Drug Design Research Articles

Biomaterials’ enhancement of immunotherapy for breast cancer by targeting functional cells in the tumor micro-environment

Immunotherapy for breast cancer is now being considered clinically, and more recently, the number of investigations aimed specifically at nano-biomaterials-assisted immunotherapy for breast cancer treatment is growing. Alterations of the breast cancer micro-environment can play a critical role in anti-tumor immunity and cancer development, progression and metastasis. The improvement and rearrangement of tumor micro-environment (TME) may enhance the permeability of anti-tumor drugs. Therefore, targeting the TME is also an ideal and promising option during the selection of effective nano-biomaterial-based immuno-therapeutic strategies excepted for targeting intrinsic resistant mechanisms of the breast tumor. Although nano-biomaterials designed to specifically release loaded anti-tumor drugs in response to tumor hypoxia and low pH conditions have shown promises and the diversity of the TME components also supports a broad targeting potential for anti-tumor drug designs, yet the applications of nano-biomaterials for targeting immunosuppressive cells/immune cells in the TME for improving the breast cancer treating outcomes, have scarcely been addressed in a scientific review. This review provides a thorough discussion for the application of the different forms of nano-biomaterials, as carrier vehicles for breast cancer immunotherapy, targeting specific types of immune cells in the breast tumor microenvironment. In parallel, the paper provides a critical analysis of current advances/challenges with leading nano-biomaterial-mediated breast cancer immunotherapeutic strategies. The current review is timely and important to the cancer research field and will provide a critical tool for nano-biomaterial design and research groups pushing the clinical translation of new nano-biomaterial-based immuno-strategies targeting breast cancer TME, to further open new avenues for the understanding, prevention, diagnosis and treatment of breast cancer, as well as other cancer types.

SARS-CoV-2 main protease (M-pro) mutational profiling: An insight into mutation coldspots

SARS-CoV-2 and the COVID-19 pandemic have marked a milestone in the history of scientific research worldwide. To ensure that treatments are successful in the mid-long term, it is crucial to characterize SARS-CoV-2 mutations, as they might lead to viral resistance. Data from >5,700,000 SARS-CoV-2 genomes available at GISAID was used to report SARS-CoV-2 mutations. Given the pivotal role of its main protease (M-pro) in virus replication, a detailed analysis of SARS-CoV-2 M-pro mutations was conducted, with particular attention to mutation-resistant residues or mutation coldspots, defined as those residues that have mutated in five or fewer genomes. 32 mutation coldspots were identified, most of which mediate interprotomer interactions or funneling interaction networks from the substrate-binding site towards the dimerization surface and vice versa. Besides, mutation coldspots were virtually conserved in all main proteases from other CoVs. Our results provide valuable information about key residues to M-pro structure that could be useful in rational target-directed drug design and establish a solid groundwork based on mutation analyses for the inhibition of M-pro dimerization, with a potential applicability to future coronavirus outbreaks.

Action Mechanism of Bioactive Compounds from Plants and Spices Mixture as Promising Anticancer Agents: In Silico Drug Design Predictions Targeting Cancer Cell Proliferation Through Cell Cycle Arrest Pathway

Although cancer has been the main reason for death for decades, until now, no safe certified drugs have been discovered to efficiently inhibit its progression. Currently, we aim to employ an in-silico model to predict the action mechanism of phenolic components that we previously demonstrated as inhibitors of colon, prostate and lung cancer. The work was performed by testing pharmacokinetics and pharmacodynamics proprieties of selected molecules. Four compounds: Kaempferol, chrysin, vanillin and p-hydroxybenzoic acid widely obeyed the five rules and the ADME/T criteria with minimum toxicity. Docking prediction recorded an intense affinity between kaempferol-CDK1(−17.72 ± 0.02 kcal/mol), chrysin-CDK1 (−17.61 ± 0.01 kcal/mol) and kaempferol-caspase 8 (−9.30 ± 0.00 kcal/mol), chrysin-caspase 8 (−8.18 ± 0.12 kcal/mol), vanillin-MMP9 (−9.83 ± 0.04 kcal/mol) and p-hydroxybenzoic acid-Ca2 (−8.91 ± 0.01 kcal/mol). Likewise, MMGBSA results demonstrated that these compounds are evidently bound to the targets’ active sites with solid interactions. The passed compounds might target the responsible factors for transcription as inactivators. The molecular mechanism based on kaempferol and vanillin actions was predicted to target CDK1 and MMP9 via cell cycle arrest and apoptosis. All results were confirmed through RMSD analysis suggesting that kaempferol and vanillin are the best ligands to suppress cancer cell proliferation. Thus, these findings allowed us to imagine a potential molecular mechanism of these binaries, phenolic compounds/target proteins to lead further trails on cancer, of which the synergistic activity between phenolic compounds could promote the therapeutic capacity.

