Determination of amino acid pairs in human p53 protein sensitive to mutations/variants by means of a random approach

This is the continuation of our studies using random approaches to analyse the p53 protein family. In this data-based theoretical analysis, we use the random approach to analyse the amino acid pairs in human p53 protein in order to determine which amino acid pairs are more sensitive to 190 human p53 mutations/variants. The rationale of this study is based on our hypothesis and findings that a harmful mutation is more likely to occur at randomly unpredictable amino acid pairs, and a harmless mutation is more likely to occur at randomly predictable amino acid pairs. This is because we argue that the randomly predictable amino acid pairs should not be deliberately evolved, whereas the randomly unpredictable amino acid pairs should be deliberately evolved with a connection to protein function. The results show, for example, that 93.16% of 190 mutations/variants occur at randomly unpredictable amino acid pairs. Thus, the randomly unpredictable amino acid pairs are more sensitive to mutations/variants in human p53 protein. The results also suggest that the human p53 protein has a tendency for the occurrence of mutation/variants.     

Analysis of amino acid pairs sensitive to variants in human collagen alpha5(IV) chain precursor by means of a random approach

In this data-based theoretical analysis, we use the random approach to analyze the amino acid pairs in 5(IV) chain precursor (CA54) in order to determine which amino acid pairs are more sensitive to 151 variants from missense mutant human CA54 protein. The rationale of this study is based on our hypothesis and previous findings that harmful variance is more likely to occur at randomly unpredictable amino acid pair position rather than at randomly predictable positions. This is reasonable to argue as randomly predictable amino acid pairs are less likely to be deliberately evolved, whereas randomly unpredictable amino acid pairs are probably deliberately evolved in connection with protein function. The results show that all 151 variants occurred at randomly unpredictable amino acid pairs and the chance of a variant occurring is markedly higher in randomly unpredictable amino acid pairs than in predictable pairs. Thus, randomly unpredictable amino acid pairs are more sensitive to variance in human CA54. The results also suggest that the human CA54 protein has a natural tendency towards variants.     

Determination of amino acid pairs sensitive to variants in human beta-glucocerebrosidase by means of a random approach

In this data-based theoretical analysis, we use the random approach to analyse the amino acid pairs in human beta-glucocerebrosidase in order to determine which amino acid pairs are more sensitive to 109 variants from missense mutant human glucocerebrosidase. The rationale of this study is based on our hypothesis and findings that the harmful variants are more likely to occur at randomly unpredictable amino acid pairs and the non-harmful variants are more likely to occur at randomly predictable amino acid pairs. This is because we argue that the randomly predictable amino acid pairs should not be deliberately evolved, whereas the randomly unpredictable amino acid pairs should be deliberately evolved with connection of protein function. The results show, for example, that 93.58% of 109 variants occur at randomly unpredictable amino acid pairs, which account for 71.40% of amino acid pairs in glucocerebrosidase, and the chance of occurrence of the variant is about 4.4 times higher in randomly unpredictable amino acid pairs than in predictable pairs. Hence the randomly unpredictable amino acid pairs are more sensitive to variants in human glucocerebrosidase. The results also suggest that human glucocerebrosidase has a natural tendency to variants.     

Prediction of amino acid pairs sensitive to mutations in the spike protein from SARS related coronavirus

In this study, we analyzed the amino acid pairs affected by mutations in two spike proteins from human coronavirus strains 229E and OC43 by means of random analysis in order to gain some insight into the possible mutations in the spike protein from SARS-CoV. The results demonstrate that the randomly unpredictable amino acid pairs are more sensitive to the mutations. The larger is the difference between actual and predicted frequencies, the higher is the chance of mutation occurring. The effect induced by mutations is to reduce the difference between actual and predicted frequencies. The amino acid pairs whose actual frequencies are larger than their predicted frequencies are more likely to be targeted by mutations, whereas the amino acid pairs whose actual frequencies are smaller than their predicted frequencies are more likely to be formed after mutations. These findings are identical to our several recent studies, i.e. the mutations represent a process of degeneration inducing human diseases.     

Estimation of amino acid pairs sensitive to variants in human phenylalanine hydroxylase protein by means of a random approach

In this data-based theoretical analysis, we use a random approach to estimate amino acid pairs in human phenylalanine 4-hydroxylase (PAH) protein in order to determine which amino acid pairs are more sensitive to 187 variants in human PAH protein. The rationale of this study is based on our hypothesis and previous findings that the harmful variants are more likely to occur at randomly unpredictable amino acid pairs rather than at randomly predictable pairs. This is reasonable to argue as randomly predictable amino acid pairs are less likely to be deliberately evolved, whereas randomly unpredictable amino acid pairs are probably deliberately evolved in connection with protein function. 94.12% of 187 variants occurred at randomly unpredictable amino acid pairs, which accounted for 71.84% of 451 amino acid pairs in human PAH protein. The chance of a variant occurring is five times higher in randomly unpredictable amino acid pairs than in predictable pairs. Thus, randomly unpredictable amino acid pairs are more sensitive to variance in human PAH protein. The results also suggest that the human PAH protein has a natural tendency towards variants.     

