Maxwell I. Zimmerman

Molecular Structure of RADA16-I Designer Self-Assembling Peptide Nanofibers

The designer self-assembling peptide RADA16-I forms nanofiber matrices which have shown great promise for regenerative medicine and three-dimensional cell culture. The RADA16-I amino acid sequence has a β-strand-promoting alternating hydrophobic/charged motif, but arrangement of β-strands into the nanofiber structure has not been previously determined. Here we present a structural model of RADA16-I nanofibers, based on solid-state NMR measurements on samples with different schemes for (13)C isotopic labeling. NMR peak positions and line widths indicate an ordered structure composed of β-strands. The NMR data show that the nanofibers are composed of two stacked β-sheets stabilized by a hydrophobic core formed by alanine side chains, consistent with previous proposals. However, the previously proposed antiparallel β-sheet structure is ruled out by measured (13)C-(13)C dipolar couplings. Instead, neighboring β-strands within β-sheets are parallel, with a registry shift that allows cross-strand staggering of oppositely charged arginine and aspartate side chains. The resulting structural model is compared to nanofiber dimensions observed via images taken by transmission electron microscopy and atomic force microscopy. Multiple NMR peaks for each alanine side chain were observed and could be attributed to multiple configurations of side chain packing within a single scheme for intermolecular packing.

FAST Conformational Searches by Balancing Exploration/Exploitation Trade-Offs.

Molecular dynamics simulations are a powerful means of understanding conformational changes. However, it is still difficult to simulate biologically relevant time scales without the use of specialized supercomputers. Here, we introduce a goal-oriented sampling method, called fluctuation amplification of specific traits (FAST), for extending the capabilities of commodity hardware. This algorithm rapidly searches conformational space for structures with desired properties by balancing trade-offs between focused searches around promising solutions (exploitation) and trying novel solutions (exploration). FAST was inspired by the hypothesis that many physical properties have an overall gradient in conformational space, akin to the energetic gradients that are known to guide proteins to their folded states. For example, we expect that transitioning from a conformation with a small solvent-accessible surface area to one with a large surface area will require passing through a series of conformations with steadily increasing surface areas. We demonstrate that such gradients are common through retrospective analysis of existing Markov state models (MSMs). Then we design the FAST algorithm to exploit these gradients to find structures with desired properties by (1) recognizing and amplifying structural fluctuations along gradients that optimize a selected physical property whenever possible, (2) overcoming barriers that interrupt these overall gradients, and (3) rerouting to discover alternative paths when faced with insurmountable barriers. To test FAST, we compare its performance to other methods for three common types of problems: (1) identifying unexpected binding pockets, (2) discovering the preferred paths between specific structures, and (3) folding proteins. Our conservative estimate is that FAST outperforms conventional simulations and an adaptive sampling algorithm by at least an order of magnitude. Furthermore, FAST yields both the proper thermodynamics and kinetics, allowing for a direct connection with kinetic experiments that is impossible with many other advanced sampling algorithms because they provide only thermodynamic information. Therefore, we expect FAST to be of great utility for a wide range of applications.

Solid-State NMR Evidence for β-Hairpin Structure within MAX8 Designer Peptide Nanofibers

MAX8, a designer peptide known to undergo self-assembly following changes in temperature, pH, and ionic strength, has demonstrated usefulness for tissue engineering and drug delivery. It is hypothesized that the self-assembled MAX8 nanofiber structure consists of closed β-hairpins aligned into antiparallel β-sheets. Here, we report evidence from solid-state NMR spectroscopy that supports the presence of the hypothesized β-hairpin conformation within the nanofiber structure. Specifically, our (13)C-(13)C two-dimensional exchange data indicate spatial proximity between V3 and K17, and (13)C-(13)C dipolar coupling measurements reveal proximity between the V3 and V18 backbone carbonyls. Moreover, isotopic dilution of labeled MAX8 nanofibers did not result in a loss of the (13)C-(13)C dipolar couplings, showing that these couplings are primarily intramolecular. NMR spectra also indicate the existence of a minor conformation, which is discussed in terms of previously hypothesized nanofiber physical cross-linking and possible nanofiber polymorphism.

