James A. Davey

Multistate approaches in computational protein design

Computational protein design (CPD) is a useful tool for protein engineers. It has been successfully applied towards the creation of proteins with increased thermostability, improved binding affinity, novel enzymatic activity, and altered ligand specificity. Traditionally, CPD calculations search and rank sequences using a single fixed protein backbone template in an approach referred to as single‐state design (SSD). While SSD has enjoyed considerable success, certain design objectives require the explicit consideration of multiple conformational and/or chemical states. Cases where a “multistate” approach may be advantageous over the SSD approach include designing conformational changes into proteins, using native ensembles to mimic backbone flexibility, and designing ligand or oligomeric association specificities. These design objectives can be efficiently tackled using multistate design (MSD), an emerging methodology in CPD that considers any number of protein conformational or chemical states as inputs instead of a single protein backbone template, as in SSD. In this review article, recent examples of the successful design of a desired property into proteins using MSD are described. These studies employing MSD are divided into two categories—those that utilized multiple conformational states, and those that utilized multiple chemical states. In addition, the scoring of competing states during negative design is discussed as a current challenge for MSD.

Improving the accuracy of protein stability predictions with multistate design using a variety of backbone ensembles.

Multistate computational protein design (MSD) with backbone ensembles approximating conformational flexibility can predict higher quality sequences than single‐state design with a single fixed backbone. However, it is currently unclear what characteristics of backbone ensembles are required for the accurate prediction of protein sequence stability. In this study, we aimed to improve the accuracy of protein stability predictions made with MSD by using a variety of backbone ensembles to recapitulate the experimentally measured stability of 85 Streptococcal protein G domain β1 sequences. Ensembles tested here include an NMR ensemble as well as those generated by molecular dynamics (MD) simulations, by Backrub motions, and by PertMin, a new method that we developed involving the perturbation of atomic coordinates followed by energy minimization. MSD with the PertMin ensembles resulted in the most accurate predictions by providing the highest number of stable sequences in the top 25, and by correctly binning sequences as stable or unstable with the highest success rate (≈90%) and the lowest number of false positives. The performance of PertMin ensembles is due to the fact that their members closely resemble the input crystal structure and have low potential energy. Conversely, the NMR ensemble as well as those generated by MD simulations at 500 or 1000 K reduced prediction accuracy due to their low structural similarity to the crystal structure. The ensembles tested herein thus represent on‐ or off‐target models of the native protein fold and could be used in future studies to design for desired properties other than stability. Proteins 2014; 82:771–784.

Discovery of Substrates for a SET Domain Lysine Methyltransferase Predicted by Multistate Computational Protein Design

Characterization of lysine methylation has proven challenging despite its importance in biological processes such as gene transcription, protein turnover, and cytoskeletal organization. In contrast to other key posttranslational modifications, current proteomics techniques have thus far shown limited success at characterizing methyl-lysine residues across the cellular landscape. To complement current biochemical characterization methods, we developed a multistate computational protein design procedure to probe the substrate specificity of the protein lysine methyltransferase SMYD2. Modeling of substrate-bound SMYD2 identified residues important for substrate recognition and predicted amino acids necessary for methylation. Peptide- and protein- based substrate libraries confirmed that SMYD2 activity is dictated by the motif [LFM]-1-K(∗)-[AFYMSHRK]+1-[LYK]+2 around the target lysine K(∗). Comprehensive motif-based searches and mutational analysis further established four additional substrates of SMYD2. Our methodology paves the way to systematically predict and validate posttranslational modification sites while simultaneously pairing them with their associated enzymes.

Rational design of proteins that exchange on functional timescales

Proteins are intrinsically dynamic molecules that can exchange between multiple conformational states, enabling them to carry out complex molecular processes with extreme precision and efficiency. Attempts to design novel proteins with tailored functions have mostly failed to yield efficiencies matching those found in nature because standard methods do not allow the design of exchange between necessary conformational states on a functionally relevant timescale. Here we developed a broadly applicable computational method to engineer protein dynamics that we term meta-multistate design. We used this methodology to design spontaneous exchange between two novel conformations introduced into the global fold of Streptococcal protein G domain β1. The designed proteins, named DANCERs, for dynamic and native conformational exchangers, are stably folded and switch between predicted conformational states on the millisecond timescale. The successful introduction of defined dynamics on functional timescales opens the door to new applications requiring a protein to spontaneously access multiple conformational states.

