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Background: Oxidative stress is a significant player in the pathogenesis of diabetes mellitus and the Kelch-like ECH-associated protein1/nuclear factor erythroid 2-related factor 2/antioxidant response element (Keap1/Nrf2/ARE) signaling pathway serves as the essential defense system to mitigate oxidative stress. Nrf2 is responsible for the mitigation of oxidative stress while Keap1 represses Nrf2’s activation upon binding. Identification of Nrf2 activators has started to pick up enthusiasm as they can be used as therapeutic agents against diabetes mellitus. One of the ongoing mechanisms in the activation of Nrf2 is to disrupt Keap1/Nrf2 protein-protein interaction. This study aimed at using computational analysis to screen natural compounds capable of inhibiting Keap1/Nrf2 protein-protein interaction. Methods: A manual curated library of natural compounds was screened against crystal structure of Keap1 using glide docking algorithm. Binding free energy of the docked complexes, and adsorption, digestion, metabolism and excretion (ADME) properties were further employed to identify the hit compounds. The bioactivity of the identified hit against Keap1 was predicted using quantitative structure-activity relationship (QSAR) model. Results: A total of 7 natural compounds (Compound 222, 230, 310, 208, 210, 229 and 205) identified from different medicinal plants were found to be potent against Keap1 based on their binding affinity and binding free energy. The internal validated model kpls_radial_30 with R2 of 0.9109, Q2 of 0.7287 was used to predict the compounds’ bioactivities. Compound 205 was considered as the ideal drug candidate because it showed moderation for ADME properties, had predicted pIC50 of 6.614 and obeyed Lipinski’s rule of five. Conclusion: This study revealed that Compound 205, a compound isolated from Amphipterygium adstringens is worth considering for further experimental analysis.

Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction with their target protein. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. We carried out validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set. We demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other baseline scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.

In the three-dimensional structure of proteins, domains are stably folded structural units that retain molecular function independent of the rest of the larger protein. Here we review the basic building blocks of protein structure, provide an overview of molecular functions of protein domains for macromolecular interactions, ligand binding, and catalysis, summarize structural and bioinformatic approaches for identifying and classifying protein domains, and discuss the roles of protein domains as modules in the combinatorial evolution of protein structure and function.

Accurate prediction of protein–ligand binding free energies is important in enzyme engineering and drug discovery. The molecular mechanics/generalized Born surface area (MM/GBSA) approach is widely used to estimate ligand-binding affinities, but its performance heavily relies on the accuracy of its energy components. A hybrid strategy combining MM/GBSA and machine learning (ML) has been developed to predict the binding free energies of protein–ligand systems. Based on the MM/GBSA energy terms and several features associated with protein–ligand interactions, our ML-based scoring function, GXLE, shows much better performance than MM/GBSA without entropy. In particular, the good transferability of the GXLE model is highlighted by its good performance in ranking power for prediction of the binding affinity of different ligands for either the docked structures or crystal structures. The GXLE scoring function and its code are freely available and can be used to correct the binding free energies computed by MM/GBSA.

INTRODUCTION Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods. AREAS COVERED In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed. EXPERT OPINION Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an effective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (Mpro), which has been extensively studied in the last few months. There is an urgency for developing effective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein–ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 Mpro with an α-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein–ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 Mpro and the α-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.

Graphical abstract, Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.

Immune checkpoint inhibitor (ICI) responses vary, and biomarkers for predicting responders are urgently needed. Growing evidence points to the association between programmed cell death protein ligand 2 (PDL2) and ICI benefits, while clinical evidences were lacking. Thus, we consolidated five public ICI-treated cohorts to investigate the association between PDL2 expression and ICI treatment prognosis. Immune cell signatures and IFN-γ signatures are investigated in The Cancer Genome Atlas (TCGA) dataset and later in ICI-treated cohorts to explore the association between PDL2 and antitumor immunity in the tumor microenvironment (TME). We found that immune cell signatures and IFN-γ signatures were enriched in the PDL2-high group in TCGA pooled cohorts and most cancers. Consistently, in ICI-treated cohorts, patients with high PDL2 expression experienced longer overall survival time (OS) and were more likely responsive to ICIs than patients with low PDL2 expression. Immune cell scores of the high PDL2 expression patients were significantly higher ( P < 0.05 ) than those of the low PDL2 expression patients in ICI-treated cohorts. In conclusion, our findings suggest that PDL2 is a potential predictive biomarker for ICIs.

