The accuracy of several multiple sequence alignment programs for proteins

Allosteric regulation of kinase activity in living cells

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities, making it difficult to specifically target one kinase, and allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss the methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or 'sensors,' are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Cooption of regulatory modules for tektin paralogs during ciliary band formation in a marine annelid larva

Tektins are a highly conserved family of coiled-coil domain containing proteins known to play a role in structure, stability and function of cilia and flagella. Tektin proteins are thought to form filaments which run the length of the axoneme along the inner surface of the A tubule of each microtubule doublet. Phylogenetic analyses suggest that the tektin family arose via duplications from a single tektin gene in a unicellular organism giving rise to four and five tektin genes in bilaterians and in spiralians, respectively. Although tektins are found in most metazoans, little is known about their expression and function outside of a handful of model species. Here we present the first comprehensive study of tektin family gene expression in any animal system, in the spiralian annelid Platynereis dumerilii. This indirect developing species retains a full ancient spiralian complement of five tektin genes. We show that all five tektins are expressed almost exclusively in known ciliary structures following the expression of the motile cilia master regulator foxJ1. The three older bilaterian tektin-1, tektin-2, and tektin-4 genes, show a high degree of spatial and temporal co-regulation, while the spiralian specific tektin-3/5A and tektin-3/5B show a delay in onset of expression in every ciliary structure. In addition, tektin-3/5B transcripts show a restricted subcellular localization to the most apical region near the multiciliary arrays. The exact recapitulation of the sequence of expression and localization of the five tektins at different times during larval development indicates the cooption of a fixed regulatory and cellular program during the formation of each ciliary band and multiciliated cell type in this spiralian.

Allosteric regulation of kinase activity in living cells

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities making it difficult to specifically target one kinase, allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or "sensors" are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Genetic Analysis of Orf Virus (ORFV) Strains Isolated from Goats in China: Insights into Epidemiological Characteristics and Evolutionary Patterns

Contagious ecthyma (CE) is an acute infectious zoonosis caused by orf virus (ORFV) that mainly infects sheep and goats and causes obvious lesions and low market value of livestock, resulting in huge economic losses for farmers. In this study, two strains of ORFV were isolated from Shaanxi Province and Yunnan Province in China, named FX and LX. The two ORFVs were located in the major clades of domestic strains respectively, and exhibited distinct sequence homology. We analyzed the genetic data of core genes (B2L, F1L, VIR, ORF109) and variable genes (GIF, ORF125 and vIL-10) of ORFV to investigate its epidemiological and evolutionary characteristics. The sequences from 2007 to 2018 constituted the majority of the viral population, predominantly concentrated in India and China. Most genes were clustered into SA00-like type and IA82-like type, and the hotspots in East and South Asia were identified in the ORFV transmission trajectories. For these genes, VIR had the highest substitution rate of 4.85 × 10-4, both VIR and vIL-10 suffered the positive selection pressure during ORFV evolution. Many motifs associated with viral survival were distributed among ORFVs. In addition, some possible viral epitopes have been predicted, which still require validation in vivo and in vitro. This work gives more insight into the prevalence and phylogenetic relationships of existing orf viruses and facilitate better vaccine design.

Viral Instant Mutation Viewer: A Tool to Speed Up the Identification and Analysis of New SARS-CoV-2 Emerging Variants and Beyond

The appearance of genetic variants impacts vaccination efficiency and therapeutic options, generating a need to map and relate mutations observed in the proteome and the genome. We develop an user-friendly web service software (Viral Instant Mutation Viewer or VIMVer) which allows a direct identification of mutations in the genome and its counterpart in the viral proteome. Since its emergence in 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, has generated an overwhelming amount of data while becoming one of the most studied viruses of the Nidovirales order. We originally developed this tool during the COVID pandemic; thus, for any SARS-CoV-2 nucleotide sequence, the web service gives a fast identification, mapping, and display of new mutations simultaneously at the nucleotide and amino acid level in comparison to a reference sequence (Wuhan-1). Furthermore, the lineage or the relative position to the known lineage of the variant of interest is available on the link to Phylogenetic Assignment of Named Global Outbreak LINeages (PANGOLIN COVID-19). The workflow presented here is available online. The source code is released under public license and can be easily adapted for further development to other viruses.

Reclassification of family A DNA polymerases reveals novel functional subfamilies and distinctive structural features

Family A DNA polymerases (PolAs) form an important and well-studied class of extant polymerases participating in DNA replication and repair. Nonetheless, despite the characterization of multiple subfamilies in independent, dedicated works, their comprehensive classification thus far is missing. We therefore re-examine all presently available PolA sequences, converting their pairwise similarities into positions in Euclidean space, separating them into 19 major clusters. While 11 of them correspond to known subfamilies, eight had not been characterized before. For every group, we compile their general characteristics, examine their phylogenetic relationships and perform conservation analysis in the essential sequence motifs. While most subfamilies are linked to a particular domain of life (including phages), one subfamily appears in Bacteria, Archaea and Eukaryota. We also show that two new bacterial subfamilies contain functional enzymes. We use AlphaFold2 to generate high-confidence prediction models for all clusters lacking an experimentally determined structure. We identify new, conserved features involving structural alterations, ordered insertions and an apparent structural incorporation of a uracil-DNA glycosylase (UDG) domain. Finally, genetic and structural analyses of a subset of T7-like phages indicate a splitting of the 3'-5' exo and pol domains into two separate genes, observed in PolAs for the first time.

MAGUS+eHMMs: improved multiple sequence alignment accuracy for fragmentary sequences

SUMMARY

Multiple sequence alignment is an initial step in many bioinformatics pipelines, including phylogeny estimation, protein structure prediction and taxonomic identification of reads produced in amplicon or metagenomic datasets, etc. Yet, alignment estimation is challenging on datasets that exhibit substantial sequence length heterogeneity, and especially when the datasets have fragmentary sequences as a result of including reads or contigs generated by next-generation sequencing technologies. Here, we examine techniques that have been developed to improve alignment estimation when datasets contain substantial numbers of fragmentary sequences. We find that MAGUS, a recently developed MSA method, is fairly robust to fragmentary sequences under many conditions, and that using a two-stage approach where MAGUS is used to align selected 'backbone sequences' and the remaining sequences are added into the alignment using ensembles of Hidden Markov Models further improves alignment accuracy. The combination of MAGUS with the ensemble of eHMMs (i.e. MAGUS+eHMMs) clearly improves on UPP, the previous leading method for aligning datasets with high levels of fragmentation.

