Wangmurphy5672

Protein complexes can be computationally identified from protein-interaction networks with community detection methods, suggesting new multi-protein assemblies. Most community detection algorithms tend to be un- or semi-supervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and by using parallel algorithms, respectively. Here, we present Super.Complex, a distributed supervised machine learning pipeline for community detection in networks. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities with epsilon-greedy and pseudo-metropolis criteria, and an embarrassingly us to better understand the association of protein and disease. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. In this work, we present Super.Complex, a generalizable and scalable supervised machine learning-based community detection algorithm that outperforms existing methods by accurately learning and using patterns from known communities. We propose 3 novel evaluation measures to compare learned and known communities, an outstanding issue. We use Super.Complex to identify 1028 human protein complexes, including 234 complexes linked to SARS-CoV-2, the virus causing COVID-19, and 103 complexes containing 111 uncharacterized proteins.

Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed integrative multi-omics analysis from genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.

First, we cluster gene co-expression networks and identify gene modules for various AD phenotypes given population gene expression data. Next, we predict the transcription factors (TFs) that significantly regulate the genes in each module and the AD risk variants (e.g., SNPs) interrupting the TF binding sites on the regulatory elements. Finally, we construct a full gene regulatory network linking SNPs, interrupted TFs, and regulatory elements to targe and AD phenotypes, including disease progression and Covid response. Our analysis is open-source available at https//github.com/daifengwanglab/ADSNPheno .With global vaccination efforts against SARS-CoV-2 underway, there is a need for rapid quantification methods for neutralizing antibodies elicited by vaccination and characterization of their strain dependence. Here, we describe a designed protein biosensor that enables sensitive and rapid detection of neutralizing antibodies against wild type and variant SARS-CoV-2 in serum samples. More generally, our thermodynamic coupling approach can better distinguish sample to sample differences in analyte binding affinity and abundance than traditional competition based assays.A lipid nanoparticle (LNP) formulation is a state-of-the-art delivery system for genetic drugs such as DNA, mRNA, and siRNA, which is successfully applied to COVID-19 vaccines and gains tremendous interest in therapeutic applications. Despite its importance, a molecular-level understanding of the LNP structures and dynamics is still lacking, which makes a rational LNP design almost impossible. In this work, we present an extension of CHARMM-GUI Membrane Builder to model and simulate all-atom LNPs with various (ionizable) cationic lipids and PEGylated lipids (PEG-lipids). These new lipid types can be mixed with any existing lipid types with or without a biomolecule of interest, and the generated systems can be simulated using various molecular dynamics engines. As a first illustration, we considered model LNP membranes with DLin-KC2-DMA (KC2) or DLin-MC3-DMA (MC3) without PEG-lipids. The results from these model membranes are consistent with those from the two previous studies albeit with mild accumulation of neutral MC3 in the bilayer center. To demonstrate Membrane Builder ’s capability of building a realistic LNP patch, we generated KC2- or MC3-containing LNP membranes with high concentrations of cholesterol and ionizable cationic lipids together with 2 mol% PEG-lipids. We observe that PEG-chains are flexible, which can be more preferentially extended laterally in the presence of cationic lipids due to the attractive interactions between their head groups and PEG oxygen. The presence of PEG-lipids also relaxes the lateral packing in LNP membranes, and the area compressibility modulus ( K A ) of LNP membranes with cationic lipids fit into typical K A of fluid-phase membranes. Interestingly, the interactions between PEG oxygen and head group of ionizable cationic lipids induce a negative curvature. We hope that this LNP capability in Membrane Builder can be useful to better characterize various LNPs with or without genetic drugs for a rational LNP design.Companion animals are susceptible to SARS-CoV-2 infection and sporadic cases of pet infections have occurred in the United Kingdom. Here we present the first large-scale serological survey of SARS-CoV-2 neutralising antibodies in dogs and cats in the UK. Results are reported for 688 sera (454 canine, 234 feline) collected by a large veterinary diagnostic laboratory for routine haematology during three time periods; pre-COVID-19 (January 2020), during the first wave of UK human infections (April-May 2020) and during the second wave of UK human infections (September 