Actin-binding protein profilin1 is an important determinant of cellular phosphoinositide control.

Membrane polyphosphoinositides (PPIs) are lipid-signaling molecules that undergo metabolic turnover and influence a diverse range of cellular functions. PPIs regulate the activity and/or spatial localization of a number of actin-binding proteins (ABPs) through direct interactions; however, it is much less clear whether ABPs could also be an integral part in regulating PPI signaling. In this study, we show that ABP profilin1 (Pfn1) is an important molecular determinant of the cellular content of PI(4,5)P2 (the most abundant PPI in cells). In growth factor (EGF) stimulation setting, Pfn1 depletion does not impact PI(4,5)P2 hydrolysis but enhances plasma membrane (PM) enrichment of PPIs that are produced downstream of activated PI3-kinase, including PI(3,4,5)P3 and PI(3,4)P2, the latter consistent with increased PM recruitment of SH2-containing inositol 5' phosphatase (SHIP2) (a key enzyme for PI(3,4)P2 biosynthesis). Although Pfn1 binds to PPIs in vitro, our data suggest that Pfn1's affinity to PPIs and PM presence in actual cells, if at all, is negligible, suggesting that Pfn1 is unlikely to directly compete with SHIP2 for binding to PM PPIs. Additionally, we provide evidence for Pfn1's interaction with SHIP2 in cells and modulation of this interaction upon EGF stimulation, raising an alternative possibility of Pfn1 binding as a potential restrictive mechanism for PM recruitment of SHIP2. In conclusion, our findings challenge the dogma of Pfn1's binding to PM by PPI interaction, uncover a previously unrecognized role of Pfn1 in PI(4,5)P2 homeostasis and provide a new mechanistic avenue of how an ABP could potentially impact PI3K signaling byproducts in cells through lipid phosphatase control.

Deciphering the Role of Fatty Acid-Metabolizing CYP4F11 in Lung Cancer and Its Potential As a Drug Target.

Lung cancer is the leading cause of cancer deaths worldwide. We found that the cytochrome P450 isoform CYP4F11 is significantly overexpressed in patients with lung squamous cell carcinoma. CYP4F11 is a fatty acid ω-hydroxylase and catalyzes the production of the lipid mediator 20-hydroxyeicosatetraenoic acid (20-HETE) from arachidonic acid. 20-HETE promotes cell proliferation and migration in cancer. Inhibition of 20-HETE-generating cytochrome P450 enzymes has been implicated as novel cancer therapy for more than a decade. However, the exact role of CYP4F11 and its potential as drug target for lung cancer therapy has not been established yet. Thus, we performed a transient knockdown of CYP4F11 in the lung cancer cell line NCI-H460. Knockdown of CYP4F11 significantly inhibits lung cancer cell proliferation and migration while the 20-HETE production is significantly reduced. For biochemical characterization of CYP4F11-inhibitor interactions, we generated recombinant human CYP4F11. Spectroscopic ligand binding assays were conducted to evaluate CYP4F11 binding to the unselective CYP4A/F inhibitor HET0016. HET0016 shows high affinity to recombinant CYP4F11 and inhibits CYP4F11-mediated 20-HETE production in vitro with a nanomolar IC 50 Cross evaluation of HET0016 in NCI-H460 cells shows that lung cancer cell proliferation is significantly reduced together with 20-HETE production. However, HET0016 also displays antiproliferative effects that are not 20-HETE mediated. Future studies aim to establish the role of CYP4F11 in lung cancer and the underlying mechanism and investigate the potential of CYP4F11 as a therapeutic target for lung cancer. SIGNIFICANCE STATEMENT: Lung cancer is a deadly cancer with limited treatment options. Cytochrome P450 4F11 (CYP4F11) is significantly upregulated in lung squamous cell carcinoma. Knockdown of CYP4F11 in a lung cancer cell line significantly attenuates cell proliferation and migration with reduced production of the lipid mediator 20-hydroxyeicosatetraenoic acid (20-HETE). Studies with the unselective inhibitor HET0016 show a high inhibitory potency of CYP4F11-mediated 20-HETE production using recombinant enzyme. Overall, our studies demonstrate the potential of targeting CYP4F11 for new transformative lung cancer treatment.

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.

We present a novel and interpretable approach for assessing small-molecule binding using context explanation networks. Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of precalculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each precalculated term to the final affinity prediction, with implications for subsequent lead optimization.

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening.