AVAILABILITY AND IMPLEMENTATION

UPP is available on https://github.com/smirarab/sepp, and MAGUS is available on https://github.com/vlasmirnov/MAGUS. MAGUS+eHMMs can be performed by running MAGUS to obtain the backbone alignment, and then using the backbone alignment as an input to UPP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A novel framework for engineering protein loops exploring length and compositional variation

Insertions and deletions (indels) are known to affect function, biophysical properties and substrate specificity of enzymes, and they play a central role in evolution. Despite such clear significance, this class of mutation remains an underexploited tool in protein engineering with few available platforms capable of systematically generating and analysing libraries of varying sequence composition and length. We present a novel DNA assembly platform (InDel assembly), based on cycles of endonuclease restriction digestion and ligation of standardised dsDNA building blocks, that can generate libraries exploring both composition and sequence length variation. In addition, we developed a framework to analyse the output of selection from InDel-generated libraries, combining next generation sequencing and alignment-free strategies for sequence analysis. We demonstrate the approach by engineering the well-characterized TEM-1 β-lactamase Ω-loop, involved in substrate specificity, identifying multiple novel extended spectrum β-lactamases with loops of modified length and composition-areas of the sequence space not previously explored. Together, the InDel assembly and analysis platforms provide an efficient route to engineer protein loops or linkers where sequence length and composition are both essential functional parameters.

Sequence and Structure-Based Analysis of Specificity Determinants in Eukaryotic Protein Kinases

Protein kinases lie at the heart of cell-signaling processes and are often mutated in disease. Kinase target recognition at the active site is in part determined by a few amino acids around the phosphoacceptor residue. However, relatively little is known about how most preferences are encoded in the kinase sequence or how these preferences evolved. Here, we used alignment-based approaches to predict 30 specificity-determining residues (SDRs) for 16 preferences. These were studied with structural models and were validated by activity assays of mutant kinases. Cancer mutation data revealed that kinase SDRs are mutated more frequently than catalytic residues. We have observed that, throughout evolution, kinase specificity has been strongly conserved across orthologs but can diverge after gene duplication, as illustrated by the G protein-coupled receptor kinase family. The identified SDRs can be used to predict kinase specificity from sequence and aid in the interpretation of evolutionary or disease-related genomic variants.

Protein multiple alignments: sequence-based versus structure-based programs

MOTIVATION

Multiple sequence alignment programs have proved to be very useful and have already been evaluated in the literature yet not alignment programs based on structure or both sequence and structure. In the present article we wish to evaluate the added value provided through considering structures.

RESULTS

We compared the multiple alignments resulting from 25 programs either based on sequence, structure or both, to reference alignments deposited in five databases (BALIBASE 2 and 3, HOMSTRAD, OXBENCH and SISYPHUS). On the whole, the structure-based methods compute more reliable alignments than the sequence-based ones, and even than the sequence+structure-based programs whatever the databases. Two programs lead, MAMMOTH and MATRAS, nevertheless the performances of MUSTANG, MATT, 3DCOMB, TCOFFEE+TM_ALIGN and TCOFFEE+SAP are better for some alignments. The advantage of structure-based methods increases at low levels of sequence identity, or for residues in regular secondary structures or buried ones. Concerning gap management, sequence-based programs set less gaps than structure-based programs. Concerning the databases, the alignments of the manually built databases are more challenging for the programs.

AVAILABILITY AND IMPLEMENTATION

All data and results presented in this study are available at: http://wwwabi.snv.jussieu.fr/people/mathilde/download/AliMulComp/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SpCLUST: Towards a fast and reliable clustering for potentially divergent biological sequences

This paper presents SpCLUST, a new C++ package that takes a list of sequences as input, aligns them with MUSCLE, computes their similarity matrix in parallel and then performs the clustering. SpCLUST extends a previously released software by integrating additional scoring matrices which enables it to cover the clustering of amino-acid sequences. The similarity matrix is now computed in parallel according to the master/slave distributed architecture, using MPI. Performance analysis, realized on two real datasets of 100 nucleotide sequences and 1049 amino-acids ones, show that the resulting library substantially outperforms the original Python package. The proposed package was also intensively evaluated on simulated and real genomic and protein data sets. The clustering results were compared to the most known traditional tools, such as UCLUST, CD-HIT and DNACLUST. The comparison showed that SpCLUST outperforms the other tools when clustering divergent sequences, and contrary to the others, it does not require any user intervention or prior knowledge about the input sequences.

Graph Splitting: A Graph-Based Approach for Superfamily-Scale Phylogenetic Tree Reconstruction

A protein superfamily contains distantly related proteins that have acquired diverse biological functions through a long evolutionary history. Phylogenetic analysis of the early evolution of protein superfamilies is a key challenge because existing phylogenetic methods show poor performance when protein sequences are too diverged to construct an informative multiple sequence alignment (MSA). Here, we propose the Graph Splitting (GS) method, which rapidly reconstructs a protein superfamily-scale phylogenetic tree using a graph-based approach. Evolutionary simulation showed that the GS method can accurately reconstruct phylogenetic trees and be robust to major problems in phylogenetic estimation, such as biased taxon sampling, heterogeneous evolutionary rates, and long-branch attraction when sequences are substantially diverge. Its application to an empirical data set of the triosephosphate isomerase (TIM)-barrel superfamily suggests rapid evolution of protein-mediated pyrimidine biosynthesis, likely taking place after the RNA world. Furthermore, the GS method can also substantially improve performance of widely used MSA methods by providing accurate guide trees.

