How well can the accuracy of comparative protein structure models be predicted?

Comparative structure models are available for two orders of magnitude more protein sequences than are experimentally determined structures. These models, however, suffer from two limitations that experimentally determined structures do not: They frequently contain significant errors, and their accuracy cannot be readily assessed. We have addressed the latter limitation by developing a protocol optimized specifically for predicting the Calpha root-mean-squared deviation (RMSD) and native overlap (NO3.5A) errors of a model in the absence of its native structure. In contrast to most traditional assessment scores that merely predict one model is more accurate than others, this approach quantifies the error in an absolute sense, thus helping to determine whether or not the model is suitable for intended applications. The assessment relies on a model-specific scoring function constructed by a support vector machine. This regression optimizes the weights of up to nine features, including various sequence similarity measures and statistical potentials, extracted from a tailored training set of models unique to the model being assessed: If possible, we use similarly sized models with the same fold; otherwise, we use similarly sized models with the same secondary structure composition. This protocol predicts the RMSD and NO3.5A errors for a diverse set of 580,317 comparative models of 6174 sequences with correlation coefficients (r) of 0.84 and 0.86, respectively, to the actual errors. This scoring function achieves the best correlation compared to 13 other tested assessment criteria that achieved correlations ranging from 0.35 to 0.71.     

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.     

How do trabeculae affect the calculation of structural properties of bone?

One difficulty that arises in an analysis of the cross-sectional properties of bone is whether to include cancellous bone in the analysis. The purpose of this paper is to determine how different amounts of cancellous bone affect the measurement of structural properties of bone cross-sections. Thirty-two tibial and femoral cross-sections were chosen at random from a series of cross-sectioned nonhuman primate bones. Geometrical properties were calculated for the cross-sections, and torsional and bending stress analyses were performed. The results suggest that the effect of including cancellous bone in the analysis is closely related to the amount of bone, where it lies within the cross-section, and the type of analysis performed. Including cancellous bone in calculations of structural properties of bone cross-sections may cause the strength and stiffness of the bone to be exaggerated.     

How reliably can we predict the reliability of protein structure predictions?

Background  

Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences.     

How good is prediction of protein structural class by the component-coupled method?

Proteins of known structures are usually classified into four structural classes: all-alpha, all-beta, alpha+beta, and alpha/beta type of proteins. A number of methods to predicting the structural class of a protein based on its amino acid composition have been developed during the past few years. Recently, a component-coupled method was developed for predicting protein structural class according to amino acid composition. This method is based on the least Mahalanobis distance principle, and yields much better predicted results in comparison with the previous methods. However, the success rates reported for structural class prediction by different investigators are contradictory. The highest reported accuracies by this method are near 100%, but the lowest one is only about 60%. The goal of this study is to resolve this paradox and to determine the possible upper limit of prediction rate for structural classes. In this paper, based on the normality assumption and the Bayes decision rule for minimum error, a new method is proposed for predicting the structural class of a protein according to its amino acid composition. The detailed theoretical analysis indicates that if the four protein folding classes are governed by the normal distributions, the present method will yield the optimum predictive result in a statistical sense. A non-redundant data set of 1,189 protein domains is used to evaluate the performance of the new method. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein. The apparent relatively high accuracy level (more than 90%) attained in the previous studies was due to the preselection of test sets, which may not be adequately representative of all unrelated proteins.     

Membrane protein structural biology – How far can the bugs take us? (Review)

Membrane proteins are core components of many essential cellular processes, and high-resolution structural data is therefore highly sought after. However, owing to the many bottlenecks associated with membrane protein crystallization, progress has been slow. One major problem is our inability to obtain sufficient quantities of membrane proteins for crystallization trials. Traditionally, membrane proteins have been isolated from natural sources, or for prokaryotic proteins, expressed by recombinant techniques. We are however a long way away from a streamlined overproduction of eukaryotic proteins. With this technical limitation in mind, we have probed the question as to how far prokaryotic homologues can take us towards a structural understanding of the eukaryotic/human membrane proteome(s).     

