•Structural coverage has long been a hurdle to structure-based proteome-wide screens.•Current coverage nears 70% for the human proteome, 95% for human drug targets.•50% of human proteins are covered ...by four or more homologous structures.•Nearly any protein is capable of binding small-molecule drugs.•Insufficient structural coverage is a fading issue for drug discovery.
Structure-based computational drug discovery efforts have traditionally focused on the structure of a single, well-known drug target. Important applications, such as target deconvolution and the analysis of polypharmacology, require proteome-scale molecular docking and have been inaccessible to structure-based in silico approaches. One important reason for this inaccessibility was that the structure of most proteins was not known. Lately, this ‘structure gap’ has been closing rapidly, and proteome-scale molecular docking seems within reach. Here, we survey the current state of structural coverage of the human genome and find that coverage is truly proteome-wide, both overall and in most pharmaceutically relevant categories of proteins. The time is right for structure-based approaches to target deconvolution and polypharmacology.
The brain’s default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar ...disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging–genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.
The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus ...SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.
Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation of concerns for de novo drug design, categorizing the task into ...three main categories: generation, discrimination and exploration. We demonstrate that changes to any of these three concerns impacts benchmark performance for drug design tasks. In this report, we present Deriver, an open‐source Python package that acts as a modular framework for molecule generation, with a focus on integrating multiple generative methods. Using Deriver, we demonstrate that changing parameters related to each of these three concerns impacts chemical space traversal significantly, and that the freedom to independently adjust each is critical to real‐world applications having conflicting priorities. We find that combining multiple generative methods can improve optimization of molecular properties and lower the chance of becoming trapped in local minima. In addition, filtering molecules for drug‐likeness (based on physicochemical properties and SMARTS pattern matching) before they are scored may hinder exploration, but can also improve the quality of the final molecules. Finally, we demonstrate that any given task has an exploration algorithm best suited to it, though in practice linear probabilistic sampling generally results in the best outcomes, when compared to Monte Carlo sampling or greedy sampling. Deriver is being made freely available, to help others interested in collaboratively improving existing methods in de novo drug design centered around inheritance of molecular structure, modularity, extensibility, and separation of concerns.
Background Schizophrenia is a complex genetic disorder, with multiple putative risk genes and many reports of reduced cortical gray matter. Identifying the genetic loci contributing to these ...structural alterations in schizophrenia (and likely also to normal structural gray matter patterns) could aid understanding of schizophrenia's pathophysiology. We used structural parameters as potential intermediate illness markers to investigate genomic factors derived from single nucleotide polymorphism (SNP) arrays. Method We used research quality structural magnetic resonance imaging (sMRI) scans from European American subjects including 33 healthy control subjects and 18 schizophrenia patients. All subjects were genotyped for 367 SNPs. Linked sMRI and genetic (SNP) components were extracted to reveal relationships between brain structure and SNPs, using parallel independent component analysis, a novel multivariate approach that operates effectively in small sample sizes. Results We identified an sMRI component that significantly correlated with a genetic component ( r = −.536, p < .00005); components also distinguished groups. In the sMRI component, schizophrenia gray matter deficits were in brain regions consistently implicated in previous reports, including frontal and temporal lobes and thalamus ( p < .01). These deficits were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal central nervous system development, including AKT, PI3K, SLC6A4, DRD2, CHRM2, and ADORA2A. Conclusions Despite the small sample size, this novel analysis method identified an sMRI component including brain areas previously reported to be abnormal in schizophrenia and an associated genetic component containing several putative schizophrenia risk genes. Thus, we identified multiple genes potentially underlying specific structural brain abnormalities in schizophrenia.
