Two types of amino acid substitutions in protein evolution

Summary The frequency of amino acid substitutions, relative to the frequency expected by chance, decreases linearly with the increase in physico-chemical differences between amino acid pairs involved in a substitution. This correlation does not apply to abnormal human hemoglobins. Since abnormal hemoglobins mostly reflect the process of mutation rather than selection, the correlation manifest during protein evolution between substitution frequency and physico-chemical difference in amino acids can be attributed to natural selection. Outside of abnormal proteins, the correlation also does not apply to certain regions of proteins characterized by rapid rates of substitution. In these cases again, except for the largest physico-chemical differences between amino acid pairs, the substitution frequencies seem to be independent of the physico-chemical parameters. The limination of the substituents involving the largest physicochemical differences can once more be attributed to natural selection. For smaller physico-chemical differences, natural selection, if it is operating in the polypeptide regions, must be based on parameters other than those examined.     

Phylogeny reconstruction for protein sequences based on amino acid properties

This paper presents an essentially new method used to construct phylogenetic trees from related amino acid sequences. The method is based on a new distance measure which describes sequence relationships by means of typical steric and physicochemical properties of the amino acids and is advantageous in some essential points. The method was applied to different sets of protein sequences and the results were compared with other well-established methods.     

Simulation studies on the evolution of amino acid sequences

Summary A model of molecular evolution in which the parameter (intrinsic rate of amino acid substitution) fluctuates from time to time was investigated by simulating the process. It was found that the usual method of estimation such as Poisson fitting underestimates this variation of the parameter when remote comparisons are made. At the same time, four distance measures (minimum base difference, Poisson fitting, random nucleotide substitutions and negative binomial fitting) were tested for their accuracy. When the substitution rate is not uniform among the amino acid sites, the negative binomial fitting gives most satisfactory results, however, one needs to know the parameter beforehand in order to use this method. It was pointed out that the fluctuation of the evolutionary rate is expected if the nearly neutral but very slightly deleterious mutations play an important role on molecular evolution.Contribution No. 1087 from the National Institute of Genetics, Mishima, Shizuoka-ken, 411 Japan.     

Nonrandom amino acid substitution and estimation of the number of nucleotide substitutions in evolution

Summary A method of estimating the number of nucleotide substitutions from amino acid sequence data is developed by using Dayhoff's mutation probability matrix. This method takes into account the effect of nonrandom amino acid substitutions and gives an estimate which is similar to the value obtained by Fitch's counting method, but larger than the estimate obtained under the assumption of random substitutions (Jukes and Cantor's formula). Computer simulations based on Dayhoff's mutation probability matrix have suggested that Jukes and Holmquist's method of estimating the number of nucleotide substitutions gives an overestimate when amino acid substitution is not random and the variance of the estimate is generally very large. It is also shown that when the number of nucleotide substitutions is small, this method tends to give an overestimate even when amino acid substitution is purely at random.     

Metabolic costs of amino acid and protein production in Escherichia coli

Escherichia coli is the most popular microorganism for the production of recombinant proteins and is gaining increasing importance for the production of low-molecular weight compounds such as amino acids. The metabolic cost associated with the production of amino acids and (recombinant) proteins from glucose, glycerol and acetate was determined using three different computational techniques to identify those amino acids that put the highest burden on the biosynthetic machinery of E. coli. Comparing the costs of individual amino acids, we find that methionine is the most expensive amino acid in terms of consumed mol of ATP per molecule produced, while leucine is the most expensive amino acid when taking into account the cellular abundances of amino acids. Moreover, we show that the biosynthesis of a large number of amino acids from glucose and particularly from glycerol provides a surplus of energy, which can be used to balance the high energetic cost of amino acid polymerization.     

Effects of amino acid analogues on protein degradation in isolated rat hepatocytes

Analogues and derivatives of six of the amino acids which most effectively inhibit protein degradation in isolated rat hepatocytes (leucine, asparagine, glutamine, histidine, phenylalanine and tryptophan) were investigated to see if they could antagonize or mimic the effect of the parent compound. No antagonists were found. Amino alcohols and amino acid amides tended to inhibit protein degradation strongly, apparently by a direct lysosomotropic effect as indicated by their ability to cause lysosomal vacuolation. Amino acid alkyl esters and dipeptides inhibited degradation to approximately the same extent as did their parent amino acids, possibly by being converted to free amino acids intracellularly. Of several leucine analogues tested, four (L-norleucine, L-norvaline, D-norleucine and L-allo-isoleucine) were found to be as effective as leucine in inhibiting protein degradation. None of the analogues had any effect on protein synthesis. Since leucine appears to play a unique role as a regulator of bulk autophagy in hepatocytes, the availability of active leucine agonists may help tj elucidate the biochemical mechanism for control of this important process.     

