Linking protein structural and functional change to mutation using amino acid networks.
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ABSTRACT:
SUBMITTER: Sotomayor-Vivas C
PROVIDER: S-EPMC8782487 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Mutagenesis of protein-encoding sequences occurs ubiquitously; it enables evolution, accumulates during aging, and is associated with disease. Many biotechnological methods exploit random mutations to evolve novel proteins. To quantitate protein tolerance to random change, it is vital to understand the probability that a random amino acid replacement will lead to a protein's functional inactivation. We define this probability as the "x factor." Here, we develop a broadly applicable approach to calculate x factors and demonstrate this method using the human DNA repair enzyme 3-methyladenine DNA glycosylase (AAG). Three gene-wide mutagenesis libraries were created, each with 10(5) diversity and averaging 2.2, 4.6, and 6.2 random amino acid changes per mutant. After determining the percentage of functional mutants in each library using high-stringency selection (>19,000-fold), the x factor was found to be 34% +/- 6%. Remarkably, reanalysis of data from studies of diverse proteins reveals similar inactivation probabilities. To delineate the nature of tolerated amino acid substitutions, we sequenced 244 surviving AAG mutants. The 920 tolerated substitutions were characterized by substitutability index and mapped onto the AAG primary, secondary, and known tertiary structures. Evolutionarily conserved residues show low substitutability indices. In AAG, beta strands are on average less substitutable than alpha helices; and surface loops that are not involved in DNA binding are the most substitutable. Our results are relevant to such diverse topics as applied molecular evolution, the rate of introduction of deleterious alleles into genomes in evolutionary history, and organisms' tolerance of mutational burden.
| S-EPMC438954 | biostudies-literature
Project description:Correlated networks of amino acids have been proposed to play a fundamental role in allostery and enzyme catalysis. These networks of amino acids can be traced from surface-exposed residues all the way into the active site, and disruption of these networks can decrease enzyme activity. Substitution of the distal Gly121 residue in Escherichia coli dihydrofolate reductase results in an up to 200-fold decrease in the hydride transfer rate despite the fact that the residue is located 15 Å from the active-site center. In this study, nuclear magnetic resonance relaxation experiments are used to demonstrate that dynamics on the picosecond to nanosecond and microsecond to millisecond time scales are changed significantly in the G121V mutant of dihydrofolate reductase. In particular, picosecond to nanosecond time scale dynamics are decreased in the FG loop (containing the mutated residue at position 121) and the neighboring active-site loop (the Met20 loop) in the mutant compared to those of the wild-type enzyme, suggesting that these loops are dynamically coupled. Changes in methyl order parameters reveal a pathway by which dynamic perturbations can be propagated more than 25 Å across the protein from the site of mutation. All of the enzyme complexes, including the model Michaelis complex with folate and nicotinamide adenine dinucleotide phosphate bound, assume an occluded ground-state conformation, and we do not observe sampling of a higher-energy closed conformation by (15)N R2 relaxation dispersion experiments. This is highly significant, because it is only in the closed conformation that the cofactor and substrate reactive centers are positioned for reaction. The mutation also impairs microsecond to millisecond time scale fluctuations that have been implicated in the release of product from the wild-type enzyme. Our results are consistent with an important role for Gly121 in controlling protein dynamics critical for enzyme function and further validate the dynamic energy landscape hypothesis of enzyme catalysis.
| S-EPMC3838469 | biostudies-literature
Project description:Switchgrass, a forage and bioenergy crop, occurs as two main ecotypes with different but overlapping ranges of adaptation. The two ecotypes differ in a range of characteristics, including flowering time. Flowering time determines the duration of vegetative development and therefore biomass accumulation, a key trait in bioenergy crops. No causal variants for flowering time differences between switchgrass ecotypes have, as yet, been identified. In this study, we mapped a robust flowering time quantitative trait locus (QTL) on chromosome 4K in a biparental F2 population and characterized the flowering-associated transcription factor gene PvHd1, an ortholog of CONSTANS in Arabidopsis and Heading date 1 in rice, as the underlying causal gene. Protein modeling predicted that a serine to glycine substitution at position 35 (p.S35G) in B-Box domain 1 greatly altered the global structure of the PvHd1 protein. The predicted variation in protein compactness was supported in vitro by a 4 °C shift in denaturation temperature. Overexpressing the PvHd1-p.35S allele in a late-flowering CONSTANS-null Arabidopsis mutant rescued earlier flowering, whereas PvHd1-p.35G had a reduced ability to promote flowering, demonstrating that the structural variation led to functional divergence. Our findings provide us with a tool to manipulate the timing of floral transition in switchgrass cultivars and, potentially, expand their cultivation range.
| S-EPMC10540729 | biostudies-literature
Project description:BackgroundThe functional sites of a protein present important information for determining its cellular function and are fundamental in drug design. Accordingly, accurate methods for the prediction of functional sites are of immense value. Most available methods are based on a set of homologous sequences and structural or evolutionary information, and assume that functional sites are more conserved than the average. In the analysis presented here, we have investigated the conservation of location and type of amino acids at functional sites, and compared the behaviour of functional sites between different protein domains.ResultsFunctional sites were extracted from experimentally determined structural complexes from the Protein Data Bank harbouring a conserved protein domain from the SMART database. In general, functional (i.e. interacting) sites whose location is more highly conserved are also more conserved in their type of amino acid. However, even highly conserved functional sites can present a wide spectrum of amino acids. The degree of conservation strongly depends on the function of the protein domain and ranges from highly conserved in location and amino acid to very variable. Differentiation by binding partner shows that ion binding sites tend to be more conserved than functional sites binding peptides or nucleotides.ConclusionThe results gained by this analysis will help improve the accuracy of functional site prediction and facilitate the characterization of unknown protein sequences.