Reptile enamel matrix proteins: Selection, divergence, and functional constraint

The three major enamel matrix proteins (EMPs): amelogenin (AMEL), ameloblastin (AMBN), and enamelin (ENAM), are intrinsically linked to tooth development in tetrapods. However, reptiles and mammals exhibit significant differences in dental patterning and development, potentially affecting how EMPs evolve in each group. In most reptiles, teeth are replaced continuously throughout life, while mammals have reduced replacement to only one or two generations. Reptiles also form structurally simple, aprismatic enamel while mammalian enamel is composed of highly organized hydroxyapatite prisms. These differences, combined with reported low sequence homology in reptiles, led us to predict that reptiles may experience lower selection pressure on their EMPs as compared with mammals. However, we found that like mammals, reptile EMPs are under moderate purifying selection, with some differences evident between AMEL, AMBN, and ENAM. We also demonstrate that sequence homology in reptile EMPs is closely associated with divergence times, with more recently diverged lineages exhibiting high homology, along with strong phylogenetic signal. Lastly, despite sequence divergence, none of the reptile species in our study exhibited mutations consistent with diseases that cause degeneration of enamel (e.g. amelogenesis imperfecta). Despite short tooth retention time and simplicity in enamel structure, reptile EMPs still exhibit purifying selection required to form durable enamel.

Taxon-specific expansion and loss of tektins inform metazoan ciliary diversity

Background

Cilia and flagella are complex cellular structures thought to have first evolved in a last ciliated eukaryotic ancestor due to the conserved 9 + 2 microtubule doublet structure of the axoneme and associated proteins. The Tektin family of coiled-coil domain containing proteins was previously identified in cilia of organisms as diverse as green algae and sea urchin. While studies have shown that some Tektins are necessary for ciliary function, there has been no comprehensive phylogenetic survey of tektin genes. To fill this gap, we sampled tektin sequences broadly among metazoan and unicellular lineages in order to determine how the tektin gene complements evolved in over 100 different extant species.

Results

Using Bayesian and Maximum Likelihood analyses, we have ascertained with high confidence that all metazoan tektins arose from a single ancestral tektin gene in the last common ancestor of metazoans and choanoflagellates. Gene duplications gave rise to two tektin genes in the metazoan ancestor, and a subsequent expansion to three and four tektin genes in early bilaterian ancestors. While all four tektin genes remained highly conserved in most deuterostome and spiralian species surveyed, most tektin genes in ecdysozoans are highly derived with extensive gene loss in several lineages including nematodes and some crustaceans. In addition, while tektin-1, − 2, and − 4 have remained as single copy genes in most lineages, tektin-3/5 has been duplicated independently several times, notably at the base of the spiralian, vertebrate and hymenopteran (Ecdysozoa) clades.

Conclusions

We provide a solid description of tektin evolution supporting one, two, three, and four ancestral tektin genes in a holozoan, metazoan, bilaterian, and nephrozoan ancestor, respectively. The isolated presence of tektin in a cryptophyte and a chlorophyte branch invokes events of horizontal gene transfer, and that the last common ciliated eukaryotic ancestor lacked a tektin gene. Reconstructing the evolutionary history of the tektin complement in each extant metazoan species enabled us to pinpoint lineage specific expansions and losses. Our analysis will help to direct future studies on Tektin function, and how gain and loss of tektin genes might have contributed to the evolution of various types of cilia and flagella.

Electronic supplementary material

The online version of this article (10.1186/s12862-019-1360-0) contains supplementary material, which is available to authorized users.

High-Throughput Reconstruction of Ancestral Protein Sequence, Structure, and Molecular Function

Ancestral protein sequence reconstruction is a powerful technique for explicitly testing hypotheses about the evolution of molecular function, allowing researchers to meticulously dissect how historical changes in protein sequence impacted functional repertoire by altering the protein's 3D structure. These techniques have provided concrete, experimentally validated insights into ancient evolutionary processes and help illuminate the complex relationship between protein sequence, structure, and function. Inferring the protein family phylogenies on which ancestral sequence reconstruction depends and reconstructing the sequences, themselves, are amenable to high-throughput computational analysis. However, determining the structures of ancestral-reconstructed proteins and characterizing their functions typically rely on time-consuming and expensive laboratory analyses, limiting most current studies to examining a relatively small number of specific hypotheses. For this reason, we have little detailed, unbiased information about how molecular function evolves across large protein family phylogenies. Here we describe a generalized protocol that integrates ancestral sequence reconstruction with structural homology modeling and structure-based molecular affinity prediction to characterize historical changes in protein function across families with thousands of individual sequences. We highlight key steps in the analysis protocol requiring particularly careful attention to avoid introducing potential errors as well as steps for which computationally efficient subroutines can be substituted for more intensive approaches, allowing researchers to scale the analysis up or down, depending on available resources and requirements for reproducibility and scientific rigor. In our view, this approach provides a compelling compliment to more laboratory-intensive procedures, generating important contextual information that can help guide detailed experiments.

Multiple Sequence Alignment Averaging Improves Phylogeny Reconstruction

The classic methodology of inferring a phylogenetic tree from sequence data is composed of two steps. First, a multiple sequence alignment (MSA) is computed. Then, a tree is reconstructed assuming the MSA is correct. Yet, inferred MSAs were shown to be inaccurate and alignment errors reduce tree inference accuracy. It was previously proposed that filtering unreliable alignment regions can increase the accuracy of tree inference. However, it was also demonstrated that the benefit of this filtering is often obscured by the resulting loss of phylogenetic signal. In this work we explore an approach, in which instead of relying on a single MSA, we generate a large set of alternative MSAs and concatenate them into a single SuperMSA. By doing so, we account for phylogenetic signals contained in columns that are not present in the single MSA computed by alignment algorithms. Using simulations, we demonstrate that this approach results, on average, in more accurate trees compared to 1) using an unfiltered MSA and 2) using a single MSA with weights assigned to columns according to their reliability. Next, we explore in which regions of the MSA space our approach is expected to be beneficial. Finally, we provide a simple criterion for deciding whether or not the extra effort of computing a SuperMSA and inferring a tree from it is beneficial. Based on these assessments, we expect our methodology to be useful for many cases in which diverged sequences are analyzed. The option to generate such a SuperMSA is available at http://guidance.tau.ac.il.

