Dennis R. Livesay

Probalign: multiple sequence alignment using partition function posterior probabilities

MOTIVATION The maximum expected accuracy optimization criterion for multiple sequence alignment uses pairwise posterior probabilities of residues to align sequences. The partition function methodology is one way of estimating these probabilities. Here, we combine these two ideas for the first time to construct maximal expected accuracy sequence alignments. RESULTS We bridge the two techniques within the program Probalign. Our results indicate that Probalign alignments are generally more accurate than other leading multiple sequence alignment methods (i.e. Probcons, MAFFT and MUSCLE) on the BAliBASE 3.0 protein alignment benchmark. Similarly, Probalign also outperforms these methods on the HOMSTRAD and OXBENCH benchmarks. Probalign ranks statistically highest (P-value < 0.005) on all three benchmarks. Deeper scrutiny of the technique indicates that the improvements are largest on datasets containing N/C-terminal extensions and on datasets containing long and heterogeneous length proteins. These points are demonstrated on both real and simulated data. Finally, our method also produces accurate alignments on long and heterogeneous length datasets containing protein repeats. Here, alignment accuracy scores are at least 10% and 15% higher than the other three methods when standard deviation of length is >300 and 400, respectively. AVAILABILITY Open source code implementing Probalign as well as for producing the simulated data, and all real and simulated data are freely available from http://www.cs.njit.edu/usman/probalign

Predicting protein functional sites with phylogenetic motifs.

In this report, we demonstrate that phylogenetic motifs, sequence regions conserving the overall familial phylogeny, represent a promising approach to protein functional site prediction. Across our structurally and functionally heterogeneous data set, phylogenetic motifs consistently correspond to functional sites defined by both surface loops and active site clefts. Additionally, the partially buried prosthetic group regions of cytochrome P450 and succinate dehydrogenase are identified as phylogenetic motifs. In nearly all instances, phylogenetic motifs are structurally clustered, despite little overall sequence proximity, around key functional site features. Based on calculated false‐positive expectations and standard motif identification methods, we show that phylogenetic motifs are generally conserved in sequence. This result implies that they can be considered motifs in the traditional sense as well. However, there are instances where phylogenetic motifs are not (overall) well conserved in sequence. This point is enticing, because it implies that phylogenetic motifs are able to identify key sequence regions that traditional motif‐based approaches would not. Further, phylogenetic motif results are also shown to be consistent with evolutionary trace results, and bootstrapping is used to demonstrate tree significance. Proteins 2005.

A flexible approach for understanding protein stability

A distance constraint model (DCM) is presented that identifies flexible regions within protein structure consistent with specified thermodynamic condition. The DCM is based on a rigorous free energy decomposition scheme representing structure as fluctuating constraint topologies. Entropy non‐additivity is problematic for naive decompositions, limiting the success of heat capacity predictions. The DCM resolves non‐additivity by summing over independent entropic components determined by an efficient network‐rigidity algorithm. A minimal 3‐parameter DCM is demonstrated to accurately reproduce experimental heat capacity curves. Free energy landscapes and quantitative stability‐flexibility relationships are obtained in terms of global flexibility. Several connections to experiment are made.

Conserved quantitative stability/flexibility relationships (QSFR) in an orthologous RNase H pair

Many reports qualitatively describe conserved stability and flexibility profiles across protein families, but biophysical modeling schemes have not been available to robustly quantify both. Here we investigate an orthologous RNase H pair by using a minimal distance constraint model (DCM). The DCM is an all atom microscopic model [Jacobs and Dallakyan, Biophys J 2005;88(2):903–915] that accurately reproduces heat capacity measurements [Livesay et al., FEBS Lett 2004;576(3):468–476], and is unique in its ability to harmoniously calculate thermodynamic stability and flexibility in practical computing times. Consequently, quantified stability/flexibility relationships (QSFR) can be determined using the DCM. For the first time, a comparative QSFR analysis is performed, serving as a paradigm study to illustrate the utility of a QSFR analysis for elucidating evolutionarily conserved stability and flexibility profiles. Despite global conservation of QSFR profiles, distinct enthalpy‐entropy compensation mechanisms are identified between the RNase H pair. In both cases, local flexibility metrics parallel H/D exchange experiments by correctly identifying the folding core and several flexible regions. Remarkably, at appropriately shifted temperatures (e.g., melting temperature), these differences lead to a global conservation in Landau free energy landscapes, which directly relate thermodynamic stability to global flexibility. Using ensemble‐based sampling within free energy basins, rigidly, and flexibly correlated regions are quantified through cooperativity correlation plots. Five conserved flexible regions are identified within the structures of the orthologous pair. Evolutionary conservation of these flexibly correlated regions is strongly suggestive of their catalytic importance. Conclusions made herein are demonstrated to be robust with respect to the DCM parameterization. Proteins 2006.

How accurate and statistically robust are catalytic site predictions based on closeness centrality

BackgroundWe examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid α-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex i and all other vertices.ResultsWe benchmark the approach against 283 structurally unique proteins within the Catalytic Site Atlas. Our results, which are inline with previous investigations of smaller datasets, indicate closeness centrality predictions are statistically significant. However, unlike previous approaches, we specifically focus on residues with the very best scores. Over the top five closeness centrality scores, we observe an average true to false positive rate ratio of 6.8 to 1. As demonstrated previously, adding a solvent accessibility filter significantly improves predictive power; the average ratio is increased to 15.3 to 1. We also demonstrate (for the first time) that filtering the predictions by residue identity improves the results even more than accessibility filtering. Here, we simply eliminate residues with physiochemical properties unlikely to be compatible with catalytic requirements from consideration. Residue identity filtering improves the average true to false positive rate ratio to 26.3 to 1. Combining the two filters together has little affect on the results. Calculated p-values for the three prediction schemes range from 2.7E-9 to less than 8.8E-134. Finally, the sensitivity of the predictions to structure choice and slight perturbations is examined.ConclusionOur results resolutely confirm that closeness centrality is a viable prediction scheme whose predictions are statistically significant. Simple filtering schemes substantially improve the methods predicted power. Moreover, no clear effect on performance is observed when comparing ligated and unligated structures. Similarly, the CC prediction results are robust to slight structural perturbations from molecular dynamics simulation.

Conferring Thermostability to Mesophilic Proteins through Optimized Electrostatic Surfaces

Recently, there have been several experimental reports of proteins displaying appreciable stability gains through mutation of one or two amino acid residues. Here, we employ a simple theoretical model to quickly screen mutant structures for increased thermostability through optimization of the proteins electrostatic surface. Our results are able to reproduce the experimental observation that elimination of like-charge repulsions and creation of opposite-charge attractions on the protein surface is an efficient method to confer thermostability to a mesophilic protein. Using Poisson-Boltzmann electrostatics, we calculate relative protein stabilities for the exhaustive surface mutagenesis of the cold shock, RNase T1, and CheY proteins. Comparison with 25 experimentally characterized cold shock protein mutants reveals an average correlation of 0.86. The model is also quantitatively accurate when reproducing the experimental D49A and D49H mutant stabilities of RNase T1. This work represents the first comprehensive in silico screening of mutant candidates likely to confer thermostability to mesophilic proteins through optimization of surface electrostatics. Systematic single mutant, followed by double mutant, screening yields a limited number of mutant structures displaying significant stability gains suitable for subsequent experimental characterization.

Hydrogen bond networks determine emergent mechanical and thermodynamic properties across a protein family

BackgroundGram-negative bacteria use periplasmic-binding proteins (bPBP) to transport nutrients through the periplasm. Despite immense diversity within the recognized substrates, all members of the family share a common fold that includes two domains that are separated by a conserved hinge. The hinge allows the protein to cycle between open (apo) and closed (ligated) conformations. Conformational changes within the proteins depend on a complex interplay of mechanical and thermodynamic response, which is manifested as an increase in thermal stability and decrease of flexibility upon ligand binding.ResultsWe use a distance constraint model (DCM) to quantify the give and take between thermodynamic stability and mechanical flexibility across the bPBP family. Quantitative stability/flexibility relationships (QSFR) are readily evaluated because the DCM links mechanical and thermodynamic properties. We have previously demonstrated that QSFR is moderately conserved across a mesophilic/thermophilic RNase H pair, whereas the observed variance indicated that different enthalpy-entropy mechanisms allow similar mechanical response at their respective melting temperatures. Our predictions of heat capacity and free energy show marked diversity across the bPBP family. While backbone flexibility metrics are mostly conserved, cooperativity correlation (long-range couplings) also demonstrate considerable amount of variation. Upon ligand removal, heat capacity, melting point, and mechanical rigidity are, as expected, lowered. Nevertheless, significant differences are found in molecular cooperativity correlations that can be explained by the detailed nature of the hydrogen bond network.ConclusionNon-trivial mechanical and thermodynamic variation across the family is explained by differences within the underlying H-bond networks. The mechanism is simple; variation within the H-bond networks result in altered mechanical linkage properties that directly affect intrinsic flexibility. Moreover, varying numbers of H-bonds and their strengths control the likelihood for energetic fluctuations as H-bonds break and reform, thus directly affecting thermodynamic properties. Consequently, these results demonstrate how unexpected large differences, especially within cooperativity correlation, emerge from subtle differences within the underlying H-bond network. This inference is consistent with well-known results that show allosteric response within a family generally varies significantly. Identifying the hydrogen bond network as a critical determining factor for these large variances may lead to new methods that can predict such effects.

