Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Augmented Reality for Enhanced Visualization of MOF Adsorbents.

Augmented reality (AR) is an emerging technique used to improve visualization and comprehension of complex 3D materials. This approach has been applied not only in the field of chemistry but also in real estate, physics, mechanical engineering, and many other areas. Here, we demonstrate the workflow for an app-free AR technique for visualization of metal-organic frameworks (MOFs) and other porous materials to investigate their crystal structures, topology, and gas adsorption sites. We think this workflow will serve as an additional tool for computational and experimental scientists working in the field for both research and educational purposes.

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

Development of a Patient-Reported Outcomes Tool to Assess Pain and Discomfort in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Pain has been identified as a core outcome for individuals with autosomal dominant polycystic kidney disease (ADPKD), but no disease-specific pain assessment has been developed using current development methodology for patient-reported outcomes (PRO) instruments. We developed and validated an ADPKD-specific pain questionnaire: the ADPKD Pain and Discomfort Scale (ADPKD-PDS). METHODS: Conceptual underpinnings were drawn from literature review, concept elicitation, expert consultation, and measurement performance. In the qualitative analysis phase, concepts were elicited from focus groups of adults with ADPKD, and the resulting draft instrument was refined using cognitive debriefing interviews with individuals with ADPKD. For quantitative analysis, adults with ADPKD completed the draft instrument and other PRO tools in an online survey, and a follow-up survey was conducted 3-4 weeks later. Survey responses were analyzed for item-level descriptive statistics, latent model fit statistics, item discrimination, item- and domain-level psychometric statistics, test-retest reliability, responsiveness to change, and convergent validity. RESULTS: In the qualitative phase, 46 focus groups were conducted in 18 countries with 293 participants. Focus groups described three conceptually distinct types of ADPKD-related pain and discomfort (dull kidney pain, sharp kidney pain, and fullness/discomfort). In the quantitative phase, 298 adults with ADPKD completed the online survey, and 108 participants completed the follow-up survey. After iterative refinement of the instrument, latent variable measurement models showed very good fit (comparative fit and nonnormed fit indices both 0.99), as did item- and domain-level psychometric characteristics. The final ADPKD-PDS contains 20 items assessing pain severity and interference with activities over a 7-day recall period. CONCLUSIONS: The ADPKD-PDS is the first validated tool for systematically assessing pain and discomfort in ADPKD.

Caregiver Burden of Autosomal Dominant Polycystic Kidney Disease: A Qualitative Study.

Rationale & Objective: There is limited published research on how autosomal dominant polycystic kidney disease (ADPKD) impacts caregivers. This study explored how caregivers of individuals with ADPKD perceive the burdens placed on them by the disease. Study Design: Qualitative study consisting of focus groups and interviews. Discussions were conducted by trained interviewers using semi-structured interview guides. Setting & Participants: The research was conducted in 14 countries in North America, South America, Asia, Australia, and Europe. Eligible participants were greater than or equal to 18 years old and caring for a child or adult diagnosed with ADPKD. Analytical Approach: The concepts reported were coded using qualitative research software. Data saturation was reached when subsequent discussions introduced no new key concepts. Results: Focus groups and interviews were held with 139 participants (mean age, 44.9 years; 66.9% female), including 25 participants who had a diagnosis of ADPKD themselves. Caregivers reported significant impact on their emotional (74.1%) and social life (38.1%), lost work productivity (26.6%), and reduced sleep (25.2%). Caregivers also reported worry about their financial situation (23.7%). In general, similar frequencies of impact were reported among caregivers with ADPKD versus caregivers without ADPKD, with the exception of sleep (8.0% vs 28.9%, respectively), leisure activities (28.0% vs 40.4% respectively), and work/employment (12.0% vs 29.8%, respectively). Limitations: The study was observational and designed to elicit concepts, and only descriptive analyses were conducted. Conclusions: These findings highlight the unique burden on caregivers in ADPKD, which results in substantial emotional, social, and professional/financial impact.

Identifying a Hidden Conglomerate Chiral Pool in the CSD.

