CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases

In the context of the global damage caused by coronavirus disease 2019 (COVID-19) and the emergence of the monkeypox virus (MPXV) outbreak as a public health emergency of international concern, research into methods that can rapidly test potential therapeutics during an outbreak of a new infectious disease is urgently needed. Computational drug discovery is an effective way to solve such problems. The existence of various large open databases has mitigated the time and resource consumption of traditional drug development and improved the speed of drug discovery. However, the diversity of cell lines used in various databases remains limited, and previous drug discovery methods are ineffective for cross-cell prediction. In this study, we propose a correlation-dependent connectivity map (CDCM) to achieve cross-cell predictions of drug similarity. The CDCM mainly identifies drug-drug or disease-drug relationships from the perspective of gene networks by exploring the correlation changes between genes and identifying similarities in the effects of drugs or diseases on gene expression. We validated the CDCM on multiple datasets and found that it performed well for drug identification across cell lines. A comparison with the Connectivity Map revealed that our method was more stable and performed better across different cell lines. In the application of the CDCM to COVID-19 and MPXV data, the predictions of potential therapeutic compounds for COVID-19 were consistent with several previous studies, and most of the predicted drugs were found to be experimentally effective against MPXV. This result confirms the practical value of the CDCM. With the ability to predict across cell lines, the CDCM outperforms the Connectivity Map, and it has wider application prospects and a reduced cost of use.

Stapled Peptides: An Innovative and Ultimate Future Drug Offering a Highly Powerful and Potent Therapeutic Alternative

Peptide-based therapeutics have traditionally faced challenges, including instability in the bloodstream and limited cell membrane permeability. However, recent advancements in α-helix stapled peptide modification techniques have rekindled interest in their efficacy. Notably, these developments ensure a highly effective method for improving peptide stability and enhancing cell membrane penetration. Particularly in the realm of antimicrobial peptides (AMPs), the application of stapled peptide techniques has significantly increased peptide stability and has been successfully applied to many peptides. Furthermore, constraining the secondary structure of peptides has also been proven to enhance their biological activity. In this review, the entire process through which hydrocarbon-stapled antimicrobial peptides attain improved drug-like properties is examined. First, the essential secondary structural elements required for their activity as drugs are validated, specific residues are identified using alanine scanning, and stapling techniques are strategically incorporated at precise locations. Additionally, the mechanisms by which these structure-based stapled peptides function as AMPs are explored, providing a comprehensive and engaging discussion.

EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks

Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell biology, systems biology, disease formation, and disease treatment. Accurately determining the quality of multimeric protein models is still computationally challenging, as the space of possible conformations is significantly larger when proteins form in complex with one another. Here, we present EGG (energy and graph-based architectures) to assess the accuracy of predicted multimeric protein models. We implemented message-passing and transformer layers to infer the overall fold and interface accuracy scores of predicted multimeric protein models. When evaluated with CASP15 targets, our methods achieved promising results against single model predictors: fourth and third place for determining the highest-quality model when estimating overall fold accuracy and overall interface accuracy, respectively, and first place for determining the top three highest quality models when estimating both overall fold accuracy and overall interface accuracy.

Time-Lapse Macro Imaging with Dissolution Tests for Exploring the Interrelationship Between Disintegration and Dissolution Behaviors of Solid Dosages

OBJECTIVE

This study aims to establish a Flow-through Visualization Dissolution System (FVDS) that combines time-lapse macro-imaging and a flow-through cell to simultaneously elucidate dissolution and disintegration profiles.

METHODS

Three cefaclor extended-release tablets (CEC-1, CEC-2, CEC-3) from different manufacturers were subjected to dissolution tests using both the US Pharmacopeia basket method and the FVDS method. Two dissolution media plans were implemented in FVDS: i) Plan I involved dissolution in pH1.0 medium for 12 h; ii) Plan II initiated dissolution in pH1.0 medium for 1 h, followed by pH6.8 phosphate buffer for 11 h. The resulting dissolution data were fitted using classic mathematical models. Pixel information was further extracted from images obtained using FVDS and plotted over time.

