In the deep end: pooling data and other statistical challenges of zoo and aquarium research

Zoo and aquarium research presents many logistic challenges, including extremely small sample sizes and lack of independent data points, which lend themselves to the misuse of statistics. Pseudoreplication and pooling of data are two statistical problems common in research in the biological sciences. Although the prevalence of these and other statistical miscues have been documented in other fields, little attention has been paid to the practice of statistics in the field of zoo biology. A review of articles published in the journal Zoo Biology between 1999–2004 showed that approximately 40% of the 146 articles utilizing inferential statistics during that span contained some evidence of pseudoreplication or pooling of data. Nearly 75% of studies did not provide degrees of freedom for all statistics and approximately 20% did not report test statistic values. Although the level of pseudoreplication in this dataset is not outside the levels found in other branches of biology, it does indicate the challenges of dealing with appropriate data analysis in zoo and aquarium studies. The standardization of statistical techniques to deal with the methodological challenges of zoo and aquarium populations can help advance zoo research by guiding the production and analysis of applied studies. This study recommends techniques for dealing with these issues, including complete disclosure of data manipulation and reporting of statistical values, checking and control for institutional effects in statistical models, and avoidance of pseudoreplicated observations. Additionally, zoo biologists should seek out other models such as hierarchical or factorial models or randomization tests to supplement their repertoire of t‐tests and ANOVA. These suggestions are intended to stimulate conversation and examination of the current use of statistics in zoo biology in an effort to develop more consistent requirements for publication. Zoo Biol 0:1–14, 2006. © 2006 Wiley‐Liss, Inc.     

A scientific basis for restoring fish spawning habitat in the St. Clair and Detroit Rivers of the Laurentian Great Lakes

Loss of functional habitat in riverine systems is a global fisheries issue. Few studies, however, describe the decision‐making approach taken to abate loss of fish spawning habitat. Numerous habitat restoration efforts are underway and documentation of successful restoration techniques for spawning habitat of desirable fish species in large rivers connecting the Laurentian Great Lakes are reported here. In 2003, to compensate for the loss of fish spawning habitat in the St. Clair and Detroit Rivers that connect the Great Lakes Huron and Erie, an international partnership of state, federal, and academic scientists began restoring fish spawning habitat in both of these rivers. Using an adaptive management approach, we created 1,100 m² of productive fish spawning habitat near Belle Isle in the Detroit River in 2004; 3,300 m² of fish spawning habitat near Fighting Island in the Detroit River in 2008; and 4,000 m² of fish spawning habitat in the Middle Channel of the St. Clair River in 2012. Here, we describe the adaptive‐feedback management approach that we used to guide our decision making during all phases of spawning habitat restoration, including problem identification, team building, hypothesis development, strategy development, prioritization of physical and biological imperatives, project implementation, habitat construction, monitoring of fish use of the constructed spawning habitats, and communication of research results. Numerous scientific and economic lessons learned from 10 years of planning, building, and assessing fish use of these three fish spawning habitat restoration projects are summarized in this article.     

Toward intensifying design of experiments in upstream bioprocess development: An industrial Escherichia coli feasibility study

In this study, step variations in temperature, pH, and carbon substrate feeding rate were performed within five high cell density Escherichia coli fermentations to assess whether intraexperiment step changes, can principally be used to exploit the process operation space in a design of experiment manner. A dynamic process modeling approach was adopted to determine parameter interactions. A bioreactor model was integrated with an artificial neural network that describes biomass and product formation rates as function of varied fed‐batch fermentation conditions for heterologous protein production. A model reliability measure was introduced to assess in which process region the model can be expected to predict process states accurately. It was found that the model could accurately predict process states of multiple fermentations performed at fixed conditions within the determined validity domain. The results suggest that intraexperimental variations of process conditions could be used to reduce the number of experiments by a factor, which in limit would be equivalent to the number of intraexperimental variations per experiment. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1343–1352, 2016     

Chiral Separation of Uncharged Pomalidomide Enantiomers Using Carboxymethyl‐β‐Cyclodextrin: A Validated Capillary Electrophoretic Method

