NMR-based fragment screening and lead discovery accelerated by principal component analysis

Structural Relationships to Efficacy for Prazole-Derived Antivirals

Here, an in vitro characterization of a family of prazole derivatives that covalently bind to the C73 site on Tsg101 and assay their ability to inhibit viral particle production is presented. Structurally, increased steric bulk on the 4-pyridyl of the prazole expands the prazole site on the UEV domain toward the β-hairpin in the Ub-binding site and is coupled to increased inhibition of virus-like particle production in HIV-1. Increased bulk also increased toxicity, which is alleviated by increasing flexibility. Further, the formation of a novel secondary Tsg101 adduct for several of the tested compounds and the commercial drug lansoprazole. The secondary adduct involved the loss of the 4-pyridyl substituent to form an irreversible species, with implications for increasing the half-life of the active species or its specificity toward Tsg101 UEV. It is also determined that sulfide derivatives display effective viral inhibition, presumably through cellular sulfoxidation, allowing for delayed conversion within the cellular environment, and identify SARS-COV-2 as a target of prazole inhibition. These results open multiple avenues for the design of prazole derivatives for antiviral applications.

Medicinal Chemistry and NMR Driven Discovery of Novel UDP-glucuronosyltransferase 1A Inhibitors That Overcome Therapeutic Resistance in Cells

The UDP glucuronosyltransferases (UGT) deactivate many therapeutics via glucuronidation while being required for clearance of normal metabolites and xenobiotics. There are 19 UGT enzymes categorized into UGT1A and UGT2B families based on sequence conservation. This presents a challenge in terms of targeting specific UGTs to overcome drug resistance without eliciting overt toxicity. Here, we identified for the first time that UGT1A4 is highly elevated in acute myeloid leukemia (AML) patients and its reduction corresponded to objective clinical responses. To develop inhibitors to UGT1A4, we leveraged previous NMR-based fragment screening data against the C-terminal domain of UGT1A (UGT1A-C). NMR and medicinal chemistry strategies identified novel chemical matter based on fragment compounds with the capacity to bind ∼20 fold more tightly to UGT1A-C (Kd ∼ 600 μM vs ∼30 μM). Some compounds differentially inhibited UGT1A4 versus UGT1A1 enzyme activity and restored drug sensitivity in resistant human cancer cells. NMR-based NOE experiments revealed these novel compounds recognised a region distal to the catalytic site suggestive of allosteric regulation. This binding region is poorly conserved between UGT1A and UGT2B C-terminal sequences, which otherwise exhibit high similarity. Consistently, these compounds did not bind to the C-terminal domain of UGT2B7 nor a triple mutant of UGT1A-C replaced with UGT2B7 residues in this region. Overall, we discovered a site on UGTs that can be leveraged to differentially target UGT1As and UGT2Bs, identified UGT1A4 as a therapeutic target, and found new chemical matter that binds the UGT1A C-terminus, inhibits glucuronidation and restores drug sensitivity.

Principal Component Analysis (PCA) of Molecular Descriptors for Improving Permeation through the Blood-Brain Barrier of Quercetin Analogues

Despite its beneficial pharmacological effects in the brain, partly by modulating inositol phosphate multikinase (IPMK) activity, the therapeutic use of quercetin is limited due to its poor solubility, low oral bioavailability, and low permeability through the blood-brain barrier (BBB). We aimed to identify quercetin analogues with improved BBB permeability and preserved binding affinities towards IPMK and to identify the molecular characteristics required for them to permeate the BBB. Binding affinities of quercetin analogues towards IPMK were determined by molecular docking. Principal component analysis (PCA) was applied to identify the molecular descriptors contributing to efficient permeation through the BBB. Among 34 quercetin analogues, 19 compounds were found to form more stable complexes with IPMK, and the vast majority were found to be more lipophilic than quercetin. Using two distinct in silico techniques, insufficient BBB permeation was determined for all quercetin analogues. However, using the PCA method, the descriptors related to intrinsic solubility and lipophilicity (logP) were identified as mainly responsible for clustering four quercetin analogues (trihydroxyflavones) with the highest BBB permeability. The application of PCA revealed that quercetin analogues could be classified with respect to their structural characteristics, which may be utilized in further analogue syntheses and lead optimization of BBB-penetrating IPMK modulators as neuroprotective agents.