RESULTS

The basket method showed the cumulative dissolution of all three tablets in pH1.0, pH4.0 and water reached 80% within 6 h, but remained below 60% in the pH6.8 medium. The f2 values indicated CEC-2 was similar to CEC-1 in the pH4.0 medium, pH6.8 medium and water. Using FVDS with medium plan II, the cumulative dissolution of CEC-1 and CEC-2 reached about 80% showing similarity, while no similarity was observed between CEC-3 and CEC-1. The f2 factor of the percentage area change profiles also showed consistent results in the dissolution profile of medium plan II. However, FVDS with medium plan I cannot distinguish between CEC-2 and CEC-3.

CONCLUSION

FVDS offers an alternative to traditional dissolution methods by integrating imaging analysis as a complementary tool to disintegration and dissolution testing methods.

Aberrant MET Receptor Tyrosine Kinase Signaling in Glioblastoma: Targeted Therapy and Future Directions

Brain tumors represent a heterogeneous group of neoplasms characterized by a high degree of aggressiveness and a poor prognosis. Despite recent therapeutic advances, the treatment of brain tumors, including glioblastoma (GBM), an aggressive primary brain tumor associated with poor prognosis and resistance to therapy, remains a significant challenge. Receptor tyrosine kinases (RTKs) are critical during development and in adulthood. Dysregulation of RTKs through activating mutations and gene amplification contributes to many human cancers and provides attractive therapeutic targets for treatment. Under physiological conditions, the Met RTK, the hepatocyte growth factor/scatter factor (HGF/SF) receptor, promotes fundamental signaling cascades that modulate epithelial-to-mesenchymal transition (EMT) involved in tissue repair and embryogenesis. In cancer, increased Met activity promotes tumor growth and metastasis by providing signals for proliferation, survival, and migration/invasion. Recent clinical genomic studies have unveiled multiple mechanisms by which MET is genetically altered in GBM, including focal amplification, chromosomal rearrangements generating gene fusions, and a splicing variant mutation (exon 14 skipping, METex14del). Notably, MET overexpression contributes to chemotherapy resistance in GBM by promoting the survival of cancer stem-like cells. This is linked to distinctive Met-induced pathways, such as the upregulation of DNA repair mechanisms, which can protect tumor cells from the cytotoxic effects of chemotherapy. The development of MET-targeted therapies represents a major step forward in the treatment of brain tumours. Preclinical studies have shown that MET-targeted therapies (monoclonal antibodies or small molecule inhibitors) can suppress growth and invasion, enhancing the efficacy of conventional therapies. Early-phase clinical trials have demonstrated promising results with MET-targeted therapies in improving overall survival for patients with recurrent GBM. However, challenges remain, including the need for patient stratification, the optimization of treatment regimens, and the identification of mechanisms of resistance. This review aims to highlight the current understanding of mechanisms underlying MET dysregulation in GBM. In addition, it will focus on the ongoing preclinical and clinical assessment of therapies targeting MET dysregulation in GBM.

Synthetic GPR40/FFAR1 agonists: An exhaustive survey on the most recent chemical classes and their structure-activity relationships

