Benjamin D. Allen

Combinatorial methods for small-molecule placement in computational enzyme design

The incorporation of small-molecule transition state structures into protein design calculations poses special challenges because of the need to represent the added translational, rotational, and conformational freedoms within an already difficult optimization problem. Successful approaches to computational enzyme design have focused on catalytic side-chain contacts to guide placement of small molecules in active sites. We describe a process for modeling small molecules in enzyme design calculations that extends previously described methods, allowing favorable small-molecule positions and conformations to be explored simultaneously with sequence optimization. Because all current computational enzyme design methods rely heavily on sampling of possible active site geometries from discrete conformational states, we tested the effects of discretization parameters on calculation results. Rotational and translational step sizes as well as side-chain library types were varied in a series of computational tests designed to identify native-like binding contacts in three natural systems. We find that conformational parameters, especially the type of rotamer library used, significantly affect the ability of design calculations to recover native binding-site geometries. We describe the construction and use of a crystallographic conformer library and find that it more reliably captures active-site geometries than traditional rotamer libraries in the systems tested.

Generation of longer emission wavelength red fluorescent proteins using computationally designed libraries

The longer emission wavelengths of red fluorescent proteins (RFPs) make them attractive for whole-animal imaging because cells are more transparent to red light. Although several useful RFPs have been developed using directed evolution, the quest for further red-shifted and improved RFPs continues. Herein, we report a structure-based rational design approach to red-shift the fluorescence emission of RFPs. We applied a combined computational and experimental approach that uses computational protein design as an in silico prescreen to generate focused combinatorial libraries of mCherry mutants. The computational procedure helped us identify residues that could fulfill interactions hypothesized to cause red-shifts without destabilizing the protein fold. These interactions include stabilization of the excited state through H-bonding to the acylimine oxygen atom, destabilization of the ground state by hydrophobic packing around the charged phenolate, and stabilization of the excited state by a π-stacking interaction. Our methodology allowed us to identify three mCherry mutants (mRojoA, mRojoB, and mRouge) that display emission wavelengths > 630 nm, representing red-shifts of 20–26 nm. Moreover, our approach required the experimental screening of a total of ∼5,000 clones, a number several orders of magnitude smaller than those previously used to achieve comparable red-shifts. Additionally, crystal structures of mRojoA and mRouge allowed us to verify fulfillment of the interactions hypothesized to cause red-shifts, supporting their contribution to the observed red-shifts.

Dramatic performance enhancements for the FASTER optimization algorithm

FASTER is a combinatorial optimization algorithm useful for finding low‐energy side‐chain configurations in side‐chain placement and protein design calculations. We present two simple enhancements to FASTER that together improve the computational efficiency of these calculations by as much as two orders of magnitude with no loss of accuracy. Our results highlight the importance of choosing appropriate initial configurations, and show that efficiency can be improved by stringently limiting the number of positions that are allowed to relax in response to a perturbation. The changes we describe improve the quality of solutions found for large‐scale designs, and allow them to be found in hours rather than days. The improved FASTER algorithm finds low‐energy solutions more efficiently than common optimization schemes based on the dead‐end elimination theorem and Monte Carlo. These advances have prompted investigations into new methods for force field parameterization and multiple state design.

Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles

The stability, activity, and solubility of a protein sequence are determined by a delicate balance of molecular interactions in a variety of conformational states. Even so, most computational protein design methods model sequences in the context of a single native conformation. Simulations that model the native state as an ensemble have been mostly neglected due to the lack of sufficiently powerful optimization algorithms for multistate design. Here, we have applied our multistate design algorithm to study the potential utility of various forms of input structural data for design. To facilitate a more thorough analysis, we developed new methods for the design and high-throughput stability determination of combinatorial mutation libraries based on protein design calculations. The application of these methods to the core design of a small model system produced many variants with improved thermodynamic stability and showed that multistate design methods can be readily applied to large structural ensembles. We found that exhaustive screening of our designed libraries helped to clarify several sources of simulation error that would have otherwise been difficult to ascertain. Interestingly, the lack of correlation between our simulated and experimentally measured stability values shows clearly that a design procedure need not reproduce experimental data exactly to achieve success. This surprising result suggests potentially fruitful directions for the improvement of computational protein design technology.

An efficient algorithm for multistate protein design based on FASTER.

Most of the methods that have been developed for computational protein design involve the selection of side‐chain conformations in the context of a single, fixed main‐chain structure. In contrast, multistate design (MSD) methods allow sequence selection to be driven by the energetic contributions of multiple structural or chemical states simultaneously. This methodology is expected to be useful when the design target is an ensemble of related states rather than a single structure, or when a protein sequence must assume several distinct conformations to function. MSD can also be used with explicit negative design to suggest sequences with altered structural, binding, or catalytic specificity. We report implementation details of an efficient multistate design optimization algorithm based on FASTER (MSD‐FASTER). We subjected the algorithm to a battery of computational tests and found it to be generally applicable to various multistate design problems; designs with a large number of states and many designed positions are completely feasible. A direct comparison of MSD‐FASTER and multistate design Monte Carlo indicated that MSD‐FASTER discovers low‐energy sequences much more consistently. MSD‐FASTER likely performs better because amino acid substitutions are chosen on an energetic basis rather than randomly, and because multiple substitutions are applied together. Through its greater efficiency, MSD‐FASTER should allow protein designers to test experimentally better‐scoring sequences, and thus accelerate progress in the development of improved scoring functions and models for computational protein design.