The C-terminal regulatory domain of p53 contains a functional docking site for cyclin A

Radiation injury to cells enhances C-terminal phosphorylation of p53 at both Ser315 and Ser392 in vivo, suggesting the existence of two cooperating DNA damage-responsive pathways that play a role in stimulating p53-dependent gene expression. Our previous data has shown that cyclin A-cdk2 is the major enzyme responsible for modifying p53 at Ser315 in vivo after irradiation damage and in this report we dissect the mechanism of cyclinA-cdk2 binding to and phosphorylation of p53. Although cyclin B(1)-dependent protein kinases can phosphorylate small peptides containing the Ser315 site, cyclin A-cdk2 does not phosphorylate such small peptides suggesting that additional determinants are required for cyclin A-cdk2 interaction with p53. Peptide competition studies have localized a cyclin A interaction site to a Lys381Lys382Leu383Met384Phe385 sequence within C-terminal negative regulatory domain of human p53. An alanine mutation at any one of four key positions abrogates the efficacy of a synthetic peptide containing this motif as an inhibitor of cyclin A-cdk2 phosphorylation of p53 protein. Single amino acid mutations of full-length p53 protein at Lys382, Leu383, or Phe385 decreases cyclin A-cdk2 dependent phosphorylation at Ser315. Cyclin B(1)-cdk2 complexes are not inhibited by KKLMF motif-containing peptides nor is p53 phosphorylation by cyclin B-cdk2 reduced by mutation of the cyclin A interaction site. These data identifying a KKLMF cyclin A docking site on p53 protein highlight a common cyclin A interaction motif that is shared between the tumour suppressor proteins pRb and p53.     

Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza A virus

This is the continuation of our studies on the prediction of mutation engineered by randomness in proteins from influenza A virus. In our previous studies, we have demonstrated that randomness plays a role in engineering mutations because the measures of randomness in protein are different before and after mutations. Thus we built a cause-mutation relationship to count the mutation engineered by randomness, and conducted several concept-initiated studies to predict the mutations in proteins from influenza A virus, which demonstrated the possibility of prediction of mutations along this line of thought. On the other hand, these concept-initiated studies indicate the directions forwards the enhancement of predictability, of which we need to use the neural network instead of logistic regression that was used in those concept-initiated studies to enhance the predictability. In this proof-of-concept study, we attempt to apply the neural network to modeling the cause-mutation relationship to predict the possible mutation positions, and then we use the amino acid mutating probability to predict the would-be-mutated amino acids at predicted positions. The results confirm the possibility of use of internal cause-mutation relationship with neural network model to predict the mutation positions and use of amino acid mutating probability to predict the would-be-mutated amino acids.     

Selective pressures at a codon-level predict deleterious mutations in human disease genes

Deleterious mutations affecting biological function of proteins are constantly being rejected by purifying selection from the gene pool. The non-synonymous/synonymous substitution rate ratio (omega) is a measure of selective pressure on amino acid replacement mutations for protein-coding genes. Different methods have been developed in order to predict non-synonymous changes affecting gene function. However, none has considered the estimation of selective constraints acting on protein residues. Here, we have used codon-based maximum likelihood models in order to estimate the selective pressures on the individual amino acid residues of a well-known model protein: p53. We demonstrate that the number of residues under strong purifying selection in p53 is much higher than those that are strictly conserved during the evolution of the species. In agreement with theoretical expectations, residues that have been noted to be of structural relevance, or in direct association with DNA, were among those showing the highest signals of purifying selection. Conversely, those changing according to a neutral, or nearly neutral mode of evolution, were observed to be irrelevant for protein function. Finally, using more than 40 human disease genes, we demonstrate that residues evolving under strong selective pressures (omega<0.1) are significantly associated (p<0.01) with human disease. We hypothesize that non-synonymous change on amino acids showing omega<0.1 will most likely affect protein function. The application of this evolutionary prediction at a genomic scale will provide an a priori hypothesis of the phenotypic effect of non-synonymous coding single nucleotide polymorphisms (SNPs) in the human genome.     

