Structure-Based Virtual Screening: From Classical to Artificial Intelligence

Novel dual-targeting inhibitors of NSD2 and HDAC2 for the treatment of liver cancer: structure-based virtual screening, molecular dynamics simulation, and in vitro and in vivo biological activity evaluations

Liver cancer exhibits a high degree of heterogeneity and involves intricate mechanisms. Recent research has revealed the significant role of histone lysine methylation and acetylation in the epigenetic regulation of liver cancer development. In this study, five inhibitors capable of targeting both histone lysine methyltransferase nuclear receptor-binding SET domain 2 (NSD2) and histone deacetylase 2 (HDAC2) were identified using a structure-based virtual screening approach. Notably, DT-NH-1 displayed a potent inhibition of NSD2 (IC50 = 0.08 ± 0.03 μM) and HDAC2 (IC50 = 5.24 ± 0.87 nM). DT-NH-1 also demonstrated a strong anti-proliferative activity against various liver cancer cell lines, particularly HepG2 cells, and exhibited a high level of biological safety. In an experimental xenograft model involving HepG2 cells, DT-NH-1 showed a significant reduction in tumour growth. Consequently, these findings indicate that DT-NH-1 will be a promising lead compound for the treatment of liver cancer with epigenetic dual-target inhibitors.

Computational and Experimental Approaches Towards Understanding the Role of ATG8 in Autophagy: A Therapeutic Paradigm in Leishmaniasis

In the realm of parasitology, autophagy has emerged as a critical focal point, particularly in combating Leishmaniasis. Central to this endeavour is the recognition of the protein ATG8 as pivotal for the survival and infectivity of the parasitic organism Leishmania major, thereby making it a potential target for therapeutic intervention. Consequently, there is a pressing need to delve into the structural characteristics of ATG8 to facilitate the design of effective drugs. In this study, our efforts centered on the purification of ATG8 from Leishmania major, which enabled novel insights into its structural features through meticulous spectroscopic analysis. We aimed to comprehensively assess the stability and behaviour of ATG8 in the presence of various denaturants, including urea, guanidinium chloride, and SDS-based chemicals. Methodically, our approach included secondary structural analysis utilizing CD spectroscopy, which not only validated but also augmented computationally predicted structures of ATG8 reported in previous investigations. Remarkably, our findings unveiled that the purified ATG8 protein retained its folded conformation, exhibiting the anticipated secondary structure. Moreover, our exploration extended to the influence of lipids on ATG8 stability, yielding intriguing revelations. We uncovered a nuanced perspective suggesting that targeting both the lipid composition of Leishmania major and ATG8 could offer a promising strategy for future therapeutic approaches in combating leishmaniasis. Collectively, our study underscores the importance of understanding the structural intricacies of ATG8 in driving advancements towards the development of targeted therapies against Leishmaniasis, thereby providing a foundation for future investigations in this field.

In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for in silico hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources.

Toward high-resolution modeling of small molecule-ion channel interactions

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.

DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.

Comprehensive computational study in the identification of novel potential cholesterol lowering agents targeting proprotein convertase subtilisin/kexin type 9

The enzymatic target proprotein convertase subtilisin/kexin type 9 (PCSK9) is critically involved in the regulation of the lipoprotein metabolism leading to the degradation of low-density lipoprotein receptors (LDLRs) upon binding. Drugs that lower LDL cholesterol (LDL-C) through the inhibition of PCSK9 are useful in the management of hypercholesterolemia which greatly reduces the associated risk of atherosclerotic cardiovascular disease (CVD). In 2015, anti-PCSK9 monoclonal antibodies (mAbs), alirocumab and evolocumab were approved but owing to their high costs their prior authorization practices were impeded, reducing their long-term adherence. This has drawn considerable attention for the development of small-molecule PCSK9 inhibitors. In this research work, novel and diverse molecules with affinity towards PCSK9 thereby having ability to lower cholesterol. A hierarchical multistep docking was implemented to identify small molecules from chemical libraries with a score cutoff -8.00 kcal/mol, thereby weeding all the non-potential molecules. A set of seven representative molecules Z1139749023, Z1142698190, Z2242867634, Z2242893449, Z2242894417, Z2242909019, and Z2242914794 have been identified from a comprehensive computational study which included assessment of pharmacokinetics and toxicity profiles and binding interactions along with in-depth analysis of structural dynamics and integrity using prolong molecular dynamics (MD) simulation (in-duplicate). Furthermore the binding affinity of these PCSK9 inhibitory candidates molecules was ascertained over 1000 trajectory frames using MM-GBSA calculations. The molecules reported herein are propitious candidates for further development through necessary experimental considerations.Communicated by Ramaswamy H. Sarma.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: Ligand Search and Screening

G protein-coupled receptors (GPCRs) are transmembrane proteins that participate in many physiological processes and represent major pharmacological targets. Recent advances in structural biology of GPCRs have enabled the development of drugs based on the receptor structure (structure-based drug design, SBDD). SBDD utilizes information about the receptor-ligand complex to search for suitable compounds, thus expanding the chemical space of possible receptor ligands without the need for experimental screening. The review describes the use of structure-based virtual screening (SBVS) for GPCR ligands and approaches for the functional testing of potential drug compounds, as well as discusses recent advances and successful examples in the application of SBDD for the identification of GPCR ligands.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Anti-Obesity Therapeutic Targets Studied In Silico and In Vivo: A Systematic Review

