HopDock: a probabilistic search algorithm for decoy sampling in protein-protein docking

Background

Elucidating the three-dimensional structure of a higher-order molecular assembly formed by interacting molecular units, a problem commonly known as docking, is central to unraveling the molecular basis of cellular activities. Though protein assemblies are ubiquitous in the cell, it is currently challenging to predict the native structure of a protein assembly in silico.

Methods

This work proposes HopDock, a novel search algorithm for protein-protein docking. HopDock efficiently obtains an ensemble of low-energy dimeric configurations, also known as decoys, that can be effectively used by ab-initio docking protocols. HopDock is based on the Basin Hopping (BH) framework which perturbs the structure of a dimeric configuration and then follows it up with an energy minimization to explicitly sample a local minimum of a chosen energy function. This process is repeated in order to sample consecutive energy minima in a trajectory-like fashion. HopDock employs both geometry and evolutionary conservation analysis to narrow down the interaction search space of interest for the purpose of efficiently obtaining a diverse decoy ensemble.

Results and conclusions

A detailed analysis and a comparative study on seventeen different dimers shows HopDock obtains a broad view of the energy surface near the native dimeric structure and samples many near-native configurations. The results show that HopDock has high sampling capability and can be employed to effectively obtain a large and diverse ensemble of decoy configurations that can then be further refined in greater structural detail in ab-initio docking protocols.

     

Menthol Binding and Inhibition of α7-Nicotinic Acetylcholine Receptors

Menthol is a common compound in pharmaceutical and commercial products and a popular additive to cigarettes. The molecular targets of menthol remain poorly defined. In this study we show an effect of menthol on the α₇ subunit of the nicotinic acetylcholine (nACh) receptor function. Using a two-electrode voltage-clamp technique, menthol was found to reversibly inhibit α7-nACh receptors heterologously expressed in Xenopus oocytes. Inhibition by menthol was not dependent on the membrane potential and did not involve endogenous Ca²⁺-dependent Cl⁻ channels, since menthol inhibition remained unchanged by intracellular injection of the Ca²⁺ chelator BAPTA and perfusion with Ca²⁺-free bathing solution containing Ba²⁺. Furthermore, increasing ACh concentrations did not reverse menthol inhibition and the specific binding of [¹²⁵I] α-bungarotoxin was not attenuated by menthol. Studies of α₇- nACh receptors endogenously expressed in neural cells demonstrate that menthol attenuates α₇ mediated Ca²⁺ transients in the cell body and neurite. In conclusion, our results suggest that menthol inhibits α7-nACh receptors in a noncompetitive manner.     

Prolactin activation of the long form of its cognate receptor causes increased visceral fat and obesity in males as shown in transgenic mice expressing only this receptor subtype

To date the best defined function of prolactin (PRL) is its action on the ovary and mammary gland, although it has also been shown to have an effect on lipid metabolism. Using mice engineered to express only the long form of the prolactin receptor (PRL-RL), we demonstrate that PRL acting through PRL-RL alone causes severe adipose accumulation in visceral fat of males at 6 months of age. The increase in visceral fat accumulation is attributed to loss of adipose-derived leptin, which results in diminished lipolysis. The reduction in leptin also corresponds to decreased activation of AMP-activated protein kinase (AMPK), which further results in diminished fatty acid oxidation and increased fatty acid synthesis. Interestingly, the blunted AMPK response was only observed in adipose tissue and not in liver suggesting that this PRL mediated effect is tissue specific. A glucose tolerance study inferred that PRL-RL mice may suffer from insulin resistance or a reduction in insulin production that is not due to aberrant expression of glucose transporter 4 (Glut4). Collectively, our findings demonstrate that PRL signaling through the long form receptor causes reduced fatty acid oxidation, increased lipid storage, glucose intolerance, and obesity. These findings are of great importance towards understanding the etiology of obesity associated with hyperprolactinemia in humans as well as the role of PRL as a metabolic regulator in adipose tissue.     

Effectiveness of a monthly schedule of follow-up for the treatment of uncomplicated severe acute malnutrition in Sokoto,Nigeria: A cluster randomized crossover trial

