Methods for facile synthesis of extraordinarily diverse peptide-like oligomers have placed peptoids at the center of a broad and vibrant area of foldamer science and technology. The 7th Peptoid Summit offered a perspective on the current state of peptoid science and technology and on prospects for engineering supramolecular assemblies that rival the complexity of biomolecular systems. Methods for engineering biomolecular systems based on DNA and protein are advancing rapidly, building a technology platform for engineering increasingly large and complex self-assembled nanosystems. A comparative review of the physical basis for DNA, protein, and peptoid engineering indicates that the characteristics of peptoids suit them for a strong role in developing self-assembled nanosystems. Physical parallels between peptoids and proteins indicate that peptoid engineering, like protein engineering, will require specialized software to support design. Access to novel side-chain functionality will enable peptoid designers to exploit novel binding interactions, including many that have been discovered and exploited in crystal engineering, a field that has extensively explored the self-assembly of small organic molecules to form well-ordered structures. Developments in DNA, protein, and inorganic nanotechnologies are converging to provide a technology platform for the design and fabrication of complex, functional, atomically precise nanosystems. Peptoid-based foldamer technologies, can contribute to this convergence, expanding the scope of the emerging field of atomically precise macromolecular nanosystems.

Protein engineering is altering the structure of a protein to improve or change its properties. This unit summarizes concepts for protein engineering using rational design, directed evolution, and combinations of them. Different strategies are presented for identifying the best mutagenesis method, how to identify desired variants by screening or selection, and examples for successful applications are given. This should enable researchers to choose the most promising tools to solve their protein engineering challenges.

The manipulation of protein backbone structure to control interaction and function is a challenge for protein engineering. We integrated computational design with experimental selection for grafting the backbone and side chains of a two-segment HIV gp120 epitope, targeted by the cross-neutralizing antibody b12, onto an unrelated scaffold protein. The final scaffolds bound b12 with high specificity and with affinity similar to that of gp120, and crystallographic analysis of a scaffold bound to b12 revealed high structural mimicry of the gp120-b12 complex structure. The method can be generalized to design other functional proteins through backbone grafting.

Microbial production of desired compounds provides an efficient framework for the development of renewable energy resources. To be competitive to traditional chemistry, one requirement is to utilize the full capacity of the microorganism to produce target compounds with high yields and turnover rates. We use integrated computational methods to generate and quantify the performance of novel biosynthetic routes that contain highly optimized catalysts. Engineering a novel reaction pathway entails addressing feasibility on multiple levels, which involves handling the complexity of large-scale biochemical networks while respecting the critical chemical phenomena at the atomistic scale. To pursue this multi-layer challenge, our strategy merges knowledge-based metabolic engineering methods with computational chemistry methods. By bridging multiple disciplines, we provide an integral computational framework that could accelerate the discovery and implementation of novel biosynthetic production routes. Using this approach, we have identified and optimized a novel biosynthetic route for the production of 3HP from pyruvate. Biotechnol. Bioeng. © 2011 Wiley Periodicals, Inc.

We describe RosettaRemodel, a generalized framework for flexible protein design that provides a versatile and convenient interface to the Rosetta modeling suite. RosettaRemodel employs a unified interface, called a blueprint, which allows detailed control over many aspects of flexible backbone protein design calculations. RosettaRemodel allows the construction and elaboration of customized protocols for a wide range of design problems ranging from loop insertion and deletion, disulfide engineering, domain assembly, loop remodeling, motif grafting, symmetrical units, to de novo structure modeling.

Developments in biocatalysis have been largely fuelled by consumer demands for new products, industrial attempts to improving existing process and minimizing waste, coupled with governmental measures to regulate consumer safety along with scientific advancements. One of the major hurdles to application of biocatalysis to chemical synthesis is unavailability of the desired enzyme to catalyse the reaction to allow for a viable process development. Even when the desired enzyme is available it often forces the process engineers to alter process parameters due to inadequacies of the enzyme, such as instability, inhibition, low yield or selectivity, etc. Developments in the field of enzyme or reaction engineering have allowed access to means to achieve the ends, such as directed evolution, de novo protein design, use of non-conventional media, using new substrates for old enzymes, active-site imprinting, altering temperature, etc. Utilization of enzyme discovery and improvement tools therefore provides a feasible means to overcome this problem. Judicious employment of these tools has resulted in significant advancements that have leveraged the research from laboratory to market thus impacting economic growth; however, there are further opportunities that have not yet been explored. The present review attempts to highlight some of these achievements and potential opportunities.