Efficiently driving protein-based fragment screening and lead discovery using two-dimensional NMR

Fragment-based drug discovery (FBDD) and validation of small molecule binders using NMR spectroscopy is an established and widely used method in the early stages of drug discovery. Starting from a library of small compounds, ligand- or protein-observed NMR methods are employed to detect binders, typically weak, that become the starting points for structure-activity relationships (SAR) by NMR. Unlike the more frequently used ligand-observed 1D NMR techniques, protein-observed 2D ¹H-¹⁵N or ¹H-¹³C heteronuclear correlation (HSQC or HMQC) methods offer insights that include the mechanism of ligand engagement on the target and direct binding affinity measurements in addition to routine screening. We hereby present the development of a set of software tools within the MestReNova (Mnova) package for analyzing 2D NMR for FBDD and hit validation purposes. The package covers three main tasks: (1) unsupervised profiling of raw data to identify outlier data points to exclude in subsequent analyses; (2) batch processing of single-point spectra to identify and rank binders based on chemical shift perturbations or spectral peak intensity changes; and (3) batch processing of multiple titration series to derive binding affinities (K_D) by tracing the changes in peak locations or measuring global spectral changes. Toward this end, we implemented and evaluated a set of algorithms for automated peak tracing, spectral binning, and variance analysis by PCA, and a new tool for spectral data intensity comparison using ECHOS. The accuracy and speed of the tools are demonstrated on 2D NMR binding data collected on ligands used in the development of potential inhibitors of the anti-apoptotic MCL-1 protein.

Solid-State NMR Characterization of Lyophilized Formulations of Monoclonal Antibody Therapeutics

Monoclonal antibodies (mAbs) are an important and growing class of biotherapeutic drugs. Method development for the characterization of critical quality attributes, including higher-order structure (HOS), of mAbs remains an area of active inquiry. Recently, solution-state nuclear magnetic resonance (NMR) spectroscopy has received increased attention and is a means for reliable, high-resolution HOS characterization of aqueous-based preparations of mAbs. While mAbs are predominantly formulated in solution, up to 20% are prepared as solid amorphous powders and techniques for the robust characterization of HOS in the solid state remain limited. We propose here the use of solid-state NMR (ssNMR) fingerprinting to inform directly on the HOS of solid preparations of mAbs. Using lyophilized samples of the NISTmAb reference material prepared with different formulation conditions, we demonstrate that ¹H-¹³C cross-polarization (hC-CP) buildup spectral series at natural isotopic abundance mAb samples are sensitive to differences in formulation. We also demonstrate that principal component analysis (PCA) can be used to differentiate the samples from one another in a user-independent manner while also highlighting areas where expert analysis can provide structural details about important molecular interactions in solid-phase protein formulations. Results from this study contribute to establishing the foundation for the use of ssNMR for HOS characterization of solid-phase biotherapeutics.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Application of Improved Convolution Neural Network in Financial Forecasting

Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, we recalculate the mean and variance of all kinds of observation vectors, and use the new mean and covariance matrix as credit financial credit, and then determine the best model parameters.The empirical results based on specific data from China's stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.

Using NMR Spectroscopy in the Fragment-Based Drug Discovery of Small-Molecule Anticancer Targeted Therapies

Against the challenge of providing personalized cancer care, the development of targeted therapies stands as a promising approach. The discovery of these agents can benefit from fragment-based drug discovery (FBDD) methods that help guide ligand design and provide key structural information on the targets of interest. In particular, nuclear magnetic resonance spectroscopy is a promising biophysical tool in fragment discovery due to its detection capabilities and versatility. This review provides an overview of FBDD, describes the basis of NMR-based fragment screening, summarizes some exciting technical advances reported over the past decades, and closes with a discussion of selected case studies where this technique has been used as part of drug discovery campaigns to produce lead compounds towards the design of anti-cancer targeted therapies.