A critical evaluation of correlated mutation algorithms and coevolution within allosteric mechanisms.

The notion of using the evolutionary history encoded within multiple sequence alignments to predict allosteric mechanisms is appealing. In this approach, correlated mutations are expected to reflect coordinated changes that maintain intramolecular coupling between residue pairs. Despite much early fanfare, the general suitability of correlated mutations to predict allosteric couplings has not yet been established. Lack of progress along these lines has been hindered by several algorithmic limitations including phylogenetic artifacts within alignments masking true covariance and the computational intractability of consideration of more than two correlated residues at a time. Recent progress in algorithm development, however, has been substantial with a new generation of correlated mutation algorithms that have made fundamental progress toward solving these difficult problems. Despite these encouraging results, there remains little evidence to suggest that the evolutionary constraints acting on allosteric couplings are sufficient to be recovered from multiple sequence alignments. In this review, we argue that due to the exquisite sensitivity of protein dynamics, and hence that of allosteric mechanisms, the latter vary widely within protein families. If it turns out to be generally true that even very similar homologs display a wide divergence of allosteric mechanisms, then even a perfect correlated mutation algorithm could not be reliably used as a general mechanism for discovery of allosteric pathways.

On the role of structural class of a protein with two-state folding kinetics in determining correlations between its size, topology, and folding rate

The time it takes for proteins to fold into their native states varies over several orders of magnitude depending on their native‐state topology, size, and amino acid composition. In a number of previous studies, it was found that there is strong correlation between logarithmic folding rates and contact order for proteins that fold with two‐state kinetics, while such correlation is absent for three‐state proteins. Conversely, strong correlations between folding rates and chain length occur within three‐state proteins, but not in two‐state proteins. Here, we demonstrate that chain lengths and folding rates of two‐state proteins are not correlated with each other only when all‐α, all‐β, and mixed‐class proteins are considered together, which is typically the case. However, when considering all‐α and all‐β two‐state proteins separately, there is significant linear correlation between folding rate and size. Moreover, the sets of data points for the all‐α and all‐β classes define asymptotes of lower and upper limits on folding rates of mixed‐class proteins. By analyzing correlation of other topological parameters with folding rates of two‐state proteins, we find that only the long‐range order exhibits correlation with folding rates that is uniform over all three classes. It is also the only descriptor to provide statistically significant correlations for each of the three structural classes. We give an interpretation of this observation in terms of Makarov and Plaxcos diffusion‐based topomer‐search model.

The evolutionary origins and catalytic importance of conserved electrostatic networks within TIM-barrel proteins

Conservation of function is the basic tenet of protein evolution. Conservation of key electrostatic properties is a frequently employed mechanism that leads to conserved function. In a previous report, we identified several conserved electrostatic properties in four protein families and one functionally diverse enzyme superfamily. In this report, we demonstrate the evolutionary and catalytic importance of electrostatic networks in three ubiquitous metabolic enzymes: triosephosphate isomerase, enolase, and transaldolase. Evolutionary importance is demonstrated using phylogenetic motifs (sequence fragments that parallel the overall familial phylogeny). Phylogenetic motifs frequently correspond to both catalytic residues and conserved interactions that fine‐tune catalytic residue pKa values. Further, in the case of triosephosphate isomerase, quantitative differences in the catalytic Glu169 pKa values parallel subfamily differentiation. Finally, phylogenetic motifs are shown to structurally cluster around the active sites of eight different TIM‐barrel families. Depending upon the mechanistic requisites of each reaction catalyzed, interruptions to the canonical fold may or may not be identified as phylogenetic motifs.