Polynucleotides

Nagy

Oostenbrink

2014

J. Chem. Inf. Model.

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“…70,71 More recently, it has been shown that secondary structure conformations can be classified solely on the basis of backbone dihedrals of a protein. 72,73 As a demonstration of our classification approach to realistic periodic data, we have applied our method to analyze the roughly to β sheets, α helices, turns, etc. (see Fig.…”

Section: Local Motifs Of a β-Hairpine Peptidementioning

confidence: 99%

Recognizing Local and Global Structural Motifs at the Atomic Scale

Gasparotto

Meißner

Ceriotti

2018

J. Chem. Theory Comput.

109

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Most of the current understanding of structure-property relations at the molecular and the supramolecular scales can be formulated in terms of the stability of and the interactions between a limited number of recurring structural motifs (e.g., H-bonds, coordination polyhedra, and protein secondary structure). Here we demonstrate an algorithm to automatically recognize such patterns, based on the identification of local maxima in the probability distributions observed in atomistic computer simulations, which is robust to the dimensionality and the sparsity of the reference atomistic data. We first discuss its main features, demonstrating some on artificial data sets, and then show how it can be applied to identify coordination environments in Lennard-Jones clusters and to recognize secondary-structure patterns in the simulation of an oligopeptide. To assess the applicability of this algorithm for motifs that involve several interdependent degrees of freedom, we also employ it to identify groups of conformers of the cluster and the polypeptide, considered in their entirety. The motifs identified by analyzing atomistic simulations can be used to interpret and rationalize the stability and behavior of the system at hand, and also as a tool to accelerate sampling, in association with biased molecular dynamics schemes.

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“…The SS of all proteins was determined from the protein structure using three different algorithms: DSSP (Dictionary of Secondary Structure for Proteins) 28 , DISICL (DIhedral based Segment Identification and CLassification) 29 , and the in-house algorithm HbSS (Hydrogen-bond based Secondary Structure) described below. Table S4) based on their distinctive backbone hydrogenbonding patterns.…”

Section: Secondary Structure Determinationmentioning

confidence: 99%

SESCA: Predicting Circular Dichroism Spectra from Protein Molecular Structures

Nagy

Igaev

Hoffmann

et al. 2018

Preprint

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50Circular dichroism spectroscopy is a highly sensitive, but low-resolution technique to study 51 the structure of proteins. Combed with molecular modelling and other complementary 52 techniques, CD spectroscopy can also provide essential information at higher resolution. To 53 this aim, we introduce a new computational method to calculate the electronic circular 54 dichroism spectra of proteins from a three dimensional-model structure or structural 55 ensemble. The method determines the CD spectrum from the average secondary structure 56 composition of the protein using a pre-calculated set of basis spectra. We derived several 57 basis spectrum sets obtained from the experimental CD spectra and secondary structure 58 information of 71 reference proteins and tested the prediction accuracy of these basis 59 spectrum sets through cross-validation. Furthermore, we investigated how prediction 60 accuracy is affected by contributions from amino acid side chain groups and protein 61 flexibility, potential experimental errors of the reference protein spectra, as well as the choice 62 of the secondary structure classification algorithm and the number of basis spectra. We 63 compared the predictive power of our method to previous spectrum prediction algorithms  64 such as DichroCalc and PDB2CD  and found that SESCA predicts the CD spectra with up 65 to 50% smaller deviation. Our results indicate that SESCA basis sets are robust to 66 experimental error in the reference spectra, and the choice of the secondary structure 67 classification algorithm. For over 80% of the globular reference proteins, SESCA basis sets 68 could accurately predict the experimental spectrum solely from their secondary structure 69 composition. To improve SESCA predictions for the remaining proteins, we applied 70 corrections to account for intensity normalization, contributions from the amino side chains, 71 and conformational flexibility. For globular proteins only intensity scaling improved the 72 prediction accuracy significantly, but our models indicate that side chain contributions and 73 . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/279752 doi: bioRxiv preprint first posted online Mar. 9, 2018; 3 structural flexibility are pivotal for the prediction of shorter peptides and intrinsically 74 disordered proteins. 75Author summary 76 Proteins are biomolecules that perform almost all of active task in living organisms, and how 77 they perform these task is defined by their structure. By understanding the structure of 78 proteins, we can alter and regulate their biological functions, which may lead to many 79 medical, scientific, and technological advancements. Here we present SESCA, a new method 80 that allows the assessment, and refinement of protein model structures. SESCA predicts the 81 expected circular dichroism spectrum of a proposed protein model and compares it to an 82 experimental...

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Dihedral-Based Segment Identification and Classification of Biopolymers I: Proteins

Cited by 36 publications

References 26 publications

Dihedral-Based Segment Identification and Classification of Biopolymers II: Polynucleotides

Dihedral-Based Segment Identification and Classification of Biopolymers II: Polynucleotides

Recognizing Local and Global Structural Motifs at the Atomic Scale

SESCA: Predicting Circular Dichroism Spectra from Protein Molecular Structures

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