| S-EPMC1215474 | biostudies-literature
Project description:The native three-dimensional structure of a single protein is determined by the physicochemical nature of its constituent amino acids. The 20 different types of amino acids, depending on their physicochemical properties, can be grouped into three major classes: hydrophobic, hydrophilic, and charged. The anatomy of the weighted and unweighted networks of hydrophobic, hydrophilic, and charged residues separately for a large number of proteins were studied. Results showed that the average degree of the hydrophobic networks has a significantly larger value than that of hydrophilic and charged networks. The average degree of the hydrophilic networks is slightly higher than that of the charged networks. The average strength of the nodes of hydrophobic networks is nearly equal to that of the charged network, whereas that of hydrophilic networks has a smaller value than that of hydrophobic and charged networks. The average strength for each of the three types of networks varies with its degree. The average strength of a node in a charged network increases more sharply than that of the hydrophobic and hydrophilic networks. Each of the three types of networks exhibits the "small-world" property. Our results further indicate that the all-amino-acids networks and hydrophobic networks are of assortative type. Although most of the hydrophilic and charged networks are of the assortative type, few others have the characteristics of disassortative mixing of the nodes. We have further observed that all-amino-acids networks and hydrophobic networks bear the signature of hierarchy, whereas the hydrophilic and charged networks do not have any hierarchical signature.
| S-EPMC1914426 | biostudies-literature
Project description:To study the molecular basis for antibody diversity and the structural basis for antigen binding, we have characterized the loss of phosphocholine (P-Cho) binding both experimentally and computationally in U10, a somatic mutant of the antibody S107. Nucleotide sequencing of U10 shows a single base change in JH1, substituting Asp-101 with Ala, over 9 A distant from the P-Cho-binding pocket. Probing with antiidiotypic antibodies suggests local, not global, conformational changes. Computational results support a specific structural mechanism for the loss of P-Cho binding. The U10 mutation eliminates the charged interaction between Asp-101 and Arg-94, which allows the Arg-94 side chain to disrupt P-Cho binding sterically and electrostatically by folding into the P-Cho-binding site. These results specifically show the importance of the Arg-94 to Asp-101 side chain salt bridge in the heavy-chain CDR3 conformation and suggest that residues distant from the binding site play an important role in antibody diversity and inducible complementarity.
| S-EPMC297657 | biostudies-other
Project description:BackgroundThe completion of maize genome sequencing has resulted in the identification of a large number of uncharacterized genes. Gene annotation and functional characterization of gene products are important to uncover novel protein functionality.ResultsIn this paper, we identify, and annotate members of all the maize aldehyde dehydrogenase (ALDH) gene superfamily according to the revised nomenclature criteria developed by ALDH Gene Nomenclature Committee (AGNC). The maize genome contains 24 unique ALDH sequences encoding members of ten ALDH protein families including the previously identified male fertility restoration RF2A gene, which encodes a member of mitochondrial class 2 ALDHs. Using computational modeling analysis we report here the identification, the physico-chemical properties, and the amino acid residue analysis of a novel tunnel like cavity exclusively found in the maize sterility restorer protein, RF2A/ALDH2B2 by which this protein is suggested to bind variably long chain molecular ligands and/or potentially harmful molecules.ConclusionsOur finding indicates that maize ALDH superfamily is the most expanded of plant ALDHs ever characterized, and the mitochondrial maize RF2A/ALDH2B2 is the only plant ALDH that harbors a newly defined pocket/cavity with suggested functional specificity.
| S-EPMC3022562 | biostudies-literature
Project description:Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.
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Project description:Carcinogenesis is a complex process with multiple genetic and environmental factors contributing to the development of one or more tumors. Understanding the underlying mechanism of this process and identifying related markers to assess the outcome of this process would lead to more directed treatment and thus significantly reduce the mortality rate of cancers. Recently, molecular diagnostics and prognostics based on the identification of patterns within gene expression profiles in the context of protein interaction networks were reported. However, the predictive performances of these approaches were limited. In this study we propose a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain interaction network in human proteome. We first developed a model to score each protein by quantifying the domain connections to its interacting partners and the somatic mutations present in the domain. We then defined proteins as gene signatures if their scores were above a preset threshold. Next, for each gene signature, we quantified the correlation of the expression levels between this gene signature and its neighboring proteins. The results of the quantification in each patient were then used to predict cancer outcome by a modified naïve Bayes classifier. In this study we achieved a favorable accuracy of 88.3%, sensitivity of 87.2%, and specificity of 88.9% on a set of well-documented gene expression profiles of 253 consecutive breast cancer patients with different outcomes. We also compiled a list of cancer-associated gene signatures and domains, which provided testable hypotheses for further experimental investigation. Our approach proved successful on different independent breast cancer data sets as well as an ovarian cancer data set. This study constitutes the first predictive method to classify cancer outcomes based on the relationship between the domain organization and protein network.
| S-EPMC3068924 | biostudies-literature
Project description:All the protein sequences from SWISS-PROT database were analyzed for occurrence of single amino acid repeats, tandem oligo-peptide repeats, and periodically conserved amino acids. Single amino acid repeats of glutamine, serine, glutamic acid, glycine, and alanine seem to be tolerated to a considerable extent in many proteins. Tandem oligo-peptide repeats of different types with varying levels of conservation were detected in several proteins and found to be conspicuous, particularly in structural and cell surface proteins. It appears that repeated sequence patterns may be a mechanism that provides regular arrays of spatial and functional groups, useful for structural packing or for one to one interactions with target molecules. To facilitate further explorations, a database of Tandem Repeats in Protein Sequences (TRIPS) has been developed and is available at URL: http://www.ncl-india.org/trips.
| S-EPMC2144659 | biostudies-other