A statistical score for assessing the quality of multiple sequence alignments

Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides

Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.

End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification

The prediction of mechanical and dynamical properties of proteins is an important frontier, especially given the greater availability of proteins structures. Here we report a series of models that provide end-to-end predictions of nanodynamical properties of proteins, focused on high-throughput normal mode predictions directly from the amino acid sequence. Using neural network models within the family of Natural Language Processing and graph-based methods, we offer atomistically based mechanistic predictions of key protein mechanical features. The models include an end-to-end long short-term memory (LSTM) model, an end-to-end transformer model, a graph-based transformer model, and an equivariant graph neural network. All four models show exceptional performance, with the graph-based transformer architecture offering the best results but at the cost of requiring a graph structure as input. Conversely, the LSTM and transformer models offer end-to-end sequence-to-property prediction capabilities, providing efficient avenues for protein engineering, analysis, and design. We compare our results against published data based on a Principal Neighborhood Aggregation graph neural network, revealing that the transformer model offers better performance while also being able to predict a large set of the first 64 normal mode frequencies, simultaneously. The use of the end-to-end transformer model may facilitate other downstream applications through the use of transfer learning, and it offers a comprehensive prediction of dynamical properties without any structural knowledge, directly from the amino acid sequence. We demonstrate a potential application in scientific sonification, where the normal mode frequencies are transposed to generate audible signals for a detailed analysis of subtle changes of protein sequences.

RPfam: A refiner towards curated-like multiple sequence alignments of the Pfam protein families

High-quality multiple sequence alignments can provide insights into the architecture and function of protein families. The existing MSA tools often generate results inconsistent with biological distribution of conserved regions because of positioning amino acid residues and gaps only by symbols. We propose RPfam, a refiner towards curated-like MSAs for modeling the protein families in the Pfam database. RPfam refines the automatic alignments via scoring alignments based on the PFASUM matrix, restricting realignments within badly aligned blocks, optimizing the block scores by dynamic programming, and running refinements iteratively using the Simulated Annealing algorithm. Experiments show RPfam effectively refined the alignments produced by the MSA tools ClustalO and Muscle with reference to the curated seed alignments of the Pfam protein families. Especially RPfam improved the quality of the ClustalO alignments by 4.4% and the Muscle alignments by 2.8% on the gp32 DNA binding protein-like family. Supplementary Table is available at http://www.worldscinet.com/jbcb/ .

Scoring protein sequence alignments using deep Learning

BACKGROUND

A high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a protein sequence, there are no methods to predict which SA may lead to more accurate models without actually building the models. In this work, we describe a method to predict the quality of a protein's SA.

METHODS

We created our own dataset by generating a variety of SAs for a set of 1,351 representative proteins and investigated various deep learning architectures to predict the local distance difference test (lDDT) scores of distance maps predicted with SAs as the input. These lDDT scores serve as indicators of the quality of the SAs.

RESULTS

Using two independent test datasets consisting of CASP13 and CASP14 targets, we show that our method is effective for scoring and ranking SAs when a pool of SAs is available for a protein sequence. With an example, we further discuss that SA selection using our method can lead to improved structure prediction.

AVAILABILITY

Code and datasets are available at https://github.com/ba-lab/Alignment-Score/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ancestral Sequence Reconstruction for Exploring Alkaloid Evolution

The complex and bioactive monoterpene indole alkaloids (MIAs) found in Catharanthus roseus and related species are the products of many millions of years of evolution through mutation and natural selection. Ancestral sequence reconstruction (ASR) is a method that combines phylogenetic analysis and experimental biochemistry to infer details about past events in protein evolution. Here, I propose that ASR could be leveraged to understand how enzymes catalyzing the formation of complex alkaloids arose over evolutionary time. I discuss the steps of ASR, including sequence selection, multiple sequence alignment, tree inference, and the generation and characterization of inferred ancestral enzymes.

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.

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.

Building (Viral) Phylogenetic Trees Using a Maximum Likelihood Approach

Phylogenetic analyses allow for inferring a hypothesis about the evolutionary history of a set of homologous molecular sequences. This hypothesis can be used as the basis for further molecular and computational studies. In this unit, we offer one specific method to construct a Maximum Likelihood phylogenetic tree. We outline how to identify homologous sequences and construct a multiple sequence alignment. Following alignment, sequences are screened for potentially confounding factors such as recombination and genetic saturation. Finally, a Maximum Likelihood phylogenetic tree can be constructed implementing a rigorously tested model of evolution. The workflow outlined in this unit provides sufficient background for inferring a robust phylogenetic tree starting from a particular gene of interest. © 2018 by John Wiley & Sons, Inc.

