Daniel Veltri

Are nicotinic acetylcholine receptors coupled to G proteins

It was, until recently, accepted that the two classes of acetylcholine (ACh) receptors are distinct in an important sense: muscarinic ACh receptors signal via heterotrimeric GTP binding proteins (G proteins), whereas nicotinic ACh receptors (nAChRs) open to allow flux of Na+, Ca2+, and K+ ions into the cell after activation. Here we present evidence of direct coupling between G proteins and nAChRs in neurons. Based on proteomic, biophysical, and functional evidence, we hypothesize that binding to G proteins modulates the activity and signaling of nAChRs in cells. It is important to note that while this hypothesis is new for the nAChR, it is consistent with known interactions between G proteins and structurally related ligand‐gated ion channels. Therefore, it underscores an evolutionarily conserved metabotropic mechanism of G protein signaling via nAChR channels.

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.

MENTHOL INHIBITS 5-HT3 RECEPTOR-MEDIATED CURRENTS

The effects of alcohol monoterpene menthol, a major active ingredient of the peppermint plant, were tested on the function of human 5-hydroxytryptamine type 3 (5-HT3) receptors expressed in Xenopus laevis oocytes. 5-HT (1 μM)-evoked currents recorded by two-electrode voltage-clamp technique were reversibly inhibited by menthol in a concentration-dependent (IC50 = 163 μM) manner. The effects of menthol developed gradually, reaching a steady-state level within 10–15 minutes and did not involve G-proteins, since GTPγS activity remained unaltered and the effect of menthol was not sensitive to pertussis toxin pretreatment. The actions of menthol were not stereoselective as (−), (+), and racemic menthol inhibited 5-HT3 receptor–mediated currents to the same extent. Menthol inhibition was not altered by intracellular 1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic acid injections and transmembrane potential changes. The maximum inhibition observed for menthol was not reversed by increasing concentrations of 5-HT. Furthermore, specific binding of the 5-HT3 antagonist [3H]GR65630 was not altered in the presence of menthol (up to 1 mM), indicating that menthol acts as a noncompetitive antagonist of the 5-HT3 receptor. Finally, 5-HT3 receptor–mediated currents in acutely dissociated nodose ganglion neurons were also inhibited by menthol (100 μM). These data demonstrate that menthol, at pharmacologically relevant concentrations, is an allosteric inhibitor of 5-HT3 receptors.

Systematic analysis of global features and model building for recognition of antimicrobial peptides

With growing bacterial resistance to antibiotics, it is becoming paramount to seek out new antibacterials. Antimicrobial peptides (AMPs) provide interesting templates for antibacterial drug research. Our understanding of what it is that confers to these peptides their antimicrobial activity is currently poor. Yet, such understanding is the first step towards modification or design of novel AMPs for treatment. Research in machine learning is beginning to focus on recognition of AMPs from non-AMPs as a means of understanding what features confer to an AMP its activity. Methods either seek new features and test them in the context of classification or measure the classification power of features provided by biologists. In this paper, we provide a rigorous evaluation of features provided by a biologist or resulting from a combination of experimental and computational research. We present a statistics-based approach to carefully measure the significance of each feature and use this knowledge to construct predictive models. We present here logistic regression models, which are capable of associating probabilities on whether a peptide is antimicrobial or not with the feature values of the peptide. We provide access to the proposed methodology through a web server. The server allows users to replicate the findings in this paper or evaluate their own features.We believe research in this direction will allow the community to make further progress and elucidate features that capture antimicrobial activity. This is an important first step towards assisting modification and/or de novo design of AMPs in the wet laboratory.

Deep learning improves antimicrobial peptide recognition

Abstract Motivation Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com. Supplementary information Supplementary data are available at Bioinformatics online.

Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming

Growing bacterial resistance to antibiotics is spurring research on utilizing naturally-occurring antimicrobial peptides (AMPs) as templates for novel drug design. While experimentalists mainly focus on systematic point mutations to measure the effect on antibacterial activity, the computational community seeks to understand what determines such activity in a machine learning setting. The latter seeks to identify the biological signals or features that govern activity. In this paper, we advance research in this direction through a novel method that constructs and selects complex sequence-based features which capture information about distal patterns within a peptide. Comparative analysis with state-of-the-art methods in AMP recognition reveals our method is not only among the top performers, but it also provides transparent summarizations of antibacterial activity at the sequence level. Moreover, this paper demonstrates for the first time the capability not only to recognize that a peptide is an AMP or not but also to predict its target selectivity based on models of activity against only Gram-positive, only Gram-negative, or both types of bacteria. The work described in this paper is a step forward in computational research seeking to facilitate AMP design or modification in the wet laboratory.

