G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs. This work addresses two significant research questions. First, do ligand interaction fingerprints provide a substantial advantage over automated methods of binding site selection for classical docking? Second, can the functional status of prospective screening candidates be predicted from ligand interaction fingerprints using a random forest classifier? Ligand interaction fingerprints were found to offer modest advantages in sampling accurate poses, but no substantial advantage in the final set of top-ranked poses after scoring, and, thus, were not used in the generation of the ligand–receptor complexes used to train and test the random forest classifier. A binary classifier which treated agonists, antagonists, and inverse agonists as active and all other ligands as inactive proved highly effective in ligand function prediction in an external test set of GPR31 and TAAR2 candidate ligands with a hit rate of 82.6% actual actives within the set of predicted actives.

Aims Due to antibiotic tolerance of microbes within biofilm, non-antibiotic methods for prevention and treatment of implant-related infections are preferable. The goal of this work is to evaluate a facile loading strategy for medium-chain fatty-acid signaling molecules 2-heptycyclopropane-1-carboxylic acid (2CP), cis-2-decenoic acid (C2DA), and trans-2-decenoic acid, which all act as diffusible signaling factors (DSFs), onto titanium surfaces for comparison of their antimicrobial efficacy. Methods and results Titanium coupons were drop-coated with 0.75 mg of DSF in ethanol and dried. Surface characteristics and the presence of DSF were confirmed with Fourier Transform infrared spectroscopy, x-ray photoelectron spectroscopy, and water contact angle. Antimicrobial assays analyzing biofilm and planktonic Staphylococcus aureus, Escherichia coli, or Candida albicans viability showed that planktonic growth was reduced after 24-h incubation but only sustained through 72 h for S. aureus and C. albicans. Biofilm formation on the titanium coupons was also reduced for all strains at the 24-h time point, but not through 72 h for E. coli. Although & SIM;60% of the loaded DSF was released within the first 2 days, enough remained on the surface after 4 days of elution to significantly inhibit E. coli and C. albicans biofilm. Cytocompatibility evaluations with a fibroblast cell line showed that none of the DSF-loaded groups decreased viability, while C2DA and 2CP increased viability by up to 50%. Conclusions In this study, we found that DSF-loaded titanium coupons can inhibit planktonic microbes and prevent biofilm attachment, without toxicity to mammalian cells.

Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Wound dressings serve to protect tissue from contamination, alleviate pain, and facilitate wound healing. The biopolymer chitosan is an exemplary choice in wound dressing material as it is biocompatible and has intrinsic antibacterial properties. Infection can be further prevented by loading dressings with cis-2-decenoic acid (C2DA), a non-antibiotic antimicrobial agent, as well as bupivacaine (BUP), a local anesthetic that also has antibacterial capabilities. This study utilized a series of assays to elucidate the responses of dermal cells to decanoic anhydride-modified electrospun chitosan membranes (DA-ESCMs) loaded with C2DA and/or BUP. Cytocompatibility studies determined the toxic loading ranges for C2DA, BUP, and combinations, revealing that higher concentrations (0.3 mg of C2DA and 1.0 mg of BUP) significantly decreased the viability of fibroblasts and keratinocytes. These high concentrations also inhibited collagen production by fibroblasts, with lower loading concentrations promoting collagen deposition. These findings provide insight into preliminary cellular responses to DA-ESCMs and can guide future research on their clinical application as wound dressings.

Pharmacophore models are three-dimensional arrangements of molecular features required for biological activity that are used in ligand identification efforts for many biological targets, including G protein-coupled receptors (GPCR). Though GPCR are integral membrane proteins of considerable interest as targets for drug development, many of these receptors lack known ligands or experimentally determined structures necessary for ligand-or structure-based pharmacophore model generation, respectively. Thus, we here present a structure-based pharmacophore modeling approach that uses fragments placed with Multiple Copy Simultaneous Search (MCSS) to generate high-performing pharmacophore models in the context of experimentally determined, as well as modeled GPCR structures. Moreover, we have addressed the oft-neglected topic of pharmacophore model se-lection via development of a cluster-then-predict machine learning workflow. Herein score-based pharmaco-phore models were generated in experimentally determined and modeled structures of 13 class A GPCR and resulted in pharmacophore models exhibiting high enrichment factors when used to search a database containing 569 class A GPCR ligands. In addition, classification of pharmacophore models with the best performing cluster-then-predict logistic regression classifier resulted in positive predictive values (PPV) of 0.88 and 0.76 for selecting high enrichment pharmacophore models from among those generated in experimentally determined and modeled structures, respectively.

Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental screening. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Pharmacophore models are most typically ligand-based and constructed via the identification of structural commonalities between known bioactive ligands. However, structure-based pharmacophore models (requiring only a target protein's structure) provide an alternative to ligand-based pharmacophore models. Although both ligand-based and structure-based pharmacophore models have exhibited success in prior virtual screening studies, most pharmacophore modeling efforts are not applicable to GPCR targets lacking known ligands. Consequently, the development of a purely structure-based pharmacophore modeling protocol for GPCR has become an increasingly attractive approach as the number of publicly available, high-resolution GPCR structures has increased (113 unique GPCR represented as of November 24, 2021). Thus, the aim of this work was to develop a structure-based pharmacophore modeling approach that can generate well-performing pharmacophore models in GPCR crystal structures and homology models. Pharmacophore models were generated in crystal structures and homology models of 13 class A GPCR via automated feature annotation of differing subsets of functional group fragments placed with Multiple Copy Simultaneous Search (MCSS). During the feature annotation process, distance cutoffs were enforced to generate combinations of pharmacophore features that reflect spatial arrangements of interactions typically observed in GPCR pharmacophore models. Resulting pharmacophore models for each target GPCR were used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor (EF) and goodness-of-hit (GH) metrics to assess performance. To identify well-performing pharmacophore models for cases where active ligands are unknown, pharmacophore models were classified using cluster-then-predict logistic regression. As a proof-of principle for this method, pharmacophore models were generated for the orphan GPCR GPR101 to elucidate candidate ligands that may possess activity for GPR101. Application of this method to the set of 13 class A GPCR targets resulted in the generation of pharmacophore models possessing EF scores ≥ 2 in both crystal structures (12 of 13 cases) and homology models (9 of 13 cases). In addition, classification of pharmacophore models with cluster-then-predict logistic regression resulted in positive predictive values (PPV) of at least 0.67 for pharmacophore models generated with selected MCSS fragment subsets. Lastly, implementation of generated pharmacophore models in a virtual screening workflow identified 33 candidate ligands for GPR101.

G protein-coupled receptors (GPCR) are the largest family of cell surface receptors in vertebrates. Their abundance and role in nearly all physiological systems make GPCR the largest protein family targeted for development of pharmaceuticals. Ligand discovery aimed at identification of chemical tools and drug leads is aided by molecular docking simulations that allow critical analysis of the potential interactions between small molecules and proteins in resulting complexes. However, blind assessments of ligand pose quality and affinity prediction have thus far not provided broadly generalizable performance expectations for docking into experimentally-characterized GPCR targets. Likewise, the relative importance of receptor activation state and ligand function differences have also not been systematically assessed. This study compares performance when docking ligands of varied function into varied GPCR activation states in the absence of extensive resampling of the input GPCR structure, and only limited sidechain flexibility after ligand placement. Simulations were performed using 37 experimental structures of 11 Class A GPCR crystallized in multiple activation states (giving rise to 37 self-docking and 68 cross docking simulations). Our results show that one specific subset of cross-docking simulations gave results of similar quality to self-docking. Median ligand RMSD values for top-scored poses were 1.2 Å and 2.0 Å for self-docking and StateMatch/FunctionMatch cross-docking, respectively. The distributions of ligand RMSD values were not statistically different for these two conditions, according to a Kolmogorov-Smirnov test. Therefore, docking performance against GPCR targets can be estimated in advance based on docking target structure activation states, with higher accuracy expected when docking agonists into active state structures and inverse agonists or antagonists into inactive state structures. Receptor conformational sampling in advance of docking or receptor conformational adjustment after docking are more likely to produce substantial improvements for other pairings of receptor activation state and ligand function.

Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental screening. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require a resolved or predictive structure for a protein) have gained more attention as the number of publicly available, high-resolution GPCR structures have increased (113 unique GPCR represented as of November 24, 2021). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR with few known ligands. Pharmacophore models have been generated using the active sites of 8 class A GPCR crystal structures via automated feature annotation of 5 randomly selected functional group fragments in an attempt to sample diverse combinations of pharmacophore features to aid in virtual screening efforts. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. As proof-of-principle for this method, pharmacophore models were also generated within two dopamine receptor 2 (D2) crystal structures to elucidate novel ligands possessing activity for this receptor. Application of this method to the set of 8 class A GPCR generated pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and predictive models (7 of 8 cases), implying that generated pharmacophore models will prove useful in the context of virtual screening. Furthermore, application of this protocol to the D2 receptor resulted in the identification of novel candidate ligands whose activity has been confirmed with two in vitroactivity assays.

