Site Identification by Ligand Competitive Saturation-Biologics Approach for Structure-Based Protein Charge Prediction.

Protein-based therapeutics typically require high concentrations of the active protein, which can lead to protein aggregation and high solution viscosity. Such solution behaviors can limit the stability, bioavailability, and manufacturability of protein-based therapeutics and are directly influenced by the charge of a protein. Protein charge is a system property affected by its environment, including the buffer composition, pH, and temperature. Thus, the charge calculated by summing the charges of each residue in a protein, as is commonly done in computational methods, may significantly differ from the effective charge of the protein as these calculations do not account for contributions from bound ions. Here, we present an extension of the structure-based approach termed site identification by ligand competitive saturation-biologics (SILCS-Biologics) to predict the effective charge of proteins. The SILCS-Biologics approach was applied on a range of protein targets in different salt environments for which membrane-confined electrophoresis-determined charges were previously reported. SILCS-Biologics maps the 3D distribution and predicted occupancy of ions, buffer molecules, and excipient molecules bound to the protein surface in a given salt environment. Using this information, the effective charge of the protein is predicted such that the concentrations of the ions and the presence of excipients or buffers are accounted for. Additionally, SILCS-Biologics also produces 3D structures of the binding sites of ions on the proteins, which enable further analyses such as the characterization of protein surface charge distribution and dipole moments in different environments. Notable is the capability of the method to account for competition between salts, excipients, and buffers on the calculated electrostatic properties in different protein formulations. Our study demonstrates the ability of the SILCS-Biologics approach to predict the effective charge of proteins and its applicability in uncovering protein-ion interactions and their contributions to protein solubility and function.

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, µex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating µex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced µex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.

Characterization of 5'-methylthioadenosine/S-adenosylhomocysteine nucleosidases from Borrelia burgdorferi: Antibiotic targets for Lyme disease.

BACKGROUND: Borrelia burgdorferi causes Lyme disease, the most common tick-borne illness in the United States. The Center for Disease Control and Prevention estimates that the occurrence of Lyme disease in the U.S. has now reached approximately 300,000 cases annually. Early stage Borrelia burgdorferi infections are generally treatable with oral antibiotics, but late stage disease is more difficult to treat and more likely to lead to post-treatment Lyme disease syndrome. METHODS: Here we examine three unique 5'-methylthioadenosine/S-adenosylhomocysteine (MTA/SAH) nucleosidases (MTNs or MTANs, EC 3.2.2.9) responsible for salvage of adenine and methionine in B. burgdorferi and explore their potential as antibiotic targets to treat Lyme disease. Recombinant Borrelia MTNs were expressed and purified from E. coli. The enzymes were extensively characterized for activity, specificity, and inhibition using a UV spectrophotometric assay. In vitro antibiotic activities of MTN inhibitors were assessed using a bioluminescent BacTiter-Glo™ assay. RESULTS: The three Borrelia MTNs showed unique activities against the native substrates MTA, SAH, and 5'-deoxyadenosine. Analysis of substrate analogs revealed that specific activity rapidly dropped as the length of the 5'-alkylthio substitution increased. Non-hydrolysable nucleoside transition state analogs demonstrated sub-nanomolar enzyme inhibition constants. Lastly, two late stage transition state analogs exerted in vitro IC50 values of 0.3-0.4 µg/mL against cultured B. burgdorferi cells. CONCLUSION: B. burgdorferi is unusual in that it expresses three distinct MTNs (cytoplasmic, membrane bound, and secreted) that are effectively inactivated by nucleoside analogs. GENERAL SIGNIFICANCE: The Borrelia MTNs appear to be promising targets for developing new antibiotics to treat Lyme disease.

ezCADD: A Rapid 2D/3D Visualization-Enabled Web Modeling Environment for Democratizing Computer-Aided Drug Design.

As abundant and user-friendly as computer-aided drug design (CADD) software may seem, there is still a large underserved population of biomedical researchers around the world, particularly those with no computational training and limited research funding. To address this important need and help scientists overcome barriers that impede them from leveraging CADD in their drug discovery work, we have developed ezCADD, a web-based CADD modeling environment that manifests four simple design concepts: easy, quick, user-friendly, and 2D/3D visualization-enabled. In this paper, we describe the features of three fundamental applications that have been implemented in ezCADD: small-molecule docking, protein-protein docking, and binding pocket detection, and their applications in drug design against a pathogenic microbial enzyme as an example. To assess user experience and the effectiveness of our implementation, we introduced ezCADD to first-year pharmacy students as an active learning exercise in the Principles of Drug Action course. The web service robustly handled 95 simultaneous molecular docking jobs. Our survey data showed that among the 95 participating students, 97% completed the molecular docking experiment on their own at least partially without extensive training; 88% considered ezCADD easy and user-friendly; 99-100% agreed that ezCADD enhanced the understanding of drug-receptor structures and recognition; and the student experience in molecular modeling and visualization was significantly improved from zero to a higher level. The student feedback represents the baseline data of user experience from noncomputational researchers. It is demonstrated that in addition to supporting drug discovery research, ezCADD is also an effective tool for promoting science, technology, engineering, and mathematics (STEM) education. More advanced CADD applications are being developed and added to ezCADD, available at http://dxulab.org/software .

Molecular insights into the improved clinical performance of PEGylated interferon therapeutics: a molecular dynamics perspective.

