Graphical models of residue coupling in protein families

Thomas, John; Ramakrishnan, Naren; Bailey‐Kellogg, Chris

doi:10.1145/1134030.1134033

Cited by 24 publications

(40 citation statements)

References 20 publications

(30 reference statements)

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“…By applying statistical techniques that can distinguish mere correlation from direct statistical coupling of residue positions [33, 54,58], many false positive predictions could be eliminated. The adoption of this class 20 of statistical models, known as Markov random fields (MRFs), or Potts models in statistical physics, led to a breakthrough in de-novo (template-free) protein structure prediction: The predicted contacts proved sufficiently accurate to be used as spatial restraints to reli-25 ably predict protein 3D structures purely from sequence information [23, 26-28, 32, 35, 36, 44-47].…”

Section: Introductionmentioning

confidence: 99%

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Vorberg

Seemayer

Soeding

2018

Preprint

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Compensatory mutations between protein residues that are in physical contact with each other can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, high coupling coefficients predict residues contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on boosting contact prediction quality by adding external information, little progress has been made to improve the statistical procedure at the core. In that regard, our lack of understanding of the sources of noise poses a major obstacle. We have developed CCMgen, the first method for simulating protein evolution by providing full control over the generation of realistic synthetic MSAs with pairwise statistical couplings between residue positions. This procedure requires an exact statistical model that reliably reproduces observed alignment statistics. With CCMpredPy we also provide an implementation of persistent contrastive divergence (PCD), a precise inference technique that enables us to learn the required high-quality statistical models. We demonstrate how CCMgen can facilitate the development and testing of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. For that purpose we propose a simple entropy correction (EC) strategy which disentangles the correction for both sources of noise. We find that entropy contributes typically roughly twice as much noise as phylogeny.

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Section: Introductionmentioning

confidence: 99%

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Vorberg

Seemayer

Soeding

2018

Preprint

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“…As in previous work [4,17,8,18,[26][27][28][29][30], we assume that constraints on amino acid choices required to maintain structure and function are revealed in the sequence record, and devise an objective function seeking to satisfy those constraints. (In fact, related contact potentials have long been the basis for many protein structure prediction techniques [31,32].)…”

Section: Potential Scorementioning

confidence: 99%

Protein Fragment Swapping: A Method for Asymmetric, Selective Site-Directed Recombination

Zheng

Griswold

Bailey‐Kellogg

2009

Lecture Notes in Computer Science

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Abstract. This paper presents a new approach to site-directed recombination, swapping combinations of selected discontiguous fragments from a source protein in place of corresponding fragments of a target protein. By being both asymmetric (differentiating source and target) and selective (swapping discontiguous fragments), our method focuses experimental effort on a more restricted portion of sequence space, constructing hybrids that are more likely to have the properties that are the objective of the experiment. Furthermore, since the source and target need to be structurally homologous only locally (rather than overall), our method supports swapping fragments from functionally important regions of a source into a target "scaffold"; e.g., to humanize an exogenous therapeutic protein. A protein fragment swapping plan is defined by the residue position boundaries of the fragments to be swapped; it is assessed by an average potential score over the resulting hybrid library, with singleton and pairwise terms evaluating the importance and fit of the swapped residues. While we prove that it is NP-hard to choose an optimal set of fragments under such a potential score, we develop an integer programming approach, which we call SWAGMER, that works very well in practice. We demonstrate the effectiveness of our method in two types of swapping problem: selective recombination between beta-lactamases and activity swapping between glutathione transferases. We show that the selective recombination approach generates a better plan (in terms of resulting potential score) than a traditional site-directed recombination approach. We also show that in both cases the optimized experiment is significantly better than one that would result from stochastic methods.

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“…A variety of recent studies have used MSAs to calculate correlations in mutations at several positions within an alignment and between alignments [15,10,19,14]. These correlations have been hypothesized to result from structural/functional coupling between these positions within the protein [8].…”

Section: Introductionmentioning

confidence: 99%

“…Going beyond sequence conservation, couplings provide additional information about potentially important structural/functional connections between residues within a protein family. Previous studies [15,8,10] show that residue couplings play key roles in transducing signals in cellular systems.…”

Section: Introductionmentioning

confidence: 99%

“…Our underlying hypothesis is that if structural and functional behaviors are the underlying cause of residue couplings within MSAs, then couplings are more naturally studied at the level of amino acid properties. We are motivated by the prior work of Thomas et al [9,10] which proposes probabilistic graphical models for capturing couplings in a protein family in terms of amino acids. Graphical models are useful for support- ing better investigation, characterization, and design of proteins.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using physicochemical properties of amino acids to induce graphical models of residue couplings

Hossain

Bailey‐Kellogg

Friedman

et al. 2011

Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics

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Residue coupling in protein families is an important indicator for structural and functional conservation. Two residues are coupled if changes of amino acid at one residue location are correlated with changes in the other. Many algorithmic techniques have been proposed to discover couplings in protein families. These approaches discover couplings over amino acid combinations but do not yield mechanistic or other explanations for such couplings. We propose to study couplings in terms of amino acid classes such as polarity, hydrophobicity, size, and reactivity, and present two algorithms for learning probabilistic graphical models of amino acid class-based residue couplings. Our probabilistic graphical models provide a sound basis for predictive, diagnostic, and abductive reasoning. Further, our methods can take optional structural priors into account for building graphical models. The resulting models are useful in assessing the likelihood of a new protein to be a member of a family and for designing new protein sequences by sampling from the graphical model. We apply our approaches to understand couplings in two protein families: Nickel-responsive transription factors (NikR) and G-protein coupled receptors (GPCRs). The results demonstrate that our graphcial models based on sequences, physicochemical properties, and protein structure are capable of detecting amino acid classbased couplings between important residues that play roles in activities of these two families.

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Graphical models of residue coupling in protein families

Cited by 24 publications

References 20 publications

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Protein Fragment Swapping: A Method for Asymmetric, Selective Site-Directed Recombination

Using physicochemical properties of amino acids to induce graphical models of residue couplings

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