Chitosan nanofiber membranes are recognized as functional antimicrobial materials, as they can effectively provide a barrier that guides tissue growth and supports healing. Methods to stabilize nanofibers in aqueous solutions include acylation with fatty acids. Modification with fatty acids that also have antimicrobial and biofilm-resistant properties may be particularly beneficial in tissue regeneration applications. This study investigated the ability to customize the fatty acid attachment by acyl chlorides to include antimicrobial 2-decenoic acid. Synthesis of 2-decenoyl chloride was followed by acylation of electrospun chitosan membranes in pyridine. Physicochemical properties were characterized through scanning electron microscopy, FTIR, contact angle, and thermogravimetric analysis. The ability of membranes to resist biofilm formation by S. aureus and P. aeruginosa was evaluated by direct inoculation. Cytocompatibility was evaluated by adding membranes to cultures of NIH3T3 fibroblast cells. Acylation with chlorides stabilized nanofibers in aqueous media without significant swelling of fibers and increased hydrophobicity of the membranes. Acyl-modified membranes reduced both S. aureus and P.aeruginosa bacterial biofilm formation on membrane while also supporting fibroblast growth. Acylated chitosan membranes may be useful as wound dressings, guided regeneration scaffolds, local drug delivery, or filtration.

Wounds resulting from surgeries, implantation of medical devices, and musculoskeletal trauma result in pain and can also result in infection of damaged tissue. Up to 80% of these infections are due to biofilm formation either on the surface of implanted devices or on surrounding wounded tissue. Bacteria within a biofilm have intrinsic growth and development characteristics that allow them to withstand up to 1,000 times the minimum inhibitory concentration of antibiotics, demonstrating the need for new therapeutics to prevent and treat these infections. Cis-2-decenoic acid (C2DA) is known to disperse preformed biofilms and can prevent biofilm formation entirely for some strains of bacteria. Additionally, local anesthetics like bupivacaine have been shown to have antimicrobial effects against multiple bacterial strains. This study sought to evaluate hexanoic acid-treated electrospun chitosan membranes (HA-ESCM) as wound dressings that release C2DA and bupivacaine to simultaneously prevent infection and alleviate pain associated with musculoskeletal trauma. Release profiles of both therapeutics were evaluated, and membranes were tested in vitro against Methicillin-resistant Staphylococcus aureus (MRSA) to determine efficacy in preventing biofilm infection and bacterial growth. Results indicate that membranes release both therapeutics for 72 hr, and release profile can be tailored by loading concentration. Membranes were effective in preventing biofilm growth but were toxic to fibroblasts when loaded with 2.5 or 5 mg of bupivacaine.

Fatty-acid signaling molecules can inhibit biofilm formation, signal dispersal events, and revert dormant cells within biofilms to a metabolically active state. We synthesized 2-heptylcyclopropane-1-carboxylic acid (2CP), an analog of cis -2-decenoic acid (C2DA), which contains a cyclopropanated bond that may lock the signaling factor in an active state and prevent isomerization to its least active trans -configuration (T2DA). 2CP was compared to C2DA and T2DA for ability to disperse biofilms formed by Staphylococcus aureus and Pseudomonas aeruginosa . 2CP at 125 μg/ml dispersed approximately 100% of S. aureus cells compared to 25% for C2DA; both 2CP and C2DA had significantly less S. aureus biofilm remaining compared to T2DA, which achieved no significant dispersal. 2CP at 125 μg/ml dispersed approximately 60% of P. aeruginosa biofilms, whereas C2DA and T2DA at the same concentration dispersed 40%. When combined with antibiotics tobramycin, tetracycline, or levofloxacin, 2CP decreased the minimum concentration required for biofilm inhibition and eradication, demonstrating synergistic and additive responses for certain combinations. Furthermore, 2CP supported fibroblast viability above 80% for concentrations below 1 mg/ml. This study demonstrates that 2CP shows similar or improved efficacy in biofilm dispersion, inhibition, and eradication compared to C2DA and T2DA and thus may be promising for use in preventing infection for healthcare applications.

