Omecamtiv Mecarbil Enhances the Duty Ratio of Human \u3cem\u3eβ\u3c/em\u3e-Cardiac Myosin Resulting in Increased Calcium Sensitivity and Slowed Force Development in Cardiac Muscle

The small molecule drug omecamtiv mecarbil (OM) specifically targets cardiac muscle myosin and is known to enhance cardiac muscle performance, yet its impact on human cardiac myosin motor function is unclear. We expressed and purified human β-cardiac myosin subfragment 1 (M2β-S1) containing a C-terminal Avi tag. We demonstrate that the maximum actin-activated ATPase activity of M2β-S1 is slowed more than 4-fold in the presence of OM, whereas the actin concentration required for half-maximal ATPase was reduced dramatically (30-fold). We find OM does not change the overall actin affinity. Transient kinetic experiments suggest that there are two kinetic pathways in the presence of OM. The dominant pathway results in a slow transition between actomyosin·ADP states and increases the time myosin is strongly bound to actin. However, OM also traps a population of myosin heads in a weak actin affinity state with slow product release. We demonstrate that OM can reduce the actin sliding velocity more than 100-fold in the in vitro motility assay. The ionic strength dependence of in vitro motility suggests the inhibition may be at least partially due to drag forces from weakly attached myosin heads. OM causes an increase in duty ratio examined in the motility assay. Experiments with permeabilized human myocardium demonstrate that OM increases calcium sensitivity and slows force development (ktr) in a concentration-dependent manner, whereas the maximally activated force is unchanged. We propose that OM increases the myosin duty ratio, which results in enhanced calcium sensitivity but slower force development in human myocardium

Regression applied to protein binding site prediction and comparison with classification

<p>Abstract</p> <p>Background</p> <p>The structural genomics centers provide hundreds of protein structures of unknown function. Therefore, developing methods enabling the determination of a protein function automatically is imperative. The determination of a protein function can be achieved by studying the network of its physical interactions. In this context, identifying a potential binding site between proteins is of primary interest. In the literature, methods for predicting a potential binding site location generally are based on classification tools. The aim of this paper is to show that regression tools are more efficient than classification tools for patches based binding site predictors. For this purpose, we developed a patches based binding site localization method usable with either regression or classification tools.</p> <p>Results</p> <p>We compared predictive performances of regression tools with performances of machine learning classifiers. Using leave-one-out cross-validation, we showed that regression tools provide better predictions than classification ones. Among regression tools, Multilayer Perceptron ranked highest in the quality of predictions. We compared also the predictive performance of our patches based method using Multilayer Perceptron with the performance of three other methods usable through a web server. Our method performed similarly to the other methods.</p> <p>Conclusion</p> <p>Regression is more efficient than classification when applied to our binding site localization method. When it is possible, using regression instead of classification for other existing binding site predictors will probably improve results. Furthermore, the method presented in this work is flexible because the size of the predicted binding site is adjustable. This adaptability is useful when either false positive or negative rates have to be limited.</p

A Structure-Based Approach for Detection of Thiol Oxidoreductases and Their Catalytic Redox-Active Cysteine Residues

Cysteine (Cys) residues often play critical roles in proteins, for example, in the formation of structural disulfide bonds, metal binding, targeting proteins to the membranes, and various catalytic functions. However, the structural determinants for various Cys functions are not clear. Thiol oxidoreductases, which are enzymes containing catalytic redox-active Cys residues, have been extensively studied, but even for these proteins there is little understanding of what distinguishes their catalytic redox Cys from other Cys functions. Herein, we characterized thiol oxidoreductases at a structural level and developed an algorithm that can recognize these enzymes by (i) analyzing amino acid and secondary structure composition of the active site and its similarity to known active sites containing redox Cys and (ii) calculating accessibility, active site location, and reactivity of Cys. For proteins with known or modeled structures, this method can identify proteins with catalytic Cys residues and distinguish thiol oxidoreductases from the enzymes containing other catalytic Cys types. Furthermore, by applying this procedure to Saccharomyces cerevisiae proteins containing conserved Cys, we could identify the majority of known yeast thiol oxidoreductases. This study provides insights into the structural properties of catalytic redox-active Cys and should further help to recognize thiol oxidoreductases in protein sequence and structure databases

Recruitment of rare 3-grams at functional sites: Is this a mechanism for increasing enzyme specificity?