A benchmark study of sequence alignment methods for protein clustering

Background

Protein sequence alignment analyses have become a crucial step for many bioinformatics studies during the past decades. Multiple sequence alignment (MSA) and pair-wise sequence alignment (PSA) are two major approaches in sequence alignment. Former benchmark studies revealed drawbacks of MSA methods on nucleotide sequence alignments. To test whether similar drawbacks also influence protein sequence alignment analyses, we propose a new benchmark framework for protein clustering based on cluster validity. This new framework directly reflects the biological ground truth of the application scenarios that adopt sequence alignments, and evaluates the alignment quality according to the achievement of the biological goal, rather than the comparison on sequence level only, which averts the biases introduced by alignment scores or manual alignment templates. Compared with former studies, we calculate the cluster validity score based on sequence distances instead of clustering results. This strategy could avoid the influence brought by different clustering methods thus make results more dependable.

Results

Results showed that PSA methods performed better than MSA methods on most of the BAliBASE benchmark datasets. Analyses on the 80 re-sampled benchmark datasets constructed by randomly choosing 90% of each dataset 10 times showed similar results.

Conclusions

These results validated that the drawbacks of MSA methods revealed in nucleotide level also existed in protein sequence alignment analyses and affect the accuracy of results.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2524-4) 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.

Biocuration in the structure-function linkage database: the anatomy of a superfamily

With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and methods used in the structure-function linkage database (SFLD) to help address some of the challenges. We discuss how the hierarchy within the SFLD allows us to infer detailed functional properties for functionally diverse enzyme superfamilies in which all members are homologous, conserve an aspect of their chemical function and have associated conserved structural features that enable the chemistry. Also presented is the Enzyme Structure-Function Ontology (ESFO), which has been designed to capture the relationships between enzyme sequence, structure and function that underlie the SFLD and is used to guide the biocuration processes within the SFLD.

Database URL:http://sfld.rbvi.ucsf.edu/

An improved and general streamlined phylogenetic protocol applied to the fatty acid desaturase family

Numerous tools to generate phylogenetic estimates are available, but there is no single protocol that will produce an accurate phylogenetic tree for any dataset. Here, we investigated some of those tools, paying particular attention to different alignment algorithms, in order to produce a phylogeny for the integral membrane fatty acid desaturase (FAD) family. Herein, we report a novel streamlined protocol which utilises peptide pattern recognition (PPR). This protocol can theoretically be applied universally to generate accurate multiple sequence alignments and improve downstream phylogenetic analyses. Applied to the desaturases, the protocol generated the first detailed phylogenetic estimates for the family since 2003, which suggested they may have evolved from three functionally distinct desaturases and further, that desaturases evolved first in cyanobacteria. In addition to the phylogenetic outputs, we mapped PPR sequence motifs onto an X-ray protein structure to provide insights into biochemical function and demonstrate the complementarity of PPR and phylogenetics.

Determination of optimal parameters of MAFFT program based on BAliBASE3.0 database

Background

Multiple sequence alignment (MSA) is one of the most important research contents in bioinformatics. A number of MSA programs have emerged. The accuracy of MSA programs highly depends on the parameters setting, mainly including gap open penalties (GOP), gap extension penalties (GEP) and substitution matrix (SM). This research tries to obtain the optimal GOP, GEP and SM rather than MAFFT default parameters.

Results

The paper discusses the MAFFT program benchmarked on BAliBASE3.0 database, and the optimal parameters of MAFFT program are obtained, which are better than the default parameters of CLUSTALW and MAFFT program.

Conclusions

The optimal parameters can improve the results of multiple sequence alignment, which is feasible and efficient.

Structural Determinants of Sleeping Beauty Transposase Activity

Transposases are important tools in genome engineering, and there is considerable interest in engineering more efficient ones. Here, we seek to understand the factors determining their activity using the Sleeping Beauty transposase. Recent work suggests that protein coevolutionary information can be used to classify groups of physically connected, coevolving residues into elements called "sectors", which have proven useful for understanding the folding, allosteric interactions, and enzymatic activity of proteins. Using extensive mutagenesis data, protein modeling and analysis of folding energies, we show that (i) The Sleeping Beauty transposase contains two sectors, which span across conserved domains, and are enriched in DNA-binding residues, indicating that the DNA binding and endonuclease functions of the transposase coevolve; (ii) Sector residues are highly sensitive to mutations, and most mutations of these residues strongly reduce transposition rate; (iii) Mutations with a strong effect on free energy of folding in the DDE domain of the transposase significantly reduce transposition rate. (iv) Mutations that influence DNA and protein-protein interactions generally reduce transposition rate, although most hyperactive mutants are also located on the protein surface, including residues with protein-protein interactions. This suggests that hyperactivity results from the modification of protein interactions, rather than the stabilization of protein fold.

Consensus protein design

A popular and successful strategy in semi-rational design of protein stability is the use of evolutionary information encapsulated in homologous protein sequences. Consensus design is based on the hypothesis that at a given position, the respective consensus amino acid contributes more than average to the stability of the protein than non-conserved amino acids. Here, we review the consensus design approach, its theoretical underpinnings, successes, limitations and challenges, as well as providing a detailed guide to its application in protein engineering.

Sequence analysis of the L protein of the Ebola 2014 outbreak: Insight into conserved regions and mutations

The 2014 Ebola outbreak was one of the largest that have occurred; it started in Guinea and spread to Nigeria, Liberia and Sierra Leone. Phylogenetic analysis of the current virus species indicated that this outbreak is the result of a divergent lineage of the Zaire ebolavirus. The L protein of Ebola virus (EBOV) is the catalytic subunit of the RNA‑dependent RNA polymerase complex, which, with VP35, is key for the replication and transcription of viral RNA. Earlier sequence analysis demonstrated that the L protein of all non‑segmented negative‑sense (NNS) RNA viruses consists of six domains containing conserved functional motifs. The aim of the present study was to analyze the presence of these motifs in 2014 EBOV isolates, highlight their function and how they may contribute to the overall pathogenicity of the isolates. For this purpose, 81 2014 EBOV L protein sequences were aligned with 475 other NNS RNA viruses, including Paramyxoviridae and Rhabdoviridae viruses. Phylogenetic analysis of all EBOV outbreak L protein sequences was also performed. Analysis of the amino acid substitutions in the 2014 EBOV outbreak was conducted using sequence analysis. The alignment demonstrated the presence of previously conserved motifs in the 2014 EBOV isolates and novel residues. Notably, all the mutations identified in the 2014 EBOV isolates were tolerant, they were pathogenic with certain examples occurring within previously determined functional conserved motifs, possibly altering viral pathogenicity, replication and virulence. The phylogenetic analysis demonstrated that all sequences with the exception of the 2014 EBOV sequences were clustered together. The 2014 EBOV outbreak has acquired a great number of mutations, which may explain the reasons behind this unprecedented outbreak. Certain residues critical to the function of the polymerase remain conserved and may be targets for the development of antiviral therapeutic agents.

ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules

The degree of evolutionary conservation of an amino acid in a protein or a nucleic acid in DNA/RNA reflects a balance between its natural tendency to mutate and the overall need to retain the structural integrity and function of the macromolecule. The ConSurf web server (http://consurf.tau.ac.il), established over 15 years ago, analyses the evolutionary pattern of the amino/nucleic acids of the macromolecule to reveal regions that are important for structure and/or function. Starting from a query sequence or structure, the server automatically collects homologues, infers their multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relations. These data are then used, within a probabilistic framework, to estimate the evolutionary rates of each sequence position. Here we introduce several new features into ConSurf, including automatic selection of the best evolutionary model used to infer the rates, the ability to homology-model query proteins, prediction of the secondary structure of query RNA molecules from sequence, the ability to view the biological assembly of a query (in addition to the single chain), mapping of the conservation grades onto 2D RNA models and an advanced view of the phylogenetic tree that enables interactively rerunning ConSurf with the taxa of a sub-tree.

Role of bifunctional ammonia-lyase in grass cell wall biosynthesis

L-Phenylalanine ammonia-lyase (PAL) is the first enzyme in the biosynthesis of phenylpropanoid-derived plant compounds such as flavonoids, coumarins and the cell wall polymer lignin. The cell walls of grasses possess higher proportions of syringyl (S)-rich lignins and high levels of esterified coumaric acid compared with those of dicotyledonous plants, and PAL from grasses can also possess tyrosine ammonia-lyase (TAL) activity, the reason for which has remained unclear. Using phylogenetic, transcriptomic and in vitro biochemical analyses, we identified a single homotetrameric bifunctional ammonia-lyase (PTAL) among eight BdPAL enzymes in the model grass species Brachypodium distachyon. (13)C isotope labelling experiments along with BdPTAL1-downregulation in transgenic plants showed that the TAL activity of BdPTAL1 can provide nearly half of the total lignin deposited in Brachypodium, with a preference for S-lignin and wall-bound coumarate biosynthesis, indicating that PTAL function is linked to the characteristic features of grass cell walls. Furthermore, isotope dilution experiments suggest that the pathways to lignin from L-phenylalanine and L-tyrosine are distinct beyond the formation of 4-coumarate, supporting the organization of lignin synthesis enzymes in one or more metabolons.

Signaling via the CytoR/JAK/STAT/SOCS pathway: Emergence during evolution

Cell-cell signaling represents an essential hallmark of multicellular organisms, which necessarily require a means of communicating between different cell populations, particularly immune cells. Cytokine receptor signaling through the Janus kinase/Signal Transducer and Activator of Transcription/Suppressor of Cytokine Signaling (CytoR/JAK/STAT/SOCS) pathway embodies one important paradigm by which this is achieved. This pathway has been extensively studied in vertebrates and protostomes and shown to play fundamental roles in development and function of immune and other cells. However, our understanding of the origins of the individual pathway components and their assembly into a functional pathway has remained limited. This study examined the origins of each component of this pathway through bioinformatics analysis of key extant species. This has revealed step-wise accretion of individual components over a large evolutionary time-frame, but only in bilateria did a series of innovations allow their final coalescence to form a complete pathway. Assembly of the CytoR/JAK/STAT pathway has followed the retrograde model of pathway evolution, whereas addition of the SOCS component has adhered to the patchwork model.

Structure, phylogeny, and expression of the frizzled-related gene family in the lophotrochozoan annelid Platynereis dumerilii

Background

Wnt signaling pathways are highly conserved signal transduction pathways important for axis formation, cell fate specification, and organogenesis throughout metazoan development. Within the various Wnt pathways, the frizzled transmembrane receptors (Fzs) and secreted frizzled-related proteins (sFRPs) play central roles in receiving and antagonizing Wnt signals, respectively. Despite their importance, very little is known about the frizzled-related gene family (fzs & sfrps) in lophotrochozoans, especially during early stages of spiralian development. Here we ascertain the frizzled-related gene complement in six lophotrochozoan species, and determine their spatial and temporal expression pattern during early embryogenesis and larval stages of the marine annelid Platynereis dumerilii.

Results

Phylogenetic analyses confirm conserved homologs for four frizzled receptors (Fz1/2/7, Fz4, Fz5/8, Fz9/10) and sFRP1/2/5 in five of six lophotrochozoan species. The sfrp3/4 gene is conserved in one, divergent in two, and evidently lost in three lophotrochozoan species. Three novel fz-related genes (fzCRD1-3) are unique to Platynereis. Transcriptional profiling and in situ hybridization identified high maternal expression of fz1/2/7, expression of fz9/10 and fz1/2/7 within animal and dorsal cell lineages after the 32-cell stage, localization of fz5/8, sfrp1/2/5, and fzCRD-1 to animal-pole cell lineages after the 80-cell stage, and no expression for fz4, sfrp3/4, and fzCRD-2, and -3 in early Platynereis embryos. In later larval stages, all frizzled-related genes are expressed in distinct patterns preferentially in the anterior hemisphere and less in the developing trunk.

Conclusions

Lophotrochozoans have retained a generally conserved ancestral bilaterian frizzled-related gene complement (four Fzs and two sFRPs). Maternal expression of fz1/2/7, and animal lineage-specific expression of fz5/8 and sfrp1/2/5 in early embryos of Platynereis suggest evolutionary conserved roles of these genes to perform Wnt pathway functions during early cleavage stages, and the early establishment of a Wnt inhibitory center at the animal pole, respectively. Numerous frizzled receptor-expressing cells and embryonic territories were identified that might indicate competence to receive Wnt signals during annelid development. An anterior bias for frizzled-related gene expression in embryos and larvae might point to a polarity of Wnt patterning systems along the anterior–posterior axis of this annelid.

