Multiple-basin energy landscapes for large-amplitude conformational motions of proteins: Structure-based molecular dynamics simulations

Bidirectional Allostery Mechanism in Catch-Bond Formation of CD44 Mediated Cell Adhesion

Catch-bonds, whereby noncovalent ligand-receptor interactions are counterintuitively reinforced by tensile forces, play a major role in cell adhesion under mechanical stress. A basic prerequisite for catch-bond formation, as implicated in classic catch-bond models, is that force-induced remodeling of the ligand binding interface occurs prior to bond rupture. However, what strategy receptor proteins utilize to meet such specific kinetic control remains elusive. Here we report a bidirectional allostery mechanism of catch-bond formation based on theoretical and molecular dynamics simulation studies. Binding of ligand allosterically reduces the threshold force for unlocking of otherwise stably folded force-sensing element (i.e., forward allostery), so that a much smaller tensile force can trigger the conformational switching of receptor protein to high binding-strength state via backward allosteric coupling before bond rupture. Such bidirectional allostery fulfills the specific kinetic control required by catch-bond formation and is likely to be commonly utilized in cell adhesion. The essential thermodynamic and kinetic features of receptor proteins essential for catch-bond formation were identified.

MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

A crucial mechanism for controlling the actions of proteins is allostery. Allosteric modulators have the potential to provide many benefits compared to orthosteric ligands, such as increased selectivity and saturability of their effect. The identification of new allosteric sites presents prospects for the creation of innovative medications and enhances our comprehension of fundamental biological mechanisms. Allosteric sites are increasingly found in different protein families through various techniques, such as machine learning applications, which opens up possibilities for creating completely novel medications with a diverse variety of chemical structures. Machine learning methods, such as PASSer, exhibit limited efficacy in accurately finding allosteric binding sites when relying solely on 3D structural information.Scientific ContributionPrior to conducting feature selection for allosteric binding site identification, integration of supporting amino-acid-based information to 3D structural knowledge is advantageous. This approach can enhance performance by ensuring accuracy and robustness. Therefore, we have developed an accurate and robust model called Multimodel Ensemble Feature Selection for Allosteric Site Identification (MEF-AlloSite) after collecting 9460 relevant and diverse features from the literature to characterise pockets. The model employs an accurate and robust multimodal feature selection technique for the small training set size of only 90 proteins to improve predictive performance. This state-of-the-art technique increased the performance in allosteric binding site identification by selecting promising features from 9460 features. Also, the relationship between selected features and allosteric binding sites enlightened the understanding of complex allostery for proteins by analysing selected features. MEF-AlloSite and state-of-the-art allosteric site identification methods such as PASSer2.0 and PASSerRank have been tested on three test cases 51 times with a different split of the training set. The Student's t test and Cohen's D value have been used to evaluate the average precision and ROC AUC score distribution. On three test cases, most of the p-values ( < 0.05 ) and the majority of Cohen's D values ( > 0.5 ) showed that MEF-AlloSite's 1-6% higher mean of average precision and ROC AUC than state-of-the-art allosteric site identification methods are statistically significant.

Decomposition of Forces in Protein: Methodology and General Properties

In contrast to the central role played by the structure of biomolecules, the complementary force-based view has received little attention in past studies. Here, we proposed a simple method for the force decomposition of multibody interactions and provided some techniques to analyze and visualize the general behavior of forces in proteins. It was shown that atomic forces fluctuate at a magnitude of about 3000 pN, which is huge in the context of cell biology. Remarkably, the average scalar product between atomic force and displacement universally approximates -3kBT. This is smaller by an order of magnitude than the simple product of their fluctuation magnitudes due to the unexpectedly weak correlation between the directions of force and displacement. The pairwise forces are highly anisotropic, with elongated fluctuation ellipsoids. Residue-residue forces can be attractive or repulsive (despite being more likely to be attractive), forming some kind of tensegrity structure stabilized by a complicated network of forces. Being able to understand and predict the interaction network provides a basis for rational drug design and uncovering molecular recognition mechanisms.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Switching Go̅-Martini for Investigating Protein Conformational Transitions and Associated Protein-Lipid Interactions

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport.

The ATPase asymmetry: Novel computational insight into coupling diverse FO motors with tripartite F1

ATP synthase, a crucial enzyme for cellular bioenergetics, operates via the coordinated coupling of an FO motor, which presents variable symmetry, and a tripartite F1 motor. Despite extensive research, the understanding of their coupling dynamics, especially with non-10-fold symmetrical FO motors, remains incomplete. This study investigates the coupling patterns between eightfold and ninefold FO motors and the constant threefold F1 motor using coarse-grained molecular dynamics simulations. We unveil that in the case of a ninefold FO motor, a 3-3-3 motion is most likely to occur, whereas a 3-3-2 motion predominates with an eightfold FO motor. Furthermore, our findings propose a revised model for the coupling method, elucidating that the pathways' energy usage is primarily influenced by F1 rotation and conformational changes hindered by the b-subunits. Our results present a crucial step toward comprehending the energy landscape and mechanisms governing ATP synthase operation.

Identification of Residues Potentially Involved in Optical Shifts in the Water-Soluble Chlorophyll a-Binding Protein through Molecular Dynamics Simulations

Reversible light and thermally induced spectral shifts are universally observed in a wide variety of pigment-protein complexes at temperatures ranging from cryogenic to ambient. In this paper, we employed large-scale molecular dynamics (MD) simulations of a prototypical pigment-protein complex to better understand these shifts at a molecular scale. Although multiple mechanisms have been proposed over the years, no verification of these proposals via MD simulations has thus far been performed; our work represents the first step in this direction. From simulations of the water-soluble chlorophyll-binding protein complex, we determined that rearrangements of long hydrogen bonds were unlikely to be the origin of the multiwell landscape features necessary to explain observed spectral shifts. We also assessed small motions of amino acid residues and identified side chain rotations of some of these residues as likely candidates for the origin of relevant multiwell landscape features. The protein free-energy landscapes associated with side chain rotations feature energy barriers of around 1100-1600 cm-1, in agreement with optical spectroscopy results, with the most promising residue type associated with experimental signatures being serine, which possesses a symmetric triple-well landscape and moment of inertia of a relevant magnitude.