The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein-ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. In this work, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.

Dissociation of a ligand isoniazid from a protein catalase was investigated using all-atom molecular dynamics (MD) simulations. Random acceleration MD (τ-RAMD) was used, in which a random artificial force applied to the ligand facilitates its dissociation. We have suggested a novel approach to extrapolate such obtained dissociation times to the zero-force limit assuming never before attempted universal exponential dependence of the bond strength on the applied force, allowing direct comparison with experimentally measured values. We have found that our calculated dissociation time was equal to 36.1 s with statistically significant values distributed in the interval of 0.2-72.0 s, which quantitatively matches the experimental value of 50 ± 8 s despite the extrapolation over 9 orders of magnitude in time.

Microcrystal electron diffraction (MicroED) is an electron cryo-microscopy (cryo-EM) technique used to determine molecular structures with crystals that are a millionth the size needed for traditional single-crystal X-ray crystallography. An exciting use of MicroED is in drug discovery and development, where it can be applied to the study of proteins and small molecule interactions, and for structure determination of natural products. The structures are then used for rational drug design and optimization. In this Perspective, we discuss the current applications of MicroED for structure determination of protein–ligand complexes and potential future applications in drug discovery. Microcrystal electron diffraction (MicroED) can determine the structure of proteins from crystals that are orders of magnitude smaller than those used by X-ray methods. Here, the application of MicroED to protein–ligand complexes is reviewed.

Aims: The genomic and non-genomic actions of human estrogen receptor α (hERα) play a crucial role in breast epithelial cell proliferation and survival, as well as mammary tumorigenesis. hERα has been proved as a potential target for breast cancer therapy over the last 3 decades. Hence designing novel inhibitors targeting hERα can be a valuable approach in breast cancer therapy. Study Design: In the present study, the goal is to identify novel hERα inhibitors through molecular docking, AI based virtual screening and interaction fingerprint analysis. Place and Duration of Study: Department of Bioinformatics, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India in between July 2021-sep 2021. Methodology: Molecular docking studies were performed using the human estrogen receptor alpha ligand-binding domain in complex with 4-hydroxytamoxifen (PDB: 3ERT) against existing compounds from literature. The best docked existing compound and co-crystal ligand were subjected to shape screening against 28 million compounds and resulted compounds constituted the hERα inhibitor dataset which was subjected to rigid receptor docking. Further, interaction fingerprint analysis was applied as complimentary method to docking for comparing the similarity of predicted binding poses of proposed leads with that of reference binding pose. Results: Co-crystal ligand (4-OHT) and E99 exhibited better binding affinity among existing ligand set. Rigid receptor docking studies resulted in four lead compounds possessing better docking scores than 4-OHT and E99. Moreover, leads showed favorable absorption, distribution, metabolism, excretion and toxicity properties within the range of 95% FDA approved drugs. Proposed leads showed interactions with binding site residues of hERα similar to that of 4-OHT with better binding affinity. The ability of obtained leads to retrieve actives was validated using receiver operative characteristic (ROC) curve. Conclusion: From above results, it has been observed that the proposed leads have potential to act as novel hERα inhibitors.

DNA-encoded chemical libraries (DELs) are increasingly being used for the discovery of protein ligands and can be constructed displaying either one or two molecules at the extremities of the two complementary DNA strands. Here, we describe that DELs, featuring the simultaneous display of two molecules, can be encoded using various types of DNA structures, which go beyond the use of conventional double-stranded DNA fragments. Specifically, we compared dual-display methodologies in hairpin, circular or linear formats in terms of polymerase chain reaction (PCR) amplifiability and performance in affinity capture selections. The methods reported in this article highlight the feasibility and modularity of the described encoding strategies and may thus further expand the scope of DNA-encoded chemistry, particularly for the identification of compounds which recognize adjacent epitopes on the surface of target proteins of interest., Modular assembly methodologies of DNA-encoded chemical libraries featuring dual-display of organic molecules.