In the age of information technology and the additional computational search tools and software available, this systematic review aimed to identify potential therapeutic targets for obesity, evaluated in silico and subsequently validated in vivo. The systematic review was initially guided by the research question "What therapeutic targets have been used in in silico analysis for the treatment of obesity?" and structured based on the acronym PECo (P, problem; E, exposure; Co, context). The systematic review protocol was formulated and registered in PROSPERO (CRD42022353808) in accordance with the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis Protocols (PRISMA-P), and the PRISMA was followed for the systematic review. The studies were selected according to the eligibility criteria, aligned with PECo, in the following databases: PubMed, ScienceDirect, Scopus, Web of Science, BVS, and EMBASE. The search strategy yielded 1142 articles, from which, based on the evaluation criteria, 12 were included in the systematic review. Only seven these articles allowed the identification of both in silico and in vivo reassessed therapeutic targets. Among these targets, five were exclusively experimental, one was exclusively theoretical, and one of the targets presented an experimental portion and a portion obtained by modeling. The predominant methodology used was molecular docking and the most studied target was Human Pancreatic Lipase (HPL) (n = 4). The lack of methodological details resulted in more than 50% of the papers being categorized with an "unclear risk of bias" across eight out of the eleven evaluated criteria. From the current systematic review, it seems evident that integrating in silico methodologies into studies of potential drug targets for the exploration of new therapeutic agents provides an important tool, given the ongoing challenges in controlling obesity.

Discovery of novel positive allosteric modulators targeting GluN1/2A NMDARs as anti-stroke therapeutic agents

Excitotoxicity due to excessive activation of NMDARs is one of the main mechanisms of neuronal death during ischemic stroke. Previous studies have suggested that activation of either synaptic or extrasynaptic GluN2B-containing NMDARs results in neuronal damage, whereas activation of GluN2A-containing NMDARs promotes neuronal survival against ischemic insults. This study applied a systematic in silico, in vitro, and in vivo approach to the discovery of novel and potential GluN1/2A NMDAR positive allosteric modulators (PAMs). Ten compounds were obtained and identified as potential GluN1/2A PAMs by structure-based virtual screening and calcium imaging. The neuroprotective activity of the candidate compounds was demonstrated in vitro. Subsequently, compound 15 (aegeline) was tested further in the model of transient middle cerebral artery occlusion (tMCAO) in vivo, which significantly decreased cerebral infarction. The mechanism by which aegeline exerts its effect on allosteric modulation was revealed using molecular dynamics simulations. Finally, we found that the neuroprotective effect of aegeline was significantly correlated with the enhanced phosphorylation of cAMP response element-binding protein (CREB). Our study discovered the neuroprotective effect of aegeline as a novel PAM targeting GluN1/2A NMDAR, which provides a potential opportunity for the development of therapeutic agents for ischemic stroke.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

Discovery of potential TAAR1 agonist targeting neurological and psychiatric disorders: An in silico approach

Trace amine-associated receptor 1 (TAAR1) is a G-protein-coupled receptor which is primarily expressed in the brain. It is activated by trace amines which play a role in regulating neurotransmitters like dopamine, serotonin and norepinephrine. TAAR1 agonists have potential applications in the treatment of neurological and psychiatric disorders, especially schizophrenia. In this study, we have used a structure-based virtual screening approach to identify potential TAAR1 agonist(s). We have modelled the structure of TAAR1 and predicted the binding pocket. Further, molecular docking of a few well-known antipsychotic drugs was carried out with TAAR1 model, which showed key interactions with the binding pocket. From screening a library of 5 million compounds from the Enamine REAL Database using structure-based virtual screening method, we shortlisted 12 compounds which showed good docking score, glide energy and interactions with the key residues. One lead compound (Z31378290) was finally selected. The lead compound showed promising binding affinity and stable interactions with TAAR1 during molecular dynamics simulations and demonstrated better van der Waals and binding energy than the known agonist, ulotaront. Our findings suggest that the lead compound may serve as a potential TAAR1 agonist, offering a promising avenue for the development of new therapies for neurological and psychiatric disorders.

Discovery of novel IDO1/TDO2 dual inhibitors: a consensus Virtual screening approach with molecular dynamics simulations, and binding free energy analysis

The pursuit of effective cancer immunotherapy drugs remains challenging, with overexpression of indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) allowing cancer cells to evade immune attacks. While several IDO1 inhibitors have undergone clinical testing, only three dual IDO1/TDO2 inhibitors have reached human trials. Hence, this study focuses on identifying novel IDO1/TDO2 dual inhibitors through consensus structure-based virtual screening (SBVS). ZINC15 natural products library was refined based on molecular descriptors, and the selected compounds were docked to the holo form IDO1 and TDO2 using two different software programs and ranked according to their consensus docking scores. The top-scoring compounds underwent in silico evaluations for pharmacokinetics, toxicity, CYP3A4 affinity, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations. Five compounds (ZINC00000079405/10, ZINC00004028612/11, ZINC00013380497/12, ZINC00014613023/13, and ZINC00103579819/14) were identified as potential IDO1/TDO2 dual inhibitors due to their high consensus docking scores, key residue interactions with the enzymes, favorable pharmacokinetics, and avoidance of CYP3A4 binding. MD simulations of the top three hits with IDO1 indicated conformational changes and compactness, while MM-GBSA analysis revealed strong binding free energy for compounds 10 (ΔG: -20.13 kcal/mol) and 11 (ΔG: -16.22 kcal/mol). These virtual hits signify a promising initial step in identifying candidates as supplementary therapeutics to immune checkpoint inhibitors in cancer treatment. Their potential to deliver potent dual inhibition of IDO1/TDO2, along with safety and favorable pharmacokinetics, makes them compelling. Validation through in vitro and in vivo assays should be conducted to confirm their activity, selectivity, and preclinical potential as holo IDO1/TDO2 dual inhibitors.Communicated by Ramaswamy H. Sarma.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.