Free fatty acid receptor 1 (FFAR1 or GPR40) is a potential target for treating type 2 diabetes mellitus (T2DM) and related disorders that have been extensively researched for many years. GPR40/FFAR1 is a promising anti-diabetic target because it can activate insulin, promoting glucose metabolism. It controls T2DM by regulating glucose levels in the body through two separate mechanisms: glucose-stimulated insulin secretion and incretin production. In the last few years, various synthetic GPR40/FFAR1 agonists have been discovered that fall under several chemical classes, viz. phenylpropionic acid, phenoxyacetic acid, and dihydrobenzofuran acetic acid. However, only a few synthetic agonists have entered clinical trials due to various shortcomings like poor efficacy, low lipophilicity and toxicity issues. As a result, pharmaceutical firms and research institutions are interested in developing synthetic GPR40/FFAR1 agonists with superior effectiveness, lipophilicity, and safety profiles. This review encompasses the most recent research on synthetic GPR40/FFAR1 agonists, including their chemical classes, design strategies and structure-activity relationships. Additionally, we have emphasised the structural characteristics of the most potent GPR40/FFAR1 agonists from each chemical class of synthetic derivatives and analysed their chemico-biological interactions. This work will hopefully pave the way for developing more potent and selective synthetic GPR40/FFAR1 agonists for treating T2DM and related disorders.

Open Structural Data in Precision Medicine

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Protein Folding Interdiction Strategy for Therapeutic Drug Development in Viral Diseases: Ebola VP40 and Influenza A M1

In a recent paper, we proposed the folding interdiction target region (FITR) strategy for therapeutic drug design in SARS-CoV-2. This paper expands the application of the FITR strategy by proposing therapeutic drug design approaches against Ebola virus disease and influenza A. We predict target regions for folding interdicting drugs on correspondingly relevant structural proteins of both pathogenic viruses: VP40 of Ebola, and matrix protein M1 of influenza A. Identification of the protein targets employs the sequential collapse model (SCM) for protein folding. It is explained that the model predicts natural peptide candidates in each case from which to start the search for therapeutic drugs. The paper also discusses how these predictions could be tested, as well as some challenges likely to be found when designing effective therapeutic drugs from the proposed peptide candidates. The FITR strategy opens a potential new avenue for the design of therapeutic drugs that promises to be effective against infectious diseases.

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Mimicking H3 Substrate Arginine in the Design of G9a Lysine Methyltransferase Inhibitors for Cancer Therapy: A Computational Study for Structure-Based Drug Design

G9a protein methyltransferase is a potential epigenetic drug target in different cancers and other disease conditions overexpressing the enzyme. G9a is responsible for the H3K9 dimethylation mark, which epigenetically regulates gene expression. Arg8 and Lys9 of the H3 substrate peptide are the two crucial residues for substrate-specific recognition and methylation. Several substrate competitive inhibitors are reported for the potent inhibition of G9a by incorporating lysine mimic groups in the inhibitor design. In this study, we explored the concept of arginine mimic strategy. The hydrophobic segment of the reported inhibitors BIX-01294 and UNC0638 was replaced by a guanidine moiety (side-chain moiety of arginine). The newly substituted guanidine moieties of the inhibitors were positioned similar to the Arg8 of the substrate peptide in molecular docking. Additionally, improved reactivity of the guanidine-substituted inhibitors was observed in density functional theory studies. Molecular dynamics, molecular mechanics Poisson-Boltzmann surface area binding free energy, linear interaction energy, and potential mean force calculated from steered molecular dynamics simulations of the newly designed analogues show enhanced conformational stability and improved H-bond potential and binding affinity toward the target G9a. Moreover, the presence of both lysine and arginine mimics together shows a drastic increase in the binding affinity of the inhibitor towards G9a. Hence, we propose incorporating a guanidine group to imitate the substrate arginine's side chain in the inhibitor design to improve the potency of G9a inhibitors.

