Programmable Carbon Nanotube Networks: Controlling Optical Properties Through Orientation and Interaction.

The lattice geometry of natural materials and the structural geometry of artificial materials are crucial factors determining their physical properties. Most materials have predetermined geometries that lead to fixed physical characteristics. Here, the demonstration of a carbon nanotube network serves as an example of a system with controllable orientation achieving on-demand optical properties. Such a network allows programming their optical response depending on the orientation of the constituent carbon nanotubes and leads to the switching of its dielectric tensor from isotropic to anisotropic. Furthermore, it also allows for the achievement of wavelength-dispersion for their principal optical axes - a recently discovered phenomenon in van der Waals triclinic crystals. The results originate from two unique carbon nanotubes features: uniaxial anisotropy from the well-defined cylindrical geometry and the intersection interaction among individual carbon nanotubes. The findings demonstrate that shaping the relative orientations of carbon nanotubes or other quasi-one-dimensional materials of cylindrical symmetry within a network paves the way to a universal method for the creation of systems with desired optical properties.

Atomistic insights into the reentrant phase-transitions in polyuracil and polylysine mixtures.

The phase separation of protein and RNA mixtures underpins the assembly and regulation of numerous membraneless organelles in cells. The ubiquity of protein-RNA condensates in cellular regulatory processes is in part due to their sensitivity to RNA concentration, which affects their physical properties and stability. Recent experiments with poly-cationic peptide-RNA mixtures have revealed closed-loop phase diagrams featuring lower and upper critical solution temperatures. These diagrams indicate reentrant phase transitions shaped by biomolecular interactions and entropic forces such as solvent and ion reorganization. We employed atomistic simulations to study mixtures with various RNA-polylysine stoichiometries and temperatures to elucidate the microscopic driving forces behind reentrant phase transitions in protein-RNA mixtures. Our findings reveal an intricate interplay between hydration, ion condensation, and specific RNA-polylysine hydrogen bonding, resulting in distinct stoichiometry-dependent phase equilibria governing stabilities and structures of the condensate phase. Our simulations show that reentrant transitions are accompanied by desolvation around the phosphate groups of RNA, with increased contacts between phosphate and lysine side chains. In RNA-rich systems at lower temperatures, RNA molecules can form an extensive pi-stacking and hydrogen bond network, leading to percolation. In protein-rich systems, no such percolation-induced transitions are observed. Furthermore, we assessed the performance of three prominent water force fields-Optimal Point Charge (OPC), TIP4P-2005, and TIP4P-D-in capturing reentrant phase transitions. OPC provided a superior balance of interactions, enabling effective capture of reentrant transitions and accurate characterization of changes in solvent reorganization. This study offers atomistic insights into the nature of reentrant phase transitions using simple model peptide and nucleotide mixtures. We believe that our results are broadly applicable to larger classes of peptide-RNA mixtures exhibiting reentrant phase transitions.

Illuminating Protein Allostery by Chemically Accurate Contact Response Analysis (ChACRA).

Decoding allostery at the atomic level is essential for understanding the relationship between a protein's sequence, structure, and dynamics. Recently, we have shown that decomposing temperature responses of inter-residue contacts can reveal allosteric couplings and provide useful insight into the functional dynamics of proteins. The details of this Chemically Accurate Contact Response Analysis (ChACRA) are presented here along with its application to two well-known allosteric proteins. The first protein, IGPS, is a model of ensemble allostery that lacks clear structural differences between the active and inactive states. We show that the application of ChACRA reveals the experimentally identified allosteric coupling between effector and active sites of IGPS. The second protein, ATCase, is a classic example of allostery with distinct active and inactive structural states. Using ChACRA, we directly identify the most significant residue level interactions underlying the enzyme's cooperative behavior. Both test cases demonstrate the utility of ChACRA's unsupervised machine learning approach for dissecting allostery at the residue level.

An α-ketoglutarate conformational switch controls iron accessibility, activation, and substrate selection of the human FTO protein.

The fat mass and obesity-associated fatso (FTO) protein is a member of the Alkb family of dioxygenases and catalyzes oxidative demethylation of N6-methyladenosine (m6A), N1-methyladenosine (m1A), 3-methylthymine (m3T), and 3-methyluracil (m3U) in single-stranded nucleic acids. It is well established that the catalytic activity of FTO proceeds via two coupled reactions. The first reaction involves decarboxylation of alpha-ketoglutarate (αKG) and formation of an oxyferryl species. In the second reaction, the oxyferryl intermediate oxidizes the methylated nucleic acid to reestablish Fe(II) and the canonical base. However, it remains unclear how binding of the nucleic acid activates the αKG decarboxylation reaction and why FTO demethylates different methyl modifications at different rates. Here, we investigate the interaction of FTO with 5-mer DNA oligos incorporating the m6A, m1A, or m3T modifications using solution NMR, molecular dynamics (MD) simulations, and enzymatic assays. We show that binding of the nucleic acid to FTO activates a two-state conformational equilibrium in the αKG cosubstrate that modulates the O2 accessibility of the Fe(II) catalyst. Notably, the substrates that provide better stabilization to the αKG conformation in which Fe(II) is exposed to O2 are demethylated more efficiently by FTO. These results indicate that i) binding of the methylated nucleic acid is required to expose the catalytic metal to O2 and activate the αKG decarboxylation reaction, and ii) the measured turnover of the demethylation reaction (which is an ensemble average over the entire sample) depends on the ability of the methylated base to favor the Fe(II) state accessible to O2.

Four-Dimensional Mesoscale Liquid Model of Nucleus Resolves Chromatin's Radial Organization.

Recent advances chromatin capture, imaging techniques, and polymer modeling have dramatically enhanced quantitative understanding of chromosomal folding. However, the dynamism inherent in genome architectures due to physical and biochemical forces and their impact on nuclear architecture and cellular functions remains elusive. While imaging of chromatin in four dimensions is becoming more common, there is a conspicuous lack of physics-based computational tools appropriate for revealing the forces that shape nuclear architecture and dynamics. To this end, we have developed a multiphase liquid model of the nucleus, which can resolve chromosomal territories, compartments, and nuclear lamina using a physics-based and data-informed free-energy function. The model enables rapid hypothesis-driven prototyping of nuclear dynamics in four dimensions, thereby facilitating comparison with whole nucleus imaging experiments. As an application, we model the Drosophila nucleus and map phase diagram of various possible nuclear morphologies. We shed light on the interplay of adhesive and cohesive interactions which give rise to distinct radial organization seen in conventional, inverted, and senescent nuclear architectures. The results also show the highly dynamic nature of the radial organization, the disruption of which leads to significant variability in domain coarsening dynamics and consequently variability of chromatin architecture. The model also highlights the impact of oblate nuclear geometry and heterochromatin-subtype interactions on the global chromatin architecture and local asymmetry of chromatin compartments.

Energy landscapes of homopolymeric RNAs revealed by deep unsupervised learning.

Conformational dynamics of RNA plays important roles in a variety of cellular functions such as transcriptional regulation, catalysis, scaffolding, and sensing. Recently, RNAs with low-complexity sequences have been shown to phase separate and form condensate phases similar to lowcomplexity protein domains. The affinity for phase separation and the material characteristics of RNA condensates are strongly dependent on sequence composition and patterning. We hypothesize that differences in the affinities for RNA phase separation can be uncovered by studying sequence-dependent conformational dynamics of single RNA chains. To this end, we have employed atomistic simulations and deep dimensionality reduction techniques to map temperature-dependent conformational free energy landscapes for 20 base-long homopolymeric RNA sequences: poly(U), poly(G), poly(C), and poly(A). The energy landscapes of homopolymeric RNAs reveal a plethora of metastable states with qualitatively different populations stemming from differences in base chemistry. Through detailed analysis of base, phosphate, and sugar interactions, we show that experimentally observed temperature-driven shifts in metastable state populations align with experiments on RNA phase transitions. Specifically, we find that the thermodynamics of unfolding of homopolymeric RNA follows the poly(G) > poly(A) > poly(C) > poly(U) order of stability, mirroring the propensity of RNA to form condensates. To conclude, this work shows that at least for homopolymeric RNA sequences the single-chain conformational dynamics contains sufficient information for predicting and quantifying condensate forming affinities of RNAs. Thus, we anticipate that atomically detailed studies of temeprature -dependent energy landscapes of RNAs will be a useful guide for understanding the propensity of various RNA molecules to form condensates.

Infection of Human Macrophage-Like Cells by African Swine Fever Virus.

BACKGROUND: The African swine fever (ASF) virus (ASFV) and ASF-like viral sequences were identified in human samples and sewage as well as in different water environments. Pigs regularly experience infections by the ASFV. The considerable stability of the virus in the environment suggests that there is ongoing and long-term contact between humans and the ASFV. However, humans exhibit resistance to the ASFV, and the decisive factor in developing infection in the body is most likely the reaction of target macrophages to the virus. Therefore, this study aimed to characterize the responses of human macrophages to the virus and explore the distinct features of the viral replication cycle within human macrophages. METHODS: The ASFV Armenia/07 strain was used in all experiments. In this study, quantitative real-time polymerase chain reaction (qRT-PCR) was used to determine the ASFV gene expression; flow cytometry analysis was performed to evaluate the effects of the inactive and active ASFV (inASFV and aASFV) treatments on the phenotype of THP-1-derived macrophages (Mφ0) and inflammatory markers. Moreover, other methods such as cell viability and apoptosis assays, staining techniques, phagocytosis assay, lysosome-associated membrane protein (LAMP-1) cytometry, and cytokine detection were used during experiments. RESULTS: Our findings showed that the virus initiated replication by entering human macrophages. Subsequently, the virus shed its capsid and initiated the transcription of numerous viral genes, and at least some of these genes executed their functions. In THP-1-derived macrophages (Mφ0), the ASFV implemented several functions to suppress cell activity, although the timing of their implementation was slower compared with virus-sensitive porcine alveolar macrophages (PAMs). Additionally, the virus could not complete the entire replication cycle in human Mφ0, as indicated by the absence of viral factories and a decrease in infectious titers of the virus with each subsequent passage. Overall, the infection of Mφ0 with the ASFV caused significant alterations in their phenotype and functions, such as increased TLR2, TLR3, CD80, CD36, CD163, CXCR2, and surface LAMP-1 expression. Increased production of the tumor necrosis factor (TNF) and interleukin (IL)-10 and decreased production of interferon (IFN)-α were also observed. Taken together, the virus enters human THP-1-derived macrophages, starts transcription, and causes immunological responses by target cells but cannot complete the replicative cycle. CONCLUSION: These findings suggest that there may be molecular limitations within human macrophages that at least partially restrict the complete replication of the ASFV. Understanding the factors that hinder viral replication in Mφ0 can provide valuable insights into the host-virus interactions and the mechanisms underlying the resistance of human macrophages to the ASFV.

Wandering principal optical axes in van der Waals triclinic materials.

Nature is abundant in material platforms with anisotropic permittivities arising from symmetry reduction that feature a variety of extraordinary optical effects. Principal optical axes are essential characteristics for these effects that define light-matter interaction. Their orientation - an orthogonal Cartesian basis that diagonalizes the permittivity tensor, is often assumed stationary. Here, we show that the low-symmetry triclinic crystalline structure of van der Waals rhenium disulfide and rhenium diselenide is characterized by wandering principal optical axes in the space-wavelength domain with above π/2 degree of rotation for in-plane components. In turn, this leads to wavelength-switchable propagation directions of their waveguide modes. The physical origin of wandering principal optical axes is explained using a multi-exciton phenomenological model and ab initio calculations. We envision that the wandering principal optical axes of the investigated low-symmetry triclinic van der Waals crystals offer a platform for unexplored anisotropic phenomena and nanophotonic applications.

Exploring van der Waals materials with high anisotropy: geometrical and optical approaches.

The emergence of van der Waals (vdW) materials resulted in the discovery of their high optical, mechanical, and electronic anisotropic properties, immediately enabling countless novel phenomena and applications. Such success inspired an intensive search for the highest possible anisotropic properties among vdW materials. Furthermore, the identification of the most promising among the huge family of vdW materials is a challenging quest requiring innovative approaches. Here, we suggest an easy-to-use method for such a survey based on the crystallographic geometrical perspective of vdW materials followed by their optical characterization. Using our approach, we found As2S3 as a highly anisotropic vdW material. It demonstrates high in-plane optical anisotropy that is ~20% larger than for rutile and over two times as large as calcite, high refractive index, and transparency in the visible range, overcoming the century-long record set by rutile. Given these benefits, As2S3 opens a pathway towards next-generation nanophotonics as demonstrated by an ultrathin true zero-order quarter-wave plate that combines classical and the Fabry-Pérot optical phase accumulations. Hence, our approach provides an effective and easy-to-use method to find vdW materials with the utmost anisotropic properties.

Protein thermal sensing regulates physiological amyloid aggregation.

To survive, cells must respond to changing environmental conditions. One way that eukaryotic cells react to harsh stimuli is by forming physiological, RNA-seeded subnuclear condensates, termed amyloid bodies (A-bodies). The molecular constituents of A-bodies induced by different stressors vary significantly, suggesting this pathway can tailor the cellular response by selectively aggregating a subset of proteins under a given condition. Here, we identify critical structural elements that regulate heat shock-specific amyloid aggregation. Our data demonstrates that manipulating structural pockets in constituent proteins can either induce or restrict their A-body targeting at elevated temperatures. We propose a model where selective aggregation within A-bodies is mediated by the thermal stability of a protein, with temperature-sensitive structural regions acting as an intrinsic form of post-translational regulation. This system would provide cells with a rapid and stress-specific response mechanism, to tightly control physiological amyloid aggregation or other cellular stress response pathways.

Determinants of viscoelasticity and flow activation energy in biomolecular condensates.

The form and function of biomolecular condensates are intimately linked to their material properties. Here, we integrate microrheology with molecular simulations to dissect the physical determinants of condensate fluid phase dynamics. By quantifying the timescales and energetics of network relaxation in a series of heterotypic viscoelastic condensates, we uncover distinctive roles of sticker motifs, binding energy, and chain length in dictating condensate dynamical properties. We find that the mechanical relaxation times of condensate-spanning networks are determined by both intermolecular interactions and chain length. We demonstrate, however, that the energy barrier for network reconfiguration, termed flow activation energy, is independent of chain length and only varies with the strengths of intermolecular interactions. Biomolecular diffusion in the dense phase depends on a complex interplay between viscoelasticity and flow activation energy. Our results illuminate distinctive roles of chain length and sequence-specific multivalent interactions underlying the complex material and transport properties of biomolecular condensates.

Phase separation of multicomponent peptide mixtures into dehydrated clusters with hydrophilic cores.

Phase separation of biomolecules underlies the formation and regulation of various membraneless condensates in cells. How condensates function reliably while surrounded by heterogeneous and dynamic mixtures of biomolecular components with specific and nonspecific interactions is yet to be understood. Studying multicomponent biomolecular mixtures with designer peptides has recently become an attractive avenue for learning about physicochemical principles governing cellular condensates. In this work, we employed long-timescale atomistic simulations of multicomponent tripeptide mixtures with all residue substitutions to illuminate the nature of direct and water-mediated interactions in a prototypical cellular condensate environment. We find that peptide mixtures form clusters with inverse hydrophobic order. Most multivalent and charged residues are localized in the cluster's core, with a large fraction of nonaromatic hydrophobic residues remaining on the surface. This inverse hydrophobic order in peptide clusters is partly driven by the expulsion of nonspecifically bound water molecules following peptide cluster growth. The growth of clusters is also accompanied by the formation of increasing numbers of specific water-mediated interactions between polar and charged residues. While the present study focused on the condensation of short peptide motifs, the general findings and analysis techniques should be helpful for future studies on larger peptides and protein condensation.

Temperature sensitive contact modes allosterically gate TRPV3.

TRPV Ion channels are sophisticated molecular sensors designed to respond to distinct temperature thresholds. The recent surge in cryo-EM structures has provided numerous insights into the structural rearrangements accompanying their opening and closing; however, the molecular mechanisms by which TRPV channels establish precise and robust temperature sensing remain elusive. In this work we employ molecular simulations, multi-ensemble contact analysis, graph theory, and machine learning techniques to reveal the temperature-sensitive residue-residue interactions driving allostery in TRPV3. We find that groups of residues exhibiting similar temperature-dependent contact frequency profiles cluster at specific regions of the channel. The dominant mode clusters on the ankyrin repeat domain and displays a linear melting trend while others display non-linear trends. These modes describe the residue-level temperature response patterns that underlie the channel's functional dynamics. With network analysis, we find that the community structure of the channel changes with temperature. And that a network of high centrality contacts connects distant regions of the protomer to the gate, serving as a means for the temperature-sensitive contact modes to allosterically regulate channel gating. Using a random forest model, we show that the contact states of specific temperature-sensitive modes are indeed predictive of the channel gate's state. Supporting the physical validity of these modes and networks are several residues identified with our analyses that are reported in literature to be functionally critical. Our results offer high resolution insight into thermo-TRP channel function and demonstrate the utility of temperature-sensitive contact analysis.

Possibility of Exciton Bose-Einstein Condensation in CdSe Nanoplatelets.

The quasi-two-dimensional exciton subsystem in CdSe nanoplatelets is considered. It is theoretically shown that Bose-Einstein condensation (BEC) of excitons is possible at a nonzero temperature in the approximation of an ideal Bose gas and in the presence of an "energy gap" between the ground and the first excited states of the two-dimensional exciton center of inertia of the translational motion. The condensation temperature (Tc) increases with the width of the "gap" between the ground and the first excited levels of size quantization. It is shown that when the screening effect of free electrons and holes on bound excitons is considered, the BEC temperature of the exciton subsystem increases as compared to the case where this effect is absent. The energy spectrum of the exciton condensate in a CdSe nanoplate is calculated within the framework of the weakly nonideal Bose gas approximation, considering the specifics of two-dimensional Born scattering.

Re-entrant transitions of locally stiff RNA chains in the presence of polycations leads to gelated architectures.

The liquid-liquid phase separation of protein and nucleic acid mixtures drives the formation of numerous membraneless compartments in cells. Temperature variation is commonly used for mapping condensate phase diagrams, which often display unique upper critical temperatures. Recent report on peptide-RNA mixtures has shown the existence of lower and upper critical solution temperatures, highlighting the importance of temperature-dependent solvent and ion-mediated forces. In the present work, we employ residue-level coarse-grained models of RNA and polycation peptide chains for simulating temperature-induced re-entrant transitions and shedding light on the role played by mobile ions, temperature-dependent dielectric permittivity, and local chain stiffness. We show that differences in bending rigidity can significantly modulate condensate topology leading to the formation of gelated or fibril like architectures. The study also finds that temperature dependence of water permittivity is generally sufficient for recapitulating experimentally observed closed loop and LCST phase diagrams of highly charged protein-RNA mixtures. However, we find that similar-looking closed-loop phase diagrams can correspond to vastly different condensate topologies.

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR.

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

Temperature-Sensitive Contact Modes Allosterically Gate TRPV3.

TRPV Ion channels are sophisticated molecular sensors designed to respond to distinct temperature thresholds. The recent surge in cryo-EM structures has provided numerous insights into the structural rearrangements accompanying their opening and closing; however, the molecular mechanisms by which TRPV channels establish precise and robust temperature sensing remain elusive. In this work we employ molecular simulations, multi-ensemble contact analysis, graph theory, and machine learning techniques to reveal the temperature-sensitive residue-residue interactions driving allostery in TRPV3. We find that groups of residues exhibiting similar temperature-dependent contact frequency profiles cluster at specific regions of the channel. The dominant mode clusters on the ankyrin repeat domain and displays a linear melting trend while others display non-linear trends. These modes describe the residue-level temperature response patterns that underlie the channel's functional dynamics. With network analysis, we find that the community structure of the channel changes with temperature. And that a network of high centrality contacts connects distant regions of the protomer to the gate, serving as a means for the temperature-sensitive contact modes to allosterically regulate channel gating. Using a random forest model, we show that the contact states of specific temperature-sensitive modes are indeed predictive of the channel gate's state. Supporting the physical validity of these modes and networks are several residues identified with our analyses that are reported in literature to be functionally critical. Our results offer high resolution insight into thermo-TRP channel function and demonstrate the utility of temperature-sensitive contact analysis.

