A broader context for understanding amino acid alphabet optimality.

A series of prior publications has reported unusual properties of the set of genetically encoded amino acids shared by all known life. This work uses quantitative measures (descriptors) of size, charge and hydrophobicity to compare the distribution of the genetically encoded amino acids with random samples of plausible alternatives. Results show that the standard "alphabet" of amino acids established by the time of LUCA is distributed with unusual evenness over a broad range for the three, key physicochemical properties. However, different publications have used slightly different assumptions, including variations in the precise descriptors used, the set of plausible alternative molecules considered, and the format in which results have been presented. Here we consolidate these findings into a unified framework in order to clarify unusual features. We find that in general, the remarkable features of the full set of 20 genetically encoded amino acids are robust when compared with random samples drawn from a densely populated picture of plausible, alternative L-α-amino acids. In particular, the genetically encoded set is distributed across an exceptionally broad range of volumes, and distributed exceptionally evenly within a modest range of hydrophobicities. Surprisingly, range and evenness of charge (pKa) is exceptional only for the full amino acid structures, not for their sidechains - a result inconsistent with prior interpretations involving the role that amino acid sidechains play within protein sequences. In stark contrast, these remarkable features are far less clear when the prebiotically plausible subset of genetically encoded amino acids is compared with a much smaller pool of prebiotically plausible alternatives. By considering the nature of the "optimality theory" approach taken to derive these and prior insights, we suggest productive avenues for further research.

Unearthing the root of amino acid similarity.

Similarities and differences between amino acids define the rates at which they substitute for one another within protein sequences and the patterns by which these sequences form protein structures. However, there exist many ways to measure similarity, whether one considers the molecular attributes of individual amino acids, the roles that they play within proteins, or some nuanced contribution of each. One popular approach to representing these relationships is to divide the 20 amino acids of the standard genetic code into groups, thereby forming a simplified amino acid alphabet. Here, we develop a method to compare or combine different simplified alphabets, and apply it to 34 simplified alphabets from the scientific literature. We use this method to show that while different suggestions vary and agree in non-intuitive ways, they combine to reveal a consensus view of amino acid similarity that is clearly rooted in physico-chemistry.

Beyond terrestrial biology: charting the chemical universe of α-amino acid structures.

α-Amino acids are fundamental to biochemistry as the monomeric building blocks with which cells construct proteins according to genetic instructions. However, the 20 amino acids of the standard genetic code represent a tiny fraction of the number of α-amino acid chemical structures that could plausibly play such a role, both from the perspective of natural processes by which life emerged and evolved, and from the perspective of human-engineered genetically coded proteins. Until now, efforts to describe the structures comprising this broader set, or even estimate their number, have been hampered by the complex combinatorial properties of organic molecules. Here, we use computer software based on graph theory and constructive combinatorics in order to conduct an efficient and exhaustive search of the chemical structures implied by two careful and precise definitions of the α-amino acids relevant to coded biological proteins. Our results include two virtual libraries of α-amino acid structures corresponding to these different approaches, comprising 121 044 and 3 846 structures, respectively, and suggest a simple approach to exploring much larger, as yet uncomputed, libraries of interest.

SGDB: a database of synthetic genes re-designed for optimizing protein over-expression.

Here we present the Synthetic Gene Database (SGDB): a relational database that houses sequences and associated experimental information on synthetic (artificially engineered) genes from all peer-reviewed studies published to date. At present, the database comprises information from more than 200 published experiments. This resource not only provides reference material to guide experimentalists in designing new genes that improve protein expression, but also offers a dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that influence gene expression. The SGDB was built under MySQL database management system. We also offer an XML schema for standardized data description of synthetic genes. Users can access the database at http://www.evolvingcode.net/codon/sgdb/index.php, or batch downloads all information through XML files. Moreover, users may visually compare the coding sequences of a synthetic gene and its natural counterpart with an integrated web tool at http://www.evolvingcode.net/codon/sgdb/aligner.php, and discuss questions, findings and related information on an associated e-forum at http://www.evolvingcode.net/forum/viewforum.php?f=27.

A quantitative investigation of the chemical space surrounding amino acid alphabet formation.

