Size and structure of the sequence space of repeat proteins

AbstractThe coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability. We show that the coding space of a given protein family —the total number of sequences in that family— can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences. We analyzed and calculated the size of three abundant repeat proteins families, whose members are large proteins made of many repetitions of conserved portions of ∼30 amino acids. While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences, we found that correlations between amino acid usage at different positions significantly impact that diversity. We quantified the impact of different types of correlations, functional and evolutionary, on sequence diversity. Analysis of the detailed structure of the coding space of the families revealed a rugged landscape, with many local energy minima of varying sizes with a hierarchical structure, reminiscent of fustrated energy landscapes of spin glass in physics. This clustered structure indicates a multiplicity of subtypes within each family, and suggests new strategies for protein design.

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Transcriptome Ortholog Alignment Sequence Tools (TOAST) for Phylogenomic Dataset Assembly

10.21203/rs.2.16269/v4 ◽

2020 ◽

Author(s):

Dustin J. Wcisel ◽

J. Thomas Howard ◽

Jeffrey A. Yoder ◽

Alex Dornburg

Keyword(s):

Missing Data ◽

Open Source ◽

Research Question ◽

Single Copy ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Sequencing Technologies ◽

Phylogenomic Analyses ◽

The Impact

Abstract Background Advances in next-generation sequencing technologies have reduced the cost of whole transcriptome analyses, allowing characterization of non-model species at unprecedented levels. The rapid pace of transcriptomic sequencing has driven the public accumulation of a wealth of data for phylogenomic analyses, however lack of tools aimed towards phylogeneticists to efficiently identify orthologous sequences currently hinders effective harnessing of this resource. Results We introduce TOAST, an open source R software package that can utilize the ortholog searches based on the software Benchmarking Universal Single-Copy Orthologs (BUSCO) to assemble multiple sequence alignments of orthologous loci from transcriptomes for any group of organisms. By streamlining search, query, and alignment, TOAST automates the generation of locus and concatenated alignments, and also presents a series of outputs from which users can not only explore missing data patterns across their alignments, but also reassemble alignments based on user-defined acceptable missing data levels for a given research question. Conclusions TOAST provides a comprehensive set of tools for assembly of sequence alignments of orthologs for comparative transcriptomic and phylogenomic studies. This software empowers easy assembly of public and novel sequences for any target database of candidate orthologs, and fills a critically needed niche for tools that enable quantification and testing of the impact of missing data. As open-source software, TOAST is fully customizable for integration into existing or novel custom informatic pipelines for phylogenomic inference.

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Transcriptome Ortholog Alignment Sequence Tools (TOAST) for Phylogenomic Dataset Assembly

10.21203/rs.2.16269/v1 ◽

2019 ◽

Author(s):

Alex Dornburg ◽

Dustin J. Wcisel ◽

J. Thomas Howard ◽

Jeffrey A. Yoder

Keyword(s):

Missing Data ◽

Open Source ◽

Single Copy ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Sequencing Technologies ◽

Phylogenomic Analyses ◽

The Cost ◽

The Impact

Abstract Background Advances in next-generation sequencing technologies have reduced the cost of whole transcriptome analyses, allowing characterization of non-model species at unprecedented levels. The rapid pace of transcriptomic sequencing has driven the public accumulation of a wealth of data for phylogenomic analyses, however lack of tools aimed towards phylogeneticists to efficiently identify orthologous sequences currently hinders effective harnessing of this resource.Results We introduce TOAST, an open source R software package that can utilize the ortholog searches based on the software Benchmarking Universal Single-Copy Orthologs (BUSCO) to assemble multiple sequence alignments of orthologous loci from transcriptomes for any group of organisms. By streamlining search, query, and alignment, TOAST automates the generation of locus and concatenated alignments, and also presents a series of outputs from which users can not only explore missing data patterns across their alignments, but also reassemble alignments based on user-defined acceptable missing data levels for a given research question.Conclusions TOAST provides a comprehensive set of tools for assembly of sequence alignments of orthologs for comparative transcriptomic and phylogenomic studies. This software empowers easy assembly of public and novel sequences for any target database of candidate orthologs, and fills a critically needed niche for tools that enable quantification and testing of the impact of missing data. As open-source software, TOAST is fully customizable for integration into existing or novel custom informatic pipelines for phylogenomic inference.

