Inclusion of Enclosed Hydration Effects in the Binding Free Energy Estimation of Dopamine D3 Receptor Complexes

Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.Comment: This is the first report of using enclosed hydration in estimating binding free energies of protein-ligand complexes using implicit solvatio

Water Pharmacophore: Designing Ligands using Molecular Dynamics Simulations with Water

In this study, we demonstrate a method to construct a water-based pharmacophore model which can be utilized in the absence of known ligands. This method utilizes waters found in the binding pocket, sampled through molecular dynamics. Screening of compound databases against this water-based pharmacophore model reveals that this approach can successfully identify known binders to a target protein. The method was tested by enrichment studies of 7 therapeutically important targets and compared favourably to screening-by-docking with Glide. Our results suggest that even without experimentally known binders, pharmacophore models can be generated using molecular dynamics with waters and used for virtual screening

A molecular reconstruction approach to sitebased 3D-RISM and comparison to GIST hydration thermodynamic maps in an enzyme active site

Computed, high-resolution, spatial distributions of solvation energy and entropy can provide detailed information about the role of water in molecular recognition. While grid inhomogeneous solvation theory (GIST) provides rigorous, detailed thermodynamic information from explicit solvent molecular dynamics simulations, recent developments in the 3D reference interaction site model (3D-RISM) theory allow many of the same quantities to be calculated in a fraction of the time. However, 3D-RISM produces atomic-site, rather than molecular, density distributions, which are difficult to extract physical meaning from. To overcome this difficulty, we introduce a method to reconstruct molecular density distributions from atomic site density distributions. Furthermore, we assess the quality of the resulting solvation thermodynamics density distributions by analyzing the binding site of coagulation Factor Xa with both GIST and 3D-RISM. We find good qualitative agreement between the methods for oxygen and hydrogen densities as well as direct solute-solvent energetic interactions. However, 3D-RISM predicts lower energetic and entropic penalties for moving water from the bulk to the binding site

Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening

Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliable protein-ligand x-ray structures and binding affinity data has required the use of constructed datasets for the training and evaluation of CNN molecular recognition models. Here, we outline various sources of bias in one such widely-used dataset, the Directory of Useful Decoys: Enhanced (DUDE). We have constructed and performed tests to investigate whether CNN models developed using DUD-E are properly learning the underlying physics of molecular recognition, as intended, or are instead learning biases inherent in the dataset itself. We find that superior enrichment efficiency in CNN models can be attributed to the analogue and decoy bias hidden in the DUD-E dataset rather than successful generalization of the pattern of proteinligand interactions. Comparing additional deep learning models trained on PDBbind datasets, we found that their enrichment performances using DUD-E are not superior to the performance of the docking program AutoDock Vina. Together, these results suggest that biases that could be present in constructed datasets should be thoroughly evaluated before applying them to machine learning based methodology development

Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening

Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliable protein-ligand x-ray structures and binding affinity data has required the use of constructed datasets for the training and evaluation of CNN molecular recognition models. Here, we outline various sources of bias in one such widely-used dataset, the Directory of Useful Decoys: Enhanced (DUDE). We have constructed and performed tests to investigate whether CNN models developed using DUD-E are properly learning the underlying physics of molecular recognition, as intended, or are instead learning biases inherent in the dataset itself. We find that superior enrichment efficiency in CNN models can be attributed to the analogue and decoy bias hidden in the DUD-E dataset rather than successful generalization of the pattern of proteinligand interactions. Comparing additional deep learning models trained on PDBbind datasets, we found that their enrichment performances using DUD-E are not superior to the performance of the docking program AutoDock Vina. Together, these results suggest that biases that could be present in constructed datasets should be thoroughly evaluated before applying them to machine learning based methodology development

Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry Identification of Yeasts Is Contingent on Robust Reference Spectra