To date, explanations for the origin and emergence of the alphabet of amino acids encoded by the standard genetic code have been largely qualitative and speculative. Here, with the help of computational chemistry, we present the first quantitative exploration of nature's "choices" set against various models for plausible alternatives. Specifically, we consider the chemical space defined by three fundamental biophysical properties (size, charge, and hydrophobicity) to ask whether the amino acids that entered the genetic code exhibit a higher diversity than random samples of similar size drawn from several different definitions of amino acid possibility space. We found that in terms of the properties studied, the full, standard set of 20 biologically encoded amino acids is indeed significantly more diverse than an equivalently sized group drawn at random from the set of plausible, prebiotic alternatives (using the Murchison meteorite as a model for pre-biotic plausibility). However, when the set of possible amino acids is enlarged to include those that are produced by standard biosynthetic pathways (reflecting the widespread idea that many members of the standard alphabet were recruited in this way), then the genetically encoded amino acids can no longer be distinguished as more diverse than a random sample. Finally, if we turn to consider the overlap between biologically encoded amino acids and those that are prebiotically plausible, then we find that the biologically encoded subset are no more diverse as a group than would be expected from a random sample, unless the definition of "random sample" is adjusted to reflect possible prebiotic abundance (again, using the contents of the Murchison meteorite as our estimator). This final result is contingent on the accuracy of our computational estimates for amino acid properties, and prebiotic abundances, and an exploration of the likely effect of errors in our estimation reveals that our results should be treated with caution. We thus present this work as a first step in quantifying and thus testing various origin-of-life hypotheses regarding the origin and evolution of life's amino acid alphabet, and advocate the progress that would add valuable information in the future.

Amino acid quantitative structure property relationship database: a web-based platform for quantitative investigations of amino acids.

Here, we present the AA-QSPR Db (Amino Acid Quantitative Structure Property Relationship Database): a novel, freely available web-resource of data pertaining to amino acids, both engineered and naturally occurring. In addition to presenting fundamental molecular descriptors of size, charge and hydrophobicity, it also includes online visualization tools for users to perform instant, interactive analyses of amino acid sub-sets in which they are interested. The database has been designed with extensible markup language technology to provide a flexible structure, suitable for future development. In addition to providing easy access for queries by external computers, it also offers a user-friendly web-based interface that facilitates human interactions (submission, storage and retrieval of amino acid data) and an associated e-forum that encourages users to question and discuss current and future database contents.

An interactive visualization tool to explore the biophysical properties of amino acids and their contribution to substitution matrices.

BACKGROUND: Quantitative descriptions of amino acid similarity, expressed as probabilistic models of evolutionary interchangeability, are central to many mainstream bioinformatic procedures such as sequence alignment, homology searching, and protein structural prediction. Here we present a web-based, user-friendly analysis tool that allows any researcher to quickly and easily visualize relationships between these bioinformatic metrics and to explore their relationships to underlying indices of amino acid molecular descriptors. RESULTS: We demonstrate the three fundamental types of question that our software can address by taking as a specific example the connections between 49 measures of amino acid biophysical properties (e.g., size, charge and hydrophobicity), a generalized model of amino acid substitution (as represented by the PAM74-100 matrix), and the mutational distance that separates amino acids within the standard genetic code (i.e., the number of point mutations required for interconversion during protein evolution). We show that our software allows a user to recapture the insights from several key publications on these topics in just a few minutes. CONCLUSION: Our software facilitates rapid, interactive exploration of three interconnected topics: (i) the multidimensional molecular descriptors of the twenty proteinaceous amino acids, (ii) the correlation of these biophysical measurements with observed patterns of amino acid substitution, and (iii) the causal basis for differences between any two observed patterns of amino acid substitution. This software acts as an intuitive bioinformatic exploration tool that can guide more comprehensive statistical analyses relating to a diverse array of specific research questions.

Simplified gene synthesis: a one-step approach to PCR-based gene construction.

PCR-based gene synthesis conventionally requires two steps: first, all overlapping oligonucleotides are assembled by self-priming; then an additional pair of primers is used to amplify the full-length gene product. Here we propose a simplified method of gene synthesis which combines these two steps into one. We have found that the efficiency of this one-step method, which we term "Simplified Gene Synthesis", is affected by multiple parameters of the PCR reactions. In particular, the choice of polymerase is critical for successful one-step assembly. Other important factors include the concentration of assembly oligonucleotides and amplification primers. Moreover, we offer a general method to estimate, given a known mutation rate, how many clones should be sequenced in order to be confident of obtaining at least one correct gene product. Having determined the accuracy of gene products synthesized under optimal conditions with Simplified Gene Synthesis, we show that our estimation works well. Overall, the simplified gene synthesis provides an easier and more efficient approach to gene synthesis, providing a further step towards the future goal of generalized automation for this process.