Prediction of Mutations Initiated by Internal Power in H3N2 Hemagglutinins of Influenza A Virus from North America

Probably the best way to predict mutations is to find the cause for mutations, by which the cause–mutation relationship can be built. However, many causes which have resulted in mutations in the past might not leave any trace due to the changes in environments. As well, the current proteins may not be sensitive to the causes, which led to mutations in the past, because of evolution. Thus we might have recorded many mutations, but few of their corresponding causes, and it would be difficult to establish the one-to-one cause–mutation relationship. However, the internal power engineering mutations within a protein would exist, of which randomness should play an important role. Since 1999, we have developed three methods to quantify the randomness within a protein by which we can build a cause–mutation relationship because we can classify the occurrence and non-occurrence of mutation as unity and zero, and transfer this relationship into the classification problem, which can be solved using logistic regression. Recently, we used the logistic regression to predict the mutation positions in H5N1 hemagglutinins from influenza A virus, and applied the amino-acid mutating probability to predict the would-be-mutated amino acids at predicted positions as the concept-initiated study. However, we still need to conduct many proof-of-concept studies to test whether this cause–mutation relationship is independent of protein subtypes, whether the logistic regression is powerful enough, etc. In this study, we attempted to use the logistic regression to predict the mutation positions in H3N2 hemagglutinins of influenza A virus from North America to answer the questions in the proof-of-concept stage.     

Point mutation of the p53 gene resulting in splicing inhibition in small cell lung carcinoma

The p53 gene is functionally inactivated mostly by point mutations resulting in amino acid substitutions in a wide variety of human cancers. We found a novel mutation of the p53 gene in a small cell lung carcinoma cell line, Lu-143. One of the allelic p53 genes was lost accompanied by loss of heterozygosity for chromosome 17. In the remaining allelic p53 gene, there was a single-base substitution of G to T at position 1 within the splice donor site of intron 7, and the mutated intron was not spliced out during the mRNA maturation process. As a result of this mutation, larger sized p53 mRNA was expressed and no p53 specific protein was detected in this cell line. These results suggest that mutations causing splicing abnormalities are one of the molecular mechanisms for the p53 gene inactivation in human cancer.     

Molecular Evolution of the Tissue-nonspecific Alkaline Phosphatase Allows Prediction and Validation of Missense Mutations Responsible for Hypophosphatasia

ALPL encodes the tissue nonspecific alkaline phosphatase (TNSALP), which removes phosphate groups from various substrates. Its function is essential for bone and tooth mineralization. In humans, ALPL mutations lead to hypophosphatasia, a genetic disorder characterized by defective bone and/or tooth mineralization. To date, 275 ALPL mutations have been reported to cause hypophosphatasia, of which 204 were simple missense mutations. Molecular evolutionary analysis has proved to be an efficient method to highlight residues important for the protein function and to predict or validate sensitive positions for genetic disease. Here we analyzed 58 mammalian TNSALP to identify amino acids unchanged, or only substituted by residues sharing similar properties, through 220 millions years of mammalian evolution. We found 469 sensitive positions of the 524 residues of human TNSALP, which indicates a highly constrained protein. Any substitution occurring at one of these positions is predicted to lead to hypophosphatasia. We tested the 204 missense mutations resulting in hypophosphatasia against our predictive chart, and validated 99% of them. Most sensitive positions were located in functionally important regions of TNSALP (active site, homodimeric interface, crown domain, calcium site, …). However, some important positions are located in regions, the structure and/or biological function of which are still unknown. Our chart of sensitive positions in human TNSALP (i) enables to validate or invalidate at low cost any ALPL mutation, which would be suspected to be responsible for hypophosphatasia, by contrast with time consuming and expensive functional tests, and (ii) displays higher predictive power than in silico models of prediction.     

Genetic studies of the lac repressor. XIII. Extensive amino acid replacements generated by the use of natural and synthetic nonsense suppressors

We have altered the amino acid sequence of the lac repressor one residue at a time by utilizing a collection of nonsense suppressors that permit the insertion of 13 different amino acids in response to the amber (UAG) codon, as well as an additional amino acid in response to the UGA codon. We used this collection to suppress nonsense mutations at 141 positions in the lacI gene, which encodes the 360 amino acid long lac repressor, including 53 new nonsense mutations which we constructed by oligonucleotide-directed mutagenesis. This method has generated over 1600 single amino acid substitutions in the lac repressor. We have cataloged the effects of these replacements and have interpreted the results with the objective of gaining a better understanding of lac repressor structure, and protein structure in general. The DNA binding domain of the repressor, involving the amino-terminal 59 amino acids, is extremely sensitive to substitution, with 70% of the replacements resulting in the I- phenotype. However, the remaining 301 amino acid core of the repressor is strikingly tolerant of substitutions, with only 30% of the amino acids introduced causing the I- phenotype. This analysis reveals the location of sites in the protein involved in inducer binding, tighter binding to operator and thermal stability, and permits a virtual genetic image reconstruction of the lac repressor protein.     

