Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning

In Silico Drug Repurposing Endorse Amprenavir, Darunavir and Saquinavir to Target Enzymes of Multidrug Resistant Uropathogenic E. Coli

Multidrug resistance is a paramount impediment to successful treatment of most hospital acquired bacterial infections. A plethora of bacterial genera exhibit differential levels of resistance to the existing antibiotics. Prevalent Uropathogenic Escherichia coli or UPEC conduce high mortality among them. Multi-Drug Resistant bacterial strains utilize precise mechanisms to bypass effects of antibiotics. This is probably due to their familiar genomic origin. In this article drug repositioning method have been utilised to target 23 enzymes of UPEC strains viz. CFT073, 536 and UTI89. 3-D drug binding motifs have been predicted using SPRITE and ASSAM servers that compare amino acid side chain similarities. From the hit results anti-viral drugs have been considered for their uniqueness and specificity. Out of 14 anti-viral drugs 3 anti-HIV drugs viz. Amprenavir, Darunavir and Saquinavir have selected for maximum binding score or drug targetability. Finally, active sites of the enzymes were analyzed using GASS-WEB for eloquent drug interference. Further analyses with the active sites of all the enzymes showed that the three selected anti-HIV drugs were very much potent to inhibit their active sites. Combination or sole application of Amprenavir, Darunavir and Saquinavir to MDR-UPEC infections may leads to cure and inhibition of mortality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-024-01282-x.

Potentials and future perspectives of multi-target drugs in cancer treatment: the next generation anti-cancer agents

Cancer is a major public health problem worldwide with more than an estimated 19.3 million new cases in 2020. The occurrence rises dramatically with age, and the overall risk accumulation is combined with the tendency for cellular repair mechanisms to be less effective in older individuals. Conventional cancer treatments, such as radiotherapy, surgery, and chemotherapy, have been used for decades to combat cancer. However, the emergence of novel fields of cancer research has led to the exploration of innovative treatment approaches focused on immunotherapy, epigenetic therapy, targeted therapy, multi-omics, and also multi-target therapy. The hypothesis was based on that drugs designed to act against individual targets cannot usually battle multigenic diseases like cancer. Multi-target therapies, either in combination or sequential order, have been recommended to combat acquired and intrinsic resistance to anti-cancer treatments. Several studies focused on multi-targeting treatments due to their advantages include; overcoming clonal heterogeneity, lower risk of multi-drug resistance (MDR), decreased drug toxicity, and thereby lower side effects. In this study, we'll discuss about multi-target drugs, their benefits in improving cancer treatments, and recent advances in the field of multi-targeted drugs. Also, we will study the research that performed clinical trials using multi-target therapeutic agents for cancer treatment.

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases .

GrAfSS: a webserver for substructure similarity searching and comparisons in the structures of proteins and RNA

The GrAfSS (Graph theoretical Applications for Substructure Searching) webserver is a platform to search for three-dimensional substructures of: (i) amino acid side chains in protein structures; and (ii) base arrangements in RNA structures. The webserver interfaces the functions of five different graph theoretical algorithms – ASSAM, SPRITE, IMAAAGINE, NASSAM and COGNAC – into a single substructure searching suite. Users will be able to identify whether a three-dimensional (3D) arrangement of interest, such as a ligand binding site or 3D motif, observed in a protein or RNA structure can be found in other structures available in the Protein Data Bank (PDB). The webserver also allows users to determine whether a protein or RNA structure of interest contains substructural arrangements that are similar to known motifs or 3D arrangements. These capabilities allow for the functional annotation of new structures that were either experimentally determined or computationally generated (such as the coordinates generated by AlphaFold2) and can provide further insights into the diversity or conservation of functional mechanisms of structures in the PDB. The computed substructural superpositions are visualized using integrated NGL viewers. The GrAfSS server is available at http://mfrlab.org/grafss/.