Modeling the Influenza A NP-vRNA-Polymerase Complex in Atomic Detail

Seasonal flu is an acute respiratory disease that exacts a massive toll on human populations, healthcare systems and economies. The disease is caused by an enveloped Influenza virus containing eight ribonucleoprotein (RNP) complexes. Each RNP incorporates multiple copies of nucleoprotein (NP), a fragment of the viral genome (vRNA), and a viral RNA-dependent RNA polymerase (POL), and is responsible for packaging the viral genome and performing critical functions including replication and transcription. A complete model of an Influenza RNP in atomic detail can elucidate the structural basis for viral genome functions, and identify potential targets for viral therapeutics. In this work we construct a model of a complete Influenza A RNP complex in atomic detail using multiple sources of structural and sequence information and a series of homology-modeling techniques, including a motif-matching fragment assembly method. Our final model provides a rationale for experimentally-observed changes to viral polymerase activity in numerous mutational assays. Further, our model reveals specific interactions between the three primary structural components of the RNP, including potential targets for blocking POL-binding to the NP-vRNA complex. The methods developed in this work open the possibility of elucidating other functionally-relevant atomic-scale interactions in additional RNP structures and other biomolecular complexes.

Perspective on Crystallographic Studies of Antibody Structures

In the past 50 years, there has been a great progress made in understanding and deploying antibodies in biology, medicine, and therapy. In this study, a brief overview is presented on how the crystal structures of antibody fragments guided therapeutic strategies emanating from our laboratories along with some historical perspective.

In Silico Design, Synthesis and Evaluation of Novel Series of Benzothiazole- Based Pyrazolidinediones as Potent Hypoglycemic Agents

Background:

The discovery of novel ligand binding domain (LBD) of peroxisome proliferator- activated receptor γ (PPARγ) has recently attracted attention to few research groups in order to develop more potent and safer antidiabetic agents.

Objective:

This study is focused on docking-based design and synthesis of novel compounds combining benzothiazole and pyrazolidinedione scaffold as potential antidiabetic agents.

Methods:

Several benzothiazole-pyrazolidinedione hybrids were synthesized and tested for their in vivo anti-hyperglycemic activity. Interactions profile of title compounds against PPARγ was examined through molecular modelling approach.

Results:

All tested compounds exhibited anti-hyperglycemic activity similar or superior to the reference drug Rosiglitazone. Introducing chlorine atom and alkyl group at position-6 and -5 respectively on benzothiazole core resulted in enhancing the anti-hyperglycemic effect. Docking study revealed that such groups demonstrated favorable hydrophobic interactions with novel LBD Ω- pocket of PPARγ protein.

Conclusion:

Among the tested compounds, N-(6-chloro-5-methylbenzo[d]thiazol-2-yl-4-(4((3,5- dioxopyrazolidin-4-ylidene)methyl)phenoxy)butanamide 5b was found to be the most potent compound and provided valuable insights to further develop novel hybrids as anti-hyperglycemic agents.

Small Sequence-Sensitive Compounds for Specific Recognition of the G⋅C Base Pair in DNA Minor Groove

A series of small diamidines with thiophene and modified N-alkylbenzimidazole σ-hole module represent specific binding to single G⋅C base pair (bp) DNA sequence. The variation of N-alkyl or aromatic rings were sensitive to microstructures of the DNA minor groove. Thirteen new compounds were synthesized to test their binding affinity and selectivity. The dicyanobenzimidazoles needed to synthesize the target diamidines were made via condensation/cyclization reactions of different aldehydes with different 3-amino-4-(alkyl- or phenyl-amino) benzonitriles. The final diamidines were synthesized using lithium bis-trimethylsilylamide (LiN[Si(CH3 )3 ]2 ) or Pinner methods. The newly synthesized compounds showed strong binding and selectivity to AAAGTTT compared to similar sequences AAATTT and AAAGCTTT investigated by several biophysical methods including biosensor-SPR, fluorescence spectroscopy, DNA thermal melting, ESI-MS spectrometry, circular dichroism, and molecular dynamics. The binding affinity results determined by fluorescence spectroscopy are in accordance with those obtained by biosensor-SPR. These small size single G⋅C bp highly specific binders extend the compound database for future biological applications.

