Empirical analysis of protein insertions and deletions determining parameters for the correct placement of gaps in protein sequence alignments

A cellular selection identifies elongated flavodoxins that support electron transfer to sulfite reductase

Flavodoxins (Flds) mediate the flux of electrons between oxidoreductases in diverse metabolic pathways. To investigate whether Flds can support electron transfer to a sulfite reductase (SIR) that evolved to couple with a ferredoxin, we evaluated the ability of Flds to transfer electrons from a ferredoxin-NADP reductase (FNR) to a ferredoxin-dependent SIR using growth complementation of an Escherichia coli strain with a sulfur metabolism defect. We show that Flds from cyanobacteria complement this growth defect when coexpressed with an FNR and an SIR that evolved to couple with a plant ferredoxin. When we evaluated the effect of peptide insertion on Fld-mediated electron transfer, we observed a sensitivity to insertions within regions predicted to be proximal to the cofactor and partner binding sites, while a high insertion tolerance was detected within loops distal from the cofactor and within regions of helices and sheets that are proximal to those loops. Bioinformatic analysis showed that natural Fld sequence variability predicts a large fraction of the motifs that tolerate insertion of the octapeptide SGRPGSLS. These results represent the first evidence that Flds can support electron transfer to assimilatory SIRs, and they suggest that the pattern of insertion tolerance is influenced by interactions with oxidoreductase partners.

Bridging the gaps in statistical models of protein alignment

Summary

Sequences of proteins evolve by accumulating substitutions together with insertions and deletions (indels) of amino acids. However, it remains a common practice to disconnect substitutions and indels, and infer approximate models for each of them separately, to quantify sequence relationships. Although this approach brings with it computational convenience (which remains its primary motivation), there is a dearth of attempts to unify and model them systematically and together. To overcome this gap, this article demonstrates how a complete statistical model quantifying the evolution of pairs of aligned proteins can be constructed using a time-parameterized substitution matrix and a time-parameterized alignment state machine. Methods to derive all parameters of such a model from any benchmark collection of aligned protein sequences are described here. This has not only allowed us to generate a unified statistical model for each of the nine widely used substitution matrices (PAM, JTT, BLOSUM, JO, WAG, VTML, LG, MIQS and PFASUM), but also resulted in a new unified model, MMLSUM. Our underlying methodology measures the Shannon information content using each model to explain losslessly any given collection of alignments, which has allowed us to quantify the performance of all the above models on six comprehensive alignment benchmarks. Our results show that MMLSUM results in a new and clear overall best performance, followed by PFASUM, VTML, BLOSUM and MIQS, respectively, amongst the top five. We further analyze the statistical properties of MMLSUM model and contrast it with others.

Supplementary information

Supplementary data are available at Bioinformatics online.

Using the Evolutionary History of Proteins to Engineer Insertion-Deletion Mutants from Robust, Ancestral Templates Using Graphical Representation of Ancestral Sequence Predictions (GRASP)

Analyzing the natural evolution of proteins by ancestral sequence reconstruction (ASR) can provide valuable information about the changes in sequence and structure that drive the development of novel protein functions. However, ASR has also been used as a protein engineering tool, as it often generates thermostable proteins which can serve as robust and evolvable templates for enzyme engineering. Importantly, ASR has the potential to provide an insight into the history of insertions and deletions that have occurred in the evolution of a protein family. Indels are strongly associated with functional change during enzyme evolution and represent a largely unexplored source of genetic diversity for designing proteins with novel or improved properties. Current ASR methods differ in the way they handle indels; inclusion or exclusion of indels is often managed subjectively, based on assumptions the user makes about the likelihood of each recombination event, yet most currently available ASR tools provide limited, if any, opportunities for evaluating indel placement in a reconstructed sequence. Graphical Representation of Ancestral Sequence Predictions (GRASP) is an ASR tool that maps indel evolution throughout a reconstruction and enables the evaluation of indel variants. This chapter provides a general protocol for performing a reconstruction using GRASP and using the results to create indel variants. The method addresses protein template selection, sequence curation, alignment refinement, tree building, ancestor reconstruction, evaluation of indel variants and approaches to library development.

The length scale of multivalent interactions is evolutionarily conserved in fungal and vertebrate phase-separating proteins

One key feature of proteins that form liquid droplets by phase separation inside a cell is multivalency-the presence of multiple sites that mediate interactions with other proteins. We know little about the variation of multivalency on evolutionary time scales. Here, we investigated the long-term evolution (∼600 million years) of multivalency in fungal mRNA decapping subunit 2 protein (Dcp2), and in the FET (FUS, EWS and TAF15) protein family. We found that multivalency varies substantially among the orthologs of these proteins. However, evolution has maintained the length scale at which sequence motifs that enable protein-protein interactions occur. That is, the total number of such motifs per hundred amino acids is higher and less variable than expected by neutral evolution. To help explain this evolutionary conservation, we developed a conformation classifier using machine-learning algorithms. This classifier demonstrates that disordered segments in Dcp2 and FET proteins tend to adopt compact conformations, which is necessary for phase separation. Thus, the evolutionary conservation we detected may help proteins preserve the ability to undergo phase separation. Altogether, our study reveals that the length scale of multivalent interactions is an evolutionarily conserved feature of two classes of phase-separating proteins in fungi and vertebrates.

