Designing molecular dynamics simulations to shift populations of the conformational states of calmodulin

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Kinetic Barrier to Enzyme Inhibition Is Manipulated by Dynamical Local Interactions in E. coli DHFR

Dihydrofolate reductase (DHFR) is an important drug target and a highly studied model protein for understanding enzyme dynamics. DHFR's crucial role in folate synthesis renders it an ideal candidate to understand protein function and protein evolution mechanisms. In this study, to understand how a newly proposed DHFR inhibitor, 4'-deoxy methyl trimethoprim (4'-DTMP), alters evolutionary trajectories, we studied interactions that lead to its superior performance over that of trimethoprim (TMP). To elucidate the inhibition mechanism of 4'-DTMP, we first confirmed, both computationally and experimentally, that the relative binding free energy cost for the mutation of TMP and 4'-DTMP is the same, pointing the origin of the characteristic differences to be kinetic rather than thermodynamic. We then employed an interaction-based analysis by focusing first on the active site and then on the whole enzyme. We confirmed that the polar modification in 4'-DTMP induces additional local interactions with the enzyme, particularly, the M20 loop. These changes are propagated to the whole enzyme as shifts in the hydrogen bond networks. To shed light on the allosteric interactions, we support our analysis with network-based community analysis and show that segmentation of the loop domain of inhibitor-bound DHFR must be avoided by a successful inhibitor.

Molecular dynamics simulations reveal the hidden EF-hand of EF-SAM as a possible key thermal sensor for STIM1 activation by temperature

Intracellular calcium signaling is essential for many cellular processes, including store-operated Ca2+ entry (SOCE), which is initiated by stromal interaction molecule 1 (STIM1) detecting endoplasmic reticulum (ER) Ca2+ depletion. STIM1 is also activated by temperature independent of ER Ca2+ depletion. Here we provide evidence, from advanced molecular dynamics simulations, that EF-SAM may act as a true temperature sensor for STIM1, with the prompt and extended unfolding of the hidden EF-hand subdomain (hEF) even at slightly elevated temperatures, exposing a highly conserved hydrophobic Phe108. Our study also suggests an interplay between Ca2+ and temperature sensing, as both, the canonical EF-hand subdomain (cEF) and the hidden EF-hand subdomain (hEF), exhibit much higher thermal stability in the Ca2+-loaded form compared to the Ca2+-free form. The SAM domain, surprisingly, displays high thermal stability compared to the EF-hands and may act as a stabilizer for the latter. We propose a modular architecture for the EF-hand-SAM domain of STIM1 composed of a thermal sensor (hEF), a Ca2+ sensor (cEF), and a stabilizing domain (SAM). Our findings provide important insights into the mechanism of temperature-dependent regulation of STIM1, which has broad implications for understanding the role of temperature in cellular physiology.

Experiment and Simulation Reveal Residue Details for How Target Binding Tunes Calmodulin's Calcium-Binding Properties

We aim to elucidate the molecular mechanism of the reciprocal relation of calmodulin's (CaM) target binding and its affinity for calcium ions (Ca²⁺), which is central to decoding CaM-dependent Ca²⁺ signaling in a cell. We employed stopped-flow experiments and coarse-grained molecular simulations that learn the coordination chemistry of Ca²⁺ in CaM from first-principle calculations. The associative memories as part of the coarse-grained force fields built on known protein structures further influence CaM's selection of its polymorphic target peptides in the simulations. We modeled the peptides from the Ca²⁺/CaM-binding domain of Ca²⁺/CaM-dependent kinase II (CaMKII), CaMKIIp (293-310) and selected distinctive mutations at the N-terminus. Our stopped-flow experiments have shown that the CaM's affinity for Ca²⁺ in the bound complex of Ca²⁺/CaM/CaMKIIp decreased significantly when Ca²⁺/CaM bound to the mutant peptide (296-AAA-298) compared to that bound to the wild-type peptide (296-RRK-298). The coarse-grained molecular simulations revealed that the 296-AAA-298 mutant peptide destabilized the structures of Ca²⁺-binding loops at the C-domain of CaM (c-CaM) due to both loss of electrostatic interactions and differences in polymorphic structures. We have leveraged a powerful coarse-grained approach to advance a residue-level understanding of the reciprocal relation in CaM, that could not be possibly achieved by other computational approaches.

