Personality Predictors of Communication Skills Among Orthopedic Surgery Residents.

INTRODUCTION: This study examined the relationship between personality traits and interpersonal communication skills among first-year orthopedic surgery residents. METHOD: This study performed a retrospective analysis on the data collected in the 2 phases among the 6 cohorts of first-year orthopedic surgery residents (n = 73) during a 6-year period at an urban academic medical hospital. Resident personality was assessed through self-report prior to entry into the program and included a total of 7 personality traits. These traits were broken down into 2 categories, day to day, or usual, tendencies, which measured personality traits when no stress was present and stress tendencies, which measured personality traits when stressed or fatigued. The "day to day" tendencies measured were Emotional Stability, Agreeableness, Conscientiousness and Openness) and "stress" tendencies measured were Excitable, Skeptical and Imaginative. Communication skills were measured across 4 specific dimensions of patient communication (Engage, Empathy, Educate, Enlist) in an Objective Structured Clinical Examination (OSCE). RESULTS: Multiple regression analyses showed that the personality traits identified as "stress" tendencies predicted performance on 2 of the 4 communication skills dimensions measured by the OSCE and accounted for up to 34.8% of the total variance in the ratings of empathic communication and up to 67.2% of the total variance in education-related communication. CONCLUSIONS: Our research identifies specific personality traits that affect resident communication skills related to patient education and empathy in simulated encounters. Three stress-related personality traits (Excitable, Skeptical, Imaginative) had a strong negative influence on communication skills, while day to day personality traits (Emotional Stability, Agreeableness, Conscientiousness) positively influenced communication skills.

Personality Assessment in Orthopaedic Surgery: AOA Critical Issues.

Personality assessment tools are used effectively in many arenas of business, but they have not been embraced by the medical profession. There is increasing evidence that these tools have promise for helping to match resident candidates to specific fields of medicine, for mentoring residents, and for developing improved leadership in our field. This paper reviews many aspects of personality assessment tools and their use in orthopaedic surgery.

Personality Factors Associated With Resident Performance: Results From 12 Accreditation Council for Graduate Medical Education Accredited Orthopaedic Surgery Programs.

OBJECTIVES: To understand the personality factors associated with orthopedic surgery resident performance. DESIGN: A prospective, cross-sectional survey of orthopedic surgery faculty that assessed their perceptions of the personality traits most highly associated with resident performance. Residents also completed a survey to determine their specific personality characteristics. A subset of faculty members rated the performance of those residents within their respective program on 5 dimensions. Multiple regression models tested the relationship between the set of resident personality measures and each aspect of performance; relative weights analyses were then performed to quantify the contribution of the individual personality measures to the total variance explained in each performance domain. Independent samples t-tests were conducted to examine differences between the personality characteristics of residents and those faculty identified as relevant to successful resident performance. SETTING: Data were collected from 12 orthopedic surgery residency programs1 throughout the United States. The level of clinical care provided by participating institutions varied. PARTICIPANTS: Data from 175 faculty members and 266 residents across 12 programs were analyzed. RESULTS: The personality features of residents were related to faculty evaluations of resident performance (for all, p < 0.01); the full set of personality measures accounted for 4%-11% of the variance in ratings of resident performance. Particularly, the characteristics of agreeableness, neuroticism, and learning approach were found to be most important for explaining resident performance. Additionally, there were significant differences between the personality features that faculty members identified as important for resident performance and the personality features that residents possessed. CONCLUSION: Personality assessments can predict orthopedic surgery resident performance. However, results suggest the traits that faculty members value or reward among residents could be different from the traits associated with improved resident performance.

Stoichiometries and affinities of interacting proteins from concentration series of solution scattering data: decomposition by least squares and quadratic optimization.

In studying interacting proteins, complementary insights are provided by analyzing both the association model (the stoichiometry and affinity constants of the intermediate and final complexes) and the quaternary structure of the resulting complexes. Many current methods for analyzing protein interactions either give a binary answer to the question of association and no information about quaternary structure or at best provide only part of the complete picture. Presented here is a method to extract both types of information from X-ray or neutron scattering data for a series of equilibrium mixtures containing the initial components at different concentrations. The method determines the association pathway and constants, along with the scattering curves of the individual members of the mixture, so as to best explain the scattering data for the mixtures. The derived curves then enable reconstruction of the intermediate and final complexes. Using simulated solution scattering data for four hetero-oligomeric complexes with different structures, molecular weights and association models, it is demonstrated that this method accurately determines the simulated association model and scattering profiles for the initial components and complexes. Recognizing that experimental mixtures contain static contaminants and nonspecific complexes with the lowest affinities (inter-particle interference) as well as the desired specific complex(es), a new analytical method is also employed to extend this approach to evaluating the association models and scattering curves in the presence of static contaminants, testing both a nonparticipating monomer and a large homo-oligomeric aggregate. It is demonstrated that the method is robust to both random noise and systematic noise from such contaminants, and the treatment of nonspecific complexes is discussed. Finally, it is shown that this method is applicable over a large range of weak association constants typical of specific but transient protein-protein complexes.