A novel method to improve recognition of antimicrobial peptides through distal sequence-based features

Growing bacterial resistance to antibiotics is urging the development of new lines of treatment. The discovery of naturally-occurring antimicrobial peptides (AMPs) is motivating many experimental and computational researchers to pursue AMPs as possible templates. In the experimental community, the focus is generally on systematic point mutation studies to measure the effect on antibacterial activity. In the computational community, the goal is to understand what determines such activity in a machine learning setting. In the latter, it is essential to identify biological signals or features in AMPs that are predictive of antibacterial activity. Construction of effective features has proven challenging. In this paper, we advance research in this direction. We propose a novel method to construct and select complex sequence-based features able to capture information about distal patterns within a peptide. Thorough comparative analysis in this paper indicates that such features compete with the state-of-the-art in AMP recognition while providing transparent summarizations of antibacterial activity at the sequence level. We demonstrate that these features can be combined with additional physicochemical features of interest to a biological researcher to facilitate specific AMP design or modification in the wet laboratory. Code, data, results, and analysis accompanying this paper are publicly available online at: http://cs.gmu.edu/~ashehu/?q=OurTools.

Binary Response Models for Recognition of Antimicrobial Peptides

There is now great urgency in developing new antibiotics to combat bacterial resistance. Recent attention has turned to naturally-occurring antimicrobial peptides (AMPs) that can serve as templates for antibacterial drug research. As natural AMPs have a wide range of activity against various bacteria, current research is focusing on modifying existing peptides or designing new ones to increase potency. This paper presents a computational approach to further our understanding of what physicochemical properties or features confer to a peptide antimicrobial activity. One of the contributions of this paper is the ability to rigorously test the relevance of features obtained by biological or computational researchers in the context of AMP recognition. A second contribution is the construction of a predictive model that employs relevant features and their combinations to associate with a novel peptide sequence a probability to have antimicrobial activity. Taken together, the work in this paper seeks to help researchers elucidate features of importance for antimicrobial activity. This is an important first step towards modification or design of novel AMPs for treatment. With this goal in mind, we provide access to the proposed methodology through a web server, which allows users to replicate the findings here or evaluate their own feature set.

Hypomorphic caspase activation and recruitment domain 11 (CARD11) mutations associated with diverse immunologic phenotypes with or without atopic disease

Background Caspase activation and recruitment domain 11 (CARD11) encodes a scaffold protein in lymphocytes that links antigen receptor engagement with downstream signaling to nuclear factor &kgr;B, c‐Jun N‐terminal kinase, and mechanistic target of rapamycin complex 1. Germline CARD11 mutations cause several distinct primary immune disorders in human subjects, including severe combined immune deficiency (biallelic null mutations), B‐cell expansion with nuclear factor &kgr;B and T‐cell anergy (heterozygous, gain‐of‐function mutations), and severe atopic disease (loss‐of‐function, heterozygous, dominant interfering mutations), which has focused attention on CARD11 mutations discovered by using whole‐exome sequencing. Objectives We sought to determine the molecular actions of an extended allelic series of CARD11 and to characterize the expanding range of clinical phenotypes associated with heterozygous CARD11 loss‐of‐function alleles. Methods Cell transfections and primary T‐cell assays were used to evaluate signaling and function of CARD11 variants. Results Here we report on an expanded cohort of patients harboring novel heterozygous CARD11 mutations that extend beyond atopy to include other immunologic phenotypes not previously associated with CARD11 mutations. In addition to (and sometimes excluding) severe atopy, heterozygous missense and indel mutations in CARD11 presented with immunologic phenotypes similar to those observed in signal transducer and activator of transcription 3 loss of function, dedicator of cytokinesis 8 deficiency, common variable immunodeficiency, neutropenia, and immune dysregulation, polyendocrinopathy, enteropathy, X‐linked–like syndrome. Pathogenic variants exhibited dominant negative activity and were largely confined to the CARD or coiled‐coil domains of the CARD11 protein. Conclusion These results illuminate a broader phenotypic spectrum associated with CARD11 mutations in human subjects and underscore the need for functional studies to demonstrate that rare gene variants encountered in expected and unexpected phenotypes must nonetheless be validated for pathogenic activity.

Knowledge-based search and multi-objective filters: proposed structural models of GPCR dimerization

Many experimental studies point to the ubiquitous role of protein complexation in the cell while lamenting the lack of structural models to permit structure-function studies. This scarcity is due to persisting challenges in protein-protein docking. Methods based on energetic optimization have to handle vast and high-dimensional configuration spaces and inaccurate energy functions only to arrive at the wrong interface. Methods that employ learned models to replace or precede energetic evaluations are limited by the generality of these models. Computational approaches designed to be general often fail to provide realistic models on protein classes of interest in the wet laboratory. One such class are G protein-coupled receptors, which wet-lab studies suggest undergo complexation, possibly affecting drug efficacy. In this paper, we propose a computational protocol to address the unique challenges posed by these receptors. To deal with challenges, such as receptor size and inaccuracy of energy functions, the protocol takes a geometry-driven approach and integrates in the search geometric constraints posed by the environment where the receptors operate. Various filters are designed to handle the computational cost of energetic evaluation, and analysis techniques based on new scoring strategies, including multi-objective analysis, are employed to reduce the sampled ensemble to a few credible structural models. We demonstrate that dimeric models of the Dopamine D2 receptor targeted to treat psychotic disorders reproduce macroscopic knowledge extracted in the wet-laboratory and can be employed to further spur detailed structure-function studies.