Electronic supplementary material

The online version of this article (doi:10.1186/s13227-015-0032-4) contains supplementary material, which is available to authorized users.

Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors

BACKGROUND

The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences.

RESULTS

In this study, we describe a systematic approach, using Support Vector Machine classifiers, to the analysis of G protein-coupled receptor misclassifications. As a proof of concept, this approach is used to assist the discovery of labeling quality problems in a curated, publicly accessible database of this type of proteins. We also investigate the extent to which physico-chemical transformations of the protein sequences reflect G protein-coupled receptor subtype labeling. The candidate mislabeled cases detected with this approach are externally validated with phylogenetic trees and against further trusted sources such as the National Center for Biotechnology Information, Universal Protein Resource, European Bioinformatics Institute and Ensembl Genome Browser information repositories.

CONCLUSIONS

In quantitative classification problems, class labels are often by default assumed to be correct. Label noise, though, is bound to be a pervasive problem in bioinformatics, where labels may be obtained indirectly through complex, many-step similarity modelling processes. In the case of G protein-coupled receptors, methods capable of singling out and characterizing those sequences with consistent misclassification behaviour are required to minimize this problem. A systematic, Support Vector Machine-based method has been proposed in this study for such purpose. The proposed method enables a filtering approach to the label noise problem and might become a support tool for database curators in proteomics.

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

Robust sequence alignment using evolutionary rates coupled with an amino acid substitution matrix

Background

Selective pressures at the DNA level shape genes into profiles consisting of patterns of rapidly evolving sites and sites withstanding change. These profiles remain detectable even when protein sequences become extensively diverged. A common task in molecular biology is to infer functional, structural or evolutionary relationships by querying a database using an algorithm. However, problems arise when sequence similarity is low. This study presents an algorithm that uses the evolutionary rate at codon sites, the dN/dS (ω) parameter, coupled to a substitution matrix as an alignment metric for detecting distantly related proteins. The algorithm, called BLOSUM-FIRE couples a newer and improved version of the original FIRE (Functional Inference using Rates of Evolution) algorithm with an amino acid substitution matrix in a dynamic scoring function. The enigmatic hepatitis B virus X protein was used as a test case for BLOSUM-FIRE and its associated database EvoDB.

Results

The evolutionary rate based approach was coupled with a conventional BLOSUM substitution matrix. The two approaches are combined in a dynamic scoring function, which uses the selective pressure to score aligned residues. The dynamic scoring function is based on a coupled additive approach that scores aligned sites based on the level of conservation inferred from the ω values. Evaluation of the accuracy of this new implementation, BLOSUM-FIRE, using MAFFT alignment as reference alignments has shown that it is more accurate than its predecessor FIRE. Comparison of the alignment quality with widely used algorithms (MUSCLE, T-COFFEE, and CLUSTAL Omega) revealed that the BLOSUM-FIRE algorithm performs as well as conventional algorithms. Its main strength lies in that it provides greater potential for aligning divergent sequences and addresses the problem of low specificity inherent in the original FIRE algorithm. The utility of this algorithm is demonstrated using the Hepatitis B virus X (HBx) protein, a protein of unknown function, as a test case.

Conclusion

This study describes the utility of an evolutionary rate based approach coupled to the BLOSUM62 amino acid substitution matrix in inferring protein domain function. We demonstrate that such an approach is robust and performs as well as an array of conventional algorithms.

GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters

Inference of multiple sequence alignments (MSAs) is a critical part of phylogenetic and comparative genomics studies. However, from the same set of sequences different MSAs are often inferred, depending on the methodologies used and the assumed parameters. Much effort has recently been devoted to improving the ability to identify unreliable alignment regions. Detecting such unreliable regions was previously shown to be important for downstream analyses relying on MSAs, such as the detection of positive selection. Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms. We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks. We show that GUIDANCE2 outperforms all previously developed methodologies. Furthermore, GUIDANCE2 also provides a set of alternative MSAs which can be useful for downstream analyses. The novel algorithm is implemented as a web-server, available at: http://guidance.tau.ac.il.

GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters

Inference of multiple sequence alignments (MSAs) is a critical part of phylogenetic and comparative genomics studies. However, from the same set of sequences different MSAs are often inferred, depending on the methodologies used and the assumed parameters. Much effort has recently been devoted to improving the ability to identify unreliable alignment regions. Detecting such unreliable regions was previously shown to be important for downstream analyses relying on MSAs, such as the detection of positive selection. Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms. We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks. We show that GUIDANCE2 outperforms all previously developed methodologies. Furthermore, GUIDANCE2 also provides a set of alternative MSAs which can be useful for downstream analyses. The novel algorithm is implemented as a web-server, available at: http://guidance.tau.ac.il.

Effective alignment of RNA pseudoknot structures using partition function posterior log-odds scores

Background

RNA pseudoknots play important roles in many biological processes. Previous methods for comparative pseudoknot analysis mainly focus on simultaneous folding and alignment of RNA sequences. Little work has been done to align two known RNA secondary structures with pseudoknots taking into account both sequence and structure information of the two RNAs.

Results

In this article we present a novel method for aligning two known RNA secondary structures with pseudoknots. We adopt the partition function methodology to calculate the posterior log-odds scores of the alignments between bases or base pairs of the two RNAs with a dynamic programming algorithm. The posterior log-odds scores are then used to calculate the expected accuracy of an alignment between the RNAs. The goal is to find an optimal alignment with the maximum expected accuracy. We present a heuristic to achieve this goal. The performance of our method is investigated and compared with existing tools for RNA structure alignment. An extension of the method to multiple alignment of pseudoknot structures is also discussed.

Conclusions

The method described here has been implemented in a tool named RKalign, which is freely accessible on the Internet. As more and more pseudoknots are revealed, collected and stored in public databases, we anticipate a tool like RKalign will play a significant role in data comparison, annotation, analysis, and retrieval in these databases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0464-9) contains supplementary material, which is available to authorized users.