Bayesian reconstruction of P(r) directly from two-dimensional detector images via a Markov chain Monte Carlo method.

The interatomic distance distribution, P(r), is a valuable tool for evaluating the structure of a molecule in solution and represents the maximum structural information that can be derived from solution scattering data without further assumptions. Most current instrumentation for scattering experiments (typically CCD detectors) generates a finely pixelated two-dimensional image. In contin-uation of the standard practice with earlier one-dimensional detectors, these images are typically reduced to a one-dimensional profile of scattering inten-sities, I(q), by circular averaging of the two-dimensional image. Indirect Fourier transformation methods are then used to reconstruct P(r) from I(q). Substantial advantages in data analysis, however, could be achieved by directly estimating the P(r) curve from the two-dimensional images. This article describes a Bayesian framework, using a Markov chain Monte Carlo method, for estimating the parameters of the indirect transform, and thus P(r), directly from the two-dimensional images. Using simulated detector images, it is demonstrated that this method yields P(r) curves nearly identical to the reference P(r). Furthermore, an approach for evaluating spatially correlated errors (such as those that arise from a detector point spread function) is evaluated. Accounting for these errors further improves the precision of the P(r) estimation. Experimental scattering data, where no ground truth reference P(r) is available, are used to demonstrate that this method yields a scattering and detector model that more closely reflects the two-dimensional data, as judged by smaller residuals in cross-validation, than P(r) obtained by indirect transformation of a one-dimensional profile. Finally, the method allows concurrent estimation of the beam center and Dmax, the longest interatomic distance in P(r), as part of the Bayesian Markov chain Monte Carlo method, reducing experimental effort and providing a well defined protocol for these parameters while also allowing estimation of the covariance among all parameters. This method provides parameter estimates of greater precision from the experimental data. The observed improvement in precision for the traditionally problematic Dmax is particularly noticeable.

Algorithms for optimizing cross-overs in DNA shuffling.

BACKGROUND: DNA shuffling generates combinatorial libraries of chimeric genes by stochastically recombining parent genes. The resulting libraries are subjected to large-scale genetic selection or screening to identify those chimeras with favorable properties (e.g., enhanced stability or enzymatic activity). While DNA shuffling has been applied quite successfully, it is limited by its homology-dependent, stochastic nature. Consequently, it is used only with parents of sufficient overall sequence identity, and provides no control over the resulting chimeric library. RESULTS: This paper presents efficient methods to extend the scope of DNA shuffling to handle significantly more diverse parents and to generate more predictable, optimized libraries. Our CODNS (cross-over optimization for DNA shuffling) approach employs polynomial-time dynamic programming algorithms to select codons for the parental amino acids, allowing for zero or a fixed number of conservative substitutions. We first present efficient algorithms to optimize the local sequence identity or the nearest-neighbor approximation of the change in free energy upon annealing, objectives that were previously optimized by computationally-expensive integer programming methods. We then present efficient algorithms for more powerful objectives that seek to localize and enhance the frequency of recombination by producing "runs" of common nucleotides either overall or according to the sequence diversity of the resulting chimeras. We demonstrate the effectiveness of CODNS in choosing codons and allocating substitutions to promote recombination between parents targeted in earlier studies: two GAR transformylases (41% amino acid sequence identity), two very distantly related DNA polymerases, Pol X and ß (15%), and beta-lactamases of varying identity (26-47%). CONCLUSIONS: Our methods provide the protein engineer with a new approach to DNA shuffling that supports substantially more diverse parents, is more deterministic, and generates more predictable and more diverse chimeric libraries.

A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.

In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria.

Planning combinatorial disulfide cross-links for protein fold determination.

