Quantile distributions of amino acid usage in protein classes

Bücher²

1992

We present a comparative study of residue usage correlations of various organism protein sets of diverse phylogenetic species and ofopen reading frames ofseveral large human viral genomes. Our correlation analysis reveals three major tendencies: (i) charge compensation reflected by the high correlation of basic with acidic residues; (it) the positive correlations of functionally and structurally similar amino acids including many pairs of hydrophobic amino acids, all pairs of aromatic amino acids, the anionic pair (glutamate and aspartate), but not the cationic pair (lysine and arginine), moderately the hydroxyl pair (serine and threonine), the small amino acids (glycine and alanine), and many (but not all) of those having high values in the Dayhoff substitutability matrix (characteristics such as amino acid polarity or codon usage agreement, except for the wobble position, do not nearily imply significant positive correlations); (iii) a widespread negative correlation of the aggregate strong codon group amino acids (Ala, Gly, Pro) versus the weak codon group amino acids (Lys, HIe, Tyr, Asn, Phe). Discussion and speculations relate amino acid usage correlations to protein function/strucre, cellular localization, proximity in amino acid biosynthetic pathways, amino acid relative abundances, tRNA and aminoacyl synthetase availabilities, and evolutionary processes. This paper presents a detailed correlational analysis of the amino acid compositional spectrum in protein sequences. Invariants and contrasts are determined from protein sequence collections of a broad phylogenetic range, including representative mammalian, avian, amphibian, invertebrate, fungal, and bacterial species, for open reading frames (ORFs) of several large human viral genomes and for several protein function classes. The correlation estimates are determined for amino acid types in several alphabets (individual amino acids, charge and hydrophobic categories, and codon types). Difficulties for these analyses are data heterogeneities and uncertainties inherent to protein function and structure determinations. Large sequence collections invariably contain subpopulations that differ significantly in their amino acid composition and length. Lacking generally applicable criteria for protein classification, we consider each protein sequence as providing a sample point. Accordingly, for each protein in a specified collection and a prescribed pair of amino acids (or amino acid types), the occurrence frequency of the pair is observed. A Kendall T correlation (see Data and Methods) is calculated relative to the specified collection of protein sequences. Thus, the percentages of positively and negatively charged residues per protein for prokaryotic and eukaryotic protein sequence collections are significantly positively correlated with coefficients generally exceeding 0.4 (see Results and Tables 3 and 4).The analyses presented here are distinct from other types of amino acid usage correlation statistics reported elsewhere (1-6). In ref. 1 the sampli...

Section: Discussionmentioning

confidence: 99%

Correlation analysis of amino acid usage in protein classes.

Bücher²

1992

“…(iv) Multiplets. There are several levels and forms of repetitive structures (18). Multiplets comprise all homodipeptides XX, homotripeptides XXX, etc., where X denotes any specific amino acid.…”

mentioning

confidence: 99%

Amino acid runs in eukaryotic proteomes and disease associations

Brocchieri

Bergman

et al. 2002

Self Cite

215

222

We present a comparative proteome analysis of the five complete eukaryoticgenomes(human,Drosophilamelanogaster,Caenorhabditis elegans, Saccharomyces cerevisiae, Arabidopsis thaliana), focusing on individual and multiple amino acid runs, charge and hydrophobic runs. We found that human proteins with multiple long runs are often associated with diseases; these include long glutamine runs that induce neurological disorders, various cancers, categories of leukemias (mostly involving chromosomal translocations), and an abundance of Ca 2 ؉ and K ؉ channel proteins. Many human proteins with multiple runs function in development and͞or transcription regulation and are Drosophila homeotic homologs. A large number of these proteins are expressed in the nervous system. More than 80% of Drosophila proteins with multiple runs seem to function in transcription regulation. The most frequent amino acid runs in Drosophila sequences occur for glutamine, alanine, and serine, whereas human sequences highlight glutamate, proline, and leucine. The most frequent runs in yeast are of serine, glutamine, and acidic residues. Compared with the other eukaryotic proteomes, amino acid runs are significantly more abundant in the fly. This finding might be interpreted in terms of innate differences in DNA-replication processes, repair mechanisms, DNA-modification systems, and mutational biases. There are striking differences in amino acid runs for glutamine, asparagine, and leucine among the five proteomes. Several human inherited neurodegenerative diseases are triplet-repeat diseases associated with proteins containing long runs of glutamine (long CAG codon iterations; for reviews, see refs. 1 and 2). Disease severity seems to be correlated with the extent of iterations of the CAG codon above a threshold (3). Strikingly, many of the triplet-repeat disease proteins contain multiple long runs of amino acids other than glutamine. Listing all runs of lengths of at least five residues (and using the standard one-letter amino acid code), the huntingtin protein contains Q 23 , P 11 , P 10 , E 5 , E 6 ; atrophin-1 (dentatorubral pallidoluysian atrophy, DRPLA) contains Q 20 , S 7 , S 10 , P 6 , H 5 ; the androgenreceptor protein (Kennedy's disease) contains Q 26 , Q 6 , Q 5 , P 8 , A 5 , G 24 ; and the brain-voltage-dependent calcium channel protein CCAA (spinocerebellar ataxia 6) contains H 10 and Q 11 .Consequences of hyperexpansion of DNA-triplet repeats might include altered rates of transcription or translation, mRNA instability, and aberrant DNA-hairpin structures (4, 5). Protein aggregation attributed to attachment of glutamine-rich proteins to unrelated molecules may lead to inappropriate multimerization or to formation of ''polar zippers,'' in which a long stretch of glutamine residues link strands by hydrogen bonds (6 -8).The foregoing examples motivate our comparative analysis of eukaryotic proteomes focusing on proteins containing multiple amino acid runs. The complete genomes investigated are those of the Human Genome Project tentative dr...

“…Living cells tend to be acidic because of phosphate heads on membrane surfaces and the intrinsic acidic backbone of RNA, DNA, and ATP molecules (12). Moreover, the majority of species proteins favor a net negative charge (13). Residues on the protein surface presumably need to be selective to be able to interact with appropriate structures and avoid interacting with other structures.…”

Section: Discussionmentioning

confidence: 99%

Atom density in protein structures

Zhu

Baud

1999

Self Cite

The residue environment in protein structures is studied with respect to the density of carbon (C), oxygen (O), and nitrogen (N) atoms within a certain distance (say 5 Å) of each residue. Two types of environments are evaluated: one based on side-chain atom contacts (abbreviated S-S) and the other based on all atom (sidechain ؉ backbone) contacts (abbreviated A-A). Different atom counts are observed about nine-residue structural categories defined by three solvent accessibility levels and three secondary structure states. Among the structural categories, the S-S atom count ratios generally vary more than the A-A atom count ratios because of the fact that the backbone (O) and (N) atoms contribute equal counts. Secondary structure affects the (C) density for the A-A contacts whereas secondary structure has little influence on the (C) density for the S-S contacts. For S-S contacts, a greater density of (O) over (N) atom neighbors stands out in the environment of most amino acid types. By contrast, for A-A contacts, independent of the solvent accessibility levels, the ratio (O)͞(N) is Ϸ1 in helical states, consistent with the geometry of ␣-helical residues whose side-chains tilt oppositely to the amino to carboxy ␣-helical axis. The highest ratio of neighbor (O)͞(N) is achieved under solvent exposed conditions. This (O) vs. (N) prevalence is advantageous at the protein surface that generally exhibits an acid excess that helps to enhance protein solubility in the cell and to avoid nonspecific interactions with phosphate groups of DNA, RNA, and other plasma constituents.residue associations ͉ oxygen ͉ nitrogen