Brighter Red Fluorescent Proteins by Rational Design of Triple-Decker Motif.

Red fluorescent proteins (RFPs) are used extensively in chemical biology research as fluorophores for live cell imaging, as partners in FRET pairs, and as signal transducers in biosensors. For all of these applications, brighter RFP variants are desired. Here, we used rational design to increase the quantum yield of monomeric RFPs in order to improve their brightness. We postulated that we could increase quantum yield by restricting the conformational degrees of freedom of the RFP chromophore. To test our hypothesis, we introduced aromatic residues above the chromophore of mRojoA, a dim RFP containing a π-stacked Tyr residue directly beneath the chromophore, in order to reduce chromophore conformational flexibility via improved packing and steric complementarity. The best mutant identified displayed an absolute quantum yield increase of 0.07, representing an over 3-fold improvement relative to mRojoA. Remarkably, this variant was isolated following the screening of only 48 mutants, a library size that is several orders of magnitude smaller than those previously used to achieve equivalent gains in quantum yield in other RFPs. The crystal structure of the highest quantum yield mutant showed that the chromophore is sandwiched between two Tyr residues in a triple-decker motif of aromatic rings. Presence of this motif increases chromophore rigidity, as evidenced by the significantly reduced temperature factors compared to dim RFPs. Overall, the approach presented here paves the way for the rapid development of fluorescent proteins with higher quantum yield and overall brightness.

Prediction of Stable Globular Proteins Using Negative Design with Non-native Backbone Ensembles

Accurate predictions of protein stability have great potential to accelerate progress in computational protein design, yet the correlation of predicted and experimentally determined stabilities remains a significant challenge. To address this problem, we have developed a computational framework based on negative multistate design in which sequence energy is evaluated in the context of both native and non-native backbone ensembles. This framework was validated experimentally with the design of ten variants of streptococcal protein G domain β1 that retained the wild-type fold, and showed a very strong correlation between predicted and experimental stabilities (R(2) = 0.86). When applied to four different proteins spanning a range of fold types, similarly strong correlations were also obtained. Overall, the enhanced prediction accuracies afforded by this method pave the way for new strategies to facilitate the generation of proteins with novel functions by computational protein design.

Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design

Computational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side‐chain rotamers for wild‐type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side‐chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics.

Multistate Computational Protein Design with Backbone Ensembles

The ability of computational protein design (CPD) to identify protein sequences possessing desired characteristics in vast sequence spaces makes it a highly valuable tool in the protein engineering toolbox. CPD calculations are typically performed using a single-state design (SSD) approach in which amino-acid sequences are optimized on a single protein structure. Although SSD has been successfully applied to the design of numerous protein functions and folds, the approach can lead to the incorrect rejection of desirable sequences because of the combined use of a fixed protein backbone template and a set of rigid rotamers. This fixed backbone approximation can be addressed by using multistate design (MSD) with backbone ensembles. MSD improves the quality of predicted sequences by using ensembles approximating conformational flexibility as input templates instead of a single fixed protein structure. In this chapter, we present a step-by-step guide to the implementation and analysis of MSD calculations with backbone ensembles. Specifically, we describe ensemble generation with the PertMin protocol, execution of MSD calculations for recapitulation of Streptococcal protein G domain β1 mutant stability, and analysis of computational predictions by sequence binning. Furthermore, we provide a comparison between MSD and SSD calculation results and discuss the benefits of multistate approaches to CPD.

Turning a Negative into a Positive: Conversion of a Homodimer into a Heterodimer Using Negative State Repertoires

In this issue of Structure, Leaver-Fay et al. (2016) engineer bispecific antibodies using multistate computational protein design with negative state repertoires. In combination with additional mutations selected rationally, they produced antibodies that assembled as heterodimers with up to 93% purity.