Estimation of the number of amino acid substitutions per site when the substitution rate varies among sites

A general model for estimating the number of amino acid substitutions per site (d) from the fraction of identical residues between two sequences (q) is proposed. The well-known Poisson-correction formula q = e ^–d corresponds to a site-independent and amino-acid-independent substitution rate. Equation q = (1 – e ^–2d )/2d, derived for the case of substitution rates that are site-independent, but vary among amino acids, approximates closely the empirical method, suggested by Dayhoff et al. (1978). Equation q = 1/(1 + d) describes the case of substitution rates that are amino acid-independent but vary among sites. Lastly, equation q = [ln(1 + 2d)]/2d accounts for the general case where substitution rates can differ for both amino acids and sites.     

Glucocorticoids inhibit precursor incorporation into protein in splenic lymphocytes by stimulating protein degradation and expanding intracellular amino acid pools

In the presence of tracer concentrations of extracellular leucine (5 μM), treatment of rat splenic lymphocyte suspensions in vitro with 1 μM dexamethasone for 2.5–4 h caused a 30–35% inhibition of [³H]leucine incorporation into protein. As the extracellular leucine concentration was raised to 5 mM, this inhibition was progressively reduced to 0–12%. This phenomenon correlated with a marked dependence on extracellular leucine concentration of the dexamethasone-dependent enlargement of free intracellular leucine pools in splenic lymphocytes: a 123% increase in pool size with tracer extracellular leucine; a 10% increase with 5 mM leucine. Varying extracellular leucine had no effect on: (1) nuclear [³H]dexamethasone binding by the cells; (2) the concentration of dexamethasone needed for half-maximal inhibition of [³H]leucine incorporation; (3) the time course of onset and maximal expression of the hormonal inhibition of [³H]leucine incorporation; or (4) the magnitude of dexamethasone-dependent inhibition of [³H]uridine incorporation into RNA by these cells. There was no detectable effect of dexamethasone on uptake and retention of [³H]leucine by the cells, regardless of the extracellular leucine concentration. Treatment of splenic lymphocytes for 4 h in vitro with 1 μM dexamethasone caused a small shift of ribosomes from larger aggregate polysomes to smaller forms. Thus, glucocorticoid-induced inhibition of amino acid incorporation in splenic lymphocytes is a multicomponent response, of which an actual decrease in protein synthesis is only a small part. Enlargement of free intracellular amino acid pools, probably resulting from increased protein degradation, is the major contributing factor to the hormonal inhibition of amino acid incorporation.     

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor.     

The amino acid sequence of two small ribosomal proteins from Bacillus stearothermophilus

The low-Mr proteins (tentatively called protein I and II) were purified from 2 M NaCl extracts of the Bacillus stearothermophilus ribosome. Their amino acid sequences have been determined from the peptides obtained by digestion with trypsin, chymotrypsin, and pepsin, and by cleavage with CNBr, using the micro-DABITC/PITC double-coupling method [FEBS Lett. (1978) 93, 205-214]. Protein I contains 56 residues and has an Mr of 6514. Protein II had 37 residues with an Mr of 4361. The amino acid sequence of protein I shows significant similarity to L32 from E. coli, whereas that of protein II is slightly, if at all, related to ribosomal protein L34 from E. coli.     

The broken-stick model for amino acid composition in proteins

Summary The relative abundances among the amino acids, which are functionally similar to one another, were explained by random partition of a unit interval.     

Mobilization of maternal mRNA in amphibian eggs with special reference to the possible role of membraneous supramolecular structures

Current knowledge of the mechanism of mobilization of maternal mRNA is summarized herein and a working hypothesis has been constructed to explain the mechanism on the assumption that the mRNA enters the cytoplasm in association with the cytoplasmic membraneous structures and is then stored in the structures until liberation and relocation at the step of oocyte maturation. An extensive turnover of poly(A) sequences as well as the occurrence of repetitive sequences in the maternal mRNA may be relevant to mRNA activation.     

Degeneracy of the information contained in amino acid sequences: Evidence from overlaid genes

Summary The observed gene overlays in the viruses X174 and SV40 show a surprising economy of information storage; two different amino acid sequences are read in different frames from the same stretch of DNA. This phenomenon appears contradictory in that the information in the two overlaid amino acid sequences is strongly interdependent, yet each of the two proteins has evolved to its own well-defined function. The contradiction can be resolved by assuming sufficiently large degeneracy of the information contents of amino acid sequences with respect to function. Such a degeneracy is familiar from homologous proteins where a given biological function is implemented by many different amino acid sequences. It is shown that the very existence of viral overlays allows to derive a lower limit for the magnitude of this degeneracy: The degeneracy is equal to, or greater than fourfold; on the average, at each position of the chain a choice of 1 out of 5 or less amino acids, and not a choice of 1 out of 20 is necessary for constructing a protein with a specified function. In addition, the strong dependence of overlay probabilities on chain length allows the definition of a maximal length of overlays; in bacterial viruses overlay regions should be shorter than about 150 residues.     