Evaluating the accuracy and efficiency of multiple sequence alignment methods

A comparison of 10 most popular Multiple Sequence Alignment (MSA) tools, namely, MUSCLE, MAFFT(L-INS-i), MAFFT (FFT-NS-2), T-Coffee, ProbCons, SATe, Clustal Omega, Kalign, Multalin, and Dialign-TX is presented. We also focused on the significance of some implementations embedded in algorithm of each tool. Based on 10 simulated trees of different number of taxa generated by R, 400 known alignments and sequence files were constructed using indel-Seq-Gen. A total of 4000 test alignments were generated to study the effect of sequence length, indel size, deletion rate, and insertion rate. Results showed that alignment quality was highly dependent on the number of deletions and insertions in the sequences and that the sequence length and indel size had a weaker effect. Overall, ProbCons was consistently on the top of list of the evaluated MSA tools. SATe, being little less accurate, was 529.10% faster than ProbCons and 236.72% faster than MAFFT(L-INS-i). Among other tools, Kalign and MUSCLE achieved the highest sum of pairs. We also considered BALiBASE benchmark datasets and the results relative to BAliBASE- and indel-Seq-Gen-generated alignments were consistent in the most cases.

Indel reliability in indel-based phylogenetic inference

It is often assumed that it is unlikely that the same insertion or deletion (indel) event occurred at the same position in two independent evolutionary lineages, and thus, indel-based inference of phylogeny should be less subject to homoplasy compared with standard inference which is based on substitution events. Indeed, indels were successfully used to solve debated evolutionary relationships among various taxonomical groups. However, indels are never directly observed but rather inferred from the alignment and thus indel-based inference may be sensitive to alignment errors. It is hypothesized that phylogenetic reconstruction would be more accurate if it relied only on a subset of reliable indels instead of the entire indel data. Here, we developed a method to quantify the reliability of indel characters by measuring how often they appear in a set of alternative multiple sequence alignments. Our approach is based on the assumption that indels that are consistently present in most alternative alignments are more reliable compared with indels that appear only in a small subset of these alignments. Using simulated and empirical data, we studied the impact of filtering and weighting indels by their reliability scores on the accuracy of indel-based phylogenetic reconstruction. The new method is available as a web-server at http://guidance.tau.ac.il/RELINDEL/.

PAPNC, a novel method to calculate nucleotide diversity from large scale next generation sequencing data

Estimating viral diversity in infected patients can provide insight into pathogen evolution and emergence of drug resistance. With the widespread adoption of deep sequencing, it is important to develop tools to accurately calculate population diversity from very large datasets. Current methods for estimating diversity that are based on multiple alignments are not practical to apply to such data. In this study, the authors report a novel method (Pairwise Alignment Positional Nucleotide Counting, PAPNC) for estimating population diversity from 454 sequence data. The diversity measurements determined using this method were comparable to those calculated by average pairwise difference (APD) of multiply aligned sequences using MEGA5. Diversities were estimated for 9 patient plasma HIV samples sequenced with Titanium 454 technology and by single-genome sequencing (SGS). Diversities calculated from deep sequencing using PAPNC ranged from 0.002 to 0.021 while APD measurements calculated from SGS data ranged proximately from 0.001 to 0.018, with the difference being attributable to PCR error (contributing background diversity of 0.0016 in a control sample). Comparison of APDs estimated from 100 sets of sequences drawn at random from 454 generated data and from corresponding SGS data showed very close correlation between the two methods with R(2) of 0.96, and differing on average by about 1% (after correction for PCR error). The authors have developed a novel method that is good for calculating genetic diversities for large scale datasets from next generation sequencing. It can be implemented easily as a function in available variation calling programs like SAMtools or haplotype reconstruction software for nucleotide genetic diversity calculation. A Perl script implementing this method is available upon request.

AST: an automated sequence-sampling method for improving the taxonomic diversity of gene phylogenetic trees

A challenge in phylogenetic inference of gene trees is how to properly sample a large pool of homologous sequences to derive a good representative subset of sequences. Such a need arises in various applications, e.g. when (1) accuracy-oriented phylogenetic reconstruction methods may not be able to deal with a large pool of sequences due to their high demand in computing resources; (2) applications analyzing a collection of gene trees may prefer to use trees with fewer operational taxonomic units (OTUs), for instance for the detection of horizontal gene transfer events by identifying phylogenetic conflicts; and (3) the pool of available sequences is biased towards extensively studied species. In the past, the creation of subsamples often relied on manual selection. Here we present an Automated sequence-Sampling method for improving the Taxonomic diversity of gene phylogenetic trees, AST, to obtain representative sequences that maximize the taxonomic diversity of the sampled sequences. To demonstrate the effectiveness of AST, we have tested it to solve four problems, namely, inference of the evolutionary histories of the small ribosomal subunit protein S5 of E. coli, 16 S ribosomal RNAs and glycosyl-transferase gene family 8, and a study of ancient horizontal gene transfers from bacteria to plants. Our results show that the resolution of our computational results is almost as good as that of manual inference by domain experts, hence making the tool generally useful to phylogenetic studies by non-phylogeny specialists. The program is available at http://csbl.bmb.uga.edu/~zhouchan/AST.php.

Assessing the efficiency of multiple sequence alignment programs

Background

Multiple sequence alignment (MSA) is an extremely useful tool for molecular and evolutionary biology and there are several programs and algorithms available for this purpose. Although previous studies have compared the alignment accuracy of different MSA programs, their computational time and memory usage have not been systematically evaluated. Given the unprecedented amount of data produced by next generation deep sequencing platforms, and increasing demand for large-scale data analysis, it is imperative to optimize the application of software. Therefore, a balance between alignment accuracy and computational cost has become a critical indicator of the most suitable MSA program. We compared both accuracy and cost of nine popular MSA programs, namely CLUSTALW, CLUSTAL OMEGA, DIALIGN-TX, MAFFT, MUSCLE, POA, Probalign, Probcons and T-Coffee, against the benchmark alignment dataset BAliBASE and discuss the relevance of some implementations embedded in each program’s algorithm. Accuracy of alignment was calculated with the two standard scoring functions provided by BAliBASE, the sum-of-pairs and total-column scores, and computational costs were determined by collecting peak memory usage and time of execution.