Protein Conformational Dynamics Underlie Selective Recognition of Thermophilic over Mesophilic Enzyme I by a Substrate Analogue.

Substrate selectivity is an important preventive measure to decrease the possibility of cross interactions between enzymes and metabolites that share structural similarities. In addition, understanding the mechanisms that determine selectivity towards a particular substrate increases the knowledge base for designing specific inhibitors for target enzymes. Here, we combine NMR, molecular dynamics (MD) simulations, and protein engineering to investigate how two substrate analogues, allylicphosphonate (cPEP) and sulfoenolpyruvate (SEP), recognize the mesophilic (eEIC) and thermophilic (tEIC) homologues of the receptor domain of bacterial Enzyme I, which has been proposed as a target for antimicrobial research. Chemical Shift Perturbation (CSP) experiments show that cPEP and SEP recognize tEIC over the mesophilic homologue. Combined Principal Component Analysis of half-microsecond-long MD simulations reveals that incomplete quenching of a breathing motion in the eEIC-ligand complex destabilizes the interaction and makes the investigated substrate analogues selective toward the thermophilic enzyme. Our results indicate that residual protein motions need to be considered carefully when optimizing small molecule inhibitors of EI. In general, our work demonstrates that protein conformational dynamics can be exploited in the rational design and optimization of inhibitors with subfamily selectivity.

Multiscale Modeling of Protein-RNA Condensation in and Out of Equilibrium.

A vast number of intracellular membraneless bodies also known as biomolecular condensates form through a liquid-liquid phase separation (LLPS) of biomolecules. To date, phase separation has been identified as the main driving force for a membraneless organelles such as nucleoli, Cajal bodies, stress granules, and chromatin compartments. Recently, the protein-RNA condensation is receiving increased attention, because it is closely related to the biological function of cells such as transcription, translation, and RNA metabolism. Despite the multidisciplinary efforts put forth to study the biophysical properties of protein-RNA condensates, there are many fundamental unanswered questions regarding the mechanism of formation and regulation of protein-RNA condensates in eukaryotic cells. Major challenges in studying protein-RNA condensation stem from (i) the molecular heterogeneity and conformational flexibility of RNA and protein chains and (ii) the nonequilibrium nature of transcription and cellular environment. Computer simulations, bioinformatics, and mathematical models are uniquely positioned for shedding light on the microscopic nature of protein-RNA phase separation. To this end, there is an urgent need for innovative models with the right spatiotemporal resolution for confronting the experimental observables in a comprehensive and physics-based manner. In this chapter, we will summarize the currently emerging research efforts, which employ atomistic and coarse-grained molecular models and field theoretical models to understand equilibrium and nonequilibrium aspects of protein-RNA condensation.

Temperature-sensitive contacts in disordered loops tune enzyme I activity.

Homologous enzymes with identical folds often exhibit different thermal and kinetic behaviors. Understanding how an enzyme sequence encodes catalytic activity at functionally optimal temperatures is a fundamental problem in biophysics. Recently it was shown that the residues that tune catalytic activities of thermophilic/mesophilic variants of the C-terminal domain of bacterial enzyme I (EIC) are largely localized within disordered loops, offering a model system with which to investigate this phenomenon. In this work, we use molecular dynamics simulations and mutagenesis experiments to reveal a mechanism of sequence-dependent activity tuning of EIC homologs. We find that a network of contacts in the catalytic loops is particularly sensitive to changes in temperature, with some contacts exhibiting distinct linear or nonlinear temperature-dependent trends. Moreover, these trends define structurally clustered dynamical modes and can distinguish regions that tend toward order or disorder at higher temperatures. Assaying several thermophilic EIC mutants, we show that complementary mesophilic mutations to the most temperature-sensitive positions exhibit the most enhanced activity, while mutations to relatively temperature insensitive positions exhibit the least enhanced activities. These results provide a mechanistic explanation of sequence-dependent temperature tuning and offer a computational method for rational enzyme modification.