Design of structurally distinct proteins using strategies inspired by evolution

Natural recombination combines pieces of pre-existing proteins to create new tertiary structures and functions. We describe a computational protocol, called SEWING, which is inspired by this process and builds new proteins from connected or disconnected pieces of existing structures. Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and in some cases remain folded to over 100 °C. High resolution structures of the designed proteins CA01 and DA05R1 were solved by X-ray crystallography (2.2 Å resolution) and NMR respectively, and there was excellent agreement with the design models. This method provides a new strategy to rapidly create large numbers of diverse and designable protein scaffolds

Combining Experiments and Simulations Using the Maximum Entropy Principle

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges

De Novo Proteins Designed From Evolutionary Principles

Protein engineering has rapidly developed into a powerful method for the optimization, alteration, and creation of protein functions. Current protein engineering methods fall into the category of either high-throughput directed evolution techniques, or engineering through the use of computational models of protein structure. Despite significant innovation in both of these categories, neither is capable of handling the most difficult and desirable protein engineering goals. The combination of these two categories is an area of active research, and the development and testing of combination methods is the focus of this dissertation. Chapters 2 and 3 describe the development of a computational framework for de novo protein design called SEWING (Structural Extension WIth Native-fragment Graphs). In contrast to existing methods of de novo design, which attempt to design proteins that match a designer-supplied target topology, SEWING generates large numbers of diverse protein structures. We show that this strategy is highly effective at creating diverse helical backbones. Experimental characterization of SEWING designs shows that the experimental structures match the design models with sub-angstrom root mean square deviation (RMSD). Chapter 3 extends this methodology to the creation of protein interfaces. Using this method, several de novo designed proteins are created that bind their designated target. Chapter 4 describes the combination of directed evolution and computational modeling through the improvement of directed evolution techniques. In this chapter, a web tool called SwiftLib is developed, which allows rapid generation of degenerate codon libraries. SwiftLib allows protein engineers to determine optimal degenerate codon primers for the incorporation of desired sequences, such as sequence profiles generated from computational modeling and evolutionary data. Together, these chapters outline the creation of tools for the engineering of protein functions, and provide additional evidence that computational modeling and evolutionary principles can be combined for the improvement of protein engineering methods.Doctor of Philosoph

Improving Computational Methods for Designing Polar Protein-Protein Interfaces

Computational protein design has come a long way over the past few decades, but there is still room to improve. Even state-of-the-art computational protein modeling software has challenges when attempting to design protein-protein interactions. Current rotamer optimization protocols have problems when performing sequence design at the interface of multiple protein chains, specifically with respect to desolvation penalties. Additionally, modern docking protocols use artificial energy landscapes that are poorly suited for protein interface design. In this study, I inspect the current state-of-the-art protocols, identify their shortcomings, and develop and benchmark improvements and/or replacements. I lay out three improvements (two for rotamer optimization at the interface and one for docking), benchmark them, and show that they all improve our ability to sample the energy landscape provided. The benchmarks show that, based on computational metrics, our new protocols are able to minimize risk of desolvation penalties without any energetic tradeoffs compared to existing standards.Doctor of Philosoph

ENGINEERING IODOTYROSINE DEIODINASE TOWARDS BIOREMEDIATION OF HALOPHENOLS AND CHARACTERIZATION OF A UNIQUE THERMOPHILIC IODOTYROSINE DEIODINASE FROM Thermotoga neapolitana