Identifying functionally informative evolutionary sequence profiles

Motivation

Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually.

Results

We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git.

Contact

andras.fiser@einstein.yu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Three stress-responsive NAC transcription factors from Populus euphratica differentially regulate salt and drought tolerance in transgenic plants

Stress-responsive NAM, Arabidopsis transcription activation factor 1/2 (ATAF1/2) and CUC2 (SNAC) genes are being used to alter stress tolerance in Arabidopsis or grasses through genetic engineering. However, limited reports are available about the functional characteristics of SNAC in trees. In this study, three putative NAC proteins were identified from Populus euphratica. PeNAC034 and PeNAC045 were classified into the ATAF subgroup and PeNAC036 into the ANAC072 subgroup. These three SNAC transcription factors were localized in the nucleus and contained the transcription activation domain in their C-terminal. Under drought and salt stresses, PeNAC036 was strongly induced in the whole plant, but PeNAC034 was significantly suppressed in the roots and stems, and PeNAC045 was inhibited in the roots. PeNAC036 overexpression in Arabidopsis wild-type (WT) (OEPeNAC036) and PeNAC036 complementation in mutant anac072 (anac072/PeNAC036) lines increased tolerance to salt and drought, whereas PeNAC034 overexpression in WT (OEPeNAC034) and PeNAC034 complementation in mutant ataf1 (ataf1/PeNAC034) lines enhanced salt and drought sensitivity. After drought and salt treatments, the expression levels of COR47, RD29B, ERD11, RD22 and DREB2A were upregulated in OEPeNAC036 and anac072/PeNAC036 lines, but were downregulated in OEPeNAC034 and ataf1/PeNAC034 plants. Compared with WT and Vector lines, PeNAC045 overexpression in poplar WT (OEPeNAC045) led to a significant decrease in the net photosynthesis rate, stomatal conductance and transpiration rate under salinity and drought conditions. These results suggest that P. euphratica can adapt to the environment of high salinity and drought, which may be related to the differential expression patterns of SNAC genes.

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.

The role of peu-miR164 and its target PeNAC genes in response to abiotic stress in Populus euphratica

Plant miR164 family is highly conserved and miR164 members regulate conserved targets belonging to NAC transcription factors. Our previous studies have revealed that peu-miR164a-e and its target gene POPTR_0007s08420 participate in abiotic stress response in Populus euphratica according to deep sequencing and degradome sequencing. In this study, miR164 family comprises six members that generate two mature products (miR164a-e and miR164f) and target seven NAC genes in P. euphratica. Co-expression in Nicotiana benthamiana and 5' RACE confirmed that peu-miR164 directs PeNAC070, PeNAC012 and PeNAC028 mRNAs cleavage. Expression profiles of primary peu-miR164 a/b/c/d/e bear similarity to those of peu-miR164a-e, whereas PeNAC070 and PeNAC081 showed inverse expression patterns with peu-miR164a-e under abiotic stresses. Existence of cis-acting elements in PeNAC070 promoter (ABRE,MBs, Box-W1, GC-motif, and W-box) and in peu-MIR164b promoter (HSE) further confirmed different responses of peu-miR164 and PeNAC070 to abiotic stresses. Histochemical β-glucuronidase (GUS) staining revealed that GUS activities increased when ProPeNAC070::GUS transgenic Arabidopsis plants were exposed to NaCl, mannitol and abscisic acid (ABA), whereas GUS activity of Propeu-MIR164b::GUS plants decreased under ABA treatment. Subcellular localization and transactivation assays showed that PeNAC070 protein was localized to the nucleus and exhibited transactivation activity at the C-terminal. Overexpression of PeNAC070 in Arabidopsis promoted lateral root development, delayed stem elongation, and increased sensitivity of transgenic plants to drought and salt stresses. This study aids in understanding the adaptability of P. euphratica to extreme drought and salt environment by analysing tissue-specific expression patterns of miR164-regulated and specific promoter-regulated PeNAC genes.

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.

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.

