RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase.

The main proteinase (Mpro) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of Mpro has both scientific and practical significance. In this study, we explored the binding characteristics and inhibiting process of Mpro activity by two recently reported allosteric inhibitors, pelitinib and AT7519 which were obtained by the X-ray screening experiments, to probe the allosteric mechanism via molecular dynamic (MD) simulations. We found that pelitinib and AT7519 can stably bind to Mpro far from the active site. The binding affinity is estimated to be -24.37 ± 4.14 and - 26.96 ± 4.05 kcal/mol for pelitinib and AT7519, respectively, which is considerably stable compared with orthosteric drugs. Furthermore, the strong binding caused clear changes in the catalytic site of Mpro, thus decreasing the substrate accessibility. The community network analysis also validated that pelitinib and AT7519 strengthened intra- and inter-domain communication of Mpro dimer, resulting in a rigid Mpro, which could negatively impact substrate binding. In summary, our findings provide the detailed working mechanism for the two experimentally observed allosteric sites of Mpro. These allosteric sites greatly enhance the 'druggability' of Mpro and represent attractive targets for the development of new Mpro inhibitors.

Concentration-Driven Evolution of Adaptive Artificial Ion Channels or Nanopores with Specific Anticancer Activities.

In nature, ceramides are a class of sphingolipids possessing a unique ability to self-assemble into protein-permeable channels with intriguing concentration-dependent adaptive channel cavities. However, within the realm of artificial ion channels, this interesting phenomenon is scarcely represented. Herein, we report on a novel class of adaptive artificial channels, Pn-TPPs, based on PEGylated cholic acids bearing triphenylphosphonium (TPP) groups as anion binding motifs. Interestingly, the molecules self-assemble into chloride ion channels at low concentrations while transforming into small molecule-permeable nanopores at high concentrations. Moreover, the TPP groups endow the molecules with mitochondria-targeting properties, enabling them to selectively drill holes on the mitochondrial membrane of cancer cells and subsequently trigger the caspase 9 apoptotic pathway. The anticancer efficacies of Pn-TPPs correlate with their abilities to form nanopores. Significantly, the most active ensembles formed by P5-TPP exhibits impressive anticancer activity against human liver cancer cells, with an IC50 value of 3.8 µM. While demonstrating similar anticancer performance to doxorubicin, P5-TPP exhibits a selectivity index surpassing that of doxorubicin by a factor of 16.8.

Structures, Interactions and Activity of the N-Terminal Truncated Variants of Antimicrobial Peptide Thanatin.

Gram-negative bacteria are intrinsically more resistant to many frontline antibiotics, which is attributed to the permeability barrier of the outer membrane, drug efflux pumps and porins. Consequently, discovery of new small molecules antibiotics to kill drug-resistant Gram-negative bacteria presents a significant challenge. Thanatin, a 21-residue insect-derived antimicrobial peptide, is known for its potent activity against Enterobacter Gram-negative bacteria, including drug-resistant strains. Here, we investigated a 15-residue N-terminal truncated analog PM15 (P1IIYCNRRTGKCQRM15) of thanatin to determine modes of action and antibacterial activity. PM15 and the P1 to Y and A substituted variants PM15Y and PM15A delineated interactions and permeabilization of the LPS-outer membrane. In antibacterial assays, PM15 and the analogs showed growth inhibition of strains of Gram-negative bacteria that is largely dependent on the composition of the culture media. Atomic-resolution structures of PM15 and PM15Y in free solution and in complex with LPS micelle exhibited persistent ß-hairpin structures similar to native thanatin. However, in complex with LPS, the structures of peptides are more compact, with extensive packing interactions among residues across the two anti-parallel strands of the ß-hairpin. The docked complex of PM15/LPS revealed a parallel orientation of the peptide that may be sustained by potential ionic and van der Waals interactions with the lipid A moiety of LPS. Further, PM15 and PM15Y bind to LptAm, a monomeric functional variant of LptA, the periplasmic component of the seven-protein (A-G) complex involved in LPS transport. Taken together, the structures, target interactions and antibacterial effect of PM15 presented in the current study could be useful in designing thanatin-based peptide analogs.

zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15.

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions. This module enables the extraction of protein-ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic-level protein-ligand feature encoding and fusion to output refined ligand poses and ligand per-atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation R = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF-2016 protein-ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein-ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side-chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein-ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

Membrane Potential-Dependent Uptake of Cationic Oligoimidazolium Mediates Bacterial DNA Damage and Death.

The treatment of bacterial infections is becoming increasingly challenging with the emergence of antimicrobial resistance. Thus, the development of antimicrobials with novel mechanisms of action is much needed. Previously, we designed several cationic main-chain imidazolium compounds and identified the polyimidazolium PIM1 as a potent antibacterial against a wide panel of multidrug-resistant nosocomial pathogens, and it had relatively low toxicity against mammalian epithelial cells. However, little is known about the mechanism of action of PIM1. Using an oligomeric version of PIM1 with precisely six repeating units (OIM1-6) to control for consistency, we showed that OIM1-6 relies on an intact membrane potential for entry into the bacterial cytoplasm, as resistant mutants to OIM1-6 have mutations in their electron transport chains. These mutants demonstrate reduced uptake of the compound, which can be circumvented through the addition of a sub-MIC dose of colistin. Once taken up intracellularly, OIM1-6 exerts double-stranded DNA breaks. Its potency and ability to kill represents a promising class of drugs that can be combined with membrane-penetrating drugs to potentiate activity and hedge against the rise of resistant mutants. In summary, we discovered that cationic antimicrobial OIM1-6 exhibits an antimicrobial property that is dissimilar to the conventional cationic antimicrobial compounds. Its killing mechanism does not involve membrane disruption but instead depends on the membrane potential for uptake into bacterial cells so that it can exert its antibacterial effect intracellularly.

Gradient Nanoconfinement Facilitates Binding of Transcriptional Factor NF-κB to Histone- and Protamine-DNA Complexes.

Mechanically induced chromosome reorganization plays important roles in transcriptional regulation. However, the interplay between chromosome reorganization and transcription activities is complicated, such that it is difficult to decipher the regulatory effects of intranuclear geometrical cues. Here, we simplify the system by introducing DNA, packaging proteins (i.e., histone and protamine), and transcription factor NF-κB into a well-defined fluidic chip with changing spatical confinement ranging from 100 to 500 nm. It is uncovered that strong nanoconfinement suppresses higher-order folding of histone- and protamine-DNA complexes, the fracture of which exposes buried DNA segments and causes increased quantities of NF-κB binding to the DNA chain. Overall, these results reveal a pathway of how intranuclear geometrical cues alter the open/closed state of a DNA-protein complex and therefore affect transcription activities: i.e., NF-κB binding.

Release of frustration drives corneal amyloid disaggregation by brain chaperone.

TGFBI-related corneal dystrophy (CD) is characterized by the accumulation of insoluble protein deposits in the corneal tissues, eventually leading to progressive corneal opacity. Here we show that ATP-independent amyloid-ß chaperone L-PGDS can effectively disaggregate corneal amyloids in surgically excised human cornea of TGFBI-CD patients and release trapped amyloid hallmark proteins. Since the mechanism of amyloid disassembly by ATP-independent chaperones is unknown, we reconstructed atomic models of the amyloids self-assembled from TGFBIp-derived peptides and their complex with L-PGDS using cryo-EM and NMR. We show that L-PGDS specifically recognizes structurally frustrated regions in the amyloids and releases those frustrations. The released free energy increases the chaperone's binding affinity to amyloids, resulting in local restructuring and breakage of amyloids to protofibrils. Our mechanistic model provides insights into the alternative source of energy utilized by ATP-independent disaggregases and highlights the possibility of using these chaperones as treatment strategies for different types of amyloid-related diseases.

Modulation of Mechanical Properties of Short Bioinspired Peptide Materials by Single Amino-Acid Mutations.

The occurrence of modular peptide repeats in load-bearing (structural) proteins is common in nature, with distinctive peptide sequences that often remain conserved across different phylogenetic lineages. These highly conserved peptide sequences endow specific mechanical properties to the material, such as toughness or elasticity. Here, using bioinformatic tools and phylogenetic analysis, we have identified the GX8 peptide with the sequence GLYGGYGX (where X can be any residue) in a wide range of organisms. By simple mutation of the X residue, we demonstrate that GX8 can be self-assembled into various supramolecular structures, exhibiting vastly different physicochemical and viscoelastic properties, from liquid-like coacervate microdroplets to hydrogels to stiff solid materials. A combination of spectroscopic, electron microscopy, mechanical, and molecular dynamics studies is employed to obtain insights into molecular scale interactions driving self-assembly of GX8 peptides, underscoring that π-π stacking and hydrophobic interactions are the drivers of peptide self-assembly, whereas the X residue determines the extent of hydrogen bonding that regulates the macroscopic mechanical response. This study highlights the ability of single amino-acid polymorphism to tune the supramolecular assembly and bulk material properties of GX8 peptides, enabling us to cover a broad range of potential biomedical applications such as hydrogels for tissue engineering or coacervates for drug delivery.

Genomic, transcriptomic, and metabolomic analysis of Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites.

Plants accumulate a vast array of secondary metabolites, which constitute a natural resource for pharmaceuticals. Oldenlandia corymbosa belongs to the Rubiaceae family, and has been used in traditional medicine to treat different diseases, including cancer. However, the active metabolites of the plant, their biosynthetic pathway and mode of action in cancer are unknown. To fill these gaps, we exposed this plant to eight different stress conditions and combined different omics data capturing gene expression, metabolic profiles, and anti-cancer activity. Our results show that O. corymbosa extracts are active against breast cancer cell lines and that ursolic acid is responsible for this activity. Moreover, we assembled a high-quality genome and uncovered two genes involved in the biosynthesis of ursolic acid. Finally, we also revealed that ursolic acid causes mitotic catastrophe in cancer cells and identified three high-confidence protein binding targets by Cellular Thermal Shift Assay (CETSA) and reverse docking. Altogether, these results constitute a valuable resource to further characterize the biosynthesis of active metabolites in the Oldenlandia group, while the mode of action of ursolic acid will allow us to further develop this valuable compound.

Revealing the biotoxicity of phosphorene oxide nanosheets based on the villin headpiece.

Phosphorene, a novel member of the two-dimensional nanomaterial family, has demonstrated great potential in biomedical applications, such as photothermal therapy, drug delivery and antibacterial. However, phosphorene is unstable and easily oxidized in an aerobic environment. In this paper, using larger-scale molecular dynamics simulations, we investigated the disruption of phosphorene oxide (PO) to the structure of a model protein, villin headpiece subdomain (HP35). It shows that the disruption of PO nanosheets to the protein structure is enhanced with increasing oxidation concentration of PO, while PO's oxidation mode has very little effect on the PO-HP35 interaction. PO with a low oxidation concentration has certain biocompatibility to HP35. Oxygen atoms filling into the groove region in the puckered surface of phosphorene enhance the dispersion interaction between phosphorene and HP35, which enhances the disruption of phosphorene to the structure of HP35. Compared with the dispersion interaction, the electrostatic interaction between PO and the protein has a negligible effect on the structural damage of HP35. These findings might shed light on the biological toxicity of PO nanosheets and would be helpful for future potential biomedical applications of PO nanosheets, such as nanodrugs and antibacterial agents.

A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.

Hypoxia deactivates epigenetic feedbacks via enzyme-derived clicking proteolysis-targeting chimeras.

Epigenetic mediation through bromodomain and extraterminal (BET) proteins have progressively translated protein imbalance into effective cancer treatment. Perturbation of druggable BET proteins through proteolysis-targeting chimeras (PROTACs) has recently contributed to the discovery of effective therapeutics. Unfortunately, precise and microenvironment-activatable BET protein degradation content with promising tumor selectivity and pharmacological suitability remains elusive. Here, we present an enzyme-derived clicking PROTACs (ENCTACs) capable of orthogonally cross-linking two disparate small-molecule warhead ligands that recognize BET bromodomain-containing protein 4 (BRD4) protein and E3 ligase within tumors only upon hypoxia-induced activation of nitroreductase enzyme. This localized formation of heterobifunctional degraders promotes specific down-regulation of BRD4, which subsequently alters expression of epigenetic targets and, therefore, allows precise modulation of hypoxic signaling in live cells, zebrafish, and living mice with solid tumors. Our activation-feedback system demonstrates compelling superiorities and may enable the PROTAC technology with more flexible practicality and druggable potency for precision medicine in the near future.

SARS-CoV-2 accessory protein 7b forms homotetramers in detergent.

A global pandemic is underway caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 genome, like its predecessor SARS-CoV, contains open reading frames that encode accessory proteins involved in virus-host interactions active during infection and which likely contribute to pathogenesis. One of these accessory proteins is 7b, with only 44 (SARS-CoV) and 43 (SARS-CoV-2) residues. It has one predicted transmembrane domain fully conserved, which suggests a functional role, whereas most variability is contained in the predicted cytoplasmic C-terminus. In SARS-CoV, 7b protein is expressed in infected cells, and the transmembrane domain was necessary and sufficient for Golgi localization. Also, anti-p7b antibodies have been found in the sera of SARS-CoV convalescent patients. In the present study, we have investigated the hypothesis that SARS-2 7b protein forms oligomers with ion channel activity. We show that in both SARS viruses 7b is almost completely α-helical and has a single transmembrane domain. In SDS, 7b forms various oligomers, from monomers to tetramers, but only monomers when exposed to reductants. Combination of SDS gel electrophoresis and analytical ultracentrifugation (AUC) in both equilibrium and velocity modes suggests a dimer-tetramer equilibrium, but a monomer-dimer-tetramer equilibrium in the presence of reductant. This data suggests that although disulfide-linked dimers may be present, they are not essential to form tetramers. Inclusion of pentamers or higher oligomers in the SARS-2 7b model were detrimental to fit quality. Preliminary models of this association was generated with AlphaFold2, and two alternative models were exposed to a molecular dynamics simulation in presence of a model lipid membrane. However, neither of the two models provided any evident pathway for ions. To confirm this, SARS-2 p7b was studied using Planar Bilayer Electrophysiology. Addition of p7b to model membranes produced occasional membrane permeabilization, but this was not consistent with bona fide ion channels made of a tetrameric assembly of α-helices.

Cholesterol-stabilized membrane-active nanopores with anticancer activities.

Cholesterol-enhanced pore formation is one evolutionary means cholesterol-free bacterial cells utilize to specifically target cholesterol-rich eukaryotic cells, thus escaping the toxicity these membrane-lytic pores might have brought onto themselves. Here, we present a class of artificial cholesterol-dependent nanopores, manifesting nanopore formation sensitivity, up-regulated by cholesterol of up to 50 mol% (relative to the lipid molecules). The high modularity in the amphiphilic molecular backbone enables a facile tuning of pore size and consequently channel activity. Possessing a nano-sized cavity of ~ 1.6 nm in diameter, our most active channel Ch-C1 can transport nanometer-sized molecules as large as 5(6)-carboxyfluorescein and display potent anticancer activity (IC50 = 3.8 µM) toward human hepatocellular carcinomas, with high selectivity index values of 12.5 and >130 against normal human liver and kidney cells, respectively.

In Silico Repurposed Drugs against Monkeypox Virus.