A probabilistic model for indel evolution: differentiating insertions from deletions

Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here, we introduce several improvements to indel modeling: 1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; 2) we introduce numerous summary statistics that allow approximate Bayesian computation-based parameter estimation; 3) we develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical data sets; and 4) using a model-selection scheme, we test whether the richer model better fits biological data compared with the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical data sets and that, for the majority of these data sets, the deletion rate is higher than the insertion rate.

Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy

Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here, we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.

100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design

The design of synthetic proteins with the desired function is a long-standing goal in biomolecular science, with broad applications in biochemical engineering, agriculture, medicine, and public health. Rational de novo design and experimental directed evolution have achieved remarkable successes but are challenged by the requirement to find functional "needles" in the vast "haystack" of protein sequence space. Data-driven models for fitness landscapes provide a predictive map between protein sequence and function and can prospectively identify functional candidates for experimental testing to greatly improve the efficiency of this search. This Viewpoint reviews the applications of machine learning and, in particular, deep learning as part of data-driven protein engineering platforms. We highlight recent successes, review promising computational methodologies, and provide an outlook on future challenges and opportunities. The article is written for a broad audience comprising both polymer and protein scientists and computer and data scientists interested in an up-to-date review of recent innovations and opportunities in this rapidly evolving field.

Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond

As both a computational and an experimental endeavor, ancestral sequence reconstruction remains a timely and important technique. Modern approaches to conduct ancestral sequence reconstruction for proteins are built upon a conceptual framework from journal founder Emile Zuckerkandl. On top of this, work on maximum likelihood phylogenetics published in Journal of Molecular Evolution in 1996 was one of the first approaches for generating maximum likelihood ancestral sequences of proteins. From its computational history, future model development needs as well as potential applications in areas as diverse as computational systems biology, molecular community ecology, infectious disease therapeutics and other biomedical applications, and biotechnology are discussed. From its past in this journal, there is a bright future for ancestral sequence reconstruction in the field of evolutionary biology.

SimSpliceEvol: alternative splicing-aware simulation of biological sequence evolution

Background

It is now well established that eukaryotic coding genes have the ability to produce more than one type of transcript thanks to the mechanisms of alternative splicing and alternative transcription. Because of the lack of gold standard real data on alternative splicing, simulated data constitute a good option for evaluating the accuracy and the efficiency of methods developed for splice-aware sequence analysis. However, existing sequence evolution simulation methods do not model alternative splicing, and so they can not be used to test spliced sequence analysis methods.

Results

We propose a new method called SimSpliceEvol for simulating the evolution of sets of alternative transcripts along the branches of an input gene tree. In addition to traditional sequence evolution events, the simulation also includes gene exon-intron structure evolution events and alternative splicing events that modify the sets of transcripts produced from genes. SimSpliceEvol was implemented in Python. The source code is freely available at https://github.com/UdeS-CoBIUS/SimSpliceEvol.

Conclusions

Data generated using SimSpliceEvol are useful for testing spliced RNA sequence analysis methods such as methods for spliced alignment of cDNA and genomic sequences, multiple cDNA alignment, orthologous exons identification, splicing orthology inference, transcript phylogeny inference, which requires to know the real evolutionary relationships between the sequences.

Contributions of substitutions and indels to the structural variations in ancient protein superfamilies

Background

Quantitative evaluation of protein structural evolution is important for our understanding of protein biological functions and their evolutionary adaptation, and is useful in guiding protein engineering. However, compared to the models for sequence evolution, the quantitative models for protein structural evolution received less attention. Ancient protein superfamilies are often considered versatile, allowing genetic and functional diversifications during long-term evolution. In this study, we investigated the quantitative impacts of sequence variations on the structural evolution of homologues in 68 ancient protein superfamilies that exist widely in sequenced eukaryotic, bacterial and archaeal genomes.

Results

We found that the accumulated structural variations within ancient superfamilies could be explained largely by a bilinear model that simultaneously considers amino acid substitution and insertion/deletion (indel). Both substitutions and indels are essential for explaining the structural variations within ancient superfamilies. For those ancient superfamilies with high bilinear multiple correlation coefficients, the influence of each unit of substitution or indel on structural variations is almost constant within each superfamily, but varies greatly among different superfamilies. The influence of each unit indel on structural variations is always larger than that of each unit substitution within each superfamily, but the accumulated contributions of indels to structural variations are lower than those of substitutions in most superfamilies. The total contributions of sequence indels and substitutions (46% and 54%, respectively) to the structural variations that result from sequence variations are slightly different in ancient superfamilies.

Conclusions

Structural variations within ancient protein superfamilies accumulated under the significantly bilinear influence of amino acid substitutions and indels in sequences. Both substitutions and indels are essential for explaining the structural variations within ancient superfamilies. For those structural variations resulting from sequence variations, the total contribution of indels is slightly lower than that of amino acid substitutions. The regular clock exists not only in protein sequences, but also probably in protein structures.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5178-8) contains supplementary material, which is available to authorized users.