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes— such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

GPR88 is an orphan G protein-coupled receptor (GPCR) expressed at high levels in the striatum and is a target for the development of therapeutics to treat multiple neuropsychiatric disorders. While a few small-molecule synthetic agonists have been reported, no synthetic antagonists or endogenous ligands have been identified. Typical 2nd messenger, cell-based assays for agonist and antagonist identification and signaling pathway elucidation rely on complex downstream events, with interpretation of results complicated from effects of transfected receptor overexpression and interference from endogenous receptors and proteins. The NLuc complementation assay, based on an engineered luciferase from the deep-sea shrimp, Oplophorus gracilirostris, can monitor receptor-G protein interaction in live cells and profile compounds according to the G proteins they activate. The synthetic agonists, 2-PCCA [(1R,2R)-2-(pyridin-2-yl)cyclopropane carboxylic acid], and 2-OMPP [(2R)-N-(2-hydroxy-1-(4-((2-methylpentyl)oxy)phenyl)ethyl)-2-phenylpropanamide] are suspected to activate GPR88 via a Gαi-coupled signaling pathway. In this study we show the activation of GPR88 by 2-PCCA and 2-OMPP inhibited isoproterenol induced cAMP production in transiently transfected GPR88 HEK-293T cells with relative EC50 values of 250 nM and 1.0 μM, respectively, consistent with prior reports. The EC50 value for PCCA in HEK-293T/GPR88 transients is five times that in RH-7777 cells with COS-7 cells showing no cAMP response. These results highlight the importance of the cellular background in engineered systems. Additionally, the NLuc complementation assay shows PCCA receptor binding triggers activation of several G-proteins including Gαi1(EC50= 760 nM), Gαi0 (EC50=3.3μM) and GαS (EC50=4.5 nM) indicating the effects of varying G-protein expression in different cell lines. In combination with our computational endeavors, 2nd messenger and the NLuc complementation assays are valuable tools in our research as we develop high-throughput screening processes and probe signaling pathways in search of GPCR based therapeutics.

G protein‐coupled receptors (GPCR) comprise a family of over 800 integral membrane proteins involved in signal transduction and serve as the primary cellular sensors for chemical stimuli. About 35% of current drugs act on GPCR targets. In addition, of the 300 agents in current clinical trials, greater than 20% target novel GPCRs for which there are no approved drugs (orphan GPCRs). Ligand discovery and deorphanization efforts seeking to assign function to understudied GPCRs are not trivial. Notoriously complex in nature, GPCR‐dependent signaling centers around receptor‐ligand interactions that serve to stabilize conformational access to intracellular signaling partners, G proteins. In GPCR signaling, receptors and heterotrimeric G proteins (made of the Gα and Gβγ subunits) work together to transmit signals via downstream effectors and distinct pathways. The measurement of GPCR mediated second messengers such as cyclic AMP (cAMP), intracellular calcium (Ca2+) mobilization, and ERK activation (MAP kinase) are commonly used as tools to detect ligand induced GPCR activation. However, experimental pathway elucidation is complicated by the tendencies for pathway crosstalk, receptor desensitization, and receptor‐G protein interaction promiscuity. Cell‐based assays that monitor second messenger responses are often used as screening tools for potential GPCR agonists and antagonists, however, commonly used cell‐lines such as HEK‐293, express many endogenous GPCRs that can complicate data analysis. Many classes of Gα subunits exist: Gαs and Gα i/o G‐proteins modulate cyclic AMP (cAMP) through stimulation or inhibition of adenylate cyclase while Gαq subunits activate phospholipase C and a subsequent rapid release of intracellular Ca2+. Specifically, the orphan receptor, GPR88, has been linked with psychiatric diseases including schizophrenia and bipolar disorder. Lacking a validated endogenous agonist, the GPR88 signaling pathway remains poorly understood, however, in vivo knockout experiments and in vitro preclinical studies with small molecule synthetic agonists indicate that GPR88 couples to Gαi/o G‐proteins to inhibit adenylate cyclase activity and reduce intracellular cAMP in cell‐based assays. In this work, we describe our challenges in the study of the GPR88 signaling pathway using second messenger cell‐based assays and explain our decision to move to a real‐time NanoLuc‐based GPCR/G protein complementation assay.Support or Funding InformationNIH grant 1 R15 MH109034

