Quantifying the topography of the intrinsic energy landscape of flexible biomolecular recognition

Uncovering the Association Mechanism between Two Intrinsically Flexible Proteins

The understanding of protein-protein interaction mechanisms is key to the atomistic description of cell signaling pathways and for the development of new drugs. In this context, the mechanism of intrinsically disordered proteins folding upon binding has attracted attention. The VirB9 C-terminal domain (VirB9Ct) and the VirB7 N-terminal motif (VirB7Nt) associate with VirB10 to form the outer membrane core complex of the Type IV Secretion System injectisome. Despite forming a stable and rigid complex, VirB7Nt behaves as a random coil, while VirB9Ct is intrinsically dynamic in the free state. Here we combined NMR, stopped-flow fluorescence, and computer simulations using structure-based models to characterize the VirB9Ct-VirB7Nt coupled folding and binding mechanism. Qualitative data analysis suggested that VirB9Ct preferentially binds to VirB7Nt by way of a conformational selection mechanism at lower temperatures. However, at higher temperatures, energy barriers between different VirB9Ct conformations are more easily surpassed. Under these conditions the formation of non-native initial encounter complexes may provide alternative pathways toward the native complex conformation. These observations highlight the intimate relationship between folding and binding, calling attention to the fact that the two molecular partners must search for the most favored intramolecular and intermolecular interactions on a rugged and funnelled conformational energy landscape, along which multiple intermediates may lead to the final native state.

Exploring the Folding Mechanism of Dimeric Superoxide Dismutase

The Cu/Zn Human Superoxide Dismutase (SOD1) is a dimeric metalloenzyme whose genetic mutations are directly related to amyotrophic lateral sclerosis (ALS), so understanding its folding mechanism is of fundamental importance. Currently, the SOD1 dimer formation is studied via molecular dynamics simulations using a simplified structure-based model and an all-atom model. Results from the simplified model reveal a mechanism dependent on distances between monomers, which are limited by constraints to mimic concentration dependence. The stability of intermediates (during the int state) is significantly affected by this distance, as well as by the presence of two folded monomers prior to dimer formation. The kinetics of interface formation are also highly dependent on the separation distance. The folding temperature of the dimer is about 4.2% higher than that of the monomer, a value not too different from experimental data. All-atom simulations on the apo dimer give binding free energy between monomers similar to experimental values. An intermediate state is evident for the apo form at a separation distance between monomers slightly larger than the native distance which has little formed interface between monomers. We have shown that this intermediate is stabilized by non-native intra- and intercontacts.

Learning continuous potentials from smFRET

Potential energy landscapes are useful models in describing events such as protein folding and binding. While single-molecule fluorescence resonance energy transfer (smFRET) experiments encode information on continuous potentials for the system probed, including rarely visited barriers between putative potential minima, this information is rarely decoded from the data. This is because existing analysis methods often model smFRET output assuming, from the onset, that the system probed evolves in a discretized state space to be analyzed within a hidden Markov model (HMM) paradigm. By contrast, here, we infer continuous potentials from smFRET data without discretely approximating the state space. We do so by operating within a Bayesian nonparametric paradigm by placing priors on the family of all possible potential curves. As our inference accounts for a number of required experimental features raising computational cost (such as incorporating discrete photon shot noise), the framework leverages a structured-kernel-interpolation Gaussian process prior to help curtail computational cost. We show that our structured-kernel-interpolation priors for potential energy reconstruction from smFRET analysis accurately infers the potential energy landscape from a smFRET binding experiment. We then illustrate advantages of structured-kernel-interpolation priors for potential energy reconstruction from smFRET over standard HMM approaches by providing information, such as barrier heights and friction coefficients, that is otherwise inaccessible to HMMs.

Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap

While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle’s position, and sparse sampling of the potential limits data in higher energy regions such as barriers. We develop a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large datasets. Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction (SKIPPER) is validated on 1D and 2D experimental trajectories for particles in a feedback trap.

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A feedback trap was used to generate noisy Langevin microbead trajectories

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The potential energy surface is recovered using a Bayesian formulation

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The formulation uses a structured-kernel-interpolation Gaussian process (SKI-GP) to tractably approximate Gaussian process regression for larger datasets

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Thanks to our adaptation of SKI-GP, we have broadened the use of Gaussian processes for natural science applications

Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems

We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.

Investigating the Conformational Dynamics of a Y-Family DNA Polymerase during Its Folding and Binding to DNA and a Nucleotide

During DNA polymerization, the Y-family DNA polymerases are capable of bypassing various DNA damage, which can stall the replication fork progression. It has been well acknowledged that the structures of the Y-family DNA polymerases have been naturally evolved to undertake this vital task. However, the mechanisms of how these proteins utilize their unique structural and conformational dynamical features to perform the translesion DNA synthesis are less understood. Here, we developed structure-based models to study the precatalytic DNA polymerization process, including DNA and nucleotide binding to DPO4, a paradigmatic Y-family polymerase from Sulfolobus solfataricus. We studied the interplay between the folding and the conformational dynamics of DPO4 and found that DPO4 undergoes first unraveling (unfolding) and then folding for accomplishing the functional "open-to-closed" conformational transition. DNA binding dynamically modulates the conformational equilibrium in DPO4 during the stepwise binding through different types of interactions, leading to different conformational distributions of DPO4 at different DNA binding stages. We observed that nucleotide binding induces modulation of a few contacts surrounding the active site of the DPO4-DNA complex associated with a high free energy barrier. Our simulation results resonate with the experimental evidence that the conformational change at the active site led by nucleotide is the rate-limiting step of nucleotide incorporation. In combination with localized frustration analyses, we underlined the importance of DPO4 conformational dynamics and fluctuations in facilitating DNA and nucleotide binding. Our findings offer mechanistic insights into the processes of DPO4 conformational dynamics associated with the substrate binding and contribute to the understanding of the "structure-dynamics-function" relationship in the Y-family DNA polymerases.

