Functional filter for whole-genome sequencing data identifies HHT and stress-associated non-coding SMAD4 polyadenylation site variants >5 kb from coding DNA.

Despite whole-genome sequencing (WGS), many cases of single-gene disorders remain unsolved, impeding diagnosis and preventative care for people whose disease-causing variants escape detection. Since early WGS data analytic steps prioritize protein-coding sequences, to simultaneously prioritize variants in non-coding regions rich in transcribed and critical regulatory sequences, we developed GROFFFY, an analytic tool that integrates coordinates for regions with experimental evidence of functionality. Applied to WGS data from solved and unsolved hereditary hemorrhagic telangiectasia (HHT) recruits to the 100,000 Genomes Project, GROFFFY-based filtration reduced the mean number of variants/DNA from 4,867,167 to 21,486, without deleting disease-causal variants. In three unsolved cases (two related), GROFFFY identified ultra-rare deletions within the 3' untranslated region (UTR) of the tumor suppressor SMAD4, where germline loss-of-function alleles cause combined HHT and colonic polyposis (MIM: 175050). Sited >5.4 kb distal to coding DNA, the deletions did not modify or generate microRNA binding sites, but instead disrupted the sequence context of the final cleavage and polyadenylation site necessary for protein production: By iFoldRNA, an AAUAAA-adjacent 16-nucleotide deletion brought the cleavage site into inaccessible neighboring secondary structures, while a 4-nucleotide deletion unfolded the downstream RNA polymerase II roadblock. SMAD4 RNA expression differed to control-derived RNA from resting and cycloheximide-stressed peripheral blood mononuclear cells. Patterns predicted the mutational site for an unrelated HHT/polyposis-affected individual, where a complex insertion was subsequently identified. In conclusion, we describe a functional rare variant type that impacts regulatory systems based on RNA polyadenylation. Extension of coding sequence-focused gene panels is required to capture these variants.

The discovery of novel and potent indazole NLRP3 inhibitors enabled by DNA-encoded library screening.

NLRP3 is an intracellular sensor protein that detects a broad range of danger signals and environmental insults. Its activation results in a protective pro-inflammatory response designed to impair pathogens and repair tissue damage via the formation of the NLRP3 inflammasome. Assembly of the NLRP3 inflammasome leads to caspase 1-dependent secretory release of the pro-inflammatory cytokines IL-1ß and IL-18 as well as to gasdermin d-mediated pyroptotic cell death. Herein, we describe the discovery of a novel indazole series of high affinity, reversible inhibitors of NLRP3 activation through screening of DNA-encoded libraries and the potent lead compound 3 (BAL-0028, IC50 = 25 nM) that was identified directly from the screen. SPR studies showed that compound 3 binds tightly (KD range 104-123 nM) to the NACHT domain of NLRP3. A CADD analysis of the interaction of compound 3 with the NLRP3 NACHT domain proposes a binding site that is distinct from those of ADP and MCC950 and includes specific site interactions. We anticipate that compound 3 (BAL-0028) and other members of this novel indazole class of neutral inhibitors will demonstrate significantly different physical, biochemical, and biological properties compared to NLRP3 inhibitors previously identified.

Effects of reference population size and structure on genomic prediction of maternal traits in two pig lines using whole-genome sequence-, high-density- and combined annotation-dependent depletion genotypes.

The aim of this study was to investigate the reference population size required to obtain substantial prediction accuracy within- and across-lines and the effect of using a multi-line reference population for genomic predictions of maternal traits in pigs. The data consisted of two nucleus pig populations, one pure-bred Landrace (L) and one Synthetic (S) Yorkshire/Large White line. All animals were genotyped with up to 30 K animals in each line, and all had records on maternal traits. Prediction accuracy was tested with three different marker data sets: High-density SNP (HD), whole genome sequence (WGS), and markers derived from WGS based on pig combined annotation dependent depletion-score (pCADD). Also, two different genomic prediction methods (GBLUP and Bayes GC) were compared for four maternal traits; total number piglets born (TNB), total number of stillborn piglets (STB), Shoulder Lesion Score and Body Condition Score. The main results from this study showed that a reference population of 3 K-6 K animals for within-line prediction generally was sufficient to achieve high prediction accuracy. However, when the number of animals in the reference population was increased to 30 K, the prediction accuracy significantly increased for the traits TNB and STB. For multi-line prediction accuracy, the accuracy was most dependent on the number of within-line animals in the reference data. The S-line provided a generally higher prediction accuracy compared to the L-line. Using pCADD scores to reduce the number of markers from WGS data in combination with the GBLUP method generally reduced prediction accuracies relative to GBLUP using HD genotypes. The BayesGC method benefited from a large reference population and was less dependent on the different genotype marker datasets to achieve a high prediction accuracy.