Designing small molecules to target cryptic pockets yields both positive and negative allosteric modulators

Allosteric drugs, which bind to proteins in regions other than their main ligand-binding or active sites, make it possible to target proteins considered “undruggable” and to develop new therapies that circumvent existing resistance. Despite growing interest in allosteric drug discovery, rational design is limited by a lack of sufficient structural information about alternative binding sites in proteins. Previously, we used Markov State Models (MSMs) to identify such “cryptic pockets,” and here we describe a method for identifying compounds that bind in these cryptic pockets and modulate enzyme activity. Experimental tests validate our approach by revealing both an inhibitor and two activators of TEM β-lactamase (TEM). To identify hits, a library of compounds is first virtually screened against either the crystal structure of a known cryptic pocket or an ensemble of structures containing the same cryptic pocket that is extracted from an MSM. Hit compounds are then screened experimentally and characterized kinetically in individual assays. We identify three hits, one inhibitor and two activators, demonstrating that screening for binding to allosteric sites can result in both positive and negative modulation. The hit compounds have modest effects on TEM activity, but all have higher affinities than previously identified inhibitors, which bind the same cryptic pocket but were found, by chance, via a computational screen targeting the active site. Site-directed mutagenesis of key contact residues predicted by the docking models is used to confirm that the compounds bind in the cryptic pocket as intended. Because hit compounds are identified from docking against both the crystal structure and structures from the MSM, this platform should prove suitable for many proteins, particularly targets whose crystal structures lack obvious druggable pockets, and for identifying both inhibitory and activating small-molecule modulators.

Enspara: Modeling molecular ensembles with scalable data structures and parallel computing

Markov state models (MSMs) are quantitative models of protein dynamics that are useful for uncovering the structural fluctuations that proteins undergo, as well as the mechanisms of these conformational changes. Given the enormity of conformational space, there has been ongoing interest in identifying a small number of states that capture the essential features of a protein. Generally, this is achieved by making assumptions about the properties of relevant features—for example, that the most important features are those that change slowly. An alternative strategy is to keep as many degrees of freedom as possible and subsequently learn from the model which of the features are most important. In these larger models, however, traditional approaches quickly become computationally intractable. In this paper, we present enspara, a library for working with MSMs that provides several novel algorithms and specialized data structures that dramatically improve the scalability of traditional MSM methods. This includes ragged arrays for minimizing memory requirements, MPI-parallelized implementations of compute-intensive operations, and a flexible framework for model estimation.

Exposons exploit cooperative changes in solvent exposure to detect cryptic allosteric sites and other functionally-relevant conformational transitions

Abstract Proteins are dynamic molecules that undergo conformational changes to a broad spectrum of different excited states. Unfortunately, the small populations of these states make it difficult to determine their structures or functional implications. Computer simulations are an increasingly powerful means to identify and characterize functionally-relevant excited states. However, this advance has uncovered a further challenge: it can be extremely difficult to identify the most salient features of large simulation datasets. We reasoned that many functionally-relevant conformational changes are likely to involve large, cooperative changes to the surfaces that are available to interact with potential binding partners. To examine this hypothesis, we introduce a method that returns a prioritized list of potentially functional conformational changes by segmenting protein structures into clusters of residues that undergo cooperative changes in their solvent exposure, along with the hierarchy of interactions between these groups. We term these groups exposons to distinguish them from other types of clusters that arise in this analysis and others. We demonstrate, using three different model systems, that this method identifies experimentally-validated and functionally-relevant conformational changes, including conformational switches, allosteric coupling, and cryptic pockets. Our results suggest that key functional sites are hubs in the network of exposons. As a further test of the predictive power of this approach, we apply it to discover cryptic allosteric sites in two different β-lactamase enzymes that are widespread sources of antibiotic resistance. Experimental tests confirm our predictions for both systems. Importantly, we provide the first evidence for a cryptic allosteric site in CTX-M-9 β-lactamase. Experimentally testing this prediction did not require any mutations, and revealed that this site exerts the most potent allosteric control over activity of any pockets found in β-lactamases to date. Discovery of a similar pocket that was previously overlooked in the well-studied TEM-1 β-lactamase demonstrates the utility of exposons.Conformational changes can dramatically alter a protein’s function by changing the surfaces that are accessible to interact with binding partners. However, it is often difficult to hone in on the most relevant conformational changes from the cartesian coordinates of atoms on the protein’s surface. Instead, we describe a protein’s surface in terms of groups of residues that undergo cooperative changes in their solvent exposure. We term these groups exposons. We demonstrate that Markov state models (MSMs) elegantly identify the conformational transitions that give rise to an exposon, enabling users to rapidly find the most interesting conformational changes in their system. For example, this approach readily identifies previously-known cryptic allosteric sites and other functionally-relevant conformational transitions. Moreover, it predicts a cryptic allosteric site in an important target for combating antibiotic resistance that lacks known cryptic pockets. Experimental tests confirm that targeting this site reduces catalytic efficiency 15-fold.