Cryo-electron microscopy-based drug design

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

A hierarchical approach towards identification of novel inhibitors against L, D-transpeptidase YcbB as an anti-bacterial therapeutic target

The bacterial cell wall, being a vital component for cell viability, is regarded as a promising drug target. The L, D-Transpeptidase YcbB enzyme has been implicated for a significant role in cell wall polymers cross linking during typhoid toxin release, β-lactam resistance and outer membrane defect rescue. These observations have been recorded in different bacterial pathogens such as Salmonella Typhimurium, Citrobacter rodentium, and Salmonella typhi. In this work, we have shown structure based virtual screening of diverse natural and synthetic drug libraries against the enzyme and revealed three compounds as LAS_32135590, LAS_34036730 and LAS-51380924. These compounds showed highly stable energies and the findings are very competitive with the control molecule ((1RG or (4 R,5S)-3-({(3S,5S)-5-[(3-carboxyphenyl)carbamoyl]pyrrolidin-3-yl}sulfanyl)-5-[(1S,2R)-1-formyl-2-hydroxypropyl]-4-methyl-4,5-dihydro-1H-pyrrole-2-carboxylic acid or ertapenem)) used. Compared to control (which has binding energy score of -11.63 kcal/mol), the compounds showed better binding energy. The binding energy score of LAS_32135590, LAS_34036730 and LAS-51380924 is -12.63 kcal/mol, -12.22 kcal/mol and -12.10 kcal/mol, respectively. Further, the docked snapshot of the lead compounds and control were investigated for stability under time dependent dynamics environment. All the three leads complex and control system showed significant equilibrium (mean RMSD < 3 Å) both in term of intermolecular docked conformation and binding interactions network. Further validation on the complex's stability was acquired from the end-state MMPB/GBSA analysis that observed greater contribution from van der Waals forces and electrostatic energy while less contribution was noticed from solvation part. The compounds were also showed good drug-likeness and are non-toxic and non-mutagenic. In short, the compounds can be used in experimental testing's and might be subjected to structure modification to get better results.Communicated by Ramaswamy H. Sarma.

Elucidation of protein-ligand interactions by multiple trajectory analysis methods

The identification of interaction between protein and ligand including binding positions and strength plays a critical role in drug discovery. Molecular docking and molecular dynamics (MD) techniques have been widely applied to predict binding positions and binding affinity. However, there are few works that describe the systematic exploration of the MD trajectory evolution in this context, potentially leaving out important information. To address the problem, we build a framework, Moira (molecular dynamics trajectory analysis), which enables automating the whole process ranging from docking, MD simulations and various analyses as well as visualizations. We utilized Moira to analyze 400 MD simulations in terms of their geometric features (root mean square deviation and protein-ligand interaction profiler) and energetics (molecular mechanics Poisson-Boltzmann surface area) for these trajectories. Finally, we demonstrate the performance of different analysis techniques in distinguishing native poses among four poses.

Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network

The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates. We combine high-throughput virtual screening and inverse design infused with domain knowledge and artificial intelligence to accelerate molecular generation significantly. This enabled us to explore more than 800 000 potential emitter molecules and find more than 10 000 candidates estimated to have inverted singlet-triplet gaps (INVEST) and appreciable fluorescence rates, many of which likely emit blue light. This class of molecules has the potential to realize a new generation of organic light-emitting diodes.

SPP1, a potential therapeutic target and biomarker for lung cancer: functional insights through computational studies

NIH reported 128 different types of cancer of which lung cancer is the leading cause of mortality. Globally, it is estimated that on average one in every seventeen hospitalized patients was deceased. There are plenty of studies that have been reported on lung cancer draggability and therapeutics, but yet a protein that plays a central specific to cure the disease remains unclear. So, this study is designed to identify the possible therapeutic targets and biomarkers that can be used for the potential treatment of lung cancers. In order to identify differentially expressed genes, 39 microarray datasets of lung cancer patients were obtained from various demographic regions of the GEO database available at NCBI. After annotating statistically, 6229 up-regulated genes and 10324 down-regulated genes were found. Out of 17 up-regulated genes and significant genes, we selected SPP1 (osteopontin) through virtual screening studies. We found functional interactions with the other cancer-associated genes such as VEGF, FGA, JUN, EGFR, and TGFB1. For the virtual screening studies,198 biological compounds were retrieved from the ACNPD database and docked with SPP1 protein (PDBID: 3DSF). In the results, two highly potential compounds secoisolariciresinol diglucoside (-12.9 kcal/mol), and Hesperidin (-12.0 kcal/mol) showed the highest binding affinity. The stability of the complex was accessed by 100 ns simulation in an SPC water model. From the functional insights obtained through these computational studies, we report that SPP1 could be a potential biomarker and successive therapeutic protein target for lung cancer treatment.

