FTMAP: extended protein mapping with user-selected probe molecules

New antibacterial candidates against Acinetobacter baumannii discovered by in silico-driven chemogenomics repurposing

Acinetobacter baumannii is a worldwide Gram-negative bacterium with a high resistance rate, responsible for a broad spectrum of hospital-acquired infections. A computational chemogenomics framework was applied to investigate the repurposing of approved drugs to target A. baumannii. This comprehensive approach involved compiling and preparing proteomic data, identifying homologous proteins in drug-target databases, evaluating the evolutionary conservation of targets, and conducting molecular docking studies and in vitro assays. Seven drugs were selected for experimental assays. Among them, tavaborole exhibited the most promising antimicrobial activity with a minimum inhibitory concentration (MIC) value of 2 μg/ml, potent activity against several clinically relevant strains, and robust efficacy against biofilms from multidrug-resistant strains at a concentration of 16 μg/ml. Molecular docking studies elucidated the binding modes of tavaborole in the editing and active domains of leucyl-tRNA synthetase, providing insights into its structural basis for antimicrobial activity. Tavaborole shows promise as an antimicrobial agent for combating A. baumannii infections and warrants further investigation in preclinical studies.

Unveiling crucial amino acids in the carbohydrate recognition domain of a viral protein through a structural bioinformatic approach

Carbohydrate binding modules (CBMs) are protein domains that typically reside near catalytic domains, increasing substrate-protein proximity by constraining the conformational space of carbohydrates. Due to the flexibility and variability of glycans, the molecular details of how these protein regions recognize their target molecules are not always fully understood. Computational methods, including molecular docking and molecular dynamics simulations, have been employed to investigate lectin-carbohydrate interactions. In this study, we introduce a novel approach that integrates multiple computational techniques to identify the critical amino acids involved in the interaction between a CBM located at the tip of bacteriophage J-1's tail and its carbohydrate counterparts. Our results highlight three amino acids that play a significant role in binding, a finding we confirmed through in vitro experiments. By presenting this approach, we offer an intriguing alternative for pinpointing amino acids that contribute to protein-sugar interactions, leading to a more thorough comprehension of the molecular determinants of protein-carbohydrate interactions.

Repurposing DrugBank compounds as potential Plasmodium falciparum class 1a aminoacyl tRNA synthetase multi-stage pan-inhibitors with a specific focus on mitomycin

Plasmodium falciparum aminoacyl tRNA synthetases (PfaaRSs) are potent antimalarial targets essential for proteome fidelity and overall parasite survival in every stage of the parasite's life cycle. So far, some of these proteins have been singly targeted yielding inhibitor compounds that have been limited by incidences of resistance which can be overcome via pan-inhibition strategies. Hence, herein, for the first time, we report the identification and in vitro antiplasmodial validation of Mitomycin (MMC) as a probable pan-inhibitor of class 1a (arginyl(A)-, cysteinyl(C), isoleucyl(I)-, leucyl(L), methionyl(M), and valyl(V)-) PfaaRSs which hypothetically may underlie its previously reported activity on the ribosomal RNA to inhibit protein translation and biosynthesis. We combined multiple in silico structure-based discovery strategies that first helped identify functional and druggable sites that were preferentially targeted by the compound in each of the plasmodial proteins: Ins1-Ins2 domain in Pf-ARS; anticodon binding domain in Pf-CRS; CP1-editing domain in Pf-IRS and Pf-MRS; C-terminal domain in Pf-LRS; and CP-core region in Pf-VRS. Molecular dynamics studies further revealed that MMC allosterically induced changes in the global structures of each protein. Likewise, prominent structural perturbations were caused by the compound across the functional domains of the proteins. More so, MMC induced systematic alterations in the binding of the catalytic nucleotide and amino acid substrates which culminated in the loss of key interactions with key active site residues and ultimate reduction in the nucleotide-binding affinities across all proteins, as deduced from the binding energy calculations. These altogether confirmed that MMC uniformly disrupted the structure of the target proteins and essential substrates. Further, MMC demonstrated IC50 < 5 μM against the Dd2 and 3D7 strains of parasite making it a good starting point for malarial drug development. We believe that findings from our study will be important in the current search for highly effective multi-stage antimalarial drugs.