Deep learning methods that predict protein-ligand binding have recently been used for structure-based virtual screening. Many such models have been trained using protein-ligand complexes with known crystal structures and activities from the PDBBind data set. However, because PDBbind only includes 20K complexes, models typically fail to generalize to new targets, and model performance is on par with models trained with only ligand information. Conversely, the ChEMBL database contains a wealth of chemical activity information but includes no information about binding poses. We introduce BigBind, a data set that maps ChEMBL activity data to proteins from the CrossDocked data set. BigBind comprises 583 K ligand activities and includes 3D structures of the protein binding pockets. Additionally, we augmented the data by adding an equal number of putative inactives for each target. Using this data, we developed Banana (basic neural network for binding affinity), a neural network-based model to classify active from inactive compounds, defined by a 10 µM cutoff. Our model achieved an AUC of 0.72 on BigBind's test set, while a ligand-only model achieved an AUC of 0.59. Furthermore, Banana achieved competitive performance on the LIT-PCBA benchmark (median EF1% 1.81) while running 16,000 times faster than molecular docking with Gnina. We suggest that Banana, as well as other models trained on this data set, will significantly improve the outcomes of prospective virtual screening tasks.

Interpreting forces as deep learning gradients improves quality of predicted protein structures.

Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of a protein structure capable of mapping into our software package, SidechainNet. We demonstrate our method's efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics.

Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences.

We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semiempirical methods are suitable priors for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data with small deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from the limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling and propose an alternative to the widely used "experimental torsion and knowledge distance geometry" (ETKDG) method using quantum torsion-derived distance geometry (QTDG) methods.

Conformer Generation for Structure-Based Drug Design: How Many and How Good?

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.

Open-ComBind: harnessing unlabeled data for improved binding pose prediction.

Determination of the bound pose of a ligand is a critical first step in many in silico drug discovery tasks. Molecular docking is the main tool for the prediction of non-covalent binding of a protein and ligand system. Molecular docking pipelines often only utilize the information of one ligand binding to the protein despite the commonly held hypothesis that different ligands share binding interactions when bound to the same receptor. Here we describe Open-ComBind, an easy-to-use, open-source version of the ComBind molecular docking pipeline that leverages information from multiple ligands without known bound structures to enhance pose selection. We first create distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. These similarity distributions are then combined with a per-ligand docking score to enhance overall pose selection by 5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 9.0%. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at www.github.com/drewnutt/open_combind .

Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network.

The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Relative binding free energy (RBFE, also referred to as ΔΔG) methods allow the estimation of binding free energy changes after small changes to a ligand scaffold. Here, we propose and evaluate a Siamese convolutional neural network (CNN) for the prediction of RBFE between two bound ligands. We show that our multitask loss is able to improve on a previous state-of-the-art Siamese network for RBFE prediction via increased regularization of the latent space. The Siamese network architecture is well suited to the prediction of RBFE in comparison to a standard CNN trained on the same data (Pearson's R of 0.553 and 0.5, respectively). When evaluated on a left-out protein family, our Siamese CNN shows variability in its RBFE predictive performance depending on the protein family being evaluated (Pearson's R ranging from -0.44 to 0.97). RBFE prediction performance can be improved during generalization by injecting only a few examples (few-shot learning) from the evaluation data set during model training.

Disruption of profilin1 function suppresses developmental and pathological retinal neovascularization.

Angiogenesis-mediated neovascularization in the eye is usually associated with visual complications. Pathological angiogenesis is particularly prominent in the retina in the settings of proliferative diabetic retinopathy, in which it can lead to permanent loss of vision. In this study, by bioinformatics analyses, we provide evidence for elevated expression of actin-binding protein PFN1 (profilin1) in the retinal vascular endothelial cells (VECs) of individuals with proliferative diabetic retinopathy, findings further supported by gene expression analyses for PFN1 in experimentally induced abnormal retinal neovascularization in an oxygen-induced retinopathy murine model. We observed that in a conditional knockout mouse model, postnatal deletion of the Pfn1 gene in VECs leads to defects in tip cell activity (marked by impaired filopodial protrusions) and reduced vascular sprouting, resulting in hypovascularization during developmental angiogenesis in the retina. Consistent with these findings, an investigative small molecule compound targeting the PFN1-actin interaction reduced random motility, proliferation, and cord morphogenesis of retinal VECs in vitro and experimentally induced abnormal retinal neovascularization in vivo In summary, these findings provide the first direct in vivo evidence that PFN1 is required for formation of actin-based protrusive structures and developmental angiogenesis in the retina. The proof of concept of susceptibility of abnormal angiogenesis to small molecule intervention of PFN1-actin interaction reported here lays a conceptual foundation for targeting PFN1 as a possible strategy in angiogenesis-dependent retinal diseases.