Computational protein design promises to revolutionize protein engineering

Natural evolution has produced an astounding array of proteins that perform the physical and chemical functions required for life on Earth. Although proteins can be reengineered to provide altered or novel functions, the utility of this approach is limited by the difficulty of identifying protein sequences that display the desired properties. Recently, advances in the field of computational protein design (CPD) have shown that molecular simulation can help to predict sequences with new and improved functions. In the past few years, CPD has been used to design protein variants with optimized specificity of binding to DNA, small molecules, peptides, and other proteins. Initial successes in enzyme design highlight CPDs unique ability to design function de novo. The use of CPD for the engineering of potential therapeutic agents has demonstrated its strength in real-life applications.

Molecular tandem repeat strategy for elucidating mechanical properties of high-strength proteins

Significance Squid have teeth-like structural [squid ring teeth (SRT)] proteins inside their suckers, which have segmented semicrystalline morphology with repetitive amorphous and crystalline domains. These proteins have high elastic modulus and toughness. However, a clear relationship between molecular structure and mechanical properties of this material remains elusive. To investigate the genetic basis of material properties in SRT sequences, we developed a new approach for the design and production of structural proteins. We show that the toughness and flexibility of these synthetic SRT mimics increase as a function of molecular weight, whereas the elastic modulus and yield strength remain unchanged. These results suggest that artificial proteins produced by our approach can help to illuminate the genetic basis of protein material behavior in SRT. Many globular and structural proteins have repetitions in their sequences or structures. However, a clear relationship between these repeats and their contribution to the mechanical properties remains elusive. We propose a new approach for the design and production of synthetic polypeptides that comprise one or more tandem copies of a single unit with distinct amorphous and ordered regions. Our designed sequences are based on a structural protein produced in squid suction cups that has a segmented copolymer structure with amorphous and crystalline domains. We produced segmented polypeptides with varying repeat number, while keeping the lengths and compositions of the amorphous and crystalline regions fixed. We showed that mechanical properties of these synthetic proteins could be tuned by modulating their molecular weights. Specifically, the toughness and extensibility of synthetic polypeptides increase as a function of the number of tandem repeats. This result suggests that the repetitions in native squid proteins could have a genetic advantage for increased toughness and flexibility.

Tunable thermal transport and reversible thermal conductivity switching in topologically networked bio-inspired materials

The dynamic control of thermal transport properties in solids must contend with the fact that phonons are inherently broadband. Thus, efforts to create reversible thermal conductivity switches have resulted in only modest on/off ratios, since only a relatively narrow portion of the phononic spectrum is impacted. Here, we report on the ability to modulate the thermal conductivity of topologically networked materials by nearly a factor of four following hydration, through manipulation of the displacement amplitude of atomic vibrations. By varying the network topology, or crosslinked structure, of squid ring teeth-based bio-polymers through tandem-repetition of DNA sequences, we show that this thermal switching ratio can be directly programmed. This on/off ratio in thermal conductivity switching is over a factor of three larger than the current state-of-the-art thermal switch, offering the possibility of engineering thermally conductive biological materials with dynamic responsivity to heat.Thermal conductivity in a proteinaceous semi-crystalline material can be modulated by more than three times upon hydration and dehydration cycles.

Research Update: Programmable tandem repeat proteins inspired by squid ring teeth

Cephalopods have evolved many interesting features that can serve as inspiration. Repetitive squid ring teeth (SRT) proteins from cephalopods exhibit properties such as strength, self-healing, and biocompatibility. These proteins have been engineered to design novel adhesives, self-healing textiles, and the assembly of 2d-layered materials. Compared to conventional polymers, repetitive proteins are easy to modify and can assemble in various morphologies and molecular architectures. This research update discusses the molecular biology and materials science of polypeptides inspired by SRT proteins, their properties, and perspectives for future applications.

ProtaBank: A repository for protein design and engineering data

We present ProtaBank, a repository for storing, querying, analyzing, and sharing protein design and engineering data in an actively maintained and updated database. ProtaBank provides a format to describe and compare all types of protein mutational data, spanning a wide range of properties and techniques. It features a user‐friendly web interface and programming layer that streamlines data deposition and allows for batch input and queries. The database schema design incorporates a standard format for reporting protein sequences and experimental data that facilitates comparison of results across different data sets. A suite of analysis and visualization tools are provided to facilitate discovery, to guide future designs, and to benchmark and train new predictive tools and algorithms. ProtaBank will provide a valuable resource to the protein engineering community by storing and safeguarding newly generated data, allowing for fast searching and identification of relevant data from the existing literature, and exploring correlations between disparate data sets. ProtaBank invites researchers to contribute data to the database to make it accessible for search and analysis. ProtaBank is available at https://protabank.org.