Protein Sequence Alignment Analysis by Local Covariation: Coevolution Statistics Detect Benchmark Alignment Errors

Dickson, Russell J.; Gloor, Gregory B.

doi:10.1371/journal.pone.0037645

Cited by 18 publications

(20 citation statements)

References 37 publications

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“…The evolutionary distribution of these residues was examined with an alignment of 178 singlechain LHE protein sequences generated by using Cn3D (22) to develop a structure-based guide alignment followed by LoCo (19) to identify and correct systematically misaligned segments. The resulting alignment was hand-curated to remove all sequences lacking acidic residues at the catalytic positions (19) (Fig. S1).…”

Section: Identification Of a Coevolving Network Of Catalytic And Noncmentioning

confidence: 99%

“…Moreover, the monomeric and dimeric LHEs likely evolved under different functional constraints, and the phylogenetic signal and functional information for either form is diluted in alignments that include both the monomeric and dimeric LHEs. Because LHEs are currently under investigation for use as genome-editing agents (16-18), a greater understanding of their functional constraints would aid in engineering studies.Here, we take advantage of high-quality structure-guided multiple sequence alignments of single-chain LHEs to predict coevolving networks using methods based on mutual information (5,19,20). Strikingly, the network with the strongest predictive scores included the metal-binding catalytic residues and adjacent noncatalytic residues that lie on opposite LAGLIDADG α-helices.…”

mentioning

confidence: 99%

“…Here, we take advantage of high-quality structure-guided multiple sequence alignments of single-chain LHEs to predict coevolving networks using methods based on mutual information (5,19,20). Strikingly, the network with the strongest predictive scores included the metal-binding catalytic residues and adjacent noncatalytic residues that lie on opposite LAGLIDADG α-helices.…”

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confidence: 99%

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Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues

McMurrough

Dickson

Thibert

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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The active sites of enzymes consist of residues necessary for catalysis and structurally important noncatalytic residues that together maintain the architecture and function of the active site. Examples of evolutionary interactions between catalytic and noncatalytic residues have been difficult to define and experimentally validate due to a general intolerance of these residues to substitution. Here, using computational methods to predict coevolving residues, we identify a network of positions consisting of two catalytic metal-binding residues and two adjacent noncatalytic residues in LAGLIDADG homing endonucleases (LHEs). Distinct combinations of the four residues in the network map to distinct LHE subfamilies, with a striking distribution of the metal-binding Asp (D) and Glu (E) residues. Mutation of these four positions in three LHEs-I-LtrI, I-OnuI, and I-HjeMI-indicate that the combinations of residues tolerated are specific to each enzyme. Kinetic analyses under single-turnover conditions revealed that I-LtrI activity could be modulated over an ∼100-fold range by mutation of residues in the coevolving network. I-LtrI catalytic site variants with low activity could be rescued by compensatory mutations at adjacent noncatalytic sites that restore an optimal coevolving network and vice versa. Our results demonstrate that LHE activity is constrained by an evolutionary barrier of residues with strong context-dependent effects. Creation of optimal coevolving active-site networks is therefore an important consideration in engineering of LHEs and other enzymes.amino acid coevolution | genetic selection T he active sites of enzymes are often the most conserved positions in a multiple sequence alignment, as purifying selection for maintenance of function constrains amino acid variation of residues that directly participate in catalysis. Noncatalytic residues, often in close proximity to catalytic residues, contribute to enzymatic function by maintaining the architecture and chemical environment of the active site. Noncatalytic residues often show sequence variation in multiple sequence alignments, yet nonpermissive substitutions at these positions will have an impact on enzymatic function by disrupting the architecture or chemical environment necessary for catalysis. Thus, catalytic and noncatalytic residues must coevolve with each other and surrounding residues to maintain active-site conformation and chemistry and to buffer against potentially deleterious mutations (1, 2). Coevolving residues within a protein family can be predicted by computational methods that use mutual information theory to identify residue covariation in a multiple sequence alignment (2-5). However, because the magnitude of covariation between positions varies with the magnitude of positional variation (4), the identification of catalytic residues as part of coevolving networks is problematic for the simple reason that catalytic residues show little sequence variation. Although others have identified putative coevolution between residues involv...

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Section: Identification Of a Coevolving Network Of Catalytic And Noncmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues

McMurrough

Dickson

Thibert

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…A number of studies have highlighted the impact of the choice of alignment on subsequent phylogenetic inference [ 23 - 31 ]; in many cases different alignment methods, or different guide trees, can give rise to very different phylogenies [ 23 , 32 - 36 ]. Sensitivity to the alignment is also observed in the context of many other types of downstream analysis, including homology modelling of protein structures [ 37 - 39 ], detection of correlated evolution [ 40 , 41 ], prediction of RNA secondary structure [ 42 ], and inference of positive selection [ 36 , 43 - 45 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

et al. 2015

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BackgroundA standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment.ResultsIn this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased.ConclusionsThe alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference.Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0516-1) contains supplementary material, which is available to authorized users.

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“…Many groups have observed high co-evolution between residues close in primary structure. Two or more residues can only be identified as co-evolving if they have minimum distance apart in primary structure [37,38]. Set of sequential residues in one protein chain that are close in primary structure is called subsequence.…”

Section: Introductionmentioning

confidence: 99%

New Measurement for Correlation of Co-evolution Relationship of Subsequences in Protein

Gao

Dou

et al. 2015

Interdiscip Sci Comput Life Sci

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Many computational tools have been developed to measure the protein residues co-evolution. Most of them only focus on co-evolution for pairwise residues in a protein sequence. However, number of residues participate in co-evolution might be multiple. And some co-evolved residues are clustered in several distinct regions in primary structure. Therefore, the co-evolution among the adjacent residues and the correlation between the distinct regions offer insights into function and evolution of the protein and residues. Subsequence is used to represent the adjacent multiple residues in one distinct region. In the paper, co-evolution relationship in each subsequence is represented by mutual information matrix (MIM). Then, Pearson's correlation coefficient: R value is developed to measure the similarity correlation of two MIMs. MSAs from Catalytic Data Base (Catalytic Site Atlas, CSA) are used for testing. R value characterizes a specific class of residues. In contrast to individual pairwise co-evolved residues, adjacent residues without high individual MI values are found since the co-evolved relationship among them is similar to that among another set of adjacent residues. These subsequences possess some flexibility in the composition of side chains, such as the catalyzed environment.

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Protein Sequence Alignment Analysis by Local Covariation: Coevolution Statistics Detect Benchmark Alignment Errors

Cited by 18 publications

References 37 publications

Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues

Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

New Measurement for Correlation of Co-evolution Relationship of Subsequences in Protein

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