Kinetic Proofreading.

Biochemistry and molecular biology rely on the recognition of structural complementarity between molecules. Molecular interactions must be both quickly reversible, i.e., tenuous, and specific. How the cell reconciles these conflicting demands is the subject of this article. The problem and its theoretical solution are discussed within the wider theoretical context of the thermodynamics of stochastic processes (stochastic thermodynamics). The solution-an irreversible reaction cycle that decreases internal error at the expense of entropy export into the environment-is shown to be widely employed by biological processes that transmit genetic and regulatory information.

High-Throughput Investigation of Diverse Junction Elements in RNA Tertiary Folding.

RNAs fold into defined tertiary structures to function in critical biological processes. While quantitative models can predict RNA secondary structure stability, we are still unable to predict the thermodynamic stability of RNA tertiary structure. Here, we probe conformational preferences of diverse RNA two-way junctions to develop a predictive model for the formation of RNA tertiary structure. We quantitatively measured tertiary assembly energetics of >1,000 of RNA junctions inserted in multiple structural scaffolds to generate a "thermodynamic fingerprint" for each junction. Thermodynamic fingerprints enabled comparison of junction conformational preferences, revealing principles for how sequence influences 3-dimensional conformations. Utilizing fingerprints of junctions with known crystal structures, we generated ensembles for related junctions that predicted their thermodynamic effects on assembly formation. This work reveals sequence-structure-energetic relationships in RNA, demonstrates the capacity for diverse compensation strategies within tertiary structures, and provides a path to quantitative modeling of RNA folding energetics based on "ensemble modularity."

A Quantitative and Predictive Model for RNA Binding by Human Pumilio Proteins.

High-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs). Nevertheless, quantitative approaches are needed to capture the landscape of RNA-RBP interactions responsible for cellular regulation. We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2. Despite prior findings of linear sequence motifs, our measurements revealed widespread residue flipping and instances of positional coupling. Application of our thermodynamic model to published in vivo crosslinking data reveals quantitative agreement between predicted affinities and in vivo occupancies. Our analyses suggest a thermodynamically driven, continuous Pumilio-binding landscape that is negligibly affected by RNA structure or kinetic factors, such as displacement by ribosomes. This work provides a quantitative foundation for dissecting the cellular behavior of RBPs and cellular features that impact their occupancies.

Elastin recoil is driven by the hydrophobic effect.

Elastin is an extracellular matrix material found in all vertebrates. Its reversible elasticity, robustness, and low stiffness are essential for the function of arteries, lungs, and skin. It is among the most resilient elastic materials known: During a human lifetime, arterial elastin undergoes in excess of 2 × 109 stretching/contracting cycles without replacement, and slow oxidative hardening has been identified as a limiting factor on human lifespan. For over 50 y, the mechanism of entropic recoil has been controversial. Herein, we report a combined NMR and thermomechanical study that establishes the hydrophobic effect as the primary driver of elastin function. Water ordering at the solvent:protein interface was observed as a function of stretch using double quantum 2H NMR, and the most extensive thermodynamic analysis performed to date was obtained by measuring elastin length and volume as a function of force and temperature in normal water, heavy water and with cosolvents. When stretched, elastin's heat capacity increases, water is ordered proportional to the degree of stretching, the internal energy decreases, and heat is released in excess of the work performed. These properties show that recoil in elastin under physiological conditions is primarily driven by the hydrophobic effect rather than by configurational entropy as is the case for rubber. Consistent with this conclusion are decreases in the thermodynamic signatures when cosolvents that alter the hydrophobic effect are introduced. We propose that hydrophobic effect-driven recoil, as opposed to a configurational entropy mechanism where hardening from crystallization can occur, is the origin of elastin's unusual resilience.

Observation of negative surface and interface energies of quantum dots.

Surface energy is a fundamental property of materials and is particularly important in describing nanomaterials where atoms or molecules at the surface constitute a large fraction of the material. Traditionally, surface energy is considered to be a positive quantity, where atoms or molecules at the surface are less thermodynamically stable than their counterparts in the interior of the material because they have fewer bonds or interactions at the surface. Using calorimetric methods, we show that the surface energy is negative in some prototypical colloidal semiconductor nanocrystals, or quantum dots with organic ligand coatings. This implies that the surface atoms are more thermodynamically stable than those on the interior due to the strong bonds between these atoms and surfactant molecules, or ligands, that coat their surface. In addition, we extend this work to core/shell indium phosphide/zinc sulfide nanocrystals and show that the interfacial energy between these materials is highly thermodynamically favorable in spite of their large lattice mismatch. This work challenges many of the assumptions that have guided thinking about colloidal nanomaterial thermodynamics, investigates the fundamental stability of many technologically relevant colloidal nanomaterials, and paves the way for future experimental and theoretical work on nanocrystal thermodynamics.

