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.     

Effect of amino acid substitutions on conformational stability of a protein

This paper reviews studies on thermostable proteins from thermophilic bacteria and on mutant proteins of human hemoglobin, tryptophan synthase α-subunit of E. coli, T₄ phage lysozyme, and phage λ repressor with respect to the role of the consisting amino acid residues in stabilization of conformation. The stability of a protein is easily affected by single amino acid substitutions, by which the protein undergoes change(s) of one or more of the following: a hydrogen bond, a salt bridge, a hydrophobic interaction, the volume of the residue, a disulfide bond, or the relative position of two aromatic rings.     

Accumulation pattern of amino acid substitutions in protein evolution

Summary A simple method for the evolutionary analysis of amino acid sequence data is presented and used to examine whether the number of variable sites (NVS) of a protein is constant during its evolution. The NVSs for hemoglobin and for mitochondrial cytochrome c are each found to be almost constant, and the ratio between the NVSs is close to the ratio between the unit evolutionary periods. This indicates that the substitution rate per variable site is almost uniform for these proteins, as the neutral theory claims. An advantage of the present analysis is that it can be done without knowledge of paleontological divergence times and can be extended to bacterial proteins such as bacterial c-type cytochromes. It is suggested that the NVS of cytochrome c has been almost constant even over the long period (ca. 3.0 billion years) of bacterial evolution but that at least two different substitution rates are necessary to describe the accumulated changes in the sequence. This two clock interpretation is consistent with fossil evidence for the appearance times of photosynthetic bacteria and eukaryotes.     

Sequence feature-based prediction of protein stability changes upon amino acid substitutions

Background

Protein destabilization is a common mechanism by which amino acid substitutions cause human diseases. Although several machine learning methods have been reported for predicting protein stability changes upon amino acid substitutions, the previous studies did not utilize relevant sequence features representing biological knowledge for classifier construction.

Results

In this study, a new machine learning method has been developed for sequence feature-based prediction of protein stability changes upon amino acid substitutions. Support vector machines were trained with data from experimental studies on the free energy change of protein stability upon mutations. To construct accurate classifiers, twenty sequence features were examined for input vector encoding. It was shown that classifier performance varied significantly by using different sequence features. The most accurate classifier in this study was constructed using a combination of six sequence features. This classifier achieved an overall accuracy of 84.59% with 70.29% sensitivity and 90.98% specificity.

Conclusions

Relevant sequence features can be used to accurately predict protein stability changes upon amino acid substitutions. Predictive results at this level of accuracy may provide useful information to distinguish between deleterious and tolerant alterations in disease candidate genes. To make the classifier accessible to the genetics research community, we have developed a new web server, called MuStab (http://bioinfo.ggc.org/mustab/).

     

Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines

In principle, structural information of protein sequences with no detectable homology to a protein of known structure could be obtained by predicting the arrangement of their secondary structural elements. Although some ab initio methods for protein structure prediction have been reported, the long-range interactions required to accurately predict tertiary structures of β-sheet containing proteins are still difficult to simulate. To remedy this problem and facilitate de novo prediction of β-sheet containing protein structures, we developed a support vector machine (SVM) approach that classified parallel and antiparallel orientation of β-strands by using the information of interstrand amino acid pairing preferences. Based on a second-order statistics on the relative frequencies of each possible interstrand amino acid pair, we defined an average amino acid pairing encoding matrix (APEM) for encoding β-strands as input in the prediction model. As a result, a prediction accuracy of 86.89% and a Matthew's correlation coefficient value of 0.71 have been achieved through 7-fold cross-validation on a non-redundant protein dataset from PISCES. Although several issues still remain to be studied, the method presented here to some extent could indicate the important contribution of the amino acid pairs to the β-strand orientation, and provide a possible way to further be combined with other algorithms making a full ‘identification’ of β-strands.     