Structure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of ligand-binding poses is still a major challenge for molecular docking due to deficiency of scoring functions (SFs) and ignorance of protein flexibility upon ligand binding. In this study, based on a cross-docking dataset dedicatedly constructed from the PDBbind database, we developed several XGBoost-trained classifiers to discriminate the near-native binding poses from decoys, and systematically assessed their performance with/without the involvement of the cross-docked poses in the training/test sets. The calculation results illustrate that using Extended Connectivity Interaction Features (ECIF), Vina energy terms and docking pose ranks as the features can achieve the best performance, according to the validation through the random splitting or refined-core splitting and the testing on the re-docked or cross-docked poses. Besides, it is found that, despite the significant decrease of the performance for the threefold clustered cross-validation, the inclusion of the Vina energy terms can effectively ensure the lower limit of the performance of the models and thus improve their generalization capability. Furthermore, our calculation results also highlight the importance of the incorporation of the cross-docked poses into the training of the SFs with wide application domain and high robustness for binding pose prediction. The source code and the newly-developed cross-docking datasets can be freely available at https://github.com/sc8668/ml_pose_prediction and https://zenodo.org/record/5525936, respectively, under an open-source license. We believe that our study may provide valuable guidance for the development and assessment of new machine learning-based SFs (MLSFs) for the predictions of protein–ligand binding poses.

The previously introduced ratio of frequencies (RF ) framework provides statistically sound information on the relative interaction preferences of atoms in crystal structures. By applying the methodology to protein-ligand complexes, we can investigate the significance of interactions that are employed in structure-based drug design. Here, we revisit three aspects of molecular recognition in the light of the RF framework, namely stacking interactions of heteroaromatic rings with protein amide groups, interactions of acidified C-H groups, and interaction differences between syn and anti lone pairs of carboxylate groups. In addition, we introduce a highly interactive visualization tool that facilitates design idea generation in structure-enabled drug discovery projects. Finally, we show that applying the RF analysis as a simple rescoring tool after docking improves enrichment factors for the DUD-E diverse targets subset supporting the relevance of our approach.

The growing use of hydrogen-deuterium exchange mass spectrometry (HDX-MS) for studying membrane proteins, large protein assemblies, and highly disulfide-bonded species is often challenged by the presence in the sample of large amounts of lipids, protein ligands, and/or highly ionizable reducing agents. Here, we describe how a short size-exclusion chromatography (SEC) column can be integrated with a conventional temperature-controlled HDX-MS setup to achieve fast and online removal of unwanted species from the HDX sample prior to chromatographic separation and MS analysis. Dual-mode valves permit labeled proteins eluting after SEC to be directed to the proteolytic and chromatographic columns, while unwanted sample components are led to waste. The SEC-coupled HDX-MS method allows analyses to be completed with lower or similar back-exchange compared to conventional experiments. We demonstrate the suitability of the method for the analysis of challenging protein samples, achieving efficient online removal of lipid components from protein-lipid systems, depletion of an antibody from an antigen during epitope mapping, and elimination of MS interfering compounds such as tris(2-carboxyethyl)phosphine (TCEP) during HDX-MS analysis of a disulfide-bonded protein. The implementation of the short SEC column to the conventional HDX-MS setup is straightforward and could be of significant general utility during the HDX-MS analysis of complex protein states.

Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta$$\end{document}Δ-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta$$\end{document}Δ-AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the docking and screening power of the underlying classical scoring function. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-021-00536-w.

We have investigated the active site of E. coli MurB using the Quantum Mechanics/Molecular Mechanics (QM/MM) methodology. The docking of three novel series of 4-thiazolidinone derivatives has been performed using two methods: rigid docking and flexible docking (Induced Fit Docking: IFD). The results have been compared to understand the conformational aspects of the enzyme. The docking results from rigid docking show that the ligands with highly negative ΔGbind have poor docking scores. In addition, the value of the regression coefficient (R) obtained on correlating the ΔGbind and the experimental pMIC values is insignificant. On keeping the protein flexible, there is a remarkable improvement in both the docking score and ΔGbind, along with a good value of R (0.64). Two important residues, Tyr254 and Try190 are found to be highly displaced during the flexible docking and hence their role in effective ligand binding has been confirmed. Thus, comparing the two methodologies, IFD has emerged as the more appropriate one for studying the E. coli MurB enzyme. To further substantiate the findings, MD studies over a time period of 20 ns have been performed on the IFD-LIII j and Rigid/XP-LIII j complexes and the results shows the former complex to be more stable, with lower average RMSD and higher average ΔGbind.Communicated by Ramaswamy H. Sarma.

Modifying pair-specific Lennard-Jones parameters through the nonbonded FIX (NBFIX) feature of the CHARMM36 force field has proven cost-effective for improving the description of cation-π interactions in biological objects by means of pairwise additive potential energy functions. Here, two sets of newly optimized CHARMM36 force-field parameters including NBFIX corrections, coined CHARMM36m-NBF and CHARMM36-WYF, and the original force fields, namely CHARMM36m and Amber ff14SB, are used to determine the standard binding free energies of seven protein-ligand complexes containing cation-π interactions. Compared with precise experimental measurements, our results indicate that the uncorrected, original force fields significantly underestimate the binding free energies, with a mean error of 5.3 kcal/mol, while the mean errors of CHARMM36m-NBF and CHARMM36-WYF amount to 0.8 and 2.1 kcal/mol, respectively. The present study cogently demonstrates that the use of modified parameters jointly with NBFIX corrections dramatically increases the accuracy of the standard binding free energy of protein-ligand complexes dominated by cation-π interactions, most notably with CHARMM36m-NBF.

Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein–ligand binding mode and ass...

Allostery is central to many cellular processes, by up- or down-regulating target function. However, what determines the allosteric type remains elusive and currently it is impossible to predict whether the allosteric compounds would activate or inhibit target function before experimental studies. We demonstrated that the allosteric type and allosteric pathways are governed by the forces imposed by ligand binding to target protein using the anisotropic network model and developed an allosteric type prediction method (AlloType). AlloType correctly predicted 13 of the 16 allosteric systems in the data set with experimentally determined protein and complex structures as well as verified allosteric types, which was also used to identify allosteric pathways. When applied to glutathione peroxidase 4, a protein with no complex structure information, AlloType could still be able to predict the allosteric type of the recently reported allosteric activators, demonstrating its potential application in designing specific allosteric drugs and uncovering allosteric mechanisms.

Prediction of protein–protein interaction sites plays an important role for understanding the protein interactions and functions. However, in the protein–protein interaction site prediction problem, the number of binding-site residues is usually much less than that of other amino acid residues in a protein chain, which would lead to the performance drop of standard machine learning methods on minority class, i.e., the binding-site residues. Therefore, to improve the prediction performance on binding-site residues, we propose in this paper a new boosting algorithm (SSTBoost) that consists of stochastic sensitivity measure-based undersampling method and AdaBoost algorithm. Stochastic sensitivity measure-based undersampling method aims to re-balance the dataset by selecting those samples with the highest probability to be incorrectly labeled, and AdaBoost algorithm aims to improve the performance of base hypotheses by making them to be complementary and be conjunction with each other. Twenty UCI datasets are first used to evaluate the robustness and effectiveness of the SSTBoost. After that, the SSTBoost is tested on twenty-two practical protein–protein interaction sites prediction problems. Experimental results show that the SSTBoost significantly improves the performances against state-of-the-art methods by $\text{57.3}\%$ , $\text{88.2}\%$ , and $\text{78.2}\%$ out of 110 cases in terms of Recall, F-score, and G-mean, respectively. This shows its potential to handle other bioinformatic applications in near future.

In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network (DNN) model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.

The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.

Artificial tubular molecular pockets bearing polar functionalities on their inner surface are useful model systems for understanding the mechanisms of protein-ligand interactions in living systems. We herein report a pillar[5]arene-derived molecular tube, [P4-(OH)BPO], whose endo conformational isomer endo-[P4-(OH)BPO] possesses an inwardly pointing hydrogen-bond (H-bond) donor (OH) in its deep cavity and a strong H-bond acceptor (C═O) on its predominantly hydrophobic inner surface, rendering it a perfect protein binding pocket mimetic. A fragment-based drug design model was established using endo-[P4-(OH)BPO] and a library of various shape-complementary fragment ligands (1-38). On the basis of the binding affinity data for "fragment-pocket" complexes G⊂endo-[P4-(OH)BPO] (G = 1-38), two rationally designed "lead molecules" (39 and 40) were identified as being able to enhance binding affinity significantly by forming H-bonds with both the donor and acceptor of endo-[P4-(OH)BPO]. The described work opens new avenues for developing pillar[n]arene-derived protein binding pocket-mimetic systems for studies of protein-ligand interactions and mechanisms of enzymatic reactions.

Several applications of graphene oxide (GO) have been established over the years, and it has the potential to be used as a biomedical material. Studying the effect of GO on protein-ligand (small molecules/drugs) complex systems are vital as the mechanisms involved are not well understood. The interaction of GO on the protein-ligand binding is also vital for the preparation of an effective drug carrier in the bloodstream. In this work, we have tried to explore in details the effect of GO on the interaction between a hydrophilic molecule, namely, 7-(N,N'-diethylamino) coumarin-3-carboxylic acid (7-DCA) with human serum albumin (HSA) by employing multispectroscopic, microscopic, calorimetric, and molecular docking studies. We find out that protein-ligand complexes were placed on the GO surface, and GO gives stability to the protein-ligand complex via hydrogen bonding, electrostatic interactions, hydrophobic interactions, and so forth. Due to the presence of a large surface area in GO, it offers a hydrophobic environment, and as a result, the emission maxima of 7-DCA in the ternary complex is more blue-shifted, and the average lifetime becomes higher compared to the binary system. Circular dichroism spectral studies give information about the conformational changes of HSA in the absence and presence of GO when it forms complex with 7-DCA. The fluorescence lifetime imaging study shows the presence of the 7-DCA/HSA complex on the GO sheet. Molecular docking simulation shows that the closest distance between 7-DCA and HSA is 11.9 A, and the protein interacted with the ligand through hydrogen bonding, hydrophobic interaction, and so forth.

10 pags, 3 figs, 2 tabs. -- Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s00249-021-01518-4., Equilibrium binding constants (Kb) between chemical compounds and target proteins or between interacting proteins provide a quantitative understanding of biological interaction mechanisms. Reported uncertainties of measured experimental parameters are critical for decision-making in many scientific areas, e.g., in lead compound discovery processes and in comparing computational predictions with experimental results. Uncertainties in measured Kb values are commonly represented by a symmetric normal distribution, often quoted in terms of the experimental value plus-minus the standard deviation. However, in general, the distributions of measured Kb (and equivalent Kd) values and the corresponding free energy change ΔGb are all asymmetric to varying degree. Here, using a simulation approach, we illustrate the effect of asymmetric Kb distributions within the realm of isothermal titration calorimetry (ITC) experiments. Further we illustrate the known, but perhaps not widely appreciated, fact that when distributions of any of Kb, Kd and ΔGb are transformed into each other, their degree of asymmetry is changed. Consequently, we recommend that a more accurate way of expressing the uncertainties of Kb, Kd, and ΔGb values is to consistently report 95% confidence intervals, in line with other authors' suggestions. The ways to obtain such error ranges are discussed in detail and exemplified for a binding reaction obtained by ITC., This research was funded by grant no. S-LLT-20-2 from the Research Council of Lithuania (DM). MB and MJM aknowledge Fundacao para a Ciencia e Tecnologia (FCT), Portugal, for the financial support to Projects UIDB/00081/2020 and UIDB/00313/2020, respectively. The authors also acknowledge the COST action ARBRE-MOBIEU CA15126 supported by COST (European Cooperation in Science and Technology).

JACS Au 1(4), 484-500 (2021). doi:10.1021/jacsau.0c00094, Molecular recognition is fundamental to biological signaling. A central question is how individual interactions between molecular moieties affect the thermodynamics of ligand binding to proteins and how these effects might propagate beyond the immediate neighborhood of the binding site. Here, we investigate this question by introducing minor changes in ligand structure and characterizing the effects of these on ligand affinity to the carbohydrate recognition domain of galectin-3, using a combination of isothermal titration calorimetry, X-ray crystallography, NMR relaxation, and computational approaches including molecular dynamics (MD) simulations and grid inhomogeneous solvation theory (GIST). We studied a congeneric series of ligands with a fluorophenyl-triazole moiety, where the fluorine substituent varies between the ortho, meta, and para positions (denoted O, M, and P). The M and P ligands have similar affinities, whereas the O ligand has 3-fold lower affinity, reflecting differences in binding enthalpy and entropy. The results reveal surprising differences in conformational and solvation entropy among the three complexes. NMR backbone order parameters show that the O-bound protein has reduced conformational entropy compared to the M and P complexes. By contrast, the bound ligand is more flexible in the O complex, as determined by $^{19}$F NMR relaxation, ensemble-refined X-ray diffraction data, and MD simulations. Furthermore, GIST calculations indicate that the O-bound complex has less unfavorable solvation entropy compared to the other two complexes. Thus, the results indicate compensatory effects from ligand conformational entropy and water entropy, on the one hand, and protein conformational entropy, on the other hand. Taken together, these different contributions amount to entropy���entropy compensation among the system components involved in ligand binding to a target protein., Published by ACS Publications, Washington, DC

The assessment of protein–ligand interactions is critical at early stage of drug discovery. Computational approaches for efficiently predicting such interactions facilitate drug development. Recently, methods based on deep learning, including structure- and sequence-based models, have achieved impressive performance on several different datasets. However, their application still suffers from a generalizability issue because of insufficient data, especially for structure based models, as well as a heterogeneity problem because of different label measurements and varying proteins across datasets. Here, we present an interpretable multi-task model to evaluate protein–ligand interaction (Multi-PLI). The model can run classification (binding or not) and regression (binding affinity) tasks concurrently by unifying different datasets. The model outperforms traditional docking and machine learning on both binary classification and regression tasks and achieves competitive results compared with some structure-based deep learning methods, even with the same training set size. Furthermore, combined with the proposed occlusion algorithm, the model can predict the important amino acids of proteins that are crucial for binding, thus providing a biological interpretation. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-021-00510-6.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.

Many of the recently developed methods to study the shape of molecules permit one conformation of one molecule to be compared to another conformation of the same or a different molecule: a relative shape. Other methods provide an absolute description of the shape of a conformation that does not rely on comparisons or overlays. Any absolute description of shape can be used to generate a self-organizing map (shape map) that places all molecular shapes relative to one another; in the studies reported here, the shape fingerprint and ultrafast shape recognition methods are employed to create such maps. In the shape maps, molecules that are near one another have similar shapes, and the maps for the 102 targets in the DUD-E set have been generated. By examining the distribution of actives in comparison with their physical-property-matched decoys, we show that the proteins of key-in-lock type (relatively rigid receptor and ligand) can be distinguished from those that are more of a hand-in-glove type (more flexible receptor and ligand). These are linked to known differences in protein flexibility and binding-site size.

It is frequently mentioned that QSARs have not generally lived up to expectations, especially in cases where high predictability is expected yet failed to deliver satisfactory results. Even though outliers can provide an increased opportunity in drug discovery research, outliers in SAR and QSAR can contort predictions and affect the accuracy if proper attention is not given. The percentages of outliers in QSARs have not changed appreciably over the last decade. In our previous studies, we suggested two possible sources of outliers in SAR and QSAR. In this paper, we suggest an additional possible source of outliers in QSAR. We presented several literature examples that show one or more water molecules that play a critical role in protein-ligand binding interactions as observed in their crystal structures. These examples illustrate that failing to account for the effects of water molecules in protein-ligand interactions could mislead interpretation and possibly yield outliers in SAR and QSAR. Examples include cases where QSAR, considering the role of water molecules in protein-ligand crystal structures, provided deeper insight into the understanding and interpretation of the developed QSAR.

Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were validated on the PDBbind v2019 core set and CSAR-HiQ sets. The PrtCmm IFP Score outperformed several other models in predicting protein–ligand binding affinities. Besides, conventional ECFPs were simplified to generate new IFPs, which provided consistent but faster predictions. The relationship between the base atom properties of ECFPs and the accuracy of predictions was also investigated. Availability PrtCmm IFP has been implemented in the IFP Score Toolkit on github (https://github.com/debbydanwang/IFPscore). Supplementary information Supplementary data are available at Bioinformatics online.

STING protein (stimulator of interferon genes) plays an important role in the innate immune system. A number of potent compounds regulating its activity have been reported, mostly derivatives of cyclic dinucleotides (CDNs), natural STING agonists. Here, we aim to provide complementary information to large-scale "ligand-profiling" studies by probing the importance of STING-CDN protein-ligand interactions on the protein side. We examined in detail six typical CDNs each in complex with 13 rationally devised mutations in STING: S162A, S162T, Y167F, G230A, R232K, R232H, A233L, A233I, R238K, T263A, T263S, R293Q, and G230A/R293Q. The mutations switch on and off various types of protein-ligand interactions: π-π stacking, hydrogen bonding, ionic pairing, and nonpolar contacts. We correlated experimental data obtained by differential scanning fluorimetry, X-ray crystallography, and isothermal titration calorimetry with theoretical calculations. This enabled us to provide a mechanistic interpretation of the differences in the binding of representative CDNs to STING. We observed that the G230A mutation increased the thermal stability of the protein-ligand complex, indicating an increased level of ligand binding, whereas R238K and Y167F led to a complete loss of stabilization (ligand binding). The effects of the other mutations depended on the type of ligand (CDN) and varied, to some extent. A very good correlation (R2 = 0.6) between the experimental binding affinities and interaction energies computed by quantum chemical methods enabled us to explain the effect of the studied mutations in detail and evaluate specific interactions quantitatively. Our work may inspire development of high-affinity ligands against the common STING haplotypes by targeting the key (sometimes non-intuitive) protein-ligand interactions.

Secreted and membrane-bound members of the immunoglobulin superfamily (IgSF) encompass a large, diverse array of proteins that play central roles in immune response and neural development, and are implicated in diseases ranging from cancer to rheumatoid arthritis. Despite the potential biomedical benefits of understanding IgSF:IgSF cognate receptor-ligand interactions, relatively little about them is known at a molecular level, and experimentally probing all possible receptor-ligand pairs is prohibitively costly. The Protein Ligand Interface Design (ProtLID) algorithm is a computational pharmacophore-based approach to identify cognate receptor-ligand pairs that was recently validated in a pilot study on a small set of IgSF complexes. Although ProtLID has shown a success rate of 61% at identifying at least one cognate ligand for a given receptor, it currently lacks any form of confidence measure that can prioritize individual receptor-ligand predictions to pursue experimentally. In this study, we expanded the application of ProtLID to cover all IgSF complexes with available structural data. In addition, we introduced an approach to estimate the confidence of predictions made by ProtLID based on a statistical analysis of how the ProtLID-constructed pharmacophore matches the structures of candidate ligands. The confidence score combines the physicochemical compatibility, spatial consistency, and mathematical skewness of the distribution of matches throughout a set of candidate ligands. Our results suggest that a subset of cases meeting stringent confidence criteria will always have at least one successful receptor-ligand prediction.

Trimethylsilyl (TMS) groups present outstanding NMR probes of biological macromolecules as they produce intense singlets in 1H NMR spectra near 0 ppm, where few other proton resonances occur. We report a system for genetic encoding of N6-(((trimethylsilyl)methoxy)carbonyl)-l-lysine (TMSK) for site-specific incorporation into proteins. The system is based on pyrrolysyl-tRNA synthetase mutants, which deliver proteins with high yield and purity in vivo and in cell-free protein synthesis. As the TMS signal can readily be identified in 1D 1H NMR spectra of high-molecular weight systems without the need of isotopic labeling, TMSK delivers an excellent site-specific NMR probe for the study of protein structure and function, which is both inexpensive and convenient. We demonstrate the utility of TMSK to detect ligand binding, measure the rate of conformational change, and assess protein dimerization by paramagnetic relaxation enhancement. In addition, we present a system for dual incorporation of two different unnatural amino acids (TMSK and O-tert-butyl-tyrosine) in the same protein in quantities sufficient for NMR spectroscopy. Close proximity of the TMS and tert-butyl groups was readily detected by nuclear Overhauser effects.

Investigation of protein-ligand interactions in physiological conditions is crucial for better understanding of biochemistry because the binding stoichiometry and conformations of complexes in biological processes, such as various types of regulation and transportation, could reveal key pathways in organisms. Nanoelectrospray ionization mass spectrometry is widely used in studies of biological processes and systems biology. However, non-volatile salts in biological fluid may adversely interfere with nanoelectrospray ionization mass spectrometry. In this study, the previously developed method of induced nanoelectrospray ionization was used to facilitate in situ desalting of protein in solutions with high concentrations of non-volatile salts, and direct investigation of protein-ligand interactions for the first time. In situ desalting occurred at the tip of emitters within a short period lasting for a few to tens of milliseconds, enabling the maintenance of nativelike conditions compatible with mass spectrometry measurements. Induced nanoelectrospray ionization was driven by pulsed potential and exhibited microelectrophoresis effect in each spray cycle, which is not observed in conventional nanoelectrospray ionization because the continuous spray procedure is driven by direct current. Microelectrophoresis caused desalting through micron-sized spray emitters (1–20 μm), as confirmed experimentally with proteins in 100 mM NaCl solution. The method developed in this study has been further illustrated as a potential option for fast and direct identification of protein-ligand (small molecules or metal ions) interactions in complex samples. The results of this study demonstrate that the newly developed method may represent a reliable approach for investigations of proteins and protein complexes in biological samples.

Sulfonyl-triazoles are a new class of electrophiles that mediate covalent reaction with tyrosine residues on proteins through sulfur-triazole exchange (SuTEx) chemistry. Recent studies demonstrate the broad utility and tunability of SuTEx chemistry for chemical proteomics and protein ligand discovery. Here, we present a strategy for mapping protein interaction networks of structurally complex binding elements using functionalized SuTEx probes. We show that the triazole leaving group (LG) can serve as a releasable linker for embedding hydrophobic fragments to direct molecular recognition while permitting efficient proteome-wide identification of binding sites in live cells. We synthesized a series of SuTEx probes functionalized with a lipid kinase fragment binder for discovery of ligandable tyrosines residing in catalytic and regulatory domains of protein and metabolic kinases in live cells. We performed competition studies with kinase inhibitors and substrates to demonstrate that probe binding is occurring in an activity-dependent manner. Our functional studies led to discovery of probe-modified sites within the C2 domain that were important for downregulation of protein kinase C-alpha in response to phorbol ester activation. Our proof of concept studies highlight the triazole LG of SuTEx probes as a traceless linker for locating protein binding sites targeted by complex recognition elements in live cells., Sulfonyl-triazole probes modified with a kinase recognition element are developed for live cell activity-based profiling to identify tyrosine sites located in catalytic and regulatory domains that are important for kinase function.

Intrinsically disordered proteins or intrinsically disordered regions (IDPs) have gained much attention in recent years due to their vital roles in biology and prevalence in various human diseases. Although IDPs are perceived as attractive therapeutic targets, rational drug design targeting IDPs remains challenging because of their conformational heterogeneity. Here, we propose a hierarchical computational strategy for IDP drug virtual screening (IDPDVS) and applied it in the discovery of p53 transactivation domain I (TAD1) binding compounds. IDPDVS starts from conformation sampling of the IDP target, then it combines stepwise conformational clustering with druggability evaluation to identify potential ligand binding pockets, followed by multiple docking screening runs and selection of compounds that can bind multi-conformations. p53 is an important tumor suppressor and restoration of its function provides an opportunity to inhibit cancer cell growth. TAD1 locates at the N-terminus of p53 and plays key roles in regulating p53 function. No compounds that directly bind to TAD1 have been reported due to its highly disordered structure. We successfully used IDPDVS to identify two compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells. Our study demonstrates that IDPDVS is an efficient strategy for IDP drug discovery and p53 TAD1 can be directly targeted by small molecules., A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.

Toll-like receptor 4 (TLR4) recognizes various protein ligands; however, the protein-TLR4 binding model is unclear. Here we demonstrate a Crenomytilus grayanus lectin (CGL)-TLR4/MD2 model to show that CGL interacts with a TLR4/myeloid differentiation factor 2 (MD2) complex independently of sugar-binding properties. CGL could suppress lipopolysaccharide-induced immune responses significantly, suggesting that TLR4 itself has potential as a therapeutic target.

SfmD is a heme-dependent enzyme in the biosynthetic pathway of saframycin A. Here, we present a 1.78 A resolution de novo crystal structure of SfmD, which unveils a novel heme cofactor attached to the protein with an unusual HxnHxxxC motif (n ∼ 38). This heme cofactor is unique in two respects. It contains a single thioether bond in a cysteine–vinyl link with Cys317, and the ferric heme has two axial protein ligands, i.e., His274 and His313. We demonstrated that SfmD heme is catalytically active and can utilize dioxygen and ascorbate for a single-oxygen insertion into 3-methyl-L-tyrosine. Catalytic assays using ascorbate derivatives revealed the functional groups of ascorbate essential to its function as a cosubstrate. Abolishing the thioether linkage through mutation of Cys317 resulted in catalytically inactive SfmD variants. EPR and optical data revealed that the heme center undergoes a substantial conformational change with one axial histidine ligand dissociating from the iron ion in response to substrate 3-methyl-L-tyrosine binding or chemical reduction by a reducing agent, such as the cosubstrate ascorbate. The labile axial ligand was identified as His274 through redox-linked structural determinations. Together, identifying an unusual heme cofactor with a previously unknown heme-binding motif for a monooxygenase activity and the structural similarity of SfmD to the members of the heme-based tryptophan dioxygenase superfamily will broaden understanding of heme chemistry.