Inferring Rates and Length-Distributions of Indels Using Approximate Bayesian Computation

The most common evolutionary events at the molecular level are single-base substitutions, as well as insertions and deletions (indels) of short DNA segments. A large body of research has been devoted to develop probabilistic substitution models and to infer their parameters using likelihood and Bayesian approaches. In contrast, relatively little has been done to model indel dynamics, probably due to the difficulty in writing explicit likelihood functions. Here, we contribute to the effort of modeling indel dynamics by presenting SpartaABC, an approximate Bayesian computation (ABC) approach to infer indel parameters from sequence data (either aligned or unaligned). SpartaABC circumvents the need to use an explicit likelihood function by extracting summary statistics from simulated sequences. First, summary statistics are extracted from the input sequence data. Second, SpartaABC samples indel parameters from a prior distribution and uses them to simulate sequences. Third, it computes summary statistics from the simulated sets of sequences. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC can provide an approximation to the posterior distribution of indel parameters as well as point estimates. We study the performance of our methodology and show that it provides accurate estimates of indel parameters in simulations. We next demonstrate the utility of SpartaABC by studying the impact of alignment errors on the inference of positive selection. A C ++ program implementing SpartaABC is freely available in http://spartaabc.tau.ac.il.

Solving the master equation for Indels

BACKGROUND

Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events.

MAIN TEXT

This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees.

CONCLUSIONS

While approximations that use finite-state machines (Pair HMMs and transducers) currently represent the most practical approach to problems such as sequence alignment and phylogeny, more rigorous approaches that work directly with the matrix exponential of the underlying continuous-time Markov chain also show promise, especially in view of recent advances.

Internal epitope tagging informed by relative lack of sequence conservation

Many experimental techniques rely on specific recognition and stringent binding of proteins by antibodies. This can readily be achieved by introducing an epitope tag. We employed an approach that uses a relative lack of evolutionary conservation to inform epitope tag site selection, followed by integration of the tag-coding sequence into the endogenous locus in zebrafish. We demonstrate that an internal epitope tag is accessible for antibody binding, and that tagged proteins retain wild type function.

Inferring Indel Parameters using a Simulation-based Approach

In this study, we present a novel methodology to infer indel parameters from multiple sequence alignments (MSAs) based on simulations. Our algorithm searches for the set of evolutionary parameters describing indel dynamics which best fits a given input MSA. In each step of the search, we use parametric bootstraps and the Mahalanobis distance to estimate how well a proposed set of parameters fits input data. Using simulations, we demonstrate that our methodology can accurately infer the indel parameters for a large variety of plausible settings. Moreover, using our methodology, we show that indel parameters substantially vary between three genomic data sets: Mammals, bacteria, and retroviruses. Finally, we demonstrate how our methodology can be used to simulate MSAs based on indel parameters inferred from real data sets.

DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment

BACKGROUND

Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.

RESULTS

Two predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.

CONCLUSIONS

Predicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.

Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools

Computer simulations have been widely used to validate and evaluate the power of statistical methods for genetic epidemiological studies. Although a large number of simulation methods and software packages have been developed for genome-wide association studies, methodological and bioinformatics challenges have limited their applications in simulating datasets for whole-genome and whole-exome sequencing studies. With the development of more sophisticated statistical methods that make fuller use of available data and our knowledge of the human genome, there is a pressing need for genetic simulators that capture more features of empirical data (e.g., multiallele variants, indels, use of the Variant Call Format) and the human genome (e.g., functional annotations of genetic variants). This article introduces Variant Simulation Tools (VST), a module of Variant Tools for the simulation of genetic variants for sequencing-based genetic epidemiological studies. Although multiple simulation engines are provided, the core of VST is a novel forward-time simulation engine that simulates real nucleotide sequences of the human genome using DNA mutation models, fine-scale recombination maps, and a selection model based on amino acid changes of translated protein sequences. The design of VST allows users to easily create and distribute simulation methods and simulated datasets for a variety of applications and encourages fair comparison between statistical methods through the use of existing or reproduced simulated datasets.

Orthology detection combining clustering and synteny for very large datasets

The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

Prediction and analysis of surface hydrophobic residues in tertiary structure of proteins

The analysis of protein structures provides plenty of information about the factors governing the folding and stability of proteins, the preferred amino acids in the protein environment, the location of the residues in the interior/surface of a protein and so forth. In general, hydrophobic residues such as Val, Leu, Ile, Phe, and Met tend to be buried in the interior and polar side chains exposed to solvent. The present work depends on sequence as well as structural information of the protein and aims to understand nature of hydrophobic residues on the protein surfaces. It is based on the nonredundant data set of 218 monomeric proteins. Solvent accessibility of each protein was determined using NACCESS software and then obtained the homologous sequences to understand how well solvent exposed and buried hydrophobic residues are evolutionarily conserved and assigned the confidence scores to hydrophobic residues to be buried or solvent exposed based on the information obtained from conservation score and knowledge of flanking regions of hydrophobic residues. In the absence of a three-dimensional structure, the ability to predict surface accessibility of hydrophobic residues directly from the sequence is of great help in choosing the sites of chemical modification or specific mutations and in the studies of protein stability and molecular interactions.