Effect of conserved intersubunit amino acid substitutions on Hfq protein structure and stability

Hfq is a thermostable RNA-binding bacterial protein that forms a uniquely shaped homohexamer. Based on sequence and structural similarity, Hfq belongs to the like-Sm (LSm) protein family. In spite of a rather high degree of homology between archaeal and eukaryotic LSm proteins, their quaternary structure is different, usually consisting of five to eight monomers. In this work, the importance of conserved intersubunit hydrogen bonds for the Hfq spatial organization was tested. The structures and stabilities for the Gln8Ala, Asn28Ala, Asp40Ala, and Tyr55Ala Hfq mutants were determined. All these proteins have the same hexamer organization, but their stability is different. Elimination of a single intersubunit hydrogen bond due to Gln8Ala, Asp40Ala, and Tyr55Ala substitutions results in decreased stability of the Hfq hexamer. Tyr55Ala Hfq as well as the earlier studied His57Ala Hfq has reduced protein thermostability, which seems to correspond to an opening of the protein hydrophobic core.     

Selection on amino acid substitutions in Arabidopsis

Studies of nucleotide diversity have found an excess of low-frequency amino acid polymorphisms segregating in Arabidopsis thaliana, suggesting a predominance of weak purifying selection acting on amino acid polymorphism in this inbreeding species. Here, we investigate levels of diversity and divergence at synonymous and nonsynonymous sites in 6 circumpolar populations of the outbreeding Arabidopsis lyrata and compare these results with A. thaliana, to test for differences in mutation and selection parameters across genes, populations, and species. We find that A. lyrata shows an excess of low-frequency nonsynonymous polymorphisms both within populations and species wide, consistent with weak purifying selection similar to the patterns observed in A. thaliana. Furthermore, nonsynonymous polymorphisms tend to be more restricted in their population distribution in A. lyrata, consistent with purifying selection preventing their geographic spread. Highly expressed genes show a reduced ratio of amino acid to synonymous change for both polymorphism and fixed differences, suggesting a general pattern of stronger purifying selection on high-expression proteins.     

Single amino acid substitutions on the needle tip protein IpaD increased Shigella virulence

Infection of colonic epithelial cells by Shigella is associated with the type III secretion system, which serves as a molecular syringe to inject effectors into host cells. This system includes an extracellular needle used as a conduit for secreted proteins. Two of these proteins, IpaB and IpaD, dock at the needle tip to control secretion and are also involved in the insertion of a translocation pore into host cell membrane allowing effector delivery. To better understand the function of IpaD, we substituted thirteen residues conserved among homologous proteins in other bacterial species. Generated variants were tested for their ability to surface expose IpaB and IpaD, to control secretion, to insert the translocation pore, and to invade host cells. In addition to a first group of seven ipaD variants that behaved similarly to the wild-type strain, we identified a second group with mutations V314D and I319D that deregulated secretion of all effectors, but remained fully invasive. Moreover, we identified a third group with mutations Y153A, T161D, Q165L and Y276A, that exhibited increased levels of translocators secretion, pore formation, and cell entry. Altogether, our results offer a better understanding of the role of IpaD in the control of Shigella virulence.     

Predicting protein disorder by analyzing amino acid sequence

Background

Many protein regions and some entire proteins have no definite tertiary structure, presenting instead as dynamic, disorder ensembles under different physiochemical circumstances. These proteins and regions are known as Intrinsically Unstructured Proteins (IUP). IUP have been associated with a wide range of protein functions, along with roles in diseases characterized by protein misfolding and aggregation.

Results

Identifying IUP is important task in structural and functional genomics. We exact useful features from sequences and develop machine learning algorithms for the above task. We compare our IUP predictor with PONDRs (mainly neural-network-based predictors), disEMBL (also based on neural networks) and Globplot (based on disorder propensity).

Conclusion

We find that augmenting features derived from physiochemical properties of amino acids (such as hydrophobicity, complexity etc.) and using ensemble method proved beneficial. The IUP predictor is a viable alternative software tool for identifying IUP protein regions and proteins.