BACKGROUND: Fold recognition techniques take advantage of the limited number of overall structural organizations, and have become increasingly effective at identifying the fold of a given target sequence. However, in the absence of sufficient sequence identity, it remains difficult for fold recognition methods to always select the correct model. While a native-like model is often among a pool of highly ranked models, it is not necessarily the highest-ranked one, and the model rankings depend sensitively on the scoring function used. Structure elucidation methods can then be employed to decide among the models based on relatively rapid biochemical/biophysical experiments. RESULTS: This paper presents an integrated computational-experimental method to determine the fold of a target protein by probing it with a set of planned disulfide cross-links. We start with predicted structural models obtained by standard fold recognition techniques. In a first stage, we characterize the fold-level differences between the models in terms of topological (contact) patterns of secondary structure elements (SSEs), and select a small set of SSE pairs that differentiate the folds. In a second stage, we determine a set of residue-level cross-links to probe the selected SSE pairs. Each stage employs an information-theoretic planning algorithm to maximize information gain while minimizing experimental complexity, along with a Bayes error plan assessment framework to characterize the probability of making a correct decision once data for the plan are collected. By focusing on overall topological differences and planning cross-linking experiments to probe them, our fold determination approach is robust to noise and uncertainty in the models (e.g., threading misalignment) and in the actual structure (e.g., flexibility). We demonstrate the effectiveness of our approach in case studies for a number of CASP targets, showing that the optimized plans have low risk of error while testing only a small portion of the quadratic number of possible cross-link candidates. Simulation studies with these plans further show that they do a very good job of selecting the correct model, according to cross-links simulated from the actual crystal structures. CONCLUSIONS: Fold determination can overcome scoring limitations in purely computational fold recognition methods, while requiring less experimental effort than traditional protein structure determination approaches.

Characterizing the space of interatomic distance distribution functions consistent with solution scattering data.

Scattering of neutrons and X-rays from molecules in solution offers alternative approaches to the study of a wide range of macromolecular structures in their solution state without crystallization. We study one part of the problem of elucidating three-dimensional structure from solution scattering data, determining the distribution of interatomic distances, P(r), where r is the distance between two atoms in the protein molecule. This problem is known to be ill-conditioned: for a single observed diffraction pattern, there may be many consistent distance distribution functions, and there is a risk of overfitting the observed scattering data. We propose a new approach to avoiding this problem: accepting the validity of multiple alternative P(r) curves rather than seeking a single "best." We place linear constraints to ensure that a computed P(r) is consistent with the experimental data. The constraints enforce smoothness in the P(r) curve, ensure that the P(r) curve is a probability distribution, and allow for experimental error. We use these constraints to precisely describe the space of all consistent P(r) curves as a polytope of histogram values or Fourier coefficients. We develop a linear programming approach to sampling the space of consistent, realistic P(r) curves. On both experimental and simulated scattering data, our approach efficiently generates ensembles of such curves that display substantial diversity.

Algorithms for joint optimization of stability and diversity in planning combinatorial libraries of chimeric proteins.

In engineering protein variants by constructing and screening combinatorial libraries of chimeric proteins, two complementary and competing goals are desired: the new proteins must be similar enough to the evolutionarily-selected wild-type proteins to be stably folded, and they must be different enough to display functional variation. We present here the first method, Staversity, to simultaneously optimize stability and diversity in selecting sets of breakpoint locations for site-directed recombination. Our goal is to uncover all "undominated" breakpoint sets, for which no other breakpoint set is better in both factors. Our first algorithm finds the undominated sets serving as the vertices of the lower envelope of the two-dimensional (stability and diversity) convex hull containing all possible breakpoint sets. Our second algorithm identifies additional breakpoint sets in the concavities that are either undominated or dominated only by undiscovered breakpoint sets within a distance bound computed by the algorithm. Both algorithms are efficient, requiring only time polynomial in the numbers of residues and breakpoints, while characterizing a space defined by an exponential number of possible breakpoint sets. We applied Staversity to identify 2-10 breakpoint plans for different sets of parent proteins taken from the purE family, as well as for parent proteins TEM-1 and PSE-4 from the beta-lactamase family. The average normalized distance between our plans and the lower bound for optimal plans is around 2%. Our plans dominate most (60-90% on average for each parent set) of the plans found by other possible approaches, random sampling or explicit optimization for stability with implicit optimization for diversity. The identified breakpoint sets provide a compact representation of good plans, enabling a protein engineer to understand and account for the trade-offs between two key considerations in combinatorial chimeragenesis.

Analysis of self-associating proteins by singular value decomposition of solution scattering data.