2020-February 2021). Both pre-COVID-19 sera and those from the first wave tested negative. However, in sera collected during the second wave, 1.4% (n=4) of dogs and 2.2% (n=2) cats tested positive for neutralising antibodies. The low numbers of animals testing positive suggests pet animals are unlikely to be a major reservoir for human infection in the UK. However, continued surveillance of in-contact susceptible animals should be performed as part of ongoing population health surveillance initiatives.Native mass spectrometry (MS) enjoyed tremendous success in the past two decades in a wide range of studies aiming at understanding the molecular mechanisms of physiological processes underlying a variety of pathologies and accelerating the drug discovery process. However, the success record of native MS has been surprisingly modest with respect to the most recent challenge facing the biomedical community â€" the novel coronavirus infection (COVID-19). The major reason for the paucity of successful studies that use native MS to target various aspects of SARS-CoV-2 interaction with its host is the extreme degree of structural heterogeneity of the viral protein playing a key role in the host cell invasion. Indeed, the SARS-CoV-2 spike protein (S-protein) is extensively glycosylated, presenting a formidable challenge for native mass spectrometry (MS) as a means of characterizing its interactions with both the host cell-surface receptor ACE2 and the drug candidates capable of disrupting this interaction. In this work we evaluate the utility of native MS complemented with the experimental methods using gas-phase chemistry (limited charge reduction) to obtain meaningful information on the association of the S1 domain of the S-protein with the ACE2 ectodomain, and the influence of a small synthetic heparinoid on this interaction. Native MS reveals the presence of several different S1 oligomers in solution and allows the stoichiometry of the most prominent S1/ACE2 complexes to be determined. This enables meaningful interpretation of the changes in native MS that are observed upon addition of a small synthetic heparinoid (the pentasaccharide fondaparinux) to the S1/ACE2 solution, confirming that the small polyanion destabilizes the protein/receptor binding.Background A novel coronavirus, SARS-CoV-2 (known as COVID-19), spread rapidly around the world, affecting all and creating an ongoing global pandemic. In the United States, Latinx, African American, and Indigenous populations across the country have been disproportionately affected by COVID-19 cases and death rates. An examination of the perceptions and beliefs about the spread of the virus, COVID-19 testing, and vaccination amongst racial/ethnic minority groups is needed in order to alleviate the widespread disparity in new cases and deaths. Methods From November to December 2020 the research team conducted focus groups with members of Latinx farm-working communities in the Eastern Coachella Valley, located in the inland southern California desert region. A total of seven focus groups, six in Spanish and one in Purépecha, with a total of 55 participants were conducted. Topics covered include knowledge of the coronavirus, COVID-19 testing and vaccination. Results Using theme identification techniques, the findings identify structural factors that underly perceptions held by immigrant, migrant, and indigenous Latinx community members about COVID-19, which, in turn, shape attitudes and behaviors related to COVID-19 testing and vaccination. Common themes that emerged across focus groups include misinformation, lack of trust in institutions, and insecurity around employment and residency. Conclusions This racial/ethnic minority population is structurally vulnerable to historical and present-day inequalities that put them at increased risk of COVID-19 exposure, morbidity, and mortality. Findings from the focus groups indicate a significant need for interventions that decrease structural vulnerabilities by addressing issues of (dis)trust in government and public health among this population.The intracarotid sodium amobarbital procedure (ISAP or Wada test) lateralizes cerebral functions to the cerebral hemispheres preoperatively. Functional magnetic resonance imaging (fMRI) is increasingly used to characterize preoperative language and memory lateralization. In this study, concordance of fMRI with Wada was examined in patients with medically intractable seizures. The relationship of the distance between the epileptogenic focus to functional activation area with patients' post-operative deficits in language was also analyzed. 27 epilepsy patients with preoperative fMRI and Wada data were analyzed using established fMRI paradigms for language and memory. Activation of Broca's and Wernicke's areas were measured in three dimensions. Language and memory lateralization were determined, and standard neuropsychiatry Wada test procedures were used for comparison. The shortest distance between a language area to the border of surgical focus (LAD) was also measured and compared with postoperative language deficits.