Computational insights into potential marine natural products as selective inhibitors of Mycobacterium tuberculosis InhA: A structure-based virtual screening study

Several factors are associated with the emergence of drug resistance mechanisms, such as impermeable cell walls, gene mutations, and drug efflux systems. Consequently, bacteria acquire resistance, leading to a decrease in drug efficacy. A new and innovative strategy is required to combat drug resistance in tuberculosis (TB) effectively. Therefore, targeting the mycolic acid biosynthesis pathway, which is involved in synthesising mycolic acids (MAs), essential structural components responsible for mycobacterial pathogenicity, has garnered interest in TB research and the concept of drug resistance. In this context, InhA, which plays a crucial role in the fatty acid synthase-II (FAS-II) system of the MA biosynthetic pathway, was selected as a druggable target for screening investigation. To identify potential lead molecules against InhA, diverse marine natural products (MNPs) were collected from the comprehensive marine natural products database (CMNPD). Virtual screening studies aided in selecting potential lead molecules that best fit within the substrate-binding pocket (SBP) of InhA, forming crucial hydrogen bond interaction with the catalytic residue Tyr158. Three MNPs, CMNPD30814, CMNPD1702, and CMNPD27355, were chosen as prospective alternative molecules due to their favorable pharmacokinetic properties and lack of toxicity according to ProTox-II predictions. Additionally, improved reactivity of the MNPs was observed in the results of density functional theory (DFT) studies. Furthermore, comparative molecular dynamics simulation (MDS), principal component (PC)-based free energy landscape (FEL) analysis, and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) were employed to show enhanced structural stability, increased H-bond potential, and high binding affinity toward the target InhA. Moreover, the hot spot residues that contributed to the high binding energy profile and anchored the stability of the complexes were revealed with their individual interaction energy. The computational insights from this study provide potential avenues to combat TB through the multifaceted mode of action of these marine lead molecules, which can be further explored in future experimental investigations.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Elucidating the binding mechanism of SARS-CoV-2 NSP6-TBK1 and structure-based designing of phytocompounds inhibitors for instigating the host immune response

SARS-CoV-2, also referred to as severe acute respiratory syndrome coronavirus 2, is the virus responsible for causing COVID-19, an infectious disease that emerged in Wuhan, China, in December 2019. Among its crucial functions, NSP6 plays a vital role in evading the human immune system by directly interacting with a receptor called TANK-binding kinase (TBK1), leading to the suppression of IFNβ production. Consequently, in the present study we used the structural and biophysical approaches to analyze the effect of newly emerged mutations on the binding of NSP6 and TBK1. Among the identified mutations, four (F35G, L37F, L125F, and I162T) were found to significantly destabilize the structure of NSP6. Furthermore, the molecular docking analysis highlighted that the mutant NSP6 displayed its highest binding affinity with TBK1, exhibiting docking scores of -1436.2 for the wildtype and -1723.2, -1788.6, -1510.2, and -1551.7 for the F35G, L37F, L125F, and I162T mutants, respectively. This suggests the potential for an enhanced immune system evasion capability of NSP6. Particularly, the F35G mutation exhibited the strongest binding affinity, supported by a calculated binding free energy of -172.19 kcal/mol. To disrupt the binding between NSP6 and TBK1, we conducted virtual drug screening to develop a novel inhibitor derived from natural products. From this screening, we identified the top 5 hit compounds as the most promising candidates with a docking score of -6.59 kcal/mol, -6.52 kcal/mol, -6.32 kcal/mol, -6.22 kcal/mol, and -6.21 kcal/mol. The molecular dynamic simulation of top 3 hits further verified the dynamic stability of drugs-NSP6 complexes. In conclusion, this study provides valuable insight into the higher infectivity of the SARS-CoV-2 new variants and a strong rationale for the development of novel drugs against NSP6.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Inhibitors of Immune Checkpoints: Small Molecule- and Peptide-Based Approaches

The revolutionary progress in cancer immunotherapy, particularly the advent of immune checkpoint inhibitors, marks a significant milestone in the fight against malignancies. However, the majority of clinically employed immune checkpoint inhibitors are monoclonal antibodies (mAbs) with several limitations, such as poor oral bioavailability and immune-related adverse effects (irAEs). Another major limitation is the restriction of the efficacy of mAbs to a subset of cancer patients, which triggered extensive research efforts to identify alternative approaches in targeting immune checkpoints aiming to overcome the restricted efficacy of mAbs. This comprehensive review aims to explore the cutting-edge developments in targeting immune checkpoints, focusing on both small molecule- and peptide-based approaches. By delving into drug discovery platforms, we provide insights into the diverse strategies employed to identify and optimize small molecules and peptides as inhibitors of immune checkpoints. In addition, we discuss recent advances in nanomaterials as drug carriers, providing a basis for the development of small molecule- and peptide-based platforms for cancer immunotherapy. Ongoing research focused on the discovery of small molecules and peptide-inspired agents targeting immune checkpoints paves the way for developing orally bioavailable agents as the next-generation cancer immunotherapies.

Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends

In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.

Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects

Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations

Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

New Insights for Polyphenolic Compounds as Naturally Inspired Proteasome Inhibitors

Polyphenols, an important class of natural products, are widely distributed in plant-based foods. These compounds are endowed with several biological activities and exert protective effects in various physiopathological contexts, including cancer. We herein investigated novel potential mechanisms of action of polyphenols, focusing on the proteasome, which has emerged as an attractive therapeutic target in cancers such as multiple myeloma. We carried out a structure-based virtual screening study using the DrugBank database as a repository of FDA-approved polyphenolic molecules. Starting from 86 polyphenolic compounds, based on the theoretical binding affinity and the interactions established with key residues of the chymotrypsin binding site, we selected 2 promising candidates, namely Hesperidin and Diosmin. The further assessment of the biologic activity highlighted, for the first time, the capability of these two molecules to inhibit the β5-proteasome activity and to exert anti-tumor activity against proteasome inhibitor-sensitive or resistant multiple myeloma cell lines.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.

Structural Dynamics of P-Rex1 Complexed with Natural Leads Establishes the Protein as an Attractive Target for Therapeutics to Suppress Cancer Metastasis

Phosphatidylinositol 3,4,5-trisphosphate- (PIP3-) dependent Rac exchanger 1 (P-Rex1) functions as Rho guanine nucleotide exchange factor and is activated by synergistic activity of Gβγ and PIP3 of the heterotrimeric G protein. P-Rex1 activates Rac GTPases for regulating cell invasion and migration and promotes metastasis in several human cancers including breast, prostate, and skin cancer. The protein is a promising therapeutic target because of its multifunction roles in human cancers. Herein, the present study attempts to identify selective P-Rex1 natural inhibitors by targeting PIP3-binding pocket using large-size multiple natural molecule libraries. Each library was filtered subsequently in FAF-Drugs4 based on Lipinski's rule of five (RO5), toxicity, and filter pan assay interference compounds (PAINS). The output hits were virtually screened at the PIP3-binding pocket through PyRx AutoDock Vina and cross-checked by GOLD. The best binders at the PIP3-binding pocket were prioritized using a comparative analysis of the docking scores. Top-ranked two compounds with high GOLD fitness score (>80) and lowest AutoDock binding energy (< -12.7 kcal/mol) were complexed and deciphered for molecular dynamics along with control-P-Rex1 complex to validate compound binding conformation and disclosed binding interaction pattern. Both the systems were seen in good equilibrium, and along the simulation time, the compounds are in strong contact with the P-Rex1 PIP3-binding site. Hydrogen bonding analysis towards simulation end identified the formation of 16 and 22 short- and long-distance hydrogen bonds with different percent of occupancy to the PIP3 residues for compound I and compound 2, respectively. Radial distribution function (RDF) analysis of the key hydrogen bonds between the compound and the PIP3 residues demonstrated a strong affinity of the compounds to the mentioned PIP3 pocket. Additionally, MMGB/PBSA energies were performed that confirmed the dominance of Van der Waals energy in complex formation along with favorable contribution from hydrogen bonding. These findings were also cross-validated by a more robust WaterSwap binding energy predictor, and the results are in good agreement with a strong binding affinity of the compounds for the protein. Lastly, the key contribution of residues in interaction with the compounds was understood by binding free energy decomposition and alanine scanning methods. In short, the results of this study suggest that P-Rex1 is a good druggable target to suppress cancer metastasis; therefore, the screened druglike molecules of this study need in vitro and in vivo anti-P-Rex1 validation and may serve as potent leads to fight cancer.

Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines

The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with [Formula: see text]a 70% improvement in runtime compared to the baseline model by labeling only [Formula: see text]30% of molecules compared to the baseline oracle.

Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology

Accurate binding affinity prediction is crucial to structure-based drug design. Recent work used computational topology to obtain an effective representation of protein-ligand interactions. Although persistent homology encodes geometric features, previous works on binding affinity prediction using persistent homology employed uninterpretable machine learning models and failed to explain the underlying geometric and topological features that drive accurate binding affinity prediction. In this work, we propose a novel, interpretable algorithm for protein-ligand binding affinity prediction. Our algorithm achieves interpretability through an effective embedding of distances across bipartite matchings of the protein and ligand atoms into real-valued functions by summing Gaussians centered at features constructed by persistent homology. We name these functions internuclear persistent contours (IPCs) . Next, we introduce persistence fingerprints , a vector with 10 components that sketches the distances of different bipartite matching between protein and ligand atoms, refined from IPCs. Let the number of protein atoms in the protein-ligand complex be n , number of ligand atoms be m , and ω ≈ 2.4 be the matrix multiplication exponent. We show that for any 0 < ε < 1, after an 𝒪 ( mn log( mn )) preprocessing procedure, we can compute an ε -accurate approximation to the persistence fingerprint in 𝒪 ( m log 6 ω ( m/" )) time, independent of protein size. This is an improvement in time complexity by a factor of 𝒪 (( m + n ) 3 ) over any previous binding affinity prediction that uses persistent homology. We show that the representational power of persistence fingerprint generalizes to protein-ligand binding datasets beyond the training dataset. Then, we introduce PATH , Predicting Affinity Through Homology, an interpretable, small ensemble of shallow regression trees for binding affinity prediction from persistence fingerprints. We show that despite using 1,400-fold fewer features, PATH has comparable performance to a previous state-of-the-art binding affinity prediction algorithm that uses persistent homology features. Moreover, PATH has the advantage of being interpretable. Finally, we visualize the features captured by persistence fingerprint for variant HIV-1 protease complexes and show that persistence fingerprint captures binding-relevant structural mutations. The source code for PATH is released open-source as part of the osprey protein design software package.