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumorigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, including the cleavage of its precursor, pre-miR21, by the enzyme Dicer. The present work investigates the conformational dynamics of pre-miR21, focusing on the role of adenine29 in switching between Dicer-binding-prone and inactive states. We also investigated the effect of L50, a cyclic peptide binder of pre-miR21 and a weak inhibitor of its processing. Using time series data and our novel collective variable-based enhanced sampling technique, OneOPES, we simulated these conformational changes and assessed the effect of L50 on the conformational plasticity of pre-miR21. Our results provide insight into peptide-induced conformational changes and pave the way for the development of a computational platform for the screening of inhibitors of pre-miR21 processing that considers RNA flexibility, a stepping stone for effective structure-based drug design, with potentially broad applications in drug discovery.

DigFrag as a digital fragmentation method used for artificial intelligence-based drug design.

Fragment-Based Drug Design (FBDD) plays a pivotal role in the field of drug discovery and development. The construction of high-quality fragment libraries is a critical step in FBDD. Conventional fragmentation approaches often rely on rigid rules and chemical intuition, limiting their adaptability to diverse molecular structures. The rapid development of Artificial Intelligence (AI) technology offers a transformative opportunity to rethink traditional methods. Here, we present DigFrag, a digital fragmentation method that highlights important substructures by focusing locally within the molecular graph. In addition, we feed the fragments segmented by machine intelligence and human expertise into the deep generative model to compare the preference for data from different sources. Experimental results show that the structural diversity of fragments segmented by DigFrag is higher, and more desirable compounds are generated based on these fragments. These results also demonstrate that data generated based on AI methods may be more suitable for AI models. Moreover, a user-friendly platform called MolFrag ( https://dpai.ccnu.edu.cn/MolFrag/ ) is developed based on various fragmentation techniques to support molecular segmentation.

New Insights into the Development of Donepezil-Based Hybrid and Natural Molecules as Multi-Target Drug Agents for Alzheimer’s Disease Treatment

Alzheimer’s disease (AD) involves a complex pathophysiology with multiple interconnected subpathologies, including protein aggregation, impaired neurotransmission, oxidative stress, and microglia-mediated neuroinflammation. Current treatments, which generally target a single subpathology, have failed to modify the disease’s progression, providing only temporary symptom relief. Multi-target drugs (MTDs) address several subpathologies, including impaired aggregation of pathological proteins. In this review, we cover hybrid molecules published between 2014 and 2024. We offer an overview of the strategies employed in drug design and approaches that have led to notable improvements and reduced hepatotoxicity. Our aim is to offer insights into the potential development of new Alzheimer’s disease drugs. This overview highlights the potential of multi-target drugs featuring heterocycles with N-benzylpiperidine fragments and natural compounds in improving Alzheimer’s disease treatment.