The racemic mixture of pomalidomide (POM), a second‐generation immunomodulatory uncharged drug, was separated into enantiomers by capillary zone electrophoresis for the first time. Seven different chargeable cyclodextrin (CD) derivatives were screened as complexing agents and chiral selectors, investigating the stability of the POM‐CD inclusion complexes and their enantiodiscriminating capacities. Based on preliminary experiments, carboxymethyl‐β‐CD (CM‐β‐CD) was found to be the most effective chiral selector. Factors influencing enantioseparation were systematically optimized, using an orthogonal experimental design. Optimal parameters (background electrolyte [BGE]: 50 mM Tris‐acetate buffer, pH 6.5, containing 15 mM CM‐β‐CD; capillary temperature: 20°C; voltage applied +15 kV) allowed baseline separation of POM enantiomers with a resolution as high as 4.87. The developed method was validated, in terms of sensitivity (limit of detection and limit of quantification), linearity, accuracy, repeatability, and intermediate precision. Chirality 28:199–203, 2016. © 2015 Wiley Periodicals, Inc.     

Optimization of protein–protein docking for predicting Fc–protein interactions

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc–protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc‐binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc–protein complexes are available, numerous others have not yet been determined. Protein–protein docking could be used to investigate Fc–protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking‐based structural bioinformatics approach is developed for predicting the structures of Fc–protein complexes. Based on the available set of X‐ray structures of Fc–protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc–protein complexes, as well as in the design of peptides and proteins that target Fc.     

Rational design of a monomeric and photostable far‐red fluorescent protein for fluorescence imaging in vivo

Fluorescent proteins (FPs) are powerful tools for cell and molecular biology. Here based on structural analysis, a blue‐shifted mutant of a recently engineered monomeric infrared fluorescent protein (mIFP) has been rationally designed. This variant, named iBlueberry, bears a single mutation that shifts both excitation and emission spectra by approximately 40 nm. Furthermore, iBlueberry is four times more photostable than mIFP, rendering it more advantageous for imaging protein dynamics. By tagging iBlueberry to centrin, it has been demonstrated that the fusion protein labels the centrosome in the developing zebrafish embryo. Together with GFP‐labeled nucleus and tdTomato‐labeled plasma membrane, time‐lapse imaging to visualize the dynamics of centrosomes in radial glia neural progenitors in the intact zebrafish brain has been demonstrated. It is further shown that iBlueberry can be used together with mIFP in two‐color protein labeling in living cells and in two‐color tumor labeling in mice.     

Redesigning the stereospecificity of tyrosyl‐tRNA synthetase

d ‐Amino acids are largely excluded from protein synthesis, yet they are of great interest in biotechnology. Unnatural amino acids have been introduced into proteins using engineered aminoacyl‐tRNA synthetases (aaRSs), and this strategy might be applicable to d ‐amino acids. Several aaRSs can aminoacylate their tRNA with a d ‐amino acid; of these, tyrosyl‐tRNA synthetase (TyrRS) has the weakest stereospecificity. We use computational protein design to suggest active site mutations in Escherichia coli TyrRS that could increase its d ‐Tyr binding further, relative to l ‐Tyr. The mutations selected all modify one or more sidechain charges in the Tyr binding pocket. We test their effect by probing the aminoacyl‐adenylation reaction through pyrophosphate exchange experiments. We also perform extensive alchemical free energy simulations to obtain l ‐Tyr/d ‐Tyr binding free energy differences. Agreement with experiment is good, validating the structural models and detailed thermodynamic predictions the simulations provide. The TyrRS stereospecificity proves hard to engineer through charge‐altering mutations in the first and second coordination shells of the Tyr ammonium group. Of six mutants tested, two are active towards d ‐Tyr; one of these has an inverted stereospecificity, with a large preference for d ‐Tyr. However, its activity is low. Evidently, the TyrRS stereospecificity is robust towards charge rearrangements near the ligand. Future design may have to consider more distant and/or electrically neutral target mutations, and possibly design for binding of the transition state, whose structure however can only be modeled. Proteins 2016; 84:240–253. © 2015 Wiley Periodicals, Inc.     

Protein side chain conformation predictions with an MMGBSA energy function

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an “MMGBSA” energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803–819. © 2016 Wiley Periodicals, Inc.