SIFT Indel: predictions for the functional effects of amino acid insertions/deletions in proteins

Indels in the coding regions of a gene can either cause frameshifts or amino acid insertions/deletions. Frameshifting indels are indels that have a length that is not divisible by 3 and subsequently cause frameshifts. Indels that have a length divisible by 3 cause amino acid insertions/deletions or block substitutions; we call these 3n indels. The new amino acid changes resulting from 3n indels could potentially affect protein function. Therefore, we construct a SIFT Indel prediction algorithm for 3n indels which achieves 82% accuracy, 81% sensitivity, 82% specificity, 82% precision, 0.63 MCC, and 0.87 AUC by 10-fold cross-validation. We have previously published a prediction algorithm for frameshifting indels. The rules for the prediction of 3n indels are different from the rules for the prediction of frameshifting indels and reflect the biological differences of these two different types of variations. SIFT Indel was applied to human 3n indels from the 1000 Genomes Project and the Exome Sequencing Project. We found that common variants are less likely to be deleterious than rare variants. The SIFT indel prediction algorithm for 3n indels is available at http://sift-dna.org/

Residue mutations and their impact on protein structure and function: detecting beneficial and pathogenic changes

The present review focuses on the evolution of proteins and the impact of amino acid mutations on function from a structural perspective. Proteins evolve under the law of natural selection and undergo alternating periods of conservative evolution and of relatively rapid change. The likelihood of mutations being fixed in the genome depends on various factors, such as the fitness of the phenotype or the position of the residues in the three-dimensional structure. For example, co-evolution of residues located close together in three-dimensional space can occur to preserve global stability. Whereas point mutations can fine-tune the protein function, residue insertions and deletions (‘decorations’ at the structural level) can sometimes modify functional sites and protein interactions more dramatically. We discuss recent developments and tools to identify such episodic mutations, and examine their applications in medical research. Such tools have been tested on simulated data and applied to real data such as viruses or animal sequences. Traditionally, there has been little if any cross-talk between the fields of protein biophysics, protein structure–function and molecular evolution. However, the last several years have seen some exciting developments in combining these approaches to obtain an in-depth understanding of how proteins evolve. For example, a better understanding of how structural constraints affect protein evolution will greatly help us to optimize our models of sequence evolution. The present review explores this new synthesis of perspectives.

The tree alignment problem

BACKGROUND

The inference of homologies among DNA sequences, that is, positions in multiple genomes that share a common evolutionary origin, is a crucial, yet difficult task facing biologists. Its computational counterpart is known as the multiple sequence alignment problem. There are various criteria and methods available to perform multiple sequence alignments, and among these, the minimization of the overall cost of the alignment on a phylogenetic tree is known in combinatorial optimization as the Tree Alignment Problem. This problem typically occurs as a subproblem of the Generalized Tree Alignment Problem, which looks for the tree with the lowest alignment cost among all possible trees. This is equivalent to the Maximum Parsimony problem when the input sequences are not aligned, that is, when phylogeny and alignments are simultaneously inferred.

RESULTS

For large data sets, a popular heuristic is Direct Optimization (DO). DO provides a good tradeoff between speed, scalability, and competitive scores, and is implemented in the computer program POY. All other (competitive) algorithms have greater time complexities compared to DO. Here, we introduce and present experiments a new algorithm Affine-DO to accommodate the indel (alignment gap) models commonly used in phylogenetic analysis of molecular sequence data. Affine-DO has the same time complexity as DO, but is correctly suited for the affine gap edit distance. We demonstrate its performance with more than 330,000 experimental tests. These experiments show that the solutions of Affine-DO are close to the lower bound inferred from a linear programming solution. Moreover, iterating over a solution produced using Affine-DO shows little improvement.

CONCLUSIONS

Our results show that Affine-DO is likely producing near-optimal solutions, with approximations within 10% for sequences with small divergence, and within 30% for random sequences, for which Affine-DO produced the worst solutions. The Affine-DO algorithm has the necessary scalability and optimality to be a significant improvement in the real-world phylogenetic analysis of sequence data.

Repeat or not repeat?--Statistical validation of tandem repeat prediction in genomic sequences

Tandem repeats (TRs) represent one of the most prevalent features of genomic sequences. Due to their abundance and functional significance, a plethora of detection tools has been devised over the last two decades. Despite the longstanding interest, TR detection is still not resolved. Our large-scale tests reveal that current detectors produce different, often nonoverlapping inferences, reflecting characteristics of the underlying algorithms rather than the true distribution of TRs in genomic data. Our simulations show that the power of detecting TRs depends on the degree of their divergence, and repeat characteristics such as the length of the minimal repeat unit and their number in tandem. To reconcile the diverse predictions of current algorithms, we propose and evaluate several statistical criteria for measuring the quality of predicted repeat units. In particular, we propose a model-based phylogenetic classifier, entailing a maximum-likelihood estimation of the repeat divergence. Applied in conjunction with the state of the art detectors, our statistical classification scheme for inferred repeats allows to filter out false-positive predictions. Since different algorithms appear to specialize at predicting TRs with certain properties, we advise applying multiple detectors with subsequent filtering to obtain the most complete set of genuine repeats.

