Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2 based framework combined with all-atom enhanced sampling molecular dynamics and induced fit docking, named AF2RAVE-Glide, to conduct computational model based small molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

In silico study to identify novel NEK7 inhibitors from natural sources by a combination strategy

Cancer poses a significant global health challenge and significantly contributes to mortality. NEK7, related to the NIMA protein kinase family, plays a crucial role in spindle assembly and cell division. The dysregulation of NEK7 is closely linked to the onset and progression of various cancers, especially colon and breast cancer, making it a promising target for cancer therapy. Nevertheless, the shortage of high-quality NEK7 inhibitors highlights the need for new therapeutic strategies. In this study, we utilized a multidisciplinary approach, including virtual screening, molecular docking, pharmacokinetics, molecular dynamics simulations (MDs), and MM/PBSA calculations, to evaluate natural compounds as NEK7 inhibitors comprehensively. Through various docking strategies, we identified three natural compounds: (-)-balanol, digallic acid, and scutellarin. Molecular docking revealed significant interactions at residues such as GLU112 and ALA114, with docking scores of -15.054, -13.059, and -11.547 kcal/mol, respectively, highlighting their potential as NEK7 inhibitors. MDs confirmed the stability of these compounds at the NEK7-binding site. Hydrogen bond analysis during simulations revealed consistent interactions, supporting their strong binding capacity. MM/PBSA analysis identified other crucial amino acids contributing to binding affinity, including ILE20, VAL28, ILE75, LEU93, ALA94, LYS143, PHE148, LEU160, and THR161, crucial for stabilizing the complex. This research demonstrated that these compounds exceeded dabrafenib in binding energy, according to MM/PBSA calculations, underscoring their effectiveness as NEK7 inhibitors. ADME/T predictions showed lower oral toxicity for these compounds, suggesting their potential for further development. This study highlights the promise of these natural compounds as bases for creating more potent derivatives with significant biological activities, paving the way for future experimental validation.

Discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for treating cancers with microsatellite instability

Microsatellite instability (MSI) is a hypermutable condition caused by DNA mismatch repair system defects, contributing to the development of various cancer types. Recent research has identified Werner syndrome ATP-dependent helicase (WRN) as a promising synthetic lethal target for MSI cancers. Herein, we report the first discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for MSI cancer therapy. Initial computational analysis and biological evaluation identified a new scaffold for a WRN inhibitor. Subsequent SAR study led to the discovery of a highly potent WRN inhibitor. Furthermore, we demonstrated that the optimal compound induced DNA damage and apoptotic cell death in MSI cancer cells by inhibiting WRN. This study provides a new pharmacophore for WRN inhibitors, emphasizing their therapeutic potential for MSI cancers.

An Overview of Crosslinked Enzyme Aggregates: Concept of Development and Trends of Applications

Enzymes are commonly used as biocatalysts for various biological and chemical processes in industrial applications. However, their limited operational stability, catalytic efficiency, poor reusability, and high-cost hamper further industrial usage. Thus, crosslinked enzyme aggregates (CLEAs) are developed as a better enzyme immobilization tool to extend the enzymes' operational stability. This immobilization method is appealing because it is simpler due to the absence of ballast and permits the collective use of crude enzyme cocktails. CLEAs, so far, have been successfully developed using a variety of enzymes, viz., hydrolases, proteases, amidases, lipases, esterases, and oxidoreductase. Recent years have seen the emergence of novel strategies for preparing better CLEAs, which include the combi- and multi-CLEAs, magnetics CLEAs, and porous CLEAs for various industrial applications, viz., laundry detergents, organic synthesis, food industries, pharmaceutical applications, oils, and biodiesel production. To better understand the different strategies for CLEAs' development, this review explores these strategies and highlights the relevant concerns in designing innovative CLEAs. This article also details the challenges faced during CLEAs preparation and solutions for overcoming them. Finally, the trending strategies to improve the preparation of CLEAs alongside their industrial application trends are also discussed.

Structure-Based Discovery of Mouse Trace Amine-Associated Receptor 5 Antagonists

Trace amine-associated receptors (TAARs) were discovered in 2001 as new members of class A G protein-coupled receptors (GPCRs). With the only exception of TAAR1, TAAR members (TAAR2-9, also known as noncanonical olfactory receptors) were originally described exclusively in the olfactory epithelium and believed to mediate the innate perception of volatile amines. However, most noncanonical olfactory receptors are still orphan receptors. Given its recently discovered nonolfactory expression and therapeutic potential, TAAR5 has been the focus of deorphanization campaigns that led to the discovery of a few druglike antagonists. Here, we report four novel TAAR5 antagonists identified through high-throughput screening, which, along with the four ligands published in the literature, constituted our starting point to design a computational strategy for the identification of TAAR5 ligands. We developed a structure-based virtual screening protocol that allowed us to identify three new TAAR5 antagonists with a hit rate of 10%. Despite lacking an experimental structure, we accurately modeled the TAAR5 binding site by integrating comparative sequence- and structure-based analyses of serotonin receptors with homology modeling and side-chain optimization. In summary, we have identified seven new TAAR5 antagonists that could serve as lead candidates for the development of new treatments for depression, anxiety, and neurodegenerative diseases.