Distinct residues of human p53 implicated in binding to DNA, simian virus 40 large T antigen, 53BP1, and 53BP2.

We identified a minimal domain of human p53 required for the transactivation of a p53 response element in Saccharomyces cerevisiae. This domain contains the central region of p53 sufficient for specific DNA binding, which colocalizes with the region responsible for binding simian virus 40 large T antigen, 53BP1, and 53BP2. Thirty amino acid positions, including natural mutational hot spots (R175, R213, R248, R249, and R273), in the minimal DNA-binding domain were mutated by alanine substitution. Alanine substitutions at positions R213, R248, R249, D281, R282, R283, E286, and N288 affected transactivation but allowed binding to at least one of the three interacting proteins; these amino acids may be involved in amino acid-base pair contacts. Surprisingly, alanine substitution at the mutational hot spot R175 did not affect DNA binding, transactivation, or T-antigen binding, although it nearly eliminated binding to 53BP1 and 53BP2. Mutation of H168 significantly affected only T-antigen binding, and mutation of E285 affected only 53BP1 binding. Thus, we implicate specific residues of p53 in different DNA and protein interactions.     

Mutant p53 reactivation by small molecules makes its way to the clinic

The TP53 tumor suppressor gene is mutated in many human tumors, including common types of cancer such as colon and ovarian cancer. This illustrates the key role of p53 as trigger of cell cycle arrest or cell death upon oncogenic stress. Most TP53 mutations are missense mutations that result in single amino acid substitutions in p53 and expression of high levels of dysfunctional p53 protein. Restoration of wild type p53 function in such tumor cells will induce robust cell death and allow efficient eradication of the tumor. Therapeutic targeting of mutant p53 in tumors is a rapidly developing field at the forefront of translational cancer research. Various approaches have led to the identification of small molecules that can rescue mutant p53. These include compounds that target specific p53 mutations, including PK083 and PK5174 (Y220C mutant p53) and NSC319726 (R175H mutant p53), as well as PRIMA-1 and its analog APR-246 that affect a wider range of mutant p53 proteins. APR-246 has been tested in a Phase I/II clinical trial with promising results.     

Amino acid positions subject to multiple coevolutionary constraints can be robustly identified by their eigenvector network centrality scores

As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for coevolution between pairs of positions. Coevolutionary scores are usually rank‐ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of coevolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly‐used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6‐bisphosphate aldolase protein families as models. Calculations used unthresholded coevolution scores from which column‐specific properties such as sequence entropy and random noise were subtracted; “central” positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise coevolution scores: instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints—detectable by divergent algorithms—that occur at key protein locations. Finally, we discuss the fact that multiple patterns coexist in evolutionary data that, together, give rise to emergent protein functions. Proteins 2015; 83:2293–2306. © 2015 Wiley Periodicals, Inc.     

Determination of amino acid pairs sensitive to variants in human copper-transporting ATPase 2

In this study, we use our probabilistic approach to analyze the amino acid pairs in human copper-transporting ATPase 2 (ATP7B) in order to determine which amino acid pairs are more sensitive to 125 variants from missense mutant human ATP7B. The results show 97.6% of 125 variants occur at randomly unpredictable amino acid pairs, which account for 80.9% of amino acid pairs in ATP7B, and the chance of occurring of variant is about 9 times higher in randomly unpredictable amino acid pairs than in predictable pairs. Thus, the randomly unpredictable amino acid pairs are more sensitive to variants in human ATP7B.     

Support vector machines for learning to identify the critical positions of a protein

A method for identifying the positions in the amino acid sequence, which are critical for the catalytic activity of a protein using support vector machines (SVMs) is introduced and analysed. SVMs are supported by an efficient learning algorithm and can utilize some prior knowledge about the structure of the problem. The amino acid sequences of the variants of a protein, created by inducing mutations, along with their fitness are required as input data by the method to predict its critical positions. To investigate the performance of this algorithm, variants of the beta-lactamase enzyme were created in silico using simulations of both mutagenesis and recombination protocols. Results from literature on beta-lactamase were used to test the accuracy of this method. It was also compared with the results from a simple search algorithm. The algorithm was also shown to be able to predict critical positions that can tolerate two different amino acids and retain function.