Repurposing of drugs as STAT3 inhibitors for cancer therapy

Drug repurposing is a valuable approach in delivering new cancer therapeutics rapidly into the clinic. Existing safety and patient tolerability data for drugs already in clinical use represent an untapped resource in terms of identifying therapeutic agents for off-label protein targets. The multicellular effects of STAT3 mediated by a range of various upstream signaling pathways make it an attractive therapeutic target with utility in a range of diseases including cancer, and has led to the development of a variety of STAT3 inhibitors. Moreover, heightened STAT3 transcriptional activation in tumor cells and within the cells of the tumor microenvironment contribute to disease progression. Consequently, there are many STAT3 inhibitors in preclinical development or under evaluation in clinical trials for their therapeutic efficacy predominantly in inflammatory diseases and cancer. Despite these advances, many challenges remain in ultimately providing STAT3 inhibitors to patients as cancer treatments, highlighting the need not only for a better understanding of the mechanisms associated with STAT3 activation, but also how various pharmaceutical agents suppress STAT3 activity in various cancers. In this review we discuss the importance of STAT3-dependent functions in cancer, review the status of compounds designed as direct-acting STAT3 inhibitors, and describe some of the strategies for repurposing of drugs as STAT3 inhibitors for cancer therapy.

Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins

BACKGROUND

Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies.

RESULTS

Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered.

CONCLUSIONS

Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.

Current Advancements in Addressing Key Challenges of Therapeutic Antibody Design, Manufacture, and Formulation

Therapeutic antibody technology heavily dominates the biologics market and continues to present as a significant industrial interest in developing novel and improved antibody treatment strategies. Many noteworthy advancements in the last decades have propelled the success of antibody development; however, there are still opportunities for improvement. In considering such interest to develop antibody therapies, this review summarizes the array of challenges and considerations faced in the design, manufacture, and formulation of therapeutic antibodies, such as stability, bioavailability and immunological engagement. We discuss the advancement of technologies that address these challenges, highlighting key antibody engineered formats that have been adapted. Furthermore, we examine the implication of novel formulation technologies such as nanocarrier delivery systems for the potential to formulate for pulmonary delivery. Finally, we comprehensively discuss developments in computational approaches for the strategic design of antibodies with modulated functions.

Applications of machine learning in GPCR bioactive ligand discovery

GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR ligands as lead compounds. There are tens of millions of compounds present in different chemical databases. In order to scan this immense chemical space, computational methods, especially machine learning (ML) methods, are essential components of GPCR drug discovery pipelines. ML approaches have applications in both ligand-based and structure-based virtual screening. We present here a cheminformatics overview of ML applications to different stages of GPCR drug discovery. Focusing on olfactory receptors, which are the largest family of GPCRs, a case study for predicting agonists for an ectopic olfactory receptor, OR1G1, compares four classical ML methods.

A New Generation of Minor-Groove-Binding-Heterocyclic Diamidines That Recognize G·C Base Pairs in an AT Sequence Context

We review the preparation of new compounds with good solution and cell uptake properties that can selectively recognize mixed A·T and G·C bp sequences of DNA. Our underlying aim is to show that these new compounds provide important new biotechnology reagents as well as a new class of therapeutic candidates with better properties and development potential than other currently available agents. In this review, entirely different ways to recognize mixed sequences of DNA by modifying AT selective heterocyclic cations are described. To selectively recognize a G·C base pair an H-bond acceptor must be incorporated with AT recognizing groups as with netropsin. We have used pyridine, azabenzimidazole and thiophene-N-methylbenzimidazole GC recognition units in modules crafted with both rational design and empirical optimization. These modules can selectively and strongly recognize a single G·C base pair in an AT sequence context. In some cases, a relatively simple change in substituents can convert a heterocyclic module from AT to GC recognition selectivity. Synthesis and DNA interaction results for initial example lead modules are described for single G·C base pair recognition compounds. The review concludes with a description of the initial efforts to prepare larger compounds to recognize sequences of DNA with more than one G·C base pairs. The challenges and initial successes are described along with future directions.