Spencer S. Ericksen

Bio: Spencer S. Ericksen is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Dopamine receptor & Virtual screening. The author has an hindex of 14, co-authored 26 publications receiving 638 citations. Previous affiliations of Spencer S. Ericksen include West Virginia University & Cornell University.

A marine microbiome antifungal targets urgent-threat drug-resistant fungi

Abstract: New antifungal drugs are urgently needed to address the emergence and transcontinental spread of fungal infectious diseases, such as pandrug-resistant Candida auris. Leveraging the microbiomes of marine animals and cutting-edge metabolomics and genomic tools, we identified encouraging lead antifungal molecules with in vivo efficacy. The most promising lead, turbinmicin, displays potent in vitro and mouse-model efficacy toward multiple-drug-resistant fungal pathogens, exhibits a wide safety index, and functions through a fungal-specific mode of action, targeting Sec14 of the vesicular trafficking pathway. The efficacy, safety, and mode of action distinct from other antifungal drugs make turbinmicin a highly promising antifungal drug lead to help address devastating global fungal pathogens such as C. auris.

Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening

Abstract: In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare traditional consensus scoring methods with a novel, unsupervised gradient boosting approach. We also observed increased score variation among active ligands and developed a statistical mixture model consensus score based on combining score means and variances. To evaluate performance, we used the common performance metrics ROCAUC and EF1 on 21 benchmark targets from DUD-E. Traditional consensus methods, such as taking the mean of quantile normalized docking scores, outperformed individual docking methods and are more robust to target variation. The mixture model and gradient boosting provided further improvements over the traditional consensus methods. These methods are readily applicable to new targets in academic r...

Structural Determinants for High-Affinity Zolpidem Binding to GABA-A receptors

Abstract: The imidazopyridine zolpidem (Ambien) is one of the most commonly prescribed sleep aids in the United States (Rush, 1998). Similar to classic benzodiazepines (BZDs), zolpidem binds at the extracellular N-terminal α/γ subunit interface of the GABA-A receptor (GABAR). However, zolpidem differs significantly from classic BZDs in chemical structure and neuropharmacological properties. Thus, classic BZDs and zolpidem are likely to have different requirements for high-affinity binding to GABARs. To date, three residues—γ2Met57, γ2Phe77, and γ2Met130—have been identified as necessary for high-affinity zolpidem binding (Proc Natl Acad Sci USA 94:8824-8829, 1997; Mol Pharmacol 52:874-881, 1997). In this study, we used radioligand binding techniques, γ2/α1 chimeric subunits (χ), site-directed mutagenesis, and molecular modeling to identify additional γ2 subunit residues important for high-affinity zolpidem binding. Whereas α1β2χ receptors containing only the first 161 amino-terminal residues of the γ2 subunit bind the classic BZD flunitrazepam with wild-type affinity, zolpidem affinity is decreased ∼8-fold. By incrementally restoring γ2 subunit sequence, we identified a seven-amino acid stretch in the γ2 subunit loop F region (amino acids 186-192) that is required to confer high-affinity zolpidem binding to GABARs. When mapped to a homology model, these seven amino acids make up part of loop F located at the α/γ interface. Based on in silico zolpidem docking, three residues within loop F, γ2Glu189, γ2Thr193, and γ2Arg194, emerge as being important for stabilizing zolpidem in the BZD binding pocket and probably interact with other loop F residues to maintain the structural integrity of the BZD binding site.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Fluorescent chemosensors based on spiroring-opening of xanthenes and related derivatives.

Abstract: Fluorescent Chemosensors Based on Spiroring-Opening of Xanthenes and Related Derivatives Xiaoqiang Chen, Tuhin Pradhan, Fang Wang, Jong Seung Kim,* and Juyoung Yoon* Departments of Chemistry and Nano Science and of Bioinspired Science (WCU), Ewha Womans University, Seoul 120-750, Korea State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemistry and Chemical Engineering, Nanjing University of Technology, Nanjing 210009, China Department of Chemistry, Korea University, Seoul 136-701, Korea

International Union of Pharmacology. LXX. Subtypes of γ-Aminobutyric AcidA Receptors: Classification on the Basis of Subunit Composition, Pharmacology, and Function. Update

Abstract: In this review we attempt to summarize experimental evidence on the existence of defined native GABA(A) receptor subtypes and to produce a list of receptors that actually seem to exist according to current knowledge. This will serve to update the most recent classification of GABA(A) receptors (Pharmacol Rev 50:291-313, 1998) approved by the Nomenclature Committee of the International Union of Pharmacology. GABA(A) receptors are chloride channels that mediate the major form of fast inhibitory neurotransmission in the central nervous system. They are members of the Cys-loop pentameric ligand-gated ion channel (LGIC) superfamily and share structural and functional homology with other members of that family. GABA(A) receptors are assembled from a family of 19 homologous subunit gene products and form numerous, mostly hetero-oligomeric, pentamers. Such receptor subtypes with properties that depend on subunit composition vary in topography and ontogeny, in cellular and subcellular localization, in their role in brain circuits and behaviors, in their mechanisms of regulation, and in their pharmacology. We propose several criteria, which can be applied to all the members of the LGIC superfamily, for including a receptor subtype on a list of native hetero-oligomeric subtypes. With these criteria, we develop a working GABA(A) receptor list, which currently includes 26 members, but will undoubtedly be modified and grow as information expands. The list is divided into three categories of native receptor subtypes: "identified," "existence with high probability," and "tentative."

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

Abstract: Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein-ligand binding, protein-protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields.

Enzyme immobilization on electrospun polymer nanofibers: An overview

Abstract: Enzyme immobilization has attracted continuous attention in the fields of fine chemistry, biomedicine, and biosensor. The performance of immobilized enzyme largely depends on the structure of supports. Nanostructured supports are believed to be able to retain the catalytic activity as well as ensure the immobilization efficiency of enzyme to a high extent. Electrospinning provides a simple and versatile method to fabricate nanofibrous supports. Compared with other nanostructured supports (e.g. mesoporous silica, nanoparticles), nanofibrous supports show many advantages for their high porosity and interconnectivity. This review mainly discusses the recent advances in using nanofibers as hosts for enzyme immobilization by two different methods, surface attachment and encapsulation. Surface attachment refers to physical adsorption or covalent attachment of enzymes on pristine or modified nanofibrous supports, and encapsulation means electrospinning a mixture of enzyme and polymer. We make a detailed comparison between these two immobilization approaches and highlight their distinct characteristics. The prospective applications of enzyme immobilized electrospun nanofibers in the development of biosensors, biofuel cells and biocatalysts are also discussed.