Conglomerate crystallization is the spontaneous generation of individually enantioenriched crystals from a nonenantioenriched material. This behavior is responsible for spontaneous resolution and the discovery of molecular chirality by Pasteur. The phenomenon of conglomerate crystallization of chiral organic molecules has been left largely undocumented, with no actively curated list available in the literature. While other crystallographic behaviors can be interrogated by automated searching, conglomerate crystallizations are not identified within the Cambridge Structural Database (CSD) and are therefore not accessible by conventional automated searching. By conducting a manual search of the CSD and literature, a list of over 1800 chiral species capable of conglomerate crystallization was curated by inspection of the racemic synthetic routes described in each publication. The majority of chiral conglomerate crystals are produced and published by synthetic chemists who seldom note and rarely exploit the implications this phenomenon can have on the enantiopurity of their crystalline materials. With their structures revealed, we propose that this list of compounds represents a new chiral pool which is not tied to biological sources of chirality.

A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.

We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.

Polycystic Kidney Disease-Related Disease Burden in Adolescents With Autosomal Dominant Polycystic Kidney Disease: An International Qualitative Study.

Rationale & Objective: Little is known about symptoms and disease impacts in adolescents with autosomal dominant polycystic kidney disease (ADPKD). The objective of the study was to explore these issues from the adolescent patient's perspective. Study Design: Observational, qualitative study. Setting & Participants: Eligible participants were 12-17 years old and had a diagnosis of ADPKD. Semi-structured interviews were conducted in 18 cities in 13 countries to elicit participant experiences of ADPKD-related symptoms and physical, social, and emotional impacts. Analytical Approach: Interviews were recorded, transcribed, and coded. Symptom and impact frequencies from the interviews were calculated, and representative quotes concerning elicited concepts were collated. Results: Thirty-three participants (mean age, 14.6 years; 42.4% female) completed interviews. Frequently reported symptoms included urinary urgency (n = 10; 30.3%) and back pain (n = 9; 27.3%). Consistent with previous findings in adults, participants experienced 3 primary types of pain: dull kidney pain, severe or sharp kidney pain, and a feeling of fullness and/or discomfort. Reported disease impacts included avoiding sports and physical activity (n = 10; 30.3%), missing school (n = 6; 18.2%) and social activities (n = 6; 18.2%), and feeling worried (n = 6; 18.2%), sad (n = 4; 12.1%), or frustrated (n = 3; 9.1%) about the disease and their future. Approximately one-fifth of participants (n = 7; 21.2%) reported that they were bothered or impacted by dietary limitations (primarily the need for reduced sodium intake and increased water intake). Limitations: The study had a small sample size. The researchers were unable to conduct focus groups with participants because of parental preferences. Conclusions: The findings from this exploratory study indicate that a substantial proportion of adolescents with ADPKD experience physical, social, and emotional impacts from their disease.

Results from Patient Interviews on Fatigue in Progressive Multiple Sclerosis and Evaluation of Fatigue Patient-Reported Outcome (PRO) Instruments.

INTRODUCTION: Fatigue is one of the most common and debilitating symptoms of multiple sclerosis (MS) but is challenging to assess and has not been comprehensively characterized in patients with progressive MS. This study aimed to (1) obtain qualitative evidence from patients with progressive MS to characterize MS-related fatigue concepts and their impacts on health-related quality of life (HRQoL), and (2) evaluate the conceptual frameworks of existing MS-specific fatigue patient-reported outcome (PRO) instruments using study data to determine the most suitable PRO instrument in this population. METHODS: Qualitative interviews were conducted with 30 US participants with confirmed progressive MS and fatigue in the last 6 months to assess their MS-related fatigue. Data were compared with concepts in existing PRO instruments to evaluate their relevance in progressive MS. RESULTS: Physical and mental concepts of fatigue were identified and characterized distinctly from patients with progressive MS. Most patients characterized fatigue as occurring daily and lasting several hours, with negative impacts on HRQoL. Concept mapping to existing MS-specific fatigue PRO instruments supported the Fatigue Severity Impact Questionnaire-Relapsing Multiple Sclerosis (FSIQ-RMS) as the most suitable existing option for assessing fatigue in patients with progressive MS, as it separates physical and mental aspects of fatigue and includes every highly endorsed concept reported by the interviewed patients. CONCLUSIONS: This qualitative study identified meaningful physical and mental fatigue concepts in patients with progressive MS and preliminarily supports the use of the FSIQ-RMS for this population. More research is needed to fully validate this instrument for progressive MS.

Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins.

Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.

Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods.