Monkeypox is an emerging epidemic of concern. The disease is caused by the monkeypox virus and an increasing global incidence with a 2022 outbreak that has spread to Europe amid the COVID-19 pandemic. The new outbreak is associated with novel, previously undiscovered mutations and variants. Currently, the US Food and Drug Administration (FDA) approved poxvirus treatment involves the use of tecovirimat. However, there is otherwise limited pharmacopoeia and research interest in monkeypox. In this study, virtual screening and molecular dynamics were employed to explore the potential repurposing of multiple drugs previously approved by the FDA or other jurisdictions for other applications. Several drugs are predicted to tightly bind to viral proteins, which are crucial in viral replication, including molecules which show high potential for binding the monkeypox D13L capsid protein, whose inhibition has previously been demonstrated to suppress viral replication.

Crystal structure of the Rubella virus protease reveals a unique papain-like protease fold.

Rubella, a viral disease characterized by a red skin rash, is well controlled because of an effective vaccine, but outbreaks are still occurring in the absence of available antiviral treatments. The Rubella virus (RUBV) papain-like protease (RubPro) is crucial for RUBV replication, cleaving the nonstructural polyprotein p200 into two multifunctional proteins, p150 and p90. This protease could represent a potential drug target, but structural and mechanistic details important for the inhibition of this enzyme are unclear. Here, we report a novel crystal structure of RubPro at a resolution of 1.64 Å. The RubPro adopts a unique papain-like protease fold, with a similar catalytic core to that of proteases from Severe acute respiratory syndrome coronavirus 2 and foot-and-mouth disease virus while having a distinctive N-terminal fingers domain. RubPro has well-conserved sequence motifs that are also found in its newly discovered Rubivirus relatives. In addition, we show that the RubPro construct has protease activity in trans against a construct of RUBV protease-helicase and fluorogenic peptides. A protease-helicase construct, exogenously expressed in Escherichia coli, was also cleaved at the p150-p90 cleavage junction, demonstrating protease activity of the protease-helicase protein. We also demonstrate that RubPro possesses deubiquitylation activity, suggesting a potential role of RubPro in modulating the host's innate immune responses. We anticipate that these structural and functional insights of RubPro will advance our current understanding of its function and help facilitate more structure-based research into the RUBV replication machinery, in hopes of developing antiviral therapeutics against RUBV.

Genome instability is associated with ethnic differences between Asians and Europeans in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the deadliest cancer types with diverse etiological factors across the world. Although large scale genomic studies have been conducted in different countries, integrative analysis of HCC genomes and ethnic comparison across cohorts are lacking. Methods: We first integrated genomes of 1,349 HCC patients from five large cohorts across the world and applied multiple statistical methods in identifying driver genes. Subsequently, we systematically compared HCC genomes and transcriptomes between Asians and Europeans using the TCGA cohort. Results: We identified 29 novel candidate driver genes, many of which are infrequent tumor suppressors driving late-stage tumor progression. When we systematically compared ethnic differences in the genomic landscape between Asian and European HCCs using the TCGA cohort (n = 348), we found little differences in driver frequencies. Through multi-modal integrative analysis, we found higher genomic instability in Asians together with a collection of molecular events ranging from tumor mutation burden (TMB), copy number alterations as well as transcriptomic subtypes segregating distinctively between two ethnic backgrounds. Strikingly, we identified an Asian specific transcriptomic subtype with multiple ethnically enriched genomic alterations, in particular chromosome 16 deletion, leading to a clinically aggressive RNA subgroup unique to Asians. Integrating multi-modal information, we found that survival models predict patient prognosis much better in Asians than in Europeans, demonstrating a higher potential for precision medicine applications in Asia. Conclusion: For the first time, we have uncovered an unprecedented amount of genomic differences segregating distinctively across ethnicities in HCC and highlighted the importance of differential disease biology and management in HCC across ethnic backgrounds.

Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein-ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.