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC50 values in the reference data set. Then, IC50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

Structure-based virtual screening and molecular docking approaches to identify potential inhibitors against KIF2C to combat glioma

Glioma, a kind of malignant brain tumor, is extremely lethal. Kinesin family member 2C (KIF2C) was found to have an aberrant expression in several cancer types, including lung cancer and glioma. KIF2C may therefore be a useful therapeutic target for the treatment of glioma. In the current study, new drug candidates that may function as KIF2C enzyme inhibitors were discovered. MTi OpenScreen was used to carry out the structure-based virtual screening of an inbuilt drug library containing 150,000 compounds. These compounds belong to different classes, such as natural product-based compounds (NP-lib), purchasable approved drugs (Drugs-lib), and food constituents compound collection (FOOD-lib). Based on their binding affinities, a total of 84 compounds were further pushed to calculate ADMET properties. The compounds (16) meeting the ADMET cutoff ranges were then further docked to the receptor to find their plausible binding modes using the Glide tool's standard precision (SP) technique. The docking results were examined using the Glide gscore, and the best binding compounds (Rimacalib and Sarizotan) were chosen to test their stability with KIF2C protein through molecular dynamics (MD) simulation. Similarly, Principal Component Analysis and cross-correlation matrix were also examined. The MM/GBSA binding free energies showed a considerable energy contribution in the binding of hits with the KIF2C. Collectively, these findings strongly suggest the potential of the lead compounds to inhibit the biological function of KIF2C, emphasizing the need for further investigation in this area.Communicated by Ramaswamy H. Sarma.

Repurposing Drugs for Inhibition against ALDH2 via a 2D/3D Ligand-Based Similarity Search and Molecular Simulation

Aldehyde dehydrogenase-2 (ALDH2) is a crucial enzyme participating in intracellular aldehyde metabolism and is acknowledged as a potential therapeutic target for the treatment of alcohol use disorder and other addictive behaviors. Using previously reported ALDH2 inhibitors of Daidzin, CVT-10216, and CHEMBL114083 as reference molecules, here we perform a ligand-based virtual screening of world-approved drugs via 2D/3D similarity search methods, followed by the assessments of molecular docking, toxicity prediction, molecular simulation, and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis. The 2D molecular fingerprinting of ECFP4 and FCFP4 and 3D molecule-shape-based USRCAT methods show good performances in selecting compounds with a strong binding behavior with ALDH2. Three compounds of Zeaxanthin (q = 0), Troglitazone (q = 0), and Sequinavir (q = +1 e) are singled out as potential inhibitors; Zeaxanthin can only be hit via USRCAT. These drugs displayed a stronger binding strength compared to the reported potent inhibitor CVT-10216. Sarizotan (q = +1 e) and Netarsudil (q = 0/+1 e) displayed a strong binding strength with ALDH2 as well, whereas they displayed a shallow penetration into the substrate-binding tunnel of ALDH2 and could not fully occupy it. This likely left a space for substrate binding, and thus they were not ideal inhibitors. The MM-PBSA results indicate that the selected negatively charged compounds from the similarity search and Vina scoring are thermodynamically unfavorable, mainly due to electrostatic repulsion with the receptor (q = -6 e for ALDH2). The electrostatic attraction with positively charged compounds, however, yielded very strong binding results with ALDH2. These findings reveal a deficiency in the modeling of electrostatic interactions (in particular, between charged moieties) in the virtual screening via the 2D/3D similarity search and molecular docking with the Vina scoring system.

In Silico Molecular Docking and Dynamics Simulation Analysis of Potential Histone Lysine Methyl Transferase Inhibitors for Managing β-Thalassemia

A decreased hemoglobin synthesis is contemplated as a pathological indication of β-thalassemia. Recent studies show that EPZ035544 from Epizyme could induce fetal hemoglobin (HbF) levels due to its proven capability to inhibit euchromatin histone lysine methyl transferase (EHMT2). Therefore, the development of EHMT2 inhibitors is considered promising in managing β-thalassemia. Our strategy to find novel compounds that are EHMT2 inhibitors relies on the virtual screening of ligands that have a structural similarity to N2-[4-methoxy-3-(2,3,4,7-tetrahydro-1H-azepin-5-yl) phenyl]-N4,6-dimethyl-pyrimidine-2,4-diamine (F80) using the PubChem database. In silico docking studies using Autodock Vina were employed to screen a library of 985 compounds and evaluate their binding ability with EHMT2. The selection of hit compounds was based on the docking score and mode of interaction with the protein. The top two ranked compounds were selected for further investigations, including pharmacokinetic properties analysis and molecular dynamics simulations (MDS). Based on the obtained docking score and interaction analysis, N-(4-methoxy-3-methylphenyl)-4,6-diphenylpyrimidin-2-amine (TP1) and 2-N-[4-methoxy-3-(5-methoxy-3H-indol-2-yl)phenyl]-4-N,6-dimethylpyrimidine-2,4-diamine (TP2) were found to be promising candidates, and TP1 exhibited better stability in the MDS study compared to TP2. In summary, our approach helps identify potential EHMT2 inhibitors, and further validation using in vitro and in vivo experiments could certainly enable this molecule to be used as a therapeutic drug in managing β-thalassemia disease.

