CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Novel coumarin-6-sulfonamide-chalcone hybrids as glutathione transferase P1-1 inhibitors

Multidrug resistance (MDR) mechanisms in cancer cells are greatly influenced by glutathione transferase P1-1 (hGSTP1-1). The use of synthetic or natural compounds as hGSTP1-1 inhibitors is considered an effective approach to overcome MDR. Nine compounds consisting of coumarin-6-sulfonamide linked to chalcone derivatives were synthesized and evaluated for their ability to inhibit hGSTP1-1. Among the synthetic derivatives, compounds 5g, 5f, and 5a displayed the most potent inhibitory effect, with IC50 values of 12.2 ± 0.5 μΜ, 12.7 ± 0.7 and 16.3 ± 0.6, respectively. Kinetic inhibition analysis of the most potent molecule, 5g, showed that it behaves as a mixed-type inhibitor of the target enzyme. An in vitro cytotoxicity assessment of 5a, 5f, and 5g against the human prostate cancer cell lines DU-145 and PC3, as well as the breast cancer cell line MCF-7, demonstrated that compound 5g exhibited the most pronounced cytotoxic effect on all tested cell lines. Molecular docking studies were performed to predict the structural and molecular determinants of 5g, 5f, and 5a binding to hGSTP1-1. In agreement with the experimental data, the results revealed that 5g exhibited the lowest docking score among the three studied inhibitors as a consequence of shape complementarity, governed by van der Waals, hydrogen bonds and a π-π stacking interaction. These findings suggest that coumarin-chalcone hybrids offer new perspectives for the development of safe and efficient natural product-based sensitizers that can target hGSTP1-1 for anticancer purposes.

Alkynyl nicotinamides show antileukemic activity in drug-resistant acute myeloid leukemia

Activating mutations of FLT3 contribute to deregulated hematopoietic stem and progenitor cell (HSC/Ps) growth and survival in patients with acute myeloid leukemia (AML), leading to poor overall survival. AML patients treated with investigational drugs targeting mutant FLT3, including Quizartinib and Crenolanib, develop resistance to these drugs. Development of resistance is largely due to acquisition of cooccurring mutations and activation of additional survival pathways, as well as emergence of additional FLT3 mutations. Despite the high prevalence of FLT3 mutations and their clinical significance in AML, there are few targeted therapeutic options available. We have identified 2 novel nicotinamide-based FLT3 inhibitors (HSN608 and HSN748) that target FLT3 mutations at subnanomolar concentrations and are potently effective against drug-resistant secondary mutations of FLT3. These compounds show antileukemic activity against FLT3ITD in drug-resistant AML, relapsed/refractory AML, and in AML bearing a combination of epigenetic mutations of TET2 along with FLT3ITD. We demonstrate that HSN748 outperformed the FDA-approved FLT3 inhibitor Gilteritinib in terms of inhibitory activity against FLT3ITD in vivo.

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction

Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the understanding of disease pathogenesis and lead to more effective drug targets. Additionally, it can aid in determining drug parameters, ensuring proper absorption, distribution, and metabolism within the body. Due to incomplete feature representation or the model's inadequate adaptation to protein-ligand complexes, the existing methodologies suffer from suboptimal predictive accuracy. To address these pitfalls, in this study, we designed a new deep learning method based on transformer and GCN. We first utilized the transformer network to grasp crucial information of the original protein sequences within the smile sequences and connected them to prevent falling into a local optimum. Furthermore, a series of dilation convolutions are performed to obtain the pocket features and smile features, subsequently subjected to graphical convolution to optimize the connections. The combined representations are fed into the proposed model for classification prediction. Experiments conducted on various protein-ligand binding prediction methods prove the effectiveness of our proposed method. It is expected that the PfgPDI can contribute to drug prediction and accelerate the development of new drugs, while also serving as a valuable partner for drug testing and Research and Development engineers.

Modeling Molecular Mechanisms of Pirfenidone Interaction with Kinases

Scar formation is a process that occurs due to increased collagen deposition and uncontrolled inflammation. Previous studies have demonstrated that Pirfenidone (Pf), an FDA approved anti-inflammatory and antifibrotic drug can reduce inflammation in vivo as well as regulate activation of LPS-stimulated neutrophils. However, the molecular level mechanism of Pf's action is not well understood. Here, we used neural networks to identify new targets and molecular modeling methods to investigate the Pf's action pathways at the molecular level that are related to its ability to reduce both the inflammatory and remodeling phases of the wound healing process. Out of all the potential targets identified, both molecular docking and molecular dynamics results suggest that Pf has a noteworthy binding preference towards the active conformation of the p38 mitogen activated protein kinase-14 (MAPK14) and it is potentially a type I inhibitor-like molecule. In addition to p38 MAPK (MAPK14), additional potential targets of Pf include AKT1, MAP3K4, MAP2K3, MAP2K6, MSK2, MAP2K2, ERK1, ERK2, and PDK1. We conclude that several proteins/kinases, rather than a single target, are involved in Pf's wound healing ability to regulate signaling, inflammation, and proliferation.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Variability in phenylalanine side chain conformations facilitates broad substrate tolerance of fatty acid binding in cockroach milk proteins

Diploptera punctata, also known as the Pacific beetle cockroach, is a viviparous cockroach that gives birth to live offspring and secretes a highly concentrated mixture of glycosylated proteins as a source of nourishment for developing embryos. These proteins are lipocalins that bind to lipids and crystallize in the gut of the embryo. A structure of milk crystals harvested from the embryos showed that the milk-derived crystals were heterogeneous and made of three proteins (called Lili-Mips). We hypothesized that the isoforms of Lili-Mip would display different affinities for fatty acids due to the ability of the pocket to bind multiple acyl chain lengths. We previously reported the structures of Lili-Mip from crystals grown in vivo and recombinantly expressed Lili-Mip2. These structures are similar, and both bind to several fatty acids. This study explores the specificity and affinity of fatty acid binding to recombinantly expressed Lili-Mip 1, 2 & 3. We show that all isoforms can bind to different fatty acids with similar affinities. We also report the thermostability of Lili-Mip is pH dependent, where stability is highest at acidic pH and declines as the pH increases to physiological levels near 7.0. We show that thermostability is an inherent property of the protein, and glycosylation and ligand binding do not change it significantly. Measuring the pH in the embryo's gut lumen and gut cells suggests that the pH in the gut is acidic and the pH inside the gut cells is closer to neutral pH. In various crystal structures (reported here and previously by us), Phe-98 and Phe-100 occupy multiple conformations in the binding pocket. In our earlier work, we had shown that the loops at the entrance could adapt various conformations to change the size of the binding pocket. Here we show Phe-98 and Phe-100 can reorient to stabilize interactions at the bottom of the cavity-and change the volume of the cavity from 510 Å3 to 337 Å3. Together they facilitate the binding of fatty acids of different acyl chain lengths.

Monocarbonyl Curcumin Analogues as Potent Inhibitors against Human Glutathione Transferase P1-1

The isoenzyme of human glutathione transferase P1-1 (hGSTP1-1) is involved in multi-drug resistance (MDR) mechanisms in numerous cancer cell lines. In the present study, the inhibition potency of two curcuminoids and eleven monocarbonyl curcumin analogues against hGSTP1-1 was investigated. Demethoxycurcumin (Curcumin II) and three of the monocarbonyl curcumin analogues exhibited the highest inhibitory activity towards hGSTP1-1 with IC₅₀ values ranging between 5.45 ± 1.08 and 37.72 ± 1.02 μM. Kinetic inhibition studies of the most potent inhibitors demonstrated that they function as non-competitive/mixed-type inhibitors. These compounds were also evaluated for their toxicity against the prostate cancer cells DU-145. Interestingly, the strongest hGSTP1-1 inhibitor, (DM96), exhibited the highest cytotoxicity with an IC₅₀ of 8.60 ± 1.07 μΜ, while the IC₅₀ values of the rest of the compounds ranged between 44.59-48.52 μΜ. Structural analysis employing molecular docking, molecular dynamics (MD) simulations, and binding-free-energy calculations was performed to study the four most potent curcumin analogues as hGSTP1-1 inhibitors. According to the obtained computational results, DM96 exhibited the lowest binding free energy, which is in agreement with the experimental data. All studied curcumin analogues were found to form hydrophobic interactions with the residue Gln52, as well as hydrogen bonds with the nearby residues Gln65 and Asn67. Additional hydrophobic interactions with the residues Phe9 and Val36 as well as π-π stacking interaction with Phe9 contributed to the superior inhibitory activity of DM96. The van der Waals component through shape complementarity was found to play the most important role in DM96-inhibitory activity. Overall, our results revealed that the monocarbonyl curcumin derivative DM96 acts as a strong hGSTP1-1 inhibitor, exerts high prostate cancer cell cytotoxicity, and may, therefore, be exploited for the suppression and chemosensitization of cancer cells. This study provides new insights into the development of safe and effective GST-targeted cancer chemosensitizers.

Inverse Molecular Docking Elucidating the Anticarcinogenic Potential of the Hop Natural Product Xanthohumol and Its Metabolites

Natural products from plants exert a promising potential to act as antioxidants, antimicrobials, anti-inflammatory, and anticarcinogenic agents. Xanthohumol, a natural compound from hops, is indeed known for its anticarcinogenic properties. Xanthohumol is converted into three metabolites: isoxanthohumol (non-enzymatically) as well as 8- and 6-prenylnaringenin (enzymatically). An inverse molecular docking approach was applied to xanthohumol and its three metabolites to discern their potential protein targets. The aim of our study was to disclose the potential protein targets of xanthohumol and its metabolites in order to expound on the potential anticarcinogenic mechanisms of xanthohumol based on the found target proteins. The investigated compounds were docked into the predicted binding sites of all human protein structures from the Protein Data Bank, and the best docking poses were examined. Top scoring human protein targets with successfully docked compounds were identified, and their experimental connection with the anticarcinogenic function or cancer was investigated. The obtained results were carefully checked against the existing experimental findings from the scientific literature as well as further validated using retrospective metrics. More than half of the human protein targets of xanthohumol with the highest docking scores have already been connected with the anticarcinogenic function, and four of them (including two important representatives of the matrix metalloproteinase family, MMP-2 and MMP-9) also have a known experimental correlation with xanthohumol. Another important protein target is acyl-protein thioesterase 2, to which xanthohumol, isoxanthohumol, and 6-prenylnaringenin were successfully docked with the lowest docking scores. Moreover, the results for the metabolites show that their most promising protein targets are connected with the anticarcinogenic function as well. We firmly believe that our study can help to elucidate the anticarcinogenic mechanisms of xanthohumol and its metabolites as after consumption, all four compounds can be simultaneously present in the organism.

ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison

The protein data bank (PDB) is a rich source of protein ligand structures, but ligands are not explicitly used in current docking algorithms. We have developed ProBiS-Dock, a docking algorithm complementary to the ProBiS-Dock Database (J. Chem. Inf. Model. 2021, 61, 4097-4107) that treats small molecules and proteins as fully flexible entities and allows conformational changes in both after ligand binding. A new scoring function is described that consists of a binding site-specific scoring function (ProBiS-Score) and a general statistical scoring function. ProBiS-Dock enables rapid docking of small molecules to proteins and has been successfully validated in silico against standard benchmarks. It enables rapid search for new active ligands by leveraging existing knowledge in the PDB. The potential of the software for drug development has been confirmed in vitro by the discovery of new inhibitors of human indoleamine 2,3-dioxygenase 1, an enzyme that is an attractive target for cancer therapy and catalyzes the first rate-determining step of l-tryptophan metabolism via the kynurenine pathway. The software is freely available to academic users at http://insilab.org/probisdock.

Drug repurposing in silico screening platforms

Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.

Evaluating the performance of drug-repurposing technologies

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

Repurposing of Drugs for SARS-CoV-2 Using Inverse Docking Fingerprints

Severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 is a virus that belongs to the Coronaviridae family. This group of viruses commonly causes colds but possesses a tremendous pathogenic potential. In humans, an outbreak of SARS caused by the SARS-CoV virus was first reported in 2003, followed by 2012 when the Middle East respiratory syndrome coronavirus (MERS-CoV) led to an outbreak of Middle East respiratory syndrome (MERS). Moreover, COVID-19 represents a serious socioeconomic and global health problem that has already claimed more than four million lives. To date, there are only a handful of therapeutic options to combat this disease, and only a single direct-acting antiviral, the conditionally approved remdesivir. Since there is an urgent need for active drugs against SARS-CoV-2, the strategy of drug repurposing represents one of the fastest ways to achieve this goal. An in silico drug repurposing study using two methods was conducted. A structure-based virtual screening of the FDA-approved drug database on SARS-CoV-2 main protease was performed, and the 11 highest-scoring compounds with known 3CLpro activity were identified while the methodology was used to report further 11 potential and completely novel 3CLpro inhibitors. Then, inverse molecular docking was performed on the entire viral protein database as well as on the Coronaviridae family protein subset to examine the hit compounds in detail. Instead of target fishing, inverse docking fingerprints were generated for each hit compound as well as for the five most frequently reported and direct-acting repurposed drugs that served as controls. In this way, the target-hitting space was examined and compared and we can support the further biological evaluation of all 11 newly reported hits on SARS-CoV-2 3CLpro as well as recommend further in-depth studies on antihelminthic class member compounds. The authors acknowledge the general usefulness of this approach for a full-fledged inverse docking fingerprint screening in the future.

Mechanistic Insights into Biological Activities of Polyphenolic Compounds from Rosemary Obtained by Inverse Molecular Docking

Rosemary (Rosmarinus officinalis L.) represents a medicinal plant known for its various health-promoting properties. Its extracts and essential oils exhibit antioxidative, anti-inflammatory, anticarcinogenic, and antimicrobial activities. The main compounds responsible for these effects are the diterpenes carnosic acid, carnosol, and rosmanol, as well as the phenolic acid ester rosmarinic acid. However, surprisingly little is known about the molecular mechanisms responsible for the pharmacological activities of rosemary and its compounds. To discern these mechanisms, we performed a large-scale inverse molecular docking study to identify their potential protein targets. Listed compounds were separately docked into predicted binding sites of all non-redundant holo proteins from the Protein Data Bank and those with the top scores were further examined. We focused on proteins directly related to human health, including human and mammalian proteins as well as proteins from pathogenic bacteria, viruses, and parasites. The observed interactions of rosemary compounds indeed confirm the beforementioned activities, whereas we also identified their potential for anticoagulant and antiparasitic actions. The obtained results were carefully checked against the existing experimental findings from the scientific literature as well as further validated using both redocking procedures and retrospective metrics.

Exact Maximum Clique Algorithm for Different Graph Types Using Machine Learning

Finding a maximum clique is important in research areas such as computational chemistry, social network analysis, and bioinformatics. It is possible to compare the maximum clique size between protein graphs to determine their similarity and function. In this paper, improvements based on machine learning (ML) are added to a dynamic algorithm for finding the maximum clique in a protein graph, Maximum Clique Dynamic (MaxCliqueDyn; short: MCQD). This algorithm was published in 2007 and has been widely used in bioinformatics since then. It uses an empirically determined parameter, Tlimit, that determines the algorithm’s flow. We have extended the MCQD algorithm with an initial phase of a machine learning-based prediction of the Tlimit parameter that is best suited for each input graph. Such adaptability to graph types based on state-of-the-art machine learning is a novel approach that has not been used in most graph-theoretic algorithms. We show empirically that the resulting new algorithm MCQD-ML improves search speed on certain types of graphs, in particular molecular docking graphs used in drug design where they determine energetically favorable conformations of small molecules in a protein binding site. In such cases, the speed-up is twofold.

A fluorescence-based, gain-of-signal, live cell system to evaluate SARS-CoV-2 main protease inhibition

The likelihood of continued circulation of COVID-19 and its variants, and novel coronaviruses due to future zoonotic transmissions, combined with the current paucity of coronavirus antivirals, emphasize the need for improved screening in developing effective antivirals for the treatment of infection by SARS-CoV-2 (CoV2) and other coronaviruses. Here we report the development of a live-cell based assay for evaluating the intracellular function of the critical, highly-conserved CoV2 target, the Main 3C-like protease (Mpro). This assay is based on expression of native wild-type mature CoV2 Mpro, the function of which is quantitatively evaluated in living cells through cleavage of a biosensor leading to loss of fluorescence. Evaluation does not require cell harvesting, allowing for multiple measurements from the same cells facilitating quantification of Mpro inhibition, as well as recovery of function upon removal of inhibitory drugs. The pan-coronavirus Mpro inhibitor, GC376, was utilized in this assay and effective inhibition of intracellular CoV2 Mpro was found to be consistent with levels required to inhibit CoV2 infection of human lung cells. We demonstrate that GC376 is an effective inhibitor of intracellular CoV2 Mpro at low micromolar levels, while other predicted Mpro inhibitors, bepridil and alverine, are not. Results indicate this system can provide a highly effective high-throughput coronavirus Mpro screening system.

A synthetic small molecule stalls pre-mRNA splicing by promoting an early-stage U2AF2-RNA complex

Dysregulated pre-mRNA splicing is an emerging Achilles heel of cancers and myelodysplasias. To expand the currently limited portfolio of small-molecule drug leads, we screened for chemical modulators of the U2AF complex, which nucleates spliceosome assembly and is mutated in myelodysplasias. A hit compound specifically enhances RNA binding by a U2AF2 subunit. Remarkably, the compound inhibits splicing of representative substrates and stalls spliceosome assembly at the stage of U2AF function. Computational docking, together with structure-guided mutagenesis, indicates that the compound bridges the tandem U2AF2 RNA recognition motifs via hydrophobic and electrostatic moieties. Cells expressing a cancer-associated U2AF1 mutant are preferentially killed by treatment with the compound. Altogether, our results highlight the potential of trapping early spliceosome assembly as an effective pharmacological means to manipulate pre-mRNA splicing. By extension, we suggest that stabilizing assembly intermediates may offer a useful approach for small-molecule inhibition of macromolecular machines.

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

DeepDTAF: a deep learning method to predict protein-ligand binding affinity

Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many computational methods have been proposed to predict binding affinity, most of which usually require protein 3D structures that are not often available. Therefore, new methods that can fully take advantage of sequence-level features are greatly needed to predict protein-ligand binding affinity and accelerate the drug discovery process. We developed a novel deep learning approach, named DeepDTAF, to predict the protein-ligand binding affinity. DeepDTAF was constructed by integrating local and global contextual features. More specifically, the protein-binding pocket, which possesses some special properties for directly binding the ligand, was firstly used as the local input feature for protein-ligand binding affinity prediction. Furthermore, dilated convolution was used to capture multiscale long-range interactions. We compared DeepDTAF with the recent state-of-art methods and analyzed the effectiveness of different parts of our model, the significant accuracy improvement showed that DeepDTAF was a reliable tool for affinity prediction. The resource codes and data are available at https: //github.com/KailiWang1/DeepDTAF.

Insight into Inhibitory Mechanism of PDE4D by Dietary Polyphenols Using Molecular Dynamics Simulations and Free Energy Calculations

Phosphodiesterase 4 (PDE4), mainly present in immune, epithelial, and brain cells, represents a family of key enzymes for the degradation of cyclic adenosine monophosphate (cAMP), which modulates inflammatory response. In recent years, the inhibition of PDE4 has been proven to be an effective therapeutic strategy for the treatment of neurological disorders. PDE4D constitutes a high-interest therapeutic target primarily for the treatment of Alzheimer's disease, as it is highly involved in neuroinflammation, learning ability, and memory dysfunctions. In the present study, a thorough computational investigation consisting of molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations based on the linear response approximation (LRA) method was performed to study dietary polyphenols as potential PDE4D inhibitors. The obtained results revealed that curcumin, 6-gingerol, capsaicin, and resveratrol represent potential PDE4D inhibitors; however, the predicted binding free energies of 6-gingerol, capsaicin, and resveratrol were less negative than in the case of curcumin, which exhibited the highest inhibitory potency in comparison with a positive control rolipram. Our results also revealed that the electrostatic component through hydrogen bonding represents the main driving force for the binding and inhibitory activity of curcumin, 6-gingerol, and resveratrol, while the van der Waals component through shape complementarity plays the most important role in capsaicin's inhibitory activity. All investigated compounds form hydrophobic interactions with residues Gln376 and Asn602 as well as hydrogen bonds with nearby residues Asp438, Met439, and Ser440. The binding mode of the studied natural compounds is consequently very similar; however, it significantly differs from the binding of known PDE4 inhibitors. The uncovered molecular inhibitory mechanisms of four investigated natural polyphenols, curcumin, 6-gingerol, capsaicin, and resveratrol, form the basis for the design of novel PDE4D inhibitors for the treatment of Alzheimer's disease with a potentially wider therapeutic window and fewer adverse side effects.

Evaluating the potency of Sulawesi propolis compounds as ACE-2 inhibitors through molecular docking for COVID-19 drug discovery preliminary study

Coronavirus disease (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Up to date, there has been no specific cure to treat the disease. Indonesia is one of the countries that is still fighting to control virus transmission. Yet, at the same time, Indonesia has a rich biodiversity of natural medicinal products that potentially become an alternative cure. Thus, this study examined the potency of a natural medicinal product, Sulawesi propolis compounds produced by Tetragonula sapiens, inhibiting angiotensin-converting activity enzyme-2 (ACE-2), a receptor of SARS-CoV-2 in the human body. In this study, molecular docking was done to analyze the docking scores as the representation of binding affinity and the interaction profiles of propolis compounds toward ACE-2. The results illustrated that by considering the docking score and the presence of interaction with targeted sites, five compounds, namely glyasperin A, broussoflavonol F, sulabiroins A, (2S)-5,7-dihydroxy-4'-methoxy-8-prenylflavanone and isorhamnetin are potential to inhibit the binding of ACE-2 and SARS-CoV-2, with the docking score of -10.8, -9.9, -9.5, -9.3 and -9.2 kcal/mol respectively. The docking scores are considered to be more favorable compared to MLN-4760 as a potent inhibitor.

Mechanistic Insights into Side Effects of Troglitazone and Rosiglitazone Using a Novel Inverse Molecular Docking Protocol

Thiazolidinediones form drugs that treat insulin resistance in type 2 diabetes mellitus. Troglitazone represents the first drug from this family, which was removed from use by the FDA due to its hepatotoxicity. As an alternative, rosiglitazone was developed, but it was under the careful watch of FDA for a long time due to suspicion, that it causes cardiovascular diseases, such as heart failure and stroke. We applied a novel inverse molecular docking protocol to discern the potential protein targets of both drugs. Troglitazone and rosiglitazone were docked into predicted binding sites of >67,000 protein structures from the Protein Data Bank and examined. Several new potential protein targets with successfully docked troglitazone and rosiglitazone were identified. The focus was devoted to human proteins so that existing or new potential side effects could be explained or proposed. Certain targets of troglitazone such as 3-oxo-5-beta-steroid 4-dehydrogenase, neutrophil collagenase, stromelysin-1, and VLCAD were pinpointed, which could explain its hepatoxicity, with additional ones indicating that its application could lead to the treatment/development of cancer. Results for rosiglitazone discerned its interaction with members of the matrix metalloproteinase family, which could lead to cancer and neurodegenerative disorders. The concerning cardiovascular side effects of rosiglitazone could also be explained. We firmly believe that our results deepen the mechanistic understanding of the side effects of both drugs, and potentially with further development and research maybe even help to minimize them. On the other hand, the novel inverse molecular docking protocol on the other hand carries the potential to develop into a standard tool to predict possible cross-interactions of drug candidates potentially leading to adverse side effects.

Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug-Protein-Disease Data

Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking data sets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly create benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to the protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking data sets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 min to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify this to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking data sets in virtual screening. The DUBS package is available on GitHub at https://github.com/chopralab/lemon/tree/master/dubs.

Rational Design, Synthesis, Characterization and Evaluation of Iodinated 4,4'-Bipyridines as New Transthyretin Fibrillogenesis Inhibitors

The 3,3',5,5'-tetrachloro-2-iodo-4,4'-bipyridine structure is proposed as a novel chemical scaffold for the design of new transthyretin (TTR) fibrillogenesis inhibitors. In the frame of a proof-of-principle exploration, four chiral 3,3',5,5'-tetrachloro-2-iodo-2'-substituted-4,4'- bipyridines were rationally designed and prepared from a simple trihalopyridine in three steps, including a Cu-catalysed Finkelstein reaction to introduce iodine atoms on the heteroaromatic scaffold, and a Pd-catalysed coupling reaction to install the 2'-substituent. The corresponding racemates, along with other five chiral 4,4'-bipyridines containing halogens as substituents, were enantioseparated by high-performance liquid chromatography in order to obtain pure enantiomer pairs. All stereoisomers were tested against the amyloid fibril formation (FF) of wild type (WT)-TTR and two mutant variants, V30M and Y78F, in acid mediated aggregation experiments. Among the 4,4'-bipyridine derivatives, interesting inhibition activity was obtained for both enantiomers of the 3,3',5,5'-tetrachloro-2'-(4-hydroxyphenyl)-2-iodo-4,4'-bipyridine. In silico docking studies were carried out in order to explore possible binding modes of the 4,4'-bipyridine derivatives into the TTR. The gained results point out the importance of the right combination of H-bond sites and the presence of iodine as halogen-bond donor. Both experimental and theoretical evidences pave the way for the utilization of the iodinated 4,4'-bipyridine core as template to design new promising inhibitors of TTR amyloidogenesis.