G protein-coupled receptors (GPCR) comprise the largest family of membrane proteins and are of considerable interest as targets for drug development. However, many GPCR structures remain unsolved. To address the structural ambiguity of these receptors, computational tools such as homology modeling and loop modeling are often employed to generate predictive receptor structures. Here we combined both methods to benchmark a protocol incorporating homology modeling based on a locally selected template and extracellular loop modeling that additionally evaluates the presence of template ligands during these modeling steps. Ligands were also docked using three docking methods and two pose selection methods to elucidate an optimal ligand pose selection method. Results suggest that local template-based homology models followed by loop modeling produce more accurate and predictive receptor models than models produced without loop modeling, with decreases in average receptor and ligand RMSD of 0.54 Å and 2.91 Å, respectively. Ligand docking results showcased the ability of MOE induced fit docking to produce ligand poses with atom root-mean-square deviation (RMSD) values at least 0.20 Å lower (on average) than the other two methods benchmarked in this study. In addition, pose selection methods (software-based scoring, ligand complementation) selected lower RMSD poses with MOE induced fit docking than either of the other methods (averaging at least 1.57 Å lower), indicating that MOE induced fit docking is most suited for docking into GPCR homology models in our hands. In addition, target receptor models produced with a template ligand present throughout the modeling process most often produced target ligand poses with RMSD values ≤ 4.5 Å and Tanimoto coefficients > 0.6 after selection based on ligand complementation than target receptor models produced in the absence of template ligands. Overall, the findings produced by this study support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions.

G protein-coupled receptors (GPCR) are important drug discovery targets. Despite progress, many GPCR structures have not yet been solved. For these targets, comparative modeling is used in virtual ligand screening to prioritize experimental efforts. However, the structure of extracellular loop 2 (ECL2) is often poorly predicted. This is significant due to involvement of ECL2 in ligand binding for many Class A GPCR. Here we examine the performance of loop modeling protocols available in the Rosetta (cyclic coordinate descent [CCD], KIC with fragments [KICF] and next generation KIC [NGK]) and Molecular Operating Environment (MOE) software suites (de novo search). ECL2 from GPCR crystal structures served as the structure prediction targets and were divided into four sets depending on loop length. Results suggest that KICF and NGK sampled and scored more loop models with sub-angstrom and near-atomic accuracy than CCD or de novo search for loops of 24 or fewer residues. None of the methods were able to sample loop conformations with near-atomic accuracy for the longest targets ranging from 25 to 32 residues based on 1000 models generated. For these long loop targets, increased conformational sampling is necessary. The strongly conserved disulfide bond between Cys3.25 and Cys45.50 in ECL2 proved an effective filter. Setting an upper limit of 5.1 Å on the S–S distance improved the lowest RMSD model included in the top 10 scored structures in Groups 1–4 on average between 0.33 and 1.27 Å. Disulfide bond formation and geometry optimization of ECL2 provided an additional incremental benefit in structure quality.

Nipsnap1 (4-nitrophenylphosphatase domain and nonneuronal SNAP25-like protein homolog 1) is a neuron-specific mitochondrial protein that binds to the cytoplasmic domain of APP family proteins. The C-terminal region of Nipsnap1 is evolutionarily conserved from humans to bacteria. The molecular and cellular functions of Nipsnap1 remain unknown. A homology model of C-terminal region of mouse Nipsnap1 was generated using bacterial Nipsnap proteins NP_356412.1 (PDB ID 1VQS) and NP_396154.1 (PDB ID 1VQY), and virtual ligand screening was performed using FINDSITE. Recombinant Nipsnap1 proteins (full-length, C-terminal fragment, and N-terminal fragment) were affinity purified from bacteria and mammalian cells. The interaction between recombinant Nipsnap1 proteins and APP cytoplasmic peptide (APP-C18), NAD(H), or NADP(H) were analyzed using biochemical pulldown assays and circular dichroism spectroscopy. FINDSITE virtual ligand screening of over 70,000 compounds identified NADH and NADPH as the top ligand candidates for Nipsnap1 C-terminal region. Using biochemical pull-down assays, we show that purified recombinant Nipsnap1 binds directly to NADH and NADPH. In addition, Nipsnap1 interaction with NADH and NADPH was validated using near-UV circular dichroism spectroscopy. Current experiments are focused on mapping the interaction domain of Nipsnap1 with APP and NADH, and determining if APP interferes with Nipsnap1-NADH binding. The C-terminal region of APP family proteins interacts with mitochondrial Nipsnap1, which also directly binds NAD(P)H. These results suggest that APP C-terminal fragment may regulate NAD(P)H metabolism through an interaction with Nipnsap1.