Both protein dynamics and ligand concentration can shift the binding mechanism between conformational selection and induced fit

This study aimed to shed light on the long debate over whether conformational selection (CS) or induced fit (IF) is the governing mechanism for protein–ligand binding. The main difference between the two scenarios is whether the conformational transition of the protein from the unbound form to the bound form occurs before or after encountering the ligand. Here we introduce the IF fraction (i.e., the fraction of binding events achieved via IF), to quantify the binding mechanism. Using simulations of a model protein–ligand system, we demonstrate that both the rate of the conformational transition and the concentration of ligand molecules can affect the IF fraction. CS dominates at slow conformational transition and low ligand concentration. An increase in either quantity results in a higher IF fraction. Despite the many-body nature of the system and the involvement of multiple, disparate types of dynamics (i.e., ligand diffusion, protein conformational transition, and binding reaction), the overall binding kinetics over wide ranges of parameters can be fit to a single exponential, with the apparent rate constant exhibiting a linear dependence on ligand concentration. The present study may guide future kinetics experiments and dynamics simulations in determining the IF fraction.The binding of proteins to small molecules (i.e., ligands) is central to many essential biological functions, including enzyme catalysis, receptor activation, and drug action. Generally, significant differences in protein conformation exist between the unbound and bound states, as exemplified by hemoglobin upon binding oxygen (1–4) and HIV-1 protease upon binding a substrate or a drug molecule (5). In the latter as well as some other cases (6–10), loops and other groups collapse around the bound ligand, leading to a closed binding pocket. The conformational redistribution and dynamics of the protein molecule exhibited during the binding process can potentially play a critical role in determining the magnitude of the rate constant as well as the mechanism of ligand binding (11, 12). Two mechanistic models have emerged as archetypes. In the induced-fit (IF) model, one assumes that, owing to interactions with the incoming ligand, the protein transitions from an “inactive” conformation to an “active” conformation (13). In the conformational-selection (CS) model, one assumes that the protein can preexist in the active conformation with a low probability, and it is when the protein is in this conformation that the ligand comes into contact, leading to productive binding (14). Both models have garnered defenders and detractors (15–19). This study aimed to shed light on the long debate over whether CS or IF is the governing mechanism for protein–ligand binding.It has been suggested that observation of the active conformation without the ligand, akin to constitutive activity of receptors, is direct evidence of CS (17, 19). However, detractors of CS have noted that, at least for cases with a closed binding pocket in the active conformation, direct binding to the latter conformation cannot proceed (9, 15). In some cases, a partially closed conformation has been observed by a sensitive probe such as paramagnetic relaxation enhancement (20) or in molecular dynamics simulations. Accordingly, a revised model known as extended CS has been put forward (21–28), whereby the ligand binds to the partially closed conformation and then the protein–ligand system evolves to the bound state with the closed binding pocket. Although the divide between CS and IF is somewhat blurred by extended CS, strictly speaking the latter is an IF model, in the sense that the ligand binds to an inactive conformation (i.e., the partially closed conformation) before the protein adopts the final active conformation with the closed binding pocket. Indeed, a strict CS mechanism is not possible for a protein whose active conformation features a closed binding pocket. In any event, mere observation of the active conformation in the unbound state cannot be taken as proof of the CS mechanism. According to the Boltzmann distribution, every conformation, including the active conformation, has a certain equilibrium probability. Whether the active conformation can be observed depends on the magnitude of its equilibrium probability as well as the sensitivity of the probe. The binding mechanism should not change just because the probe has become more sensitive.It thus seems that neither CS nor IF should be the sole dominant mechanism governing protein–ligand binding. What, then, are the determinants of binding mechanism? Hammes et al. (29) and Daniels et al. (30) have suggested that an increase in ligand concentration can shift the binding mechanism from CS to IF, because a higher ligand concentration would make binding more likely. The assumption is that that would increase the chance for the binding to occur before the conformational transition, but one cannot be certain without additional information about the dynamics and interactions of the protein and ligand molecules. Others have suggested that the timescale of the protein conformational transition, relative to the timescale of the ligand diffusional approach to the binding pocket, controls the binding mechanism (12), but the effect of ligand concentration was not studied.To unequivocally determine the binding mechanism, one has to follow the protein–ligand relative translation and the protein internal motion, from the unbound state until two reactant molecules form the bound product. This process involves disparate types of dynamics, including ligand diffusion, protein conformational transition, and the final binding reaction. As the simplest model, protein conformational transition has been treated as gating, that is, the transitions between two conformational states are approximated as rate processes (31–33). The transition rates were initially assumed to be unaffected by protein–ligand interactions. More recently it was recognized that protein–ligand interactions necessarily influence the conformational transition rates and such influence is an essential ingredient of molecular recognition (12, 34, 35). Accordingly, the transition rates were assigned different values depending on whether the ligand is inside or outside the binding pocket, resulting in the dual-transition-rates model.Here we studied the binding mechanism and kinetics of a system consisting of a concentration of ligand molecules surrounding a protein molecule whose conformational dynamics follows the dual-transition-rates model (Fig. 1A). From dynamics simulations, we calculate the IF fraction (i.e., the fraction of binding events achieved via IF) and show that the binding mechanism is shifted by both the rate of protein conformational transition and the concentration of ligand molecules. CS dominates at slow conformational transition and low ligand concentration. With the increase of either quantity, the binding mechanism shifts from CS to IF. The overall binding kinetics over wide ranges of parameters can be fit to a single exponential, with the apparent binding rate constant exhibiting a linear dependence on ligand concentration. The concentration dependence of the binding kinetics thus yields little information on the binding mechanism, but kinetics experiments and dynamics simulations can be designed to determine the IF fraction.Open in a separate windowFig. 1.The model protein–ligand system and its binding mechanism. (A) A spherical protein is surrounded by point-like ligand molecules inside a spherical container (with radius R_w). The protein can transition between an inactive conformation and active conformation, and the transition rates depend on whether a ligand molecule is in the binding pocket (with inner and outer radii R and R₁, respectively). (B) A binding event achieved through either the conformational selection (Upper) or the induced fit (Lower) mechanism. The crucial difference is whether the last inactive-to-active transition (at time t_c) before the binding reaction (at t_r) occurs with or without a loosely bound ligand molecule.     