Ligand-based methods play a crucial role in virtual screening when the 3D structure of the target is not available. This study discusses the results of a validation study of the CSD field-based ligand screener using a novel benchmarking data set containing 56 targets. The data set was created starting from the target UniProt IDs in a previously published data set (i.e., the AZ data set), by mining ChEMBL to find known active molecules for these targets and by using DUD-E to generate property-matched decoys of the identified actives. Several experiments were performed to assess the virtual screening performance of the new method. One of its strengths is that it can use an overlay of multiple flexible ligands as a query without the need to run several parallel calculations with one ligand at a time. Here, we discuss how changes to different parameter settings or adoption of different query models can influence the final performance compared to the performance when using the experimentally observed overlay of ligands. We have also generated the enrichment scores based on three external benchmark data sets to enable the comparison with existing methods previously validated using these data sets. Here, we present results for the standard DUD-E data set, the DUD-E+ data set, as well as the DUD_Lib_VS_1.0 data set which was designed for ligand-based virtual screening validation and hence is more suitable for this type of methods.

Augmenting Structure-Based Design with Experimental Protein-Ligand Interaction Data: Molecular Recognition, Interactive Visualization, and Rescoring.

The previously introduced ratio of frequencies (RF ) framework provides statistically sound information on the relative interaction preferences of atoms in crystal structures. By applying the methodology to protein-ligand complexes, we can investigate the significance of interactions that are employed in structure-based drug design. Here, we revisit three aspects of molecular recognition in the light of the RF framework, namely stacking interactions of heteroaromatic rings with protein amide groups, interactions of acidified C-H groups, and interaction differences between syn and anti lone pairs of carboxylate groups. In addition, we introduce a highly interactive visualization tool that facilitates design idea generation in structure-enabled drug discovery projects. Finally, we show that applying the RF analysis as a simple rescoring tool after docking improves enrichment factors for the DUD-E diverse targets subset supporting the relevance of our approach.

Use of molecular docking computational tools in drug discovery.

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Qualitative Findings on the Impact of Disease in Epstein-Barr Virus-Driven Post-Transplant Lymphoproliferative Disease Patients, as Measured by EQ-5D, SF-36, and the FACT-LYM.

INTRODUCTION: There are no validated patient-reported outcome (PRO) instruments for Epstein-Barr virus-driven post-transplant lymphoproliferative disease (EBV+ PTLD). The aim of this study was to assess the content applicability for three frequently used PRO instruments from the perspective of patients with EBV+ PTLD. METHODS: A moderated focus group comprising adult patients with EBV+ PTLD was conducted using a concept confirmation and an open-ended concept elicitation approach. The domains of the EuroQoL Group-5 Dimension (EQ-5D) instrument, Short Form Health Survey-Version 2 (SF-36v2) questionnaire, and Functional Assessment of Chronic Illness Therapy-Lymphoma (FACT-LYM) questionnaire were discussed. The concept elicitation portion was a general discussion of symptoms and patient burden of EBV+ PTLD. RESULTS: Six patients participated in this study: five women and one man. Most participants reported acute pain in the location of their EBV+ PTLD. All participants reported significant physical fatigue and experienced productivity loss. Patients reported emotional fatigue, feelings of dissociation, lack of motivation, and persistent fear of disease progression, including mortality. Patients described their social functioning as disjointed, behaving differently with loved ones/caregivers than when alone. The EQ-5D was relevant for the pain/discomfort and anxiety/depression domains; most SF-36v2 domains were relevant, with the exception of the general health perception domain, which was not applicable; all domains in the FACT-LYM were relevant. The open-ended portion drew no new content. CONCLUSIONS: This qualitative research identified meaningful concepts in patients with EBV+ PTLD, with physical, emotional, and social functioning being impacted. The FACT-LYM questionnaire was the most relevant of the three PROs studied, with all domains relevant to this population. It is important to properly analyze PRO data in patients with EBV+ PTLD.

Identification of Noncompetitive Protein-Ligand Interactions for Structural Optimization.

For efficient structure-guided drug design, it is important to have an excellent understanding of the quality of interactions between the target receptor and bound ligands. Identification and characterization of poor intermolecular contacts offers the possibility to focus design efforts directly on ligand regions with suboptimal molecular recognition. To enable a more straightforward identification of these in a structural model, we use a suitably enhanced version of our previously introduced statistical ratio of frequencies (RF) approach. This allows us to highlight protein-ligand interactions and geometries that occur much less often in the Protein Data Bank than would be expected from the exposed surface areas of the interacting atoms. We provide a comprehensive overview of such noncompetitive interactions and geometries for a set of common ligand substituents. Through retrospective case studies on congeneric series and single-point mutations for several pharmaceutical targets, we illustrate how knowledge of noncompetitive interactions could be exploited in the drug design process.