Characterization of all possible single-nucleotide change caused amino acid substitutions in the kinase domain of Bruton tyrosine kinase

Knowledge about features distinguishing deleterious and neutral variations is crucial for interpretation of novel variants. Bruton tyrosine kinase (BTK) contains the highest number of unique disease-causing variations among the human protein kinases, still it is just 10% of all the possible single-nucleotide substitution-caused amino acid variations (SNAVs). In the BTK kinase domain (BTK-KD) can appear altogether 1,495 SNAVs. We investigated them all with bioinformatic and protein structure analysis methods. Most disease-causing variations affect conserved and buried residues disturbing protein stability. Minority of exposed residues is conserved, but strongly tied to pathogenicity. Sixty-seven percent of variations are predicted to be harmful. In 39% of the residues, all the variants are likely harmful, whereas in 10% of sites, all the substitutions are tolerated. Results indicate the importance of the entire kinase domain, involvement in numerous interactions, and intricate functional regulation by conformational change. These results can be extended to other protein kinases and organisms.

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.

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.

Accuracy estimation and parameter advising for protein multiple sequence alignment

Abstract We develop a novel and general approach to estimating the accuracy of multiple sequence alignments without knowledge of a reference alignment, and use our approach to address a new task that we call parameter advising: the problem of choosing values for alignment scoring function parameters from a given set of choices to maximize the accuracy of a computed alignment. For protein alignments, we consider twelve independent features that contribute to a quality alignment. An accuracy estimator is learned that is a polynomial function of these features; its coefficients are determined by minimizing its error with respect to true accuracy using mathematical optimization. Compared to prior approaches for estimating accuracy, our new approach (a) introduces novel feature functions that measure nonlocal properties of an alignment yet are fast to evaluate, (b) considers more general classes of estimators beyond linear combinations of features, and (c) develops new regression formulations for learning an estimator from examples; in addition, for parameter advising, we (d) determine the optimal parameter set of a given cardinality, which specifies the best parameter values from which to choose. Our estimator, which we call Facet (for "feature-based accuracy estimator"), yields a parameter advisor that on the hardest benchmarks provides more than a 27% improvement in accuracy over the best default parameter choice, and for parameter advising significantly outperforms the best prior approaches to assessing alignment quality.

A method of alignment masking for refining the phylogenetic signal of multiple sequence alignments

Inaccurate inference of positional homologies in multiple sequence alignments and systematic errors introduced by alignment heuristics obfuscate phylogenetic inference. Alignment masking, the elimination of phylogenetically uninformative or misleading sites from an alignment before phylogenetic analysis, is a common practice in phylogenetic analysis. Although masking is often done manually, automated methods are necessary to handle the much larger data sets being prepared today. In this study, we introduce the concept of subsplits and demonstrate their use in extracting phylogenetic signal from alignments. We design a clustering approach for alignment masking where each cluster contains similar columns-similarity being defined on the basis of compatible subsplits; our approach then identifies noisy clusters and eliminates them. Trees inferred from the columns in the retained clusters are found to be topologically closer to the reference trees. We test our method on numerous standard benchmarks (both synthetic and biological data sets) and compare its performance with other methods of alignment masking. We find that our method can eliminate sites more accurately than other methods, particularly on divergent data, and can improve the topologies of the inferred trees in likelihood-based analyses. Software available upon request from the author.

The ability of land plants to synthesize glucuronoxylans predates the evolution of tracheophytes

Glucuronoxylans with a backbone of 1,4-linked β-D-xylosyl residues are ubiquitous in the secondary walls of gymnosperms and angiosperms. Xylans have been reported to be present in hornwort cell walls, but their structures have not been determined. In contrast, the presence of xylans in the cell walls of mosses and liverworts remains a subject of debate. Here we present data that unequivocally establishes that the cell walls of leafy tissue and axillary hair cells of the moss Physcomitrella patens contain a glucuronoxylan that is structurally similar to glucuronoxylans in the secondary cell walls of vascular plants. Some of the 1,4-linked β-D-xylopyranosyl residues in the backbone of this glucuronoxylan bear an α-D-glucosyluronic acid (GlcpA) sidechain at O-2. In contrast, the lycopodiophyte Selaginella kraussiana synthesizes a glucuronoxylan substituted with 4-O-Me-α-D-GlcpA sidechains, as do many hardwood species. The monilophyte Equisetum hyemale produces a glucuronoxylan with both 4-O-Me-α-D-GlcpA and α-D-GlcpA sidechains, as does Arabidopsis. The seedless plant glucuronoxylans contain no discernible amounts of the reducing-end sequence that is characteristic of gymnosperm and eudicot xylans. Phylogenetic studies showed that the P. patens genome contains genes with high sequence similarity to Arabidopsis CAZy family GT8, GT43 and GT47 glycosyltransferases that are likely involved in xylan synthesis. We conclude that mosses synthesize glucuronoxylan that is structurally similar to the glucuronoxylans present in the secondary cell walls of lycopodiophytes, monilophytes, and many seed-bearing plants, and that several of the glycosyltransferases required for glucuronoxylan synthesis evolved before the evolution of tracheophytes.