Molecular Simulations of Intrinsically Disordered Proteins and Their Binding Mechanisms

Intrinsically disordered proteins (IDPs) lack well-defined secondary or tertiary structures in solution but are found to be involved in a wide range of critical cellular processes that highlight their functional importance. IDPs usually undergo folding upon binding to their targets. Such binding coupled to folding behavior has widened our perspective on the protein structure-dynamics-function paradigm in molecular biology. However, characterizing the folding upon binding mechanism of IDPs experimentally remains quite challenging. Molecular simulations emerge as a potentially powerful tool that offers information complementary to experiments. Here we present a general computational framework for the molecular simulations of IDP folding upon binding processes that combines all-atom molecular dynamics (MD) and coarse-grained simulations. The classical all-atom molecular dynamics approach using GPU acceleration allows the researcher to explore the properties of the IDP conformational ensemble, whereas coarse-grained structure-based models implemented with parameters carefully calibrated to available experimental measurements can be used to simulate the entire folding upon binding process. We also discuss a set of tools for the analysis of MD trajectories and describe the details of the computational protocol to follow so that it can be adapted by the user to study any IDP in isolation and in complex with partners.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

Shedding Light on the Inhibitory Mechanisms of SARS-CoV-1/CoV-2 Spike Proteins by ACE2-Designed Peptides

Angiotensin-converting enzyme 2 (ACE2) is the host cellular receptor that locks onto the surface spike protein of the 2002 SARS coronavirus (SARS-CoV-1) and of the novel, highly transmissible and deadly 2019 SARS-CoV-2, responsible for the COVID-19 pandemic. One strategy to avoid the virus infection is to design peptides by extracting the human ACE2 peptidase domain α1-helix, which would bind to the coronavirus surface protein, preventing the virus entry into the host cells. The natural α1-helix peptide has a stronger affinity to SARS-CoV-2 than to SARS-CoV-1. Another peptide was designed by joining α1 with the second portion of ACE2 that is far in the peptidase sequence yet grafted in the spike protein interface with ACE2. Previous studies have shown that, among several α1-based peptides, the hybrid peptidic scaffold is the one with the highest/strongest affinity for SARS-CoV-1, which is comparable to the full-length ACE2 affinity. In this work, binding and folding dynamics of the natural and designed ACE2-based peptides were simulated by the well-known coarse-grained structure-based model, with the computed thermodynamic quantities correlating with the experimental binding affinity data. Furthermore, theoretical kinetic analysis of native contact formation revealed the distinction between these processes in the presence of the different binding partners SARS-CoV-1 and SARS-CoV-2 spike domains. Additionally, our results indicate the existence of a two-state folding mechanism for the designed peptide en route to bind to the spike proteins, in contrast to a downhill mechanism for the natural α1-helix peptides. The presented low-cost simulation protocol demonstrated its efficiency in evaluating binding affinities and identifying the mechanisms involved in the neutralization of spike-ACE2 interaction by designed peptides. Finally, the protocol can be used as a computer-based screening of more potent designed peptides by experimentalists searching for new therapeutics against COVID-19.

Investigating the trade-off between folding and function in a multidomain Y-family DNA polymerase

The way in which multidomain proteins fold has been a puzzling question for decades. Until now, the mechanisms and functions of domain interactions involved in multidomain protein folding have been obscure. Here, we develop structure-based models to investigate the folding and DNA-binding processes of the multidomain Y-family DNA polymerase IV (DPO4). We uncover shifts in the folding mechanism among ordered domain-wise folding, backtracking folding, and cooperative folding, modulated by interdomain interactions. These lead to ‘U-shaped’ DPO4 folding kinetics. We characterize the effects of interdomain flexibility on the promotion of DPO4–DNA (un)binding, which probably contributes to the ability of DPO4 to bypass DNA lesions, which is a known biological role of Y-family polymerases. We suggest that the native topology of DPO4 leads to a trade-off between fast, stable folding and tight functional DNA binding. Our approach provides an effective way to quantitatively correlate the roles of protein interactions in conformational dynamics at the multidomain level.

Conformational state switching and pathways of chromosome dynamics in cell cycle

The cell cycle is a process and function of a cell with different phases essential for cell growth, proliferation, and replication. It depends on the structure and dynamics of the underlying DNA molecule, which underpins the genome function. A microscopic structural-level understanding of how a genome or its functional module chromosome performs the cell cycle in terms of large-scale conformational transformation between different phases, such as the interphase and the mitotic phase, is still challenging. Here, we develop a non-equilibrium, excitation-relaxation energy landscape-switching model to quantify the underlying chromosome conformational transitions through (de-)condensation for a complete microscopic understanding of the cell cycle. We show that the chromosome conformational transition mechanism from the interphase to the mitotic phase follows a two-stage scenario, in good agreement with the experiments. In contrast, the mitotic exit pathways show the existence of an over-expanded chromosome that recapitulates the chromosome in the experimentally identified intermediate state at the telophase. We find the conformational pathways are heterogeneous and irreversible as a result of the non-equilibrium dynamics of the cell cycle from both structural and kinetic perspectives. We suggest that the irreversibility is mainly due to the distinct participation of the ATP-dependent structural maintenance of chromosomal protein complexes during the cell cycle. Our findings provide crucial insights into the microscopic molecular structural and dynamical physical mechanism for the cell cycle beyond the previous more macroscopic descriptions. Our non-equilibrium landscape framework is general and applicable to study diverse non-equilibrium physical and biological processes such as active matter, differentiation/development, and cancer.

Investigations of the underlying mechanisms of HIF-1α and CITED2 binding to TAZ1

The TAZ1 domain of CREB binding protein is crucial for transcriptional regulation and recognizes multiple targets. The interactions between TAZ1 and its specific targets are related to the cellular hypoxic negative feedback regulation. Previous experiments reported that one of the TAZ1 targets, CITED2, is an efficient competitor of another target, HIF-1α. Here, by developing the structure-based models of TAZ1 complexes, we have uncovered the underlying mechanisms of the competitions between the two intrinsic disordered proteins (IDPs) HIF-1α and CITED2 binding to TAZ1. Our results support the experimental hypothesis on the competition mechanisms and the apparent affinity. Furthermore, the simulations locate the dominant position of forming TAZ1-CITED2 complex in both thermodynamics and kinetics. For thermodynamics, TAZ1-CITED2 is the lowest basin located on the free energy surface of binding in the ternary system. For kinetics, the results suggest that CITED2 binds to TAZ1 faster than HIF-1α. In addition, the analysis of contact map and Φ values is important for guiding further experimental studies to understand the biomolecular functions of IDPs.

Efficient flexible backbone protein-protein docking for challenging targets

Motivation

Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates.

Results

Here, we develop and benchmark RosettaDock 4.0, which efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20-80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, the method successfully docks the complex.

Availability and implementation

As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee.

Supplementary information

Supplementary data are available at Bioinformatics online.

Explicit Characterization of the Free Energy Landscape of pKID-KIX Coupled Folding and Binding

The most fundamental aspect of the free energy landscape of proteins is that it is globally funneled such that protein folding is energetically biased. Then, what are the distinctive characteristics of the landscape of intrinsically disordered proteins, apparently lacking such energetic bias, that nevertheless fold upon binding? Here, we address this fundamental issue through the explicit characterization of the free energy landscape of the paradigmatic pKID-KIX system (pKID, phosphorylated kinase-inducible domain; KIX, kinase interacting domain). This is done based on unguided, fully atomistic, explicit-water molecular dynamics simulations with an aggregated simulation time of >30 μs and on the computation of the free energy that defines the landscape. We find that, while the landscape of pKID before binding is considerably shallower than the one for a protein that autonomously folds, it becomes progressively more funneled as the binding of pKID with KIX proceeds. This explains why pKID is disordered in a free state, and the binding of pKID with KIX is a prerequisite for pKID's folding. In addition, we observe that the key event in completing the pKID-KIX coupled folding and binding is the directed self-assembly where pKID is docked upon the KIX surface to maximize the surface electrostatic complementarity, which, in turn, require pKID to adopt the correct folded structure. This key process shows up as the free energy barrier in the pKID landscape separating the intermediate nonspecific complex state and the specific complex state. The present work not only provides a detailed molecular picture of the coupled folding and binding of pKID but also expands the funneled landscape perspective to intrinsically disordered proteins.

Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space

Flexibility is often a key determinant of protein function. To elucidate the link between their molecular structure and role in an organism, computational techniques such as molecular dynamics can be leveraged to characterize their conformational space. Extensive sampling is, however, required to obtain reliable results, useful to rationalize experimental data or predict outcomes before experiments are carried out. We demonstrate that a generative neural network trained on protein structures produced by molecular simulation can be used to obtain new, plausible conformations complementing pre-existing ones. To demonstrate this, we show that a trained neural network can be exploited in a protein-protein docking scenario to account for broad hinge motions taking place upon binding. Overall, this work shows that neural networks can be used as an exploratory tool for the study of molecular conformational space.

Structure, stability and specificity of the binding of ssDNA and ssRNA with proteins

Recognition of single-stranded DNA (ssDNA) or single-stranded RNA (ssRNA) is important for many fundamental cellular functions. A variety of single-stranded DNA-binding proteins (ssDBPs) and single-stranded RNA-binding proteins (ssRBPs) have evolved that bind ssDNA and ssRNA, respectively, with varying degree of affinities and specificities to form complexes. Structural studies of these complexes provide key insights into their recognition mechanism. However, computational modeling of the specific recognition process and to predict the structure of the complex is challenging, primarily due to the heterogeneity of their binding energy landscape and the greater flexibility of ssDNA or ssRNA compared with double-stranded nucleic acids. Consequently, considerably fewer computational studies have explored interactions between proteins and single-stranded nucleic acids compared with protein interactions with double-stranded nucleic acids. Here, we report a newly developed energy-based coarse-grained model to predict the structure of ssDNA–ssDBP and ssRNA–ssRBP complexes and to assess their sequence-specific interactions and stabilities. We tuned two factors that can modulate specific recognition: base–aromatic stacking strength and the flexibility of the single-stranded nucleic acid. The model was successfully applied to predict the binding conformations of 12 distinct ssDBP and ssRBP structures with their cognate ssDNA and ssRNA partners having various sequences. Estimated binding energies agreed well with the corresponding experimental binding affinities. Bound conformations from the simulation showed a funnel-shaped binding energy distribution where the native-like conformations corresponded to the energy minima. The various ssDNA–protein and ssRNA–protein complexes differed in the balance of electrostatic and aromatic energies. The lower affinity of the ssRNA–ssRBP complexes compared with the ssDNA–ssDBP complexes stems from lower flexibility of ssRNA compared to ssDNA, which results in higher rate constants for the dissociation of the complex (k_off) for complexes involving the former.

Quantifying bimolecular self-assembly is pivotal to understanding cellular function. In recent years, a large progress has been made in understanding the structure and biophysics of protein-protein interactions. Particularly, various computational tools are available for predicting these structures and to estimate their stability and the driving forces of their formation. The understating of the interactions between proteins and nucleic acids, however, is still limited, presumably due to the involvement of non-specific interactions as well as the high conformational plasticity that may demand an induced-fit mechanism. In particular, the interactions between proteins and single-stranded nucleic acids (i.e., single-stranded DNA and RNA) is very challenging due to their high flexibility. Furthermore, the interface between proteins and single-stranded nucleic acids is often chemically more heterogeneous than the interface between proteins and double-stranded DNA. In this study, we developed a coarse-grained computational model to predict the structure of complexes between proteins and single-stranded nucleic acids. The model was applied to estimate binding affinities and the estimated binding energies agreed well with the corresponding experimental binding affinities. The kinetics of association as well as the specificity of the complexes between proteins and ssDNA are different than those with ssRNA, mostly due to differences in their conformational flexibility.