Utilization of extracellular information before ligand-receptor binding reaches equilibrium expands and shifts the input dynamic range

Cell signaling systems sense and respond to ligands that bind cell surface receptors. These systems often respond to changes in the concentration of extracellular ligand more rapidly than the ligand equilibrates with its receptor. We demonstrate, by modeling and experiment, a general “systems level” mechanism cells use to take advantage of the information present in the early signal, before receptor binding reaches a new steady state. This mechanism, pre-equilibrium sensing and signaling (PRESS), operates in signaling systems in which the kinetics of ligand-receptor binding are slower than the downstream signaling steps, and it typically involves transient activation of a downstream step. In the systems where it operates, PRESS expands and shifts the input dynamic range, allowing cells to make different responses to ligand concentrations so high as to be otherwise indistinguishable. Specifically, we show that PRESS applies to the yeast directional polarization in response to pheromone gradients. Consideration of preexisting kinetic data for ligand-receptor interactions suggests that PRESS operates in many cell signaling systems throughout biology. The same mechanism may also operate at other levels in signaling systems in which a slow activation step couples to a faster downstream step.Detecting and responding to a chemical gradient is a central feature of a multitude of biological processes (1). For this behavior, organisms use signaling systems that sense information about the extracellular world, transmit this information into the cell, and orchestrate a response. Measurements of the direction and proximity of the extracellular stimuli usually rely on the binding of diffusing chemical particles (ligands) to specific cell surface receptors. Different organisms have evolved different strategies to make use of this information. Small motile organisms, including certain bacteria, use a temporal sensing strategy, measuring and comparing concentration signals over time along their swimming tracks (2). In contrast, some eukaryotic cells, including Saccharomyces cerevisiae, are sufficiently large to implement a spatial sensing mechanism, measuring concentration differences across their cell bodies (3).The observation that some eukaryotes that use spatial sensing exhibit remarkable precision in response to shallow gradients (1–2% differences in ligand concentration between front and rear) (4, 5) has led to several proposed models in which large amplification is achieved by positive feedback loops in the signaling pathways triggered by the ligand-receptor binding (6, 7). Here, we describe a different mechanism, dependent on ligand-receptor binding dynamics, which improves gradient sensing when the concentration of external ligand is close to saturation. We use the budding yeast S. cerevisiae to study the efficiency of this mechanism.Haploid yeast cells exist in two mating types, MATa and MATα (also referred to as a and α cells). Mating occurs when a and α cells sense each other’s secreted mating pheromones: a-factor and α-factor (αF) (8). The pheromone secreted by the nearby mating partner diffuses, forming a gradient surrounding the sensing cell. Pheromone binds a membrane receptor, Ste2, in MATa yeast (9) that activates a pheromone response system (PRS), which cells use to decide whether to fuse with a mating partner or not. At high enough αF concentrations, cells develop a polarized chemotropic growth toward the pheromone source (4). To do that, the nonmotile yeast determines the direction of the potential mating partner measuring on which side there are more bound pheromone receptors, which are initially distributed homogeneously on the cell surface (10). However, this sensing modality can only work when external pheromone is nonsaturating: If all receptors are bound, cells should not be able to determine the direction of the gradient. Surprisingly, even at high pheromone concentrations, yeast tend to polarize in the correct direction (4, 11). Different amplification mechanisms have been proposed to account for the conversion of small differences in ligand concentration across the yeast cell, as is the case for dense mating mixtures, into chemotropic growth (6).We previously studied induction of reporter gene output by the PRS after step increases in the concentration of αF. We found large cell-to-cell variability, the bulk of which was due to large differences in the ability of individual cells to send signal through the system and in their general capacity to express proteins (12). The level of induced gene expression matches well the equilibrium binding curve of αF to receptor (13, 14), a phenomenon known as dose–response alignment (DoRA), common to many other signaling systems (14). In the PRS, DoRA persists for several hours of stimulation.During these studies, we realized that the binding dynamics of αF to its receptor is remarkably slow: At concentrations near the dissociation constant (K_d), binding takes about 20 min to reach 90% of the equilibrium level (15, 16). This dynamics is slow relative not only to the 90-min cell division cycle but also to the pheromone-dependent activation of the mitogen-activated protein kinase (MAPK) Fus3, which takes 2 to 5 min to reach steady-state levels (14). An unavoidable conclusion is that the machinery downstream of the αF receptor must be using pre-equilibrium binding information for its operation.This observation led us to study the consequences of fast and slow ligand-receptor dynamics on the ability of cells to sense extracellular cues. In biology, the rates of ligand binding and unbinding to membrane receptors span a large range, including many cases with dynamics similar to, or even slower than, that of mating pheromone (e.g., rates for EGF, insulin, glucagon, IFN-α1a, and IL-2 in

From the Cover: 1.36 million years of Mediterranean forest refugium dynamics in response to glacial–interglacial cycle strength

The sediment record from Lake Ohrid (Southwestern Balkans) represents the longest continuous lake archive in Europe, extending back to 1.36 Ma. We reconstruct the vegetation history based on pollen analysis of the DEEP core to reveal changes in vegetation cover and forest diversity during glacial–interglacial (G–IG) cycles and early basin development. The earliest lake phase saw a significantly different composition rich in relict tree taxa and few herbs. Subsequent establishment of a permanent steppic herb association around 1.2 Ma implies a threshold response to changes in moisture availability and temperature and gradual adjustment of the basin morphology. A change in the character of G–IG cycles during the Early–Middle Pleistocene Transition is reflected in the record by reorganization of the vegetation from obliquity- to eccentricity-paced cycles. Based on a quantitative analysis of tree taxa richness, the first large-scale decline in tree diversity occurred around 0.94 Ma. Subsequent variations in tree richness were largely driven by the amplitude and duration of G–IG cycles. Significant tree richness declines occurred in periods with abundant dry herb associations, pointing to aridity affecting tree population survival. Assessment of long-term legacy effects between global climate and regional vegetation change reveals a significant influence of cool interglacial conditions on subsequent glacial vegetation composition and diversity. This effect is contrary to observations at high latitudes, where glacial intensity is known to control subsequent interglacial vegetation, and the evidence demonstrates that the Lake Ohrid catchment functioned as a refugium for both thermophilous and temperate tree species.