PEGylation is a widely adopted process to covalently attach a polyethylene glycol (PEG) polymer to a protein drug for the purpose of optimizing drug clinical performance. While the outcomes of PEGylation in imparting pharmacological advantages have been examined through experimental studies, the underlying molecular mechanisms remain poorly understood. Using interferon (IFN) as a representative model system, we carried out comparative molecular dynamics (MD) simulations of free PEGx, apo-IFN, and PEGx-IFN (x = 50, 100, 200, 300) to characterize the molecular-level changes in IFN introduced by PEGylation. The simulations yielded molecular evidence directly linked to the improved protein stability, bioavailability, retention time, as well as the decrease in protein bioactivity with PEG conjugates. Our results indicate that there is a tradeoff between the benefits and costs of PEGylation. The optimal PEG chain length used in PEGylation needs to strike a good balance among the competing factors and maximizes the overall therapeutic efficacy of the protein drug. We anticipate the study will have a broad implication for protein drug design and development, and provide a unique computational approach in the context of optimizing PEGylated protein drug conjugates.

MSBIS: A Multi-Step Biomedical Informatics Screening Approach for Identifying Medications that Mitigate the Risks of Metoclopramide-Induced Tardive Dyskinesia.

In 2009 the U.S. Food and Drug Administration (FDA) placed a black box warning on metoclopramide (MCP) due to the increased risks and prevalence of tardive dyskinesia (TD). In this study, we developed a multi-step biomedical informatics screening (MSBIS) approach leveraging publicly available bioactivity and drug safety data to identify concomitant drugs that mitigate the risks of MCP-induced TD. MSBIS includes (1) TargetSearch (http://dxulab.org/software) bioinformatics scoring for drug anticholinergic activity using CHEMBL bioactivity data; (2) unadjusted odds ratio (UOR) scoring for indications of TD-mitigating effects using the FDA Adverse Event Reporting System (FAERS); (3) adjusted odds ratio (AOR) re-scoring by removing the effect of cofounding factors (age, gender, reporting year); (4) logistic regression (LR) coefficient scoring for confirming the best TD-mitigating drug candidates. Drugs with increasing TD protective potential and statistical significance were obtained at each screening step. Fentanyl is identified as the most promising drug against MCP-induced TD (coefficient: -2.68; p-value<0.01). The discovery is supported by clinical reports that patients fully recovered from MCP-induced TD after fentanyl-induced general anesthesia. Loperamide is identified as a potent mitigating drug against a broader range of drug-induced movement disorders through pharmacokinetic modifications. Using drug-induced TD as an example, we demonstrated that MSBIS is an efficient in silico tool for unknown drug-drug interaction detection, drug repurposing, and combination therapy design.

Assessing and predicting drug-induced anticholinergic risks: an integrated computational approach.

BACKGROUND: Anticholinergic (AC) adverse drug events (ADEs) are caused by inhibition of muscarinic receptors as a result of designated or off-target drug-receptor interactions. In practice, AC toxicity is assessed primarily based on clinician experience. The goal of this study was to evaluate a novel concept of integrating big pharmacological and healthcare data to assess clinical AC toxicity risks. METHODS: AC toxicity scores (ATSs) were computed using drug-receptor inhibitions identified through pharmacological data screening. A longitudinal retrospective cohort study using medical claims data was performed to quantify AC clinical risks. ATS was compared with two previously reported toxicity measures. A quantitative structure-activity relationship (QSAR) model was established for rapid assessment and prediction of AC clinical risks. RESULTS: A total of 25 common medications, and 575,228 exposed and unexposed patients were analyzed. Our data indicated that ATS is more consistent with the trend of AC outcomes than other toxicity methods. Incorporating drug pharmacokinetic parameters to ATS yielded a QSAR model with excellent correlation to AC incident rate (R2 = 0.83) and predictive performance (cross validation Q2 = 0.64). Good correlation and predictive performance (R2 = 0.68/Q2 = 0.29) were also obtained for an M2 receptor-specific QSAR model and tachycardia, an M2 receptor-specific ADE. CONCLUSIONS: Albeit using a small medication sample size, our pilot data demonstrated the potential and feasibility of a new computational AC toxicity scoring approach driven by underlying pharmacology and big data analytics. Follow-up work is under way to further develop the ATS scoring approach and clinical toxicity predictive model using a large number of medications and clinical parameters.

Experimental identification and computational characterization of a novel extracellular metalloproteinase produced by Clostridium sordellii.

Clostridium sordellii is a lethal pathogen for both animals and humans. Severe capillary leakage, toxic shock syndrome, and an extreme leukemoid reaction (LR), are hallmark features of C. sordellii infections and contribute to its high mortality rate. Here we report the discovery of a previously unknown and uncharacterized metalloproteinase of C. sordellii (referred as Mcs1) that cleaves human vascular cell adhesion molecule (VCAM)-1 in vitro, an adhesion molecule critical to hematopoietic precursor retention and leukocyte diapedesis. We successfully identified the open reading frame encoding Mcs1 within the ATCC 9714 genome and developed an Δmcs1 mutant strain using the ClosTron mutagenesis technology. No VCAM-1 proteolysis was observed from exotoxins collected from mutant strain cultures. Using advanced protein structural modeling and molecular dynamics simulation techniques, the 3D molecular structure and conformational features of Mcs1 were also characterized. Our data demonstrates that Mcs1 proteolytic activity is controlled by the electrostatic interactions between Glu113 and Arg227 residues and the gating motions within its cleft region. This pilot interdisciplinary investigation provided crucial experimental evidence of the existence of Mcs1 in C. sordellii and molecular insights into its 3D structure and proteolytic activity. These findings have the potential to help advance new therapeutics and diagnostics against deadly C. sordellii infections. Follow-up in vitro and in vivo work is under way to further characterize Mcs1 enzymatic kinetics and its role in C. sordellii pathogenesis.