CcpNmr AnalysisScreen, a new software programme with dedicated automated analysis tools for fragment-based drug discovery by NMR

Fragment-based drug discovery or FBDD is one of the main methods used by industry and academia for identifying drug-like candidates in early stages of drug discovery. NMR has a significant impact at any stage of the drug discovery process, from primary identification of small molecules to the elucidation of binding modes for guiding optimisations. The essence of NMR as an analytical tool, however, requires the processing and analysis of relatively large amounts of single data items, e.g. spectra, which can be daunting when managed manually. One bottleneck in FBDD by NMR is a lack of adequate and well-integrated resources for NMR data analysis that are freely available to the community. Thus, scientists typically resort to manually inspecting large datasets and relying predominantly on subjective interpretations. In this manuscript, we present CcpNmr AnalysisScreen, a software package that provides computational tools for automated analysis of FBDD data by NMR. We outline how the quality of collected spectra can be evaluated quickly, and how robust workflows can be optimised for reliable and rapid hit identification. With an intuitive graphical user interface and powerful algorithms, AnalysisScreen enables easy analysis of the large datasets needed in the early process of drug discovery by NMR.

Principal component analysis for automated classification of 2D spectra and interferograms of protein therapeutics: influence of noise, reconstruction details, and data preparation

Protein therapeutics have numerous critical quality attributes (CQA) that must be evaluated to ensure safety and efficacy, including the requirement to adopt and retain the correct three-dimensional fold without forming unintended aggregates. Therefore, the ability to monitor protein higher order structure (HOS) can be valuable throughout the lifecycle of a protein therapeutic, from development to manufacture. 2D NMR has been introduced as a robust and precise tool to assess the HOS of a protein biotherapeutic. A common use case is to decide whether two groups of spectra are substantially different, as an indicator of difference in HOS. We demonstrate a quantitative use of principal component analysis (PCA) scores to perform this decision-making, and demonstrate the effect of acquisition and processing details on class separation using samples of NISTmAb monoclonal antibody Reference Material subjected to two different oxidative stress protocols. The work introduces an approach to computing similarity from PCA scores based upon the technique of histogram intersection, a method originally developed for retrieval of images from large databases. Results show that class separation can be robust with respect to random noise, reconstruction method, and analysis region selection. By contrast, details such as baseline distortion can have a pronounced effect, and so must be controlled carefully. Since the classification approach can be performed without the need to identify peaks, results suggest that it is possible to use even more efficient measurement strategies that do not produce spectra that can be analyzed visually, but nevertheless allow useful decision-making that is objective and automated.

The BADC and BCCP subunits of chloroplast acetyl-CoA carboxylase sense the pH changes of the light-dark cycle

Acetyl-CoA carboxylase (ACCase) catalyzes the first committed step in the de novo synthesis of fatty acids. The multisubunit ACCase in the chloroplast is activated by a shift to pH 8 upon light adaptation and is inhibited by a shift to pH 7 upon dark adaptation. Here, titrations with the purified ACCase biotin attachment domain-containing (BADC) and biotin carboxyl carrier protein (BCCP) subunits from Arabidopsis indicated that they can competently and independently bind biotin carboxylase (BC) but differ in responses to pH changes representing those in the plastid stroma during light or dark conditions. At pH 7 in phosphate buffer, BADC1 and BADC2 gain an advantage over BCCP1 and BCCP2 in affinity for BC. At pH 8 in KCl solution, however, BCCP1 and BCCP2 had more than 10-fold higher affinity for BC than did BADC1. The pH-modulated shifts in BC preferences for BCCP and BADC partners suggest they contribute to light-dependent regulation of heteromeric ACCase. Using NMR spectroscopy, we found evidence for increased intrinsic disorder of the BADC and BCCP subunits at pH 7. We propose that this intrinsic disorder potentially promotes fast association with BC through a "fly-casting mechanism." We hypothesize that the pH effects on the BADC and BCCP subunits attenuate ACCase activity by night and enhance it by day. Consistent with this hypothesis, Arabidopsis badc1 badc3 mutant lines grown in a light-dark cycle synthesized more fatty acids in their seeds. In summary, our findings provide evidence that the BADC and BCCP subunits function as pH sensors required for light-dependent switching of heteromeric ACCase activity.