Position-, disorder-, and salt-dependent diffusion in binding-coupled-folding of intrinsically disordered proteins

Successful extensions of protein-folding energy landscape theory to intrinsically disordered proteins' (IDPs') binding-coupled-folding transition can enormously simplify this biomolecular process into diffusion along a limited number of reaction coordinates, and the dynamics subsequently is described by Kramers' rate theory. As the critical pre-factor, the diffusion coefficient D has direct implications on the binding kinetics. Here, we employ a structure-based model (SBM) to calculate D in the binding-folding of an IDP prototype. We identify a strong position-dependent D during binding by applying a reaction coordinate that directly measures the fluctuations in a Cartesian configuration space. Using the malleability of the SBM, we modulate the degree of conformational disorder in an isolated IDP and determine complex effects of intrinsic disorder on D varying for different binding stages. Here, D tends to increase with disorder during initial binding but shows a non-monotonic relationship with disorder in terms of a decrease-followed-by-increase in D during the late binding stage. The salt concentration, which correlates with electrostatic interactions via Debye-Hückel theory in our SBM, also modulates D in a stepwise way. The speeding up of diffusion by electrostatic interactions is observed during the formation of the encounter complex at the beginning of binding, while the last diffusive binding dynamics is hindered by non-native salt bridges. Because D describes the diffusive speed locally, which implicitly reflects the roughness of the energy landscape, we are eventually able to portray the binding energy landscape, including that from IDPs' binding, then to binding with partial folding, and finally to rigid docking, as well as that under different environmental salt concentrations. Our theoretical results provide key mechanistic insights into IDPs' binding-folding, which is internally conformation- and externally salt-controlled with respect to diffusion.

Quantifying the Intrinsic Conformation Energy Landscape Topography of Proteins with Large-Scale Open-Closed Transition

Large-scale conformational changes of proteins, including the open-closed transitions, are crucial for a variety of protein functions. These open-closed transitions are often associated with ligand binding. However, the understandings of the underlying mechanisms of the conformational changes within proteins during the open-closed transitions are still challenging at present. In this study, we quantified the intrinsic underlying conformational energy landscapes of five different proteins with large-scale open-closed transitions. This is realized by exploring the underlying density of states and the intrinsic conformational energy landscape topography measure Λ. Λ is a dimensionless ratio of conformational energy gap δE versus conformational energy roughness δE and configurational entropy S or size of the intrinsic conformational energy landscape. By quantifying the Λ of intrinsic open-closed conformational (Λ^oc) and intrinsic global folding (Λ^global) energy landscapes, we show that both intrinsic open-closed conformation energy and entropy landscapes are funneled toward the closed state. Furthermore, our results indicate the strong correlations between Λ and thermodynamics (conformational state transition temperature against trapping temperature) as well as between Λ and kinetics (open-closed kinetic time) of these proteins. This shows that the intrinsic conformational landscape topography determines both the conformational thermodynamic stability and kinetic speed of the conformational dynamics. Our investigations provide important insights for understanding the fundamental mechanisms of the protein conformational dynamics in a physical and global way.

Composition-related structural transition of random peptides: insight into the boundary between intrinsically disordered proteins and folded proteins

Previous studies based on bioinformatics showed that there is a sharp distinction of structural features and residue composition between the intrinsically disordered proteins and the folded proteins. What induces such a composition-related structural transition? How do various kinds of interactions work in such processes? In this work, we investigate these problems based on a survey on peptides randomly composed of charged residues (including glutamic acids and lysines) and the residues with different hydrophobicity, such as alanines, glycines, or phenylalanines. Based on simulations using all-atom model and replica-exchange Monte Carlo method, a coil-globule transition is observed for each peptide. The corresponding transition temperature is found to be dependent on the contents of the hydrophobic and charged residues. For several cases, when the mean hydrophobicity is larger than a certain threshold, the transition temperature is higher than the room temperature, and vise versa. These thresholds of hydrophobicity and net charge are quantitatively consistent with the border line observed from the study of bioinformatics. These results outline the basic physical reasons for the compositional distinction between the intrinsically disordered proteins and the folded proteins. Furthermore, the contributions of various interactions to the structural variation of peptides are analyzed based on the contact statistics and the charge-pattern dependence of the gyration radii of the peptides. Our observations imply that the hydrophobicity contributes essentially to such composition-related transitions. Thus, we achieve a better understanding on composition-structure relation of the natural proteins and the underlying physics.

SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity

Nucleic acids have been widely recognized as potential targets in drug discovery and aptamer selection. Quantifying the interactions between small molecules and nucleic acids is critical to discover lead compounds and design novel aptamers. Scoring function is normally employed to quantify the interactions in structure-based virtual screening. However, the predictive power of nucleic acid–ligand scoring functions is still a challenge compared to other types of biomolecular recognition. With the rapid growth of experimentally determined nucleic acid–ligand complex structures, in this work, we develop a knowledge-based scoring function of nucleic acid–ligand interactions, namely SPA-LN. SPA-LN is optimized by maximizing both the affinity and specificity of native complex structures. The development strategy is different from those of previous nucleic acid–ligand scoring functions which focus on the affinity only in the optimization. The native conformation is stabilized while non-native conformations are destabilized by our optimization, making the funnel-like binding energy landscape more biased toward the native state. The performance of SPA-LN validates the development strategy and provides a relatively more accurate way to score the nucleic acid–ligand interactions.

Opposing Intermolecular Tuning of Ca2+ Affinity for Calmodulin by Neurogranin and CaMKII Peptides

We investigated the impact of bound calmodulin (CaM)-target compound structure on the affinity of calcium (Ca²⁺) by integrating coarse-grained models and all-atomistic simulations with nonequilibrium physics. We focused on binding between CaM and two specific targets, Ca²⁺/CaM-dependent protein kinase II (CaMKII) and neurogranin (Ng), as they both regulate CaM-dependent Ca²⁺ signaling pathways in neurons. It was shown experimentally that Ca²⁺/CaM (holoCaM) binds to the CaMKII peptide with overwhelmingly higher affinity than Ca²⁺-free CaM (apoCaM); the binding of CaMKII peptide to CaM in return increases the Ca²⁺ affinity for CaM. However, this reciprocal relation was not observed in the Ng peptide (Ng_13–49), which binds to apoCaM or holoCaM with binding affinities of the same order of magnitude. Unlike the holoCaM-CaMKII peptide, whose structure can be determined by crystallography, the structural description of the apoCaM-Ng_13–49 is unknown due to low binding affinity, therefore we computationally generated an ensemble of apoCaM-Ng_13–49 structures by matching the changes in the chemical shifts of CaM upon Ng_13–49 binding from nuclear magnetic resonance experiments. Next, we computed the changes in Ca²⁺ affinity for CaM with and without binding targets in atomistic models using Jarzynski’s equality. We discovered the molecular underpinnings of lowered affinity of Ca²⁺ for CaM in the presence of Ng_13–49 by showing that the N-terminal acidic region of Ng peptide pries open the β-sheet structure between the Ca²⁺ binding loops particularly at C-domain of CaM, enabling Ca²⁺ release. In contrast, CaMKII peptide increases Ca²⁺ affinity for the C-domain of CaM by stabilizing the two Ca²⁺ binding loops. We speculate that the distinctive structural difference in the bound complexes of apoCaM-Ng_13–49 and holoCaM-CaMKII delineates the importance of CaM’s progressive mechanism of target binding on its Ca²⁺ binding affinities.

Manipulating motions of targeted single cells in solution by an integrated double-ring magnetic tweezers imaging microscope

Controlling and manipulating living cell motions in solution hold a high promise in developing new biotechnology and biological science. Here, we developed a magnetic tweezers device that employs a combination of two permanent magnets in up-down double-ring configuration axially fitting with a microscopic objective, allowing a picoNewton (pN) bidirectional force and motion control on the sample beyond a single upward pulling direction. The experimental force calibration and magnetic field simulation using finite element method magnetics demonstrate that the designed magnetic tweezers covers a linear-combined pN force with positive-negative polarization changes in a tenability of sub-pN scale, which can be utilized to further achieve motion manipulation by shifting the force balance. We demonstrate an application of the up-down double-ring magnetic tweezers for single cell manipulation, showing that the cells with internalized paramagnetic beads can be selectively picked up and guided in a controlled fine motion.

Quantifying Nonnative Interactions in the Protein-Folding Free-Energy Landscape

Protein folding is a central problem in biological physics. Energetic roughness is an important aspect that controls protein-folding stability and kinetics. The roughness is associated with conflicting interactions in the protein and is also known as frustration. Recent studies indicate that an addition of a small amount of energetic frustration may enhance folding speed for certain proteins. In this study, we have investigated the conditions under which frustration increases the folding rate. We used a Cα structure-based model to simulate a group of proteins. We found that the free-energy barrier at the transition state (ΔF) correlates with nonnative-contact variation (ΔA), and the simulated proteins are clustered according to their fold motifs. These findings are corroborated by the Clementi-Plotkin analytical model. As a consequence, the optimum frustration regime for protein folding can be predicted analytically.

Conformation Dynamics of the Intrinsically Disordered Protein c-Myb with the ff99IDPs Force Field

The intrinsically disordered protein c-Myb plays a critical role in cellular proliferation and differentiation. Loss of c-myb function results in embryonic lethality due to failure of fetal hepatic hematopoiesis. The conformation dynamics of the intrinsically disordered c-Myb are still unknown. Here, molecular dynamics (MD) simulations with the intrinsically disordered protein force field ff99IDPs were used to study the conformation dynamics. In comparison with ff99SBildn, ff99IDPs can reproduce more diverse disordered conformers of c-Myb. The predicted secondary chemical shift under ff99IDPs is more close to that of experiment data than that under ff99SBildn. Therefore, ff99IDPs can sample native molten globule, native pre-molten globule and native coil conformers for c-Myb. These results are consistent with those of other intrinsically disordered proteins. Kinetic analysis of MD simulations shows that c-Myb folds via a two-state process and indicates that c-Myb folds in the order of tertiary folding and helical folding. The folding nucleus of KEL plays an essential role in stabilizing the folding state with dynamic correlation networks. The influences of solvent models for TIP3P, TIP4P-EW and TIP5P were also investigated and it was found that TIP3P and ff99IDPs are the best combination to research the conformer sampling of c-Myb. These results reveal the conformation dynamics of c-Myb and confirm that the ff99IDPs force field can be used to research the relationship between structure and function of other intrinsically disordered proteins.

Electrostatic forces govern the binding mechanism of intrinsically disordered histone chaperones

A unified picture to understand the protein recognition and function must include the native binding complex structure ensembles and the underlying binding mechanisms involved in specific biological processes. However, quantifications of both binding complex structures and dynamical mechanisms are still challenging for IDP. In this study, we have investigated the underlying molecular mechanism of the chaperone Chz1 and histone H2A.Z-H2B association by equilibrium and kinetic stopped-flow fluorescence spectroscopy. The dependence of free energy and kinetic rate constant on electrolyte mean activity coefficient and urea concentration are uncovered. Our results indicate a previous unseen binding kinetic intermediate. An initial conformation selection step of Chz1 is also revealed before the formation of this intermediate state. Based on these observations, a mixed mechanism of three steps including both conformation selection and induced fit is proposed. By combination of the ion- and denaturant-induced experiments, we demonstrate that electrostatic forces play a dominant role in the recognition of bipolar charged intrinsically disordered protein Chz1 to its preferred partner H2A.Z-H2B. Both the intra-chain and inter-chain electrostatic interactions have direct impacts on the native collapsed structure and binding mechanism.

Molecular mechanism of multispecific recognition of Calmodulin through conformational changes

Calmodulin (CaM) is found to have the capability to bind multiple targets. Investigations on the association mechanism of CaM to its targets are crucial for understanding protein-protein binding and recognition. Here, we developed a structure-based model to explore the binding process between CaM and skMLCK binding peptide. We found the cooperation between nonnative electrostatic interaction and nonnative hydrophobic interaction plays an important role in nonspecific recognition between CaM and its target. We also found that the conserved hydrophobic anchors of skMLCK and binding patches of CaM are crucial for the transition from high affinity to high specificity. Furthermore, this association process involves simultaneously both local conformational change of CaM and global conformational changes of the skMLCK binding peptide. We found a landscape with a mixture of the atypical "induced fit," the atypical "conformational selection," and "simultaneously binding-folding," depending on the synchronization of folding and binding. Finally, we extend our discussions on multispecific binding between CaM and its targets. These association characteristics proposed for CaM and skMLCK can provide insights into multispecific binding of CaM.

Effects of flexibility and electrostatic interactions on the coupled binding–folding mechanisms of Chz.core and H2A.z–H2B

The intrinsically disordered protein (IDP) Chz.core, which is the interaction core of Chz1, shows binding preference to histone variant H2A.z. The coupled folding–binding mechanism of the complex can be quantified by the free energy landscapes.

Understanding the roles of intrinsic disorder in subunits of hemoglobin and the disease process of sickle cell anemia

One of the common genetic disorders is sickle cell anemia, in which 2 recessive alleles must meet to allow for destruction and alteration in the morphology of red blood cells. This usually leads to loss of proper binding of oxygen to hemoglobin and curved, sickle-shaped erythrocytes. The mutation causing this disease occurs in the 6^th codon of the HBB gene encoding the hemoglobin subunit β (β-globin), a protein, serving as an integral part of the adult hemoglobin A (HbA), which is a heterotetramer of 2 α chains and 2 β chains that is responsible for binding to the oxygen in the blood. This mutation changes a charged glutamic acid to a hydrophobic valine residue and disrupts the tertiary structure and stability of the hemoglobin molecule. Since in the field of protein intrinsic disorder, charged and polar residues are typically considered as disorder promoting, in opposite to the order-promoting non-polar hydrophobic residues, in this study we attempted to answer a question if intrinsic disorder might have a role in the pathogenesis of sickle cell anemia. To this end, several disorder predictors were utilized to evaluate the presence of intrinsically disordered regions in all subunits of human hemoglobin: α, β, δ, ε, ζ, γ1, and γ2. Then, structural analysis was completed by using the SWISS-MODEL Repository to visualize the outputs of the disorder predictors. Finally, Uniprot STRING and D²P² were used to determine biochemical interactome and protein partners for each hemoglobin subunit along with analyzing their posttranslational modifications. All these properties were used to determine any differences between the 6 different types of subunits of hemoglobin and to correlate the mutation leading to sickle cell anemia with intrinsic disorder propensity.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Molecular dynamics simulations and CD spectroscopy reveal hydration-induced unfolding of the intrinsically disordered LEA proteins COR15A and COR15B from Arabidopsis thaliana

The LEA (late embryogenesis abundant) proteins COR15A and COR15B from Arabidopsis thaliana are intrinsically disordered under fully hydrated conditions, but obtain α-helical structure during dehydration, which is reversible upon rehydration. To understand this unusual structural transition, both proteins were investigated by circular dichroism (CD) and molecular dynamics (MD) approaches. MD simulations showed unfolding of the proteins in water, in agreement with CD data obtained with both HIS-tagged and untagged recombinant proteins. Mainly intramolecular hydrogen bonds (H-bonds) formed by the protein backbone were replaced by H-bonds with water molecules. As COR15 proteins function in vivo as protectants in leaves partially dehydrated by freezing, unfolding was further assessed under crowded conditions. Glycerol reduced (40%) or prevented (100%) unfolding during MD simulations, in agreement with CD spectroscopy results. H-bonding analysis indicated that preferential exclusion of glycerol from the protein backbone increased stability of the folded state.

Universal statistical fluctuations in thermodynamics and kinetics of single molecular recognition

We investigated the main universal statistical distributions of single molecular recognition. The distributions of the single molecule binding free energy spectrum or density of states were characterized in the ligand-receptor binding energy landscape. The analytical results are consistent with the microscopic molecular simulations. The free energy distribution of different binding modes or states for a single molecule ligand receptor pair is approximately Gaussian near the mean and exponential at the tail. The equilibrium constant of single molecule binding is log-normal distributed near the mean and power law distributed near the tail. Additionally, we found that the kinetics distribution of single molecule ligand binding can be characterized by log-normal around the mean and power law distribution near the tail. This distribution is caused by exploration of the underlying inhomogeneous free energy landscape. Different ligand-receptor binding complexes have the same universal form of distribution but differ in parameters.

Coupled folding and binding with 2D Window-Exchange Umbrella Sampling

Intrinsically disordered regions of proteins can gain structure by binding to a partner. This process, of coupled folding and binding (CFaB), is a fundamental part of many important biological processes. Structure-based models have proven themselves capable of revealing fundamental aspects of how CFaB occurs, however, typical methods to enhance the sampling of these transitions, such as replica exchange, do not adequately sample the transition state region of this extremely rare process. Here, we use a variant of Umbrella Sampling to enforce sampling of the transition states of CFaB of HdeA monomers at neutral pH, an extremely rare process that occurs over timescales ranging from seconds to hours. Using high resolution sampling in the transition state region, we cluster states along the principal transition path to obtain a detailed description of coupled binding and folding for the HdeA dimer, revealing new insight into the ensemble of states that are accessible to client recognition. We then demonstrate that exchanges between umbrella sampling windows, as done in previous work, significantly improve relaxation in variables orthogonal to the restraints used. Altogether, these results show that Window-Exchange Umbrella Sampling is a promising approach for systems that exhibit flexible binding, and can reveal transition state ensembles of these systems in high detail. © 2015 Wiley Periodicals, Inc.

Interrogating the activities of conformational deformed enzyme by single-molecule fluorescence-magnetic tweezers microscopy

Characterizing the impact of fluctuating enzyme conformation on enzymatic activity is critical in understanding the structure-function relationship and enzymatic reaction dynamics. Different from studying enzyme conformations under a denaturing condition, it is highly informative to manipulate the conformation of an enzyme under an enzymatic reaction condition while monitoring the real-time enzymatic activity changes simultaneously. By perturbing conformation of horseradish peroxidase (HRP) molecules using our home-developed single-molecule total internal reflection magnetic tweezers, we successfully manipulated the enzymatic conformation and probed the enzymatic activity changes of HRP in a catalyzed H2O2-amplex red reaction. We also observed a significant tolerance of the enzyme activity to the enzyme conformational perturbation. Our results provide a further understanding of the relation between enzyme behavior and enzymatic conformational fluctuation, enzyme-substrate interactions, enzyme-substrate active complex formation, and protein folding-binding interactions.

Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect

Solvation effect is an important factor for protein-ligand binding in aqueous water. Previous scoring function of protein-ligand interactions rarely incorporates the solvation model into the quantification of protein-ligand interactions, mainly due to the immense computational cost, especially in the structure-based virtual screening, and nontransferable application of independently optimized atomic solvation parameters. In order to overcome these barriers, we effectively combine knowledge-based atom-pair potentials and the atomic solvation energy of charge-independent implicit solvent model in the optimization of binding affinity and specificity. The resulting scoring functions with optimized atomic solvation parameters is named as specificity and affinity with solvation effect (SPA-SE). The performance of SPA-SE is evaluated and compared to 20 other scoring functions, as well as SPA. The comparative results show that SPA-SE outperforms all other scoring functions in binding affinity prediction and "native" pose identification. Our optimization validates that solvation effect is an important regulator to the stability and specificity of protein-ligand binding. The development strategy of SPA-SE sets an example for other scoring function to account for the solvation effect in biomolecular recognitions.

Protein-protein docking: from interaction to interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.

Conformational frustration in calmodulin-target recognition

Calmodulin (CaM) is a primary calcium (Ca(2+) )-signaling protein that specifically recognizes and activates highly diverse target proteins. We explored the molecular basis of target recognition of CaM with peptides representing the CaM-binding domains from two Ca(2+) -CaM-dependent kinases, CaMKI and CaMKII, by employing experimentally constrained molecular simulations. Detailed binding route analysis revealed that the two CaM target peptides, although similar in length and net charge, follow distinct routes that lead to a higher binding frustration in the CaM-CaMKII complex than in the CaM-CaMKI complex. We discovered that the molecular origin of the binding frustration is caused by intermolecular contacts formed with the C-domain of CaM that need to be broken before the formation of intermolecular contacts with the N-domain of CaM. We argue that the binding frustration is important for determining the kinetics of the recognition process of proteins involving large structural fluctuations.

Specificity and affinity quantification of flexible recognition from underlying energy landscape topography

Flexibility in biomolecular recognition is essential and critical for many cellular activities. Flexible recognition often leads to moderate affinity but high specificity, in contradiction with the conventional wisdom that high affinity and high specificity are coupled. Furthermore, quantitative understanding of the role of flexibility in biomolecular recognition is still challenging. Here, we meet the challenge by quantifying the intrinsic biomolecular recognition energy landscapes with and without flexibility through the underlying density of states. We quantified the thermodynamic intrinsic specificity by the topography of the intrinsic binding energy landscape and the kinetic specificity by association rate. We found that the thermodynamic and kinetic specificity are strongly correlated. Furthermore, we found that flexibility decreases binding affinity on one hand, but increases binding specificity on the other hand, and the decreasing or increasing proportion of affinity and specificity are strongly correlated with the degree of flexibility. This shows more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Our work provides a theoretical foundation and quantitative explanation of the previous qualitative studies on the relationship among flexibility, affinity and specificity. In addition, we found that the folding energy landscapes are more funneled with binding, indicating that binding helps folding during the recognition. Finally, we demonstrated that the whole binding-folding energy landscapes can be integrated by the rigid binding and isolated folding energy landscapes under weak flexibility. Our results provide a novel way to quantify the affinity and specificity in flexible biomolecular recognition.

Flexibility in biomolecular recognition is crucial for the function. Flexibility often leads to moderate binding affinity but high binding specificity, challenging the conventional wisdom that high specificity is guaranteed by high affinity. Currently, understanding of the relationship between affinity and specificity in flexible biomolecular recognition is still obscure, even in a qualitative way. By exploring the intrinsic biomolecular recognition energy landscapes, we provided a novel way to quantify the thermodynamic intrinsic specificity by energy landscape topography and kinetic specificity by association rate. We show quantitatively that flexibility decreases binding affinity while increases binding specificity, and the relative changes in affinity and specificity are strongly correlated with the degree of flexibility. Our results show that more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Importantly, we demonstrated that flexibility modulates affinity and specificity through the underlying energy landscape. Our study establishes the quantitative relationship among flexibility, affinity and specificity, bridging the gap between theory and experiments.

PolyUbiquitin chain linkage topology selects the functions from the underlying binding landscape

Ubiquitin (Ub) can generate versatile molecular signals and lead to different celluar fates. The functional poly-valence of Ub is believed to be resulted from its ability to form distinct polymerized chains with eight linkage types. To provide a full picture of ubiquitin code, we explore the binding landscape of two free Ub monomers and also the functional landscapes of of all eight linkage types by theoretical modeling. Remarkably, we found that most of the compact structures of covalently connected dimeric Ub chains (diUbs) pre-exist on the binding landscape. These compact functional states were subsequently validated by corresponding linkage models. This leads to the proposal that the folding architecture of Ub monomer has encoded all functional states into its binding landscape, which is further selected by different topologies of polymeric Ub chains. Moreover, our results revealed that covalent linkage leads to symmetry breaking of interfacial interactions. We further propose that topological constraint not only limits the conformational space for effective switching between functional states, but also selects the local interactions for realizing the corresponding biological function. Therefore, the topological constraint provides a way for breaking the binding symmetry and reaching the functional specificity. The simulation results also provide several predictions that qualitatively and quantitatively consistent with experiments. Importantly, the K48 linkage model successfully predicted intermediate states. The resulting multi-state energy landscape was further employed to reconcile the seemingly contradictory experimental data on the conformational equilibrium of K48-diUb. Our results further suggest that hydrophobic interactions are dominant in the functional landscapes of K6-, K11-, K33- and K48 diUbs, while electrostatic interactions play a more important role in the functional landscapes of K27, K29, K63 and linear linkages.

The free energy landscape in translational science: how can somatic mutations result in constitutive oncogenic activation?

The free energy landscape theory has transformed the field of protein folding. The significance of perceiving function in terms of conformational heterogeneity is gradually shifting the interest in the community from folding to function. From the free energy landscape standpoint the principles are unchanged: rather than considering the entire protein conformational landscape, the focus is on the ensemble around the bottom of the folding funnel. The protein can be viewed as populating one of two states: active or inactive. The basins of the two states are separated by a surmountable barrier, which allows the conformations to switch between the states. Unless the protein is a repressor, under physiological conditions it typically populates the inactive state. Ligand binding (or post-translational modification) triggers a switch to the active state. Constitutive allosteric mutations work by shifting the population from the inactive to the active state and keeping it there. This can happen by either destabilizing the inactive state, stabilizing the active state, or both. Identification of the mechanism through which they work is important since it may assist in drug discovery. Here we spotlight the usefulness of the free energy landscape in translational science, illustrating how oncogenic mutations can work in key proteins from the EGFR/Ras/Raf/Erk/Mek pathway, the main signaling pathway in cancer. Finally, we delineate the key components which are needed in order to trace the mechanism of allosteric events.

Atomistic picture for the folding pathway of a hybrid-1 type human telomeric DNA G-quadruplex

In this work we studied the folding process of the hybrid-1 type human telomeric DNA G-quadruplex with solvent and K(+) ions explicitly modeled. Enabled by the powerful bias-exchange metadynamics and large-scale conventional molecular dynamic simulations, the free energy landscape of this G-DNA was obtained for the first time and four folding intermediates were identified, including a triplex and a basically formed quadruplex. The simulations also provided atomistic pictures for the structures and cation binding patterns of the intermediates. The results showed that the structure formation and cation binding are cooperative and mutually supporting each other. The syn/anti reorientation dynamics of the intermediates was also investigated. It was found that the nucleotides usually take correct syn/anti configurations when they form native and stable hydrogen bonds with the others, while fluctuating between two configurations when they do not. Misfolded intermediates with wrong syn/anti configurations were observed in the early intermediates but not in the later ones. Based on the simulations, we also discussed the roles of the non-native interactions. Besides, the formation process of the parallel conformation in the first two G-repeats and the associated reversal loop were studied. Based on the above results, we proposed a folding pathway for the hybrid-1 type G-quadruplex with atomistic details, which is new and more complete compared with previous ones. The knowledge gained for this type of G-DNA may provide a general insight for the folding of the other G-quadruplexes.

Manipulating and probing enzymatic conformational fluctuations and enzyme–substrate interactions by single-molecule FRET-magnetic tweezers microscopy

To investigate the critical role of the enzyme–substrate interactions in enzymatic reactions, the enzymatic conformation and enzyme–substrate interaction at a single-molecule level are manipulated by magnetic tweezers, and the impact of the manipulation on enzyme–substrate interactions are simultaneously probed by single-molecule FRET spectroscopy.

Protein recognition and selection through conformational and mutually induced fit

Protein-protein interactions drive most every biological process, but in many instances the domains mediating recognition are disordered. How specificity in binding is attained in the absence of defined structure contrasts with well-established experimental and theoretical work describing ligand binding to protein. The signaling protein calmodulin presents a unique opportunity to investigate mechanisms for target recognition given that it interacts with several hundred different targets. By advancing coarse-grained computer simulations and experimental techniques, mechanistic insights were gained in defining the pathways leading to recognition and in how target selectivity can be achieved at the molecular level. A model requiring mutually induced conformational changes in both calmodulin and target proteins was necessary and broadly informs how proteins can achieve both high affinity and high specificity.

Polycation-π interactions are a driving force for molecular recognition by an intrinsically disordered oncoprotein family

Molecular recognition by intrinsically disordered proteins (IDPs) commonly involves specific localized contacts and target-induced disorder to order transitions. However, some IDPs remain disordered in the bound state, a phenomenon coined “fuzziness”, often characterized by IDP polyvalency, sequence-insensitivity and a dynamic ensemble of disordered bound-state conformations. Besides the above general features, specific biophysical models for fuzzy interactions are mostly lacking. The transcriptional activation domain of the Ewing's Sarcoma oncoprotein family (EAD) is an IDP that exhibits many features of fuzziness, with multiple EAD aromatic side chains driving molecular recognition. Considering the prevalent role of cation-π interactions at various protein-protein interfaces, we hypothesized that EAD-target binding involves polycation- π contacts between a disordered EAD and basic residues on the target. Herein we evaluated the polycation-π hypothesis via functional and theoretical interrogation of EAD variants. The experimental effects of a range of EAD sequence variations, including aromatic number, aromatic density and charge perturbations, all support the cation-π model. Moreover, the activity trends observed are well captured by a coarse-grained EAD chain model and a corresponding analytical model based on interaction between EAD aromatics and surface cations of a generic globular target. EAD-target binding, in the context of pathological Ewing's Sarcoma oncoproteins, is thus seen to be driven by a balance between EAD conformational entropy and favorable EAD-target cation-π contacts. Such a highly versatile mode of molecular recognition offers a general conceptual framework for promiscuous target recognition by polyvalent IDPs.

Understanding how proteins recognize each other is central to deciphering the inner workings of living things and for biomedical research. It has long been known that the sequence of a protein, which is a string of different amino acids, can dictate how a protein molecule folds into a well-defined shape required for biological tasks. Many folded proteins recognize and bind with each other by a tight geometric fit similar to that between a lock and its key. Recently, however, it has become clear that some proteins function as a flexible string, in constant motion, without forming a stable shape. Understanding how such “disordered” proteins work is challenging. To gain insight, we studied a disordered protein region that causes a large family of human cancers. Employing an innovative combination of experimental and theoretical techniques, we describe a new mode of protein interaction based on multiple simple contacts between one type of amino acid (aromatic) in the disordered protein and another type (positively charged) on the partner protein. Because this mechanism also underlies the ability of the disordered protein to cause cancer, further investigation of this unprecedented mode of protein-protein interaction may open up new avenues for cancer therapy.

Digested disorder: Quarterly intrinsic disorder digest (April-May-June, 2013)

The current literature on intrinsically disordered proteins is overwhelming. To keep interested readers up to speed with this literature, we continue a "Digested Disorder" project and represent a series of reader's digest type articles objectively representing the research papers and reviews on intrinsically disordered proteins. The only 2 criteria for inclusion in this digest are the publication date (a paper should be published within the covered time frame) and topic (a paper should be dedicated to any aspect of protein intrinsic disorder). The current digest issue covers papers published during the period of April, May, and June of 2013. The papers are grouped hierarchically by topics they cover, and for each of the included paper a short description is given on its major findings.