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RELATIVE VON NEUMANN ENTROPY FOR EVALUATING AMINO ACID CONSERVATION

Journal of Bioinformatics and Computational Biology ◽

10.1142/s021972001000494x ◽

2010 ◽

Vol 08 (05) ◽

pp. 809-823 ◽

Cited By ~ 13

Author(s):

FREDRIK JOHANSSON ◽

HIROYUKI TOH

Keyword(s):

Amino Acid ◽

Sequence Analysis ◽

Shannon Entropy ◽

Von Neumann Entropy ◽

Sequence Alignments ◽

Multiple Sequence ◽

Von Neumann ◽

Multiple Sequence Alignments ◽

Previous Definition ◽

Definition Of

The Shannon entropy is a common way of measuring conservation of sites in multiple sequence alignments, and has also been extended with the relative Shannon entropy to account for background frequencies. The von Neumann entropy is another extension of the Shannon entropy, adapted from quantum mechanics in order to account for amino acid similarities. However, there is yet no relative von Neumann entropy defined for sequence analysis. We introduce a new definition of the von Neumann entropy for use in sequence analysis, which we found to perform better than the previous definition. We also introduce the relative von Neumann entropy and a way of parametrizing this in order to obtain the Shannon entropy, the relative Shannon entropy and the von Neumann entropy at special parameter values. We performed an exhaustive search of this parameter space and found better predictions of catalytic sites compared to any of the previously used entropies.

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Relationships between amino acid sequences determined through optimum alignments, clustering, and specific distance patterns: application to a group of scorpion toxins

Genome ◽

10.1139/g92-055 ◽

1992 ◽

Vol 35 (2) ◽

pp. 360-371 ◽

Cited By ~ 6

Author(s):

Hugh Tyson

Keyword(s):

Amino Acid ◽

Diallel Analysis ◽

Distance Matrix ◽

Diallel Cross ◽

Amino Acid Sequences ◽

Scorpion Toxins ◽

Sequence Alignments ◽

Sequence Relationships ◽

The Impact ◽

Specific Distance

Optimum alignment in all pairwise combinations among a group of amino acid sequences generated a distance matrix. These distances were clustered to evaluate relationships among the sequences. The degree of relationship among sequences was also evaluated by calculating specific distances from the distance matrix and examining correlations between patterns of specific distances for pairs of sequences. The sequences examined were a group of 20 amino acid sequences of scorpion toxins originally published and analyzed by M.J. Dufton and H. Rochat in 1984. Alignment gap penalties were constant for all 190 pairwise sequence alignments and were chosen after assessing the impact of changing penalties on resultant distances. The total distances generated by the 190 pairwise sequence aligments were clustered using complete (farthest neighbour) linkage. The square, symmetrical input distance matrix is analogous to diallel cross data where reciprocal and parental values are absent. Diallel analysis methods provided analogues for the distance matrix to genetical specific combining abilities, namely specific distances between all sequence pairs that are independent of the average distances shown by individual sequences. Correlation of specific distance patterns, with transformation to modified z values and a stringent probability level, were used to delineate subgroups of related sequences. These were compared with complete linkage clustering results. Excellent agreement between the two approaches was found. Three originally outlying sequences were placed within the four new subgroups.Key words: sequence alignment, specific distances, sequence relationships.

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Experimental Assessment of the Importance of Amino Acid Positions Identified by an Entropy-Based Correlation Analysis of Multiple-Sequence Alignments

Biochemistry ◽

10.1021/bi300747r ◽

2012 ◽

Vol 51 (28) ◽

pp. 5633-5641 ◽

Cited By ~ 11

Author(s):

Susanne Dietrich ◽

Nadine Borst ◽

Sandra Schlee ◽

Daniel Schneider ◽

Jan-Oliver Janda ◽

...