Results

Our results indicate that mostly the consistency-based programs Probcons, T-Coffee, Probalign and MAFFT outperformed the other programs in accuracy. Whenever sequences with large N/C terminal extensions were present in the BAliBASE suite, Probalign, MAFFT and also CLUSTAL OMEGA outperformed Probcons and T-Coffee. The drawback of these programs is that they are more memory-greedy and slower than POA, CLUSTALW, DIALIGN-TX, and MUSCLE. CLUSTALW and MUSCLE were the fastest programs, being CLUSTALW the least RAM memory demanding program.

Conclusions

Based on the results presented herein, all four programs Probcons, T-Coffee, Probalign and MAFFT are well recommended for better accuracy of multiple sequence alignments. T-Coffee and recent versions of MAFFT can deliver faster and reliable alignments, which are specially suited for larger datasets than those encountered in the BAliBASE suite, if multi-core computers are available. In fact, parallelization of alignments for multi-core computers should probably be addressed by more programs in a near future, which will certainly improve performance significantly.

Early lignin pathway enzymes and routes to chlorogenic acid in switchgrass (Panicum virgatum L.)

Studying lignin biosynthesis in Panicum virgatum (switchgrass) has provided a basis for generating plants with reduced lignin content and increased saccharification efficiency. Chlorogenic acid (CGA, caffeoyl quinate) is the major soluble phenolic compound in switchgrass, and the lignin and CGA biosynthetic pathways potentially share intermediates and enzymes. The enzyme hydroxycinnamoyl-CoA: quinate hydroxycinnamoyltransferase (HQT) is responsible for CGA biosynthesis in tobacco, tomato and globe artichoke, but there are no close orthologs of HQT in switchgrass or in other monocotyledonous plants with complete genome sequences. We examined available transcriptomic databases for genes encoding enzymes potentially involved in CGA biosynthesis in switchgrass. The protein products of two hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyltransferase (HCT) genes (PvHCT1a and PvHCT2a), closely related to lignin pathway HCTs from other species, were characterized biochemically and exhibited the expected HCT activity, preferring shikimic acid as acyl acceptor. We also characterized two switchgrass coumaroyl shikimate 3'-hydroxylase (C3'H) enzymes (PvC3'H1 and PvC3'H2); both of these cytochrome P450s had the capacity to hydroxylate 4-coumaroyl shikimate or 4-coumaroyl quinate to generate caffeoyl shikimate or CGA. Another switchgrass hydroxycinnamoyl transferase, PvHCT-Like1, is phylogenetically distant from HCTs or HQTs, but exhibits HQT activity, preferring quinic acid as acyl acceptor, and could therefore function in CGA biosynthesis. The biochemical features of the recombinant enzymes, the presence of the corresponding activities in plant protein extracts, and the expression patterns of the corresponding genes, suggest preferred routes to CGA in switchgrass.

Early lignin pathway enzymes and routes to chlorogenic acid in switchgrass (Panicum virgatum L.)

Studying lignin biosynthesis in Panicum virgatum (switchgrass) has provided a basis for generating plants with reduced lignin content and increased saccharification efficiency. Chlorogenic acid (CGA, caffeoyl quinate) is the major soluble phenolic compound in switchgrass, and the lignin and CGA biosynthetic pathways potentially share intermediates and enzymes. The enzyme hydroxycinnamoyl-CoA: quinate hydroxycinnamoyltransferase (HQT) is responsible for CGA biosynthesis in tobacco, tomato and globe artichoke, but there are no close orthologs of HQT in switchgrass or in other monocotyledonous plants with complete genome sequences. We examined available transcriptomic databases for genes encoding enzymes potentially involved in CGA biosynthesis in switchgrass. The protein products of two hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyltransferase (HCT) genes (PvHCT1a and PvHCT2a), closely related to lignin pathway HCTs from other species, were characterized biochemically and exhibited the expected HCT activity, preferring shikimic acid as acyl acceptor. We also characterized two switchgrass coumaroyl shikimate 3'-hydroxylase (C3'H) enzymes (PvC3'H1 and PvC3'H2); both of these cytochrome P450s had the capacity to hydroxylate 4-coumaroyl shikimate or 4-coumaroyl quinate to generate caffeoyl shikimate or CGA. Another switchgrass hydroxycinnamoyl transferase, PvHCT-Like1, is phylogenetically distant from HCTs or HQTs, but exhibits HQT activity, preferring quinic acid as acyl acceptor, and could therefore function in CGA biosynthesis. The biochemical features of the recombinant enzymes, the presence of the corresponding activities in plant protein extracts, and the expression patterns of the corresponding genes, suggest preferred routes to CGA in switchgrass.

MAFFT: iterative refinement and additional methods

This chapter outlines several methods implemented in the MAFFT package. MAFFT is a popular multiple sequence alignment (MSA) program with various options for the progressive method, the iterative refinement method and other methods. We first outline basic usage of MAFFT and then describe recent practical extensions, such as dot plot and adjustment of direction in DNA alignment. We also refer to MUSCLE, another high-performance MSA program.

A genomics approach to deciphering lignin biosynthesis in switchgrass

It is necessary to overcome recalcitrance of the biomass to saccharification (sugar release) to make switchgrass (Panicum virgatum) economically viable as a feedstock for liquid biofuels. Lignin content correlates negatively with sugar release efficiency in switchgrass, but selecting the right gene candidates for engineering lignin biosynthesis in this tetraploid outcrossing species is not straightforward. To assist this endeavor, we have used an inducible switchgrass cell suspension system for studying lignin biosynthesis in response to exogenous brassinolide. By applying a combination of protein sequence phylogeny with whole-genome microarray analyses of induced cell cultures and developing stem internode sections, we have generated a list of candidate monolignol biosynthetic genes for switchgrass. Several genes that were strongly supported through our bioinformatics analysis as involved in lignin biosynthesis were confirmed by gene silencing studies, in which lignin levels were reduced as a result of targeting a single gene. However, candidate genes encoding enzymes involved in the early steps of the currently accepted monolignol biosynthesis pathway in dicots may have functionally redundant paralogues in switchgrass and therefore require further evaluation. This work provides a blueprint and resources for the systematic genome-wide study of the monolignol pathway in switchgrass, as well as other C4 monocot species.