Consensus design, the selection of mutations based on the most common amino acid in each position of a multiple sequence alignment, has proven to be an efficient way to engineer stabilized mutants and even to design entire proteins. However, its application has been limited to small motifs or small families of highly related proteins. Also, we have little idea of how information that specifies a protein's properties is distributed between positional effects (consensus) and interactions between positions (correlated occurrences of amino acids). Here, we designed several consensus variants of triosephosphate isomerase (TIM), a large, diverse family of complex enzymes. The first variant was only weakly active, had molten globular characteristics, and was monomeric at 25 °C despite being based on nearly all dimeric enzymes. A closely related variant from curation of the sequence database resulted in a native-like dimeric TIM with near-diffusion-controlled kinetics. Both enzymes vary substantially (30-40%) from any natural TIM, but they differ from each other in only a relatively small number of unconserved positions. We demonstrate that consensus design is sufficient to engineer a sophisticated protein that requires precise substrate positioning and coordinated loop motion. The difference in oligomeric states and native-like properties for the two consensus variants is not a result of defects in the dimerization interface but rather disparate global properties of the proteins. These results have important implications for the role of correlated amino acids, the ability of TIM to function as a monomer, and the ability of molten globular proteins to carry out complex reactions.

Some protein design tasks cannot be modeled by the traditional single state design strategy of finding a sequence that is optimal for a single fixed backbone. Such cases require multistate design, where a single sequence is threaded onto multiple backbones (states) and evaluated for its strengths and weaknesses on each backbone. For example, to design a protein that can switch between two specific conformations, it is necessary to to find a sequence that is compatible with both backbone conformations. We present in this paper a generic implementation of multistate design that is suited for a wide range of protein design tasks and demonstrate in silico its capabilities at two design tasks: one of redesigning an obligate homodimer into an obligate heterodimer such that the new monomers would not homodimerize, and one of redesigning a promiscuous interface to bind to only a single partner and to no longer bind the rest of its partners. Both tasks contained negative design in that multistate design was asked to find sequences that would produce high energies for several of the states being modeled. Success at negative design was assessed by computationally redocking the undesired protein-pair interactions; we found that multistate design's accuracy improved as the diversity of conformations for the undesired protein-pair interactions increased. The paper concludes with a discussion of the pitfalls of negative design, which has proven considerably more challenging than positive design.

A loop closure-based sequential algorithm, PRODA_MATCH, was developed to match catalytic residues onto a scaffold for enzyme design in silico. The computational complexity of this algorithm is polynomial with respect to the number of active sites, the number of catalytic residues, and the maximal iteration number of cyclic coordinate descent steps. This matching algorithm is independent of a rotamer library that enables the catalytic residue to take any required conformation during the reaction coordinate. The catalytic geometric parameters defined between functional groups of transition state (TS) and the catalytic residues are continuously optimized to identify the accurate position of the TS. Pseudo-spheres are introduced for surrounding residues, which make the algorithm take binding into account as early as during the matching process. Recapitulation of native catalytic residue sites was used as a benchmark to evaluate the novel algorithm. The calculation results for the test set show that the native catalytic residue sites were successfully identified and ranked within the top 10 designs for 7 of the 10 chemical reactions. This indicates that the matching algorithm has the potential to be used for designing industrial enzymes for desired reactions.

d(6)-piano-stool complexes bearing an arylsulfonamide anchor display sub-micromolar affinity towards human Carbonic Anhydrase II (hCA II). The 1.3 Å resolution X-ray crystal structure of [(η(6)-C(6)Me(6))Ru(bispy 3)Cl](+)⊂ hCA II highlights the nature of the host-guest interactions.

Few existing protein-protein interface design methods allow for extensive backbone rearrangements during the design process. There is also a dichotomy between redesign methods, which take advantage of the native interface, and de novo methods, which produce novel binders. Here, we propose a new method for designing novel protein reagents that combines advantages of redesign and de novo methods and allows for extensive backbone motion. This method requires a bound structure of a target and one of its natural binding partners. A key interaction in this interface, the anchor, is computationally grafted out of the partner and into a surface loop on the design scaffold. The design scaffold's surface is then redesigned with backbone flexibility to create a new binding partner for the target. Careful choice of a scaffold will bring experimentally desirable characteristics into the new complex. The use of an anchor both expedites the design process and ensures that binding proceeds against a known location on the target. The use of surface loops on the scaffold allows for flexible-backbone redesign to properly search conformational space. This protocol was implemented within the Rosetta3 software suite. To demonstrate and evaluate this protocol, we have developed a benchmarking set of structures from the PDB with loop-mediated interfaces. This protocol can recover the correct loop-mediated interface in 15 out of 16 tested structures, using only a single residue as an anchor.