Keyword(s):

Amino Acid ◽

Correlation Analysis ◽

Sequence Alignments ◽

Multiple Sequence ◽

Experimental Assessment ◽

Multiple Sequence Alignments

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QMaker: Fast and accurate method to estimate empirical models of protein evolution

10.1101/2020.02.20.958819 ◽

2020 ◽

Cited By ~ 1

Author(s):

Bui Quang Minh ◽

Cuong Cao Dang ◽

Le Sy Vinh ◽

Robert Lanfear

Keyword(s):

Amino Acid ◽

Amino Acid Substitution ◽

Search Algorithm ◽

Phylogenetic Analyses ◽

Accurate Method ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Substitution Models ◽

Protein Alignments

AbstractAmino acid substitution models play a crucial role in phylogenetic analyses. Maximum likelihood (ML) methods have been proposed to estimate amino acid substitution models, however, they are typically complicated and slow. In this paper, we propose QMaker, a new ML method to estimate a general time-reversible Q matrix from a large protein dataset consisting of multiple sequence alignments. QMaker combines an efficient ML tree search algorithm, a model selection for handling the model heterogeneity among alignments, and the consideration of rate mixture models among sites. We provide QMaker as a user-friendly function in the IQ-TREE software package (http://www.iqtree.org) supporting the use of multiple CPU cores so that biologists can easily estimate amino acid substitution models from their own protein alignments. We used QMaker to estimate new empirical general amino acid substitution models from the current Pfam database as well as five clade-specific models for mammals, birds, insects, yeasts, and plants. Our results show that the new models considerably improve the fit between model and data and in some cases influence the inference of phylogenetic tree topologies.

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The impact of single substitutions on multiple sequence alignments

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2008.0140 ◽

2008 ◽

Vol 363 (1512) ◽

pp. 4041-4047 ◽

Cited By ~ 5

Author(s):

Steffen Klaere ◽

Tanja Gesell ◽

Arndt von Haeseler

Keyword(s):

Stochastic Matrix ◽

Branch Length ◽

Sequence Evolution ◽

Single Mutation ◽

Sequence Alignments ◽

Multiple Sequence ◽

Alignment Column ◽

Multiple Sequence Alignments ◽

Posterior Probability Distribution ◽

The Impact

We introduce another view of sequence evolution. Contrary to other approaches, we model the substitution process in two steps. First we assume (arbitrary) scaled branch lengths on a given phylogenetic tree. Second we allocate a Poisson distributed number of substitutions on the branches. The probability to place a mutation on a branch is proportional to its relative branch length. More importantly, the action of a single mutation on an alignment column is described by a doubly stochastic matrix, the so-called one-step mutation matrix. This matrix leads to analytical formulae for the posterior probability distribution of the number of substitutions for an alignment column.

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Protein design and variant prediction using autoregressive generative models

Nature Communications ◽

10.1038/s41467-021-22732-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jung-Eun Shin ◽

Adam J. Riesselman ◽

Aaron W. Kollasch ◽

Conor McMahon ◽

Elana Simon ◽

...

Keyword(s):

Language Processing ◽

Protein Design ◽

Generative Models ◽

Disordered Proteins ◽

Evolutionary Information ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Complementarity Determining Regions ◽

State Of Art

AbstractThe ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.

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Numerical Encodings of Amino Acids in Multivariate Gaussian Modeling of Protein Multiple Sequence Alignments

Molecules ◽

10.3390/molecules24010104 ◽

2018 ◽

Vol 24 (1) ◽

pp. 104

Author(s):

Patrice Koehl ◽

Henri Orland ◽

Marc Delarue

Keyword(s):

Amino Acids ◽

Amino Acid ◽

Principal Components ◽

Gaussian Model ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Substitution Matrices ◽

Multivariate Gaussian ◽

Multivariate Gaussian Model

Residues in proteins that are in close spatial proximity are more prone to covariate as their interactions are likely to be preserved due to structural and evolutionary constraints. If we can detect and quantify such covariation, physical contacts may then be predicted in the structure of a protein solely from the sequences that decorate it. To carry out such predictions, and following the work of others, we have implemented a multivariate Gaussian model to analyze correlation in multiple sequence alignments. We have explored and tested several numerical encodings of amino acids within this model. We have shown that 1D encodings based on amino acid biochemical and biophysical properties, as well as higher dimensional encodings computed from the principal components of experimentally derived mutation/substitution matrices, do not perform as well as a simple twenty dimensional encoding with each amino acid represented with a vector of one along its own dimension and zero elsewhere. The optimum obtained from representations based on substitution matrices is reached by using 10 to 12 principal components; the corresponding performance is less than the performance obtained with the 20-dimensional binary encoding. We highlight also the importance of the prior when constructing the multivariate Gaussian model of a multiple sequence alignment.

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