Identification and protection of past forest refugia, supporting a relict population, has gained interest in light of projected forest responses to anthropogenic climate change (1–4). Understanding the past and present composition of Mediterranean forest refugia is central to the study of long-term survival of tree taxa and the systematic relation between forest dynamics and climate (5). The Quaternary vegetation history of Europe, studied for over a century, is characterized by successive loss of tree species (6–8). Species loss was originally explained by the repeated migration across east–west oriented mountain chains during glacial–interglacial (G–IG) cycles (9). Later views gave more importance to the survival of tree populations during warm and arid stages in southern refugia (10, 11). Tree survival likely depends on persistence of suitable climate and tolerable levels of climate variability, as well as niche differentiation and population size at the refugium (12), although the precise relation between regional extinctions, climate variability, and local edaphic factors is not well known (13). Mediterranean mountain regions are considered to serve as forest refugia over multiple glacial cycles and frequently coincide with present-day biodiversity hotspots (14). Across the Mediterranean, increases in aridity and fire occurrence have impacted past vegetation communities (15–18). Comprehensive review of available Quaternary Mediterranean records indicates that Early (2.58 to 0.77 Ma) and Middle Pleistocene (0.77 to 0.129 Ma) tree diversity was higher compared to the present (13, 19–21). Particularly drought intolerant, thermophilic taxa were more abundant and diverse (8) but with strong spatial and temporal variations in tree diversity across the region. Long-term relationships between refugia function and environmental change over multiple G–IG cycles are hard to quantify due to the rarity of long, uninterrupted records.The Early–Middle Pleistocene Transition (EMPT), between 1.4 and 0.4 Ma (22), is of particular importance for understanding the relation between past climate change, vegetation dynamics, and biodiversity in the Mediterranean region. The EMPT is characterized by a gradual transition of G–IG cycle duration from obliquity (41 thousand years; kyr) to eccentricity (100 kyr) scale with increasing amplitude of each G–IG cycle (e.g., refs. 23, 24). The EMPT was accompanied by long-term cooling of the deep and surface ocean and was likely caused by atmospheric CO₂ decline and ice-sheet feedbacks (25–30). In Europe, the EMPT is associated with pronounced vegetation changes and local extinction and isolation of small tree populations (31).Here, we document vegetation history of the last 1.36 Ma in the Lake Ohrid (LO) catchment, located at the Albanian/North Macedonian border at 693 m above sea level (m asl, Fig. 1), the longest continuous sedimentary lake record in Europe (32, 33). The chronology of the DEEP core (International Continental Scientific Drilling Program site 5045-1; 41°02’57’’ N, 20°42’54’’ E, Fig. 1) is based on tuning of biogeochemical proxy data to orbital parameters with independent tephrostratigraphic and paleomagnetic age control (32, 33). The Balkan Peninsula has long been considered an important glacial forest refugium for presently widespread taxa such as Abies, Picea, Carpinus, Corylus, Fagus Ostrya, Quercus, Tilia, and Ulmus (7, 34–36). More than 60% of the Balkans is currently located >1,000 m asl (36), providing steep latitudinal and elevational gradients to support refugia under both cold and warm conditions. Today, the LO catchment is dominated by (semi) deciduous oak (Quercus) and hornbeam (Carpinus/Ostrya) forests. Above 1,250 m elevation, mixed mesophyllous forest with montane elements occurs (Fagus and at higher elevations Abies), which above 1,800 m elevation develops into subalpine grassland with Juniperus shrubs (see ref. 37 for site details). Isolated populations of Pinus peuce and Pinus nigra currently grow in the area (37–40).Open in a separate windowFig. 1.(A) Location of LO and TP on the Balkan Peninsula. (B) Local setting around LO, bathymetry (81), and DEEP coring site (adapted from ref. 32).Previous analysis of pollen composition of the last 500 kyr at the DEEP site revealed that the LO has been an important refugium. Arboreal pollen (AP) is deposited continuously and changes in abundance on multimillennial timescales in association with G–IG cycles, whereas millennial-scale variability is tightly coupled to Mediterranean sea-surface temperature variations (37, 41–45). Subsequent studies confirm the refugial character of the site recording Early Pleistocene (1.365 to 1.165 Ma) high relict tree diversity and abundance—and significant hydrological changes, including an increase in lake size and depth (38). Here, we present a continuous palynological record from LO with millennial resolution (∼2 kyr) back to 1.36 Ma to assess the systematic relationships between tree pollen abundance, forest diversity, and G–IG climate variability.Our objective is as follows: 1) infer the impact of past climate variability on local vegetation across the EMPT, 2) estimate tree species diversity in the catchment, and 3) examine how the amplitude and duration of preceding G–IG intervals affected the vegetation development and plant species diversity in this refugial area.     

Substrate binding site flexibility of the small heat shock protein molecular chaperones

Small heat shock proteins (sHSPs) serve as a first line of defense against stress-induced cell damage by binding and maintaining denaturing proteins in a folding-competent state. In contrast to the well-defined substrate binding regions of ATP-dependent chaperones, interactions between sHSPs and substrates are poorly understood. Defining substrate-binding sites of sHSPs is key to understanding their cellular functions and to harnessing their aggregation-prevention properties for controlling damage due to stress and disease. We incorporated a photoactivatable cross-linker at 32 positions throughout a well-characterized sHSP, dodecameric PsHsp18.1 from pea, and identified direct interaction sites between sHSPs and substrates. Model substrates firefly luciferase and malate dehydrogenase form strong contacts with multiple residues in the sHSP N-terminal arm, demonstrating the importance of this flexible and evolutionary variable region in substrate binding. Within the conserved α-crystallin domain both substrates also bind the β-strand (β7) where mutations in human homologs result in inherited disease. Notably, these binding sites are poorly accessible in the sHSP atomic structure, consistent with major structural rearrangements being required for substrate binding. Detectable differences in the pattern of cross-linking intensity of the two substrates and the fact that substrates make contacts throughout the sHSP indicate that there is not a discrete substrate binding surface. Our results support a model in which the intrinsically-disordered N-terminal arm can present diverse geometries of interaction sites, which is likely critical for the ability of sHSPs to protect efficiently many different substrates.     

Remote control of DNA-acting enzymes by varying the Brownian dynamics of a distant DNA end

Enzyme rates are usually considered to be dependent on local properties of the molecules involved in reactions. However, for large molecules, distant constraints might affect reaction rates by affecting dynamics leading to transition states. In single-molecule experiments we have found that enzymes that relax DNA torsional stress display rates that depend strongly on how the distant ends of the molecule are constrained; experiments with different-sized particles tethered to the end of 10-kb DNAs reveal enzyme rates inversely correlated with particle drag coefficients. This effect can be understood in terms of the coupling between molecule extension and local molecular stresses: The rate of bead thermal motion controls the rate at which transition states are visited in the middle of a long DNA. Importantly, we have also observed this effect for reactions on unsupercoiled DNA; other enzymes show rates unaffected by bead size. Our results reveal a unique mechanism through which enzyme rates can be controlled by constraints on macromolecular or supramolecular substrates.     

Chloride Diffusion Property of Hybrid Basalt–Polypropylene Fibre-Reinforced Concrete in a Chloride–Sulphate Composite Environment under Drying–Wetting Cycles

The effect of fibre reinforcement on the chloride diffusion property of concrete is controversial, and the coupling effect of sulphate erosion and drying–wetting cycles in marine environments has been neglected in previous studies. In this study, the chloride diffusion property of hybrid basalt–polypropylene fibre-reinforced concrete subjected to a combined chloride–sulphate solution under drying–wetting cycles was investigated. The effects of basalt fibre (BF), polypropylene fibre (PF), and hybrid BP–PF on the chloride diffusion property were analysed. The results indicate that the presence of sulphate inhibits the diffusion of chloride at the early stage of erosion. However, at the late stage of erosion, sulphate does not only accelerate the diffusion of chloride by causing cracking of the concrete matrix but also leads to a decrease in the alkalinity of the pore solution, which further increases the risk of corrosion of the reinforcing steel. An appropriate amount of fibre can improve the chloride attack resistance of concrete at the early stage. With the increase in erosion time, the fibre effectively prevents the formation and development of sulphate erosion microcracks, thus reducing the adverse effects of sulphate on the resistance of concrete to chloride attack. The effects of sulphate and fibre on the chloride diffusion property were also elucidated in terms of changes in corrosion products, theoretical porosity, and the fibre-matrix interface transition zone.     

Interplay between partner and ligand facilitates the folding and binding of an intrinsically disordered protein

Protein–protein interactions are at the heart of regulatory and signaling processes in the cell. In many interactions, one or both proteins are disordered before association. However, this disorder in the unbound state does not prevent many of these proteins folding to a well-defined, ordered structure in the bound state. Here we examine a typical system, where a small disordered protein (PUMA, p53 upregulated modulator of apoptosis) folds to an α-helix when bound to a groove on the surface of a folded protein (MCL-1, induced myeloid leukemia cell differentiation protein). We follow the association of these proteins using rapid-mixing stopped flow, and examine how the kinetic behavior is perturbed by denaturant and carefully chosen mutations. We demonstrate the utility of methods developed for the study of monomeric protein folding, including β-Tanford values, Leffler α, Φ-value analysis, and coarse-grained simulations, and propose a self-consistent mechanism for binding. Folding of the disordered protein before binding does not appear to be required and few, if any, specific interactions are required to commit to association. The majority of PUMA folding occurs after the transition state, in the presence of MCL-1. We also examine the role of the side chains of folded MCL-1 that make up the binding groove and find that many favor equilibrium binding but, surprisingly, inhibit the association process.For many proteins, correct folding to a specific 3D structure is essential for their function inside the cell; once folded, some of these have the appropriate shape and accessible chemical groups to interact specifically with, and bind to, another protein (1). However, for a number of protein–protein interactions, folding and binding do not appear to be separate, sequential events (2, 3). Many intrinsically disordered proteins (IDPs) will appear largely unfolded in isolation, only forming a specific structure when bound to an appropriate partner protein and undergoing coupled folding and binding (4–6). Such reactions are abundant in signaling and regulatory processes (7, 8). Protein folding does not simply provide correctly shaped building blocks for the cell; it can play an intimate role in molecular recognition.Over the past decade, bioinformatics studies have revealed that protein disorder (7, 9), and coupled folding and binding (10), are widespread in biology. Many structures of bound, folded IDPs have been solved and have shown the wide range of topologies that can be formed (11). Biophysical techniques (12), NMR in particular (13), can characterize isolated IDPs in detail. Despite this progress, the number of studies examining kinetics and the mechanisms of binding remains relatively small (14–21) given that the most commonly observed function of IDPs is in coupled folding and binding reactions (22).To describe coupled folding and binding, two extreme mechanisms are often discussed, focusing on whether an IDP needs to fold before interacting productively with its binding partner. In isolation an IDP could, perhaps only transiently, occupy a conformation that resembles the bound state. In the pure conformational selection mechanism, the IDP must be in this conformation at the start of the eventually successful encounter with the partner protein (23, 24) (Fig. 1A). Arguments in support of this mechanism largely come from NMR studies that have successfully detected these lowly populated, folded states in unbound IDPs (25–27). In the contrasting induced-fit mechanism, there is no requirement for the IDP to fold in isolation (28). Instead, the potentially transient interactions with the partner protein lead to the folding of the IDP (Fig. 1A). Complex mixtures of these two extreme mechanisms can also be imagined: e.g., perhaps only a proportion of the IDP needs to fold before the encounter, i.e., conformational selection followed by induced fit of the remaining peptide chain (29). To add to the potential complexity, flux through different pathways could occur simultaneously, and may depend on the concentrations of protein involved (23, 30). Further, confirming the degree of induced fit and conformational selection is only one aspect of the binding mechanism. There remain a large number of mechanistic possibilities beyond the state of the IDP prior to successful encounters.Open in a separate windowFig. 1.PUMA–MCL-1 binding. (A) Cartoon of binding mechanisms. IDP PUMA (blue) can undergo coupled folding and binding with structured MCL-1 (white) to form a single, contiguous α-helix. Structures based on PDB 2ROC (39) and 1WSX (58). Unbound PUMA and encounter complex built using Chimera (University of California, San Francisco). Figure prepared using PyMol. (B) Representative fluorescence stopped-flow traces for binding. Increasing the concentration of urea from 0 to 3.5 M (in 0.5-M increments) slows association. (C) The urea dependence of the natural log of the association rate constant (k+) for the wild-type PUMA peptide used in this study. (D) The urea dependence of the dissociation rate constant (k−). k− was determined by preforming the PUMA–MCL-1 complex at micromolar concentrations and manually diluting to nanomolar concentrations to induce dissociation. The resulting kinetic trace was fit to a reversible model, fixing k+ from the association experiments (41). Gradient of the linear fits corresponds to the m values discussed in the main text. A, B, and C adapted from ref. 37.It is largely agreed that most protein folding (and unfolding) reactions are limited by the requirement to populate a high-energy transition state (31). Kinetic, time-resolved experiments, in combination with site-directed mutagenesis and Φ-value analysis (32), have been applied successfully to describe these transition states (33, 34). With carefully chosen mutations, the distribution of Φ values (classically between 0 and 1) offers an average picture of the interactions formed at this critical stage of the folding reaction, at residue-level resolution. This picture, in conjunction with other evidence, can offer invaluable insights into the mechanisms of folding (35, 36).We have previously reported the kinetics of a model coupled folding and binding reaction (37, 38); the BH3 motif of PUMA (an IDP) can associate with the structured protein MCL-1 and fold to a single contiguous α-helix (39). The solvent and temperature dependence of the association reaction suggested that this reaction is limited by a free energy barrier, or transition state (TS) (37). Here we systematically make structurally conservative mutations to the IDP and the partner protein, apply Φ-value analysis, and describe the transition state for binding. Molecular dynamics simulations using a coarse-grained, topology-based model of the binding process are consistent with our experimental results. We bring together all available evidence to propose a mechanism of binding.     

Molecular mechanism of the dual activity of 4EGI-1: Dissociating eIF4G from eIF4E but stabilizing the binding of unphosphorylated 4E-BP1

The eIF4E-binding protein (4E-BP) is a phosphorylation-dependent regulator of protein synthesis. The nonphosphorylated or minimally phosphorylated form binds translation initiation factor 4E (eIF4E), preventing binding of eIF4G and the recruitment of the small ribosomal subunit. Signaling events stimulate serial phosphorylation of 4E-BP, primarily by mammalian target of rapamycin complex 1 (mTORC1) at residues T₃₇/T₄₆, followed by T₇₀ and S₆₅. Hyperphosphorylated 4E-BP dissociates from eIF4E, allowing eIF4E to interact with eIF4G and translation initiation to resume. Because overexpression of eIF4E is linked to cellular transformation, 4E-BP is a tumor suppressor, and up-regulation of its activity is a goal of interest for cancer therapy. A recently discovered small molecule, eIF4E/eIF4G interaction inhibitor 1 (4EGI-1), disrupts the eIF4E/eIF4G interaction and promotes binding of 4E-BP1 to eIF4E. Structures of 14- to 16-residue 4E-BP fragments bound to eIF4E contain the eIF4E consensus binding motif, ⁵⁴YXXXXLΦ⁶⁰ (motif 1) but lack known phosphorylation sites. We report here a 2.1-Å crystal structure of mouse eIF4E in complex with m⁷GTP and with a fragment of human 4E-BP1, extended C-terminally from the consensus-binding motif (4E-BP1_50–84). The extension, which includes a proline-turn-helix segment (motif 2) followed by a loop of irregular structure, reveals the location of two phosphorylation sites (S₆₅ and T₇₀). Our major finding is that the C-terminal extension (motif 3) is critical to 4E-BP1–mediated cell cycle arrest and that it partially overlaps with the binding site of 4EGI-1. The binding of 4E-BP1 and 4EGI-1 to eIF4E is therefore not mutually exclusive, and both ligands contribute to shift the equilibrium toward the inhibition of translation initiation.Translation control of gene expression allows cells to respond quickly to external cues. In eukaryotic cells, this regulation occurs mainly at the translation initiation step (reviewed in ref. 1). Cellular eukaryotic mRNAs have a cap structure at their 5′ terminus, which is a modified nucleotide (7-methylguanosine triphosphate, m⁷GpppN, where N is any nucleotide) (2). The translational preinitiation complex assembles at the m⁷GpppN cap via the translation initiation complex 4F (eIF4F) (3), which comprises a cap-binding protein, eIF4E, a DEAD-Box RNA helicase, eIF4A, and a large scaffold protein, eIF4G. The scaffold protein eIF4G interacts with eIF4E through a consensus motif, YXXXXLΦ, where X is any amino acid and Φ is a hydrophobic residue. This motif is also shared by eIF4E binding proteins (4E-BPs). The interaction between eIF4E and 4E-BP is phosphorylation-dependent (4–8). When hypophosphorylated, 4E-BP binds tightly to eIF4E. Hyperphosphorylation of 4E-BP, however, decreases its affinity for eIF4E, enabling eIF4G to interact with eIF4E.Altered regulation of translation initiation has been linked to prion formation (9) and to several human diseases, including autism (10) and cancer (11). eIF4E is overexpressed in a variety of tumor cells (12, 13). This overexpression has been implicated in oncogenic transformation (14, 15), a process that 4E-BPs can effectively revert (14–16). Mammalian target of rapamycin (mTOR) inhibitors, such as rapamycin and its analogs, exert antitumor activity by suppressing 4E-BP1’s phosphorylation, thus enabling its interaction with eIF4E (17). The ability of 4E-BPs to compete with eIF4G for eIF4E binding is explained by the shared YXXXXLΦ binding motif (18, 19). Crystal structures of mouse eIF4E complexed with either 4E-BP1_51–64, eIF4G-I_569–580, or eIF4G-II_621–637, all short fragments containing the consensus-binding motif, are virtually identical (19–22). NMR spectroscopy titration experiments (23) and small angle X-ray scattering of full-length 4E-BP1 bound to eIF4E (24) suggested that 4E-BP1 has a larger binding interface on eIF4E than eIF4G. In agreement with this observation, mutagenesis analysis and affinity binding measurements showed that the C-terminal segment of 4E-BPs is auxiliary for binding to eIF4E (25, 26). More recently, a conserved ⁷⁹PGVTS/T⁸³ motif found in the C terminus of 4E-BPs was shown to enhance its binding affinity to eIF4E from micromolar to nanomolar range (27, 28), revealing that 4E-BP1 has, in fact, a bipartite binding interface with eIF4E.Our group has identified a small-molecule inhibitor, eIF4E/eIF4G interaction inhibitor 1 (4EGI-1), which specifically disrupts association of eIF4G-derived peptides with eIF4E but stabilizes the eIF4E/4E-BP1 interaction (29). 4EGI-1 is of particular interest because it inhibits cap-dependent translation, is active against numerous cancer cell lines, and reduces growth of human cancer xenografts in vivo (29–31). Its effect is partially explained by the recent crystal structure of an eIF4E/4EGI-1 complex, in which the inhibitor binds to a site located remotely from the YXXXXLΦ binding interface, suggesting that it allosterically represses translation initiation (32). However, the mechanism by which 4EGI-1 stabilized 4E-BP1 binding remains unclear.In this study, we describe a crystal structure of eIF4E bound to a 35-residue fragment of 4E-BP1. This fragment comprises the consensus-binding motif and also a proline-turn-helix segment containing two phosphorylation sites (S₆₅ and T₇₀) followed by a loop of irregular structure. We find that the C-terminal loop of 4E-BP1 partially overlaps with the binding site of 4EGI-1, which enables us to understand the molecular mechanism through which 4EGI-1 inhibits translation initiation: by dissociating eIF4G from eIF4E but also stabilizing the interaction between eIF4E and the unphosphorylated form of 4E-BP1. We further find that the C-terminal loop of 4E-BP1 is required to inhibit cap-dependent translation and mediates cell cycle arrest in mammalian cells.     

Kinetics of nucleotide-dependent structural transitions in the kinesin-1 hydrolysis cycle

To dissect the kinetics of structural transitions underlying the stepping cycle of kinesin-1 at physiological ATP, we used interferometric scattering microscopy to track the position of gold nanoparticles attached to individual motor domains in processively stepping dimers. Labeled heads resided stably at positions 16.4 nm apart, corresponding to a microtubule-bound state, and at a previously unseen intermediate position, corresponding to a tethered state. The chemical transitions underlying these structural transitions were identified by varying nucleotide conditions and carrying out parallel stopped-flow kinetics assays. At saturating ATP, kinesin-1 spends half of each stepping cycle with one head bound, specifying a structural state for each of two rate-limiting transitions. Analysis of stepping kinetics in varying nucleotides shows that ATP binding is required to properly enter the one-head–bound state, and hydrolysis is necessary to exit it at a physiological rate. These transitions differ from the standard model in which ATP binding drives full docking of the flexible neck linker domain of the motor. Thus, this work defines a consensus sequence of mechanochemical transitions that can be used to understand functional diversity across the kinesin superfamily.Kinesin-1 is a motor protein that steps processively toward microtubule plus-ends, tracking single protofilaments and hydrolyzing one ATP molecule per step (1–6). Step sizes corresponding to the tubulin dimer spacing of 8.2 nm are observed when the molecule is labeled by its C-terminal tail (7–10) and to a two-dimer spacing of 16.4 nm when a single motor domain is labeled (4, 11, 12), consistent with the motor walking in a hand-over-hand fashion. Kinesin has served as an important model system for advancing single-molecule techniques (7–10) and is clinically relevant for its role in neurodegenerative diseases (13), making dissection of its step a popular ongoing target of study.Despite decades of work, many essential components of the mechanochemical cycle remain disputed, including (i) how much time kinesin-1 spends in a one-head–bound (1HB) state when stepping at physiological ATP concentrations, (ii) whether the motor waits for ATP in a 1HB or two-heads–bound (2HB) state, and (iii) whether ATP hydrolysis occurs before or after tethered head attachment (4, 11, 14–20). These questions are important because they are fundamental to the mechanism by which kinesins harness nucleotide-dependent structural changes to generate mechanical force in a manner optimized for their specific cellular tasks. Addressing these questions requires characterizing a transient 1HB state in the stepping cycle in which the unattached head is located between successive binding sites on the microtubule. This 1HB intermediate is associated with the force-generating powerstroke of the motor and underlies the detachment pathway that limits motor processivity. Optical trapping (7, 19, 21, 22) and single-molecule tracking studies (4, 8–11) have failed to detect this 1HB state during stepping. Single-molecule fluorescence approaches have detected a 1HB intermediate at limiting ATP concentrations (11, 12, 14, 15), but apart from one study that used autocorrelation analysis to detect a 3-ms intermediate (17), the 1HB state has been undetectable at physiological ATP concentrations.Single-molecule microscopy is a powerful tool for studying the kinetics of structural changes in macromolecules (23). Tracking steps and potential substeps for kinesin-1 at saturating ATP has until now been hampered by the high stepping rates of the motor (up to 100 s⁻¹), which necessitates high frame rates, and the small step size (8.2 nm), which necessitates high spatial precision (7). Here, we apply interferometric scattering microscopy (iSCAT), a recently established single-molecule tool with high spatiotemporal resolution (24–27) to directly visualize the structural changes underlying kinesin stepping. By labeling one motor domain in a dimeric motor, we detect a 1HB intermediate state in which the tethered head resides over the bound head for half the duration of the stepping cycle at saturating ATP. We further show that at physiological stepping rates, ATP binding is required to enter this 1HB state and that ATP hydrolysis is required to exit it. This work leads to a significant revision of the sequence and kinetics of mechanochemical transitions that make up the kinesin-1 stepping cycle and provides a framework for understanding functional diversity across the kinesin superfamily.     

Dissecting the roles of MuB in Mu transposition: ATP regulation of DNA binding is not essential for target delivery

Collaboration between MuA transposase and its activator protein, MuB, is essential for properly regulated transposition. MuB activates MuA catalytic activity, selects target DNA, and stimulates transposition into the selected target site. Selection of appropriate target DNA requires ATP hydrolysis by the MuB ATPase. By fusing MuB to a site-specific DNA-binding protein, the Arc repressor, we generated a MuB variant that could select target DNA independently of ATP. This Arc-MuB fusion protein allowed us to test whether ATP binding and hydrolysis by MuB are necessary for stimulation of transposition into selected DNA, a process termed target delivery. We find that with the fusion proteins, MuB-dependent target delivery occurs efficiently under conditions where ATP hydrolysis is prevented by mutation or use of ADP. In contrast, no delivery was detected in the absence of nucleotide. These data indicate that the ATP- and MuA-regulated DNA-binding activity of MuB is not essential for target delivery but that activation of MuA by MuB strictly requires nucleotide-bound MuB. Furthermore, we find that the fusion protein directs transposition to regions of the DNA within 40–750 bp of its own binding site. Taken together, these results suggest that target delivery by MuB occurs as a consequence of the ability of MuB to stimulate MuA while simultaneously tethering MuA to a selected target DNA. This tethered-activator model provides an attractive explanation for other examples of protein-stimulated control of target site selection.     

Polymorphisms in fibronectin binding protein A of Staphylococcus aureus are associated with infection of cardiovascular devices

Medical implants, like cardiovascular devices, improve the quality of life for countless individuals but may become infected with bacteria like Staphylococcus aureus. Such infections take the form of a biofilm, a structured community of bacterial cells adherent to the surface of a solid substrate. Every biofilm begins with an attractive force or bond between bacterium and substratum. We used atomic force microscopy to probe experimentally forces between a fibronectin-coated surface (i.e., proxy for an implanted cardiac device) and fibronectin-binding receptors on the surface of individual living bacteria from each of 80 clinical isolates of S. aureus. These isolates originated from humans with infected cardiac devices (CDI; n = 26), uninfected cardiac devices (n = 20), and the anterior nares of asymptomatic subjects (n = 34). CDI isolates exhibited a distinct binding-force signature and had specific single amino acid polymorphisms in fibronectin-binding protein A corresponding to E652D, H782Q, and K786N. In silico molecular dynamics simulations demonstrate that residues D652, Q782, and N786 in fibronectin-binding protein A form extra hydrogen bonds with fibronectin, complementing the higher binding force and energy measured by atomic force microscopy for the CDI isolates. This study is significant, because it links pathogenic bacteria biofilms from the length scale of bonds acting across a nanometer-scale space to the clinical presentation of disease at the human dimension.     

Primitive selection of the fittest emerging through functional synergy in nucleopeptide networks

Many fundamental cellular and viral functions, including replication and translation, involve complex ensembles hosting synergistic activity between nucleic acids and proteins/peptides. There is ample evidence indicating that the chemical precursors of both nucleic acids and peptides could be efficiently formed in the prebiotic environment. Yet, studies on nonenzymatic replication, a central mechanism driving early chemical evolution, have focused largely on the activity of each class of these molecules separately. We show here that short nucleopeptide chimeras can replicate through autocatalytic and cross-catalytic processes, governed synergistically by the hybridization of the nucleobase motifs and the assembly propensity of the peptide segments. Unequal assembly-dependent replication induces clear selectivity toward the formation of a certain species within small networks of complementary nucleopeptides. The selectivity pattern may be influenced and indeed maximized to the point of almost extinction of the weakest replicator when the system is studied far from equilibrium and manipulated through changes in the physical (flow) and chemical (template and inhibition) conditions. We postulate that similar processes may have led to the emergence of the first functional nucleic-acid–peptide assemblies prior to the origin of life. Furthermore, spontaneous formation of related replicating complexes could potentially mark the initiation point for information transfer and rapid progression in complexity within primitive environments, which would have facilitated the development of a variety of functions found in extant biological assemblies.