MDexciteR: Enhanced Sampling Molecular Dynamics by Excited Normal Modes or Principal Components Obtained from Experiments

Molecular dynamics with excited normal modes (MDeNM) is an enhanced sampling method for exploring conformational changes in proteins with minimal biases. The excitation corresponds to injecting kinetic energy along normal modes describing intrinsic collective motions. Herein, we developed a new automated open-source implementation, MDexciteR (https://github.com/mcosta27/MDexciteR), enabling the integration of MDeNM with two commonly used simulation programs with GPU support. Second, we generalized the method to include the excitation of principal components calculated from experimental ensembles. Finally, we evaluated whether the use of coarse-grained normal modes calculated with elastic network representations preserved the performance and accuracy of the method. The advantages and limitations of these new approaches are discussed based on results obtained for three different protein test cases: two globular and a protein/membrane system.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

The mechanism of complex formation between calmodulin and voltage gated calcium channels revealed by molecular dynamics

Calmodulin, a ubiquitous eukaryotic calcium sensor responsible for the regulation of many fundamental cellular processes, is a highly flexible protein and exhibits an unusually wide range of conformations. Furthermore, CaM is known to interact with more than 300 cellular targets. Molecular dynamics (MD) simulation trajectories suggest that EF-hand loops show different magnitudes of flexibility. Therefore, the four EF-hand motifs have different affinities for Ca2+ ions, which enables CaM to function on wide range of Ca2+ ion concentrations. EF-hand loops are 2-3 times more flexible in apo CaM whereas least flexible in Ca2+/CaM-IQ motif complexes. We report a unique intermediate conformation of Ca2+/CaM while transitioning from extended to compact form. We also report the complex formation process between Ca2+/CaM and IQ CaM-binding motifs. Our results showed how IQ motif recognise its binding site on the CaM and how CaM transforms from extended to compact form upon binding to IQ motif.

The structural basis of Akt PH domain interaction with calmodulin

Akt plays a key role in the Ras/PI3K/Akt/mTOR signaling pathway. In breast cancer, Akt translocation to the plasma membrane is enabled by the interaction of its pleckstrin homology domain (PHD) with calmodulin (CaM). At the membrane, the conformational change promoted by PIP3 releases CaM and facilitates Thr308 and Ser473 phosphorylation and activation. Here, using modeling and molecular dynamics simulations, we aim to figure out how CaM interacts with Akt's PHD at the atomic level. Our simulations show that CaM-PHD interaction is thermodynamically stable and involves a β-strand rather than an α-helix, in agreement with NMR data, and that electrostatic and hydrophobic interactions are critical. The PHD interacts with CaM lobes; however, multiple modes are possible. IP4, the polar head of PIP3, weakens the CaM-PHD interaction, implicating the release mechanism at the plasma membrane. Recently, we unraveled the mechanism of PI3Kα activation at the atomistic level and the structural basis for Ras role in the activation. Here, our atomistic structural data clarify the mechanism of how CaM interacts, delivers, and releases Akt-the next node in the Ras/PI3K pathway-at the plasma membrane.

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.

A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs

We address the problem of triggering dissociation events between proteins that have formed a complex. We have collected a set of 25 non-redundant, functionally diverse protein complexes having high-resolution three-dimensional structures in both the unbound and bound forms. We unify elastic network models with perturbation response scanning (PRS) methodology as an efficient approach for predicting residues that have the propensity to trigger dissociation of an interacting protein pair, using the three-dimensional structures of the bound and unbound proteins as input. PRS reveals that while for a group of protein pairs, residues involved in the conformational shifts are confined to regions with large motions, there are others where they originate from parts of the protein unaffected structurally by binding. Strikingly, only a few of the complexes have interface residues responsible for dissociation. We find two main modes of response: In one mode, remote control of dissociation in which disruption of the electrostatic potential distribution along protein surfaces play the major role; in the alternative mode, mechanical control of dissociation by remote residues prevail. In the former, dissociation is triggered by changes in the local environment of the protein, e.g., pH or ionic strength, while in the latter, specific perturbations arriving at the controlling residues, e.g., via binding to a third interacting partner is required for decomplexation. We resolve the observations by relying on an electromechanical coupling model which reduces to the usual elastic network result in the limit of the lack of coupling. We validate the approach by illustrating the biological significance of top residues selected by PRS on select cases where we show that the residues whose perturbation leads to the observed conformational changes correspond to either functionally important or highly conserved residues in the complex.

Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis

Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal-modes analysis (NMA) using elastic network models (ENMs) provides an efficient procedure to explore global dynamics of proteins commonly associated with conformational transitions. In the present work, we present an iterative approach to explore protein conformational spaces by introducing structural distortions according to their equilibrium dynamics at room temperature. The approach can be used either to perform unbiased explorations of conformational space or to explore guided pathways connecting two different conformations, e.g., apo and holo forms. In order to test its performance, four proteins with different magnitudes of structural distortions upon ligand binding have been tested. In all cases, the conformational selection model has been confirmed and the conformational space between apo and holo forms has been encompassed. Different strategies have been tested that impact on the efficiency either to achieve a desired conformational change or to achieve a balanced exploration of the protein conformational multiplicity.

Structural analysis of the PATZ1 BTB domain homodimer

PATZ1 is a ubiquitously expressed transcriptional repressor belonging to the ZBTB family that is functionally expressed in T lymphocytes. PATZ1 targets the CD8 gene in lymphocyte development and interacts with the p53 protein to control genes that are important in proliferation and in the DNA-damage response. PATZ1 exerts its activity through an N-terminal BTB domain that mediates dimerization and co-repressor interactions and a C-terminal zinc-finger motif-containing domain that mediates DNA binding. Here, the crystal structures of the murine and zebrafish PATZ1 BTB domains are reported at 2.3 and 1.8 Å resolution, respectively. The structures revealed that the PATZ1 BTB domain forms a stable homodimer with a lateral surface groove, as in other ZBTB structures. Analysis of the lateral groove revealed a large acidic patch in this region, which contrasts with the previously resolved basic co-repressor binding interface of BCL6. A large 30-amino-acid glycine- and alanine-rich central loop, which is unique to mammalian PATZ1 amongst all ZBTB proteins, could not be resolved, probably owing to its flexibility. Molecular-dynamics simulations suggest a contribution of this loop to modulation of the mammalian BTB dimerization interface.

Perturb-Scan-Pull: A Novel Method Facilitating Conformational Transitions in Proteins

Conformational transitions in proteins facilitate precise physiological functions. Therefore, it is crucial to understand the mechanisms underlying these processes to modulate protein function. Yet, studying structural and dynamical properties of proteins is notoriously challenging due to the complexity of the underlying potential energy surfaces (PES). We have previously developed the perturbation-response scanning (PRS) method to identify key residues that participate in the communication network responsible for specific conformational transitions. PRS is based on a residue-by-residue scan of the protein to determine the subset of residues/forces which provide the closest conformational change leading to a target conformational state, inasmuch as linear response theory applies to these motions. Here, we develop a novel method to further evaluate if conformational transitions may be triggered on the PES. We aim to study functionally relevant conformational transitions in proteins by using results obtained from PRS and feeding them as inputs to steered molecular dynamics simulations. The success and the transferability of the method are evaluated on three protein systems having different complexities of motion on the PES: calmodulin, adenylate kinase, and bacterial ferric binding protein. We find that the method captures the target conformation, while providing key residues and the optimum paths with relatively low free energy profiles.