Evolution and function of the plant cell wall synthesis-related glycosyltransferase family 8

Carbohydrate-active enzyme glycosyltransferase family 8 (GT8) includes the plant galacturonosyltransferase1-related gene family of proven and putative alpha-galacturonosyltransferase (GAUT) and GAUT-like (GATL) genes. We computationally identified and investigated this family in 15 fully sequenced plant and green algal genomes and in the National Center for Biotechnology Information nonredundant protein database to determine the phylogenetic relatedness of the GAUTs and GATLs to other GT8 family members. The GT8 proteins fall into three well-delineated major classes. In addition to GAUTs and GATLs, known or predicted to be involved in plant cell wall biosynthesis, class I also includes a lower plant-specific GAUT and GATL-related (GATR) subfamily, two metazoan subfamilies, and proteins from other eukaryotes and cyanobacteria. Class II includes galactinol synthases and plant glycogenin-like starch initiation proteins that are not known to be directly involved in cell wall synthesis, as well as proteins from fungi, metazoans, viruses, and bacteria. Class III consists almost entirely of bacterial proteins that are lipooligo/polysaccharide alpha-galactosyltransferases and alpha-glucosyltransferases. Sequence motifs conserved across all GT8 subfamilies and those specific to plant cell wall-related GT8 subfamilies were identified and mapped onto a predicted GAUT1 protein structure. The tertiary structure prediction identified sequence motifs likely to represent key amino acids involved in catalysis, substrate binding, protein-protein interactions, and structural elements required for GAUT1 function. The results show that the GAUTs, GATLs, and GATRs have a different evolutionary origin than other plant GT8 genes, were likely acquired from an ancient cyanobacterium (Synechococcus) progenitor, and separate into unique subclades that may indicate functional specialization.

Switchgrass (Panicum virgatum) possesses a divergent family of cinnamoyl CoA reductases with distinct biochemical properties

The down-regulation of enzymes of the monolignol pathway results in reduced recalcitrance of biomass for lignocellulosic ethanol production. Cinnamoyl CoA reductase (CCR) catalyzes the first step of the phenylpropanoid pathway specifically dedicated to monolignol biosynthesis. However, plants contain multiple CCR-like genes, complicating the selection of lignin-specific targets. This study was undertaken to understand the complexity of the CCR gene family in tetraploid switchgrass (Panicum virgatum) and to determine the biochemical properties of the encoded proteins. Four switchgrass cDNAs (most with multiple variants) encoding putative CCRs were identified by phylogenetic analysis, heterologously expressed in Escherichia coli, and the corresponding enzymes were characterized biochemically. Two cDNAs, PvCCR1 and PvCCR2, encoded enzymes with CCR activity. They are phylogenetically distinct, differentially expressed, and the corresponding enzymes exhibited different biochemical properties with regard to substrate preference. PvCCR1 has higher specific activity and prefers feruloyl CoA as substrate, whereas PvCCR2 prefers caffeoyl and 4-coumaroyl CoAs. Allelic variants of each cDNA were detected, but the two most diverse variants of PvCCR1 encoded enzymes with similar catalytic activity. Based on its properties and expression pattern, PvCCR1 is probably associated with lignin biosynthesis during plant development (and is therefore a target for the engineering of improved biomass), whereas PvCCR2 may function in defense.

Functional characterization of PAS and HES family bHLH transcription factors during the metamorphosis of the red flour beetle, Tribolium castaneum