A finely balanced order-disorder equilibrium sculpts the folding-binding landscape of an antibiotic sequestering protein.

TipA, a MerR family transcription factor from Streptomyces lividans, promotes antibiotic resistance by sequestering broad-spectrum thiopeptide-based antibiotics, thus counteracting their inhibitory effect on ribosomes. TipAS, a minimal binding motif which is expressed as an isoform of TipA, harbors a partially disordered N-terminal subdomain that folds upon binding multiple antibiotics. The extent and nature of the underlying molecular heterogeneity in TipAS that shapes its promiscuous folding-function landscape is an open question and is critical for understanding antibiotic-sequestration mechanisms. Here, combining equilibrium and time-resolved experiments, statistical modeling, and simulations, we show that the TipAS native ensemble exhibits a pre-equilibrium between binding-incompetent and binding-competent substates, with the fully folded state appearing only as an excited state under physiological conditions. The binding-competent state characterized by a partially structured N-terminal subdomain loses structure progressively in the physiological range of temperatures, swells on temperature increase, and displays slow conformational exchange across multiple conformations. Binding to the bactericidal antibiotic thiostrepton follows a combination of induced-fit and conformational-selection-like mechanisms, via partial binding and concomitant stabilization of the binding-competent substate. These ensemble features are evolutionarily conserved across orthologs from select bacteria that infect humans, underscoring the functional role of partial disorder in the native ensemble of antibiotic-sequestering proteins belonging to the MerR family.

Deep learning probability flows and entropy production rates in active matter.

Active matter systems, from self-propelled colloids to motile bacteria, are characterized by the conversion of free energy into useful work at the microscopic scale. They involve physics beyond the reach of equilibrium statistical mechanics, and a persistent challenge has been to understand the nature of their nonequilibrium states. The entropy production rate and the probability current provide quantitative ways to do so by measuring the breakdown of time-reversal symmetry. Yet, their efficient computation has remained elusive, as they depend on the system's unknown and high-dimensional probability density. Here, building upon recent advances in generative modeling, we develop a deep learning framework to estimate the score of this density. We show that the score, together with the microscopic equations of motion, gives access to the entropy production rate, the probability current, and their decomposition into local contributions from individual particles. To represent the score, we introduce a spatially local transformer network architecture that learns high-order interactions between particles while respecting their underlying permutation symmetry. We demonstrate the broad utility and scalability of the method by applying it to several high-dimensional systems of active particles undergoing motility-induced phase separation (MIPS). We show that a single network trained on a system of 4,096 particles at one packing fraction can generalize to other regions of the phase diagram, including to systems with as many as 32,768 particles. We use this observation to quantify the spatial structure of the departure from equilibrium in MIPS as a function of the number of particles and the packing fraction.

Crowding revisited: Open questions and future perspectives.

Although biophysical studies have traditionally been performed in diluted solutions, it was pointed out in the late 1990s that the cellular milieu contains several other macromolecules, creating a condition of molecular crowding. How crowding affects protein stability is an important question heatedly discussed over the past 20 years. Theoretical estimations have suggested a 5-20°C effect of fold stabilisation. This estimate, however, is at variance with what has been verified experimentally that proposes only a limited increase of stability, opening the question whether some of the assumptions taken for granted should be reconsidered. The present review critically analyses the causes of this discrepancy and discusses the limitations and implications of the current concept of crowding.

Kinetic Basis for DNA Target Specificity of CRISPR-Cas12a.

Class 2 CRISPR-Cas nucleases are programmable genome editing tools with promising applications in human health and disease. However, DNA cleavage at off-target sites that resemble the target sequence is a pervasive problem that remains poorly understood mechanistically. Here, we use quantitative kinetics to dissect the reaction steps of DNA targeting by Acidaminococcus sp Cas12a (also known as Cpf1). We show that Cas12a binds DNA tightly in two kinetically separable steps. Protospacer-adjacent motif (PAM) recognition is followed by rate-limiting R-loop propagation, leading to inevitable DNA cleavage of both strands. Despite functionally irreversible binding, Cas12a discriminates strongly against mismatches along most of the DNA target sequence. This result implies substantial reversibility during R-loop formation-a late transition state-and defies common descriptions of a "seed" region. Our results provide a quantitative basis for the DNA cleavage patterns measured in vivo and observations of greater reported target specificity for Cas12a than for the Cas9 nuclease.

Radiative controls by clouds and thermodynamics shape surface temperatures and turbulent fluxes over land.

Land surface temperatures (LSTs) are strongly shaped by radiation but are modulated by turbulent fluxes and hydrologic cycling as the presence of water vapor in the atmosphere (clouds) and at the surface (evaporation) affects temperatures across regions. Here, we used a thermodynamic systems framework forced with independent observations to show that the climatological variations in LSTs across dry and humid regions are mainly mediated through radiative effects. We first show that the turbulent fluxes of sensible and latent heat are constrained by thermodynamics and the local radiative conditions. This constraint arises from the ability of radiative heating at the surface to perform work to maintain turbulent fluxes and sustain vertical mixing within the convective boundary layer. This implies that reduced evaporative cooling in dry regions is then compensated for by an increased sensible heat flux and buoyancy, which is consistent with observations. We show that the mean temperature variation across dry and humid regions is mainly controlled by clouds that reduce surface heating by solar radiation. Using satellite observations for cloudy and clear-sky conditions, we show that clouds cool the land surface over humid regions by up to 7 K, while in arid regions, this effect is absent due to the lack of clouds. We conclude that radiation and thermodynamic limits are the primary controls on LSTs and turbulent flux exchange which leads to an emergent simplicity in the observed climatological patterns within the complex climate system.

Exceptional enhancement of mechanical properties in high-entropy alloys via thermodynamically guided local chemical ordering.

Understanding the local chemical ordering propensity in random solid solutions, and tailoring its strength, can guide the design and discovery of complex, paradigm-shifting multicomponent alloys. First, we present a simple thermodynamic framework, based solely on binary enthalpies of mixing, to select optimal alloying elements to control the nature and extent of chemical ordering in high-entropy alloys (HEAs). Next, we couple high-resolution electron microscopy, atom probe tomography, hybrid Monte-Carlo, special quasirandom structures, and density functional theory calculations to demonstrate how controlled additions of Al and Ti and subsequent annealing drive chemical ordering in nearly random equiatomic face-centered cubic CoFeNi solid solution. We establish that short-range ordered domains, the precursors of long-range ordered precipitates, inform mechanical properties. Specifically, a progressively increasing local order boosts the tensile yield strengths of the parent CoFeNi alloy by a factor of four while also substantially improving ductility, which breaks the so-called strength-ductility paradox. Finally, we validate the generality of our approach by predicting and demonstrating that controlled additions of Al, which has large negative enthalpies of mixing with the constituent elements of another nearly random body-centered cubic refractory NbTaTi HEA, also introduces chemical ordering and enhances mechanical properties.

PBE-GGA predicts the B8↔B2 phase boundary of FeO at Earth's core conditions.

FeO is a crucial component of the Earth's core, and its thermodynamic properties are essential to developing more accurate core models. It is also a notorious correlated insulator in the NaCl-type (B1) phase at ambient conditions. It undergoes two polymorphic transitions at 300 K before it becomes metallic in the NiAs-type (B8) structure at ~100 GPa. Although its phase diagram is not fully mapped, it is well established that the B8 phase transforms to the CsCl-type (B2) phase at core pressures and temperatures. Here, we report a successful ab initio calculation of the B8↔B2 phase boundary in FeO at Earth's core pressures. We show that fully anharmonic free energies computed with the Perdew-Burke-Ernzerhof-generalized gradient approximation coupled with thermal electronic excitations reproduce the experimental phase boundary within uncertainties at P > 255 GPa, including the largely negative Clapeyron slope of -52 MPa/K. This study validates the applicability of a standard density functional theory functional to FeO under Earth's core conditions and demonstrates the theoretical framework that enables complex predictive studies of this region.

Implications of a temperature-dependent heat capacity for temperature-gated ion channels.

Temperature influences dynamics and state-equilibrium distributions in all molecular processes, and only a relatively narrow range of temperatures is compatible with life-organisms must avoid temperature extremes that can cause physical damage or metabolic disruption. Animals evolved a set of sensory ion channels, many of them in the family of transient receptor potential cation channels that detect biologically relevant changes in temperature with remarkable sensitivity. Depending on the specific ion channel, heating or cooling elicits conformational changes in the channel to enable the flow of cations into sensory neurons, giving rise to electrical signaling and sensory perception. The molecular mechanisms responsible for the heightened temperature-sensitivity in these ion channels, as well as the molecular adaptations that make each channel specifically heat- or cold-activated, are largely unknown. It has been hypothesized that a heat capacity difference (ΔCp) between two conformational states of these biological thermosensors can drive their temperature-sensitivity, but no experimental measurements of ΔCp have been achieved for these channel proteins. Contrary to the general assumption that the ΔCp is constant, measurements from soluble proteins indicate that the ΔCp is likely to be a function of temperature. By investigating the theoretical consequences for a linearly temperature-dependent ΔCp on the open-closed equilibrium of an ion channel, we uncover a range of possible channel behaviors that are consistent with experimental measurements of channel activity and that extend beyond what had been generally assumed to be possible for a simple two-state model, challenging long-held assumptions about ion channel gating models at equilibrium.

Adiabatic computing for optimal thermodynamic efficiency of information processing.

Landauer's principle makes a strong connection between information theory and thermodynamics by stating that erasing a one-bit memory at temperature [Formula: see text] requires an average energy larger than [Formula: see text], with [Formula: see text] Boltzmann's constant. This tiny limit has been saturated in model experiments using quasistatic processes. For faster operations, an overhead proportional to the processing speed and to the memory damping appears. In this article, we show that underdamped systems are a winning strategy to reduce this extra energetic cost. We prove both experimentally and theoretically that, in the limit of vanishing dissipation mechanisms in the memory, the physical system is thermally insulated from its environment during fast erasures, i.e., fast protocols are adiabatic as no heat is exchanged with the bath. Using a fast optimal erasure protocol, we also show that these adiabatic processes produce a maximum adiabatic temperature [Formula: see text], and that Landauer's bound for fast erasures in underdamped systems becomes the adiabatic bound: [Formula: see text].

Deciphering the interaction between PKM2 and the built-in thermodynamic properties of the glycolytic pathway in cancer cells.

Most cancer cells exhibit high glycolysis rates under conditions of abundant oxygen. Maintaining a stable glycolytic rate is critical for cancer cell growth as it ensures sufficient conversion of glucose carbons to energy, biosynthesis, and redox balance. Here we deciphered the interaction between PKM2 and the thermodynamic properties of the glycolytic pathway. Knocking down or knocking out PKM2 induced a thermodynamic equilibration in the glycolytic pathway, characterized by the reciprocal changes of the Gibbs free energy (ΔG) of the reactions catalyzed by PFK1 and PK, leading to a less exergonic PFK1-catalyzed reaction and a more exergonic PK-catalyzed reaction. The changes of the ΔGs of the two reactions causes the accumulation of intermediates, including the substrate PEP (the substrate of PK), in the segment between PFK1 and PK. The increased concentration of PEP in turn increased PK activity in the glycolytic pathway. Thus, the interaction between PKM2 and the thermodynamic properties of the glycolytic pathway maintains the reciprocal relationship between PK concentration and its substrate PEP concentration, by which, PK activity in the glycolytic pathway can be stabilized and effectively counteracts the effect of PKM2 KD or KO on glycolytic rate. In line with our previous reports, this study further validates the roles of the thermodynamics of the glycolytic pathway in stabilizing glycolysis in cancer cells. Deciphering the interaction between glycolytic enzymes and the thermodynamics of the glycolytic pathway will promote a better understanding of the flux control of glycolysis in cancer cells.

Chemical modification of hyaluronan oligosaccharides differentially modulates hyaluronan-hyaladherin interactions.

The glycosaminoglycan hyaluronan (HA) is a ubiquitous, non-sulfated polysaccharide with diverse biological roles mediated through its interactions with HA-binding proteins (HABPs). Most HABPs belong to the Link module superfamily, including the major HA receptor, CD44, and secreted protein TSG-6, which catalyzes the covalent transfer of Heavy Chains (HC) from inter-α-inhibitor (IαI) onto HA. The structures of the HA-binding domains (HABD) of CD44 (HABD_CD44) and TSG-6 (Link_TSG6) have been determined and their interactions with HA extensively characterized. The mechanisms of binding are different, with Link_TSG6 interacting with HA primarily via ionic and CH-π interactions, whereas HABD_CD44 binds solely via hydrogen bonds and van der Waals forces. Here we exploit these differences to generate HA oligosaccharides, chemically modified at their reducing ends, that bind specifically and differentially to these target HABPs. Hexasaccharides (HA6AN) modified with 2- or 3-aminobenzoic acid or 2-amino-4-methoxybenzoic acid (HA6-2AA, HA6-3AA, HA6-2A4MBA, respectively) had increased affinities for Link_TSG6 compared to unmodified HA6AN. These modifications did not increase the affinity for CD44_HABD. A model of HA6-2AA (derived from the solution dynamic 3D structure of HA4-2AA) was docked into the Link_TSG6 structure, providing evidence that the 2AA-carboxyl forms a salt bridge with Arginine-81. These modeling results informed a 2nd series of chemical modifications for HA oligosaccharides, which again showed differential binding to the two proteins. Several modifications to HA4 and HA6 were found to convert the oligosaccharide into substrates for HC-transfer, whereas unmodified HA4 and HA6 are not. This study has generated valuable research tools to further understand HA biology.

Fatty acid binding to the human transport proteins FABP3, FABP4, and FABP5 from a Ligand's perspective.

Fatty acid binding proteins (FABPs) are a family of amphiphilic transport proteins with high diversity in terms of their amino acid sequences and binding preferences. Beyond their main biological role as cytosolic fatty acid transporters, many aspects regarding their binding mechanism and functional specializations in human cells remain unclear. In this work, the binding properties and thermodynamics of FABP3, FABP4, and FABP5 were analyzed under various physical conditions. For this purpose, the FABPs were loaded with fatty acids bearing fluorescence or spin probes as model ligands, comparing their binding affinities via microscale thermophoresis (MST) and continuous-wave electron paramagnetic resonance (CW EPR) spectroscopy. The CW EPR spectra of non-covalently bound 5- and 16-DOXYL stearic acid (5/16-DSA) deliver in-depth information about the dynamics and chemical environments of ligands inside the binding pockets of the FABPs. EPR spectral simulations allow the construction of binding curves, revealing two different binding states ('intermediately' and 'strongly' bound). The proportion of bound 5/16-DSA depends strongly on the FABP concentration and the temperature but with remarkable differences between the three isoforms. Additionally, the more dynamic state ('intermediately bound') seems to dominate at body temperature with thermodynamic preference. The ligand binding studies were supplemented by aggregation studies via dynamic light scattering and bioinformatic analyses. Beyond the remarkably fine-tuned binding properties exhibited by each FABP, which were discernible with our EPR-centered approach, the results of this work attest to the power of simple spectroscopic experiments to provide new insights into the ligand binding mechanisms of proteins in general on a molecular level.

Properties of the iron-sulfur cluster electron transfer relay in an [FeFe]-hydrogenase that is tuned for H2 oxidation catalysis.

[FeFe]-hydrogenases catalyze the reversible oxidation of H2 from electrons and protons at an organometallic active site cofactor named the H-cluster. In addition to the H-cluster, most [FeFe]-hydrogenases possess accessory FeS cluster (F-cluster) relays that function in mediating electron transfer with catalysis. There is significant variation in the structural properties of F-cluster relays among the [FeFe]-hydrogenases; however, it is unknown how this variation relates to the electronic and thermodynamic properties, and thus the electron transfer properties, of enzymes. Clostridium pasteurianum [FeFe]-hydrogenase II (CpII) exhibits a large catalytic bias for H2 oxidation (compared to H2 production), making it a notable system for examining if F-cluster properties contribute to the overall function and efficiency of the enzyme. By applying a combination of multifrequency and potentiometric electron paramagnetic resonance, we resolved two electron paramagnetic resonance signals with distinct power- and temperature-dependent properties at g = 2.058 1.931 1.891 (F2.058) and g = 2.061 1.920 1.887 (F2.061), with assigned midpoint potentials of -140 ± 18 mV and -406 ± 12 mV versus normal hydrogen electrode, respectively. Spectral analysis revealed features consistent with spin-spin coupling between the two [4Fe-4S] F-clusters, and possible functional models are discussed that account for the contribution of coupling to the electron transfer landscape. The results signify the interplay of electronic coupling and free energy properties and parameters of the FeS clusters to the electron transfer mechanism through the relay and provide new insight as to how relays functionally complement the catalytic directionality of active sites to achieve highly efficient catalysis.

Stem-loop formation drives RNA folding in mechanical unzipping experiments.

Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force-distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping-rezipping process, we introduce a barrier energy landscape of the stem-loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem-loops that kinetically trap the long-lived intermediates observed in the FDC. Stem-loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.

A complete description of thermodynamic stabilities of molecular crystals.

Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differences are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations for general molecular compounds, which simultaneously account for all effects, have hitherto not been computationally viable. Here we present an efficient "end to end" framework that seamlessly combines state-of-the art electronic structure calculations, machine-learning potentials, and advanced free energy methods to calculate ab initio Gibbs free energies for general organic molecular materials. The facile generation of machine-learning potentials for a diverse set of polymorphic compounds-benzene, glycine, and succinic acid-and predictions of thermodynamic stabilities in qualitative and quantitative agreement with experiments highlight that predictive thermodynamic studies of industrially relevant molecular materials are no longer a daunting task.