Montelukast is a dual-purpose inhibitor of SARS-CoV-2 infection and virus-induced IL-6 expression identified by structure-based drug repurposing

Drug-repurposing has been instrumental to identify drugs preventing SARS-CoV-2 replication or attenuating the disease course of COVID-19. Here, we identify through structure-based drug-repurposing a dual-purpose inhibitor of SARS-CoV-2 infection and of IL-6 production by immune cells. We created a computational structure model of the receptor binding domain (RBD) of the SARS-CoV-2 spike 1 protein, and used this model for insilico screening against a library of 6171 molecularly defined binding-sites from drug molecules. Molecular dynamics simulation of candidate molecules with high RBD binding-scores in docking analysis predicted montelukast, an antagonist of the cysteinyl-leukotriene-receptor, to disturb the RBD structure, and infection experiments demonstrated inhibition of SARS-CoV-2 infection, although montelukast binding was outside the ACE2-binding site. Molecular dynamics simulation of SARS-CoV-2 variant RBDs correctly predicted interference of montelukast with infection by the beta but not the more infectious alpha variant. With distinct binding sites for RBD and the leukotriene receptor, montelukast also prevented SARS-CoV-2-induced IL-6 release from immune cells. The inhibition of SARS-CoV-2 infection through a molecule binding distal to the ACE-binding site of the RBD points towards an allosteric mechanism that is not conserved in the more infectious alpha and delta SARS-CoV-2 variants.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

BACKGROUND

Novel Coronavirus disease 2019 or COVID-19 has become a threat to human society due to fast spreading and increasing mortality. It uses vertebrate hosts and presently deploys humans. Life cycle and pathogenicity of SARS-CoV-2 have already been deciphered and possible drug target trials are on the way.

RESULTS

The present study was aimed to analyze Non-Structural Proteins that include conserved enzymes of SARS-CoV-2 like papain-like protease, main protease, Replicase, RNA-dependent RNA polymerase, methyltransferase, helicase, exoribonuclease and endoribonucleaseas targets to all known drugs. A bioinformatic based web server Drug ReposeER predicted several drug binding motifs in these analyzed proteins. Results revealed that anti-viral drugs Darunavir,Amprenavir, Rimantadine and Saquinavir were the most potent to have 3D-drug binding motifs that were closely associated with the active sites of the SARS-CoV-2 enzymes .

CONCLUSIONS

 Repurposing of the antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir to treat COVID-19 patients could be useful that can potentially prevent human mortality.

LigAdvisor: a versatile and user-friendly web-platform for drug design

Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.

NOD: a web server to predict New use of Old Drugs to facilitate drug repurposing

Computational methods accelerate the drug repurposing pipelines that are a quicker and cost-effective alternative to discovering new molecules. However, there is a paucity of web servers to conduct fast, focussed, and customized investigations for identifying new uses of old drugs. We present the NOD web server, which has the mentioned characteristics. NOD uses a sensitive sequence-guided approach to identify close and distant homologs of a protein of interest. NOD then exploits this evolutionary information to suggest potential compounds from the DrugBank database that can be repurposed against the input protein. NOD also allows expansion of the chemical space of the potential candidates through similarity searches. We have validated the performance of NOD against available experimental and/or clinical reports. In 65.6% of the investigated cases in a control study, NOD is able to identify drugs more effectively than the searches made in DrugBank. NOD is freely-available at http://pauling.mbu.iisc.ac.in/NOD/NOD/ .

Repurposing potential of posaconazole and grazoprevir as inhibitors of SARS-CoV-2 helicase

As the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic engulfs millions worldwide, the quest for vaccines or drugs against the virus continues. The helicase protein of SARS-CoV-2 represents an attractive target for drug discovery since inhibition of helicase activity can suppress viral replication. Using in silico approaches, we have identified drugs that interact with SARS-CoV-2 helicase based on the presence of amino acid arrangements matching binding sites of drugs in previously annotated protein structures. The drugs exhibiting an RMSD of ≤ 3.0 Å were further analyzed using molecular docking, molecular dynamics (MD) simulation, and post-MD analyses. Using these approaches, we found 12 drugs that showed strong interactions with SARS-CoV-2 helicase amino acids. The analyses were performed using the recently available SARS-CoV-2 helicase structure (PDB ID: 5RL6). Based on the MM-GBSA approach, out of the 12 drugs, two drugs, namely posaconazole and grazoprevir, showed the most favorable binding energy, - 54.8 and - 49.1 kcal/mol, respectively. Furthermore, of the amino acids found conserved among all human coronaviruses, 10/11 and 10/12 were targeted by, respectively, grazoprevir and posaconazole. These residues are part of the crucial DEAD-like helicase C and DEXXQc_Upf1-like/ DEAD-like helicase domains. Strong interactions of posaconazole and grazoprevir with conserved amino acids indicate that the drugs can be potent against SARS-CoV-2. Since the amino acids are conserved among the human coronaviruses, the virus is unlikely to develop resistance mutations against these drugs. Since these drugs are already in use, they may be immediately repurposed for SARS-CoV-2 therapy.