Kinetics and molecular modeling studies on the inhibition mechanism of GH13 α-glycosidases by small molecule ligands

Alpha-glucosidase inhibitors play an important role in Diabetes Mellitus (DM) treatment since they prevent postprandial hyperglycemia. The Glycoside Hydrolase family 13 (GH13) is the major family of enzymes acting on substrates containing α-glucoside linkages, such as maltose and amylose/amylopectin chains in starch. Previously, our group identified glycoconjugate 1H-1,2,3-triazoles (GCTs) inhibiting two GH13 α-glycosidases: yeast maltase (MAL12) and porcine pancreatic amylase (PPA). Here, we combined kinetic studies and computational methods on nine GCTs to characterize their inhibitory mechanism. They all behaved as reversible inhibitors, and kinetic models encompassed noncompetitive and various mechanisms of mixed-type inhibition for both enzymes. Most potent inhibitors displayed Ki values of 30 μM for MAL12 (GPESB16) and 37 μM for PPA (GPESB15). Molecular dynamics and docking simulations indicated that on MAL12, GPESB15 and GPESB16 bind in a cavity adjacent to the active site, while on the PPA, GPESB15 was predicted to bind at the entrance of the catalytic site. Notably, despite its putative location within the active site, the binding of GPESB15 does not obstruct the substrate's access to the cleavage site. Our study contributes to paving the way for developing novel therapeutic strategies for managing DM-2 through GH13 α-glycosidases inhibition.

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

Identifying Potential Ligand Binding Sites on Glycogen Synthase Kinase 3 Using Atomistic Cosolvent Simulations

Glycogen synthase kinase 3 β (GSK3β) is a serine/threonine kinase that phosphorylates several protein substrates in crucial cell signaling pathways. Owing to its therapeutic importance, there is a need to develop GSK3β inhibitors with high specificity and potency. One approach is to find small molecules that can allosterically bind to the GSK3β protein surface. We have employed fully atomistic mixed-solvent molecular dynamics (MixMD) simulations to identify three plausible allosteric sites on GSK3β that can facilitate the search for allosteric inhibitors. Our MixMD simulations narrow down the allosteric sites to precise regions on the GSK3β surface, thereby improving upon the previous predictions of the locations of these sites.

Discovery of a small-molecule NDR1 agonist for prostate cancer therapy

Prostatic cancer (PCa) is a common malignant neoplasm in men worldwide. Most patients develop castration-resistant prostate cancer (CRPC) after treatment with androgen deprivation therapy (ADT), usually resulting in death. Therefore, investigating new therapeutic targets and drugs for PCa patients is urgently needed. Nuclear Dbf2-related kinase 1 (NDR1), also known as STK38, is a serine/threonine kinase in the NDR/LATS kinase family that plays a critical role in cellular processes, including immunity, inflammation, metastasis, and tumorigenesis. It was reported that NDR1 inhibited the metastasis of prostate cancer cells by suppressing epithelial-mesenchymal transition (EMT), and decreased NDR1 expression might lead to a poorer prognosis, suggesting the enormous potential of NDR1 in antitumorigenesis. In this study, we characterized a small-molecule agonist named aNDR1, which specifically bound to NDR1 and potently promoted NDR1 expression, enzymatic activity and phosphorylation. aNDR1 exhibited drug-like properties, such as favorable stability, plasma protein binding capacity, cell membrane permeability, and PCa cell-specific inhibition, while having no obvious effect on normal prostate cells. Meanwhile, aNDR1 exhibited good antitumor activity both in vitro and in vivo. aNDR1 inhibited proliferation and migration of PCa cells and promoted apoptosis of PCa cells in vitro. We further found that aNDR1 inhibited subcutaneous tumors and lung metastatic nodules in vivo, with no obvious toxicity to the body. In summary, our study presents a potential small-molecule lead compound that targets NDR1 for clinical therapy of PCa patients.