Actin-binding protein profilin1 promotes aggressiveness of clear-cell renal cell carcinoma cells.

Clear-cell renal cell carcinoma (ccRCC), the most common subtype of renal cancer, has a poor clinical outcome. A hallmark of ccRCC is genetic loss-of-function of VHL (von Hippel-Lindau) that leads to a highly vascularized tumor microenvironment. Although many ccRCC patients initially respond to antiangiogenic therapies, virtually all develop progressive, drug-refractory disease. Given the role of dysregulated expressions of cytoskeletal and cytoskeleton-regulatory proteins in tumor progression, we performed analyses of The Cancer Genome Atlas (TCGA) transcriptome data for different classes of actin-binding proteins to demonstrate that increased mRNA expression of profilin1 (Pfn1), Arp3, cofilin1, Ena/VASP, and CapZ, is an indicator of poor prognosis in ccRCC. Focusing further on Pfn1, we performed immunohistochemistry-based classification of Pfn1 staining in tissue microarrays, which indicated Pfn1 positivity in both tumor and stromal cells; however, the vast majority of ccRCC tumors tend to be Pfn1-positive selectively in stromal cells only. This finding is further supported by evidence for dramatic transcriptional up-regulation of Pfn1 in tumor-associated vascular endothelial cells in the clinical specimens of ccRCC. In vitro studies support the importance of Pfn1 in proliferation and migration of RCC cells and in soluble Pfn1's involvement in vascular endothelial cell tumor cell cross-talk. Furthermore, proof-of-concept studies demonstrate that treatment with a novel computationally designed Pfn1-actin interaction inhibitor identified herein reduces proliferation and migration of RCC cells in vitro and RCC tumor growth in vivo Based on these findings, we propose a potentiating role for Pfn1 in promoting tumor cell aggressiveness in the setting of ccRCC.

SidechainNet: An all-atom protein structure dataset for machine learning.

Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information. We present SidechainNet, a new dataset that directly extends the ProteinNet dataset. SidechainNet includes angle and atomic coordinate information capable of describing all heavy atoms of each protein structure and can be extended by users to include new protein structures as they are released. In this article, we provide background information on the availability of protein structure data and the significance of ProteinNet. Thereafter, we argue for the potentially beneficial inclusion of sidechain information through SidechainNet, describe the process by which we organize SidechainNet, and provide a software package (https://github.com/jonathanking/sidechainnet) for data manipulation and training with machine learning models.

Inhibition of ocular neovascularization by novel anti-angiogenic compound.

Aberrant angiogenesis lies at the heart of a wide range of ocular pathologies such as proliferative diabetic retinopathy, wet age-related macular degeneration and retinopathy of prematurity. This study explores the anti-angiogenic activity of a novel small molecule investigative compound capable of inhibiting profilin1-actin interaction recently identified by our group. We demonstrate that our compound is capable of inhibiting migration, proliferation and angiogenic activity of microvascular endothelial cells in vitro as well as choroidal neovascularization (CNV) ex vivo. In mouse model of laser-injury induced CNV, intravitreal administration of this compound diminishes sub-retinal neovascularization. Finally, our preliminary structure-activity relationship study (SAR) demonstrates that this small molecule compound is amenable to improvement in biological activity through structural modifications.

SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.

While accurate prediction of aqueous solubility remains a challenge in drug discovery, machine learning (ML) approaches have become increasingly popular for this task. For instance, in the Second Challenge to Predict Aqueous Solubility (SC2), all groups utilized machine learning methods in their submissions. We present SolTranNet, a molecule attention transformer to predict aqueous solubility from a molecule's SMILES representation. Atypically, we demonstrate that larger models perform worse at this task, with SolTranNet's final architecture having 3,393 parameters while outperforming linear ML approaches. SolTranNet has a 3-fold scaffold split cross-validation root-mean-square error (RMSE) of 1.459 on AqSolDB and an RMSE of 1.711 on a withheld test set. We also demonstrate that, when used as a classifier to filter out insoluble compounds, SolTranNet achieves a sensitivity of 94.8% on the SC2 data set and is competitive with the other methods submitted to the competition. SolTranNet is distributed via pip, and its source code is available at https://github.com/gnina/SolTranNet.

Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization.

While many machine learning (ML) methods, particularly deep neural networks, have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. The methods were trained on the existing ANI-1 training set, calculated using the ωB97X/6-31G(d) single points at nonequilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and a new libmolgrid-based convolutional neural net, the Colorful CNN, show good performance.

Virtual Screening with Gnina 1.0.

Virtual screening-predicting which compounds within a specified compound library bind to a target molecule, typically a protein-is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications.

We describe libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays of voxelized molecular data. libmolgrid provides functionality for sampling batches of data suited to machine learning workflows, and it also supports temporal and spatial recurrences over that data to facilitate work with convolutional and recurrent neural networks. It was designed for seamless integration with popular deep learning frameworks and features optimized performance by leveraging graphics processing units (GPUs). libmolgrid is a free and open source project (GPLv2) that aims to democratize grid-based modeling in computational chemistry.

Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measuring generalization to new targets, and there does not exist a standard data set of sufficient size to compare performance between models. We present a new data set for structure-based machine learning, the CrossDocked2020 set, with 22.5 million poses of ligands docked into multiple similar binding pockets across the Protein Data Bank, and perform a comprehensive evaluation of grid-based convolutional neural network (CNN) models on this data set. We also demonstrate how the partitioning of the training data and test data can impact the results of models trained with the PDBbind data set, how performance improves by adding more lower-quality training data, and how training with docked poses imparts pose sensitivity to the predicted affinity of a complex. Our best performing model, an ensemble of five densely connected CNNs, achieves a root mean squared error of 1.42 and Pearson R of 0.612 on the affinity prediction task, an AUC of 0.956 at binding pose classification, and a 68.4% accuracy at pose selection on the CrossDocked2020 set. By providing data splits for clustered cross-validation and the raw data for the CrossDocked2020 set, we establish the first standardized data set for training machine learning models to recognize ligands in noncognate target structures while also greatly expanding the number of poses available for training. In order to facilitate community adoption of this data set for benchmarking protein-ligand binding affinity prediction, we provide our models, weights, and the CrossDocked2020 set at https://github.com/gnina/models.

The 3Dmol.js learning environment: a classroom response system for 3D chemical structures.

Classroom response systems are an important tool in many active learning pedagogies. They support real-time feedback on student learning and promote student engagement, even in large classrooms, by allowing instructors to solicit an answer to a question from all students and show the results. Existing classroom response systems are general purpose and not tailored to the specific needs of a chemistry classroom. In particular, it is not easy to deploy molecular representations except as static images. Here we present the 3Dmol.js learning environment, a classroom response system that uses the open source web-based 3Dmol.js JavaScript framework to provide interactive viewing and querying of 3D molecules. 3Dmol.js is available under a BSD 3-clause open source license, and the learning environment features are all available through http://3dmol.csb.pitt.edu/ without any software installation required.

Structure-based virtual screening identifies a small-molecule inhibitor of the profilin 1-actin interaction.

Profilin 1 (Pfn1) is an important regulator of the actin cytoskeleton and plays a vital role in many actin-based cellular processes. Therefore, identification of a small-molecule intervention strategy targeted against the Pfn1-actin interaction could have broad utility in cytoskeletal research and further our understanding of the role of Pfn1 in actin-mediated biological processes. Based on an already resolved Pfn1-actin complex crystal structure, we performed structure-based virtual screening of small-molecule libraries to seek inhibitors of the Pfn1-actin interaction. We identified compounds that match the pharmacophore of the key actin residues of Pfn1-actin interaction and therefore have the potential to act as competitive inhibitors of this interaction. Subsequent biochemical assays identified two candidate compounds with nearly identical structures that can mitigate the effect of Pfn1 on actin polymerization in vitro As a further proof-of-concept test for cellular effects of these compounds, we performed proximity ligation assays in endothelial cells (ECs) to demonstrate compound-induced inhibition of Pfn1-actin interaction. Consistent with the important role of Pfn1 in regulating actin polymerization and various fundamental actin-based cellular activities (migration and proliferation), treatment of these compounds reduced the overall level of cellular filamentous (F) actin, slowed EC migration and proliferation, and inhibited the angiogenic ability of ECs both in vitro and ex vivo In summary, this study provides the first proof of principle of small-molecule-mediated interference with the Pfn1-actin interaction. Our findings may have potential general utility for perturbing actin-mediated cellular activities and biological processes.