Variational Methods for Biomolecular Modeling

Structure, function and dynamics of many biomolecular systems can be characterized by the energetic variational principle and the corresponding systems of partial differential equations (PDEs). This principle allows us to focus on the identification of essential energetic components, the optimal parametrization of energies, and the efficient computational implementation of energy variation or minimization. Given the fact that complex biomolecular systems are structurally non-uniform and their interactions occur through contact interfaces, their free energies are associated with various interfaces as well, such as solute-solvent interface, molecular binding interface, lipid domain interface, and membrane surfaces. This fact motivates the inclusion of interface geometry, particular its curvatures, to the parametrization of free energies. Applications of such interface geometry based energetic variational principles are illustrated through three concrete topics: the multiscale modeling of biomolecular electrostatics and solvation that includes the curvature energy of the molecular surface, the formation of microdomains on lipid membrane due to the geometric and molecular mechanics at the lipid interface, and the mean curvature driven protein localization on membrane surfaces. By further implicitly representing the interface using a phase field function over the entire domain, one can simulate the dynamics of the interface and the corresponding energy variation by evolving the phase field function, achieving significant reduction of the number of degrees of freedom and computational complexity. Strategies for improving the efficiency of computational implementations and for extending applications to coarse-graining or multiscale molecular simulations are outlined.Comment: 36 page

Computation of Conformational Coupling in Allosteric Proteins

In allosteric regulation, an effector molecule binding a protein at one site induces conformational changes, which alter structure and function at a distant active site. Two key challenges in the computational modeling of allostery are the prediction of the structure of one allosteric state starting from the structure of the other, and elucidating the mechanisms underlying the conformational coupling of the effector and active sites. Here we approach these two challenges using the Rosetta high-resolution structure prediction methodology. We find that the method can recapitulate the relaxation of effector-bound forms of single domain allosteric proteins into the corresponding ligand-free states, particularly when sampling is focused on regions known to change conformation most significantly. Analysis of the coupling between contacting pairs of residues in large ensembles of conformations spread throughout the landscape between and around the two allosteric states suggests that the transitions are built up from blocks of tightly coupled interacting sets of residues that are more loosely coupled to one another

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew’s correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew’s correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx

The Spread of Inequality

The causes of socioeconomic inequality have been debated since the time of Plato. Many reasons for the development of stratification have been proposed, from the need for hierarchical control over large-scale irrigation systems to the accumulation of small differences in wealth over time via inheritance processes. However, none of these explains how unequal societies came to completely displace egalitarian cultural norms over time. Our study models demographic consequences associated with the unequal distribution of resources in stratified societies. Agent-based simulation results show that in constant environments, unequal access to resources can be demographically destabilizing, resulting in the outward migration and spread of such societies even when population size is relatively small. In variable environments, stratified societies spread more and are also better able to survive resource shortages by sequestering mortality in the lower classes. The predictions of our simulation are provided modest support by a range of existing empirical studies. In short, the fact that stratified societies today vastly outnumber egalitarian societies may not be due to the transformation of egalitarian norms and structures, but may instead reflect the more rapid migration of stratified societies and consequent conquest or displacement of egalitarian societies over time

Drilling their own graves:How the European oil and gas supermajors avoid sustainability tensions through mythmaking

This study explores how paradoxical tensions between economic growth and environmental protection are avoided through organizational mythmaking. By examining the European oil and gas supermajors’ ‘‘CEOspeak’’ about climate change, we show how mythmaking facilitates the disregarding, diverting, and/or displacing of sustainability tensions. In doing so, our findings further illustrate how certain defensive responses are employed: (1) regression, or retreating to the comforts of past familiarities, (2) fantasy, or escaping the harsh reality that fossil fuels and climate change are indeed irreconcilable, and (3) projecting, or shifting blame to external actors for failing to address climate change. By highlighting the discursive effects of enacting these responses, we illustrate how the European oil and gas supermajors self-determine their inability to substantively address the complexities of climate change. We thus argue that defensive responses are not merely a form of mismanagement as the paradox and corporate sustainability literature commonly suggests, but a strategic resource that poses serious ethical concerns given the imminent danger of issues such as climate change

Self or other: Directors’ attitudes towards policy initiatives for external board evaluation

Recurrent crises in corporate governance have board practice and created policy pressure to assess the effectiveness of boards. Since the 1990s boards have faced calls to undertake regular, formal evaluation. Since 2010, the UK Corporate Governance Code has urged large corporations to engage outside parties to conduct them at least every three years, a move that other jurisdictions have copied. Despite this policy importance, little research has been conducted into processes or outcomes of board evaluation. This study explores the attitudes of directors on evaluation, whether self-administered or facilitated by others. We find acceptance of the principle but reservations about the value and even honesty in questionnaire-based approaches. We find scepticism about, but also acknowledgement of, the benefits of using outside facilitators, especially for their objectivity and because their interviewing elicits insights into board dynamics. As this practice expands beyond listed companies to non-listed ones, charities, and even governance branches of government, our findings point to a need to professionalise outside facilitation