A new statistical framework to assess structural alignment quality using information compression

Collier, James H.; Allison, Lloyd; Lesk, Arthur M.; Banda, Maria García de la; Konagurthu, Arun S.

doi:10.1093/bioinformatics/btu460

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…We conclude that, as discussed previously for globular proteins, there is room for improvement in structure alignment programs, both in terms of alignment accuracy and alignment consistency . Introducing membrane location information within the superimposition procedure of membrane proteins might be one approach to improving the alignment quality.…”

Section: Discussionsupporting

confidence: 51%

“…The major challenge in assessing structural alignment accuracy is the lack of a standard score that ranks the quality of structural alignments. Although efforts have been made to address this issue recently, those strategies are not yet publicly available. Here, we rely on the expectation that a homology model built using the ideal alignment should provide sufficient information to reconstruct the structure of the other protein.…”

Section: Methodsmentioning

confidence: 99%

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Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy

Stamm

Forrest

2015

Proteins

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Protein structure alignment methods are used for the detection of evolutionary and functionally related positions in proteins. A wide array of different methods are available, but the choice of the best method is often not apparent to the user. Several studies have assessed the alignment accuracy and consistency of structure alignment methods, but none of these explicitly considered membrane proteins, which are important targets for drug development and have distinct structural features. Here, we compared 13 widely-used pairwise structural alignment methods on a test set of homologous membrane protein structures (called HOMEP3). Each pair of structures was aligned and the corresponding sequence alignment was used to construct homology models. The model accuracy compared to the known structures was assessed using scoring functions not incorporated in the tested structural alignment methods. The analysis shows that fragment-based approaches such as FR-TM-align are the most useful for aligning structures of membrane proteins. Moreover, fragment-based approaches are more suitable for comparison of protein structures that have undergone large conformational changes. Nevertheless, no method was clearly superior to all other methods. Additionally, all methods lack a measure to rate the reliability of a position within a structure alignment. To solve both of these problems, we propose a consensus-type approach, combining alignments from four different methods, namely FR-TM-align, DaliLite, MATT and FATCAT. Agreement between the methods is used to assign confidence values to each position of the alignment. Overall, we conclude that there remains scope for the improvement of structural alignment methods for membrane proteins.

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Section: Discussionsupporting

confidence: 51%

Section: Methodsmentioning

confidence: 99%

Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy

Stamm

Forrest

2015

Proteins

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“…4 The term I null ( c j ) represents the number of bits needed to transmit a single c j using the 23-component Kent mixture model whose computation is described below.…”

Section: S2 Computation Of Null Model Message Length: I Null (S) and mentioning

confidence: 99%

“…Hence, each r j is the distance between C α atom c j−1 and c j , and can be encoded efficiently using a normal distribution N with the parameters µ = 3.8Å and standard deviation of σ = 0.2Å. 4 This code length is computed as I radius (r j ) = − log 2 ( · N (r j ; µ, σ)).…”

Section: S2 Computation Of Null Model Message Length: I Null (S) and mentioning

confidence: 99%

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Statistical inference of protein structural alignments using information and compression

Collier

Allison

Lesk

et al. 2016

Preprint

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Structural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates.) Despite its importance, the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. To overcome these difficulties, we present here a framework for precise inference of structural alignments, built on the Bayesian and information-theoretic principle of Minimum Message Length (MML). The quality of any alignment is measured by its explanatory power -the amount of lossless compression achieved to explain the protein coordinates using that alignment. We have implemented this approach in the program MMLigner

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Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions

Kasarapu

Allison

2015

Mach Learn

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Mixture modelling involves explaining some observed evidence using a combination of probability distributions. The crux of the problem is the inference of an optimal number of mixture components and their corresponding parameters. This paper discusses unsupervised learning of mixture models using the Bayesian Minimum Message Length (MML) criterion. To demonstrate the effectiveness of search and inference of mixture parameters using the proposed approach, we select two key probability distributions, each handling fundamentally different types of data: the multivariate Gaussian distribution to address mixture modelling of data distributed in Euclidean space, and the multivariate von Mises-Fisher (vMF) distribution to address mixture modelling of directional data distributed on a unit hypersphere. The key contributions of this paper, in addition to the general search and inference methodology, include the derivation of MML expressions for encoding the data using multivariate Gaussian and von Mises-Fisher distributions, and the analytical derivation of the MML estimates of the parameters of the two distributions. Our approach is tested on simulated and real world data sets. For instance, we infer vMF mixtures that concisely explain experimentally determined three-dimensional protein conformations, providing an effective null model description of protein structures that is central to many inference problems in structural bioinformatics. The experimental results demonstrate that the performance of our proposed search and inference method along with the encoding schemes improve on the state of the art mixture modelling techniques.

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A new statistical framework to assess structural alignment quality using information compression

Cited by 7 publications

References 21 publications

Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy

Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy

Statistical inference of protein structural alignments using information and compression

Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions

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