The rich, highly efficient, and specific biochemistry in living cells is orchestrated by molecules belonging to a small number of families, primarily nucleic acids, proteins, fatty acids, and sugars. Many fundamental cellular and viral functions, including replication and translation, are facilitated by synergistic activity in complexes of these molecules, very often involving nucleic acids (DNA, RNA, or their constituent nucleotides/nucleobases) and proteins (or peptides/amino acids). Among the most important examples of such complexes are the nucleosome (which comprises DNA packaging units in eukaryotes), the ribosome (which translates RNA sequences into proteins), and amino acid–charged transfer RNA (t-RNA) conjugates (which are exploited during translation) (1–4). In order to harness such synergistic activity in synthetic materials, several groups (including the authors) have recently studied the coassembly of nucleic acids with (often) positively charged peptides or the self-assembly of premade nucleic-acid–peptide (NA–pep) chimeras (5–12). It is expected that such assemblies could produce new materials for various applications, such as autocatalysis, electron transfer, tissue scaffolding, and (drug) delivery (13–18). Intriguingly, the NA–pep assemblies combine “digital” molecular information for the hybridization of nucleic acids with “analog” instructions that affect peptide aggregation and, as such, are expected to show superior behavior in comparison with related nucleic-acid–only or peptide-only assemblies (19–21).We now propose that alongside the development of NA–pep conjugate assemblies for new materials, an analysis of the formation of chimeras within complex mixtures, and particularly the selection of specific sequences through replication processes, will offer insight into their emergence in the early chemical evolution. Indeed, several studies have indicated that evolution in prebiotic environments, toward the origin of life, must have involved cooperative interactions among diverse classes of molecules (22–25). Other studies, including the seminal works of Eigen (26) and Kauffman (27), have revealed the possible emergence of synergistic activity in prebiotic autocatalytic networks and, as a consequence, phase transitions toward beneficial cooperative and/or selective behavior (28, 29). Importantly, while it has been shown that highly complex functions emerge by wiring together multiple pathways—driving, for example, elaborate feedback loops—our studies, as well as others, have indicated that multiple unique dynamic features (30–36), including chemical computation (37), can be developed in relatively small networks.Despite strong evidence for prebiotic pathways that yield nucleobases and peptides—suggesting that molecules of both families were indeed present in early chemical evolution—prebiotic chemistry research has focused largely on studying each class of molecule separately (38). This approach has led to incomplete discussions on the “RNA World,” the “Peptide World,” or the “Metabolism-First World,” with minimal overlap between the different domains. In particular, studies on replicating molecules and replication networks have investigated discrete systems affected only by a single class of molecule—whether nucleic acids, peptides, lipid amphiphiles, or small organic molecules (39–41). Herein, we propose to blur the limits between families of replicators by combining nucleic-acid molecular genetic information with peptide-based assembly “phenotypes.” To this end, we sought to reveal the self-organization and selection processes taking place in mixtures containing short complementary nucleopeptide conjugates (designated R^AA and R^TT, Fig. 1). Experimental and simulation analyses of the template-directed replication processes within these networks clearly demonstrate that product formation is governed both by nucleobase hybridization and by the formation of unique supramolecular architectures by each of the nucleopeptide conjugates. Unequal assembly-dependent replication capacity induces selectivity toward the formation of R^AA. By studying the system in a flow reactor, namely, at far-from-equilibrium conditions, we show how this selectivity can be influenced and maximized—through changes in the physical (flow) and chemical (template and inhibition) conditions—to the point of almost complete extinction of R^TT, the weaker replicator. We suggest that, prior to the origin of life, processes such as these may have led to the emergence of simple functional NA–pep assemblies (42), which could facilitate further structural development into the current cellular NA–pep assemblies.Open in a separate windowFig. 1.Nucleopeptide replication networks. (A and B) NCL reactions forming the R^AA and R^TT conjugates from their respective electrophile and nucleophile precursors and time-dependent formation of these conjugates in template-free (bg), autocatalytic (ac), and cross-catalytic (cc) reactions. Note that at pH 7.4, the Glu side chain carboxylic acids would be in their deprotonated anionic form. Reactions were carried out with 100 µM E^AA or E^TT and 100 µM N, either in the absence of a template (bg) or when seeded with the designated amount of template at initiation (ac/cc). Insets show the early stages of the background reactions, highlighting the lag phase typical of product formation through autocatalysis (Top insets) and the rate enhancement (percent) in cross-catalytic reactions seeded with 60 or 30 µM template or in autocatalytic reactions seeded with 60 µM template (Bottom insets). (C and D) A time-dependent analysis of the replicator-assisted product formation of the conjugates R^AA (green) and R^TT (red) in network reactions initiated with E^AA (50 µM), E^TT (50 µM), and N (100 µM) and seeded with 20 (dashed lines) or 60 μM (solid lines) R^TT (C) or R^AA (D). HPLC chromatograms (Top) indicate the increase in R^AA and R^TT product over time in representative reactions seeded with 60 μM R^TT (C) or 60 μM R^AA (D); note that R^TT and R^AA peaks have initial intensity due to seeding (in C and D, respectively), and the * symbols denote minor (≥15%) branched product peaks, removed for clarity (see also SI Appendix, Fig. S20). All reactions were carried out in duplicate, in Hepes buffer (pH 7.4), in the presence of TCEP as a reducing agent (5 mM) and with ABA (30 μM) as the internal standard. Data were acquired by HPLC analysis of aliquots collected at the designated times (SI Appendix, Figs. S17–S20).     

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches

Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet–dry cycles (WDCs) on the resilient modulus (M_r) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet–dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviator stress (σ₄) were considered input variables, and M_r was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that M_r increased with the DMR, σ₃, and σ₄. An increase in the number of WDCs reduced the M_r value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ₄ > σ₃, (ANN model) and DMR > WDC > CSAFR > σ₄ > σ₃ (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the M_r value.