InfleCS: Clustering Free Energy Landscapes with Gaussian Mixtures

Free energy landscapes provide insights into conformational ensembles of biomolecules. In order to analyze these landscapes and elucidate mechanisms underlying conformational changes, there is a need to extract metastable states with limited noise. This has remained a formidable task, despite a plethora of existing clustering methods. We present InfleCS, a novel method for extracting well-defined core states from free energy landscapes. The method is based on a Gaussian mixture free energy estimator and exploits the shape of the estimated density landscape. The core states that naturally arise from the clustering allow for detailed characterization of the conformational ensemble. The clustering quality is evaluated on three toy models with different properties, where the method is shown to consistently outperform other conventional and state-of-the-art clustering methods. Finally, the method is applied to a temperature enhanced molecular dynamics simulation of Ca²⁺-bound Calmodulin. Through the free energy landscape, we discover a pathway between a canonical and a compact state, revealing conformational changes driven by electrostatic interactions.

Cm3+/Eu3+induced structural, mechanistic and functional implications for calmodulin

Trivalent lanthanide and actinide can strongly bind to calmodulin (CaM). The global structure of Ln/An-bound CaM were found to be similar to Ca-CaM but the local environment around Ln/An is distorted giving less structural rigidity to Ln/An-CaM.

Aqueous solution interactions with sex hormone-binding globulin and estradiol: a theoretical investigation

Sex hormone-binding globulin (SHBG) is a binding protein that regulates the availability of steroid hormones in the plasma. Although best known as a steroid carrier, recent studies have associated SHBG in modulating behavioral aspects related to sexual receptivity. Among steroids, estradiol (17β-estradiol, oestradiol or E2), documented as the most active endogenous female hormone, exerts important physiological roles in both reproductive and non-reproductive functions. In this framework, we employed molecular dynamics (MD) and docking techniques for quantifying the interaction energy between a complex aqueous solution, composed by different salts, SHBG and E2. As glucose concentration resembles measured levels in diabetes, special emphasis was devoted to analyzing the interaction energy between this carbohydrate, SHBG and E2 molecules. The calculations revealed remarkable interaction energy between glucose and SHBG surface. Surprisingly, a movement of solute components toward SHBG was observed, yielding clusters surrounding the protein. The high energy and short distance between glucose and SHBG suggests a possible scenario in favor of a detainment state between the sugar and the protein. In this context, we found that glucose clustering does not insert modification on binding site area nor over binding energy SHBG-E2 complex, in spite of protein superficial area increment. The calculations also point to a more pronounced interaction between E2 and glucose, considering the hormone immersed in the solution. In summary, our findings contribute to a better comprehension of both SHBG and E2 interplay with aqueous solution components.

Bayesian inference of protein conformational ensembles from limited structural data

Many proteins consist of folded domains connected by regions with higher flexibility. The details of the resulting conformational ensemble play a central role in controlling interactions between domains and with binding partners. Small-Angle Scattering (SAS) is well-suited to study the conformational states adopted by proteins in solution. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data. To address these challenges, we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations. The first stage of the method involves a fast model selection based on variational Bayesian inference that maximizes the model evidence of the selected ensemble. This is followed by a complete Bayesian inference of population weights in the selected ensemble. Experiments with simulated ensembles demonstrate that model evidence is capable of identifying the correct ensemble and that correct number of ensemble members can be recovered up to high level of noise. Using experimental data, we demonstrate how the method can be extended to include data from Nuclear Magnetic Resonance (NMR) and structural energies of conformers extracted from the all-atom energy functions. We show that the data from SAXS, NMR chemical shifts and energies calculated from conformers can work synergistically to improve the definition of the conformational ensemble.

Binding and backbone dynamics of protein under topological constraint: calmodulin as a model system

Herein we present the effect of artificially imposed topological constraint on calmodulin (CaM) backbone dynamics and its molecular recognition behavior. While backbone dynamics of CaM remain largely unperturbed, the thermodynamic profile of CaM binding to the smooth-muscle myosin light-chain kinase (smMLCK) peptide is modulated significantly.

