A generalized knowledge-based discriminatory function for biomolecular interactions

The impact of nucleosome structure on CRISPR/Cas9 fidelity

The clustered regularly interspaced short palindromic repeats (CRISPR) Cas system is a powerful tool that has the potential to become a therapeutic gene editor in the near future. Cas9 is the best studied CRISPR system and has been shown to have problems that restrict its use in therapeutic applications. Chromatin structure is a known impactor of Cas9 targeting and there is a gap in knowledge on Cas9's efficacy when targeting such locations. To quantify at a single base pair resolution how chromatin inhibits on-target gene editing relative to off-target editing of exposed mismatching targets, we developed the gene editor mismatch nucleosome inhibition assay (GEMiNI-seq). GEMiNI-seq utilizes a library of nucleosome sequences to examine all target locations throughout nucleosomes in a single assay. The results from GEMiNI-seq revealed that the location of the protospacer-adjacent motif (PAM) sequence on the nucleosome edge drives the ability for Cas9 to access its target sequence. In addition, Cas9 had a higher affinity for exposed mismatched targets than on-target sequences within a nucleosome. Overall, our results show how chromatin structure impacts the fidelity of Cas9 to potential targets and highlight how targeting sequences with exposed PAMs could limit off-target gene editing, with such considerations improving Cas9 efficacy and resolving current limitations.

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.

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.

Lemon: a framework for rapidly mining structural information from the Protein Data Bank

Motivation

The Protein Data Bank (PDB) currently holds over 140 000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB.

Results

Our solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in <10 min on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python bindings provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features.

Availability and implementation

The Lemon software is available as a C++ header library along with a PyPI package and example functions at https://github.com/chopralab/lemon.

Supplementary information

Supplementary data are available at Bioinformatics online.

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

Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.

Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

Combating Ebola with Repurposed Therapeutics Using the CANDO Platform

Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.

Amyloid-like ribbons of amelogenins in enamel mineralization

Enamel, the outermost layer of teeth, is an acellular mineralized tissue that cannot regenerate; the mature tissue is composed of high aspect ratio apatite nanocrystals organized into rods and inter-rod regions. Amelogenin constitutes 90% of the protein matrix in developing enamel and plays a central role in guiding the hierarchical organization of apatite crystals observed in mature enamel. To date, a convincing link between amelogenin supramolecular structures and mature enamel has yet to be described, in part because the protein matrix is degraded during tissue maturation. Here we show compelling evidence that amelogenin self-assembles into an amyloid-like structure in vitro and in vivo. We show that enamel matrices stain positive for amyloids and we identify a specific region within amelogenin that self-assembles into β-sheets. We propose that amelogenin nanoribbons template the growth of apatite mineral in human enamel. This is a paradigm shift from the current model of enamel development.

Oxido-reductive regulation of vascular remodeling by receptor tyrosine kinase ROS1

Angioplasty and stenting is the primary treatment for flow-limiting atherosclerosis; however, this strategy is limited by pathological vascular remodeling. Using a systems approach, we identified a role for the network hub gene glutathione peroxidase-1 (GPX1) in pathological remodeling following human blood vessel stenting. Constitutive deletion of Gpx1 in atherosclerotic mice recapitulated this phenotype of increased vascular smooth muscle cell (VSMC) proliferation and plaque formation. In an independent patient cohort, gene variant pair analysis identified an interaction of GPX1 with the orphan protooncogene receptor tyrosine kinase ROS1. A meta-analysis of the only genome-wide association studies of human neointima-induced in-stent stenosis confirmed the association of the ROS1 variant with pathological remodeling. Decreased GPX1 expression in atherosclerotic mice led to reductive stress via a time-dependent increase in glutathione, corresponding to phosphorylation of the ROS1 kinase activation site Y2274. Loss of GPX1 function was associated with both oxidative and reductive stress, the latter driving ROS1 activity via s-glutathiolation of critical residues of the ROS1 tyrosine phosphatase SHP-2. ROS1 inhibition with crizotinib and deglutathiolation of SHP-2 abolished GPX1-mediated increases in VSMC proliferation while leaving endothelialization intact. Our results indicate that GPX1-dependent alterations in oxido-reductive stress promote ROS1 activation and mediate vascular remodeling.