Membrane protein structural biology--how far can the bugs take us?

Membrane proteins are core components of many essential cellular processes, and high-resolution structural data is therefore highly sought after. However, owing to the many bottlenecks associated with membrane protein crystallization, progress has been slow. One major problem is our inability to obtain sufficient quantities of membrane proteins for crystallization trials. Traditionally, membrane proteins have been isolated from natural sources, or for prokaryotic proteins, expressed by recombinant techniques. We are however a long way away from a streamlined overproduction of eukaryotic proteins. With this technical limitation in mind, we have probed the question as to how far prokaryotic homologues can take us towards a structural understanding of the eukaryotic/human membrane proteome(s).     

How high is the level of technical noise in microarray data?

Background  

Microarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the MicroArray Quality Control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data.     

How well can we understand large-scale protein motions using normal modes of elastic network models?

In this article, we apply a coarse-grained elastic network model (ENM) to study conformational transitions to address the following questions: How well can a conformational change be predicted by the mode motions? Is there a way to improve the model to gain better results? To answer these questions, we use a dataset of 170 pairs having "open" and "closed" structures from Gerstein's protein motion database. Our results show that the conformational transitions fall into three categories: 1), the transitions of these proteins that can be explained well by ENM; 2), the transitions that are not explained well by ENM, but the results are significantly improved after considering the rigidity of some residue clusters and modeling them accordingly; and 3), the intrinsic nature of these transitions, specifically the low degree of collectivity, prevents their conformational changes from being represented well with the low frequency modes of any elastic network models. Our results thus indicate that the applicability of ENM for explaining conformational changes is not limited by the size of the studied protein or even the scale of the conformational change. Instead, it depends strongly on how collective the transition is.     

How well do reduced models capture the dynamics in models of interacting neurons?

This paper introduces a class of stochastic models of interacting neurons with emergent dynamics similar to those seen in local cortical populations. Rigorous results on existence and uniqueness of nonequilibrium steady states are proved. These network models are then compared to very simple reduced models driven by the same mean excitatory and inhibitory currents. Discrepancies in firing rates between network and reduced models are investigated and explained by correlations in spiking, or partial synchronization, working in concert with “nonlinearities” in the time evolution of membrane potentials. The use of simple random walks and their first passage times to simulate fluctuations in neuronal membrane potentials and interspike times is also considered.

     

How do point amino acid substitutions affect the protein structure?

In order to estimate the influence of point mutations on the protein structure, 552 pairs of structurally aligned proteins with pairwise sequence identities greater than 60% were analyzed. We selected all isolated point mismatches from these alignments. The statistics of local conformational changes corresponding to these mismatches was studied. This study revealed two surprising aspects: (a) only an extremely minor fraction (< 3%) of all observed mutations leads to significant changes in local conformations; (b) observed substitutions, which affect local conformation are more frequently those of similar (high scoring) amino acid pairs rather than the substitutions thought of as "hazardous" (low scoring), although the overall average score of the changing substitutions is still lower than the corresponding average score for the substitutions that leave the structure unchanged.     

How do misassigned paternities affect the estimation of heritability in the wild?

Studies of birds have recently played an important role in the increasing success of quantitative genetics applied to natural populations. However, these studies mostly base their pedigree relationships on social information, despite the known widespread genetic polygamy in avian species. Here, we study the influence of misassigned paternities, combined with the effect of pedigree size and depth, on the estimation of heritability. First, we compute simulations of a polygenic trait for two levels of heritability (0.1 and 0.4), several extra-pair paternity rates (ranging from 5% to 40%), and varying sample sizes (20, 50 and 100 broods) or pedigree depth (2 or 4 generations). We compare heritability estimates from the social and the genetic pedigree, running a restricted maximum-likelihood 'animal model'. Social pedigree underestimates heritability by an average of 0-17% for 5-20% extra-pair paternities and by up to 18% for 40% extra-pair paternities and a heritability of 0.4. Second, we identifyied extra-pair offspring using microsatellite loci in two populations of blue tits (Parus caeruleus) showing high levels of extra-pair paternities (15% and 25% of extra-pair offspring). We compare heritabilities of tarsus length and body mass estimated with pedigrees of increasing accuracy. These analyses suggest that the bias induced by misassigned paternities on heritability estimation depends on the level of heritability and the rate of paternity error. Typical rates of extra-pair paternities in birds (around 20% of offspring) should result in an underestimation of heritability of less than 15% when estimated over a minimum of 100 broods.     