Unveiling tetrahydroquinolines as promising BVDV entry inhibitors: Targeting the envelope protein

Bovine viral diarrhea virus (BVDV) is known to cause financial losses and decreased productivity in the cattle industry worldwide. Currently, there are no available antiviral treatments for effectively controlling BVDV infections in laboratories or farms. The BVDV envelope protein (E2) mediates receptor recognition on the cell surface and is required for fusion of virus and cell membranes after the endocytic uptake of the virus during the entry process. Therefore, E2 is an attractive target for the development of antiviral strategies. To identify BVDV antivirals targeting E2 function, we defined a binding site in silico located in domain IIIc at the interface between monomers in the disulfide linked dimer of E2. Employing a de novo design methodology to identify compounds with the potential to inhibit the E2 function, compound 9 emerged as a promising candidate with remarkable antiviral activity and minimal toxicity. In line with targeting of E2 function, compound 9 was found to block the virus entry into host cells. Furthermore, we demonstrated that compound 9 selectively binds to recombinant E2 in vitro. Molecular dynamics simulations (MD) allowed describing a possible interaction pattern between compound 9 and E2 and indicated that the S enantiomer of compound 9 may be responsible for the antiviral activity. Future research endeavors will focus on synthesizing enantiomerically pure compounds to further support these findings. These results highlight the usefulness of de novo design strategies to identify a novel class of BVDV inhibitors that block E2 function inhibiting virus entry into the host cell.

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Synergistic Inhibition Guided Fragment-Linking Strategy and Quantitative Structure-Property Relationship Modeling To Design Inhalable Therapeutics for Asthma Targeting CSF1R

The colony-stimulating factor-1 receptor (CSF1R) is a tyrosine-protein kinase that is a potential target for asthma therapeutics. We have applied a fragment-lead combination approach to identify small fragments that act synergistically with GW2580, a known inhibitor of CSF1R. Two fragment libraries were screened in combination with GW2580 by surface plasmon resonance (SPR). Binding affinity measurements confirmed that thirteen fragments bind specifically to the CSF1R, and a kinase activity assay further validated the inhibitory effect of these fragments. Several fragment compounds enhanced the inhibitory activity of the lead inhibitor. Computational solvent mapping, molecular docking, and modeling studies suggest that some of these fragments bind adjacent to the binding site of the lead inhibitor and further stabilize the inhibitor-bound state. Modeling results guided the computational fragment-linking approach to design potential next-generation compounds. The inhalability of these proposed compounds was predicted using quantitative structure-property relationships (QSPR) modeling based on an analysis of 71 drugs currently on the market. This work provides new insights into the development of inhalable small molecule therapeutics for asthma.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

Application of Mixed-Solvent Molecular Dynamics Simulations for Prediction of Allosteric Sites on G Protein-Coupled Receptors

The development of small molecule allosteric modulators acting at G protein-coupled receptors (GPCRs) is becoming increasingly attractive. Such compounds have advantages over traditional drugs acting at orthosteric sites on these receptors, in particular target specificity. However, the number and locations of druggable allosteric sites within most clinically relevant GPCRs are unknown. In the present study, we describe the development and application of a mixed-solvent molecular dynamics (MixMD)-based method for the identification of allosteric sites on GPCRs. The method employs small organic probes with druglike qualities to identify druggable hotspots in multiple replicate short-timescale simulations. As proof of principle, we first applied the method retrospectively to a test set of five GPCRs (cannabinoid receptor type 1, C-C chemokine receptor type 2, M2 muscarinic receptor, P2Y purinoceptor 1, and protease-activated receptor 2) with known allosteric sites in diverse locations. This resulted in the identification of the known allosteric sites on these receptors. We then applied the method to the μ-opioid receptor. Several allosteric modulators for this receptor are known, although the binding sites for these modulators are not known. The MixMD-based method revealed several potential allosteric sites on the mu-opioid receptor. Implementation of the MixMD-based method should aid future efforts in the structure-based drug design of drugs targeting allosteric sites on GPCRs. SIGNIFICANCE STATEMENT: Allosteric modulation of G protein-coupled receptors (GPCRs) has the potential to provide more selective drugs. However, there are limited structures of GPCRs bound to allosteric modulators, and obtaining such structures is problematic. Current computational methods utilize static structures and therefore may not identify hidden or cryptic sites. Here we describe the use of small organic probes and molecular dynamics to identify druggable allosteric hotspots on GPCRs. The results reinforce the importance of protein dynamics in allosteric site identification.

Novel Inhibitory Role of Fenofibric Acid by Targeting Cryptic Site on the RBD of SARS-CoV-2

The emergence of the recent pandemic causing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an alarming situation worldwide. It also prompted extensive research on drug repurposing to find a potential treatment for SARS-CoV-2 infection. An active metabolite of the hyperlipidemic drug fenofibrate (also called fenofibric acid or FA) was found to destabilize the receptor-binding domain (RBD) of the viral spike protein and therefore inhibit its binding to human angiotensin-converting enzyme 2 (hACE2) receptor. Despite being considered as a potential drug candidate for SARS-CoV-2, FA's inhibitory mechanism remains to be elucidated. We used molecular dynamics (MD) simulations to investigate the binding of FA to the RBD of the SARS-CoV-2 spike protein and revealed a potential cryptic FA binding site. Free energy calculations were performed for different FA-bound RBD complexes. The results suggest that the interaction of FA with the cryptic binding site of RBD alters the conformation of the binding loop of RBD and effectively reduces its binding affinity towards ACE2. Our study provides new insights for the design of SARS-CoV-2 inhibitors targeting cryptic sites on the RBD of SARS-CoV-2.

In silico prediction of molecular mechanisms of toxicity mediated by the leptospiral PF07598 gene family-encoded virulence-modifying proteins

Mechanisms of leptospirosis pathogenesis remain unclear despite the identification of a number of potential leptospiral virulence factors. We recently demonstrated potential mechanisms by which the virulence-modifying (VM) proteins-defined as containing a Domain of Unknown function (DUF1561), encoded by the PF07598 gene family-found only in group 1 pathogenic Leptospira-might mediate the clinical pathogenesis of leptospirosis. VM proteins belongs to classical AB toxin paradigm though have a unique AB domain architecture, unlike other AB toxins such as diphtheria toxin, pertussis toxin, shiga toxin, or ricin toxin which are typically encoded by two or more genes and self-assembled into a multi-domain holotoxin. Leptospiral VM proteins are secreted R-type lectin domain-containing exotoxins with discrete N-terminal ricin B-like domains involved in host cell surface binding, and a C-terminal DNase/toxin domain. Here we use the artificial intelligence-based AlphaFold algorithm and other computational tools to predict and elaborate on details of the VM protein structure-function relationship. Comparative AlphaFold and CD-spectroscopy defined the consistent secondary structure (Helix and ß-sheet) content, and the stability of the functional domains were further supported by molecular dynamics simulation. VM proteins comprises distinctive lectic family (QxW)₃ motifs, the Mycoplasma CARDS toxin (D3 domain, aromatic patches), C-terminal similarity with mammalian DNase I. In-silico study proposed that Gln412, Gln523, His533, Thr59 are the high binding energy or ligand binding residues plausibly anticipates in the functional activities. Divalent cation (Mg⁺²-Gln412) and phosphate ion (PO₄]^-3-Arg615) interaction further supports the functional activities driven by C-terminal domain. Computation-driven structure-function studies of VM proteins will guide experimentation towards mechanistic understandings of leptospirosis pathogenesis, which underlie development of new therapeutic and preventive measures for this devastating disease.

In Silico Structural and Functional Analyses of NLRP3 Inflammasomes to Provide Insights for Treating Neurodegenerative Diseases

Inflammasomes are cytoplasmic intracellular multiprotein complexes that control the innate immune system's activation of inflammation in response to derived chemicals. Recent advancements increased our molecular knowledge of activation of NLRP3 inflammasomes. Although several studies have been done to investigate the role of inflammasomes in innate immunity and other diseases, structural, functional, and evolutionary investigations are needed to further understand the clinical consequences of NLRP3 gene. The purpose of this study is to investigate the structural and functional impact of the NLRP3 protein by using a computational analysis to uncover putative protein sites involved in the stabilization of the protein-ligand complexes with inhibitors. This will allow for a deeper understanding of the molecular mechanism underlying these interactions. It was found that human NLRP3 gene coexpresses with PYCARD, NLRC4, CASP1, MAVS, and CTSB based on observed coexpression of homologs in other species. The NACHT, LRR, and PYD domain-containing protein 3 is a key player in innate immunity and inflammation as the sensor subunit of the NLRP3 inflammasome. The inflammasome polymeric complex, consisting of NLRP3, PYCARD, and CASP1, is formed in response to pathogens and other damage-associated signals (and possibly CASP4 and CASP5). Comprehensive structural and functional analyses of NLRP3 inflammasome components offer a fresh approach to the development of new treatments for a wide variety of human disorders.

Structure-based discovery and in vitro validation of inhibitors of chloride intracellular channel 4 protein

The use of computer-aided methods have continued to propel accelerated drug discovery across various disease models, interestingly allowing the specific inhibition of pathogenic targets. Chloride Intracellular Channel Protein 4 (CLIC4) is a novel class of intracellular ion channel highly implicated in tumor and vascular biology. It regulates cell proliferation, apoptosis and angiogenesis; and is involved in multiple pathologic signaling pathways. Absence of specific inhibitors however impedes its advancement to translational research. Here, we integrate structural bioinformatics and experimental research approaches for the discovery and validation of small-molecule inhibitors of CLIC4. High-affinity allosteric binders were identified from a library of 1615 Food and Drug Administration (FDA)-approved drugs via a high-performance computing-powered blind-docking approach, resulting in the selection of amphotericin B and rapamycin. NMR assays confirmed the binding and conformational disruptive effects of both drugs while they also reversed stress-induced membrane translocation of CLIC4 and inhibited endothelial cell migration. Structural and dynamics simulation studies further revealed that the inhibitory mechanisms of these compounds were hinged on the allosteric modulation of the catalytic glutathione (GSH)-like site loop and the extended catalytic β loop which may elicit interference with the catalytic activities of CLIC4. Structure-based insights from this study provide the basis for the selective targeting of CLIC4 to treat the associated pathologies.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Exploring the Surface of the Ectodomain of the PD-L1 Immune Checkpoint with Small-Molecule Fragments

Development of small molecules targeting the PD-L1/PD-1 interface is advancing both in industry and academia, but only a few have reached early-stage clinical trials. Here, we take a closer look at the general druggability of PD-L1 using in silico hot spot mapping and nuclear magnetic resonance (NMR)-based characterization. We found that the conformational elasticity of the PD-L1 surface strongly influences the formation of hot spots. We deconstructed several generations of known inhibitors into fragments and examined their binding properties using differential scanning fluorimetry (DSF) and protein-based nuclear magnetic resonance (NMR). These biophysical analyses showed that not all fragments bind to the PD-L1 ectodomain despite having the biphenyl scaffold. Although most of the binding fragments induced PD-L1 oligomerization, two compounds, TAH35 and TAH36, retain the monomeric state of proteins upon binding. Additionally, the presence of the entire ectodomain did not affect the binding of the hit compounds and dimerization of PD-L1. The data demonstrated here provide important information on the PD-L1 druggability and the structure-activity relationship of the biphenyl core moiety and therefore may aid in the design of novel inhibitors and focused fragment libraries for PD-L1.

Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries

Current fragment-based drug design relies on the efficient exploration of chemical space by using structurally diverse libraries of small fragments. However, structurally dissimilar compounds can exploit the same interactions and thus be functionally similar. Using three-dimensional structures of many fragments bound to multiple targets, we examined if a better strategy for selecting fragments for screening libraries exists. We show that structurally diverse fragments can be described as functionally redundant, often making the same interactions. Ranking fragments by the number of novel interactions they made, we show that functionally diverse selections of fragments substantially increase the amount of information recovered for unseen targets compared to the amounts recovered by other methods of selection. Using these results, we design small functionally efficient libraries that can give significantly more information about new protein targets than similarly sized structurally diverse libraries. By covering more functional space, we can generate more diverse sets of drug leads.

GraphSite: Ligand Binding Site Classification with Deep Graph Learning

The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.

Protein-Ligand Docking in the Machine-Learning Era

Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.

Fragment hopping protocol for the design of small-molecule protein-protein interaction inhibitors

Fragment-based ligand discovery (FBLD) is one of the most successful approaches to designing small-molecule protein-protein interaction (PPI) inhibitors. The incorporation of computational tools to FBLD allows the exploration of chemical space in a time- and cost-efficient manner. Herein, a computational protocol for the development of small-molecule PPI inhibitors using fragment hopping, a fragment-based de novo design approach, is described and a case study is presented to illustrate the efficiency of this protocol. Fragment hopping facilitates the design of PPI inhibitors from scratch solely based on key binding features in the PPI complex structure. This approach is an open system that enables the inclusion of different state-of-the-art programs and softwares to improve its performances.

A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola's replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.

Unraveling the Cardiac Effects Induced by Carvacrol in Spontaneously Hypertensive Rats: Involvement of Transient Receptor Potential Melastatin Subfamily 4 and 7 Channels

ABSTRACT

Accumulating evidence indicates that transient receptor potential (TRP) channels are involved in the pathophysiological process in the heart, and monoterpenes, such as carvacrol, are able to modulate these channels activity. In this article, our purpose was to evaluate the direct cardiac effect of carvacrol on the contractility of cardiomyocytes and isolated right atria from spontaneously hypertensive and Wistar Kyoto rats. In this way, in vitro experiments were used to evaluate the ventricular cardiomyocytes contractility and the Ca2+ transient measuring, in addition to heart rhythm in the right atria. The role of TRPM channels in carvacrol-mediated cardiac activities was also investigated. The results demonstrated that carvacrol induced a significant reduction in ventricular cell contractility, without changes in transient Ca2+. In addition, carvacrol promoted a significant negative chronotropic response in spontaneously hypertensive and Wistar Kyoto rats' atria. Selective blockage of TRPM channels suggests the involvement of TRP melastatin subfamily 2 (TRPM2), TRPM4, and TRPM7 in the carvacrol-mediated cardiac effects. In silico studies were conducted to further investigate the putative role of TRPM4 in carvacrol-mediated cardiac action. FTMap underscores a conserved pocket in both TRPM4 and TRPM7, revealing a potential carvacrol binding site, and morphological similarity analysis demonstrated that carvacrol shares a more than 85% similarity to 9-phenanthrol. Taken together, these results suggest that carvacrol has direct cardiac actions, leading to reduced cellular contractility and inducing a negative chronotropic effect, which may be related to TRPM7 and TRPM4 modulation.

Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design

Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.

Detection of structural and conformational changes in ALS-causing mutant profilin-1 with hydrogen/deuterium exchange mass spectrometry and bioinformatics techniques

The hydrogen/deuterium exchange (HDX) is a reliable method to survey the dynamic behavior of proteins and epitope mapping. Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) is a quantifying tool to assay for HDX in the protein of interest. We combined HDX-MALDI-TOF MS and molecular docking/MD simulation to identify accessible amino acids and analyze their contribution into the structural changes of profilin-1 (PFN-1). The molecular docking/MD simulations are computational tools for enabling the analysis of the type of amino acids that may be involved via HDX identified under the lowest binding energy condition. Glycine to valine amino acid (G117V) substitution mutation is linked to amyotrophic lateral sclerosis (ALS). This mutation is found to be in the actin-binding site of PFN-1 and prevents the dimerization/polymerization of actin and invokes a pathologic toxicity that leads to ALS. In this study, we sought to understand the PFN-1 protein dynamic behavior using purified wild type and mutant PFN-1 proteins. The data obtained from HDX-MALDI-TOF MS for PFN-1^WT and PFN-1^G117V at various time intervals, from seconds to hours, revealed multiple peaks corresponding to molecular weights from monomers to multimers. PFN-1/Benzaldehyde complexes identified 20 accessible amino acids to HDX that participate in the docking simulation in the surface of WT and mutant PFN-1. Consistent results from HDX-MALDI-TOF MS and docking simulation predict candidate amino acid(s) involved in the dimerization/polymerization of PFN^G117V. This information may shed critical light on the structural and conformational changes with details of amino acid epitopes for mutant PFN-1s' dimerization, oligomerization, and aggregation.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Tripeptides as Integrin-Linked Kinase Modulating Agents Based on a Protein-Protein Interaction with α-Parvin

Integrin-linked kinase (ILK) has emerged as a controversial pseudokinase protein that plays a crucial role in the signaling process initiated by integrin-mediated signaling. However, ILK also exhibits a scaffolding protein function inside cells, controlling cytoskeletal dynamics, and has been related to non-neoplastic diseases such as chronic kidney disease (CKD). Although this protein always acts as a heterotrimeric complex bound to PINCH and parvin adaptor proteins, the role of parvin proteins is currently not well understood. Using in silico approaches for the design, we have generated and prepared a set of new tripeptides mimicking an α-parvin segment. These derivatives exhibit activity in phenotypic assays in an ILK-dependent manner without altering kinase activity, thus allowing the generation of new chemical probes and drug candidates with interesting ILK-modulating activities.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Identification of Potential Binding Sites of Sialic Acids on the RBD Domain of SARS-CoV-2 Spike Protein

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still an emergent pandemic for humans. The virus infection is achieved by penetrating its spike protein to host cells via binding with ACE2. Moreover, recent studies show that SARS-CoV-2 may have multiple receptors that need to be further revealed. SARS-CoV-2 shares similar sequences of the spike protein with the Middle East Respiratory Syndrome Coronavirus (MERS-CoV), which can invade host cells by binding to either DPP4 or sialic acids. Sialic acids can be linked to the terminal of glycoproteins and gangliosides are used as one of the receptors of many types of viruses. Therefore, it is very interesting to determine whether sialic acid is a potential receptor of SARS-CoV-2. To address this question, we took N-Acetylneuraminic acid (Neu5Ac), a type of predominant sialic acid found in human cells, as the molecular probe to computationally search the surface of the spike protein to locate the potential binding sites of Neu5Ac. SPR analysis and mass spectrum analysis confirmed the interaction between Neu5Ac and spike protein. This study shows that sialic acids can moderately interact with the spike protein of SARS-CoV-2 by binding between the two RBDs of the spike protein, indicating it could be a potential secondary or auxiliary receptor of SARS-CoV-2.

PARP1: Structural insights and pharmacological targets for inhibition

Poly(ADP-ribose) polymerase 1 (PARP1, also known as ADPRT1) is a multifunctional human ADP-ribosyltransferase. It plays a role in multiple DNA repair pathways, including the base excision repair (BER), non-homologous end joining (NHEJ), homologous recombination (HR), and Okazaki-fragment processing pathways. In response to DNA strand breaks, PARP1 covalently attaches ADP-ribose moieties to arginine, glutamate, aspartate, cysteine, lysine, and serine acceptor sites on both itself and other proteins. This signal recruits DNA repair proteins to the site of DNA damage. PARP1 binding to these sites enhances ADP-ribosylation via allosteric communication between the distant DNA binding and catalytic domains. In this review, we provide a general overview of PARP1 and emphasize novel potential approaches for pharmacological inhibition. Clinical PARP1 inhibitors bind the catalytic pocket, where they directly interfere with ADP-ribosylation. Some inhibitors may further enhance potency by "trapping" PARP1 on DNA via an allosteric mechanism, though this proposed mode of action remains controversial. PARP1 inhibitors are used clinically to treat some cancers, but resistance is common, so novel pharmacological approaches are urgently needed. One approach may be to design novel small molecules that bind at inter-domain interfaces that are essential for PARP1 allostery. To illustrate these points, this review also includes instructive videos showing PARP1 structures and mechanisms.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

In silico studies reveal structural deviations of mutant profilin-1 and interaction with riluzole and edaravone in amyotrophic lateral sclerosis

This study aimed to investigate four of the eight PFN-1 mutations that are located near the actin-binding domain and determine the structural changes due to each mutant and unravel how these mutations alter protein structural behavior. Swapaa's command in UCSF chimera for generating mutations, FTMAP were employed and the data was analyzed by RMSD, RMSF graphs, Rg, hydrogen bonding analysis, and RRdisMaps utilizing Autodock4 and GROMACS. The functional changes and virtual screening, structural dynamics, and chemical bonding behavior changes, molecular docking simulation with two current FDA-approved drugs for ALS were investigated. The highest reduction and increase in Rg were found to exist in the G117V and M113T mutants, respectively. The RMSF data consistently shows changes nearby to this site. The in silico data described indicate that each of the mutations is capable of altering the structure of PFN-1 in vivo. The potential effect of riluzole and edaravone two FDA approved drugs for ALS, impacting the structural deviations and stabilization of the mutant PFN-1 is evaluated using in silico tools. Overall, the analysis of data collected reveals structural changes of mutant PFN-1 protein that may explain the neurotoxicity and the reason(s) for possible loss and gain of function of PFN-1 in the neurotoxic model of ALS.

Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective

The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.

Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics

Protein dynamics play an important role in small molecule binding and can pose a significant challenge in the identification of potential binding sites. Cryptic binding sites have been defined as sites which require significant rearrangement of the protein structure to become physically accessible to a ligand. Mixed-solvent MD (MixMD) is a computational protocol which maps the surface of the protein using molecular dynamics (MD) of the unbound protein solvated in a 5% box of probe molecules with explicit water. This method has successfully identified known active and allosteric sites which did not require reorganization. In this study, we apply the MixMD protocol to identify known cryptic sites of 12 proteins characterized by a wide range of conformational changes. Of these 12 proteins, three require reorganization of side chains, five require loop movements, and four require movement of more significant structures such as whole helices. In five cases, we find that standard MixMD simulations are able to map the cryptic binding sites with at least one probe type. In two cases (guanylate kinase and TIE-2), accelerated MD, which increases sampling of torsional angles, was necessary to achieve mapping of portions of the cryptic binding site missed by standard MixMD. For more complex systems where movement of a helix or domain is necessary, MixMD was unable to map the binding site even with accelerated dynamics, possibly due to the limited timescale (100 ns for individual simulations). In general, similar conformational dynamics are observed in water-only simulations and those with probe molecules. This could imply that the probes are not driving opening events but rather take advantage of mapping sites that spontaneously open as part of their inherent conformational behavior. Finally, we show that docking to an ensemble of conformations from the standard MixMD simulations performs better than docking the apo crystal structure in nine cases and even better than half of the bound crystal structures. Poorer performance was seen in docking to ensembles of conformations from the accelerated MixMD simulations.

Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

The rise of molecular simulations in fragment-based drug design (FBDD): an overview

Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.

BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

MOTIVATION

Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.

RESULTS

We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures.

AVAILABILITY AND IMPLEMENTATION

BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of protein missense alterations by combining sequence- and structure-based methods

BACKGROUND

Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play.

METHODS

Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB.

RESULTS

We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions.

CONCLUSION

Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.

Mesoscale All-Atom Influenza Virus Simulations Suggest New Substrate Binding Mechanism

Influenza virus circulates in human, avian, and swine hosts, causing seasonal epidemic and occasional pandemic outbreaks. Influenza neuraminidase, a viral surface glycoprotein, has two sialic acid binding sites. The catalytic (primary) site, which also binds inhibitors such as oseltamivir carboxylate, is responsible for cleaving the sialic acid linkages that bind viral progeny to the host cell. In contrast, the functional annotation of the secondary site remains unclear. Here, we better characterize these two sites through the development of an all-atom, explicitly solvated, and experimentally based integrative model of the pandemic influenza A H1N1 2009 viral envelope, containing ∼160 million atoms and spanning ∼115 nm in diameter. Molecular dynamics simulations of this crowded subcellular environment, coupled with Markov state model theory, provide a novel framework for studying realistic molecular systems at the mesoscale and allow us to quantify the kinetics of the neuraminidase 150-loop transition between the open and closed states. An analysis of chloride ion occupancy along the neuraminidase surface implies a potential new role for the neuraminidase secondary site, wherein the terminal sialic acid residues of the linkages may bind before transfer to the primary site where enzymatic cleavage occurs. Altogether, our work breaks new ground for molecular simulation in terms of size, complexity, and methodological analyses of the components. It also provides fundamental insights into the understanding of substrate recognition processes for this vital influenza drug target, suggesting a new strategy for the development of anti-influenza therapeutics.

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

Interaction Energetics and Druggability of the Protein-Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2)

Development of small molecule inhibitors of protein-protein interactions (PPIs) is hampered by our poor understanding of the druggability of PPI target sites. Here, we describe the combined application of alanine-scanning mutagenesis, fragment screening, and FTMap computational hot spot mapping to evaluate the energetics and druggability of the highly charged PPI interface between Kelch-like ECH-associated protein 1 (KEAP1) and nuclear factor erythroid 2 like 2 (Nrf2), an important drug target. FTMap identifies four binding energy hot spots at the active site. Only two of these are exploited by Nrf2, which alanine scanning of both proteins shows to bind primarily through E79 and E82 interacting with KEAP1 residues S363, R380, R415, R483, and S508. We identify fragment hits and obtain X-ray complex structures for three fragments via crystal soaking using a new crystal form of KEAP1. Combining these results provides a comprehensive and quantitative picture of the origins of binding energy at the interface. Our findings additionally reveal non-native interactions that might be exploited in the design of uncharged synthetic ligands to occupy the same site on KEAP1 that has evolved to bind the highly charged DEETGE binding loop of Nrf2. These include π-stacking with KEAP1 Y525 and interactions at an FTMap-identified hot spot deep in the binding site. Finally, we discuss how the complementary information provided by alanine-scanning mutagenesis, fragment screening, and computational hot spot mapping can be integrated to more comprehensively evaluate PPI druggability.