Design and Evaluation of NSAID Derivatives as AKR1C3 Inhibitors for Breast Cancer Treatment through Computer-Aided Drug Design and In Vitro Analysis.

Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure-activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (-11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment.

The Prediction of LptA and LptC Protein-Protein Interactions and Virtual Screening for Potential Inhibitors.

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation process of LPS, two components of the LPS transport (Lpt) complex, LptA and LptC, are responsible for shuttling LPS across the periplasm to the outer membrane, highlighting their potential as targets for antibacterial drug development. In the current study, a protein-protein interaction (PPI) model of LptA and LptC was constructed, and a molecular screening strategy was employed to search a protein-protein interaction compound library. The screening results indicated that compound 18593 exhibits favorable binding free energy with LptA and LptC. In comparison with the molecular dynamics (MD) simulations on currently known inhibitors, compound 18593 shows more stable target binding ability at the same level. The current study suggests that compound 18593 may exhibit an inhibitory effect on the LPS transport process, making it a promising hit compound for further research.

A comprehensive review of discovery and development of drugs discovered from 2020-2022.

To fully evaluate and define the new drug molecule for its pharmacological characteristics and toxicity profile, pre-clinical and clinical studies are conducted as part of the drug research and development process. The average time required for all drug development processes to finish various regulatory evaluations ranges from 11.4 to 13.5 years, and the expense of drug development is rising quickly. The development in the discovery of newer novel treatments is, however, largely due to the growing need for new medications. Methods to identify Hits and discovery of lead compounds along with pre-clinical studies have advanced, and one example is the introduction of computer-aided drug design (CADD), which has greatly shortened the time needed for the drug to go through the drug discovery phases. The pharmaceutical industry will hopefully be able to address the present and future issues and will continue to produce novel molecular entities (NMEs) to satisfy the expanding unmet medical requirements of the patients as the success rate of the drug development processes is increasing. Several heterocyclic moieties have been developed and tested against many targets and proved to be very effective. In-depth discussion of the drug design approaches of newly found drugs from 2020 to 2022, including their pharmacokinetic and pharmacodynamic profiles and in-vitro and in-vivo assessments, is the main goal of this review. Considering the many stages these drugs are going through in their clinical trials, this investigation is especially pertinent. It should be noted that synthetic strategies are not discussed in this review; instead, they will be in a future publication.

Exploring Relationships Between Internalizing Problems and Risky Sexual Behavior: A Twin Study.

Previous research links risky sexual behavior (RSB) to externalizing problems and to substance use, but little research has been conducted on relationships between internalizing problems (INT) and RSB. The current study addresses that literature gap, using both a twin sample from Colorado (N = 2567) and a second twin sample from Minnesota (N = 1131) in attempt to replicate initial results. We explored the hypothesis that the latent variable INT would be more strongly associated with the latent variable RSB for females than for males, examining relationships between INT and RSB via phenotypic confirmatory factor analysis and multivariate twin analyses. We found a small but significant phenotypic association between the latent variables. However, despite using two large twin samples, limited power restricted our ability to identify the genetic and environmental mechanisms underlying this association. Our sex differences hypothesis was not fully supported in either sample and requires further investigation. Our findings illustrate the complexity of the relationship between internalizing problems and risky sexual behavior.

Exploring Monkeypox: prospects for therapeutics through computational-aided drug discovery.

Monkeypox virus (MPXV) has emerged as a significant public health concern due to its potential for human transmission and its severe clinical manifestations. This review synthesizes findings from peer-reviewed articles spanning the last two decades, shedding light on diverse aspects of MPXV research. The exploration commences with an analysis of transmission dynamics, including zoonotic and human-to-human transmission, and potential reservoir hosts. Detailed insights into viral replication mechanisms illuminate its influence on disease progression and pathogenicity. Understanding the genomic and virion structure of MPXV is pivotal for targeted interventions. Genomic characteristics contributing to virulence are examined, alongside recent advancements in virion structure elucidation through cutting-edge imaging techniques. Emphasizing combat strategies, the review lists potential protein targets within the MPXV lifecycle for computer-aided drug design (CADD). The role of protein-ligand interactions and molecular docking simulations in identifying potential drug candidates is highlighted. Despite the absence of approved MPXV medications, the review outlines updates on ongoing small molecules and vaccine development efforts, spanning traditional and innovative platforms. The evolving landscape of computational drug research for MPXV is explored, encompassing advanced algorithms, machine learning, and high-performance computing. In conclusion, this review offers a holistic perspective on MPXV research by integrating insights spanning transmission dynamics to drug design. Equipping researchers with multifaceted understanding underscore the importance of innovative methodologies and interdisciplinary collaborations in addressing MPXV's challenges as research advances.

