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Local descriptors of protein structure: a systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions.

ABSTRACT: Local protein structure representations that incorporate long-range contacts between residues are often considered in protein structure comparison but have found relatively little use in structure prediction where assembly from single backbone fragments dominates. Here, we introduce the concept of local descriptors of protein structure to characterize local neighborhoods of amino acids including short- and long-range interactions. We build a library of recurring local descriptors and show that this library is general enough to allow assembly of unseen protein structures. The library could on average re-assemble 83% of 119 unseen structures, and showed little or no performance decrease between homologous targets and targets with folds not represented among domains used to build it. We then systematically evaluate the descriptor library to establish the level of the sequence signal in sets of protein fragments of similar geometrical conformation. In particular, we test whether that signal is strong enough to facilitate correct assignment and alignment of these local geometries to new sequences. We use the signal to assign descriptors to a test set of 479 sequences with less than 40% sequence identity to any domain used to build the library, and show that on average more than 50% of the backbone fragments constituting descriptors can be correctly aligned. We also use the assigned descriptors to infer SCOP folds, and show that correct predictions can be made in many of the 151 cases where PSI-BLAST was unable to detect significant sequence similarity to proteins in the library. Although the combinatorial problem of simultaneously aligning several fragments to sequence is a major bottleneck compared with single fragment methods, the advantage of the current approach is that correct alignments imply correct long range distance constraints. The lack of these constraints is most likely the major reason why structure prediction methods fail to consistently produce adequate models when good templates are unavailable or undetectable. Thus, we believe that the current study offers new and valuable insight into the prediction of sequence-structure relationships in proteins.

SUBMITTER: Hvidsten TR

PROVIDER: S-EPMC5479691 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Local descriptors of protein structure: a systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions.

Hvidsten Torgeir R TR Kryshtafovych Andriy A Fidelis Krzysztof K

Proteins 20090601 4

Local protein structure representations that incorporate long-range contacts between residues are often considered in protein structure comparison but have found relatively little use in structure prediction where assembly from single backbone fragments dominates. Here, we introduce the concept of local descriptors of protein structure to characterize local neighborhoods of amino acids including short- and long-range interactions. We build a library of recurring local descriptors and show that t ...[more]

PMID: 19025980

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Project description:The recent pandemic triggered numerous societal efforts aimed to control and limit the spread of SARS-CoV-2. One of these aspects is related on how the virion interacts with inanimate surfaces, which might be the source of secondary infection. Although recent works address the adsorption of the spike protein on surfaces, there is no information concerning the long-range interactions between spike and surfaces, experimented by the virion when is dispersed in the droplet before its possible adsorption. Some descriptors, namely the interaction potentials per single protein and global potentials, were calculated in this work. These descriptors, evaluated for the closed and open states of the spike protein, are correlated to the long-range noncovalent interactions between the SARS-CoV-2 spikes and polymeric surfaces. They are associated with the surface's affinity towards SARS-CoV-2 dispersed in respiratory droplets or water solutions. Molecular-Dynamics simulations were performed to model the surface of three synthetic polymeric materials: Polypropylene (PP), Polyethylene Terephthalate (PET), and Polylactic Acid (PLA), used in Molecular Mechanics simulations to define the above potentials. The descriptors show a similar trend for the three surfaces, highlighting a greater affinity towards the spikes of PP and PLA over PET. For closed and open structures, the long-range interactions with the surfaces decreased in the following order PP ∼ PLA > PET and PLA > PP > PET, respectively. Thus, PLA and PP interact with the virion quite distant from these surfaces to a greater extent concerning the PET surface, however, the differences among the considered surfaces were small. The global potentials show that the long-range interactions are weak compared to classic binding energy of covalent or ionic bonds. The proposed descriptors are useful most of all for a comparative study aimed at quickly preliminary screening of polymeric surfaces. The obtained results should be validated by more accurate method which will be subject of a subsequent work.

Project description:BackgroundKnowing the three-dimensional (3D) structure of the chromatin is important for obtaining a complete picture of the regulatory landscape. Changes in the 3D structure have been implicated in diseases. While there exist approaches that attempt to predict the long-range chromatin interactions, they focus only on interactions between specific genomic regions - the promoters and enhancers, neglecting other possibilities, for instance, the so-called structural interactions involving intervening chromatin.ResultsWe present a method that can be trained on 5C data using the genetic sequence of the candidate loci to predict potential genome-wide interaction partners of a particular locus of interest. We have built locus-specific support vector machine (SVM)-based predictors using the oligomer distance histograms (ODH) representation. The method shows good performance with a mean test AUC (area under the receiver operating characteristic (ROC) curve) of 0.7 or higher for various regions across cell lines GM12878, K562 and HeLa-S3. In cases where any locus did not have sufficient candidate interaction partners for model training, we employed multitask learning to share knowledge between models of different loci. In this scenario, across the three cell lines, the method attained an average performance increase of 0.09 in the AUC. Performance evaluation of the models trained on 5C data regarding prediction on an independent high-resolution Hi-C dataset (which is a rather hard problem) shows 0.56 AUC, on average. Additionally, we have developed new, intuitive visualization methods that enable interpretation of sequence signals that contributed towards prediction of locus-specific interaction partners. The analysis of these sequence signals suggests a potential general role of short tandem repeat sequences in genome organization.ConclusionsWe demonstrated how our approach can 1) provide insights into sequence features of locus-specific interaction partners, and 2) also identify their cell-line specificity. That our models deem short tandem repeat sequences as discriminative for prediction of potential interaction partners, suggests that they could play a larger role in genome organization. Thus, our approach can (a) be beneficial to broadly understand, at the sequence-level, chromatin interactions and higher-order structures like (meta-) topologically associating domains (TADs); (b) study regions omitted from existing prediction approaches using various information sources (e.g., epigenetic information); and

Dataset Information

Local descriptors of protein structure: a systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions.

Publications

Local descriptors of protein structure: a systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions.

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