Correlated Mutation Analysis on the Catalytic Domains of Serine/Threonine Protein Kinases

BACKGROUND:Protein kinases (PKs) have emerged as the largest family of signaling proteins in eukaryotic cells and are involved in every aspect of cellular regulation. Great progresses have been made in understanding the mechanisms of PKs phosphorylating their substrates, but the detailed mechanisms, by which PKs ensure their substrate specificity with their structurally conserved catalytic domains, still have not been adequately understood. Correlated mutation analysis based on large sets of diverse sequence data may provide new insights into this question. METHODOLOGY/PRINCIPAL FINDINGS:Statistical coupling, residue correlation and mutual information analyses along with clustering were applied to analyze the structure-based multiple sequence alignment of the catalytic domains of the Ser/Thr PK family. Two clusters of highly coupled sites were identified. Mapping these positions onto the 3D structure of PK catalytic domain showed that these two groups of positions form two physically close networks. We named these two networks as theta-shaped and gamma-shaped networks, respectively. CONCLUSIONS/SIGNIFICANCE:The theta-shaped network links the active site cleft and the substrate binding regions, and might participate in PKs recognizing and interacting with their substrates. The gamma-shaped network is mainly situated in one side of substrate binding regions, linking the activation loop and the substrate binding regions. It might play a role in supporting the activation loop and substrate binding regions before catalysis, and participate in product releasing after phosphoryl transfer. Our results exhibit significant correlations with experimental observations, and can be used as a guide to further experimental and theoretical studies on the mechanisms of PKs interacting with their substrates

Multidimensional mutual information methods for the analysis of covariation in multiple sequence alignments

Several methods are available for the detection of covarying positions from a multiple sequence alignment (MSA). If the MSA contains a large number of sequences, information about the proximities between residues derived from covariation maps can be sufficient to predict a protein fold. If the structure is already known, information on the covarying positions can be valuable to understand the protein mechanism. In this study we have sought to determine whether a multivariate extension of traditional mutual information (MI) can be an additional tool to study covariation. The performance of two multidimensional MI (mdMI) methods, designed to remove the effect of ternary/quaternary interdependencies, was tested with a set of 9 MSAs each containing <400 sequences, and was shown to be comparable to that of methods based on maximum entropy/pseudolikelyhood statistical models of protein sequences. However, while all the methods tested detected a similar number of covarying pairs among the residues separated by < 8 {\AA} in the reference X-ray structures, there was on average less than 65% overlap between the top scoring pairs detected by methods that are based on different principles. We have also attempted to identify whether the difference in performance among methods is due to different efficiency in removing covariation originating from chains of structural contacts. We found that the reason why methods that derive partial correlation between the columns of a MSA provide a better recognition of close contacts is not because they remove chaining effects, but because they filter out the correlation between distant residues that originates from general fitness constraints. In contrast we found that true chaining effects are expression of real physical perturbations that propagate inside proteins, and therefore are not removed by the derivation of partial correlation between variables.Comment: 21 pages, 4 figures, 1 table, supporting information containing 2 additional figures is included at the end of the manuscrip

Amino acid positions subject to multiple co-evolutionary 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 co-evolution between pairs of positions. Co-evolutionary 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 co-evolution 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 co-evolution 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 co-evolution 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 co-exist in evolutionary data that, together, give rise to emergent protein functions

