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.

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.

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.

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.

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.

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.

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.

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.

New active leads for tuberculosis booster drugs by structure-based drug discovery.

The transcriptional repressor EthR from Mycobacterium tuberculosis, a member of the TetR family of prokaryotic homo-dimeric transcription factors, controls the expression of the mycobacterial mono-oxygenase EthA. EthA is responsible for the bio-activation of the second-line tuberculosis pro-drug ethionamide, and consequently EthR inhibitors boost drug efficacy. Here, we present a comprehensive in silico structure-based screening protocol that led to the identification of a number of novel scaffolds of EthR inhibitors in subsequent biophysical screening by thermal shift assay. Growth inhibition assays demonstrated that five of the twenty biophysical hits were capable of boosting ethionamide activity in vitro, with the best novel scaffold displaying an EC50 of 34 µM. In addition, the co-crystal structures of EthR with four new ligands at resolution ranging from 2.1 to 1.4 Å confirm the binding and inactivation mode, and will enable future lead development.

Knowledge-Based Optimization of Molecular Geometries Using Crystal Structures.

This paper describes a novel way to use the structural information contained in the Cambridge Structural Database (CSD) to drive geometry optimization of organic molecules. We describe how CSD structural information is transformed into objective functions for gradient-based optimization to provide good quality geometries for a large variety of organic molecules. Performance is assessed by minimizing different sets of organic molecules reporting RMSD movements for bond lengths, valence angles, torsion angles, and heavy atom positions.

Development and Validation of the Eyelash Satisfaction Questionnaire.

BACKGROUND: Patient-reported outcome (PRO) measures have been used to assess treatment benefit in a variety of therapeutic areas and are now becoming increasingly important in aesthetic research. OBJECTIVES: The objective of the current study was to develop and validate a new PRO measure (Eyelash Satisfaction Questionnaire [ESQ]) to assess satisfaction with eyelash prominence. METHODS: The content of the questionnaire (including conceptual framework and questionnaire items) was generated by review of literature, participant interviews, and expert opinion. Cognitive interviews were conducted to pilot test the questionnaire. Psychometric properties of the questionnaire were examined in a combined sample of participants (n = 970) completing Internet- (n = 909) and paper-based (n = 61) versions. Item- and domain-level properties were examined using modern and classical psychometrics. RESULTS: Content-based analysis of qualitative data demonstrated the presence of 3 distinct domains (Length, Fullness, Overall Satisfaction; Confidence, Attractiveness, and Professionalism; and Daily Routine). Initial confirmatory factor analysis (CFA) results of 23 items revealed insufficient model-data fit (comparative fit index [CFI] of 0.86 and a non-normed fit index [NNFI] of 0.82). A revised model using 9 items (3 per domain) achieved appropriate fit (CFI of 0.99 and NNFI of 0.97). Analyses revealed measurement equivalence across the Internet- and paper-based versions. The 3 ESQ domains had strong internal consistency reliability (Cronbach's α [range] = 0.919-0.976) and adequate convergent and discriminant validity. CONCLUSIONS: The ESQ was found to be a reliable and valid PRO measure for assessing satisfaction with eyelash prominence. LEVEL OF EVIDENCE 3: Therapeutic.

Health care resource use, productivity, and costs among patients with moderate to severe plaque psoriasis in the United States.

BACKGROUND: Comprehensive studies on costs of moderate to severe plaque psoriasis (MSPP) have not been conducted in the United States. OBJECTIVE: We sought to evaluate current health care resource use, productivity, and costs among patients with MSPP in routine practice. METHODS: A total of 200 adults seeking MSPP treatment enrolled in 9 US sites. Consented patients reported symptoms, treatment, lost productivity, and costs; 6-month retrospective chart review captured health care resource use and clinical characteristics. Costs were assigned to health care resource use and lost productivity using standard algorithms. Differences by Psoriasis Area and Severity Index (PASI) group, based on PASI score (≤10, >10-≤20, >20) at enrollment, were evaluated. Analyses included descriptive statistics and analysis of variance or Kruskal-Wallis tests. RESULTS: Most patients (79.5%) were prescribed 1 or more MSPP medications (mean: 1.5); 36.0% and 9.0% received self-administered biologics and systemic therapies, respectively. Mean number of nonprescription treatments was 12.3. Differences by PASI group were observed for overall work and activity impairment (P < .02). Six-month total MSPP direct costs per patient were $11,291; indirect costs were $2101 and differed across PASI groups (P = .0008). LIMITATIONS: This study enrolled patients with MSPP actively seeking care. CONCLUSION: Despite treatment, a number of patients with MSPP continue to experience moderate to severe PASI scores, impaired functioning, and high costs suggesting a need for new treatment options.