The ability to routinely generate efficient protein catalysts of bond-forming reactions chosen by researchers, rather than nature, is a long-standing goal of the molecular life sciences. Here, we describe a directed evolution strategy for enzymes that catalyze, in principle, any bond-forming reaction. The system integrates yeast display, enzyme-mediated bioconjugation, and fluorescence-activated cell sorting to isolate cells expressing proteins that catalyze the coupling of two substrates chosen by the researcher. We validated the system using model screens for Staphylococcus aureus sortase A-catalyzed transpeptidation activity, resulting in enrichment factors of 6,000-fold after a single round of screening. We applied the system to evolve sortase A for improved catalytic activity. After eight rounds of screening, we isolated variants of sortase A with up to a 140-fold increase in LPETG-coupling activity compared with the starting wild-type enzyme. An evolved sortase variant enabled much more efficient labeling of LPETG-tagged human CD154 expressed on the surface of HeLa cells compared with wild-type sortase. Because the method developed here does not rely on any particular screenable or selectable property of the substrates or product, it represents a powerful alternative to existing enzyme evolution methods.

Directed evolution is widely used to improve enzymes, particularly for industrial biocatalytic processes. Molecular biology advances present many new strategies for directed evolution. Commonly used techniques have led to many successful examples of enzyme improvement, yet there is still a need to improve both the efficiency and capability of directed evolution. Recent strategies aimed at making directed evolution faster and more efficient take better advantage of available structural and sequence information. The underlying principles that lead to early dead-ends for directed evolution experiments are also discussed along with recent strategies designed to by-pass them. Several emerging methods for creating novel enzymes are also discussed including examples of catalytic activity for which there is no precedent in nature. Finally, the combined use of several strategies is likely to be required in practice to improve multiple target properties of an enzyme, as successfully shown by a recent industrial example.

Structure-based computational methods are popular tools for designing proteins and interactions between proteins because they provide the necessary insight and details required for rational engineering. Here, we first argue that large-scale databases of fragments contain a discrete but complete set of building blocks that can be used to design structures. We show that these structural alphabets can be saturated to provide conformational ensembles that sample the native structure space around energetic minima. Second, we show that catalogs of interaction patterns hold the key to overcome the lack of scaffolds when computationally designing protein interactions. Finally, we illustrate the power of database-driven computational protein design methods by recent successful applications and discuss what challenges remain to push this field forward.

To address the synthesis of increasingly structurally diverse small-molecule drugs, methods for the generation of efficient and selective biological catalysts are becoming increasingly important.'Directed evolution' is an umbrella term referring to a variety of methods for improving or altering the function of enzymes using a nature-inspired twofold strategy of mutagenesis followed by selection. This article provides an objective assessment of the effectiveness of directed evolution campaigns in generating enzymes with improved catalytic parameters for new substrates from the last decade, excluding studies that aimed to select for only improved physical properties and those that lack kinetic characterization. An analysis of the trends of methodologies and their success rates from 81 qualifying examples in the literature reveals the average fold improvement for k (cat)(or V (max)), K (m) and k (cat)/K (m) to be 366-, 12- and 2548-fold, respectively, whereas the median fold improvements are 5.4, 3 and 15.6. Further analysis by enzyme class, library-generation methodology and screening methodology explores relationships between successful campaigns and the methodologies employed.

Artificial metalloenzymes result from combining a catalytically active organometallic moiety with a macromolecular host. The resulting hybrid catalysts combine attractive features of both homogeneous and enzymatic systems. Herein we summarize the recent progress in this emerging field and outline the challenges ahead.

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.

Proteins are the molecules cells primarily rely on for catalysis, recognition, signaling, defense, locomotion, and structural integrity. Engineering proteins for improved function or new applications is a fast-growing segment of biotechnology and biomedicine. Experimental efforts based on the screening of large mutant libraries have led to many successes but they do not provide quantitative design principles and/or insight into the structural features that underpin the desired function. The computational de novo design of proteins promises to bridge this gap; however, it requires reliable structure prediction, provisions for protein stability, and accurate descriptions of inter-molecule interactions. Studies that successfully meet all these criteria are beginning to emerge including the design of an O(2)-binding protein and a novel enzyme that catalyzes a Diels-Alder reaction.

The carbon-fluorine bond is the strongest covalent bond in organic chemistry, yet fluoroacetate dehalogenases can readily hydrolyze this bond under mild physiological conditions. Elucidating the molecular basis of this rare biocatalytic activity will provide the fundamental chemical insights into how this formidable feat is achieved. Here, we present a series of high-resolution (1.15-1.80 Å) crystal structures of a fluoroacetate dehalogenase, capturing snapshots along the defluorination reaction: the free enzyme, enzyme-fluoroacetate Michaelis complex, glycolyl-enzyme covalent intermediate, and enzyme-product complex. We demonstrate that enzymatic defluorination requires a halide pocket that not only supplies three hydrogen bonds to stabilize the fluoride ion but also is finely tailored for the smaller fluorine halogen atom to establish selectivity toward fluorinated substrates. We have further uncovered dynamics near the active site which may play pivotal roles in enzymatic defluorination. These findings may ultimately lead to the development of novel defluorinases that will enable the biotransformation of more complex fluorinated organic compounds, which in turn will assist the synthesis, detoxification, biodegradation, disposal, recycling, and regulatory strategies for the growing markets of organofluorines across major industrial sectors.