Studying the Role of Novel Carbon Nano Tubes as a Therapeutic Agent to Treat Triple Negative Breast Cancer (TNBC) - an In Vitro and In Vivo Study

Triple Negative Breast Cancer (TNBC) is a malignant cancer with a very high mortality rate around the world. African American(AA) women are 28% more likely to die from triple-negative breast cancer (TNBC) than white women with the same diagnosis. AA patients are also more likely to be diagnosed at a later stage of the disease and have the lowest survival rates for any stage of diagnosis; There are very few existing anti TNBC drugs with therapeutic efficacy hence newer anti TNBC drug design and investigation is needed. Carbon Nano Tubes(CNT) in recent years have shown effective anti-cancer properties in various types of cancers as reported in peer reviewed journals. Henceforth, we did an investigation to study the anticancer properties of a novel CNT in both in vitro and in vivo models of TNBC. We tested the CNT drug in vitro cytotoxicity studies on TNBC model MDA-MB-231 VIM RFP cell lines and Spheroid forming assays on the same cancer cells; we also did an in vivo study on TNBC model mice to study the therapeutic efficacy of this CNT drug in reducing the tumor load. Our initial studies showed increased cell death and reduction in spheroid numbers in the CNT treated cancer cells in comparison to control and a significant reduction in the tumor volume in the TNBC model mice than in untreated animals. Thus our initial studies have shown significant therapeutic efficacy of the novel CNT as an anti TNBC agent. Additional mechanistic studies need to be done to find out the cell death mechanisms, core canonical pathways involved, pharmacokinetic studies before translational research for this novel nanoparticle as a therapeutic agent from bench to bedside.

Advancements in p53-Based Anti-Tumor Gene Therapy Research

The p53 gene is one of the genes most closely associated with human tumors and has become a popular target for tumor drug design. Currently, p53-based gene therapy techniques have been developed, but these therapies face challenges such as immaturity, high safety hazards, limited efficacy, and low patient acceptance. However, researchers are no less enthusiastic about the treatment because of its theoretical potential to treat cancer. In this paper, the advances in p53-based gene therapy and related nucleic acid delivery technologies were reviewed and prospected in order to support further development in this field.

Role of inflammasomes and neuroinflammation in epilepsy.

Epilepsy is a brain disorder characterized by recurrent seizures, which are brief episodes of abnormal electrical activity in the brain and involuntary movement that can lead to physical injury and loss of consciousness. Seizures are canonically accompanied by increased inflammatory cytokine production that promotes neuroinflammation, brain pathology, and seizure propagation. Understanding the source of pro-inflammatory cytokines which promote seizure pathogenesis could be a gateway to precision epilepsy drug design. This review discusses the inflammasome in epilepsy including its role in seizure propagation and negative impacts on brain health. The inflammasome is a multiprotein complex that coordinates IL-1β and IL-18 production in response to tissue damage, cellular stress, and infection. Clinical evidence for inflammasome signaling in epileptogenesis is reviewed followed by a discussion of emerging strategies to modulate inflammasome activity in epilepsy.

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning

ABSTRACT BACE1 has been regarded as an essential drug design target for treating Alzheimer’s disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design

Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design

The Physical Driving Forces of Conformational Transition for TTR91-96 with Proline Mutations.

Pathological aggregation of essentially dissociated Transthyretin (TTR) monomer proteins, driven by misfolding and self-interaction, is associated with Transthyretin amyloidosis (ATTR) disease. The TTR monomer proteins consist of several fragments that tend to self-aggregate. Recent experimental studies showed that the sequence of residues TTR91-96 plays an important role in self-aggregation. However, the mechanisms underlying the misfolding and aggregation of the TTR91-96 monomers are still unknown. In this study, we used microsecond molecular dynamics simulations to investigate the misfolding and self-assembly of TTR91-96 Octamers. We also investigated E92P and V94P mutants for comparative analysis. The analysis indicates that hydrophobic interactions and π-π stacking patterns play important roles in reducing the β-sheet content in the V94P and E92P mutants. Additionally, our findings reveal the conformational transition of TTR91-96 octamer from closed β-barrel, open β-barrel to the β-bilayer aggregation. We further elucidate the dynamic mechanism of the transition from intermediate states to stable states. Overall, our research may contribute to the development of drug design to combat fibrous amyloid fibrous diseases.

