Protein Design Using Continuous Rotamers

Optimizing amino acid conformation and identity is a central problem in computational protein design. Protein design algorithms must allow realistic protein flexibility to occur during this optimization, or they may fail to find the best sequence with the lowest energy. Most design algorithms implement side-chain flexibility by allowing the side chains to move between a small set of discrete, low-energy states, which we call rigid rotamers. In this work we show that allowing continuous side-chain flexibility (which we call continuous rotamers) greatly improves protein flexibility modeling. We present a large-scale study that compares the sequences and best energy conformations in 69 protein-core redesigns using a rigid-rotamer model versus a continuous-rotamer model. We show that in nearly all of our redesigns the sequence found by the continuous-rotamer model is different and has a lower energy than the one found by the rigid-rotamer model. Moreover, the sequences found by the continuous-rotamer model are more similar to the native sequences. We then show that the seemingly easy solution of sampling more rigid rotamers within the continuous region is not a practical alternative to a continuous-rotamer model: at computationally feasible resolutions, using more rigid rotamers was never better than a continuous-rotamer model and almost always resulted in higher energies. Finally, we present a new protein design algorithm based on the dead-end elimination (DEE) algorithm, which we call iMinDEE, that makes the use of continuous rotamers feasible in larger systems. iMinDEE guarantees finding the optimal answer while pruning the search space with close to the same efficiency of DEE. Availability: Software is available under the Lesser GNU Public License v3. Contact the authors for source code

Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs)

We present a new support vector machine (SVM)-based approach to predict the substrate specificity of subtypes of a given protein sequence family. We demonstrate the usefulness of this method on the example of aryl acid-activating and amino acid-activating adenylation domains (A domains) of nonribosomal peptide synthetases (NRPS). The residues of gramicidin synthetase A that are 8 Å around the substrate amino acid and corresponding positions of other adenylation domain sequences with 397 known and unknown specificities were extracted and used to encode this physico-chemical fingerprint into normalized real-valued feature vectors based on the physico-chemical properties of the amino acids. The SVM software package SVM(light) was used for training and classification, with transductive SVMs to take advantage of the information inherent in unlabeled data. Specificities for very similar substrates that frequently show cross-specificities were pooled to the so-called composite specificities and predictive models were built for them. The reliability of the models was confirmed in cross-validations and in comparison with a currently used sequence-comparison-based method. When comparing the predictions for 1230 NRPS A domains that are currently detectable in UniProt, the new method was able to give a specificity prediction in an additional 18% of the cases compared with the old method. For 70% of the sequences both methods agreed, for <6% they did not, mainly on low-confidence predictions by the existing method. None of the predictive methods could infer any specificity for 2.4% of the sequences, suggesting completely new types of specificity

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Directed evolution (DE) creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding, and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of the art of molecular modeling, discussing their strengths, limitations, and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques and a synergic effort between simulations and experimental validation.Peer ReviewedPostprint (author's final draft

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Quantum chemistry and conformational sampling meet together : a powerful approach to study and design metalloprotein reactivity