The natural history of class I primate alcohol dehydrogenases includes gene duplication, gene loss, and gene conversion

BACKGROUND

Gene duplication is a source of molecular innovation throughout evolution. However, even with massive amounts of genome sequence data, correlating gene duplication with speciation and other events in natural history can be difficult. This is especially true in its most interesting cases, where rapid and multiple duplications are likely to reflect adaptation to rapidly changing environments and life styles. This may be so for Class I of alcohol dehydrogenases (ADH1s), where multiple duplications occurred in primate lineages in Old and New World monkeys (OWMs and NWMs) and hominoids.

METHODOLOGY/PRINCIPAL FINDINGS

To build a preferred model for the natural history of ADH1s, we determined the sequences of nine new ADH1 genes, finding for the first time multiple paralogs in various prosimians (lemurs, strepsirhines). Database mining then identified novel ADH1 paralogs in both macaque (an OWM) and marmoset (a NWM). These were used with the previously identified human paralogs to resolve controversies relating to dates of duplication and gene conversion in the ADH1 family. Central to these controversies are differences in the topologies of trees generated from exonic (coding) sequences and intronic sequences.

CONCLUSIONS/SIGNIFICANCE

We provide evidence that gene conversions are the primary source of difference, using molecular clock dating of duplications and analyses of microinsertions and deletions (micro-indels). The tree topology inferred from intron sequences appear to more correctly represent the natural history of ADH1s, with the ADH1 paralogs in platyrrhines (NWMs) and catarrhines (OWMs and hominoids) having arisen by duplications shortly predating the divergence of OWMs and NWMs. We also conclude that paralogs in lemurs arose independently. Finally, we identify errors in database interpretation as the source of controversies concerning gene conversion. These analyses provide a model for the natural history of ADH1s that posits four ADH1 paralogs in the ancestor of Catarrhine and Platyrrhine primates, followed by the loss of an ADH1 paralog in the human lineage.

Local structural differences in homologous proteins: specificities in different SCOP classes

The constant increase in the number of solved protein structures is of great help in understanding the basic principles behind protein folding and evolution. 3-D structural knowledge is valuable in designing and developing methods for comparison, modelling and prediction of protein structures. These approaches for structure analysis can be directly implicated in studying protein function and for drug design. The backbone of a protein structure favours certain local conformations which include α-helices, β-strands and turns. Libraries of limited number of local conformations (Structural Alphabets) were developed in the past to obtain a useful categorization of backbone conformation. Protein Block (PB) is one such Structural Alphabet that gave a reasonable structure approximation of 0.42 Å. In this study, we use PB description of local structures to analyse conformations that are preferred sites for structural variations and insertions, among group of related folds. This knowledge can be utilized in improving tools for structure comparison that work by analysing local structure similarities. Conformational differences between homologous proteins are known to occur often in the regions comprising turns and loops. Interestingly, these differences are found to have specific preferences depending upon the structural classes of proteins. Such class-specific preferences are mainly seen in the all-β class with changes involving short helical conformations and hairpin turns. A test carried out on a benchmark dataset also indicates that the use of knowledge on the class specific variations can improve the performance of a PB based structure comparison approach. The preference for the indel sites also seem to be confined to a few backbone conformations involving β-turns and helix C-caps. These are mainly associated with short loops joining the regular secondary structures that mediate a reversal in the chain direction. Rare β-turns of type I’ and II’ are also identified as preferred sites for insertions.

The interface of protein structure, protein biophysics, and molecular evolution

Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction.

REvolver: modeling sequence evolution under domain constraints

Simulating the change of protein sequences over time in a biologically realistic way is fundamental for a broad range of studies with a focus on evolution. It is, thus, problematic that typically simulators evolve individual sites of a sequence identically and independently. More realistic simulations are possible; however, they are often prohibited by limited knowledge concerning site-specific evolutionary constraints or functional dependencies between amino acids. As a consequence, a protein's functional and structural characteristics are rapidly lost in the course of simulated evolution. Here, we present REvolver (www.cibiv.at/software/revolver), a program that simulates protein sequence alteration such that evolutionarily stable sequence characteristics, like functional domains, are maintained. For this purpose, REvolver recruits profile hidden Markov models (pHMMs) for parameterizing site-specific models of sequence evolution in an automated fashion. pHMMs derived from alignments of homologous proteins or protein domains capture information regarding which sequence sites remained conserved over time and where in a sequence insertions or deletions are more likely to occur. Thus, they describe constraints on the evolutionary process acting on these sequences. To demonstrate the performance of REvolver as well as its applicability in large-scale simulation studies, we evolved the entire human proteome up to 1.5 expected substitutions per site. Simultaneously, we analyzed the preservation of Pfam and SMART domains in the simulated sequences over time. REvolver preserved 92% of the Pfam domains originally present in the human sequences. This value drops to 15% when traditional models of amino acid sequence evolution are used. Thus, REvolver represents a significant advance toward a realistic simulation of protein sequence evolution on a proteome-wide scale. Further, REvolver facilitates the simulation of a protein family with a user-defined domain architecture at the root.