A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, inspired by the DNA-encoded compound library technique, we introduce DeepBlock, a deep learning approach for block-based ligand generation tailored to target protein sequences while enabling precise property control. DeepBlock neatly divides the generation process into two steps: building blocks generation and molecule reconstruction, accomplished by a neural network and a rule-based reconstruction algorithm we proposed, respectively. Furthermore, DeepBlock synergizes the optimization algorithm and deep learning to regulate the properties of the generated molecules. Experiments show that DeepBlock outperforms existing methods in generating ligands with affinity, synthetic accessibility and drug likeness. Moreover, when integrated with simulated annealing or Bayesian optimization using toxicity as the optimization objective, DeepBlock successfully generates ligands with low toxicity while preserving affinity with the target.

Investigation of Novel Quinoline Derivatives Targeting Epidermal Growth Factor Receptors as Anticancer Agents a Computational Approach

Background: Newer chemical entities are created and synthesis has been made feasible by a variety of computer-aided drug design (CAAD) techniques. In addition to facilitating the visualisation of the ligand-target binding process, the application of in silico methodologies and structure-based drug design (SBDD) allows for the prediction of receptor affinities and significant binding pocket locations. Objective: The goal of the current study was to identify new quinoline derivatives by computational methods specially designed to bind the EGFR receptor in the treatment of breast cancer. Materials and Methods: ChemAxon Marvin Sketch 5.11.5 was used to create derivatives of quinolines. The admetSAR online web tools and SwissADME were utilised to forecast the toxicity and pharmacokinetic characteristics of several substances. A multitude of software programmes, such as Autodock 1.1.2, MGL Tools 1.5.6, Procheck, Protparam ExPasy tool, PyMOL, and Biovia Discovery Studio Visualizer v20.1.0.19295 were also employed to ascertain the ligand-receptor interactions between quinoline derivatives and the target receptor (PDB -5GNK). Result: Almost all components were shown to be less hazardous, orally consumable and to have the appropriate pharmacokinetic characteristics based on in silico study. All newly generated derivative compounds have higher docking scores when compared to the widely used medication sorafenib. Conclusion: Interactions with quinoline analogues boost binding energy and the number of Hbonds produced, making them a suitable place to start when creating compounds for further exploration. The quinoline moiety increases its potential as a novel therapy alternative for breast cancer and could facilitate more comprehensive in vivo, in vitro, chemical-based, and pharma studies by medicinal chemists.

Emerging Technologies for the Assessment of Natural Killer Cell Activity

Understanding natural killer (NK) cell functionality is essential in developing more effective immunotherapeutic strategies that can enhance patient outcomes, especially in the context of cancer treatment. This review provides a comprehensive overview of both traditional and novel techniques for evaluating NK cell functionality, focusing on multiparameter assays and spatial methods that illuminate NK cell interactions within their microenvironment. We discuss the significance of standardized assays for assessing NK cell function across various research and clinical settings, including cancer immunotherapy, infectious diseases, and transplantation. Key factors influencing NK cell functionality include the origin of the sample, target–effector ratios, the functional state of NK cells, and the impact of pre-treatment conditions and their natural aging effect on NK cell activity. By emphasizing the importance of selecting a suitable technique for reliable measurements, especially for longitudinal monitoring, this review aims to give an overview on techniques to measure NK cell functionality in vitro and show the interaction with their microenvironment cells by spatial imaging. Ultimately, our understanding of NK cell functionality could be critical to biomarker development, drug design, and understanding of disease progression in the field of oncology or infectious disease.

DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model.

The analysis of protein-ligand binding sites plays a crucial role in the initial stages of drug discovery. Accurately predicting the ligand types that are likely to bind to protein-ligand binding sites enables more informed decision making in drug design. Our study, DeepLigType, determines protein-ligand binding sites using Fpocket and then predicts the ligand type of these pockets with the deep learning model, Convolutional Block Attention Module (CBAM) with ResNet. CBAM-ResNet has been trained to accurately predict five distinct ligand types. We classified protein-ligand binding sites into five different categories according to the type of response ligands cause when they bind to their target proteins, which are antagonist, agonist, activator, inhibitor, and others. We created a novel dataset, referred to as LigType5, from the widely recognized PDBbind and scPDB dataset for training and testing our model. While the literature mostly focuses on the specificity and characteristic analysis of protein binding sites by experimental (laboratory-based) methods, we propose a computational method with the DeepLigType architecture. DeepLigType demonstrated an accuracy of 74.30% and an AUC of 0.83 in ligand type prediction on a novel test dataset using the CBAM-ResNet deep learning model. For access to the code implementation of this research, please visit our GitHub repository at https://github.com/drorhunvural/DeepLigType.