BACKGROUND: Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) for yeast identification is limited by the requirement for protein extraction and for robust reference spectra across yeast species in databases. We evaluated its ability to identify a range of yeasts in comparison with phenotypic methods. METHODS: MALDI-TOF MS was performed on 30 reference and 167 clinical isolates followed by prospective examination of 67 clinical strains in parallel with biochemical testing (total n = 264). Discordant/unreliable identifications were resolved by sequencing of the internal transcribed spacer region of the rRNA gene cluster. PRINCIPAL FINDINGS: Twenty (67%; 16 species), and 24 (80%) of 30 reference strains were identified to species, (spectral score ≥2.0) and genus (score ≥1.70)-level, respectively. Of clinical isolates, 140/167 (84%) strains were correctly identified with scores of ≥2.0 and 160/167 (96%) with scores of ≥1.70; amongst Candida spp. (n = 148), correct species assignment at scores of ≥2.0, and ≥1.70 was obtained for 86% and 96% isolates, respectively (vs. 76.4% by biochemical methods). Prospectively, species-level identification was achieved for 79% of isolates, whilst 91% and 94% of strains yielded scores of ≥1.90 and ≥1.70, respectively (100% isolates identified by biochemical methods). All test scores of 1.70-1.90 provided correct species assignment despite being identified to "genus-level". MALDI-TOF MS identified uncommon Candida spp., differentiated Candida parapsilosis from C. orthopsilosis and C. metapsilosis and distinguished between C. glabrata, C. nivariensis and C. bracarensis. Yeasts with scores of <1.70 were rare species such as C. nivariensis (3/10 strains) and C. bracarensis (n = 1) but included 4/12 Cryptococcus neoformans. There were no misidentifications. Four novel species-specific spectra were obtained. Protein extraction was essential for reliable results. CONCLUSIONS: MALDI-TOF MS enabled rapid, reliable identification of clinically-important yeasts. The addition of spectra to databases and reduction in identification scores required for species-level identification may improve its utility

The Amsterdam Declaration on Fungal Nomenclature

The Amsterdam Declaration on Fungal Nomenclature was agreed at an international symposium convened in Amsterdam on 19–20 April 2011 under the auspices of the International Commission on the Taxonomy of Fungi (ICTF). The purpose of the symposium was to address the issue of whether or how the current system of naming pleomorphic fungi should be maintained or changed now that molecular data are routinely available. The issue is urgent as mycologists currently follow different practices, and no consensus was achieved by a Special Committee appointed in 2005 by the International Botanical Congress to advise on the problem. The Declaration recognizes the need for an orderly transitition to a single-name nomenclatural system for all fungi, and to provide mechanisms to protect names that otherwise then become endangered. That is, meaning that priority should be given to the first described name, except where that is a younger name in general use when the first author to select a name of a pleomorphic monophyletic genus is to be followed, and suggests controversial cases are referred to a body, such as the ICTF, which will report to the Committee for Fungi. If appropriate, the ICTF could be mandated to promote the implementation of the Declaration. In addition, but not forming part of the Declaration, are reports of discussions held during the symposium on the governance of the nomenclature of fungi, and the naming of fungi known only from an environmental nucleic acid sequence in particular. Possible amendments to the Draft BioCode (2011) to allow for the needs of mycologists are suggested for further consideration, and a possible example of how a fungus only known from the environment might be described is presented

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P &lt; 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Did QE lead banks to relax their lending standards? Evidence from the Federal Reserve's LSAPs

Using confidential loan officer survey data on lending standards and internal risk ratings on loans, we document an effect of large-scale asset purchase programs (LSAPs) on lending standards and risk-taking. We exploit cross-sectional variation in banks' holdings of mortgage-backed securities to show that the first and third round of quantitative easing (QE1 and QE3) significantly lowered lending standards and increased loan risk characteristics. The magnitude of the effects is about the same in QE1 and QE3, and is comparable to the effect of a one percentage point decrease in the Fed funds target rate. (c) 2018 Published by Elsevier B.V