Prediction of protein secondary structure content using amino acid composition and evolutionary information

Knowing protein structure and inferring its function from the structure are one of the main issues of computational structural biology, and often the first step is studying protein secondary structure. There have been many attempts to predict protein secondary structure contents. Previous attempts assumed that the content of protein secondary structure can be predicted successfully using the information on the amino acid composition of a protein. Recent methods achieved remarkable prediction accuracy by using the expanded composition information. The overall average error of the most successful method is 3.4%. Here, we demonstrate that even if we only use the simple amino acid composition information alone, it is possible to improve the prediction accuracy significantly if the evolutionary information is included. The idea is motivated by the observation that evolutionarily related proteins share the similar structure. After calculating the homolog-averaged amino acid composition of a protein, which can be easily obtained from the multiple sequence alignment by running PSI-BLAST, those 20 numbers are learned by a multiple linear regression, an artificial neural network and a support vector regression. The overall average error of method by a support vector regression is 3.3%. It is remarkable that we obtain the comparable accuracy without utilizing the expanded composition information such as pair-coupled amino acid composition. This work again demonstrates that the amino acid composition is a fundamental characteristic of a protein. It is anticipated that our novel idea can be applied to many areas of protein bioinformatics where the amino acid composition information is utilized, such as subcellular localization prediction, enzyme subclass prediction, domain boundary prediction, signal sequence prediction, and prediction of unfolded segment in a protein sequence, to name a few.     

Conservation of the basic pattern of cellular amino acid composition of archaeobacteria during biological evolution and the putative amino acid composition of primitive life forms

Summary. Previous studies showed that the cellular amino acid composition obtained by amino acid analysis of whole cells, differs such as eubacteria, protozoa, fungi and mammalian cells. These results suggest that the difference in the cellular amino acid composition reflects biological changes as the result of evolution. However, the basic pattern of cellular amino acid composition was relatively constant in all organisms examined. In the present study, we examined archaeobacteria, because they are considered important in understanding the relationship between biological evolution and cellular amino acid composition. The cellular amino acid compositions of Archaeoglobus fulgidus, Pyrococcus horikoshii, Methanobacterium thermoautotrophicum and Methanococcus jannaschii differed slightly from each other, but were similar to those determined from codon usage data, based on the complete genomes. Thus, the cellular amino acid composition reflects biological evolution. We suggest that primitive forms of life appearing on earth at the end of prebiotic evolution had a similar-cellular amino acid composition. Received November 28, 2000 Accepted January 30, 2001     

The infrared absorption of amino acid side chains

Amino acid side chains play fundamental roles in stabilising protein structures and in catalysing enzymatic reactions. These fields are increasingly investigated by infrared spectroscopy at the molecular level. To help the interpretation of the spectra, a review of the infrared absorption of amino acid side chains in H₂O and ²H₂O is given. The spectral region of 2600–900 cm⁻¹ is covered.     

A reexamination of the propensities of amino acids towards a particular secondary structure: classification of amino acids based on their chemical structure

The correlation between the primary and secondary structures of proteins was analysed using a large data set from the Protein Data Bank. Clear preferences of amino acids towards certain secondary structures classify amino acids into four groups: α-helix preferrers, strand preferrers, turn and bend preferrers, and His and Cys (the latter two amino acids show no clear preference for any secondary structure). Amino acids in the same group have similar structural characteristics at their Cβ and Cγ atoms that predicts their preference for a particular secondary structure. All α-helix preferrers have neither polar heteroatoms on Cβ and Cγ atoms, nor branching or aromatic group on the Cβ atom. All strand preferrers have aromatic groups or branching groups on the Cβ atom. All turn and bend preferrers have a polar heteroatom on the Cβ or Cγ atoms or do not have a Cβ atom at all. These new rules could be helpful in making predictions about non-natural amino acids.

Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network

Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area.     

The amino acid sequence of a protein from wheat kernel closely related to proteins involved in the mechanisms of plant defence

The amino acid sequence of wheatwin1, a monomeric protein of 125 residues isolated from wheat kernel (variety S. Pastore), is reported. Wheatwin1 is highly homologous (95%) to barwin, a protein from barley seed, which was shown to be related to the C-terminal domain of two proteins encoded by the wound-induced geneswin1 andwin2 in potato and to a protein encoded by the same domain of the hevein gene (hev1) in rubber tree. Similarly to barwin, wheatwin1 contains six cysteine residues all linked in disulfide bridges and the N-terminal residue is pyroglutamate. Moreover, structural studies performed on wheatwin1 andwin1 protein by predictive methods demonstrated that these proteins and barwin are closely related in the secondary structure also. The high level of homology found with the product ofwin1,win2, andhev1 genes strongly suggests that barwin and wheatwin1 play a common role in the mechanism of plant defence.