Identification of Novel β-Tubulin Inhibitors Using a Combined In Silico/In Vitro Approach

Due to their potential as leads for various therapeutic applications, including as antimitotic and antiparasitic agents, the development of tubulin inhibitors offers promise for drug discovery. In this study, an in silico pharmacophore-based virtual screening approach targeting the colchicine binding site of β-tubulin was employed. Several structure- and ligand-based models for known tubulin inhibitors were generated. Compound databases were virtually screened against the models, and prioritized hits from the SPECS compound library were tested in an in vitro tubulin polymerization inhibition assay for their experimental validation. Out of the 41 SPECS compounds tested, 11 were active tubulin polymerization inhibitors, leading to a prospective true positive hit rate of 26.8%. Two novel inhibitors displayed IC50 values in the range of colchicine. The most potent of which was a novel acetamide-bridged benzodiazepine/benzimidazole derivative with an IC50 = 2.9 μM. The screening workflow led to the identification of diverse inhibitors active at the tubulin colchicine binding site. Thus, the pharmacophore models show promise as valuable tools for the discovery of compounds and as potential leads for the development of cancer therapeutic agents.

Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction

Motivation

Extended connectivity interaction features (ECIF) is a method developed to predict protein-ligand binding affinity, allowing for detailed atomic representation. It performed very well in terms of Comparative Assessment of Scoring Functions 2016 (CASF-2016) scoring power. However, ECIF has the limitation of not being able to adequately account for interatomic distances.

Results

To investigate what kind of distance representation is effective for P-L binding affinity prediction, we have developed two algorithms that improved ECIF's feature extraction method to take distance into account. One is multi-shelled ECIF, which takes into account the distance between atoms by dividing the distance between atoms into multiple layers. The other is weighted ECIF, which weights the importance of interactions according to the distance between atoms. A comparison of these two methods shows that multi-shelled ECIF outperforms weighted ECIF and the original ECIF, achieving a CASF-2016 scoring power Pearson correlation coefficient of 0.877.

Availability and implementation

All the codes and data are available on GitHub (https://github.com/koji11235/MSECIFv2).

Cheminformatics and biomolecular dynamics studies towards the discovery of anti-staphylococcal nuclease domain-containing 1 (SND1) inhibitors to treat metastatic breast cancer

Metastatic breast cancer is a prime health concern and leading health burden across the globe. Previous efforts have shown that protein-protein interaction between Metadherin and Staphylococcal nuclease domaincontaining 1 (SND1) promotes initiation of breast cancer, progression, therapy resistance and metastasis. Therefore, small drug molecules that can interrupt the Metadherin and SND1 interaction may be ideal to suppress tumor growth, metastasis and increases chemotherapy sensitivity of triple negative breast cancer. Here, in this study, structure based virtual screening was conducted against the reported active site of SND1 enzyme, which revealed three promising lead molecules from Asinex library. These compounds were; BAS_00381028, BAS_00327287, and BAS_01293454 with binding energy score -10.25 kcal/mol, -9.65 kcal/mol and -9.32 kcal/mol, respectively. Compared to control (5-chloro-2-methoxy-N-([1,2,4]triazolo[1,5-a]pyridin-8-yl)benzene-1-sulfonamide) the lead molecules showed robust hydrophilic and hydrophobic interactions with the enzyme and revealed stable docked conformation in molecular dynamics simulation. During the simulation time, the compounds reported stable dynamics with no obvious fluctuation in binding mode and interactions noticed. The mean root mean square deviation (RMSD) of BAS_00381028, BAS_00327287, and BAS_01293454 complexes were 1.87 Å, 1.75 Å, 1.34 Å, respectively. Furthermore, the MM/GBSA analysis was conduction on the simulation trajectories of complexes that unveiled binding energy score of -19.25 kcal/mol, -27.03 kcal/mol, -34.6 kcal/mol and -29.61 kcal/mol for control, BAS_00381028, BAS_00327287, and BAS_01293454, respectively. In MM/PBSA, the binding energy value of for control, BAS_00381028, BAS_00327287, and BAS_01293454 was -20.45 kcal/mol, -27.89 kcal/mol, -36.41 kcal/mol and -32.01 kcal/mol, respectively. Additionally, the compounds were classified as druglike and have favorable pharmacokinetic properties. The compounds were predicted as promising leads and might be used in experimental investigation to study their anti-SND1 activity.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle

BACKGROUND

Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance.

RESULTS

In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method.