Boron enrichment in martian clay.

We have detected a concentration of boron in martian clay far in excess of that in any previously reported extra-terrestrial object. This enrichment indicates that the chemistry necessary for the formation of ribose, a key component of RNA, could have existed on Mars since the formation of early clay deposits, contemporary to the emergence of life on Earth. Given the greater similarity of Earth and Mars early in their geological history, and the extensive disruption of Earth's earliest mineralogy by plate tectonics, we suggest that the conditions for prebiotic ribose synthesis may be better understood by further Mars exploration.

Did evolution select a nonrandom "alphabet" of amino acids?

The last universal common ancestor of contemporary biology (LUCA) used a precise set of 20 amino acids as a standard alphabet with which to build genetically encoded protein polymers. Considerable evidence indicates that some of these amino acids were present through nonbiological syntheses prior to the origin of life, while the rest evolved as inventions of early metabolism. However, the same evidence indicates that many alternatives were also available, which highlights the question: what factors led biological evolution on our planet to define its standard alphabet? One possibility is that natural selection favored a set of amino acids that exhibits clear, nonrandom properties-a set of especially useful building blocks. However, previous analysis that tested whether the standard alphabet comprises amino acids with unusually high variance in size, charge, and hydrophobicity (properties that govern what protein structures and functions can be constructed) failed to clearly distinguish evolution's choice from a sample of randomly chosen alternatives. Here, we demonstrate unambiguous support for a refined hypothesis: that an optimal set of amino acids would spread evenly across a broad range of values for each fundamental property. Specifically, we show that the standard set of 20 amino acids represents the possible spectra of size, charge, and hydrophobicity more broadly and more evenly than can be explained by chance alone.

The effects of differential gene expression on coding sequence features: analysis by one-way ANOVA.

It is well-established that non-random patterns in coding DNA sequence (CDS) features can be partially explained by translational selection. Recent extensions of microarray and proteomic expression data have stimulated many genome-wide investigations of the relationships between gene expression and various CDS features. However, only modest correlations have been found. Here we introduced the one-way ANOVA, a more powerful extension of previous grouping methods, to re-examine these relationships at the whole genome scale for Saccharomyces cerevisiae, where genome-wide protein abundance has been recently quantified. Our results clarify that coding sequence features are inappropriate for use as genome-wide estimators for protein expression levels. This analysis also demonstrates that one-way ANOVA is a powerful and simple method to explore the influence of gene expression on CDS features.

The Synthetic Gene Designer: a flexible web platform to explore sequence manipulation for heterologous expression.

"Codon optimization" is a general approach to improving heterologous expression where genes are moved from their native genomes into alternatives that exhibit different patterns of codon usage. However, despite reports of successful manipulations and the existence of stand-alone codon optimization software packages or commercial services that offer to redesign genes, the scientific community lacks any systematic understanding of what exactly it means to optimize codon usage. Thus we present a bona fide web application, the "Synthetic Gene Designer," which contrasts with existing software by providing a centralized, free, and transparent platform for the broader scientific community to develop knowledge about synthetic gene design. Consistent with this goal, our software is associated with a moderated e-forum that promotes discussion of synthetic gene design and offers technical support. In addition, the Synthetic Gene Designer presents enhanced functionality over existing software options: for example, it enables users to work with non-standard genetic codes, with user-defined patterns of codon usage and an expanded range of methods for codon optimization. The Synthetic Gene Designer, together with on-line tutorials and the forum, is available at .

The case for an error minimizing standard genetic code.

Since discovering the pattern by which amino acids are assigned to codons within the standard genetic code, investigators have explored the idea that natural selection placed biochemically similar amino acids near to one another in coding space so as to minimize the impact of mutations and/or mistranslations. The analytical evidence to support this theory has grown in sophistication and strength over the years, and counterclaims questioning its plausibility and quantitative support have yet to transcend some significant weaknesses in their approach. These weaknesses are illustrated here by means of a simple simulation model for adaptive genetic code evolution. There remain ill explored facets of the 'error minimizing' code hypothesis, however, including the mechanism and pathway by which an adaptive pattern of codon assignments emerged, the extent to which natural selection created synonym redundancy, its role in shaping the amino acid and nucleotide languages, and even the correct interpretation of the adaptive codon assignment pattern: these represent fertile areas for future research.