Abstract Background Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder overlap with regard to symptoms, structural and functional brain abnormalities, and genetic risk factors. ...Neurobiological pathways connecting genes to clinical phenotypes across the spectrum from schizophrenia to psychotic bipolar disorder remain largely unknown. Methods We examined the relationship between structural brain changes and risk alleles across the psychosis spectrum in the multi-site Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cohort. Regional MRI brain volumes were examined in 389 subjects with a psychotic disorder (139 schizophrenia, 90 schizoaffective disorder, and 160 psychotic bipolar disorder) and 123 healthy controls. 451,701 single-nucleotide polymorphisms were screened and processed using parallel independent component analysis (para-ICA) to assess associations between genes and structural brain abnormalities in probands. Results 482 subjects were included after quality control (364 individuals with psychotic disorder and 118 healthy controls). Para-ICA identified four genetic components including several risk genes already known to contribute to schizophrenia and bipolar disorder and revealed three structural components that showed overlapping relationships with the disease risk genes across the three psychotic disorders. Functional ontologies representing these gene clusters included physiological pathways involved in brain development, synaptic transmission, and ion channel activity. Conclusions Heritable brain structural findings such as reduced cortical thickness and surface area in probands across the psychosis spectrum were associated with somewhat distinct genes related to putative disease pathways implicated in psychotic disorders. This suggests that brain structural alterations might represent discrete psychosis intermediate phenotypes along common neurobiological pathways underlying disease expression across the psychosis spectrum.
Abstract Following a prior Kentucky clinical practice study on metabolic syndrome, serum glucose and lipid levels were used in a new sample to determine whether after correcting for confounding ...factors, olanzapine hyperlipidemia risk may be higher under naturalistic non-randomized treatment. Serum glucose, total cholesterol, HDL cholesterol and triglyceride levels were assessed in 360 patients with severe mental illnesses. The initial goal was to focus on olanzapine lipid profiles, but visual data inspection indicated that quetiapine needed attention as well. Patients were divided into 3 groups: 57 (16%) on olanzapine, 105 (29%) on quetiapine, and 198 (55%) on other antipsychotics (risperidone, ziprasidone, aripiprazole or typicals). HDL and glucose levels were not significantly different across the three antipsychotic groups. When compared with other antipsychotics, olanzapine patients had a borderline significantly higher mean total serum cholesterol level (178 vs. 192 mg/dl, p = 0.06) and mean triglyceride level (172 vs. 202 mg/dl, p = 0.06). These differences became significant ( p = 0.006 and 0.03) after correcting for confounders. Quetiapine appeared overprescribed in patients with metabolic syndrome complications. When compared with other antipsychotics, quetiapine patients had a significantly higher mean total serum cholesterol level (178 vs. 194 mg/dl, p = 0.004) and mean triglyceride level (172 vs. 225 mg/dl, p < 0.001). These differences were significant ( p = 0.02 and < 0.001) after correcting for confounders. This study is consistent with emerging literature that suggests that some antipsychotics may have direct and immediate effects on lipid levels beyond obesity effects. The effect sizes of olanzapine and quetiapine on hyperlipidemia were about 0.40 in this naturalistic study.
This paper describes a methodology to calculate the binding free energy (ΔG) of a protein‐ligand complex using a continuum model of the solvent. A formal thermodynamic cycle is used to decompose the ...binding free energy into electrostatic and non‐electrostatic contributions. In this cycle, the reactants are discharged in water, associated as purely nonpolar entities, and the final complex is then recharged. The total electrostatic free energies of the protein, the ligand, and the complex in water are calculated with the finite difference Poisson‐Boltzmann (FDPB) method. The nonpolar (hydrophobic) binding free energy is calculated using a free energy‐surface area relationship, with a single alkane/water surface tension coefficient (γaw). The loss in backbone and side‐chain configurational entropy upon binding is estimated and added to the electrostatic and the nonpolar components of ΔG. The methodology is applied to the binding of the murine MHC class I protein H‐2Kb with three distinct peptides, and to the human MHC class I protein HLA‐A2 in complex with five different peptides. Despite significant differences in the amino acid sequences of the different peptides, the experimental binding free energy differences (ΔΔGexp) are quite small (<0.3 and <2.7 kcal/mol for the H‐2Kb and HLA‐A2 complexes, respectively). For each protein, the calculations are successful in reproducing a fairly small range of values for ΔΔGcalc (<4.4 and <5.2 kcal/mol, respectively) although the relative peptide binding affinities of H‐2Kb and HLA‐A2 are not reproduced. For all protein‐peptide complexes that were treated, it was found that electrostatic interactions oppose binding whereas nonpolar interactions drive complex formation. The two types of interactions appear to be correlated in that larger nonpolar contributions to binding are generally opposed by increased electrostatic contributions favoring dissociation. The factors that drive the binding of peptides to MHC proteins are discussed in light of our results.
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