CANDO and the infinite drug discovery frontier

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 'high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

A structural-based strategy for recognition of transcription factor binding sites

Scanning through genomes for potential transcription factor binding sites (TFBSs) is becoming increasingly important in this post-genomic era. The position weight matrix (PWM) is the standard representation of TFBSs utilized when scanning through sequences for potential binding sites. However, many transcription factor (TF) motifs are short and highly degenerate, and methods utilizing PWMs to scan for sites are plagued by false positives. Furthermore, many important TFs do not have well-characterized PWMs, making identification of potential binding sites even more difficult. One approach to the identification of sites for these TFs has been to use the 3D structure of the TF to predict the DNA structure around the TF and then to generate a PWM from the predicted 3D complex structure. However, this approach is dependent on the similarity of the predicted structure to the native structure. We introduce here a novel approach to identify TFBSs utilizing structure information that can be applied to TFs without characterized PWMs, as long as a 3D complex structure (TF/DNA) exists. This approach utilizes an energy function that is uniquely trained on each structure. Our approach leads to increased prediction accuracy and robustness compared with those using a more general energy function. The software is freely available upon request.

Distal effect of amino acid substitutions in CYP2C9 polymorphic variants causes differences in interatomic interactions against (S)-warfarin

Cytochrome P450 2C9 (CYP2C9) is crucial in excretion of commonly prescribed drugs. However, changes in metabolic activity caused by CYP2C9 polymorphisms inevitably result in adverse drug effects. CYP2C9*2 and *3 are prevalent in Caucasian populations whereas CYP2C9*13 is remarkable in Asian populations. Single amino acid substitutions caused by these mutations are located outside catalytic cavity but affect kinetic activities of mutants compared to wild-type enzyme. To relate distal effects of these mutations and defective drug metabolisms, simulations of CYP2C9 binding to anti-coagulant (S)-warfarin were performed as a system model. Representative (S)-warfarin-bound forms of wild-type and mutants were sorted and assessed through knowledge-based scoring function. Interatomic interactions towards (S)-warfarin were predicted to be less favorable in mutant structures in correlation with larger distance between hydroxylation site of (S)-warfarin and reactive oxyferryl heme than wild-type structure. Using computational approach could delineate complication of CYP polymorphism in management of drug therapy.

A comparison of neighbor search algorithms for large rigid molecules

Fast determination of neighboring atoms is an essential step in molecular dynamics simulations or Monte Carlo computations, and there exists a variety of algorithms to efficiently compute neighbor lists. However, most of these algorithms are general, and not specifically designed for a given type of application. As a result, although their average performance is satisfactory, they might be inappropriate in some specific application domains. In this article, we study the case of detecting neighbors between large rigid molecules, which has applications in, e.g., rigid body molecular docking, Monte Carlo simulations of molecular self-assembly or diffusion, and rigid body molecular dynamics simulations. More precisely, we compare the traditional grid-based algorithm to a series of hierarchy-based algorithms that use bounding volumes to rapidly eliminate large groups of irrelevant pairs of atoms during the neighbor search. We compare the performance of these algorithms based on several parameters: the size of the molecules, the average distance between them, the cutoff distance, as well as the type of bounding volume used in the culling hierarchy (AABB, OBB, wrapped, or layered spheres). We demonstrate that for relatively large systems (> 100,000 atoms) the algorithm based on the hierarchy of wrapped spheres shows the best results and the traditional grid-based algorithm gives the worst timings. For small systems, however, the grid-based algorithm and the one based on the wrapped sphere hierarchy are beneficial.

Potentials of mean force for protein structure prediction vindicated, formalized and generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances – so-called “potentials of mean force” (PMFs) – have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state – a necessary component of these potentials – is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities “reference ratio distributions” deriving from the application of the “reference ratio method.” This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.