A novel thieno[2,3-d]pyrimidine derivative inhibiting vascular endothelial growth factor receptor-2: A story of computer-aided drug discovery.

Following the pharmacophoric features of vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors, a novel thieno[2,3-d]pyrimidine derivative has been designed and its activity against VEGFR-2 has been demonstrated by molecular docking studies that showed an accurate binding mode and an excellent binding energy. Furthermore, the recorded binding was confirmed by a series of molecular dynamics simulation studies, which also revealed precise energetic, conformational, and dynamic changes. Additionally, molecular mechanics with generalized Born and surface area solvation and polymer-induced liquid precursors studies were conducted and verified the results of the MD simulations. Next, in silico absorption, distribution, metabolism, excretion, and toxicity studies have also been conducted to examine the general drug-like nature of the designed candidate. According to the previous results, the thieno[2,3-d]pyrimidine derivative was synthesized. Fascinatingly, it inhibited VEGFR-2 (IC50 = 68.13 nM) and demonstrated strong inhibitory activity toward human liver (HepG2), and prostate (PC3) cell lines with IC50 values of 6.60 and 11.25 µM, respectively. As well, it was safe and showed a high selectivity index against normal cell lines (WI-38). Finally, the thieno[2,3-d]pyrimidine derivative arrested the growth of the HepG2 cells at the G2/M phase inducing both early and late apoptosis. These results were further confirmed through the ability of the thieno[2,3-d]pyrimidine derivative to induce significant changes in the apoptotic genes levels of caspase-3, caspase-9, Bcl-2 associated X-protein, and B-cell lymphoma 2.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches.

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Computer-Assisted Design of Peptide-Based Radiotracers.

In medical imaging, techniques such as magnetic resonance imaging, contrast-enhanced computerized tomography, positron emission tomography (PET), and single-photon emission computed tomography (SPECT) are extensively available and routinely used for disease diagnosis. PET probes with peptide-based targeting are typically composed of small peptides especially developed to have high affinity and specificity for a range of cellular and tissue targets. These probes' key benefits include being less expensive than traditional antibody-based PET tracers and having an effective chemical modification process that allows them to be radiolabeled with almost any radionuclide, making them highly appealing for clinical usage. Currently, as with every pharmaceutical design, the use of in silico strategies is steadily growing in this field, even though it is not part of the standard toolkit used during radiopharmaceutical design. This review describes the recent applications of computational design approaches in the design of novel peptide-based radiopharmaceuticals.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics.

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Rational Computational Approaches in Drug Discovery: Potential Inhibitors for Allosteric Regulation of Mutant Isocitrate Dehydrogenase-1 Enzyme in Cancers.

Mutations in homodimeric isocitrate dehydrogenase (IDH) enzymes at specific arginine residues result in the abnormal activity to overproduce D-2 hydroxyglutarate (D-2HG), which is often projected as solid oncometabolite in cancers and other disorders. As a result, depicting the potential inhibitor for D-2HG formation in mutant IDH enzymes is a challenging task in cancer research. The mutation in the cytosolic IDH1 enzyme at R132H, especially, may be associated with higher frequency of all types of cancers. So, the present work specifically focuses on the design and screening of allosteric site binders to the cytosolic mutant IDH1 enzyme. The 62 reported drug molecules were screened along with biological activity to identify the small molecular inhibitors using computer-aided drug design strategies. The designed molecules proposed in this work show better binding affinity, biological activity, bioavailability, and potency toward the inhibition of D-2HG formation compare to the reported drugs in the in silico approach.

In silico evaluation of potential intervention against SARS-CoV-2 RNA-dependent RNA polymerase.