Advances and Challenges in Milestoning Simulations for Drug-Target Kinetics.

Molecular dynamics simulations have become indispensable for exploring complex biological processes, yet their limitations in capturing rare events hinder our understanding of drug-target kinetics. In this Perspective, we investigate the domain of milestoning simulations to understand this challenge. The milestoning approach divides the phase space of the drug-target complex into discrete cells, offering extended time scale insights. This Perspective traces the history, applications, and future potential of milestoning simulations in the context of drug-target kinetics. It explores the fundamental principles of milestoning, highlighting the importance of probabilistic transitions and transition time independence. Markovian milestoning with Voronoi tessellations is revisited to address the traditional milestoning challenges. While observing the advancements in this field, this Perspective also addresses impending challenges in estimating drug-target unbinding rate constants through milestoning simulations, paving the way for more effective drug design strategies.

Aluminum Fluorides as Noncovalent Lewis Acids in Proteins: The Case of Phosphoryl Transfer Enzymes.

The Protein Data Bank (PDB) was scrutinized for the presence of noncovalent O ⋅ ⋅ ⋅ Al Triel Bonding (TrB) interactions, involving protein residues (e. g. GLU and GLN), adenosine/guanine diphosphate moieties (ADP and GDP), water molecules and two aluminum fluorides (AlF3 and AlF4 -). The results were statistically analyzed, revealing a vast number of O ⋅ ⋅ ⋅ Al contacts in the active sites of phosphoryl transfer enzymes, with a marked directionality towards the Al σ-/π-hole. The physical nature of the TrBs studied herein was analyzed using Molecular Electrostatic Potential (MEP) maps, the Quantum Theory of Atoms in Molecules (QTAIM), the Non Covalent Interaction plot (NCIplot) visual index and Natural Bonding Orbital (NBO) studies. As far as our knowledge extends, it is the first time that O ⋅ ⋅ ⋅ Al TrBs are analyzed within a biological context, participating in protein trapping mechanisms related to phosphoryl transfer enzymes. Moreover, since they are involved in the stabilization of aluminum fluorides inside the protein's active site, we believe the results reported herein will be valuable for those scientists working in supramolecular chemistry, catalysis and rational drug design.

Application of the Linear Interaction Energy Method to Nitric Oxide Synthase Structure-Based Inhibitor Design.

The overproduction of nitric oxide by neuronal nitric oxide synthase (nNOS) is associated with several neuropathological conditions. As a result, inhibition of nNOS is a desirable therapeutic goal while avoiding the inhibition of endothelial NOS (eNOS) given its essential role in maintaining cardiovascular tone. Designing inhibitors with high specificity for nNOS over eNOS is challenging given the close similarity in the active site structure of all mammalian NOS isoforms. Computational methods like free energy perturbation (FEP) and thermodynamic integration (TI) offer attractive avenues for rational drug design, but application of these methods to NOS is hindered by several challenges, including proper handling of highly charged inhibitors with diverse structures as well as computational expense. To address these issues, we present a simplified approach combining continuum dielectric generalized born (GB) solvent models with linear interaction energy (LIE) calculations. Our method demonstrates excellent agreement with experimental data for charged inhibitors targeting mammalian NOS isoforms (mNOS). Our results highlight the utility of the GB-LIE method as a promising tool for screening NOS inhibitors and potentially other protein targets with charged active sites and diverse inhibitor structures.

Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning.

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB . SCIENTIFIC CONTRIBUTION: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions.