Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic

We investigated protein motions using normal modes within a database framework, determining on a large sample the degree to which normal modes anticipate the direction of the observed motion and were useful for motions classification. As a starting point for our analysis, we identified a large number of examples of protein flexibility from a comprehensive set of structural alignments of the proteins in the PDB. Each example consisted of a pair of proteins that were considerably different in structure given their sequence similarity. On each pair, we performed geometric comparisons and adiabatic-mapping interpolations in a high-throughput pipeline, arriving at a final list of 3,814 putative motions and standardized statistics for each. We then computed the normal modes of each motion in this list, determining the linear combination of modes that best approximated the direction of the observed motion. We integrated our new motions and normal mode calculations in the Macromolecular Motions Database, through a new ranking interface at http://molmovdb.org. Based on the normal mode calculations and the interpolations, we identified a new statistic, mode concentration, related to the mathematical concept of information content, which describes the degree to which the direction of the observed motion can be summarized by a few modes. Using this statistic, we were able to determine the fraction of the 3,814 motions where one could anticipate the direction of the actual motion from only a few modes. We also investigated mode concentration in comparison to related statistics on combinations of normal modes and correlated it with quantities characterizing protein flexibility (e.g., maximum backbone displacement or number of mobile atoms). Finally, we evaluated the ability of mode concentration to automatically classify motions into a variety of simple categories (e.g., whether or not they are "fragment-like"), in comparison to motion statistics. This involved the application of decision trees and feature selection (particular machine-learning techniques) to training and testing sets derived from merging the "list" of motions with manually classified ones.     

The morph server: a standardized system for analyzing and visualizing macromolecular motions in a database framework

The number of solved structures of macromolecules that have the same fold and thus exhibit some degree of conformational variability is rapidly increasing. It is consequently advantageous to develop a standardized terminology for describing this variability and automated systems for processing protein structures in different conformations. We have developed such a system as a 'front-end' server to our database of macromolecular motions. Our system attempts to describe a protein motion as a rigid-body rotation of a small 'core' relative to a larger one, using a set of hinges. The motion is placed in a standardized coordinate system so that all statistics between any two motions are directly comparable. We find that while this model can accommodate most protein motions, it cannot accommodate all; the degree to which a motion can be accommodated provides an aid in classifying it. Furthermore, we perform an adiabatic mapping (a restrained interpolation) between every two conformations. This gives some indication of the extent of the energetic barriers that need to be surmounted in the motion, and as a by-product results in a 'morph movie'. We make these movies available over the Web to aid in visualization. Many instances of conformational variability occur between proteins with somewhat different sequences. We can accommodate these differences in a rough fashion, generating an 'evolutionary morph'. Users have already submitted hundreds of examples of protein motions to our server, producing a comprehensive set of statistics. So far the statistics show that the median submitted motion has a rotation of approximately 10 degrees and a maximum Calpha displacement of 17 A. Almost all involve at least one large torsion angle change of >140 degrees. The server is accessible at http://bioinfo.mbb.yale. edu/MolMovDB     

Simulating protein motions with rigidity analysis.

Protein motions, ranging from molecular flexibility to large-scale conformational change, play an essential role in many biochemical processes. Despite the explosion in our knowledge of structural and functional data, our understanding of protein movement is still very limited. In previous work, we developed and validated a motion planning based method for mapping protein folding pathways from unstructured conformations to the native state. In this paper, we propose a novel method based on rigidity theory to sample conformation space more effectively, and we describe extensions of our framework to automate the process and to map transitions between specified conformations. Our results show that these additions both improve the accuracy of our maps and enable us to study a broader range of motions for larger proteins. For example, we show that rigidity-based sampling results in maps that capture subtle folding differences between protein G and its mutants, NuG1 and NuG2, and we illustrate how our technique can be used to study large-scale conformational changes in calmodulin, a 148 residue signaling protein known to undergo conformational changes when binding to Ca(2+). Finally, we announce our web-based protein folding server which includes a publicly available archive of protein motions: (http://parasol.tamu.edu/foldingserver/).     

Improving joint torque calculations: optimization-based inverse dynamics to reduce the effect of motion errors

The accuracy of joint torques calculated from inverse dynamics methods is strongly dependent upon errors in body segment motion profiles, which arise from two sources of noise: the motion capture system and movement artifacts of skin-mounted markers. The current study presents a method to increase the accuracy of estimated joint torques through the optimization of the angular position data used to describe these segment motions. To compute these angular data, we formulated a constrained nonlinear optimization problem with a cost function that minimizes the difference between the known ground reaction forces (GRFs) and the GRF calculated via a top-down inverse dynamics solution. To evaluate this approach, we constructed idealized error-free reference movements (of squatting and lifting) that produced a set of known “true” motions and associated true joint torques and GRF. To simulate real-world inaccuracies in motion data, these true motions were perturbed by artificial noise. We then applied our approach to these noise-induced data to determine optimized motions and related joint torques. To evaluate the efficacy of the optimization approach compared to traditional (bottom-up or top-down) inverse dynamics approaches, we computed the root mean square error (RMSE) values of joint torques derived from each approach relative to the expected true joint torques. Compared to traditional approaches, the optimization approach reduced the RMSE by 54% to 79%. Average reduction due to our method was 65%; previous methods only achieved an overall reduction of 30%. These results suggest that significant improvement in the accuracy of joint torque calculations can be achieved using this approach.     

Frustration‐guided motion planning reveals conformational transitions in proteins

Proteins exist as conformational ensembles, exchanging between substates to perform their function. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers. Here, we present a new, robotics‐inspired motion planning procedure called dCC‐RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non‐native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 Å root mean square deviation (RMSD), our pathways reduced the Cα atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC‐RRT to examine how collective, small‐scale motions of four side‐chains in the active site of cyclophilin A propagate through the protein. dCC‐RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non‐canonical capsid binding site 25 Å away, rationalizing NMR and multi‐temperature crystallography experiments. In all, dCC‐RRT can reveal detailed, all‐atom molecular mechanisms for small and large amplitude motions. Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/ .     

Rapid Sampling of Molecular Motions with Prior Information Constraints

Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion.     

Global and local molecular dynamics of a bacterial carboxylesterase provide insight into its catalytic mechanism

Carboxylesterases (CEs) are ubiquitous enzymes responsible for the detoxification of xenobiotics. In humans, substrates for these enzymes are far-ranging, and include the street drug heroin and the anticancer agent irinotecan. Hence, their ability to bind and metabolize substrates is of broad interest to biomedical science. In this study, we focused our attention on dynamic motions of a CE from B. subtilis (pnbCE), with emphasis on the question of what individual domains of the enzyme might contribute to its catalytic activity. We used a 10 ns all-atom molecular dynamics simulation, normal mode calculations, and enzyme kinetics to understand catalytic consequences of structural changes within this enzyme. Our results shed light on how molecular motions are coupled with catalysis. During molecular dynamics, we observed a distinct C-C bond rotation between two conformations of Glu310. Such a bond rotation would alternately facilitate and impede protonation of the active site His399 and act as a mechanism by which the enzyme alternates between its active and inactive conformation. Our normal mode results demonstrate that the distinct low-frequency motions of two loops in pnbCE, coil_5 and coil_21, are important in substrate conversion and seal the active site. Mutant CEs lacking these external loops show significantly reduced rates of substrate conversion, suggesting this sealing motion prevents escape of substrate. Overall, the results of our studies give new insight into the structure-function relationship of CEs and have implications for the entire family of α/β fold family of hydrolases, of which this CE is a member.     

Envisioning the Loop Movements and Rotation of the Two Subdomains of Dihydrofolate Reductase by Elastic Normal Mode Analysis

Abstract

Normal mode analysis, using the elastic network model, has been employed to envision the low frequency normal mode motion trends in the structures of five intermediates and a transition state in the kinetic pathway of E. coli dihydrofolate reductase (DHFR). Five of the reaction pathway analog structures and a crystal structure resembling the transition state, using X-ray analyses determined by Kraut et al., have been adapted as structural models. The motions that poise pathways of the M20 loop transitions from closed to occluded conformations and sub domain rotation to close the substrate cleft, have been predicted and envisioned for the first time by this study. Pathway entries to the movement of the substrate binding cleft helices are also envisioned. These motions play roles in transition structure stabilization and in regulating the release of the product tetrahydrofolate (THF). The motions observed push the ground state conformation of each intermediate towards a higher energy sub state conformation. A set of conserved residues involved in the catalytic reactions and conformational changes, previously studied by kinetic, theoretical and NMR, have been analyzed. The importance of these motions in terms of protein dynamics are revealed and envisioned by the normal mode analysis. Additional residues are proposed as candidates for further study of their potential promotional function.     

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

RNA molecules play integral roles in gene regulation, and understanding their structures gives us important insights into their biological functions. Despite recent developments in template-based and parameterized energy functions, the structure of RNA--in particular the nonhelical regions--is still difficult to predict. Knowledge-based potentials have proven efficient in protein structure prediction. In this work, we describe two differentiable knowledge-based potentials derived from a curated data set of RNA structures, with all-atom or coarse-grained representation, respectively. We focus on one aspect of the prediction problem: the identification of native-like RNA conformations from a set of near-native models. Using a variety of near-native RNA models generated from three independent methods, we show that our potential is able to distinguish the native structure and identify native-like conformations, even at the coarse-grained level. The all-atom version of our knowledge-based potential performs better and appears to be more effective at discriminating near-native RNA conformations than one of the most highly regarded parameterized potential. The fully differentiable form of our potentials will additionally likely be useful for structure refinement and/or molecular dynamics simulations.     

Inherent Dynamics of the Acid-Sensing Ion Channel 1 Correlates with the Gating Mechanism

The acid-sensing ion channel 1 (ASIC1) is a key receptor for extracellular protons. Although numerous structural and functional studies have been performed on this channel, the structural dynamics underlying the gating mechanism remains unknown. We used normal mode analysis, mutagenesis, and electrophysiological methods to explore the relationship between the inherent dynamics of ASIC1 and its gating mechanism. Here we show that a series of collective motions among the domains and subdomains of ASIC1 correlate with its acid-sensing function. The normal mode analysis result reveals that the intrinsic rotation of the extracellular domain and the collective motions between the thumb and finger induced by proton binding drive the receptor to experience a deformation from the extracellular domain to the transmembrane domain, triggering the channel pore to undergo “twist-to-open” motions. The movements in the transmembrane domain indicate that the likely position of the channel gate is around Leu440. These motion modes are compatible with a wide body of our complementary mutations and electrophysiological data. This study provides the dynamic fundamentals of ASIC1 gating.     

Dynamics and allosteric potential of the AMPA receptor N-terminal domain

Glutamate-gated ion channels (ionotropic glutamate receptors, iGluRs) sense the extracellular milieu via an extensive extracellular portion, comprised of two clamshell-shaped segments. The distal, N-terminal domain (NTD) has allosteric potential in NMDA-type iGluRs, which has not been ascribed to the analogous domain in AMPA receptors (AMPARs). In this study, we present new structural data uncovering dynamic properties of the GluA2 and GluA3 AMPAR NTDs. GluA3 features a zipped-open dimer interface with unconstrained lower clamshell lobes, reminiscent of metabotropic GluRs (mGluRs). The resulting labile interface supports interprotomer rotations, which can be transmitted to downstream receptor segments. Normal mode analysis reveals two dominant mechanisms of AMPAR NTD motion: intraprotomer clamshell motions and interprotomer counter-rotations, as well as accessible interconversion between AMPAR and mGluR conformations. In addition, we detect electron density for a potential ligand in the GluA2 interlobe cleft, which may trigger lobe motions. Together, these data support a dynamic role for the AMPAR NTDs, which widens the allosteric landscape of the receptor and could provide a novel target for ligand development.     

High-throughput single-molecule analysis of DNA-protein interactions by tethered particle motion

Tethered particle motion (TPM) monitors the variations in the effective length of a single DNA molecule by tracking the Brownian motion of a bead tethered to a support by the DNA molecule. Providing information about DNA conformations in real time, this technique enables a refined characterization of DNA-protein interactions. To increase the output of this powerful but time-consuming single-molecule assay, we have developed a biochip for the simultaneous acquisition of data from more than 500 single DNA molecules. The controlled positioning of individual DNA molecules is achieved by self-assembly on nanoscale arrays fabricated through a standard microcontact printing method. We demonstrate the capacity of our biochip to study biological processes by applying our method to explore the enzymatic activity of the T7 bacteriophage exonuclease. Our single molecule observations shed new light on its behaviour that had only been examined in bulk assays previously and, more specifically, on its processivity.     

Relating protein conformational changes to packing efficiency and disorder

Changes in protein conformation play key roles in facilitating various biochemical processes, ranging from signaling and phosphorylation to transport and catalysis. While various factors that drive these motions such as environmental changes and binding of small molecules are well understood, specific causative effects on the structural features of the protein due to these conformational changes have not been studied on a large scale. Here, we study protein conformational changes in relation to two key structural metrics: packing efficiency and disorder. Packing has been shown to be crucial for protein stability and function by many protein design and engineering studies. We study changes in packing efficiency during conformational changes, thus extending the analysis from a static context to a dynamic perspective and report some interesting observations. First, we study various proteins that adopt alternate conformations and find that tendencies to show motion and change in packing efficiency are correlated: residues that change their packing efficiency show larger motions. Second, our results suggest that residues that show higher changes in packing during motion are located on the changing interfaces which are formed during these conformational changes. These changing interfaces are slightly different from shear or static interfaces that have been analyzed in previous studies. Third, analysis of packing efficiency changes in the context of secondary structure shows that, as expected, residues buried in helices show the least change in packing efficiency, whereas those embedded in bends are most likely to change packing. Finally, by relating protein disorder to motions, we show that marginally disordered residues which are ordered enough to be crystallized but have sequence patterns indicative of disorder show higher dislocation and a higher change in packing than ordered ones and are located mostly on the changing interfaces. Overall, our results demonstrate that between the two conformations, the cores of the proteins remain mostly intact, whereas the interfaces display the most elasticity, both in terms of disorder and change in packing efficiency. By doing a variety of tests, we also show that our observations are robust to the solvation state of the proteins.     

Atomic resolution structure of prokaryotic phospholipase A2: analysis of internal motion and implication for a catalytic mechanism

We have found a secreted phospholipase A(2) (PLA(2), EC 3.1.1.4) from Streptomyces violaceoruber A-2688, which is the first PLA(2) identified in prokaryote, and determined its tertiary structure by NMR and X-ray analyses. In this study, we collected the X-ray diffraction data of the bacterial PLA(2) at room temperature (297 K) using conventional MoK(alpha) radiation and refined the structure at a 1.05 A resolution. The atomic resolution analysis led us to introduce disordered conformations and hydrogen atoms into a full anisotropic model. The molecular motion, which is expressed as the sum of rigid-body motion and internal motion of protein, is roughly estimated as the thermal motion when the X-ray diffraction data are collected at room temperature. In this study, we applied a TLS (rigid-body motion in terms of translation, libration, and screw motions) model to analyze the rigid-body motion of the bacterial PLA(2) and calculated the internal motion by subtracting the estimate of the rigid-body motion from the observed anisotropic temperature factor. We also subjected the TLS model to estimate the internal motion of the bovine pancreatic PLA(2) using the anisotropic temperature factor deposited in the Protein Data Bank. Both results indicate that the localization of regions exhibiting larger internal motion in the bacterial PLA(2) is almost the same as that in the bovine pancreatic PLA(2), suggesting that although the tertiary structure of the bacterial PLA(2) is strikingly different from that of the bovine pancreatic PLA(2), the internal motion, which is associated with the calcium(II) ion-binding, phospholipid-binding, and allosteric interfacial activation, is commonly observed in both PLA(2)s.     

THz time scale structural rearrangements and binding modes in lysozyme-ligand interactions

Predicting the conformational changes in proteins that are relevant for substrate binding is an ongoing challenge in the aim of elucidating the functional states of proteins. The motions that are induced by protein-ligand interactions are governed by the protein global modes. Our measurements indicate that the detected changes in the global backbone motion of the enzyme upon binding reflect a shift from the large-scale collective dominant mode in the unbound state towards a functional twisting deformation that assists in closing the binding cleft. Correlated motion in lysozyme has been implicated in enzyme function in previous studies, but detailed characterization of the internal fluctuations that enable the protein to explore the ensemble of conformations that ultimately foster large-scale conformational change is yet unknown. For this reason, we use THz spectroscopy to investigate the picosecond time scale binding modes and collective structural rearrangements that take place in hen egg white lysozyme (HEWL) when bound by the inhibitor (NAG) ₃. These protein thermal motions correspond to fluctuations that have a role in both selecting and sampling from the available protein intrinsic conformations that communicate function. Hence, investigation of these fast, collective modes may provide knowledge about the mechanism leading to the preferred binding process in HEWL-(NAG) ₃. Specifically, in this work we find that the picosecond time scale hydrogen-bonding rearrangements taking place in the protein hydration shell with binding modify the packing density within the hydrophobic core on a local level. These localized, intramolecular contact variations within the protein core appear to facilitate the large cooperative movements within the interfacial region separating the α- and β- domain that mediate binding. The THz time-scale fluctuations identified in the protein-ligand system may also reveal a molecular mechanism for substrate recognition.     

Configurational-bias sampling technique for predicting side-chain conformations in proteins

Prediction of side-chain conformations is an important component of several biological modeling applications. In this work, we have developed and tested an advanced Monte Carlo sampling strategy for predicting side-chain conformations. Our method is based on a cooperative rearrangement of atoms that belong to a group of neighboring side-chains. This rearrangement is accomplished by deleting groups of atoms from the side-chains in a particular region, and regrowing them with the generation of trial positions that depends on both a rotamer library and a molecular mechanics potential function. This method allows us to incorporate flexibility about the rotamers in the library and explore phase space in a continuous fashion about the primary rotamers. We have tested our algorithm on a set of 76 proteins using the all-atom AMBER99 force field and electrostatics that are governed by a distance-dependent dielectric function. When the tolerance for correct prediction of the dihedral angles is a <20 degrees deviation from the native state, our prediction accuracies for chi1 are 83.3% and for chi1 and chi2 are 65.4%. The accuracies of our predictions are comparable to the best results in the literature that often used Hamiltonians that have been specifically optimized for side-chain packing. We believe that the continuous exploration of phase space enables our method to overcome limitations inherent with using discrete rotamers as trials.