Multi-eGO: Model Improvements toward the Study of Complex Self-Assembly Processes

Structure-based models have been instrumental in simulating protein folding and suggesting hypotheses about the mechanisms involved. Nowadays, at least for fast-folding proteins, folding can be simulated in explicit solvent using classical molecular dynamics. However, other self-assembly processes, such as protein aggregation, are still far from being accessible. Recently, we proposed that a hybrid multistate structure-based model, multi-eGO, could help to bridge the gap toward the simulation of out-of-equilibrium, concentration-dependent self-assembly processes. Here, we further improve the model and show how multi-eGO can effectively and accurately learn the conformational ensemble of the amyloid β42 intrinsically disordered peptide, reproduce the well-established folding mechanism of the B1 immunoglobulin-binding domain of streptococcal protein G, and reproduce the aggregation as a function of the concentration of the transthyretin 105-115 amyloidogenic peptide. We envision that by learning from the dynamics of a few minima, multi-eGO can become a platform for simulating processes inaccessible to other simulation techniques.

Enhanced Coarse-Grained Molecular Dynamics Simulation with a Smoothed Hybrid Potential Using a Neural Network Model

In all-atom (AA) molecular dynamics (MD) simulations, the rugged energy profile of the force field makes it challenging to reproduce spontaneous structural changes in biomolecules within a reasonable calculation time. Existing coarse-grained (CG) models, in which the energy profile is set to a global minimum around the initial structure, are unsuitable to explore the structural dynamics between metastable states far away from the initial structure without any bias. In this study, we developed a new hybrid potential composed of an artificial intelligence (AI) potential and minimal CG potential related to the statistical bond length and excluded volume interactions to accelerate the transition dynamics while maintaining the protein character. The AI potential is trained by energy matching using a diverse structural ensemble sampled via multicanonical (Mc) MD simulation and the corresponding AA force field energy, profile of which is smoothed by energy minimization. By applying the new methodology to chignolin and TrpCage, we showed that the AI potential can predict the AA energy with significantly high accuracy, as indicated by a correlation coefficient (R-value) between the true and predicted energies exceeding 0.89. In addition, we successfully demonstrated that CGMD simulation based on the smoothed hybrid potential can significantly enhance the transition dynamics between various metastable states while preserving protein properties compared to those obtained with conventional CGMD and AAMD.

How aberrant N-glycosylation can alter protein functionality and ligand binding: An atomistic view

Protein-assembly defects due to an enrichment of aberrant conformational protein variants are emerging as a new frontier in therapeutics design. Understanding the structural elements that rewire the conformational dynamics of proteins and pathologically perturb functionally oriented ensembles is important for inhibitor development. Chaperones are hub proteins for the assembly of multiprotein complexes and an enrichment of aberrant conformers can affect the cellular proteome, and in turn, phenotypes. Here, we integrate computational and experimental tools to investigte how N-glycosylation of specific residues in glucose-regulated protein 94 (GRP94) modulates internal dynamics and alters the conformational fitness of regions fundamental for the interaction with ATP and synthetic ligands and impacts substructures important for the recognition of interacting proteins. N-glycosylation plays an active role in modulating the energy landscape of GRP94, and we provide support for leveraging the knowledge on distinct glycosylation variants to design molecules targeting GRP94 disease-associated conformational states and assemblies.

Exploring the structural acrobatics of fold-switching proteins using simplified structure-based models

Metamorphic proteins are a paradigm of the protein folding process, by encoding two or more native states, highly dissimilar in terms of their secondary, tertiary, and even quaternary structure, on a single amino acid sequence. Moreover, these proteins structurally interconvert between these native states in a reversible manner at biologically relevant timescales as a result of different environmental cues. The large-scale rearrangements experienced by these proteins, and their sometimes high mass interacting partners that trigger their metamorphosis, makes the computational and experimental study of their structural interconversion challenging. Here, we present our efforts in studying the refolding landscapes of two quintessential metamorphic proteins, RfaH and KaiB, using simplified dual-basin structure-based models (SBMs), rigorously footed on the energy landscape theory of protein folding and the principle of minimal frustration. By using coarse-grained models in which the native contacts and bonded interactions extracted from the available experimental structures of the two native states of RfaH and KaiB are merged into a single Hamiltonian, dual-basin SBM models can be generated and savvily calibrated to explore their fold-switch in a reversible manner in molecular dynamics simulations. We also describe how some of the insights offered by these simulations have driven the design of experiments and the validation of the conformational ensembles and refolding routes observed using this simple and computationally efficient models.

FO-F1 coupling and symmetry mismatch in ATP synthase resolved in every FO rotation step

FOF1 ATP synthase, a ubiquitous enzyme that synthesizes most ATP in living cells, is composed of two rotary motors: a membrane-embedded proton-driven FO motor and a catalytic F1 motor. These motors share both central and peripheral stalks. Although both FO and F1 have pseudo-symmetric structures, their symmetries do not match. How symmetry mismatch is solved remains elusive because of the missing intermediate structures of the rotational steps. Here, for the case of Bacillus PS3 ATP synthases with three- and 10-fold symmetries in F1 and FO, respectively, we uncovered the mechanical couplings between FO and F1 at every 36° rotation step via molecular dynamics simulations and comparative studies of cryoelectron microscopy (cryo-EM) structures from three species. We found that the mismatch could be solved using several elements: 1) the F1 head partially rotates relative to the FO a subunit via elastic distortion of the b subunits, 2) the rotor is twisted, and 3) comparisons of cryo-EM structures further suggest that the c ring rotary angles can deviate from the symmetric ones. In addition, the F1 motor may have non-canonical structures, relieving stronger frustration. Thus, we provide new insights for solving the symmetry mismatch problem.

How a single mutation alters the protein structure: a simulation investigation on protein tyrosine phosphatase SHP2

Protein tyrosine phosphatase SHP2 is a key regulator modulating several signaling pathways. The oncogenic mutation E76K in SHP2 releases the enzyme from an autoinhibited, closed conformation into an active, open conformation. Here, we investigated the conformational dynamics of SHP2 and the effect of the E76K mutation on its conformational ensemble via extensive molecular dynamics (MD) and metadynamics (MetaD) simulations. Our simulations provide atomistic details on how the E76K mutated SHP2 prefers the open state and also reveal that the transition between the closed and the open states is highly collective. Several intermediate metastable states during the conformational transition between the closed and the open states were also investigated. Understanding how the single E76K mutation induces the conformational change in SHP2 could facilitate the further design of SHP2 inhibitors.

Nucleosome breathing facilitates cooperative binding of pluripotency factors Sox2 and Oct4 to DNA

Critical lineage commitment events are staged by multiple transcription factors (TFs) binding to their cognate motifs, often positioned at nucleosome-enriched regions of chromatin. The underlying mechanism remains elusive due to difficulty in disentangling the heterogeneity in chromatin states. Using a novel coarse-grained model and molecular dynamics simulations, here we probe the association of Sox2 and Oct4 proteins that show clustered binding at the entry-exit region of a nucleosome. The model captures the conformational heterogeneity of nucleosome breathing dynamics that features repeated wrap-unwrap transitions of a DNA segment from one end of the nucleosome. During the dynamics, DNA forms bulges that diffuse stochastically and may regulate the target search dynamics of a protein by nonspecifically interacting with it. The overall search kinetics of the TF pair follows a "dissociation-compensated-association" mechanism, where Oct4 binding is facilitated by the association of Sox2. The cooperativity stems from a change in entropy caused by an alteration in the nucleosome dynamics upon TF binding. The binding pattern is consistent with a live-cell single-particle tracking experiment, suggesting the mechanism observed for clustered binding of a TF pair, which is a hallmark of cis-regulatory elements, has broader implications in understanding gene regulation in a complex chromatin environment.

Understanding protein diffusion on force-induced stretched DNA conformation

DNA morphology is subjected to environmental conditions and is closely coupled with its function. For example, DNA experiences stretching forces during several biological processes, including transcription and genome transactions, that significantly alter its conformation from that of B-DNA. Indeed, a well-defined 1.5 times extended conformation of dsDNA, known as Σ-DNA, has been reported in DNA complexes with proteins such as Rad51 and RecA. A striking feature in Σ-DNA is that the nucleobases are partitioned into triplets of three locally stacked bases separated by an empty rise gap of ∼ 5 Å. The functional role of such a DNA base triplet was hypothesized to be coupled with the ease of recognition of DNA bases by DNA-binding proteins (DBPs) and the physical origin of three letters (codon/anti-codon) in the genetic code. However, the underlying mechanism of base-triplet formation and the ease of DNA base-pair recognition by DBPs remain elusive. To investigate, here, we study the diffusion of a protein on a force-induced stretched DNA using coarse-grained molecular dynamics simulations. Upon pulling at the 3' end of DNA by constant forces, DNA exhibits a conformational transition from B-DNA to a ladder-like S-DNA conformation via Σ-DNA intermediate. The resulting stretched DNA conformations exhibit non-uniform base-pair clusters such as doublets, triplets, and quadruplets, of which triplets are energetically more stable than others. We find that protein favors the triplet formation compared to its unbound form while interacting non-specifically along DNA, and the relative population of it governs the ruggedness of the protein-DNA binding energy landscape and enhances the efficiency of DNA base recognition. Furthermore, we analyze the translocation mechanism of a DBP under different force regimes and underscore the significance of triplet formation in regulating the facilitated diffusion of protein on DNA. Our study, thus, provides a plausible framework for understanding the structure-function relationship between triplet formation and base recognition by a DBP and helps to understand gene regulation in complex regulatory processes.

Role of Repeated Conformational Transitions in Substrate Binding of Adenylate Kinase

The catalytic cycle of the enzyme adenylate kinase involves large conformational motions between open and closed states. A previous single-molecule experiment showed that substrate binding tends to accelerate both the opening and the closing rates and that a single turnover event often involves multiple rounds of conformational switching. In this work, we showed that the repeated conformational transitions of adenylate kinase are essential for the relaxation of incorrectly bound substrates into the catalytically competent conformation by combining all-atom and coarse-grained molecular simulations. In addition, free energy calculations based on all-atom and coarse-grained models demonstrated that the enzyme with incorrectly bound substrates has much a lower free energy barrier for domain opening compared to that with the correct substrate conformation, which may explain the the acceleration of the domain opening rate by substrate binding. The results of this work provide mechanistic understanding to previous experimental observations and shed light onto the interplay between conformational dynamics and enzyme catalysis.

Self-Effected Allosteric Coupling and Cooperativity in Hypoxic Response Regulation with Disordered Proteins

Hypersensitive regulation of cellular hypoxic response relies on cooperative displacement of one disordered protein (HIF-1α) by another disordered protein (CITED2) from the target in a negative feedback loop. Considering the weak intramolecule coupling in disordered proteins, the molecular mechanism of high cooperativity in the molecular displacement event remains elusive. Herein, we show that disordered proteins utilize a "self-effected allostery" mechanism to achieve high binding cooperativity. Different from the conventional allostery mechanisms shown by many structured or disordered proteins, this mechanism utilizes one part of the disordered protein as the effector to trigger the allosteric coupling and enhance the binding of the remaining part of the same disordered protein, contributing to high cooperativity of the displacement event. The conserved charge motif of CITED2 is the key determinant of the molecular displacement event by serving as the effector of allosteric coupling. Such self-effected allostery provides an efficient strategy to achieve high cooperativity in the molecular events involving disordered proteins.

Frustration and the Kinetic Repartitioning Mechanism of Substrate Inhibition in Enzyme Catalysis

Substrate inhibition, whereby enzymatic activity decreases with excess substrate after reaching a maximum turnover rate, is among the most elusive phenomena in enzymatic catalysis. Here, based on a dynamic energy landscape model, we investigate the underlying mechanism by performing molecular simulations and frustration analysis for a model enzyme adenylate kinase (AdK), which catalyzes the phosphoryl transfer reaction ATP + AMP ⇋ ADP + ADP. Intriguingly, these reveal a kinetic repartitioning mechanism of substrate inhibition, whereby excess substrate AMP suppresses the population of an energetically frustrated, but kinetically activated, catalytic pathway going through a substrate (ATP)-product (ADP) cobound complex with steric incompatibility. Such a frustrated pathway plays a crucial role in facilitating the bottleneck product ADP release, and its suppression by excess substrate AMP leads to a slow down of product release and overall turnover. The simulation results directly demonstrate that substrate inhibition arises from the rate-limiting product-release step, instead of the steps for populating the catalytically competent complex as often suggested in previous works. Furthermore, there is a tight interplay between the enzyme conformational equilibrium and the extent of substrate inhibition. Mutations biasing to more closed conformations tend to enhance substrate inhibition. We also characterized the key features of single-molecule enzyme kinetics with substrate inhibition effect. We propose that the above molecular mechanism of substrate inhibition may be relevant to other multisubstrate enzymes in which product release is the bottleneck step.

Multi-eGO: An in silico lens to look into protein aggregation kinetics at atomic resolution

Protein aggregation into amyloid fibrils is the archetype of aberrant biomolecular self-assembly processes, with more than 50 associated diseases that are mostly uncurable. Understanding aggregation mechanisms is thus of fundamental importance and goes in parallel with the structural characterization of the transient oligomers formed during the process. Oligomers have been proven elusive to high-resolution structural techniques, while the large sizes and long time scales, typical of aggregation processes, have limited the use of computational methods to date. To surmount these limitations, we here present multi-eGO, an atomistic, hybrid structure-based model which, leveraging the knowledge of monomers conformational dynamics and of fibril structures, efficiently captures the essential structural and kinetics aspects of protein aggregation. Multi-eGO molecular dynamics simulations can describe the aggregation kinetics of thousands of monomers. The concentration dependence of the simulated kinetics, as well as the structural features of the resulting fibrils, are in qualitative agreement with in vitro experiments carried out on an amyloidogenic peptide from Transthyretin, a protein responsible for one of the most common cardiac amyloidoses. Multi-eGO simulations allow the formation of primary nuclei in a sea of transient lower-order oligomers to be observed over time and at atomic resolution, following their growth and the subsequent secondary nucleation events, until the maturation of multiple fibrils is achieved. Multi-eGO, combined with the many experimental techniques deployed to study protein aggregation, can provide the structural basis needed to advance the design of molecules targeting amyloidogenic diseases.

Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics

Compared to all-atom molecular dynamics (AA-MD) simulations, coarse-grained (CG) MD simulations can significantly reduce calculation costs. However, existing CG-MD methods are unsuitable for sampling structures that depart significantly from the initial structure without any biased force. In this study, we developed a new adaptive CG elastic network model (ENM), in which the dynamic cross-correlation coefficient based on short-time AA-MD of at most ns order is considered. By applying Bayesian optimization to search for a suitable parameter among the vast parameter space of adaptive CG-ENM, we succeeded in reducing the searching cost to approximately 10% of those for random sampling and exhaustive sampling. To evaluate the performance of adaptive CG-ENM, we applied the new methodology to adenylate kinase (ADK) and glutamine binding protein (GBP) in the apo state. The results showed that the structural ensembles explored by adaptive CG-ENM could be considerably more diverse than those by conventional ENMs with enhanced sampling such as temperature replica exchange MD and long-time AA-MD of 1 μs. In particular, some of the structures sampled by adaptive ENM are relatively close to the holo-type structures of ADK and GBP. Furthermore, as a challenging task, to demonstrate the advantages of the CG model with lower calculation cost, we applied our new methodology to a larger biomolecule, integrin (αV) in the inactive state. Then, we sampled various structural ensembles, including extended structures that are apparently different from inactive ones.

Martini 3 Model of Cellulose Microfibrils: On the Route to Capture Large Conformational Changes of Polysaccharides

High resolution data from all-atom molecular simulations is used to parameterize a Martini 3 coarse-grained (CG) model of cellulose I allomorphs and cellulose type-II fibrils. In this case, elementary molecules are represented by four effective beads centred in the positions of O2, O3, C6, and O6 atoms in the D-glucose cellulose subunit. Non-bonded interactions between CG beads are tuned according to a low statistical criterion of structural deviation using the Martini 3 type of interactions and are capable of being indistinguishable for all studied cases. To maintain the crystalline structure of each single cellulose chain in the microfibrils, elastic potentials are employed to retain the ribbon-like structure in each chain. We find that our model is capable of describing different fibril-twist angles associated with each type of cellulose fibril in close agreement with atomistic simulation. Furthermore, our CG model poses a very small deviation from the native-like structure, making it appropriate to capture large conformational changes such as those that occur during the self-assembly process. We expect to provide a computational model suitable for several new applications such as cellulose self-assembly in different aqueous solutions and the thermal treatment of fibrils of great importance in bioindustrial applications.

The role of C-terminal helix in the conformational transition of an arginine binding protein

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Probe the role of C-ter. helix (CTH) in conformational transition of TmArgBP.

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Presence of CTH almost doubles the barrier to access the closed-state.

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In the absence of CTH, the protein can fluctuate between the two conformations.

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CTH not only constraints the open-state conformation but also guides in accessing it.

The thermotoga maritima arginine binding protein (TmArgBP) is a periplasmic binding protein that has a short helix at the C-terminal end (CTH), which is swapped between the two chains. We apply a coarse-grained structure-based model (SBM) and all-atom MD simulation on this protein to understand the mechanism and the role of CTH in the conformational transition. When the results of SBM simulations of TmArgBP in the presence and absence of CTH are compared, we find that CTH is strategically located at the back of the binding pocket restraining the open-state conformation thereby disengaging access to the closed-state. We also ran all-atom MD simulations of open-state TmArgBP with and without CTH and discovered that in the absence of CTH the protein could reach the closed-state within 250 ns, while in its presence, the protein remained predominantly in its open-state conformation. In the simulation started from unliganded closed-state conformation without CTH, the protein exhibited multiple transitions between the two states, suggesting CTH as an essential structural element to stabilize the open-state conformation. In another simulation that began with an unliganded closed-state conformation with CTH, the protein was able to access the open-state. In this simulation the CTH was observed to reorient itself to interact with the protein emphasizing its role in assisting the conformational change. Based on our findings, we believe that CTH not only acts as a structural element that constraints the protein in its open-state but it may also guide the protein back to its open-state conformation upon ligand unbinding.

SMOG 2 and OpenSMOG: Extending the limits of structure-based models

Applying simulations with structure-based G o ¯ - like models has proven to be an effective strategy for investigating the factors that control biomolecular dynamics. The common element of these models is that some (or all) of the intra/inter-molecular interactions are explicitly defined to stabilize an experimentally determined structure. To facilitate the development and application of this broad class of models, we previously released the SMOG 2 software package. This suite allows one to easily customize and distribute structure-based (i.e., SMOG) models for any type of polymer-ligand system. The force fields generated by SMOG 2 may then be used to perform simulations in highly optimized MD packages, such as Gromacs, NAMD, LAMMPS, and OpenMM. Here, we describe extensions to the software and demonstrate the capabilities of the most recent version (SMOG v2.4.2). Changes include new tools that aid user-defined customization of force fields, as well as an interface with the OpenMM simulation libraries (OpenSMOG v1.1.0). The OpenSMOG module allows for arbitrary user-defined contact potentials and non-bonded potentials to be employed in SMOG models, without source-code modifications. To illustrate the utility of these advances, we present applications to systems with millions of atoms, long polymers and explicit ions, as well as models that include non-structure-based (e.g., AMBER-based) energetic terms. Examples include large-scale rearrangements of the SARS-CoV-2 Spike protein, the HIV-1 capsid with explicit ions, and crystallographic lattices of ribosomes and proteins. In summary, SMOG 2 and OpenSMOG provide robust support for researchers who seek to develop and apply structure-based models to large and/or intricate biomolecular systems.

Design of novel cyanovirin-N variants by modulation of binding dynamics through distal mutations

We develop integrated co-evolution and dynamic coupling (ICDC) approach to identify, mutate, and assess distal sites to modulate function. We validate the approach first by analyzing the existing mutational fitness data of TEM-1 β-lactamase and show that allosteric positions co-evolved and dynamically coupled with the active site significantly modulate function. We further apply ICDC approach to identify positions and their mutations that can modulate binding affinity in a lectin, cyanovirin-N (CV-N), that selectively binds to dimannose, and predict binding energies of its variants through Adaptive BP-Dock. Computational and experimental analyses reveal that binding enhancing mutants identified by ICDC impact the dynamics of the binding pocket, and show that rigidification of the binding residues compensates for the entropic cost of binding. This work suggests a mechanism by which distal mutations modulate function through dynamic allostery and provides a blueprint to identify candidates for mutagenesis in order to optimize protein function.

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.

Structural dynamics in the evolution of a bilobed protein scaffold

Proteins conduct numerous complex biological functions by use of tailored structural dynamics. The molecular details of how these emerged from ancestral peptides remains mysterious. How does nature utilize the same repertoire of folds to diversify function? To shed light on this, we analyzed bilobed proteins with a common structural core, which is spread throughout the tree of life and is involved in diverse biological functions such as transcription, enzymatic catalysis, membrane transport, and signaling. We show here that the structural dynamics of the structural core differentiate predominantly via terminal additions during a long-period evolution. This diversifies substrate specificity and, ultimately, biological function.

Novel biophysical tools allow the structural dynamics of proteins and the regulation of such dynamics by binding partners to be explored in unprecedented detail. Although this has provided critical insights into protein function, the means by which structural dynamics direct protein evolution remain poorly understood. Here, we investigated how proteins with a bilobed structure, composed of two related domains from the periplasmic-binding protein–like II domain family, have undergone divergent evolution, leading to adaptation of their structural dynamics. We performed a structural analysis on ∼600 bilobed proteins with a common primordial structural core, which we complemented with biophysical studies to explore the structural dynamics of selected examples by single-molecule Förster resonance energy transfer and Hydrogen–Deuterium exchange mass spectrometry. We show that evolutionary modifications of the structural core, largely at its termini, enable distinct structural dynamics, allowing the diversification of these proteins into transcription factors, enzymes, and extracytoplasmic transport-related proteins. Structural embellishments of the core created interdomain interactions that stabilized structural states, reshaping the active site geometry, and ultimately altered substrate specificity. Our findings reveal an as-yet-unrecognized mechanism for the emergence of functional promiscuity during long periods of evolution and are applicable to a large number of domain architectures.

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

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

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

The dynamics of subunit rotation in a eukaryotic ribosome

Protein synthesis by the ribosome is coordinated by an intricate series of large-scale conformational rearrangements. Structural studies can provide information about long-lived states, however biological kinetics are controlled by the intervening free-energy barriers. While there has been progress describing the energy landscapes of bacterial ribosomes, very little is known about the energetics of large-scale rearrangements in eukaryotic systems. To address this topic, we constructed an all-atom model with simplified energetics and performed simulations of subunit rotation in the yeast ribosome. In these simulations, the small subunit (SSU; ~1MDa) undergoes spontaneous and reversible rotations (~8°). By enabling the simulation of this rearrangement under equilibrium conditions, these calculations provide initial insights into the molecular factors that control dynamics in eukaryotic ribosomes. Through this, we are able to identify specific inter-subunit interactions that have a pronounced influence on the rate-limiting free-energy barrier. We also show that, as a result of changes in molecular flexibility, the thermodynamic balance between the rotated and unrotated states is temperature-dependent. This effect may be interpreted in terms of differential molecular flexibility within the rotated and unrotated states. Together, these calculations provide a foundation, upon which the field may begin to dissect the energetics of these complex molecular machines.

Coarse-Grained Modeling of Multiple Pathways in Conformational Transitions of Multi-Domain Proteins

Large-scale conformational transitions in multi-domain proteins are often essential for their functions. To investigate the transitions, it is necessary to explore multiple potential pathways, which involve different intermediate structures. Here, we present a multi-basin (MB) coarse-grained (CG) structure-based Go̅ model for describing transitions in proteins with more than two moving domains. This model is an extension of our dual-basin Go̅ model in which system-dependent parameters are determined systematically using the multistate Bennett acceptance ratio method. In the MB Go̅ model for multi-domain proteins, we assume that intermediate structures may have partial inter-domain native contacts. This approach allows us to search multiple transition pathways that involve distinct intermediate structures using the CG molecular dynamics (MD) simulations. We apply this scheme to an enzyme, adenylate kinase (AdK), which has three major domains and can move along two different pathways. Using the optimized mixing parameters for each pathway, AdK shows frequent transitions between the Open, Closed, and the intermediate basins and samples a wide variety of conformations within each basin. The explored multiple transition pathways could be compared with experimental data and examined in more detail by atomistic MD simulations.

Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model

To initiate transcription, the holoenzyme (RNA polymerase [RNAP] in complex with σ factor) loads the promoter DNA via the flexible loading gate created by the clamp and β-lobe, yet their roles in DNA loading have not been characterized. We used a quasi-Markov State Model (qMSM) built from extensive molecular dynamics simulations to elucidate the dynamics of Thermus aquaticus holoenzyme's gate opening. We showed that during gate opening, β-lobe oscillates four orders of magnitude faster than the clamp, whose opening depends on the Switch 2's structure. Myxopyronin, an antibiotic that binds to Switch 2, was shown to undergo a conformational selection mechanism to inhibit clamp opening. Importantly, we reveal a critical but undiscovered role of β-lobe, whose opening is sufficient for DNA loading even when the clamp is partially closed. These findings open the opportunity for the development of antibiotics targeting β-lobe of RNAP. Finally, we have shown that our qMSMs, which encode non-Markovian dynamics based on the generalized master equation formalism, hold great potential to be widely applied to study biomolecular dynamics.

Enhanced sampling method with coarse graining of conformational space

The sampling of conformations in the molecular simulations for systems with complicated free energy landscapes is always difficult. Here, we report a method for enhanced sampling based on the coarse-graining of conformational space. In this method, the locally converged region of the conformational space is coarse-grained with its population characterized by the related average residence time and visiting number, and at the same time, the direct simulations inside it are eliminated. The detailed balance is satisfied by updating the visiting number and generating outgoing trajectories of this region. This kind of coarse-graining operation can be further carried out by merging all the neighboring regions which are already converged together. The global equilibrium is achieved when the local equilibrated regions cover all the interested areas of the landscape. We tested the method by applying it to two model potentials and one protein system with multiple-basin energy landscapes. The sampling efficiency is found to be enhanced by more than three orders of magnitude compared to conventional molecular simulations, and are comparable with other widely used enhanced sampling methods. In addition, the kinetic information can also be well captured. All these results demonstrate that our method can help to solve the sampling problems efficiently and precisely without applying high temperatures or biasing potentials.

Energy landscape remodeling mechanism of Hsp70-chaperone-accelerated protein folding

Hsp70 chaperone is one of the key protein machines responsible for the quality control of protein production in cells. Facilitating in vivo protein folding by counteracting misfolding and aggregation is the essence of its biological function. Although the allosteric cycle during its functional actions has been well characterized both experimentally and computationally, the mechanism by which Hsp70 assists protein folding is still not fully understood. In this work, we studied the Hsp70-mediated folding of model proteins with rugged energy landscape by using molecular simulations. Different from the canonical scenario of Hsp70 functioning, which assumes that folding of substrate proteins occurs spontaneously after releasing from chaperones, our results showed that the substrate protein remains in contacts with the chaperone during its folding process. The direct chaperone-substrate interactions in the open conformation of Hsp70 tend to shield the substrate sites prone to form non-native contacts, which therefore avoids the frustrated folding pathway, leading to a higher folding rate and less probability of misfolding. Our results suggest that in addition to the unfoldase and holdase functions widely addressed in previous studies, Hsp70 can facilitate the folding of its substrate proteins by remodeling the folding energy landscape and directing the folding processes, demonstrating the foldase scenario. These findings add new, to our knowledge, insights into the general molecular mechanisms of chaperone-mediated protein folding.

Using multiscale molecular dynamics simulations to obtain insights into pore forming toxin mechanisms

Pore forming toxins (PFTs) are virulent proteins released by several species, including many strains of bacteria, to attack and kill host cells. In this article, we focus on the utility of molecular dynamics (MD) simulations and the molecular insights gleaned from these techniques on the pore forming pathways of PFTs. In addition to all-atom simulations which are widely used, coarse-grained MARTINI models and structure-based models have also been used to study PFTs. Here, the emphasis is on methods and techniques involved while setting up, monitoring, and evaluating properties from MD simulations of PFTs in a membrane environment. We draw from several case studies to illustrate how MD simulations have provided molecular insights into protein-protein and protein-lipid interactions, lipid dynamics, conformational transitions and structures of both the oligomeric intermediates and assembled pore structures.

Optimizing Gō-MARTINI Coarse-Grained Model for F-BAR Protein on Lipid Membrane

Coarse-grained (CG) molecular dynamics (MD) simulations allow us to access much larger length and time scales than atomistic MD simulations, providing an attractive alternative to the conventional simulations. Based on the well-known MARTINI CG force field, the recently developed Gō-MARTINI model for proteins describes large-amplitude structural dynamics, which has not been possible with the commonly used elastic network model. Using the Gō-MARTINI model, we conduct MD simulations of the F-BAR Pacsin1 protein on lipid membrane. We observe that structural changes of the non-globular protein are largely dependent on the definition of the native contacts in the Gō model. To address this issue, we introduced a simple cutoff scheme and tuned the cutoff distance of the native contacts and the interaction strength of the Lennard-Jones potentials in the Gō-MARTINI model. With the optimized Gō-MARTINI model, we show that it reproduces structural fluctuations of the Pacsin1 dimer from atomistic simulations. We also show that two Pacsin1 dimers properly assemble through lateral interaction on the lipid membrane. Our work presents a first step towards describing membrane remodeling processes in the Gō-MARTINI CG framework by simulating a crucial step of protein assembly on the membrane.

Mechanistic basis of propofol-induced disruption of kinesin processivity

Propofol is a widely used general anesthetic to induce and maintain anesthesia, and its effects are thought to occur through impact on the ligand-gated channels including the GABA_A receptor. Propofol also interacts with a large number of proteins including molecular motors and inhibits kinesin processivity, resulting in significant decrease in the run length for conventional kinesin-1 and kinesin-2. However, the molecular mechanism by which propofol achieves this outcome is not known. The structural transition in the kinesin neck-linker region is crucial for its processivity. In this study, we analyzed the effect of propofol and its fluorine derivative (fropofol) on the transition in the neck-linker region of kinesin. Propofol binds at two crucial surfaces in the leading head: one at the microtubule-binding interface and the other in the neck-linker region. We observed in both the cases the order-disorder transition of the neck-linker was disrupted and kinesin lost its signal for forward movement. In contrast, there was not an effect on the neck-linker transition with propofol binding at the trailing head. Free-energy calculations show that propofol at the microtubule-binding surface significantly reduces the microtubule-binding affinity of the kinesin head. While propofol makes pi-pi stacking and H-bond interactions with the propofol binding cavity, fropofol is unable to make a suitable interaction at this binding surface. Therefore, the binding affinity of fropofol is much lower compared to propofol. Hence, this study provides a mechanism by which propofol disrupts kinesin processivity and identifies transitions in the ATPase stepping cycle likely affected.

Mechanistic Insights into Pore Formation by an α-Pore Forming Toxin: Protein and Lipid Bilayer Interactions of Cytolysin A

Pore forming toxins (PFTs) are the largest class of bacterial toxins playing a central role in bacterial pathogenesis. They are proteins specifically designed to form nanochannels in the membranes of target cells, ultimately resulting in cell death and establishing infection. PFTs are broadly classified as α- and β-PFTs, depending on secondary structures that form the transmembrane channel. A unique feature about this class of proteins is the drastic conformational changes and complex oligomerization pathways that occur upon exposure to the plasma membrane. A molecular understanding of pore formation has implications in designing novel intervention strategies to combat rising antimicrobial resistance, targeted-cancer therapy, as well as designing nanopores for specialized technologies. Central to unraveling the pore formation pathway is the availability of high resolution crystal structures. In this regard, β-toxins are better understood, when compared with α-toxins whose pore forming mechanisms are complicated by an incomplete knowledge of the driving forces for amphiphatic membrane-inserted helices to organize into functional pores. With the publication of the first crystal structure for an α-toxin, cytolysin A (ClyA), in 2009 we embarked on an extensive multiscale study to unravel its pore forming mechanism. This Account represents the collective mechanistic knowledge gained in our laboratories using a variety of experimental and theoretical techniques which include large scale molecular dynamics (MD) simulations, kinetic modeling studies, single-molecule fluorescence imaging, and super-resolution spectroscopy. We reported MD simulations of the ClyA protomer, oligomeric intermediates, and full pore complex in a lipid bilayer and mapped the conformational transitions that accompany membrane binding. Using single-molecule fluorescence imaging, the conformational transition was experimentally verified by analysis of various diffusion states of membrane bound ClyA. Importantly, we have uncovered a hitherto unknown putative cholesterol binding motif in the membrane-inserted helix of ClyA. Distinct binding pockets for cholesterol formed by adjacent membrane-inserted helices are revealed in MD simulations. Cholesterol appears to play a dual role by stabilizing both the membrane-inserted protomer as well as oligomeric intermediates. Molecular dynamics simulations and kinetic modeling studies suggest that the membrane-inserted arcs oligomerize reversibly to form the predominant transmembrane oligomeric intermediates during pore formation. We posit that this mechanistic understanding of the complex action of α-PFTs has implications in unraveling pore assembly across the wider family of bacterial toxins. With emerging antimicrobial resistance, alternate therapies may rely on disrupting pore functionality or oligomerization of these pathogenic determinants utilized by bacteria, and our study includes assessing the potential for dendrimers as pore blockers.

Double-Well Ultra-Coarse-Grained Model to Describe Protein Conformational Transitions

The double-well model is usually used to describe the conformational transition between two states of a protein. Since conformational changes usually occur within a relatively large time scale, coarse-grained models are often used to accelerate the dynamic process due to their inexpensive computational cost. In this work, we develop a double-well ultra-coarse-grained (DW-UCG) model to describe the conformational transitions of the adenylate kinase, glutamine-binding protein, and lactoferrin. The coarse-grained simulation results show that the DW-UCG model of adenylate kinase captures the crucial intermediate states in the LID-closing and NMP-closing pathways, reflecting the key secondary structural changes in the conformational transition. A comparison of the different DW-UCG models of adenylate kinase indicates that an appropriate choice of bead resolution could generate the free energy landscape that is comparable to that from the residue-based model. The coarse-grained simulations for the glutamine-binding protein and lactoferrin also demonstrate that the DW-UCG model is valid in reproducing the correct two-state behavior for their functional study, which indicates the potential application of the DW-UCG model in investigating the mechanism of conformational changes of large proteins.

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.

Insights into the Kinetic Partitioning Folding Dynamics of the Human Telomeric G-Quadruplex from Molecular Simulations and Machine Learning

The human telomeric DNA G-quadruplex follows a kinetic partitioning folding mechanism. The underlying folding landscape potentially has many minima separated by high free-energy barriers. However, using current theoretical models to characterize this complex folding landscape has remained a challenging problem. In this study, by developing a hybrid atomistic structure-based model that merges structural information on the hybrid-1, hybrid-2, and chair-type G-quadruplex topologies, we investigated a kinetic partitioning folding process of human telomeric DNA involving three native folds. The model was validated as it reproduced the experimental observation that the hybrid-1 conformation is the major fold and the hybrid-2 conformation is kinetically more accessible. A three-step mechanism was revealed for the formation of the hybrid-1 conformation, while a two-step mechanism was demonstrated for the formation of hybrid-2 and chair-type conformations. Likewise, a class of state in which structures adopted inappropriate combinations of syn/anti guanine nucleotides was found to greatly slow down the folding process. In addition, by employing the XGBoost machine learning algorithm, three interatom distances and six dihedral angles were identified as essential internal coordinates to represent the low-dimensional folding landscape. The strategy of coupling the multibasin model and the machine learning algorithm may be useful to investigate the conformational dynamics of other multistate biomolecules.

Structure dictates the mechanism of ligand recognition in the histidine and maltose binding proteins

Two mechanisms, induced fit (IF) and conformational selection (CS), have been proposed to explain ligand recognition coupled conformational changes. The histidine binding protein (HisJ) adopts the CS mechanism, in which a pre-equilibrium is established between the open and the closed states with the ligand binding to the closed state. Despite being structurally similar to HisJ, the maltose binding protein (MBP) adopts the IF mechanism, in which the ligand binds the open state and induces a transition to the closed state. To understand the molecular determinants of this difference, we performed molecular dynamics (MD) simulations of coarse-grained dual structure based models. We find that intra-protein contacts unique to the closed state are sufficient to promote the conformational transition in HisJ, indicating a CS-like mechanism. In contrast, additional ligand-mimicking contacts are required to “induce” the conformational transition in MBP suggesting an IF-like mechanism. In agreement with experiments, destabilizing modifications to two structural features, the spine helix (SH) and the balancing interface (BI), present in MBP but absent in HisJ, reduce the need for ligand-mimicking contacts indicating that SH and BI act as structural restraints that keep MBP in the open state. We introduce an SH like element into HisJ and observe that this can impede the conformational transition increasing the importance of ligand-mimicking contacts. Similarly, simultaneous mutations to BI and SH in MBP reduce the barrier to conformational transitions significantly and promote a CS-like mechanism. Together, our results show that structural restraints present in the protein structure can determine the mechanism of conformational transitions and even simple models that correctly capture such structural features can predict their positions. MD simulations of such models can thus be used, in conjunction with mutational experiments, to regulate protein ligand interactions, and modulate ligand binding affinities.