ALF--a simulation framework for genome evolution

In computational evolutionary biology, verification and benchmarking is a challenging task because the evolutionary history of studied biological entities is usually not known. Computer programs for simulating sequence evolution in silico have shown to be viable test beds for the verification of newly developed methods and to compare different algorithms. However, current simulation packages tend to focus either on gene-level aspects of genome evolution such as character substitutions and insertions and deletions (indels) or on genome-level aspects such as genome rearrangement and speciation events. Here, we introduce Artificial Life Framework (ALF), which aims at simulating the entire range of evolutionary forces that act on genomes: nucleotide, codon, or amino acid substitution (under simple or mixture models), indels, GC-content amelioration, gene duplication, gene loss, gene fusion, gene fission, genome rearrangement, lateral gene transfer (LGT), or speciation. The other distinctive feature of ALF is its user-friendly yet powerful web interface. We illustrate the utility of ALF with two possible applications: 1) we reanalyze data from a study of selection after globin gene duplication and test the statistical significance of the original conclusions and 2) we demonstrate that LGT can dramatically decrease the accuracy of two well-established orthology inference methods. ALF is available as a stand-alone application or via a web interface at http://www.cbrg.ethz.ch/alf.

Genome-wide influence of indel Substitutions on evolution of bacteria of the PVC superphylum, revealed using a novel computational method

Whole-genome scans for positive Darwinian selection are widely used to detect evolution of genome novelty. Most approaches are based on evaluation of nonsynonymous to synonymous substitution rate ratio across evolutionary lineages. These methods are sensitive to saturation of synonymous sites and thus cannot be used to study evolution of distantly related organisms. In contrast, indels occur less frequently than amino acid replacements, accumulate more slowly, and can be employed to characterize evolution of diverged organisms. As indels are also subject to the forces of natural selection, they can generate functional changes through positive selection. Here, we present a new computational approach to detect selective constraints on indel substitutions at the whole-genome level for distantly related organisms. Our method is based on ancestral sequence reconstruction, takes into account the varying susceptibility of different types of secondary structure to indels, and according to simulation studies is conservative. We applied this newly developed framework to characterize the evolution of organisms of the Planctomycetes, Verrucomicrobia, Chlamydiae (PVC) bacterial superphylum. The superphylum contains organisms with unique cell biology, physiology, and diverse lifestyles. It includes bacteria with simple cell organization and more complex eukaryote-like compartmentalization. Lifestyles range from free-living organisms to obligate pathogens. In this study, we conduct a whole-genome level analysis of indel substitutions specific to evolutionary lineages of the PVC superphylum and found that indels evolved under positive selection on up to 12% of gene tree branches. We also analyzed possible functional consequences for several case studies of predicted indel events.

Systematic analysis of short internal indels and their impact on protein folding

Background

Protein sequence insertions/deletions (indels) can be introduced during evolution or through alternative splicing (AS). Alternative splicing is an important biological phenomenon and is considered as the major means of expanding structural and functional diversity in eukaryotes. Knowledge of the structural changes due to indels is critical to our understanding of the evolution of protein structure and function. In addition, it can help us probe the evolution of alternative splicing and the diversity of functional isoforms. However, little is known about the effects of indels, in particular the ones involving core secondary structures, on the folding of protein structures. The long term goal of our study is to accurately predict the protein AS isoform structures. As a first step towards this goal, we performed a systematic analysis on the structural changes caused by short internal indels through mining highly homologous proteins in Protein Data Bank (PDB).

Results

We compiled a non-redundant dataset of short internal indels (2-40 amino acids) from highly homologous protein pairs and analyzed the sequence and structural features of the indels. We found that about one third of indel residues are in disordered state and majority of the residues are exposed to solvent, suggesting that these indels are generally located on the surface of proteins. Though naturally occurring indels are fewer than engineered ones in the dataset, there are no statistically significant differences in terms of amino acid frequencies and secondary structure types between the "Natural" indels and "All" indels in the dataset. Structural comparisons show that all the protein pairs with short internal indels in the dataset preserve the structural folds and about 85% of protein pairs have global RMSDs (root mean square deviations) of 2Å or less, suggesting that protein structures tend to be conserved and can tolerate short insertions and deletions. A few pairs with high RMSDs are results of relative domain positions of the proteins, probably due to the intrinsically dynamic nature of the proteins.

Conclusions

The analysis demonstrated that protein structures have the "plasticity" to tolerate short indels. This study can provide valuable guides in modeling protein AS isoform structures and homologous proteins with indels through placing the indels at the right locations since the accuracy of sequence alignments dictate model qualities in homology modeling.

