Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

Echocardiography-derived total atrial conduction time (PA-TDI duration): risk stratification and guidance in atrial fibrillation management

Atrial fibrillation (AF) is a major cause of cardiovascular morbidity and mortality. To early detect and to avoid AF-related complications, several cardiac imaging modalities and approaches aim to quantify the severity of the underlying atrial cardiomyopathy (i.e., the extent of atrial remodeling). However, most established cardiac imaging modalities just incorporate single components of atrial remodeling and do not reflect the complete multifactorial process, which may contribute to their limited predictive value. Echocardiography-derived PA-TDI duration is a sophisticated echocardiographic parameter to assess total atrial conduction time and directly reflects both electrical and structural changes to the atria. Therefore, PA-TDI duration provides a more comprehensive quantification of the extent of atrial remodeling than other imaging modalities. In this article we review the role of PA-TDI duration as a marker of atrial remodeling and summarize the available data on PA-TDI duration to identify patients at risk for AF, as well as to guide AF management. Moreover, we discuss how to assess PA-TDI duration and provide recommendations on the implementation of PA-TDI duration into routine clinical care. Graphic abstractCardiolog

Structural studies unravel the active conformation of apo RORγt nuclear receptor and a common inverse agonism of two diverse classes of RORγt inhibitors

The nuclear receptor retinoid acid receptor-related orphan receptor γt (RORγt) is a master regulator of the Th17/IL-17 pathway that plays crucial roles in the pathogenesis of autoimmunity. RORγt has recently emerged as a highly promising target for treatment of a number of autoimmune diseases. Through high-throughput screening, we previously identified several classes of inverse agonists for RORγt. Here, we report the crystal structures for the ligand-binding domain of RORγt in both apo and ligand-bound states. We show that apo RORγt adopts an active conformation capable of recruiting coactivator peptides and present a detailed analysis of the structural determinants that stabilize helix 12 (H12) of RORγt in the active state in the absence of a ligand. The structures of ligand-bound RORγt reveal that binding of the inverse agonists disrupts critical interactions that stabilize H12. This destabilizing effect is supported by ab initio calculations and experimentally by a normalized crystallographic B-factor analysis. Of note, the H12 destabilization in the active state shifts the conformational equilibrium of RORγt toward an inactive state, which underlies the molecular mechanism of action for the inverse agonists reported here. Our findings highlight that nuclear receptor structure and function are dictated by a dynamic conformational equilibrium and that subtle changes in ligand structures can shift this equilibrium in opposite directions, leading to a functional switch from agonists to inverse agonists

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Recognition and Degradation of Plant Cell Wall Polysaccharides by Two Human Gut Symbionts

Competition for nutrients contained in diverse types of plant cell wall-associated polysaccharides may explain the evolution of substrate-specific catabolic gene modules in common bacterial members of the human gut microbiota

Economic Aspects of Sanitation in Developing Countries

Improved sanitation has been shown to have great impacts on people's health and economy. However, the progress of achieving the Millennium Development Goals (MDGs) on halving the proportion of people without access to clean water and basic sanitation by 2015 has thus far been delayed. One of the reasons for the slow progress is that policy makers, as well as the general public, have not fully understood the importance of the improved sanitation solutions. This paper, by gathering relevant research findings, aims to report and discuss currently available evidence on the economic aspects of sanitation, including the economic impacts of unimproved sanitation and the costs and economic benefits of some common improved sanitation options in developing countries.; DATA USED IN THIS PAPER WERE OBTAINED FROM DIFFERENT INFORMATION SOURCES: international and national journal articles and reports, web-based statistics, and fact sheets. We used both online search and hand search methods to gather the information.; Scientific evidence has demonstrated that the economic cost associated with poor sanitation is substantial. At the global level, failure to meet the MDG water and sanitation target would have ramifications in the area of US$38 billion, and sanitation accounts for 92$142 billion (US$year 2005). This translates to a per capita spending of US$28 for sanitation. Annually, this translates to roughly US$14 million. The evidence complied in this paper demonstrates that investing in sanitation is socially and economically worthwhile. For every US$1 invested, achieving the sanitation MDG target and universal sanitation access in the non-OECD countries would result in a global return of US$9.1 and US$11.2, respectively.; Given the current state of knowledge, sanitation is undeniably a profitable investment. It is clear that achieving the MDG sanitation target not only saves lives but also provides a foundation for economic growth

A Newly Identified CG301269 Improves Lipid and Glucose Metabolism Without Body Weight Gain Through Activation of Peroxisome Proliferator–Activated Receptor α and γ

OBJECTIVE-Peroxisome proliferator-activated receptor (PPAR)alpha/gamma dual agonists have been developed to alleviate metabolic disorders. However, several PPAR alpha/gamma dual agonists are accompanied with unwanted side effects, including body weight gain, edema, and tissue failure. This study investigated the effects of a novel PPAR alpha/gamma dual agonist, CG301269, on metabolic disorders both in vitro and in vivo.RESEARCH DESIGN AND METHODS-Function of CG301269 as a PPAR alpha/gamma dual agonist was assessed in vitro by luciferase reporter assay, mammalian one-hybrid assay, and analyses of PPAR target genes. In vitro profiles on fatty acid oxidation and inflammatory responses were acquired by fatty acid oxidation assay and quantitative (q)RT-PCR of proinflammatory genes. In vivo effect of CG301269 was examined in db/db mice. Total body weight and various tissue weights were measured, and hepatic lipid profiles were analyzed. Systemic glucose and insulin tolerance were measured, and the in vivo effect of CG301269 on metabolic genes and proinflammatory genes was examined by qRT-PCR.RESULTS-CG301269 selectively stimulated the transcriptional activities of PPAR alpha and PPAR gamma. CG301269 enhanced fatty acid oxidation in vitro and ameliorated insulin resistance and hyperlipidemia in vivo. In db/db mice, CG301269 reduced inflammatory responses and fatty liver, without body weight gain.CONCLUSIONS-We demonstrate that CG301269 exhibits beneficial effects on glucose and lipid metabolism by simultaneous activation of both PPAR alpha and PPAR gamma. Our data suggest that CG301269 would be a potential lead compound against obesity and related metabolic disorders. Diabetes 60:496-506, 2011This work was supported by the Korean Science and Engineering Foundation grants funded by the Ministry of Education, Science and Technology (MEST; No. M104KH010001-06K0801-00111, SC-3230, 20100028758, 2010-0001492, 2010026035, and R31-2009-000-100320). H.W.J., S.S.C., and H.J.S. were supported by the BK21 Research Fellowship from the Ministry of Education and Human Resources Development. J.-W.L. was supported by Priority Research Centers Program through the National Research Foundation of Korea funded by the MEST (2009-0094022)

Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1)

BACKGROUND: In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1. METHOD: Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test. RESULTS: For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay. CONCLUSION: The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications

CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

ABSTRACT: A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R 2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R 2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured t