Functional Conformational Changes Research Articles

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1's Allosteric Network.

Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states of TAK1, differing just by the presence or absence of its allosteric activator TAB1, from an ensemble mixture of conformations (obtained from 2.4 μs molecular dynamics (MD) simulations). The novelty, however, lies in understanding the deeper algorithmic potentials to systematically derive a diverse set of differential residue connectivity features that reconstruct the essential mechanistic architecture for TAK1-TAB1 allostery in such a close-to-active biochemical scenario. While the recursive, random forest-based workflow displays the potential of conducting discretized, hierarchical derivation of allosteric features, a multilayer perceptron-based approach gains considerable efficacy in revealing fluid connected patterns of features when hybridized with mutual information scoring. Interestingly, both pipelines benchmark similar directions of functional conformational changes for TAK1's activation. The findings significantly advance the depth of mechanistic understanding by highlighting crucial activation signatures along a directed C-lobe → activation loop → ATP pocket channel of information flow, including (1) the αF-αE biterminal alignments and (2) the "catalytic" drift of the activation loop toward kinase active site. Besides, some novel allosteric hotspots (K253, Y206, N189, etc.) are further recognized as TAB1 sensors, transducers, and responders, including a benchmark E70 mutation site, precisely mapping the important structural segments for sequential allosteric execution. Hence, our work demonstrates how to navigate through greater structural depths and dimensions of dynamic allosteric machineries just by leveraging standard ML methods in suitable streamlined workflows adaptive to the specific system and objectives.

Methods of intrinsic and probe fluorescence in assessment of the effect of therapeutic radiation doses <i>in vitro</i> on the albumin molecule

Objective. To study in vitro the effect of therapeutic doses of radiation on the albumin molecule using intrinsic and probe fluorescence methods. Materials and methods. In order to study radiation-induced changes in serum albumin during in vitro irradiation with therapeutic doses (2 Gy, 40 Gy and 70 Gy), the study was conducted in 2 directions: therapeutic doses of ionizing radiation irradiated a buffer, which was then used to prepare an albumin solution (pre-irradiation of the buffer); therapeutic doses of ionizing radiation irradiated an albumin buffer solution. The presence of structural and functional (conformational) changes in the albumin molecule was judged by changes in the values of intrinsic (λexc=280 nm) and probe (λexc=280nm, λexc=320 nm) fluorescence. Statistical processing of the obtained data was carried out using the program GraphPad Prism 6.0. Results. Irradiation with therapeutic doses of 2 Gy, 40 Gy and 70 Gy causes conformational changes (a statistically significant decrease in fluorescence intensity) in the albumin molecule, both during preliminary irradiation of the buffer solution used later for the preparation of albumin, and during irradiation of the buffer solution of albumin. Conclusions. Quantitative changes in the fluorescence intensity, both intrinsic and probe, differ under different modes of albumin irradiation.

A sensitive and scalable fluorescence anisotropy single stranded RNA targeting approach for monitoring riboswitch conformational states.

The capacity of riboswitches to undergo conformational changes in response to binding their native ligands is closely tied to their functional roles and is an attractive target for antimicrobial drug design. Here, we established a probe-based fluorescence anisotropy assay to monitor riboswitch conformational switching with high sensitivity and throughput. Using the Bacillus subtillis yitJ S-Box (SAM-I), Fusobacterium nucleatum impX RFN element of (FMN)and class-I cyclic-di-GMP from Vibrio cholerae riboswitches as model systems, we developed short fluorescent DNA probes that specifically recognize either ligand-free or -bound riboswitch conformational states. We showed that increasing concentrations of native ligands cause measurable and reproducible changes in fluorescence anisotropy that correlate with riboswitch conformational changes observed by native gel analysis. Furthermore, we applied our assay to several ligand analogues and confirmed that it can discriminate between ligands that bind, triggering the native conformational change, from those that bind without causing the conformational change. This new platform opens the possibility of high-throughput screening compound libraries to identify potential new antibiotics that specifically target functional conformational changes in riboswitches.

Simulated pressure changes in LacI suggest a link between hydration and functional conformational changes

The functions of many proteins are associated with interconversions among conformational substates. However, these substates can be difficult to measure experimentally, and determining contributions from hydration changes can be especially difficult. Here, we assessed the use of pressure perturbations to sample the substates accessible to the Escherichia coli lactose repressor protein (LacI) in various liganded forms. In the presence of DNA, the regulatory domain of LacI adopts an Open conformation that, in the absence of DNA, changes to a Closed conformation. Increasing the simulation pressure prevented the transition from an Open to a Closed conformation, in a similar manner to the binding of DNA and anti-inducer, ONPF. The results suggest the hydration of specific residues play a significant role in determining the population of different LacI substates and that simulating pressure perturbation could be useful for assessing the role of hydration changes that accompany functionally-relevant amino acid substitutions.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Anharmonic potential energy landscapes of thermally excited protein vibrations.

At room temperature, low frequency vibrations below 6 THz are thermally excited and are not restricted to the vibrational ground state. Therefore, low frequency vibrations are also not restricted to harmonic fluctuations around a potential energy minimum. On short timescales, such low-frequency vibrations in proteins are responsible for the exploration of the potential energy surface and enable slower stochastic dynamics responsible for functional dynamics and conformational changes. However, current methods that describe and identify collective vibrational modes in proteins either rely on harmonic approximations or lack atomistic detail.

Long-range allostery mediates the regulation of plasminogen activator inhibitor-1 by cell adhesion factor vitronectin

The serpin plasminogen activator inhibitor 1 (PAI-1) spontaneously undergoes a massive structural change from a metastable and active conformation, with a solvent-accessible reactive center loop (RCL), to a stable, inactive, or latent conformation, with the RCL inserted into the central β-sheet. Physiologically, conversion to the latent state is regulated by the binding of vitronectin, which hinders the latency transition rate approximately twofold. The molecular mechanisms leading to this rate change are unclear. Here, we investigated the effects of vitronectin on the PAI-1 latency transition using all-atom path sampling simulations in explicit solvent. In simulated latency transitions of free PAI-1, the RCL is quite mobile as is the gate, the region that impedes RCL access to the central β-sheet. This mobility allows the formation of a transient salt bridge that facilitates the transition; this finding rationalizes existing mutagenesis results. Vitronectin binding reduces RCL and gate mobility by allosterically rigidifying structural elements over 40Å away from the binding site, thus blocking transition to the latent conformation. The effects of vitronectin are propagated by a network of dynamically correlated residues including a number of conserved sites that were previously identified as important for PAI-1 stability. Simulations also revealed a transient pocket populated only in the vitronectin-bound state, corresponding to a cryptic drug-binding site identified by crystallography. Overall, these results shed new light on PAI-1 latency transition regulation by vitronectin and illustrate the potential of path sampling simulations for understanding functional protein conformational changes and for facilitating drug discovery.

Integrative structure determination reveals functional global flexibility for an ultra-multimodular arabinanase

AbnA is an extracellular GH43 α-L-arabinanase from Geobacillus stearothermophilus, a key bacterial enzyme in the degradation and utilization of arabinan. We present herein its full-length crystal structure, revealing the only ultra-multimodular architecture and the largest structure to be reported so far within the GH43 family. Additionally, the structure of AbnA appears to contain two domains belonging to new uncharacterized carbohydrate-binding module (CBM) families. Three crystallographic conformational states are determined for AbnA, and this conformational flexibility is thoroughly investigated further using the “integrative structure determination” approach, integrating molecular dynamics, metadynamics, normal mode analysis, small angle X-ray scattering, dynamic light scattering, cross-linking, and kinetic experiments to reveal large functional conformational changes for AbnA, involving up to ~100 Å movement in the relative positions of its domains. The integrative structure determination approach demonstrated here may apply also to the conformational study of other ultra-multimodular proteins of diverse functions and structures.

Conformational Flexibility and Local Frustration in the Functional States of the SARS-CoV-2 Spike B.1.1.7 and B.1.351 Variants: Mutation-Induced Allosteric Modulation Mechanism of Functional Dynamics and Protein Stability.

Structural and functional studies of the SARS-CoV-2 spike proteins have recently determined distinct functional states of the B.1.1.7 and B.1.351 spike variants, providing a molecular framework for understanding the mechanisms that link the effect of mutations with the enhanced virus infectivity and transmissibility. A detailed dynamic and energetic analysis of these variants was undertaken in the present work to quantify the effects of different mutations on functional conformational changes and stability of the SARS-CoV-2 spike protein. We employed the efficient and accurate coarse-grained (CG) simulations of multiple functional states of the D614G mutant, B.1.1.7 and B.1.351 spike variants to characterize conformational dynamics of the SARS-CoV-2 spike proteins and identify dynamic signatures of the functional regions that regulate transitions between the closed and open forms. By combining molecular simulations with full atomistic reconstruction of the trajectories and the ensemble-based mutational frustration analysis, we characterized how the intrinsic flexibility of specific spike regions can control functional conformational changes required for binding with the host-cell receptor. Using the residue-based mutational scanning of protein stability, we determined protein stability hotspots and identified potential energetic drivers favoring the receptor-accessible open spike states for the B.1.1.7 and B.1.351 spike variants. The results suggested that modulation of the energetic frustration at the inter-protomer interfaces can serve as a mechanism for allosteric couplings between mutational sites and the inter-protomer hinges of functional motions. The proposed mechanism of mutation-induced energetic frustration may result in greater adaptability and the emergence of multiple conformational states in the open form. This study suggested that SARS-CoV-2 B.1.1.7 and B.1.351 variants may leverage the intrinsic plasticity of functional regions in the spike protein for mutation-induced modulation of protein dynamics and allosteric regulation to control binding with the host cell receptor.

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

Conformational changes of a membrane protein determined by infrared difference spectroscopy beyond the diffraction limit

Optogenetics is a revolutionary method for studying neural activity by exploiting the optical stimulation of neurons made possible by artificial genetic expression of light-sensitive transmembrane ``gate'' proteins. Evidence of functional conformational changes of the gate proteins can be obtained by difference infrared (IR) absorption spectroscopy triggered by light stimuli. Here we investigate the effect on the photocycle of the prototype optogenetic protein channelrhodopsin (ChR2) of gold surfaces placed in close proximity to the protein molecules. In order to do this, we bring difference IR spectroscopy to the nanoscale, using a platform based on the coupling of a tunable mid-IR quantum cascade laser and an atomic force microscope (AFM). Sensitivity to individual subwavelength-sized membrane patches, embedding fewer than a hundred ChR2 molecules, is achieved by taking advantage of a plasmonic field enhancement in the nanogap between the gold-coated AFM tip and an ultraflat gold surface used as a sample support. We obtain relative difference absorption variations smaller than ${10}^{\ensuremath{-}2}$ and benchmark nanospectroscopy difference spectra against those taken with the Fourier transform IR (FTIR) spectroscopic technique on the same sample. We identify distinct simultaneous processes in the ChR2 photocycle by performing singular value decomposition of the FTIR difference spectra, and use this procedure to compare IR nanospectroscopy data on individual membrane patches in contact with gold surfaces with those obtained on thick stacks of membrane patches unexposed to metal surfaces. ChR2 proteins maintain their gate function when placed in a 14-nm-wide gap between two gold surfaces, apart from minor modifications to their kinetics. Our results are relevant to optogenetic applications that require physical contact between nanoscale metallic probes and electrodes and the membrane of single neuronal cells. More generally, our work paves the way towards the spectroscopic study of transmembrane proteins at the nanoscale.

Parameterizing elastic network models to capture the dynamics of proteins

Coarse-grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff-based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay-based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B-factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non-linear optimization problem whose parameters describe both rigid-body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes.

Infrared nanospectroscopy study of the light-induced conformational changes of Channelrhodopsin

The channelrhodopsin-ChR2 is a light-sensitive transmembrane protein that acts as a selective ion channel between the intra- and the extra-cellular environments. In the last decade, ChR2 has proven to be essential for optogenetics, because, if expressed in mammalian neural cells, it enables the control of neuronal activity in response to visible light. Mid-infrared difference spectroscopy can probe the functional conformational changes of light-sensitive proteins, however intrinsic limitations of standard IR spectroscopy in terms of diffraction, and therefore number of probed proteins, require that the mid-IR experiments be performed on huge numbers of lipid membrane patches with overexpressed proteins. In this work, we apply for the first time IR difference nanospectroscopy, based on the use of mid-IR lasers and an atomic force microscope (AFM), to single membrane patches containing ChR2, obtaining relevant spectroscopy results for optogenetic applications and, more generally, for future experimental studies of light-sensitive proteins at the nanoscale.

The Influence of Calcium toward Order/Disorder Conformation of Repeat-in-Toxin (RTX) Structure of Family I.3 Lipase from Pseudomonas fluorescens AMS8.

Calcium-binding plays a decisive role in the folding and stabilization of many RTX proteins, especially for the RTX domain. Although many studies have been conducted to prove the contribution of Ca2+ ion toward the folding and stabilization of RTX proteins, its functional dynamics and conformational structural changes remain elusive. Here, molecular docking and molecular dynamics (MD) simulations were performed to analyze the contribution of Ca2+ ion toward the folding and stabilization of the RTX lipase (AMS8 lipase) structure. AMS8 lipase contains six Ca2+ ions (Ca1–Ca6). Three Ca2+ ions (Ca3, Ca4, and Ca5) were bound to the RTX parallel β-roll motif repeat structure (RTX domain). The metal ion (Ca2+) docking analysis gives a high binding energy, especially for Ca4 and Ca5 which are tightly bound to the RTX domain. The function of each Ca2+ ion is further analyzed using the MD simulation. The removal of Ca3, Ca4, and Ca5 caused the AMS8 lipase structure to become unstable and unfolded. The results suggested that Ca3, Ca4, and Ca5 stabilized the RTX domain. In conclusion, Ca3, Ca4, and Ca5 play a crucial role in the folding and stabilization of the RTX domain, which sustain the integrity of the overall AMS8 lipase structure.

Carbohydrate-Binding Capability and Functional Conformational Changes of AbnE, an Arabino-oligosaccharide Binding Protein

ABC importers are membrane proteins responsible for the transport of nutrients into the cells of prokaryotes. Although the structures of ABC importers vary, all contain four conserved domains: two nucleotide-binding domains (NBDs), which bind and hydrolyze ATP, and two transmembrane domains (TMDs), which help translocate the substrate. ABC importers are also dependent on an additional protein component, a high-affinity substrate-binding protein (SBP) that specifically binds the target ligand for delivery to the appropriate ABC transporter. AbnE is a SBP belonging to the ABC importer for arabino-oligosaccharides in the Gram-positive thermophilic bacterium Geobacillus stearothermophilus. Using isothermal titration calorimetry (ITC), purified AbnE was shown to bind medium-sized arabino-oligosaccharides, in the range of arabino-triose (A3) to arabino-octaose (A8), all with Kd values in the nanomolar range. We describe herein the 3D structure of AbnE in its closed conformation in complex with a wide range of arabino-oligosaccharide substrates (A2-A8). These structures provide the basis for the detailed structural analysis of the AbnE-sugar complexes, and together with complementary quantum chemical calculations, site-specific mutagenesis, and isothermal titration calorimetry (ITC) experiments, provide detailed insights into the AbnE-substrate interactions involved. Small-angle X-ray scattering (SAXS) experiments and normal mode analysis (NMA) are used to study the conformational changes of AbnE, and these data, taken together, suggest clues regarding its binding mode to the full ABC importer.