Impact of intracellular ionic strength on dimer binding in the NF-kB Inducing kinase

Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states including cancer, stroke, and viral infections. Although the signaling pathways are known, how these molecular mechanisms respond to changes in the intracellular microenvironment such as pH, ionic strength, and temperature, remains elusive. Molecular dynamics simulations were employed to differentiate the structural dynamics of the NF-κB Inducing Kinase (NIK), a protein kinase responsible for invoking the non-canonical NF-κB pathway, in its native and mutant form, and in the absence and presence of salt concentration in efforts to probe whether changes in the ionic environment stabilize or destabilize the NIK dimer. Analyses of structure-activity and conformational-activity relationships indicate that the protein-protein interactions are sensitive to changes in the ionic strength. Ligand binding pockets as well as regions between the oligomer interface either compress or expand, affecting both local and distal intermolecular interactions that result in stabilization or destabilization in the protein assembly.

Increased substrate affinity in the Escherichia coli L28R dihydrofolate reductase mutant causes trimethoprim resistance

Dihydrofolate reductase (DHFR) is a ubiquitous enzyme with an essential role in cell metabolism. DHFR catalyzes the reduction of dihydrofolate to tetrahydrofolate, which is a precursor for purine and thymidylate synthesis. Several DHFR targeting antifolate drugs including trimethoprim, a competitive antibacterial inhibitor, have therefore been developed and are clinically used. Evolution of resistance against antifolates is a common public health problem rendering these drugs ineffective. To combat the resistance problem, it is important to understand resistance-conferring changes in the DHFR structure and accordingly develop alternative strategies. Here, we structurally and dynamically characterize Escherichia coli DHFR in its wild type (WT) and trimethoprim resistant L28R mutant forms in the presence of the substrate and its inhibitor trimethoprim. We use molecular dynamics simulations to determine the conformational space, loop dynamics and hydrogen bond distributions at the active site of DHFR for the WT and the L28R mutant. We also report their experimental kcat, Km, and Ki values, accompanied by isothermal titration calorimetry measurements of DHFR that distinguish enthalpic and entropic contributions to trimethoprim binding. Although mutations that confer resistance to competitive inhibitors typically make enzymes more promiscuous and decrease affinity to both the substrate and the inhibitor, strikingly, we find that the L28R mutant has a unique resistance mechanism. While the binding affinity differences between the WT and the mutant for the inhibitor and the substrate are small, the newly formed extra hydrogen bonds with the aminobenzoyl glutamate tail of DHF in the L28R mutant leads to increased barriers for the dissociation of the substrate and the product. Therefore, the L28R mutant indirectly gains resistance by enjoying prolonged binding times in the enzyme-substrate complex. While this also leads to slower product release and decreases the catalytic rate of the L28R mutant, the overall effect is the maintenance of a sufficient product formation rate. Finally, the experimental and computational analyses together reveal the changes that occur in the energetic landscape of DHFR upon the resistance-conferring L28R mutation. We show that the negative entropy associated with the binding of trimethoprim in WT DHFR is due to water organization at the binding interface. Our study lays the framework to study structural changes in other trimethoprim resistant DHFR mutants.

Effect of Ca2+ on the promiscuous target-protein binding of calmodulin

Calmodulin (CaM) is a calcium sensing protein that regulates the function of a large number of proteins, thus playing a crucial part in many cell signaling pathways. CaM has the ability to bind more than 300 different target peptides in a Ca2+-dependent manner, mainly through the exposure of hydrophobic residues. How CaM can bind a large number of targets while retaining some selectivity is a fascinating open question. Here, we explore the mechanism of CaM selective promiscuity for selected target proteins. Analyzing enhanced sampling molecular dynamics simulations of Ca2+-bound and Ca2+-free CaM via spectral clustering has allowed us to identify distinct conformational states, characterized by interhelical angles, secondary structure determinants and the solvent exposure of specific residues. We searched for indicators of conformational selection by mapping solvent exposure of residues in these conformational states to contacts in structures of CaM/target peptide complexes. We thereby identified CaM states involved in various binding classes arranged along a depth binding gradient. Binding Ca2+ modifies the accessible hydrophobic surface of the two lobes and allows for deeper binding. Apo CaM indeed shows shallow binding involving predominantly polar and charged residues. Furthermore, binding to the C-terminal lobe of CaM appears selective and involves specific conformational states that can facilitate deep binding to target proteins, while binding to the N-terminal lobe appears to happen through a more flexible mechanism. Thus the long-ranged electrostatic interactions of the charged residues of the N-terminal lobe of CaM may initiate binding, while the short-ranged interactions of hydrophobic residues in the C-terminal lobe of CaM may account for selectivity. This work furthers our understanding of the mechanism of CaM binding and selectivity to different target proteins and paves the way towards a comprehensive model of CaM selectivity.