Hotspots API: A Python Package for the Detection of Small Molecule Binding Hotspots and Application to Structure-Based Drug Design.

Methods that survey protein surfaces for binding hotspots can help to evaluate target tractability and guide exploration of potential ligand binding regions. Fragment Hotspot Maps builds upon interaction data mined from the CSD (Cambridge Structural Database) and exploits the idea of identifying hotspots using small chemical fragments, which is now widely used to design new drug leads. Prior to this publication, Fragment Hotspot Maps was only publicly available through a web application. To increase the accessibility of this algorithm we present the Hotspots API (application programming interface), a toolkit that offers programmatic access to the core Fragment Hotspot Maps algorithm, thereby facilitating the interpretation and application of the analysis. To demonstrate the package's utility, we present a workflow which automatically derives protein hydrogen-bond constraints for molecular docking with GOLD. The Hotspots API is available from https://github.com/prcurran/hotspots under the MIT license and is dependent upon the commercial CSD Python API.

Resolving alternative organic crystal structures using density functional theory and NMR chemical shifts.

Alternative ('repeat') determinations of organic crystal structures deposited in the Cambridge Structural Database are analysed to characterise the nature and magnitude of the differences between structure solutions obtained by diffraction methods. Of the 3132 structure pairs considered, over 20% exhibited local structural differences exceeding 0.25 Å. In most cases (about 83%), structural optimisation using density functional theory (DFT) resolved the differences. Many of the cases where distinct and chemically significant structural differences remained after optimisation involved differently positioned hydroxyl groups, with obvious implications for the correct description of hydrogen bonding. 1H and 13C chemical shifts from solid-state NMR experiments are proposed as an independent methodology in cases where DFT optimisation fails to resolve discrepancies.

New insights and innovation from a million crystal structures in the Cambridge Structural Database.

The Cambridge Structural Database (CSD) is the world's largest and most comprehensive collection of organic, organometallic, and metal-organic crystal structure information. Analyses using the data have wide impact across the chemical sciences in allowing understanding of structural preferences. In this short review, we illustrate the more common methods by which CSD data influence molecular design. We show how more data could lead to more refined insights into the future using a simple example of trifluoromethylphenyl fragments, highlighting how with sufficient data one can build a reasonable model of geometric change in a chemical fragment with torsional rotation, and show some recent examples where the CSD has been used in conjunction with other methods to provide design ideas and more computationally tractable workflows for derivation of useful insights into structural design.

Knowledge-Based Conformer Generation Using the Cambridge Structural Database.

Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geometric distributions derived from the Cambridge Structural Database, it is possible to create biologically relevant conformations in the majority of cases analyzed. The paper compares the performance of the approach with previously published evaluations, and presents some cases where the method fails. The method appears to show significantly improved performance in reproduction of the conformations of structures observed in the Cambridge Structural Database and the Protein Data Bank as compared to other published methods of a similar speed.

Development of the Autosomal Dominant Polycystic Kidney Disease Impact Scale: A New Health-Related Quality-of-Life Instrument.

BACKGROUND: The impact of autosomal dominant polycystic kidney disease (ADPKD) on health-related quality of life (HRQoL) is not well understood due to a lack of instruments specific to the condition. STUDY DESIGN: Content for a new self-administered patient-reported outcome (PRO) questionnaire to assess ADPKD-related HRQoL was developed through clinical expert and patient focus group discussions. The new PRO instrument was administered to study patients with ADPKD to evaluate its reliability and validity. SETTING & PARTICIPANTS: 1,674 adult patients with ADPKD participated in this research: 285 patients in focus groups to generate questionnaire content, 15 patients in debriefing interviews to refine the PRO questionnaire, and 1,374 patients to assess the performance and measurement properties of the PRO questionnaire. OUTCOME: A new PRO questionnaire. RESULTS: The ADPKD Impact Scale (ADPKD-IS), consisting of 14 items representing 3 conceptual domains (physical, emotional, and fatigue) plus 4 additional questions, was developed. The instrument's reliability (regarding internal consistency and test-retest consistency) and validity (content and construct) were supported. LIMITATIONS: Need for more responsiveness testing when more data from clinical use become available over time. Complex concepts such as ADPKD-related pain and impact on a patient's HRQoL need further evaluation. CONCLUSIONS: The ADPKD-IS is a new patient-centric tool that reliably and validly provides a standardized method for assessing HRQoL and overall disease burden in patients with ADPKD.