The basic helix-loop-helix transcription factors are present in animals, plants and fungi and play important roles in the control of cellular proliferation, tissue differentiation, development and detoxification. Although insect genomes contain more than 50 helix-loop-helix transcription factors, the functions of only a few are known. RNAi has become a widely used tool to knock-down the expression to analyze the function of genes. As RNAi works well in Tribolium castaneum, we utilized this insect and RNAi to determine functions of 19 bHLH transcription factors belonging to PAS and HES families during the larval stages of the red flour beetle, T. castaneum. We searched the genome sequence of T. castaneum and identified 53 bHLH genes. Phylogenetic analyses classified these 53 genes into ten families; PAS, HES, Myc/USF, Hand, Mesp, Shout, p48, NeuroD/Neurogenin, Atonal and AS-C. In RNAi studies, knocking-down the expression of seven members of the PAS and HES families affected the growth and development of T. castaneum. An inability to grow to reach critical weight to undergo metamorphosis, failure to complete larval-pupal or pupal-adult ecdysis and abnormal wing development are among the most common phenotypes observed in RNAi insects. Among the bHLH transcription factors studied, the steroid receptor coactivator (SRC) showed the most severe phenotypes. Knock-down in the expression of the gene coding for SRC caused growth arrest by affecting the regulation of lipid metabolism. These studies demonstrate the power of RNAi for functional characterization of members of the multigene families in this model insect.

The cellulose synthase superfamily in fully sequenced plants and algae

BACKGROUND

The cellulose synthase superfamily has been classified into nine cellulose synthase-like (Csl) families and one cellulose synthase (CesA) family. The Csl families have been proposed to be involved in the synthesis of the backbones of hemicelluloses of plant cell walls. With 17 plant and algal genomes fully sequenced, we sought to conduct a genome-wide and systematic investigation of this superfamily through in-depth phylogenetic analyses.

RESULTS

A single-copy gene is found in the six chlorophyte green algae, which is most closely related to the CslA and CslC families that are present in the seven land plants investigated in our analyses. Six proteins from poplar, grape and sorghum form a distinct family (CslJ), providing further support for the conclusions from two recent studies. CslB/E/G/H/J families have evolved significantly more rapidly than their widely distributed relatives, and tend to have intragenomic duplications, in particular in the grape genome.

CONCLUSION

Our data suggest that the CslA and CslC families originated through an ancient gene duplication event in land plants. We speculate that the single-copy Csl gene in green algae may encode a mannan synthase. We confirm that the rest of the Csl families have a different evolutionary origin than CslA and CslC, and have proposed a model for the divergence order among them. Our study provides new insights about the evolution of this important gene family in plants.

Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods

Many gene defects are relatively easy to identify experimentally, but obtaining information about the effects of sequence variations and elucidation of the detailed molecular mechanisms of genetic diseases will be among the next major efforts in mutation research. Amino acid substitutions may have diverse effects on protein structure and function; thus, a detailed analysis of the mutations is essential. Experimental study of the molecular effects of mutations is laborious, whereas useful and reliable information about the effects of amino acid substitutions can readily be obtained by theoretical methods. Experimentally defined structures and molecular modeling can be used as a basis for interpretation of the mutations. The effects of missense mutations can be analyzed even when the 3D structure of the protein has not been determined, although structure-based analyses are more reliable. Structural analyses include studies of the contacts between residues, their implication for the stability of the protein, and the effects of the introduced residues. Investigations of steric and stereochemical consequences of substitutions provide insights on the molecular fit of the introduced residue. Mutations that change the electrostatic surface potential of a protein have wide-ranging effects. Analyses of the effects of mutations on interactions with ligands and partners have been performed for elucidation of functional mutations. We have employed numerous methods for predicting the effects of amino acid substitutions. We discuss the applicability of these methods in the analysis of genes, proteins, and diseases to reveal protein structure-function relationships, which is essential to gain insights into disease genotype-phenotype correlations.

A Monte Carlo approach successfully identifies randomness in multiple sequence alignments: a more objective means of data exclusion

Random similarity of sequences or sequence sections can impede phylogenetic analyses or the identification of gene homologies. Additionally, randomly similar sequences or ambiguously aligned sequence sections can negatively interfere with the estimation of substitution model parameters. Phylogenomic studies have shown that biases in model estimation and tree reconstructions do not disappear even with large data sets. In fact, these biases can become pronounced with more data. It is therefore important to identify possible random similarity within sequence alignments in advance of model estimation and tree reconstructions. Different approaches have been already suggested to identify and treat problematic alignment sections. We propose an alternative method that can identify random similarity within multiple sequence alignments (MSAs) based on Monte Carlo resampling within a sliding window. The method infers similarity profiles from pairwise sequence comparisons and subsequently calculates a consensus profile. This consensus profile represents a summary of all calculated single similarity profiles. In consequence, consensus profiles identify dominating patterns of nonrandom similarity or randomness within sections of MSAs. We show that the approach clearly identifies randomness in simulated and real data. After the exclusion of putative random sections, node support drastically improves in tree reconstructions of both data. It thus appears to be a powerful tool to identify possible biases of tree reconstructions or gene identification. The method is currently restricted to nucleotide data but will be extended to protein data in the near future.