Metalloprotein are proteins containing metal ion cofactors. Compared to chemical catalysts, metalloproteins have a well-defined configuration around the active site which ensures higher specificity, selectivity and reaction rates. Metalloproteins are soluble in water, their function can be optimized genetically by modifying an host (e.g. a bacteria) and are biodegradable. Therefore, they are ideal templates for the creation of novel green catalysts and therapeutics. Nonetheless, metalloproteins, as found in nature, are usually not ready for industrial scale-up and may need to be re-designed. Molecular simulations can guide the search for new metalloprotein functionality, cutting the costs of the experimental work. Modeling metalloproteins' function requires the sampling of both the electronic and nuclear degrees of freedom to exhaustively describe their chemical reactivity. The combination of conformational sampling and quantum chemical technique allows to model how they catalyze a reaction or covalently bind a ligand, without missing information about the dynamics of the whole protein. In this thesis, these computational techniques are systematically employed to study and guide present and future design efforts of laccases and hemoglobin. Laccases are copper-containing oxidoreductases that can oxidize a large variety of substrates at expenses of oxygen, which is reduced to water. Therefore, their interest in green chemistry applications: they work with air and produce water as sole by-product. So far, most efforts made to enlarge the chemical space of laccases have focused on increasing the redox potential of the first copper-based electron acceptor, a choice that yielded limited success. Here, it is proposed to focus on the desired substrate, modeling the active site of laccases to better fit and oxidize it. To do so, a computational protocol was developed, based on Monte Carlo sampling of the enzyme-substrate conformational space, followed by quick quantum chemical calculations to score oxidation. The protocol was first validated against experimental data, proving its capability to reproduce data and provide a rationale to laccases functioning. This new tool was then used to improve the oxidation of aniline by a laccase by simulating the effect of certain mutations (amino acid substitutions) which were tested in the lab by our collaborators. As a result, the design mutations significantly improved the oxidation of aniline (which leads to the formation of polyaniline, an organic semiconductor). Another design was carried out which lead to a significant improvement in activity of another laccase toward three different substrates. Therefore, the methodology developed proved to be capable of reproducing and rationalizing experimental results and rendering a la carte design of laccases toward a given (class of) substrates possible. A similar protocol, which uses an empirical computational method instead of quantum chemical techniques, was used to selectively attach a photosensitizer (a molecule that produces a chemical change in another molecule in a photochemical process) to the surface of a laccase. Hemoglobin, a heme-containing protein that carries oxygen from the lungs to the tissue in the body, is a candidate for blood substituent design. However, its design is rendered difficult by the limited knowledge of its functioning. Here, a mixed Monte Carlo-quantum chemical approach was used to support a theory about hemoglobin's allosteric mechanism and structurally characterize the tertiary end-states of the allosteric transition for the first time. These calculations, which were benchmarked against available experimental data, disclosed the role of the amino acids next to the oxygen binding site. This information was used in a subsequent molecular dynamics study which showed how the four subunits of hemoglobin give rise the allosteric response, highlighting the signalling paths and their hierarchyLas metaloproteínas son proteínas que contienen iones metálicos como cofactores. En comparación con los catalizadores químicos, las metaloproteínas tienen una configuración bien definida alrededor del sitio activo que asegura mejor especificidad, selectividad y velocidad de reacción. Las metaloproteínas son solubles en agua, su función puede ser optimizada genéticamente mediante la modificación de un huésped y son biodegradables. Por lo tanto, son ideales para la creación de nuevos catalizadores verdes y terapéuticos. No obstante, las metaloproteínas, como se encuentran en la naturaleza, no suelen estar listas para la industria y pueden necesitar ser re-diseñadas. Las simulaciones moleculares pueden orientar el diseño de mejores metaloproteınas, reduciendo el trabajo experimental. Modelar la función de las metaloproteınas necesita el muestreo de los grados de libertad electrónicos y nucleares para describir exhaustivamente su reactividad química. La combinación de muestreo conformacional y técnicas de química cuántica permite modelar la catálisis de una reacción o como un ligando se une covalentemente a una metaloproteína, sin omitir información sobre la dinámica de la proteína. En esta tesis, estas técnicas computacionales se utilizan sistemáticamente para estudiar y orientar presentes y futuros esfuerzos de diseño de lacasas y hemoglobina. Las lacasas son oxidorreductasas que pueden oxidar una gran variedad de sustratos y reducir oxígeno a agua. Por lo tanto, tienen interés en las aplicaciones de la química verde: funcionan con aire y producen agua como subproducto. Hasta ahora, la mayoría de los esfuerzos realizados para ampliar el espacio químico de lacasas se han centrado en aumentar el potencial redox del primer aceptor de electrones a base de cobre, una elección que obtuvo un éxito limitado. En este trabajo, se propone centrarse en el sustrato deseado, diseñando el sitio activo de las lacasas para mejor enlace y oxidación. Para ello, un protocolo de cálculo fue desarrollado, basado en el muestreo del espacio conformacional de la enzima-sustrato con técnicas Monte Carlo, seguido de cálculos de química cuántica para cuantificar la oxidación. El protocolo fue validado por primera vez contra datos experimentales, lo que probó su capacidad para proporcionar una justificación de las lacasas de funcionar. A continuación se utilizó esta nueva herramienta para mejorar la oxidación de la anilina por una lacasa, simulando el efecto de ciertas mutaciones que se probaron en el laboratorio por nuestros colaboradores. Como resultado, las mutaciones mejoraron significativamente la oxidación de anilina. Otro diseño produjo una mejora significativa en la actividad de otro lacasa hacía tres sustratos diferentes. Por lo tanto, la metodología desarrollada demostró ser útil para diseñar las lacasas hacia un dado sustrato. Un protocolo similar, que utiliza un método de cálculo empírico en lugar de técnicas de química cuántica, se utilizó para unir selectivamente un fotosensibilizador a la superficie de una lacasa. La hemoglobina, una proteína que contiene cuatro grupos hemo para transportar el oxígeno de los pulmones a los tejidos en el cuerpo, es un candidato para el diseño sustituyente de la sangre. Sin embargo, su diseño se ve dificultado por el limitado conocimiento de su funcionamiento. Aquí, la combinación de técnicas Monte Carlo y química cuántica se utilizó para apoyar una teoría sobre el mecanismo alostérico de la hemoglobina y caracterizar estructuralmente los estados finales de la transición alostérica terciaria por primera vez. Estos cálculos, que fueron comparados con los datos experimentales disponibles, permitieron conocer el papel de los aminoácidos próximos al sitio de unión de oxígeno. Esta información se utilizó en un estudio posterior de la dinámica molecular que mostró cómo las cuatro subunidades de la hemoglobina dan lugar a la respuesta alostérica, destacando las rutas de señalización y su jerarquía