Conserved and variable correlated mutations in the plant MADS protein network

<p>Abstract</p> <p>Background</p> <p>Plant MADS domain proteins are involved in a variety of developmental processes for which their ability to form various interactions is a key requisite. However, not much is known about the structure of these proteins or their complexes, whereas such knowledge would be valuable for a better understanding of their function. Here, we analyze those proteins and the complexes they form using a correlated mutation approach in combination with available structural, bioinformatics and experimental data.</p> <p>Results</p> <p>Correlated mutations are affected by several types of noise, which is difficult to disentangle from the real signal. In our analysis of the MADS domain proteins, we apply for the first time a correlated mutation analysis to a family of interacting proteins. This provides a unique way to investigate the amount of signal that is present in correlated mutations because it allows direct comparison of mutations in various family members and assessing their conservation. We show that correlated mutations in general are conserved within the various family members, and if not, the variability at the respective positions is less in the proteins in which the correlated mutation does not occur. Also, intermolecular correlated mutation signals for interacting pairs of proteins display clear overlap with other bioinformatics data, which is not the case for non-interacting protein pairs, an observation which validates the intermolecular correlated mutations. Having validated the correlated mutation results, we apply them to infer the structural organization of the MADS domain proteins.</p> <p>Conclusion</p> <p>Our analysis enables understanding of the structural organization of the MADS domain proteins, including support for predicted helices based on correlated mutation patterns, and evidence for a specific interaction site in those proteins.</p

Validation of Coevolving Residue Algorithms via Pipeline Sensitivity Analysis: ELSC and OMES and ZNMI, Oh My!

Correlated amino acid substitution algorithms attempt to discover groups of residues that co-fluctuate due to either structural or functional constraints. Although these algorithms could inform both ab initio protein folding calculations and evolutionary studies, their utility for these purposes has been hindered by a lack of confidence in their predictions due to hard to control sources of error. To complicate matters further, naive users are confronted with a multitude of methods to choose from, in addition to the mechanics of assembling and pruning a dataset. We first introduce a new pair scoring method, called ZNMI (Z-scored-product Normalized Mutual Information), which drastically improves the performance of mutual information for co-fluctuating residue prediction. Second and more important, we recast the process of finding coevolving residues in proteins as a data-processing pipeline inspired by the medical imaging literature. We construct an ensemble of alignment partitions that can be used in a cross-validation scheme to assess the effects of choices made during the procedure on the resulting predictions. This pipeline sensitivity study gives a measure of reproducibility (how similar are the predictions given perturbations to the pipeline?) and accuracy (are residue pairs with large couplings on average close in tertiary structure?). We choose a handful of published methods, along with ZNMI, and compare their reproducibility and accuracy on three diverse protein families. We find that (i) of the algorithms tested, while none appear to be both highly reproducible and accurate, ZNMI is one of the most accurate by far and (ii) while users should be wary of predictions drawn from a single alignment, considering an ensemble of sub-alignments can help to determine both highly accurate and reproducible couplings. Our cross-validation approach should be of interest both to developers and end users of algorithms that try to detect correlated amino acid substitutions

The Contribution of Coevolving Residues to the Stability of KDO8P Synthase

The evolutionary tree of 3-deoxy-D-manno-octulosonate 8-phosphate (KDO8P) synthase (KDO8PS), a bacterial enzyme that catalyzes a key step in the biosynthesis of bacterial endotoxin, is evenly divided between metal and non-metal forms, both having similar structures, but diverging in various degrees in amino acid sequence. Mutagenesis, crystallographic and computational studies have established that only a few residues determine whether or not KDO8PS requires a metal for function. The remaining divergence in the amino acid sequence of KDO8PSs is apparently unrelated to the underlying catalytic mechanism.The multiple alignment of all known KDO8PS sequences reveals that several residue pairs coevolved, an indication of their possible linkage to a structural constraint. In this study we investigated by computational means the contribution of coevolving residues to the stability of KDO8PS. We found that about 1/4 of all strongly coevolving pairs probably originated from cycles of mutation (decreasing stability) and suppression (restoring it), while the remaining pairs are best explained by a succession of neutral or nearly neutral covarions.Both sequence conservation and coevolution are involved in the preservation of the core structure of KDO8PS, but the contribution of coevolving residues is, in proportion, smaller. This is because small stability gains or losses associated with selection of certain residues in some regions of the stability landscape of KDO8PS are easily offset by a large number of possible changes in other regions. While this effect increases the tolerance of KDO8PS to deleterious mutations, it also decreases the probability that specific pairs of residues could have a strong contribution to the thermodynamic stability of the protein

The GC Content as a Main Factor Shaping the Amino Acid Usage During Bacterial Evolution Process