BackgroundCommunity-based management of severe acute malnutrition (SAM) involves weekly or biweekly outpatient clinic visits for clinical surveillance and distribution of therapeutic foods. Distance to outpatient clinics and high opportunity costs for caregivers can represent major barriers to access. Reducing the frequency of outpatient visits while providing training to caregivers to recognize clinical danger signs at home between outpatient visits may increase acceptability, coverage, and public health impact of SAM treatment. We investigated the effectiveness of monthly clinic visits compared to the standard weekly follow-up in the outpatient treatment of uncomplicated SAM in northwestern Nigeria.Methods and findingsWe conducted a cluster randomized crossover trial to test the noninferiority of nutritional recovery in children with uncomplicated SAM receiving monthly follow-up compared to the standard weekly schedule. From January 2018 to November 2019, 3,945 children aged 6 to 59 months were enrolled at 10 health centers (5 assigned to monthly follow-up and 5 assigned to weekly follow-up) in Sokoto, Nigeria. In total, 96% of children (n = 1,976 in the monthly follow-up group and 1,802 in the weekly follow-up group) were followed until program discharge, and 91% (n = 1,873 in the monthly follow-up group and 1,721 in the weekly follow-up group) were followed to 3 months postdischarge. The mean age at admission was 15.8 months (standard deviation [SD] 7.1), 2,097/3,945 (53.2%) were girls, and the mean midupper arm circumference (MUAC) at admission was 105.8 mm (SD 6.0). In a modified intention-to-treat analysis, the primary outcome of nutritional recovery, defined as having MUAC ≥125 mm on 2 consecutive visits, was analyzed using generalized linear models, with generalized estimating equations to account for clustering. Nutritional recovery was lower in the monthly follow-up group compared to the weekly group (1,036/1,976, 52.4% versus 1,059/1,802, 58.8%; risk difference: −6.8%), and noninferiority was not demonstrated (lower bound of the confidence interval [CI] was −11.5%, lower than the noninferiority margin of 10%). The proportion of children defaulting was lower in the monthly group than in the weekly group (109/1,976, 5.5% versus 151/1,802, 8.4%, p = 0.03). Three months postdischarge, children in the monthly group were less likely to relapse compared to those in the weekly group (58/976, 5.9% versus 78/1,005, 7.8%, p = 0.03), but cumulative mortality at 3 months postdischarge was higher in the monthly group (159/1,873, 8.5% versus 106/1,721, 6.2%, p < 0.001). Study results may depend on context-specific factors including baseline level of care and the clinical status of children presenting to health centers, and, thus, generalizability of these results may be limited.ConclusionsWhere feasible, a weekly schedule of clinic visits should be preferred to maintain effectiveness of SAM treatment. Where geographic coverage of programs is low or frequent travel to outpatient clinics is difficult or impossible, a monthly schedule of visits may provide an alternative model to deliver treatment to those in need. Modifications to the outpatient follow-up schedule, for example, weekly clinic visits until initial weight gain has been achieved followed by monthly visits, could increase the effectiveness of the model and add flexibility for program delivery.Trial registrationClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT03140904","term_id":"NCT03140904"}}NCT03140904.

Matt D.T. Hitchings and colleagues investigate the effectiveness of a monthly schedule of follow-up for the management of uncomplicated severe acute malnutrition in children aged 6-59 months in Nigeria.     

Effective Automated Feature Construction and Selection for Classification of Biological Sequences

Background

Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.

Methodology

We present an algorithmic framework (EFFECT) for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.

Results

To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification of a specific signal. Code, documentation, and all data for the applications presented here are provided for the community at http://www.cs.gmu.edu/~ashehu/?q=OurTools.     

Unfolding the fold of cyclic cysteine-rich peptides

We propose a method to extensively characterize the native state ensemble of cyclic cysteine-rich peptides. The method uses minimal information, namely, amino acid sequence and cyclization, as a topological feature that characterizes the native state. The method does not assume a specific disulfide bond pairing for cysteines and allows the possibility of unpaired cysteines. A detailed view of the conformational space relevant for the native state is obtained through a hierarchic multi-resolution exploration. A crucial feature of the exploration is a geometric approach that efficiently generates a large number of distinct cyclic conformations independently of one another. A spatial and energetic analysis of the generated conformations associates a free-energy landscape to the explored conformational space. Application to three long cyclic peptides of different folds shows that the conformational ensembles and cysteine arrangements associated with free energy minima are fully consistent with available experimental data. The results provide a detailed analysis of the native state features of cyclic peptides that can be further tested in experiment.     

Guiding protein docking with geometric and evolutionary information

Structural modeling of molecular assemblies promises to improve our understanding of molecular interactions and biological function. Even when focusing on modeling structures of protein dimers from knowledge of monomeric native structure, docking two rigid structures onto one another entails exploring a large configurational space. This paper presents a novel approach for docking protein molecules and elucidating native-like configurations of protein dimers. The approach makes use of geometric hashing to focus the docking of monomeric units on geometrically complementary regions through rigid-body transformations. This geometry-based approach improves the feasibility of searching the combined configurational space. The search space is narrowed even further by focusing the sought rigid-body transformations around molecular surface regions composed of amino acids with high evolutionary conservation. This condition is based on recent findings, where analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. Different search procedures are employed in this work to search the resulting narrowed configurational space. A proof-of-concept energy-guided probabilistic search procedure is also presented. Results are shown on a broad list of 18 protein dimers and additionally compared with data reported by other labs. Our analysis shows that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.