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MBP operates by induced fit, HisJ by the conformational selection mechanism.

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Dual structure based models (dSBMs) encode two structures of a protein.

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MD simulations of dSBMs can identify the mechanism of conformational transitions.

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Locks, absent in HisJ, hold MBP open with ligand contacts required for closing.

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Binding mechanisms can be modified by altering such structural locks.

Prediction of the terahertz absorption features with a straightforward molecular dynamics method

In this paper, we provide a straightforward method to predict the terahertz absorption spectrum based on a fixed charge model with classic molecular dynamics calculations. The absorption features in the frequency range between 1 and 3.4 THz of stearic acid B-form and between 1 and 2.7 THz of C-form were successfully calculated. Most of the absorption peaks from the simulation correspond well with those from the measurements. By calculating the spatial and time-dependent energy accumulation in the molecular system, the core idea of our calculation method is further validated. Compared with the ab initio calculations, our method provides a computationally inexpensive way to accurately predict the locations of absorption features. With regard to the traditional molecular dynamic simulations, our method is able to extract the spatial distribution of the energy accumulation as well as the local motions in the molecular system.

Binding-Induced Conformational Changes Involved in Sliding Clamp PCNA and DNA Polymerase DPO4

Cooperation between DNA polymerases and DNA sliding clamp proteins is essential for DNA replication and repair. However, it is still challenging to clarify the binding mechanism and the movements of Y-family DNA polymerase IV (DPO4) on the proliferating cell nuclear antigen (PCNA) ring. Here we develop the simulation models of DPO4–PCNA123 and DPO4–PCNA12 complexes and uncover the underlying dynamics of DPO4 during binding and the binding order of the DPO4 domains. Two important intermediate states are found on the free energy surface before reaching the final bound state. Our results suggest that both PCNA3 and DPO4 can influence the PCNA12 planar conformation, whereas the impact of PCNA3 on PCNA12 is more significant than DPO4. These findings provide the crucial information of the conformational dynamics of DPO4 and PCNA, as well as the clue of the underlying mechanism of the cooperation between DPO4 and PCNA during DNA replication.

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The mechanism of DPO4 binding to PCNA ring and PCNA dimer is investigated

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Two important intermediate states are found before reaching the final bound state

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Both PCNA3 and DPO4 can influence the PCNA12 planar conformation

Charge Interactions Modulate the Encounter Complex Ensemble of Two Differently Charged Disordered Protein Partners of KIX

Disordered proteins play important roles in cell signaling and are frequently involved in protein-protein interactions. They also have a larger proportion of charged and polar residues than their folded counterparts. Here, we developed a structure-based model and applied molecular dynamics simulations to examine the presence and importance of electrostatic interactions in the binding processes of two differently charged intrinsically disordered ligands of the KIX domain of CBP. We observed non-native opposite-charged contacts in the encounter complexes for both ligands with KIX, and this may be a general feature of coupled folding and binding reactions. The ensemble of successful encounter complexes is a diverse set of structures, and in the case of the highly charged ligand, this ensemble was found to be malleable with respect to ionic strength. There are only minor differences between encounter complex ensembles for successful and unsuccessful collisions with no key interactions that appear to make the process far more productive. The energy landscape at this early stage in the process does not appear highly funneled. Strikingly we observed many native interactions that appear to reduce chances of an encounter complex being productive. Instead it appears that collectively non-native electrostatic interactions in the encounter complex increase the likelihood of productivity by holding the proteins together long enough for folding to take place. This mechanism is more effective for the more highly charged ligand.

Amyloid assembly is dominated by misregistered kinetic traps on an unbiased energy landscape

Atomistic description of protein fibril formation has been elusive due to the complexity and long time scales of the conformational search. Here, we develop a multiscale approach combining numerous atomistic simulations in explicit solvent to construct Markov State Models (MSMs) of fibril growth. The search for the in-register fully bound fibril state is modeled as a random walk on a rugged two-dimensional energy landscape defined by β-sheet alignment and hydrogen-bonding states, whereas transitions involving states without hydrogen bonds are derived from kinetic clustering. The reversible association/dissociation of an incoming peptide and overall growth kinetics are then computed from MSM simulations. This approach is applied to derive a parameter-free, comprehensive description of fibril elongation of Aβ_16-22 and how it is modulated by phenylalanine-to-cyclohexylalanine (CHA) mutations. The trajectories show an aggregation mechanism in which the peptide spends most of its time trapped in misregistered β-sheet states connected by weakly bound states twith short lifetimes. Our results recapitulate the experimental observation that mutants CHA19 and CHA1920 accelerate fibril elongation but have a relatively minor effect on the critical concentration for fibril growth. Importantly, the kinetic consequences of mutations arise from cumulative effects of perturbing the network of productive and nonproductive pathways of fibril growth. This is consistent with the expectation that nonfunctional states will not have evolved efficient folding pathways and, therefore, will require a random search of configuration space. This study highlights the importance of describing the complete energy landscape when studying the elongation mechanism and kinetics of protein fibrils.

Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in qualitatively reproducing numerous biological processes for various biomolecules such as conformational changes and protein folding with reasonable calculation costs. However, CG-MD simulations strongly depend on various parameters, and selecting an appropriate parameter set is necessary to reproduce a particular biological process. Because exhaustive examination of all candidate parameters is inefficient, it is important to identify successful parameters. Furthermore, the successful region, in which the desired process is reproducible, is essential for describing the detailed mechanics of functional processes and environmental sensitivity and robustness. We propose an efficient search method for identifying the successful region by using two machine learning techniques, Bayesian optimization and active learning. We evaluated its performance using F1-ATPase, a biological rotary motor, with CG-MD simulations. We successfully identified the successful region with lower computational costs (12.3% in the best case) without sacrificing accuracy compared to exhaustive search. This method can accelerate not only parameter search but also biological discussion of the detailed mechanics of functional processes and environmental sensitivity based on MD simulation studies.

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.