Iodotyrosine deiodinase (IYD) promotes reductive dehalogenation of chloro-, bromo-, and iodotyrosines (Cl-Tyr, Br-Tyr, and I-Tyr, respectively). This activity decreases dramatically for phenolic substrates lacking the zwitterion due to their inability to close an active site lid. Enhancing this activity will provide an attractive approach to bioremediation of halophenols. A combination of computational design with Rosetta, library construction and screening was employed to promote IYD’s activity towards 2-iodophenol (2IP, a model for halophenol). The lid sequence of IYD from Homo sapiens (HsIYD) was redesigned by a fixed backbone approach to stabilize lid closure in the presence of 2IP. This approach successfully yielded a variant UD08 that moderately improved 2IP deiodination by 4.5-fold compared to HsIYD. UD08 expressed a disorder-to-order transition of the lid induced by 2IP as predicted by Rosetta and verified via limited proteolysis. This resembles the induced lid closure by I-Tyr in HsIYD. IYD from Haliscomenobacter hydrossis (HhIYD), as an easy-to-work with alternative to HsIYD, was subsequently redesigned via a fixed backbone approach and a loop remodeling approach to further improve IYD-2IP interactions. Rosetta once again demonstrated stabilization of the targeted enzyme•2IP complex with loop remodeling although an active 2IP deiodinase was not generated from these approaches. IYD from a thermophilic bacteria Thermotoga neapolitana (TnIYD) was characterized for its potential to facilitate engineering and provide a crystal structure of the fully reduced IYD. TnIYD is the smallest iodotyrosine deiodinase characterized to date and therefore represents a minimal structural requirement for reductive dehalogenation. TnIYD exhibits many unique properties different from those of mesophilic IYDs such as the formation of FMN semiquinone during purification and its tight binding of tyrosine (Tyr). The oxidized FMN in crystals of I-Tyr, Tyr, and fluorotyrosine (F-Tyr) bound TnIYD was readily reduced to the FMN semiquinone, but not to the hydroquinone form upon dithionite treatment. High resolution crystal structures of I-Tyr, F-Tyr, and Tyr bound TnIYD with oxidized and semiquinone FMN suggested that no major conformational changes from the oxidized structure were needed to support the FMN semiquinone and other radical intermediates generated during catalysis. An additional binding site for I-Tyr was identified on the surface of TnIYD and this might explain the substrate inhibition of IYDs observed under steady-state conditions. The reduction of TnIYD and HsIYD by dithionite was inhibited by both I-Tyr and Cl-Tyr and may cause the reduction of IYD to be rate-determining during steady-state catalysis

A Features Analysis Tool For Assessing And Improving Computational Models In Structural Biology

The protein-folding problem is to predict, from a protein's amino acid sequence, its folded 3D conformation. State of the art computational models are complex collaboratively maintained prediction software. Like other complex software, they become brittle without support for testing and refactoring. Features analysis, a language of `scientific unit testing', is the visual and quantitative comparison of distributions of features (local geometric measures) sampled from ensembles of native and predicted conformations. To support features analysis I develop a features analysis tool--a modular database framework for extracting and managing sampled feature instance and an exploratory data analysis framework for rapidly comparing feature distributions. In supporting features analysis, the tool supports the creation, tuning, and assessment of computational models, improving protein prediction and design. I demonstrate the features analysis tool through 6 case studies with the Rosetta molecular modeling suite. The first three demonstrate the tool usage mechanics through constructing and checking models. The first evaluates bond angle restraint models when used with the Backrub local sampling heuristic. The second identifies and resolves energy function derivative discontinuities that frustrate gradient-based minimization. The third constructs a model for disulfide bonds. The second three demonstrate using the tool to evaluate and improve how models represent molecular structure. I focus on modeling H-bonds because of their geometric specificity and environmental dependence lead to complex feature distributions. The fourth case study develops a novel functional form for Sp2 acceptor H-bonds. The fifth fits parameters for a refined H-bond model. The sixth combines the refined model with an electrostatics model and harmonizes them with the rest of the energy function. Next, to facilitate assessing model improvements, I develop recovery tests that measure predictive accuracy by asking models to recover native conformations that have been partially randomized. Finally, to demonstrate that the features analysis and recovery test tools support improving protein prediction and design, I evaluated the refined H-bond model and electrostatics model with additional corrections from the Rosetta community. Based on positive results, I recommend a new standard energy function, which has been accepted by the Rosetta community as the largest systematic improvement in nearly a decade.Doctor of Philosoph