     

Evaluating antioxidative activities of amino acid substitutions on mastoparan-B

Mastoparan-B is a peptide toxin isolated from the venom of Vespa basalis, the most dangerous hornet found in Taiwan. This study is aimed to evaluate the antioxidative activities of several amino acid substitutions on MP-B, and examined the influences of mast cell degranulation and hemolytic activities in parallel with antioxidative activities. The correlations between the biological function and amino acid sequence were assessed. Our study shows original MP-B is a valuable antioxidant at low concentration in competing with nitric-oxide for oxygen molecules and possesses good antioxidative enzyme activities resembled to superoxidase dismutase and glutathione peroxidase. And there are no predominant rates of mast cell degranulation and hemolytic effects in such condition. With proper substitutions, the reducing power, DPPH scavenging activity and glutathione reductase-like enzyme activity of MP-B can increase clearly. The results demonstrate that MP-B analogs are very potential to be applicable antioxidants for other antioxidative usages.     

Approaches to predicting effects of single amino acid substitutions on the function of a protein

The relative activities of 313 mutants of the gene V protein of bacteriophage f1, assayed in vivo, have been used to evaluate two approaches to predicting the effects of single amino acid substitutions on the function of a protein. First, we tested methods that only depend on the properties of the wild-type and substituting amino acids. None of the properties or measures of the functional equivalence of amino acids we tested, including the frequency of exchange of amino acids among homologous proteins as well as changes in side-chain size, hydrophobicity, and charge, were found to be more than weakly correlated with the activities of mutants. The principal reason for this poor correlation was found to be that the effect of a particular substitution varies considerably from site to site. We then tested an approach using the activities of several mutants with substitutions at a site to predict the activity of another mutant, and we find that this is a relatively good indicator of whether the other mutant at that site will be functional. A predictive scheme was developed that combines the weak information from the models depending on the properties of the wild-type and substituting amino acids with the stronger information from the tolerance of a site to substitution. Although this scheme requires no knowledge of the structure of a mutant protein, it is useful in predicting the activities of mutants.     

Prediction of protein folding class from amino acid composition

An empirical relation between the amino acid composition and three-dimensional folding pattern of several classes of proteins has been determined. Computer simulated neural networks have been used to assign proteins to one of the following classes based on their amino acid composition and size: (1) 4α-helical bundles, (2) parallel (α/β)₈ barrels, (3) nucleotide binding fold, (4) immunoglobulin fold, or (5) none of these. Networks trained on the known crystal structures as well as sequences of closely related proteins are shown to correctly predict folding classes of proteins not represented in the training set with an average accuracy of 87%. Other folding motifs can easily be added to the prediction scheme once larger databases become available. Analysis of the neural network weights reveals that amino acids favoring prediction of a folding class are usually over represented in that class and amino acids with unfavorable weights are underrepresented in composition. The neural networks utilize combinations of these multiple small variations in amino acid composition in order to make a prediction. The favorably weighted amino acids in a given class also form the most intramolecular interactions with other residues in proteins of that class. A detailed examination of the contacts of these amino acids reveals some general patterns that may help stabilize each folding class. © 1993 Wiley-Liss, Inc.     

Prediction of protein secondary structure from amino acid sequence

The conformational parametersP _k for each amino acid species (j=1–20) of sequential peptides in proteins are presented as the product ofP _i,k , wherei is the number of the sequential residues in thekth conformational state (k=-helix,-sheet,-turn, or unordered structure). Since the average parameter for ann-residue segment is related to the average probability of finding the segment in the kth state, it becomes a geometric mean of (P _k )_av=(P _i,k ) ^1/n with amino acid residuei increasing from 1 ton. We then used ln(P_k)_av to convert a multiplicative process to a summation, i.e., ln(P _k ) _av =(1/n)P _i,k (i=1 ton) for ease of operation. However, this is unlike the popular Chou-Fasman algorithm, which has the flaw of using the arithmetic mean for relative probabilities. The Chou-Fasman algorithm happens to be close to our calculations in many cases mainly because the difference between theirP _k and our InP _k is nearly constant for about one-half of the 20 amino acids. When stronger conformation formers and breakers exist, the difference become larger and the prediction at the N- and C-terminal-helix or-sheet could differ. If the average conformational parameters of the overlapping segments of any two states are too close for a unique solution, our calculations could lead to a different prediction.     