CONCLUSION

Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

Probing the Activation Mechanisms of Agonist DPI-287 to Delta-Opioid Receptor and Novel Agonists Using Ensemble-Based Virtual Screening with Molecular Dynamics Simulations

Pain drugs targeting mu-opioid receptors face major addiction problems that have caused an epidemic. The delta-opioid receptor (DOR) has shown to not cause addictive effects when bound to an agonist. While the active conformation of the DOR in complex with agonist DPI-287 has been recently solved, there are still no FDA-approved agonists targeting it, providing the opportunity for structure-based virtual screening. In this study, the conformational plasticity of the DOR was probed using molecular dynamics (MD) simulations, identifying two representative conformations from clustering analysis. The two MD conformations as well as the crystal conformation of DOR were used to screen novel compounds from the ZINC database (17 million compounds), in which 69 drugs were picked as potential compounds based on their docking scores. Notably, 37 out of the 69 compounds were obtained from the simulated conformations. The binding stability of the 69 compounds was further investigated using MD simulations. Based on the MM-GBSA binding energy and the predicted drug properties, eight compounds were chosen as the most favorable, six of which were from the simulated conformations. Using a dynamic network model, the communication between the crystal agonist and the top eight molecules with the receptor was analyzed to confirm if these novel compounds share a similar activation mechanism to the crystal ligand. Encouragingly, docking of these eight compounds to the other two opioid receptors (kappa and mu) suggests their good selectivity toward DOR.

Advances in non-hormonal male contraception targeting sperm motility

BACKGROUND

The high rates of unintended pregnancy and the ever-growing world population impose health, economic, social, and environmental threats to countries. Expanding contraceptive options, including male methods, are urgently needed to tackle these global challenges. Male contraception is limited to condoms and vasectomy, which are unsuitable for many couples. Thus, novel male contraceptive methods may reduce unintended pregnancies, meet the contraceptive needs of couples, and foster gender equality in carrying the contraceptive burden. In this regard, the spermatozoon emerges as a source of druggable targets for on-demand, non-hormonal male contraception based on disrupting sperm motility or fertilization.

OBJECTIVE AND RATIONALE

A better understanding of the molecules governing sperm motility can lead to innovative approaches toward safe and effective male contraceptives. This review discusses cutting-edge knowledge on sperm-specific targets for male contraception, focusing on those with crucial roles in sperm motility. We also highlight challenges and opportunities in male contraceptive drug development targeting spermatozoa.

SEARCH METHODS

We conducted a literature search in the PubMed database using the following keywords: 'spermatozoa', 'sperm motility', 'male contraception', and 'drug targets' in combination with other related terms to the field. Publications until January 2023 written in English were considered.

OUTCOMES

Efforts for developing non-hormonal strategies for male contraception resulted in the identification of candidates specifically expressed or enriched in spermatozoa, including enzymes (PP1γ2, GAPDHS, and sAC), ion channels (CatSper and KSper), transmembrane transporters (sNHE, SLC26A8, and ATP1A4), and surface proteins (EPPIN). These targets are usually located in the sperm flagellum. Their indispensable roles in sperm motility and male fertility were confirmed by genetic or immunological approaches using animal models and gene mutations associated with male infertility due to sperm defects in humans. Their druggability was demonstrated by the identification of drug-like small organic ligands displaying spermiostatic activity in preclinical trials.

WIDER IMPLICATIONS

A wide range of sperm-associated proteins has arisen as key regulators of sperm motility, providing compelling druggable candidates for male contraception. Nevertheless, no pharmacological agent has reached clinical developmental stages. One reason is the slow progress in translating the preclinical and drug discovery findings into a drug-like candidate adequate for clinical development. Thus, intense collaboration among academia, private sectors, governments, and regulatory agencies will be crucial to combine expertise for the development of male contraceptives targeting sperm function by (i) improving target structural characterization and the design of highly selective ligands, (ii) conducting long-term preclinical safety, efficacy, and reversibility evaluation, and (iii) establishing rigorous guidelines and endpoints for clinical trials and regulatory evaluation, thus allowing their testing in humans.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Discovery of new Schistosoma mansoni aspartyl protease inhibitors by structure-based virtual screening

BACKGROUND

Schistosomiasis is a neglected tropical disease caused by trematodes of the genus Schistosoma, with a limited treatment, mainly based on the use of praziquantel (PZQ). Currently, several aspartic proteases genes have already been identified within the genome of Schistosoma species. At least one enzyme encoded from this gene family (SmAP), named SmCD1, has been validated for the development of schistosomicidal drugs, since it has a key role in haemoglobin digestion by worms.

OBJECTIVE

In this work, we integrated a structure-based virtual screening campaign, enzymatic assays and adult worms ex vivo experiments aiming to discover the first classes of SmCD1 inhibitors.

METHODS

Initially, the 3D-structures of SmCD1, SmCD2 and SmCD3 were generated using homology modelling approach. Using these models, we prioritised 50 compounds from 20,000 compounds from ChemBridge database for further testing in adult worm aqueous extract (AWAE) and recombinant SmCD1 using enzymatic assays.

FINDINGS

Seven compounds were confirmed as hits and among them, two compounds representing new chemical scaffolds, named 5 and 19, had IC50 values against SmCD1 close to 100 μM while presenting binding efficiency indexes comparable to or even higher than pepstatin, a classical tight-binding peptide inhibitor of aspartyl proteases. Upon activity comparison against mammalian enzymes, compound 50 was selective and the most potent against the AWAE aspartic protease activity (IC50 = 77.7 μM). Combination of computational and experimental results indicate that compound 50 is a selective inhibitor of SmCD2. Compounds 5, 19 and 50 tested at low concentrations (10 uM) were neither cytotoxic against WSS-1 cells (48 h) nor could kill adult worms ex-vivo, although compounds 5 and 50 presented a slight decrease on female worms motility on late incubations times (48 or 72 h).

MAIN CONCLUSION

Overall, the inhibitors identified in this work represent promising hits for further hit-to-lead optimisation.