Understanding how proteins evolve is important, and the order of amino acids being recruited into the genetic codons was found to be an important factor shaping the amino acid composition of proteins. The latest work about the last universal common ancestor (LUCA) makes it possible to determine the potential factors shaping amino acid compositions during evolution. Those LUCA genes/proteins from Methanococcus maripaludis S2, which is one of the possible LUCA, were investigated. The evolutionary rates of these genes positively correlate with GC contents with P-value significantly lower than 0.05 for 94% homologous genes. Linear regression results showed that compositions of amino acids coded by GC-rich codons positively contribute to the evolutionary rates, while these amino acids tend to be gained in GC-rich organisms according to our results. The first principal component correlates with the GC content very well. The ratios of amino acids of the LUCA proteins coded by GC rich codons positively correlate with the GC content of different bacteria genomes, while the ratios of amino acids coded by AT rich codons negatively correlate with the increase of GC content of genomes. Next, we found that the recruitment order does correlate with the amino acid compositions, but gain and loss in codons showed newly recruited amino acids are not significantly increased along with the evolution. Thus, we conclude that GC content is a primary factor shaping amino acid compositions. GC content shapes amino acid composition to trade off the cost of amino acids with bases, which could be caused by the energy efficiency

Integrated Analysis of Residue Coevolution and Protein Structure in ABC Transporters

Intraprotein side chain contacts can couple the evolutionary process of amino acid substitution at one position to that at another. This coupling, known as residue coevolution, may vary in strength. Conserved contacts thus not only define 3-dimensional protein structure, but also indicate which residue-residue interactions are crucial to a protein’s function. Therefore, prediction of strongly coevolving residue-pairs helps clarify molecular mechanisms underlying function. Previously, various coevolution detectors have been employed separately to predict these pairs purely from multiple sequence alignments, while disregarding available structural information. This study introduces an integrative framework that improves the accuracy of such predictions, relative to previous approaches, by combining multiple coevolution detectors and incorporating structural contact information. This framework is applied to the ABC-B and ABC-C transporter families, which include the drug exporter P-glycoprotein involved in multidrug resistance of cancer cells, as well as the CFTR chloride channel linked to cystic fibrosis disease. The predicted coevolving pairs are further analyzed based on conformational changes inferred from outward- and inward-facing transporter structures. The analysis suggests that some pairs coevolved to directly regulate conformational changes of the alternating-access transport mechanism, while others to stabilize rigid-body-like components of the protein structure. Moreover, some identified pairs correspond to residues previously implicated in cystic fibrosis

Bioinformatické metody detekce koevoluce proteinů

The term coevolution describes the situation when two or more species or biomole- cules reciprocally affect each others' evolution. On the protein level, it is thought to be the main mechanism ensuring correct folding, interactions and function of a protein, and it can be observed both on the level of interacting protein families and individual amino acid residues. Coevolution studies have been proved to be a powerful tool for prediction of protein structure, function, interaction partners, etc. In this thesis, different algorithms used for detection of protein coevolution are described, as well as their applications and limitations. Keywords: coevolution, protein family, protein structure prediction, interac- tion partners, correlated mutations, mirrortree, mutual information, direct cou- pling analysisSlovem koevoluce popisujeme stav, kdy dva či více druhů nebo biomolekul vzá- jemně ovlivňují svou evoluci. Na proteinové úrovni je koevoluce považována za jeden z hlavních mechanismů zajišťujících správné sbalení, interakce a funkci pro- teinů. Pozorována může být jak na úrovni interagujících proteinových rodin, tak na úrovni jednotlivých aminokyselinových residuí. Studium koevoluce může být užitečným nástrojem při predikci struktury proteinů, jejich funkce, interakčních partnerů, apod. V této práci jsou popsány algoritmy, které jsou používány k detekci koevoluce proteinů, stejně jako jejich možné aplikace a omezení. Klíčová slova: koevoluce, proteinová rodina, predikce struktury proteinů, in- terakční partneři, korelované mutace, mirrortree, vzájemná informace, analýza přímého párováníDepartment of Cell BiologyKatedra buněčné biologieFaculty of SciencePřírodovědecká fakult