<p>Abstract</p> <p>Background</p> <p>A wealth of unannotated and functionally unknown protein sequences has accumulated in recent years with rapid progresses in sequence genomics, giving rise to ever increasing demands for developing methods to efficiently assess functional sites. Sequence and structure conservations have traditionally been the major criteria adopted in various algorithms to identify functional sites. Here, we focus on the distributions of the 20³different types of <it>3</it>-grams (or triplets of sequentially contiguous amino acid) in the entire space of sequences accumulated to date in the UniProt database, and focus in particular on the rare <it>3</it>-grams distinguished by their high entropy-based information content.</p> <p>Results</p> <p>Comparison of the UniProt distributions with those observed near/at the active sites on a non-redundant dataset of 59 enzyme/ligand complexes shows that the active sites preferentially recruit <it>3</it>-grams distinguished by their low frequency in the UniProt. Three cases, Src kinase, hemoglobin, and tyrosyl-tRNA synthetase, are discussed in details to illustrate the biological significance of the results.</p> <p>Conclusion</p> <p>The results suggest that recruitment of rare <it>3</it>-grams may be an efficient mechanism for increasing specificity at functional sites. Rareness/scarcity emerges as a feature that may assist in identifying key sites for proteins function, providing information complementary to that derived from sequence alignments. In addition it provides us (for the first time) with a means of identifying potentially functional sites from sequence information alone, when sequence conservation properties are not available.</p

Protein structure search and local structure characterization

<p>Abstract</p> <p>Background</p> <p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.</p> <p>Results</p> <p>We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at <url>http://140.113.166.178/safast/</url>.</p> <p>Conclusion</p> <p>The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.</p

Active Nuclear Receptors Exhibit Highly Correlated AF-2 Domain Motions

Nuclear receptor ligand binding domains (LBDs) convert ligand binding events into changes in gene expression by recruiting transcriptional coregulators to a conserved activation function-2 (AF-2) surface. While most nuclear receptor LBDs form homo- or heterodimers, the human nuclear receptor pregnane X receptor (PXR) forms a unique and essential homodimer and is proposed to assemble into a functional heterotetramer with the retinoid X receptor (RXR). How the homodimer interface, which is located 30 Å from the AF-2, would affect function at this critical surface has remained unclear. By using 20- to 30-ns molecular dynamics simulations on PXR in various oligomerization states, we observed a remarkably high degree of correlated motion in the PXR–RXR heterotetramer, most notably in the four helices that create the AF-2 domain. The function of such correlation may be to create “active-capable” receptor complexes that are ready to bind to transcriptional coactivators. Indeed, we found in additional simulations that active-capable receptor complexes involving other orphan or steroid nuclear receptors also exhibit highly correlated AF-2 domain motions. We further propose a mechanism for the transmission of long-range motions through the nuclear receptor LBD to the AF-2 surface. Taken together, our findings indicate that long-range motions within the LBD scaffold are critical to nuclear receptor function by promoting a mobile AF-2 state ready to bind coactivators

Mining protein loops using a structural alphabet and statistical exceptionality

<p>Abstract</p> <p>Background</p> <p>Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied.</p> <p>Results</p> <p>We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 Å). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints.</p> <p>Conclusions</p> <p>We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at <url>http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/</url>.</p

Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies

Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%–63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with “overprediction” of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation

Dynamically-Driven Inactivation of the Catalytic Machinery of the SARS 3C-Like Protease by the N214A Mutation on the Extra Domain

Despite utilizing the same chymotrypsin fold to host the catalytic machinery, coronavirus 3C-like proteases (3CLpro) noticeably differ from picornavirus 3C proteases in acquiring an extra helical domain in evolution. Previously, the extra domain was demonstrated to regulate the catalysis of the SARS-CoV 3CLpro by controlling its dimerization. Here, we studied N214A, another mutant with only a doubled dissociation constant but significantly abolished activity. Unexpectedly, N214A still adopts the dimeric structure almost identical to that of the wild-type (WT) enzyme. Thus, we conducted 30-ns molecular dynamics (MD) simulations for N214A, WT, and R298A which we previously characterized to be a monomer with the collapsed catalytic machinery. Remarkably, three proteases display distinctive dynamical behaviors. While in WT, the catalytic machinery stably retains in the activated state; in R298A it remains largely collapsed in the inactivated state, thus implying that two states are not only structurally very distinguishable but also dynamically well separated. Surprisingly, in N214A the catalytic dyad becomes dynamically unstable and many residues constituting the catalytic machinery jump to sample the conformations highly resembling those of R298A. Therefore, the N214A mutation appears to trigger the dramatic change of the enzyme dynamics in the context of the dimeric form which ultimately inactivates the catalytic machinery. The present MD simulations represent the longest reported so far for the SARS-CoV 3CLpro, unveiling that its catalysis is critically dependent on the dynamics, which can be amazingly modulated by the extra domain. Consequently, mediating the dynamics may offer a potential avenue to inhibit the SARS-CoV 3CLpro