Recruitment, effort, and retention effects of performance contracts for civil servants: Experimental evidence from Rwandan primary schools

This paper reports on a two-tiered experiment designed to separately identify the selection and effort margins of pay-for-performance (P4P). At the recruitment stage, teacher labor markets were randomly assigned to a 'pay-for-percentile' or fixed-wage contract. Once recruits were placed, an unexpected, incentive- compatible, school-level re-randomization was performed, so that some teachers who applied for a fixed-wage contract ended up being paid by P4P, and vice versa. By the second year of the study, the within-year effort effect of P4P was 0.16 standard deviations of pupil learning, with the total effect rising to 0.20 standard deviations after allowing for selection

Capturing Atomic Interactions with a Graphical Framework in Computational Protein Design

A protein's amino acid sequence determines both its chemical and its physical structures, and together these two structures determine its function. Protein designers seek new amino acid sequences with chemical and physical structures capable of performing some function. The vast size of sequence space frustrates efforts to find useful sequences. Protein designers model proteins on computers and search through amino acid sequence space computationally. They represent the three-dimensional structures for the sequences they examine, specifying the location of each atom, and evaluate the stability of these structures. Good structures are tightly packed but are free of collisions. Designers seek a sequence with a stable structure that meets the geometric and chemical requirements to function as desired; they frame their search as an optimization problem. In this dissertation, I present a graphical model of the central optimization problem in protein design, the side-chain-placement problem. This model allows the formulation of a dynamic programming solution, thus connecting side-chain placement with the class of NP-complete problems for which certain instances admit polynomial time solutions. Moreover, the graphical model suggests a natural data structure for storing the energies used in design. With this data structure, I have created an extensible framework for the representation of energies during side-chain-placement optimization and have incorporated this framework into the Rosetta molecular modeling program. I present one extension that incorporates a new degree of structural variability into the optimization process. I present another extension that includes a non-pairwise decomposable energy function, the first of its kind in protein design, laying the ground-work to capture aspects of protein stability that could not previously be incorporated into the optimization of side-chain placement

Key Biofouling Organisms in Tidal Habitats Targeted by the Offshore Renewable Energy Sector in the North Atlantic Include the Massive Barnacle Chirona hameri

Marine habitats are being targeted for the extraction of offshore renewable energy (ORE) as part of the drive to decarbonise electricity generation. Unmanaged biofouling impacts ORE devices and infrastructure by elevating drag forces, increasing weight, and accelerating corrosion, leading to decreased performance and survivability, and extending costly periods of maintenance. ORE deployments in high tidal flow locations are providing opportunities to study the biofouling unique to these habitats. In this study, surveys of numerous devices and associated infrastructure deployed at the European Marine Energy Centre in Scotland identified high tidal flow fouling assemblages. Substrate orientation relative to tidal flow appears to affect the abundance of key fouling species, including the massive barnacle Chirona hameri. This species is shown to recruit to a wide range of artificial substrates, over a prolonged period from mid-spring to mid-summer, and in maximum current speeds from 0.4–4.0 m/s. For the first time, C. hameri is reported in near-surface depths, on uncoated components of a floating tidal device. The highly gregarious settlement behaviour and rapid growth exhibited by this species may have important implications for managing fouling in the ORE industry, especially in ‘niche’ areas. Anti-fouling strategies and maintenance scheduling applicable to ORE and other marine industries are discussed

De Novo Enzyme Design Using Rosetta3

The Rosetta de novo enzyme design protocol has been used to design enzyme catalysts for a variety of chemical reactions, and in principle can be applied to any arbitrary chemical reaction of interest, The process has four stages: 1) choice of a catalytic mechanism and corresponding minimal model active site, 2) identification of sites in a set of scaffold proteins where this minimal active site can be realized, 3) optimization of the identities of the surrounding residues for stabilizing interactions with the transition state and primary catalytic residues, and 4) evaluation and ranking the resulting designed sequences. Stages two through four of this process can be carried out with the Rosetta package, while stage one needs to be done externally. Here, we demonstrate how to carry out the Rosetta enzyme design protocol from start to end in detail using for illustration the triosephosphate isomerase reaction

Computational protein design with explicit consideration of surface hydrophobic patches

De novo protein design requires the identification of amino-acid sequences that favor the target folded conformation and are soluble in water. One strategy for promoting solubility is to disallow hydrophobic residues on the protein surface during design. However, naturally occurring proteins often have hydrophobic amino acids on their surface that contribute to protein stability via the partial burial of hydrophobic surface area or play a key role in the formation of protein-protein interactions. A less restrictive approach for surface design that is used by the modeling program Rosetta is to parameterize the energy function so that the number of hydrophobic amino acids designed on the protein surface is similar to what is observed in naturally occurring monomeric proteins. Previous studies with Rosetta have shown that this limits surface hydrophobics to the naturally occurring frequency (~28%) but that it does not prevent the formation of hydrophobic patches that are considerably larger than those observed in naturally occurring proteins. Here, we describe a new score term that explicitly detects and penalizes the formation of hydrophobic patches during computational protein design. With the new term we are able to design protein surfaces that include hydrophobic amino acids at naturally occurring frequencies, but do not have large hydrophobic patches. By adjusting the strength of the new score term the emphasis of surface redesigns can be switched between maintaining solubility and maximizing folding free energy

Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta

Summary: Foldit Standalone is an interactive graphical interface to the Rosetta molecular modeling package. In contrast to most command-line or batch interactions with Rosetta, Foldit Standalone is designed to allow easy, real-time, direct manipulation of protein structures, while also giving access to the extensive power of Rosetta computations. Derived from the user interface of the scientific discovery game Foldit (itself based on Rosetta), Foldit Standalone has added more advanced features and removed the competitive game elements. Foldit Standalone was built from the ground up with a custom rendering and event engine, configurable visualizations and interactions driven by Rosetta. Foldit Standalone contains, among other features: electron density and contact map visualizations, multiple sequence alignment tools for template-based modeling, rigid body transformation controls, RosettaScripts support and an embedded Lua interpreter

Role of conformational sampling in computing mutation-induced changes in protein structure and stability

The prediction of changes in protein stability and structure resulting from single amino acid substitutions is both a fundamental test of macromolecular modeling methodology and an important current problem as high throughput sequencing reveals sequence polymorphisms at an increasing rate. In principle, given the structure of a wild-type protein and a point mutation whose effects are to be predicted, an accurate method should recapitulate both the structural changes and the change in the folding-free energy. Here, we explore the performance of protocols which sample an increasing diversity of conformations. We find that surprisingly similar performances in predicting changes in stability are achieved using protocols that involve very different amounts of conformational sampling, provided that the resolution of the force field is matched to the resolution of the sampling method. Methods involving backbone sampling can in some cases closely recapitulate the structural changes accompanying mutations but not surprisingly tend to do more harm than good in cases where structural changes are negligible. Analysis of the outliers in the stability change calculations suggests areas needing particular improvement; these include the balance between desolvation and the formation of favorable buried polar interactions, and unfolded state modeling

Modeling Symmetric Macromolecular Structures in Rosetta3

Symmetric protein assemblies play important roles in many biochemical processes. However, the large size of such systems is challenging for traditional structure modeling methods. This paper describes the implementation of a general framework for modeling arbitrary symmetric systems in Rosetta3. We describe the various types of symmetries relevant to the study of protein structure that may be modeled using Rosetta's symmetric framework. We then describe how this symmetric framework is efficiently implemented within Rosetta, which restricts the conformational search space by sampling only symmetric degrees of freedom, and explicitly simulates only a subset of the interacting monomers. Finally, we describe structure prediction and design applications that utilize the Rosetta3 symmetric modeling capabilities, and provide a guide to running simulations on symmetric systems

SwiftLib: rapid degenerate-codon-library optimization through dynamic programming

Degenerate codon (DC) libraries efficiently address the experimental library-size limitations of directed evolution by focusing diversity toward the positions and toward the amino acids (AAs) that are most likely to generate hits; however, manually constructing DC libraries is challenging, error prone and time consuming. This paper provides a dynamic programming solution to the task of finding the best DCs while keeping the size of the library beneath some given limit, improving on the existing integer-linear programming formulation. It then extends the algorithm to consider multiple DCs at each position, a heretofore unsolved problem, while adhering to a constraint on the number of primers needed to synthesize the library. In the two library-design problems examined here, the use of multiple DCs produces libraries that very nearly cover the set of desired AAs while still staying within the experimental size limits. Surprisingly, the algorithm is able to find near-perfect libraries where the ratio of amino-acid sequences to nucleic-acid sequences approaches 1; it effectively side-steps the degeneracy of the genetic code. Our algorithm is freely available through our web server and solves most design problems in about a second

Pay for locally monitored performance?:A welfare analysis for teacher attendance in Ugandan primary schools

To achieve the twin objectives of incentivizing agent performance and providing information for planning purposes, public sector organizations often rely on reports by local monitors that are costly to verify. Received wisdom has it that attaching financial incentives to these reports will result in collusion, and undermine both objectives. Simple bargaining logic, however, suggests the reverse: pay for locally monitored performance could incentivize desired behavior and improve information. To investigate this issue, we conducted a randomized controlled trial in Ugandan primary schools that explored how incentives for teachers could be designed when based on local monitoring by head teachers. Our experiment randomly varied whether head teachers' reports of teacher attendance were tied to teacher bonus payments or not. We find that local monitoring on its own is ineffective at improving teacher attendance. However, combining local monitoring with financial incentives leads to both an increase in teacher attendance (by 8 percentage points) and an improvement in the quality of information. We also observe substantial gains in pupil attainment, driven primarily by a reduction in dropouts. By placing a financial value on these enrollment gains, we demonstrate that pay for locally monitored performance passes both welfare and fiscal sustainability tests