Assessment of a Cambridge Structural Database-driven overlay program.

We recently published an improved methodology for overlaying multiple flexible ligands and an extensive data set for validating pharmacophore programs. Here, we combine these two developments and present evidence of the effectiveness of the new overlay methodology at predicting correct superimpositions for systems with varying levels of complexity. The overlay program was able to generate correct predictions for 95%, 73%, and 39% of systems classified as easy, moderate, and hard, respectively.

Impact of Alzheimer's Disease on Caregiver Questionnaire: internal consistency, convergent validity, and test-retest reliability of a new measure for assessing caregiver burden.

BACKGROUND: There is a lack of validated instruments to measure the level of burden of Alzheimer's disease (AD) on caregivers. The Impact of Alzheimer's Disease on Caregiver Questionnaire (IADCQ) is a 12-item instrument with a seven-day recall period that measures AD caregiver's burden across emotional, physical, social, financial, sleep, and time aspects. Primary objectives of this study were to evaluate psychometric properties of IADCQ administered on the Web and to determine most appropriate scoring algorithm. METHODS: A national sample of 200 unpaid AD caregivers participated in this study by completing the Web-based version of IADCQ and Short Form-12 Health Survey Version 2 (SF-12v2™). The SF-12v2 was used to measure convergent validity of IADCQ scores and to provide an understanding of the overall health-related quality of life of sampled AD caregivers. The IADCQ survey was also completed four weeks later by a randomly selected subgroup of 50 participants to assess test-retest reliability. Confirmatory factor analysis (CFA) was implemented to test the dimensionality of the IADCQ items. Classical item-level and scale-level psychometric analyses were conducted to estimate psychometric characteristics of the instrument. Test-retest reliability was performed to evaluate the instrument's stability and consistency over time. RESULTS: Virtually none (2%) of the respondents had either floor or ceiling effects, indicating the IADCQ covers an ideal range of burden. A single-factor model obtained appropriate goodness of fit and provided evidence that a simple sum score of the 12 items of IADCQ can be used to measure AD caregiver's burden. Scales-level reliability was supported with a coefficient alpha of 0.93 and an intra-class correlation coefficient (for test-retest reliability) of 0.68 (95% CI: 0.50-0.80). Low-moderate negative correlations were observed between the IADCQ and scales of the SF-12v2. CONCLUSIONS: The study findings suggest the IADCQ has appropriate psychometric characteristics as a unidimensional, Web-based measure of AD caregiver burden and is supported by strong model fit statistics from CFA, high degree of item-level reliability, good internal consistency, moderate test-retest reliability, and moderate convergent validity. Additional validation of the IADCQ is warranted to ensure invariance between the paper-based and Web-based administration and to determine an appropriate responder definition.

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.

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.

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.

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.

Validating and understanding ring conformations using small molecule crystallographic data.

Understanding the conformational preferences of ring structures is fundamental to structure-based drug design. Although the Cambridge Structural Database (CSD) provides information on the preferred conformations of small molecules, analyzing this data can be very time-consuming. In order to overcome this hurdle, tools have been developed for quickly extracting geometrical preferences from the CSD. Here we describe how the program Mogul has been extended to analyze and compare ring conformations, using a library derived from over 900 000 ring fragments in the CSD. We illustrate how these can be used to understand the conformational preferences of molecules in a crystal lattice and bound to proteins.