Can Conformational Changes of Proteins Be Represented in Torsion Angle Space? A Study with Rescaled Ridge Regression.

Torsion angles are the natural degrees of freedom of protein structures. The ability to determine torsional variations corresponding to observed changes in Cartesian coordinates is highly valuable, notably to investigate the mechanisms of functional conformational changes or to develop computational models of protein dynamics. This issue is far from trivial in practice since the impact of modifying one torsion angle strongly depends on all other angles, and the compounding effects of small variations in bond lengths and valence angles can completely disrupt a protein fold. We demonstrate that naive strategies, such as directly comparing torsion angles between structures without correcting for variations in bond lengths and valence angles or fitting torsional variations without a proper regularization scheme, fail at producing an adequate representation of conformational changes in internal coordinates. In contrast, rescaled ridge regression, a method recently introduced to regularize multidimensional regressions with correlated explanatory variables, is shown to consistently identify a minimal set of torsion angles variations that closely reproduce changes in Cartesian coordinates. This torsional representation of conformational changes is shown to be robust to the choice of experimental structures. It also provides a better agreement with theoretical models of protein dynamics than the Cartesian representation, regarding notably the predominance of low-frequency normal modes in functional motions and the presence, in predicted equilibrium dynamics, of hints of natural selection for specific functional motions. The software is available at https://github.com/ugobas/tnm .

Cellular Deformations Induced by Conical Silicon Nanowire Arrays Facilitate Gene Delivery.

Engineered cell-nanostructured interfaces generated by vertically aligned silicon nanowire (SiNW) arrays have become a promising platform for orchestrating cell behavior, function, and fate. However, the underlying mechanism in SiNW-mediated intracellular access and delivery is still poorly understood. This study demonstrates the development of a gene delivery platform based on conical SiNW arrays for mechanical cell transfection, assisted by centrifugal force, for both adherent and nonadherent cells in vitro. Cells form focal adhesions on SiNWs within 6 h, and maintain high viability and motility. Such a functional and dynamic cell-SiNW interface features conformational changes in the plasma membrane and in some cases the nucleus, promoting both direct penetration and endocytosis; this synergistically facilitates SiNW-mediated delivery of nucleic acids into immortalized cell lines, and into difficult-to-transfect primary immune T cells without pre-activation. Moreover, transfected cells retrieved from SiNWs retain the capacity to proliferate-crucial to future biomedical applications. The results indicate that SiNW-mediated intracellular delivery holds great promise for developing increasingly sophisticated investigative and therapeutic tools.

Cooperative changes in solvent exposure identify cryptic pockets, conformational switches and allosteric coupling

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.

TAPS: A traveling-salesman based automated path searching method for functional conformational changes of biological macromolecules.

Locating the minimum free energy paths (MFEPs) between two conformational states is among the most important tasks of biomolecular simulations. For example, knowledge of the MFEP is critical for focusing the effort of unbiased simulations that are used for the construction of Markov state models to the biologically relevant regions of the system. Typically, existing path searching methods perform local sampling around the path nodes in a pre-selected collective variable (CV) space to allow a gradual downhill evolution of the path toward the MFEP. Despite the wide application of such a strategy, the gradual path evolution and the non-trivial a priori choice of CVs are also limiting its overall efficiency and automation. Here we demonstrate that non-local perpendicular sampling can be pursued to accelerate the search, provided that all nodes are reordered thereafter via a traveling-salesman scheme. Moreover, path-CVs can be computed on-the-fly and used as a coordinate system, minimizing the necessary prior knowledge about the system. Our traveling-salesman based automated path searching method achieves a 5-8 times speedup over the string method with swarms-of-trajectories for two peptide systems in vacuum and solution, making it a promising method for obtaining initial pathways when investigating functional conformational changes between a pair of structures.

Cooperative Changes in Solvent Exposure Identify Cryptic Pockets, Switches, and Allosteric Coupling

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 data sets. 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, to our knowledge, 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.