Does the speed of shortening affect steady-state force depression in cat soleus muscle?

It has been stated repeatedly for the past 50 years that the steady-state force depression following shortening of an activated muscle depends on the speed of shortening. However, these statements were based on results from experiments in which muscles were shortened at different speeds but identical activation levels. Therefore, the force during shortening was changed in accordance with the force-velocity relationship of muscles: that is, increasing speeds of shortening were associated with decreasing forces, and vice versa. Consequently, it is not possible at present to distinguish whether force depression is caused by the changes in speed, as frequently stated, or the associated changes in force, or both. The purpose of this study was to test if force depression depends on the speed of shortening. We hypothesized that force depression was dependent on the force but not the speed of contraction. Our prediction is that the amount of force depression after shortening contractions at different speeds could be similar if the force during contraction was controlled at a similar level. Cat soleus muscles (n=7) were shortened by 9 or 12 mm at speeds of 3, 9, and 27 mm/s, first with a constant activation during shortening (30Hz), then with activation levels that were reduced (<30Hz) for the slow speeds (3 and 9 mm/s) to approximate the shortening forces of the fast speed contractions (27 mm/s). If done properly, force depression could be precisely matched at the three different speeds, indicating that force depression was related to the force during the shortening contraction but not to the speed. However, in order to match force depression, the forces during shortening had to be systematically greater for the slow compared to the fast speeds of shortening, suggesting that force depression also depends on the level of activation, as force depression at constant activation levels can only be matched if the force during shortening, evaluated by the mechanical work, is identical. Therefore, we conclude that force depression depends on the force and activation level during shortening, but does not depend on the speed of shortening as has been assumed for half a century. These results support, but do not prove, the current hypothesis that force depression is caused by a stress-related cross-bridge inhibition in the actin-myosin overlap zone that is newly formed during muscle shortening.     

How pregnancy can affect autoimmune diseases progression?

Autoimmune disorders are characterized by tissue damage, caused by self-reactivity of different effectors mechanisms of the immune system, namely antibodies and T cells. Their occurrence may be associated with genetic and/or environmental predisposition and to some extent, have implications for fertility and obstetrics. The relationship between autoimmunity and reproduction is bidirectional. This review only addresses the impact of pregnancy on autoimmune diseases and not the influence of autoimmunity on pregnancy development. Th17/Th1-type cells are aggressive and pathogenic in many autoimmune disorders and inflammatory diseases. The immunology of pregnancy underlies the role of Th2-type cytokines to maintain the tolerance of the mother towards the fetal semi-allograft. Non-specific factors, including hormonal changes, favor a switch to Th2-type cytokine profile. In pregnancy Th2, Th17/Th2 and Treg cells accumulate in the decidua but may also be present in the mother’s circulation and can regulate autoimmune responses influencing the progression of autoimmune diseases.     

How does averaging affect protein structure comparison on the ensemble level?