Planning the human variome project: the Spain report

The remarkable progress in characterizing the human genome sequence, exemplified by the Human Genome Project and the HapMap Consortium, has led to the perception that knowledge and the tools (e.g., microarrays) are sufficient for many if not most biomedical research efforts. A large amount of data from diverse studies proves this perception inaccurate at best, and at worst, an impediment for further efforts to characterize the variation in the human genome. Because variation in genotype and environment are the fundamental basis to understand phenotypic variability and heritability at the population level, identifying the range of human genetic variation is crucial to the development of personalized nutrition and medicine. The Human Variome Project (HVP; http://www.humanvariomeproject.org/) was proposed initially to systematically collect mutations that cause human disease and create a cyber infrastructure to link locus specific databases (LSDB). We report here the discussions and recommendations from the 2008 HVP planning meeting held in San Feliu de Guixols, Spain, in May 2008.

The Influenza Primer Design Resource: a new tool for translating influenza sequence data into effective diagnostics

BACKGROUND

Recent outbreaks of highly pathogenic avian influenza and multiple occurrences of zoonotic infection and deaths in humans have sparked a dramatic increase in influenza research. In order to rapidly identify and help prevent future influenza outbreaks, numerous laboratories around the world are working to develop new nucleotide-based diagnostics for identifying and subtyping influenza viruses. While there are several databases that have been developed for manipulating the vast amount of influenza genetic data that have been produced, significant progress can still be made in developing tools for translating the genetic data into effective diagnostics.

DESCRIPTION

The Influenza Primer Design Resource (IPDR) is the combination of a comprehensive database of influenza nucleotide sequences and a web interface that provides several important tools that aid in the development of oligonucleotides that may be used to develop better diagnostics. IPDR's database can be searched using a variety of criteria, allowing the user to align the subset of influenza sequences that they are interested in. In addition, IPDR reports a consensus sequence for the alignment along with sequence polymorphism information, a summary of most published primers and probes that match the consensus sequence, and a Primer3 analysis of potential primers and probes that could be used for amplifying the sequence subset.

CONCLUSIONS

The IPDR is a unique combination of bioinformatics tools that will greatly aid researchers in translating influenza genetic data into diagnostics, which can effectively identify and subtype influenza strains. The website is freely available at http://www.ipdr.mcw.edu.

MrBUMP: an automated pipeline for molecular replacement

A novel automation pipeline for macromolecular structure solution by molecular replacement is described. There is a special emphasis on the discovery and preparation of a large number of search models, all of which can be passed to the core molecular-replacement programs. For routine molecular-replacement problems, the pipeline automates what a crystallographer might do and its value is simply one of convenience. For more difficult cases, the pipeline aims to discover the particular template structure and model edits required to produce a viable search model and may succeed in finding an efficacious combination that would be missed otherwise. An overview of MrBUMP is given and some recent additions to its functionality are highlighted.

Protein multiple sequence alignment

Protein sequence alignment is the task of identifying evolutionarily or structurally related positions in a collection of amino acid sequences. Although the protein alignment problem has been studied for several decades, many recent studies have demonstrated considerable progress in improving the accuracy or scalability of multiple and pairwise alignment tools, or in expanding the scope of tasks handled by an alignment program. In this chapter, we review state-of-the-art protein sequence alignment and provide practical advice for users of alignment tools.

Molecular phylogenetics of ascomycotal adhesins--a novel family of putative cell-surface adhesive proteins in fission yeasts

In this work, we identify a family of putative adhesins in the fission yeasts Schizosaccharomyces pombe and Schizosaccharomyces japonicus. The members of this family share a conserved tandem repeat related to those found in the Candida albicans Als family of adhesins. Unlike previously characterised adhesins that possess conserved ligand-binding domains at the N-terminus, this group of proteins carry ligand-binding domains at their C-termini. We demonstrate that one such domain--the uncharacterised GLEYA domain, is related to the lectin-like ligand-binding domain found in the Saccharomyces cerevisiae Flo proteins. Unlike the Flo and Als proteins, the fission yeast adhesins do not contain detectable glycosyl phosphatidyl inositol (GPI) membrane anchor signals to mediate their attachment to the cell wall, which may suggest a novel cell wall attachment mechanism. Further sequence analysis identified several putative adhesins in the sub-phylum of Pezizomycotina, where only a few adhesins have been described to date.