PROMISCUOUS 2.0: a resource for drug-repositioning

The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients’ lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules (∼1 million), side-effects (∼110 000) or drug-target interactions (∼3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.

Side chain similarity comparisons for integrated drug repositioning and potential toxicity assessments in epidemic response scenarios: The case for COVID-19

Structures of protein-drug-complexes provide an atomic level profile of drug-target interactions. In this work, the three-dimensional arrangements of amino acid side chains in known drug binding sites (substructures) were used to search for similarly arranged sites in SARS-CoV-2 protein structures in the Protein Data Bank for the potential repositioning of approved compounds. We were able to identify 22 target sites for the repositioning of 16 approved drug compounds as potential therapeutics for COVID-19. Using the same approach, we were also able to investigate the potentially promiscuous binding of the 16 compounds to off-target sites that could be implicated in toxicity and side effects that had not been provided by any previous studies. The investigations of binding properties in disease-related proteins derived from the comparison of amino acid substructure arrangements allows for effective mechanism driven decision making to rank and select only the compounds with the highest potential for success and safety to be prioritized for clinical trials or treatments. The intention of this work is not to explicitly identify candidate compounds but to present how an integrated drug repositioning and potential toxicity pipeline using side chain similarity searching algorithms are of great utility in epidemic scenarios involving novel pathogens. In the case of the COVID-19 pandemic caused by the SARS-CoV-2 virus, we demonstrate that the pipeline can identify candidate compounds quickly and sustainably in combination with associated risk factors derived from the analysis of potential off-target site binding by the compounds to be repurposed.

PaccMann: a web service for interpretable anticancer compound sensitivity prediction

The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https://ibm.biz/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model's decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes.

Quasispecies dynamics in disease prevention and control

Medical interventions to prevent and treat viral disease constitute evolutionary forces that may modify the genetic composition of viral populations that replicate in an infected host and influence the genomic composition of those viruses that are transmitted and progress at the epidemiological level. Given the adaptive potential of viruses in general and the RNA viruses in particular, the selection of viral mutants that display some degree of resistance to inhibitors or vaccines is a tangible challenge. Mutant selection may jeopardize control of the viral disease. Strategies intended to minimize vaccination and treatment failures are proposed and justified based on fundamental features of viral dynamics explained in the preceding chapters. The recommended use of complex, multiepitopic vaccines, and combination therapies as early as possible after initiation of infection falls under the general concept that complexity cannot be combated with simplicity. It also follows that sociopolitical action to interrupt virus replication and spread as soon as possible is as important as scientifically sound treatment designs to control viral disease on a global scale.

The assessment of efficient representation of drug features using deep learning for drug repositioning

BACKGROUND

De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited number of candidate pairs of drugs and diseases. In other words, they are not scalable to a large number of drugs and diseases. Most of the in-silico methods mainly focus on linear approaches while non-linear models are still scarce for new indication predictions. Therefore, applying non-linear computational approaches can offer an opportunity to predict possible drug repositioning candidates.

RESULTS

In this study, we present a non-linear method for drug repositioning. We extract four drug features and two disease features to find the semantic relations between drugs and diseases. We utilize deep learning to extract an efficient representation for each feature. These representations reduce the dimension and heterogeneity of biological data. Then, we assess the performance of different combinations of drug features to introduce a pipeline for drug repositioning. In the available database, there are different numbers of known drug-disease associations corresponding to each combination of drug features. Our assessment shows that as the numbers of drug features increase, the numbers of available drugs decrease. Thus, the proposed method with large numbers of drug features is as accurate as small numbers.

CONCLUSION

Our pipeline predicts new indications for existing drugs systematically, in a more cost-effective way and shorter timeline. We assess the pipeline to discover the potential drug-disease associations based on cross-validation experiments and some clinical trial studies.