Recent algorithmic advances and continual increase in computational power have made it possible to simulate protein folding and dynamics on the level of ensembles. Furthermore, analyzing protein structure by using ensemble representation is intrinsic to certain experimental techniques, such as nuclear magnetic resonance. This creates a problem of how to compare an ensemble of molecules with a given reference structure. Recently, we used distance-based root-mean-square deviation (dRMS) to compare the native structure of a protein with its unfolded-state ensemble. We showed that for small, mostly alpha-helical proteins, the mean unfolded-state Calpha-Calpha distance matrix is significantly more nativelike than the Calpha-Calpha matrices corresponding to the individual members of the unfolded ensemble. Here, we give a mathematical derivation that shows that, for any ensemble of structures, the dRMS deviation between the ensemble-averaged distance matrix and any given reference distance matrix is always less than or equal to the average dRMS deviation of the individual members of the ensemble from the same reference matrix. This holds regardless of the nature of the reference structure or the structural ensemble in question. In other words, averaging of distance matrices can only increase their level of similarity to a given reference matrix, relative to the individual matrices comprising the ensemble. Furthermore, we show that the above inequality holds in the case of Cartesian coordinate-based root-mean-square deviation as well. We discuss this in the context of our proposal that the average structure of the unfolded ensemble of small helical proteins is close to the native structure, and demonstrate that this finding goes beyond the above mathematical fact.     

How does mutation affect the distribution of phenotypes?

The potential for mutational processes to influence patterns of neutral or adaptive phenotypic evolution is not well understood. If mutations are directionally biased, shifting trait means in a particular direction, or if mutation generates more variance in some directions of multivariate trait space than others, mutation itself might be a source of bias in phenotypic evolution. Here, we use mutagenesis to investigate the affect of mutation on trait mean and (co)variances in zebrafish, Danio rerio. Mutation altered the relationship between age and both prolonged swimming speed and body shape. These observations suggest that mutational effects on ontogeny or aging have the potential to generate variance across the phenome. Mutations had a far greater effect in males than females, although whether this is a reflection of sex‐specific ontogeny or aging remains to be determined. In males, mutations generated positive covariance between swimming speed, size, and body shape suggesting the potential for mutation to affect the evolutionary covariation of these traits. Overall, our observations suggest that mutation does not generate equal variance in all directions of phenotypic space or in each sex, and that pervasive variation in ontogeny or aging within a cohort could affect the variation available to evolution.     

How do species interactions affect species distribution models?

One of the most promising recent advances in biogeography has been the increased interest and understanding of species distribution models – estimates of the probability that a species is present given environmental data. Unfortunately, such analyses ignore many aspects of ecology, and so are difficult to interpret. In particular, we know that species interactions have a profound influence on distributions, but it is not usually possible to incorporate this knowledge into species distribution models. What is needed is a rigorous understanding of how unmeasured biotic interactions affect the inferences generated by species distribution models. To fill this gap, we develop a general mathematical approach that uses probability theory to determine how unmeasured biotic interactions affect inferences from species distribution models. Using this approach, we reanalyze one of the most important classes of mechanistic models of competition: models of consumer resource dynamics. We determine how measurements of one aspect of the environment – a single environmental variable – can be used to estimate the probability that an environment is suitable with species distribution models. We show that species distribution models, which ignore numerous facets of consumer resource dynamics such as the presence of a competitor or the dynamics of depletable resources, can furnish useful predictions for the probability that an environment is suitable in some circumstances. These results provide a rigorous link between complex mechanistic models of species interactions and species distribution models. In so doing they demonstrate that unmeasured biotic interactions can have strong and counterintuitive consequences on species distribution models.     

How does competition affect the transmission of information?

The use of social information is a prerequisite to the evolution of culture. In humans, social learning allows individuals to aggregate adaptive information and increase the complexity of technology at a level unparalleled in the animal kingdom. However, the potential to use social information is related to the availability of this type of information. Although most cultural evolution experiments assume that social learners are free to use social information, there are many examples of information withholding, particularly in ethnographic studies. In this experiment, we used a computer-based cultural game in which players were faced with a complex task and had the possibility to trade a specific part of their knowledge within their groups. The dynamics of information transmission were studied when competition was within- or exclusively between-groups. Our results show that between-group competition improved the transmission of information, increasing the amount and the quality of information. Further, informational access costs did not prevent social learners from performing better than individual learners, even when between-group competition was absent. Interestingly, between-group competition did not entirely eliminate access costs and did not improve the performance of players as compared with within-group competition. These results suggest that the field of cultural evolution would benefit from a better understanding of the factors that underlie the production and the sharing of information.