Discovery of Chemical Scaffolds as Lysophosphatidic Acid Receptor 1 Antagonists: Virtual Screening, In Vitro Validation, and Molecular Dynamics Analysis

Lysophosphatidic acid receptor 1 (LPAR1) is an emerging therapeutic target for numerous human diseases including fibrosis. However, the limited number of available core structures of LPAR1 antagonists has prompted the need for novel chemical templates. In this study, we conducted a high-throughput virtual screening to discover potential new scaffolds. We tested three existing crystal structures alongside an AlphaFold model to evaluate their suitability in structure-based virtual screening, finding that the crystal structures show superior performance compared with the predictive model. Furthermore, we also found that enhancing the precision in the screening process did not necessarily improve the enrichment of hits. From the screening campaign, we identified five structures that were validated using an LPAR1-dependent calcium flux assay. To gain a deeper insight into the protein-ligand interaction, we extensively analyzed the binding modes of these compounds using in silico techniques, laying the groundwork for the discovery of novel LPAR1 antagonists.

CHARMM-GUI-Based Induced Fit Docking Workflow to Generate Reliable Protein-Ligand Binding Modes

Molecular docking is a preferred method to predict ligand binding modes and their binding energy to target protein receptors, which is critical in early phase structure-based drug discovery. However, there is a persistent challenge in docking that can be attributed to the induced fit effect, as receptor binding sites undergo induced fit conformational changes upon ligand binding to achieve better binding modes. In this work, based on CHARMM-GUI LBS Finder& Refiner and High-Throughput Simulator, we present a straightforward CHARMM-GUI induced fit docking (CGUI-IFD) workflow to generate reliable protein-ligand binding modes. The CGUI-IFD workflow generates an ensemble of receptor binding site conformations through ligand-binding site (LBS) refinement, runs rigid receptor docking, and performs high-throughput molecular dynamics (MD) simulations of protein-ligand complex structures in explicit solvents. The results are evaluated based on the ligand root-mean-square deviation (RMSD)-based binding stability and the molecular mechanics generalized Born surface area binding energy. For a benchmark test, we used 258 cross-docking protein-ligand pairs across 41 target proteins from the Schrodinger IFD-MD data set. The application of CGUI-IFD on this data set shows 80% success rate (within 2.5 Å RMSD from the experimental structures). We expect that the CGUI-IFD workflow can be useful to generate reliable ligand binding modes for cross-docking cases.

Improving the Modeling of Extracellular Ligand Binding Pockets in RosettaGPCR for Conformational Selection

G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of Cα-Cα-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.

How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement

An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.

PDBspheres: a method for finding 3D similarities in local regions in proteins

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions ('spheres') adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA (Local-Global Alignment) structure alignment algorithm as the main engine for detecting structural similarities between the protein of interest and template spheres from the library, which currently contains >2 million spheres. To assess confidence in structural matches, an all-atom-based similarity metric takes side chain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres-a strictly structure-based method-performs on a curated dataset of 2528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4876 structures in the 'refined set' of the PDBbind 2019 dataset.

Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell's life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.

CHARMM-GUI Ligand Designer for Template-Based Virtual Ligand Design in a Binding Site

Rational drug design involves a task of finding ligands that would bind to a specific target protein. This work presents CHARMM-GUI Ligand Designer that is an intuitive and interactive web-based tool to design virtual ligands that match the shape and chemical features of a given protein binding site. Ligand Designer provides ligand modification capabilities with 3D visualization that allow researchers to modify and redesign virtual ligands while viewing how the protein-ligand interactions are affected. Virtual ligands can also be parameterized for further molecular dynamics (MD) simulations and free energy calculations. Using 8 targets from 8 different protein classes in the directory of useful decoys, enhanced (DUD-E) data set, we show that Ligand Designer can produce similar ligands to the known active ligands in the crystal structures. Ligand Designer also produces stable protein-ligand complex structures when tested using short MD simulations. We expect that Ligand Designer can be a useful and user-friendly tool to design small molecules in any given potential ligand binding site on a protein of interest.

CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement

A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.