Impact of indels on the flanking regions in structural domains

Amino acid substitution and insertions/deletions (indels) are two common events in protein evolution; however, current knowledge on indels is limited. In this study, we investigated the effects of indels on the flanking regions in protein structure superfamilies. Comprehensive analysis of structural classification of proteins superfamilies revealed that indels lead to a series of changes in the flanking regions, including the following: 1) structural shift in the tertiary structure, with a first-order exponential decay relation between structural shift and the distance to indels, 2) instability of the secondary structure elements in which parts of the α helix and β sheet are destroyed, and 3) an increase in the amino acid substitution rate of the primary structure and the nonsimilar amino acid substitution rate. In general, these quality changes are due to the combined effects of the "regional-inherent effect," "indel-accompanied effect," and "indel-following effect." Furthermore, these quality changes reflect changes in selective pressure. Indels are more likely to be preserved in regions with low selective pressure, and indels can further reduce the selective pressure on the flanking regions. These findings improve our understanding of the role of indels in protein evolution.

A law of mutation: power decay of small insertions and small deletions associated with human diseases

Indels in evolutionary studies are rapidly decayed obeying a power law. The present study analyzed the length distribution of small insertions and deletions associated with human diseases and confirmed that the decay pattern of these small mutations is similar to that of indels when the mutation datasets are large enough. The describable decay pattern of somatic mutations may have application in the evaluation of varied penetrance of different mutations and in association study of gene mutation with carcinogenesis.

Biological sequence simulation for testing complex evolutionary hypotheses: indel-Seq-Gen version 2.0

Sequence simulation is an important tool in validating biological hypotheses as well as testing various bioinformatics and molecular evolutionary methods. Hypothesis testing relies on the representational ability of the sequence simulation method. Simple hypotheses are testable through simulation of random, homogeneously evolving sequence sets. However, testing complex hypotheses, for example, local similarities, requires simulation of sequence evolution under heterogeneous models. To this end, we previously introduced indel-Seq-Gen version 1.0 (iSGv1.0; indel, insertion/deletion). iSGv1.0 allowed heterogeneous protein evolution and motif conservation as well as insertion and deletion constraints in subsequences. Despite these advances, for complex hypothesis testing, neither iSGv1.0 nor other currently available sequence simulation methods is sufficient. indel-Seq-Gen version 2.0 (iSGv2.0) aims at simulating evolution of highly divergent DNA sequences and protein superfamilies. iSGv2.0 improves upon iSGv1.0 through the addition of lineage-specific evolution, motif conservation using PROSITE-like regular expressions, indel tracking, subsequence-length constraints, as well as coding and noncoding DNA evolution. Furthermore, we formalize the sequence representation used for iSGv2.0 and uncover a flaw in the modeling of indels used in current state of the art methods, which biases simulation results for hypotheses involving indels. We fix this flaw in iSGv2.0 by using a novel discrete stepping procedure. Finally, we present an example simulation of the calycin-superfamily sequences and compare the performance of iSGv2.0 with iSGv1.0 and random model of sequence evolution.

INDELible: a flexible simulator of biological sequence evolution

Many methods exist for reconstructing phylogenies from molecular sequence data, but few phylogenies are known and can be used to check their efficacy. Simulation remains the most important approach to testing the accuracy and robustness of phylogenetic inference methods. However, current simulation programs are limited, especially concerning realistic models for simulating insertions and deletions. We implement a portable and flexible application, named INDELible, for generating nucleotide, amino acid and codon sequence data by simulating insertions and deletions (indels) as well as substitutions. Indels are simulated under several models of indel-length distribution. The program implements a rich repertoire of substitution models, including the general unrestricted model and nonstationary nonhomogeneous models of nucleotide substitution, mixture, and partition models that account for heterogeneity among sites, and codon models that allow the nonsynonymous/synonymous substitution rate ratio to vary among sites and branches. With its many unique features, INDELible should be useful for evaluating the performance of many inference methods, including those for multiple sequence alignment, phylogenetic tree inference, and ancestral sequence, or genome reconstruction.

An indel in transmembrane helix 2 helps to trace the molecular evolution of class A G-protein-coupled receptors

Class A G-protein-coupled receptors (GPCRs) constitute a large family of transmembrane receptors. Helical distortions play a major role in the overall fold of these receptors. Most are related to conserved proline residues. However, in transmembrane helix 2, the proline pattern is not conserved, and when present, proline may be located at position 2.58, 2.59, or 2.60. Sequence analysis, three-dimensional data mining, and molecular modeling were undertaken to investigate the origin of this unusual pattern. Taken together, the data strongly support the assumption that an indel led to two structural motifs for helix 2: a bulged structure in P2.59 and P2.60 receptors and a "typical" proline kink in P2.58 receptors. The proline pattern of helix 2 can be used as an evolutionary marker and helps to trace the molecular evolution of class A GPCRs. Two indel events yielding functional receptors occurred independently. One indel arose very early in GPCR evolution, in a bilaterian ancestor, before the protostome-deuterostome divergence. This indel led to the split between the P2.58 somatostatin/opioid receptors and other peptide receptors with the P2.59 pattern. A second indel also occurred in insect opsins and corresponds to a deletion. Subfamilies with proline at position 2.59 or no proline expanded earlier, whereas P2.60 receptors remained marginal throughout evolution. P2.58 receptors underwent rapid expansion in vertebrates with the development of the chemokine and purinergic receptor subfamilies from somatostatin/opioid-related ancestors.