Molecular Basis of S100A1 Activation at Saturating and Subsaturating Calcium Concentrations

The S100A1 protein mediates a wide variety of physiological processes through its binding of calcium (Ca(2+)) and endogenous target proteins. S100A1 presents two Ca(2+)-binding domains: a high-affinity "canonical" EF (cEF) hand and a low-affinity "pseudo" EF (pEF) hand. Accumulating evidence suggests that both Ca(2+)-binding sites must be saturated to stabilize an open state conducive to peptide recognition, yet the pEF hand's low affinity limits Ca(2+) binding at normal physiological concentrations. To understand the molecular basis of Ca(2+) binding and open-state stabilization, we performed 100 ns molecular dynamics simulations of S100A1 in the apo/holo (Ca(2+)-free/bound) states and a half-saturated state, for which only the cEF sites are Ca(2+)-bound. Our simulations indicate that the pattern of oxygen coordination about Ca(2+) in the cEF relative to the pEF site contributes to the former's higher affinity, whereas Ca(2+) binding strongly reshapes the protein's conformational dynamics by disrupting β-sheet coupling between EF hands. Moreover, modeling of the half-saturated configuration suggests that the open state is unstable and reverts toward a closed state in the absence of the pEF Ca(2+) ion. These findings indicate that Ca(2+) binding at the cEF site alone is insufficient to stabilize opening; thus, posttranslational modification of the protein may be required for target peptide binding at subsaturating intracellular Ca(2+) levels.

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.

Predicting long term cooperativity and specific modulators of receptor interactions in human transferrin from dynamics within a single microstate

PRS identifies regions contacting rapidly evolving residues that mechanically manipulate dissociation from the pathogen in the human transferrin–bacterial receptor complex.

Effect of Calcium Ion Removal, Ionic Strength, and Temperature on the Conformation Change in Calmodulin Protein at Physiological pH

The response of the calmodulin (CaM) protein as a function of calcium ion removal, ionic strength, and temperature at physiological pH condition was investigated using classical molecular dynamics simulations. Changing the ionic strength and temperature came out to be two of the possible routes for observing a conformation change in the protein. This behavior is similar to the conformation change observed in our previous study where a change in the pH was observed to trigger a conformation change in this protein. In the present study, as the calcium ions are removed from the protein, the protein is observed to acquire more flexibility. This flexibility is observed to be more prominent at a higher ionic strength. At a lower ionic strength of 150 mM with all the four calcium ions intact, the N- and C-lobes are observed to come close to a distance of 30 Å starting from an initial separation distance of 48 Å. This conformation change is observed to take place around 50 ns in a simulation of 100 ns. As a second parameter, temperature is observed to play a key role in the conformation change of the protein. With an increase in the temperature, the protein is observed to acquire a more compact form with the formation of different salt bridges between the residues of the N- and the C-lobes. The salt bridge formation leads to an overall lowering of the energy of the protein thus favoring the bending of the two lobes towards each other. The improper and dihedral terms show a significant shift thus leading to a more compact form on increasing the temperature. Another set of simulations is also performed at an increased temperature of 500 K to verify the reproducibility of the results. Thus a set of three possible alterations in the environmental conditions of the protein CaM are studied, with two of them giving rise to a conformation change and one adding flexibility to the protein.

Calcium Binding to Calmodulin by Molecular Dynamics with Effective Polarization

Calcium represents a key biological signaling ion with the EF-hand loops being its most prevalent binding motif in proteins. We show using molecular dynamics simulations with umbrella sampling that including electronic polarization effects via ionic charge rescaling dramatically improves agreements with experiment in terms of the strength of calcium binding and structures of the calmodulin binding sites. The present study thus opens way to accurate calculations of interactions of calcium and other computationally difficult high-charge-density ions in biological contexts.