Prediction of the changes in thermodynamic stability of proteins caused by single amino acid substitutions

The functional (synthetic) activity of blood lymphocytes and bone marrow hematopoietic cells in ground squirrels was studied in different seasons and at different stages of the torpor-arousal cycle. The effect of γ-irradiation on animals in different physiological states was also studied. The synthetic activity of cells was estimated from the amount of active RNA per unit DNA in the cell (parameter α). The α values in lymphocytes were minimal in hibernating animals (January–March), reached a peak upon their complete awakening (April), slightly decreased in the summer activity period, and decreased further in the prehibernation autumn period (November). During winter arousals between torpor bouts, this parameter reached the same values as in summer. The dynamics of parameter α in bone marrow hematopoietic cells were generally similar: minimal values in November and higher between torpor bouts than in summer. The peak of synthetic activity of proliferating hematopoietic cells recorded upon awakening from hibernation in April was mainly due to the accumulation of cells in the G₁ and G₂ phases of the cell cycle, and its decrease in summer reflected prevalent transition from G₂ to mitosis and then partly to G₀. In the torpor-arousal-euthermia cycle, two stages of awakening were distinguished, differing considerably in most of the test parameters. The synthetic activity and the total number of blood and bone marrow cells in ground squirrels irradiated in the state of torpor did not differ significantly from those in nonirradiated torpid animals. The adverse effect of radiation in animals irradiated at the initial stage of awakening was lesser than in animals irradiated in the active state, whereas animals at the second stage of awakening proved more vulnerable to acute irradiation. The physiological state of ground squirrels exposed to ionizing radiation at different phases of the torpor-arousal-euthermia cycle plays a key role in the dynamics of qualitative and quantitative characteristics of blood system cells. The results of this study indicate that the hypometabolic state of ground squirrels during hibernation is a factor of protection from the impact of ionizing radiation on the whole body and on the immune system in particular.     

Prediction of the changes in thermodynamic stability of proteins caused by single amino acid substitutions

Prediction of the effect of amino acid substitutions on the thermodynamic stability of proteins is of great importance for studies into the molecular mechanisms underlying the abnormal function of mutant proteins, interpretation of genotyping results, and purposeful design of modified proteins with improved biomedical and biotechnological properties. A set of methods was developed for predicting the changes in free energy (ΔΔG) of mutant proteins containing single substitutions using the information only about protein primary structure or also about the spatial structure. A modified KRAB algorithm was used; its higher accuracy in predicting the changes in the thermodynamic stability of mutant proteins compared with the other known methods designed for solving this problem is demonstrated. Distribution of the positions in the sequence of Malayan pit viper venom protein (kistrin) where the substitutions decrease or increase kistrin stability is analyzed. The substitutions at most positions conserved in the disintegrin family decrease the stability of this protein, except for several positions whose conservation can be determined by functional significance.     

Machine learning integration for predicting the effect of single amino acid substitutions on protein stability

Background  

Computational prediction of protein stability change due to single-site amino acid substitutions is of interest in protein design and analysis. We consider the following four ways to improve the performance of the currently available predictors: (1) We include additional sequence- and structure-based features, namely, the amino acid substitution likelihoods, the equilibrium fluctuations of the alpha- and beta-carbon atoms, and the packing density. (2) By implementing different machine learning integration approaches, we combine information from different features or representations. (3) We compare classification vs. regression methods to predict the sign vs. the output of stability change. (4) We allow a reject option for doubtful cases where the risk of misclassification is high.