New tips for structure prediction by comparative modeling

Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence identity and model quality, we carried out an analysis of a set of 4753 sequence and structure alignments. Throughout this research, the model accuracy was measured by root mean square deviations of Calpha atoms of the target-template structures. Surprisingly, the results show that sequence identity of the target protein to the template is not a good descriptor to predict the accuracy of the 3-D structure model. However, in a large number of cases, comparative modelling with lower sequence identity of target to template proteins led to more accurate 3-D structure model. As a consequence of this study, we suggest new tips for improving the quality of omparative models, particularly for models whose target-template sequence identity is below 50%.

Problems and solutions for estimating indel rates and length distributions

Insertions and deletions (indels) are fundamental but understudied components of molecular evolution. Here we present an expectation-maximization algorithm built on a pair hidden Markov model that is able to properly handle indels in neutrally evolving DNA sequences. From a data set of orthologous introns, we estimate relative rates and length distributions of indels among primates and rodents. This technique has the advantage of potentially handling large genomic data sets. We find that a zeta power-law model of indel lengths provides a much better fit than the traditional geometric model and that indel processes are conserved between our taxa. The estimated relative rates are about 12-16 indels per 100 substitutions, and the estimated power-law magnitudes are about 1.6-1.7. More significantly, we find that using the traditional geometric/affine model of indel lengths introduces artifacts into evolutionary analysis, casting doubt on studies of the evolution and diversity of indel formation using traditional models and invalidating measures of species divergence that include indel lengths.

Probabilistic phylogenetic inference with insertions and deletions

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth-death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new "concordance test" benchmark on real ribosomal RNA alignments, we show that the extended program dnamlepsilon improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.

The effectiveness of position- and composition-specific gap costs for protein similarity searches

Motivation: The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.

Results: We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. These results suggest possible improvements to the PSI-BLAST protein database search program.

Availability: The scripts for performing evaluations are available upon request from the authors.

Contact:yyu@ncbi.nlm.nih.gov

Characterizing gene family evolution

Gene families are widely used in comparative genomics, molecular evolution, and in systematics. However, they are constructed in different manners, their data analyzed and interpreted differently, with different underlying assumptions, leading to sometimes divergent conclusions. In systematics, concepts like monophyly and the dichotomy between homoplasy and homology have been central to the analysis of phylogenies. We critique the traditional use of such concepts as applied to gene families and give examples of incorrect inferences they may lead to. Operational definitions that have emerged within functional genomics are contrasted with the common formal definitions derived from systematics. Lastly, we question the utility of layers of homology and the meaning of homology at the character state level in the context of sequence evolution. From this, we move forward to present an idealized strategy for characterizing gene family evolution for both systematic and functional purposes, including recent methodological improvements.

Sequence context affects the rate of short insertions and deletions in flies and primates

BACKGROUND

Insertions and deletions (indels) are an important evolutionary force, making the evolutionary process more efficient and flexible by copying and removing genomic fragments of various lengths instead of rediscovering them by point mutations. As a mutational process, indels are known to be more active in specific sequences (like micro-satellites) but not much is known about the more general and mechanistic effect of sequence context on the insertion and deletion susceptibility of genomic loci.

RESULTS

Here we analyze a large collection of high confidence short insertions and deletions in primates and flies, revealing extensive correlations between sequence context and indel rates and building principled models for predicting these rates from sequence. According to our results, the rate of insertion or deletion of specific lengths can vary by more than 100-fold, depending on the surrounding sequence. These mutational biases can strongly influence the composition of the genome and the rate at which particular sequences appear. We exemplify this by showing how degenerate loci in human exons are selected to reduce their frame shifting indel propensity.

CONCLUSION

Insertions and deletions are strongly affected by sequence context. Consequentially, genomes must adapt to significant variation in the mutational input at indel-prone and indel-immune loci.

The relative performance of indel-coding methods in simulations

We used simulations to compare the performance of 10 approaches that have been used for treating unambiguously aligned gaps in phylogenetic analyses. We examined how these approaches perform under the ideal conditions of correct alignments, as well as how robust they are to errors caused by use of inferred alignments. Our results indicate that 5th-state coding dramatically outperformed all other coding methods, which in turn all outperformed treating gaps as missing data or excluding gapped positions. Simple indel coding (SIC) and modified complex indel coding (MCIC) performed about the same, and generally outperformed the other indel-coding methods. The high performance of 5th-state coding was largely found to be a weighting artifact. We suggest that MCIC-coded gap characters be scored for all unambiguously aligned gaps in parsimony-based molecular phylogenetic analyses. When the number of terminals sampled precludes the use of MCIC, SIC may be used as an effective substitute.

Ngila: global pairwise alignments with logarithmic and affine gap costs

Ngila is an application that will find the best alignment of a pair of sequences using log-affine gap costs, which are the most biologically realistic gap costs.

AVAILABILITY

Portable source code for Ngila can be downloaded from its development website, http://scit.us/projects/ngila/. It compiles on most operating systems.