Background: With few available effective interventions, emergence of novel mutants responding poorly to existing vaccines and ever swelling newer waves of infection, SARS-CoV-2 is posing difficult challenges to mankind. This mandates development of newer and effective therapeutics to prevent loss of life and contain the spread of this deadly virus. Nsp12 or RNA-dependent RNA polymerase (RdRp) is a suitable druggable target as it plays a central role in viral replication. Methodology: Catalytically important conserved amino acid residues of RdRp were delineated through a comprehensive literature search and multiple sequence alignments. PDB ID 7BV2 was used to create binding pockets using SeeSAR and to generate docked poses of the FDA approved drugs on the receptor and estimating their binding affinity and other properties. Result: In silico approach used in this study assisted in prediction of several potential RdRp inhibitors; and re-validation of the already reported ones. Five molecules namely Inosine, Ribavirin, 2-Deoxy-2-Fluoro-D-glucose, Guaifenesin, and Lamivudine were shortlisted which exhibited reasonable binding affinities, with neither torsional nor intermolecular or intramolecular clashes. Conclusion: This study aimed to widen the prospect of interventions against the SARS-CoV-2 RdRp. Our results also re-validate already reported molecules like 2-Deoxy-D-glucose as a similar molecule 2-deoxy-2-fluoro-D-glucose is picked up in this study. Additionally, ribavirin and lamivudine, already known antivirals with polymerase inhibition activity are also picked up as the top leads. Selected potent inhibitors of RdRp hold promise to cater for any future coronavirus-outbreak subject to in vitro and in vivo validations.

Integration of metabolomics with genomics: Metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores.

The integration of metabolomics data with sequencing data is a key step towards improving the diagnostic process for finding the disease-causing genetic variant(s) in patients suspected of having an inborn error of metabolism (IEM). The measured metabolite levels could provide additional phenotypical evidence to elucidate the degree of pathogenicity for variants found in genes associated with metabolic processes. We present a computational approach, called Reafect, that calculates for each reaction in a metabolic pathway a score indicating whether that reaction is deficient or not. When calculating this score, Reafect takes multiple factors into account: the magnitude and sign of alterations in the metabolite levels, the reaction distances between metabolites and reactions in the pathway, and the biochemical directionality of the reactions. We applied Reafect to untargeted metabolomics data of 72 patient samples with a known IEM and found that in 81% of the cases the correct deficient enzyme was ranked within the top 5% of all considered enzyme deficiencies. Next, we integrated Reafect with Combined Annotation Dependent Depletion (CADD) scores (a measure for gene variant deleteriousness) and ranked the metabolic genes of 27 IEM patients. We observed that this integrated approach significantly improved the prioritization of the genes containing the disease-causing variant when compared with the two approaches individually. For 15/27 IEM patients the correct affected gene was ranked within the top 0.25% of the set of potentially affected genes. Together, our findings suggest that metabolomics data improves the identification of affected genes in patients suffering from IEM.

Structure-based virtual screening, in silico docking, ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro.

COVID-19 is a viral pandemic caused by SARS-CoV-2. Due to its highly contagious nature, millions of people are getting affected worldwide knocking down the delicate global socio-economic equilibrium. According to the World Health Organization, COVID-19 has affected over 186 million people with a mortality of around 4 million as of July 09, 2021. Currently, there are few therapeutic options available for COVID-19 control. The rapid mutations in SARS-CoV-2 genome and development of new virulent strains with increased infection and mortality among COVID-19 patients, there is a great need to discover more potential drugs for SARS-CoV-2 on a priority basis. One of the key viral enzymes responsible for the replication and maturation of SARS-CoV-2 is Mpro protein. In the current study, structure-based virtual screening was used to identify four potential ligands against SARS-CoV-2 Mpro from a set of 8,722 ASINEX library compounds. These four compounds were evaluated using ADME filter to check their ADME profile and druggability, and all the four compounds were found to be within the current pharmacological acceptable range. They were individually docked to SARS-CoV-2 Mpro protein to assess their molecular interactions. Further, molecular dynamics (MD) simulations was carried out on protein-ligand complex using Desmond at 100 ns to explore their binding conformational stability. Based on RMSD, RMSF and hydrogen bond interactions, it was found that the stability of protein-ligand complex was maintained throughout the entire 100 ns simulations for all the four compounds. Some of the key ligand amino acid residues participated in stabilizing the protein-ligand interactions includes GLN 189, SER 10, GLU 166, ASN 142 with PHE 66 and TRP 132 of SARS-CoV-2 Mpro. Further optimization of these compounds could lead to promising drug candidates for SARS-CoV-2 Mpro target.

Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach.

Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure-activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar's website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.

Recent PELE Developments and Applications in Drug Discovery Campaigns.

Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques. We also provide new data on combining two recent new techniques, aquaPELE capable of exhaustive water sampling and fragPELE, for fragment growing.