Complex, non-native heteronuclear metal centers designed in cytochrome c peroxidase: Expanding the limits of biosynthetic modeling

Multielectron redox reactions often require enzymes with active sites that contain one or more metal centers, called metalloenzymes. Metalloenzymes may contain single metal ions, such as iron or copper, bound to the protein directly or through an organic ligand that chelates the metal, such as porphyrins (heme). Transition metals are especially useful catalytic centers because they can access multiple oxidation states with redox potentials that are tuned by the organic ligand or protein environment to facilitate coupled electron and proton movement. Metalloenzymes that accomplish some of the most challenging natural chemical reactions, such as the reduction of oxygen to water or of sulfite to hydrogen sulfide in a continuous process, require more than one metal ion as a combination of different elements or as two or more unique cofactors that contain the same metallic element; such enzymes are said to possess heteronuclear metal centers. In order to understand metalloenzymes and to translate that information into catalysts for biotechnological applications, scientists and engineers have designed artificial metalloenzymes as both structural and functional mimics of native enzymes. However, it is challenging to design artificial enzymes with heteronuclear centers because they tend to be structurally and functionally complex. Decades of research has cemented the understanding that biomimicry of the primary coordination shell around active metal ions is rarely sufficient to reproduce the fine control and stability of natural enzyme metal centers; secondary effects induced by the protein environment are required. A complementary approach to synthetic biomimicry and top-down mutation of native enzymes is biosynthetic modeling. By beginning with stable, natural enzymes that are small, but which share some crucial features with more complex metalloenzymes of interest, there has been significant success in building protein-derived interactions that are necessary and sufficient to engineer the activity and stability of natural enzyme catalysts. A prime example of heteronuclear metalloenzymes is sulfite reductase (SiR), which is an essential enzyme in sulfur assimilation and energy production pathways in bacteria and plants that reduces sulfite (SO32-) to hydrogen sulfide (HS-) at a single heteronuclear metal active center. SiR active sites comprise either a heme cofactor (siroheme) covalently linked to an iron-sulfur cluster ([4Fe-4S]) through a shared Cys ligand or a heme-copper center with linearly coordinated Cu(I) situated ~4 Å above the heme Fe. Both cofactors are biologically unique, reserved only for the six-electron process of sulfite reduction (and closely related nitrite reduction). Despite decades of research into the nature of the siroheme-[4Fe-4S] cofactor, it remains largely a mystery why such a complex cofactor is necessary for sulfite reduction and precisely how its structure and composition are related to efficient catalysis. In this dissertation, I describe the creation of a new structural and functional biosynthetic model of SiR by creating a designed heteronuclear heme-[4Fe-4S] cofactor in cytochrome c peroxidase (CcP). The model (SiRCcP) exhibits spectroscopic and ligand-binding properties of the native enzyme, and sulfite reduction activity was improved—through rational tuning of the secondary sphere interactions around the [4Fe-4S] and the substrate-binding sites—to be close to that of a native enzyme. SiRCcP represents the most complex synthetic metalloenzyme to-date, and the design process provides new insight into the boundaries of biosynthetic engineering. I also describe the design and characterization of a heme-Cu SiR biosynthetic model in the same CcP scaffold (CuICcP) for direct comparison of the mechanisms and structures of these two evolutionarily distinct enzymes. The structure and metal-binding properties of CuICcP are described, as is a relationship to the catalytic properties of heme-copper oxidase, which shares key active site structures with heme-Cu SiR. Additionally, I will describe the creation of the first binuclear Cu binding site with purple copper center properties in a natural protein that is not based on the cupredoxin fold. Together, these studies are explorations into the plasticity of enzyme active sites and their ability to meet design goals that diverge substantially from native structures and represent an expansion of the limits of biomimicry through protein engineering