We describe a method by which a single experiment can reveal both association model (pathway and constants) and low-resolution structures of a self-associating system. Small-angle scattering data are collected from solutions at a range of concentrations. These scattering data curves are mass-weighted linear combinations of the scattering from each oligomer. Singular value decomposition of the data yields a set of basis vectors from which the scattering curve for each oligomer is reconstructed using coefficients that depend on the association model. A search identifies the association pathway and constants that provide the best agreement between reconstructed and observed data. Using simulated data with realistic noise, our method finds the correct pathway and association constants. Depending on the simulation parameters, reconstructed curves for each oligomer differ from the ideal by 0.05-0.99% in median absolute relative deviation. The reconstructed scattering curves are fundamental to further analysis, including interatomic distance distribution calculation and low-resolution ab initio shape reconstruction of each oligomer in solution. This method can be applied to x-ray or neutron scattering data from small angles to moderate (or higher) resolution. Data can be taken under physiological conditions, or particular conditions (e.g., temperature) can be varied to extract fundamental association parameters (DeltaH(ass), DeltaS(ass)).

Robotic hierarchical mixing for the production of combinatorial libraries of proteins and small molecules.

We present a method to automatically plan a robotic process to mix individual combinations of reactants in individual reaction vessels (vials or wells in a multiwell plate), mixing any number of reactants in any desired stoichiometry, and ordering the mixing steps according to an arbitrarily complex treelike assembly protocol. This process enables the combinatorial generation of complete or partial product libraries in individual reaction vessels from intermediates formed in the presence of different sets of reactants. It can produce either libraries of chimeric genes constructed by ligation of fragments from different parent genes or libraries of chemical compounds constructed by convergent synthesis. Given concentrations of the input reactants and desired amounts or volumes of the products, our algorithm, RoboMix, computes the required reactant volumes and the resulting product concentrations, along with volumes and concentrations for all intermediate combinations. It outputs a sequence of robotic liquid transfer steps that ensures that each combination is correctly mixed even when individualized stoichiometries are employed and with any fractional yield for a product. It can also account for waste in robotic liquid handling and residual volume needed to ensure accurate aspiration. We demonstrate the effectiveness of the method in a test mixing dyes with different UV-vis absorption spectra, verifying the desired combinations spectroscopically.

Optimizing Bayes error for protein structure model selection by stability mutagenesis.

Site-directed mutagenesis affects protein stability in a manner dependent on the local structural environment of the mutated residue; e.g., a hydrophobic to polar substitution would behave differently in the core vs. on the surface of the protein. Thus site-directed mutagenesis followed by stability measurement enables evaluation of and selection among predicted structure models, based on consistency between predicted and experimental stability changes (DeltaDeltaGo values). This paper develops a method for planning a set of individual site-directed mutations for protein structure model selection, so as to minimize the Bayes error, i.e., the probability of choosing the wrong model. While in general it is hard to calculate exactly the multi-dimensional Bayes error defined by a set of mutations, we leverage the structure of "DeltaDeltaGo space" to develop tight upper and lower bounds. We further develop a lower bound on the Bayes error of any plan that uses a fixed number of mutations from a set of candidates. We use this bound in a branch-and-bound planning algorithm to find optimal and near-optimal plans. We demonstrate the significance and effectiveness of this approach in planning mutations for elucidating the structure of the pTfa chaperone protein from bacteriophage lambda.

Algorithms for selecting breakpoint locations to optimize diversity in protein engineering by site-directed protein recombination.

Protein engineering by site-directed recombination seeks to develop proteins with new or improved function, by accumulating multiple mutations from a set of homologous parent proteins. A library of hybrid proteins is created by recombining the parent proteins at specified breakpoint locations; subsequent screening/selection identifies hybrids with desirable functional characteristics. In order to improve the frequency of generating novel hybrids, this paper develops the first approach to explicitly plan for diversity in site-directed recombination, including metrics for characterizing the diversity of a planned hybrid library and efficient algorithms for optimizing experiments accordingly. The goal is to choose breakpoint locations to sample sequence space as uniformly as possible (which we argue maximizes diversity), under the constraints imposed by the recombination process and the given set of parents. A dynamic programming approach selects optimal breakpoint locations in polynomial time. Application of our method to optimizing breakpoints for an example biosynthetic enzyme, purE, demonstrates the significance of diversity optimization and the effectiveness of our algorithms.

Hypergraph model of multi-residue interactions in proteins: sequentially-constrained partitioning algorithms for optimization of site-directed protein recombination.

Relationships among amino acids determine stability and function and are also constrained by evolutionary history. We develop a probabilistic hypergraph model of residue relationships that generalizes traditional pairwise contact potentials to account for the statistics of multi-residue interactions. Using this model, we detected non-random associations in protein families and in the protein database. We also use this model in optimizing site-directed recombination experiments to preserve significant interactions and thereby increase the frequency of generating useful recombinants. We formulate the optimization as a sequentially-constrained hypergraph partitioning problem; the quality of recombinant libraries with respect to a set of breakpoints is characterized by the total perturbation to edge weights. We prove this problem to be NP-hard in general, but develop exact and heuristic polynomial-time algorithms for a number of important cases. Application to the beta-lactamase family demonstrates the utility of our algorithms in planning site-directed recombination.

Site-directed combinatorial construction of chimaeric genes: general method for optimizing assembly of gene fragments.

Site-directed construction of chimaeric genes by in vitro recombination "mixes-and-matches" precise building blocks from multiple parent proteins, generating libraries of hybrids to be tested for structure-function relationships and/or screened for favorable properties and novel enzymatic activities. A direct annealing and ligation method can construct chimaeric genes without requiring sequence identity between parents, except for the short (approximately 3 nt) sequences of the fragment overhangs used for specific ligation. Careful planning of the assembly process is necessary, though, in order to ensure effective construction of desired fragment assemblies and to avoid undesired assemblies (e.g., repetition of fragments, fragments out of order). We develop algorithms for specific planned ligation of short overhangs (SPLISO) that efficiently explore possible assembly plans, varying the fragment overhangs and the order of ligation steps in the assembly pathway. While there is a combinatorial explosion in the number of possible assembly plans as the number of breakpoints and parent genes increases, we employ a dynamic programming approach to find globally optimal ones in low-order polynomial time (in practice, taking only seconds for basic assembly plans). We demonstrate the effectiveness of our algorithms in planning the assembly of hybrid libraries, under a variety of experimental options and restrictions, including flexibility in the position and amino acid sequence of breakpoints. Our method promises to enable more effective application of site-directed recombination to protein investigation and engineering.

The crystal structure of the cis-proline to glycine variant (P114G) of ribonuclease A.

Replacement of a cis-proline by glycine at position 114 in ribonuclease A leads to a large decrease in thermal stability and simplifies the refolding kinetics. A crystallographic approach was used to determine whether the decrease in thermal stability results from the presence of a cis glycine peptide bond, or from a localized structural rearrangement caused by the isomerization of the mutated cis 114 peptide bond. The structure was solved at 2.0 A resolution and refined to an R-factor of 19.5% and an R(free) of 21.9%. The overall conformation of the protein was similar to that of wild-type ribonuclease A; however, there was a large localized rearrangement of the mutated loop (residues 110-117-a 9.3 A shift of the Calpha atom of residue 114). The peptide bond before Gly114 is in the trans configuration. Interestingly, a large anomalous difference density was found near residue 114, and was attributed to a bound cesium ion present in the crystallization experiment. The trans isomeric configuration of the peptide bond in the folded state of this mutant is consistent with the refolding kinetics previously reported, and the associated protein conformational change provides an explanation for the decreased thermal stability.

Probabilistic cross-link analysis and experiment planning for high-throughput elucidation of protein structure.

Emerging high-throughput techniques for the characterization of protein and protein-complex structures yield noisy data with sparse information content, placing a significant burden on computation to properly interpret the experimental data. One such technique uses cross-linking (chemical or by cysteine oxidation) to confirm or select among proposed structural models (e.g., from fold recognition, ab initio prediction, or docking) by testing the consistency between cross-linking data and model geometry. This paper develops a probabilistic framework for analyzing the information content in cross-linking experiments, accounting for anticipated experimental error. This framework supports a mechanism for planning experiments to optimize the information gained. We evaluate potential experiment plans using explicit trade-offs among key properties of practical importance: discriminability, coverage, balance, ambiguity, and cost. We devise a greedy algorithm that considers those properties and, from a large number of combinatorial possibilities, rapidly selects sets of experiments expected to discriminate pairs of models efficiently. In an application to residue-specific chemical cross-linking, we demonstrate the ability of our approach to plan experiments effectively involving combinations of cross-linkers and introduced mutations. We also describe an experiment plan for the bacteriophage lambda Tfa chaperone protein in which we plan dicysteine mutants for discriminating threading models by disulfide formation. Preliminary results from a subset of the planned experiments are consistent and demonstrate the practicality of planning. Our methods provide the experimenter with a valuable tool (available from the authors) for understanding and optimizing cross-linking experiments.