A Deep-Learning Proteomic-Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication

The Dynamic Equilibrium Between (AlOMe)_<i>n</i> Cages and (AlOMe)_<i>n</i>·(AlMe₃)_<i>m</i> Nanotubes in Methylaluminoxane (MAO): A First-Principles Investigation

Species likely to be present in methylaluminoxane (MAO) are studied via dispersion-corrected DFT, which we show is able to accurately predict thermochemical parameters for the dimerization of trimethylaluminum (TMA). Both cage-like, (AlOMe)_{<i>n</i>,<i>c</i>}, and TMA-bound nanotubes, (AlOMe)_{<i>n</i>,<i>t</i>}·(AlMe₃)_<i>m</i>, are found to be important components of MAO. The most stable structures have aluminum/oxygen atoms in environments whose average hybridization approaches sp³/sp². The (AlOMe)_{<i>n</i>,<i>t</i>}·(AlMe₃)_<i>m</i> isomers with the lowest free energies possess Al−μ-Me–Al bonds. At 298 K a novel <i>T</i>_<i>d</i>-(AlOMe)_16,c oligomer is one of the most stable structures among the six stoichiometries with the lowest free energies: (AlOMe)_20,<i>c</i>·(AlMe₃)₂, <i>T</i>_<i>d</i>-(AlOMe)_16,<i>c</i>, (AlOMe)_18,<i>c</i>, (AlOMe)_20,<i>c</i>·(AlMe₃), (AlOMe)_10,<i>t</i>·(AlMe₃)₄, and (AlOMe)_20,<i>c</i>. As the temperature rises, the abundance of (AlOMe)_{<i>n</i>,<i>t</i>}·(AlMe₃)_<i>m</i> decreases, and that of (AlOMe)_{<i>n</i>,<i>c</i>} increases. Because the former are expected to be precursors for the active species in polymerization, this may in part be the reason why the cocatalytic activity of MAO decreases at higher temperatures

Shotgun Drug Repurposing Biotechnology to Tackle Epidemics and Pandemics

In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future. <br /

Building Chemical Intuition About Physicochemical Properties of C8-Per-/Poly-fluoroalkyl Carboxylic Acids Through Computational Means

We have predicted acid dissociation constants (pKa), octanol-water partition coefficients (KOW), and DPMC lipid membrane-water partition coefficients (Klipid-w) of 150 different 8-carbon containing poly-/per-fluoroalkyl carboxylic acids (C8-PFCAs) utilizing COMSO-RS theory. Different trends associated with functionalization, degree of fluorination, degree of saturation, degree of chlorination, and branching are discussed based upon the predicted values for the partition coefficients. In general, functionalization closest to the carboxylic head group had the greatest impact on the value of the predicted physicochemical properties

Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell Lung Cancer

Pharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions using large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK expression is strongly correlated with decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. These results indicate that our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales

Proteomic Network Analysis of Bronchoalveolar Lavage Fluid in Ex-Smokers to Discover Implicated Protein Targets and Novel Drug Treatments for Chronic Obstructive Pulmonary Disease

Bronchoalveolar lavage of the epithelial lining fluid (BALF) can sample the profound changes in the airway lumen milieu prevalent in chronic obstructive pulmonary disease (COPD). We compared the BALF proteome of ex-smokers with moderate COPD who are not in exacerbation status to non-smoking healthy control subjects and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins for the treatment of COPD. Proteomic profiles of BALF were obtained from (1) never-smoker control subjects with normal lung function (n = 10) or (2) individuals with stable moderate (GOLD stage 2, FEV 50-80% predicted, FEV/FVC \u3c 0.70) COPD who were ex-smokers for at least 1 year (n = 10). After identifying potential crucial hub proteins, drug-proteome interaction signatures were ranked by the computational analysis of novel drug opportunities (CANDO) platform for multiscale therapeutic discovery to identify potentially repurposable drugs. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that most likely ameliorate inflammatory processes. Proteomic network analysis demonstrated that 233 of the \u3e1800 proteins identified in the BALF were significantly differentially expressed in COPD versus control. Functional annotation of the differentially expressed proteins was used to detail canonical pathways containing the differential expressed proteins. Topological network analysis demonstrated that four putative proteins act as central node proteins in COPD. The drugs with the most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The drugs identified using CANDO were subsequently analyzed using a knowledge-based technique to determine an optimal two-drug combination that had the most appropriate effect on the central node proteins. Network analysis of the BALF proteome identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach

Determination of the Structures of Molecularly Imprinted Polymers and Xerogels Using an Automated Stochastic Approach

An automated stochastic docking program with a graphical user interface, RANDOMDOCK (RD), has been developed to aid the development of molecularly imprinted polymers and xerogels. RD supports computations with ab initio and semiempirical quantum chemistry programs. The RD algorithms have been tested by searching for the most stable geometries of a varying number of methacrylic acid molecules interacting with nicotinamide. The optimal structures found are either as stable or more stable than those previously proposed for this molecularly imprinted polymer, illustrating that RD is capable of identifying the lowest-energy structures out of a potentially vast number of possible configurations. RD was subsequently applied to determine the most favorable binding sites between silane molecules and tetracycline (TC) as well as TC analogues. Hydrogen bonding between the templates and a silane is an important determinant of stability. Dispersion interactions are also sizable, sometimes dominant, especially between the largest silane and TC analogues not possessing a site readily available for hydrogen bonding. We highlight the importance of exploring the full intermolecular potential energy landscape when studying systems which may not afford highly specific interactions

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