Self-guided Langevin Dynamics Simulations Research Articles

CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol.

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.

Insights into four helical proteins folding via self-guided Langevin dynamics simulation

Protein folding, vital to life and death, is the basis of biological physics. The information about the protein folding process and dynamics is of tremendous significance in the protein-folding field, and the research about protein folding pathways has attracted considerable attention. Here, four protein systems (1HOD, 2AJJ, 2DX3, 2RLG) are performed the folding simulation starting from the linear structures in order to investigate their folding mechanisms using the classical molecular dynamics (MD) and the self-guided Langevin dynamics (SGLD), respectively. The latter can increase the low-frequency motion, which is particularly related to the folding process of protein. Compared with MD, there is a significant improvement through SGLD method, such as the evolution of the radius of gyration, root mean square deviation, cluster analysis, native contact, helix content, process of the protein folding and the free energy landscape. This research shows that the extended states successfully fold into the corresponding native structures within 95 ns using SGLD. Furthermore, the result of multiple trajectories again supports the above conclusion. However, classical MD ultimately fails to show any stable helix formation at the same simulation time. The detailed folding pathway and mechanism of these proteins are elucidated using the SGLD method in this report.

Enhanced Sampling of Interdomain Motion Using Map-Restrained Langevin Dynamics and NMR: Application to Pin1

Many signaling proteins consist of globular domains connected by flexible linkers that allow for substantial domain motion. Because these domains often serve as complementary functional modules, the possibility of functionally important domain motions arises. To explore this possibility, we require knowledge of the ensemble of protein conformations sampled by interdomain motion.Measurements of NMR residual dipolar couplings (RDCs) of backbone HN bonds offer a per-residue characterization of interdomain dynamics, as the couplings are sensitive to domain orientation. A challenge in reaching this potential is the need to interpret the RDCs as averages over dynamic ensembles of domain conformations.Here, we address this challenge by introducing an efficient protocol for generating conformational ensembles appropriate for flexible, multi-domain proteins. The protocol uses map-restrained self-guided Langevin dynamics simulations to promote collective, interdomain motion while restraining the internal domain motion to near rigidity. Critically, the simulations retain an all-atom description for facile inclusion of site-specific NMR RDC restraints. The result is the rapid generation of conformational ensembles consistent with the RDC data.We illustrate this protocol on human Pin1, a two-domain peptidyl-prolyl isomerase relevant for cancer and Alzheimer's disease. The results include the ensemble of domain orientations sampled by Pin1, as well as those of a dysfunctional variant, I28A-Pin1. The differences between the ensembles corroborate our previous spin relaxation results that showed weakened interdomain contact in the I28A variant relative to wild type. Our protocol extends our abilities to explore the functional significance of protein domain motions.

Probing Conformational Changes of Secondary Active Transport Proteins

Secondary active transporters (SATs) require large protein conformational changes, which some believe follow the alternating access model. The structure of each characteristic conformational state is important for understanding the complete substrate transport process. However, the structure of certain state(s) can be very difficult to obtain by experiment, while certain enhanced simulation techniques may provide an approach to predict unknown structures. Self-guided Langevin dynamics (SGLD) (Chem. Phys. Lett., 381: 512-518) simulations allows proteins to overcome energy barriers and thus it can be used to search the structures of all the conformational states efficiently by enhancing the conformational sampling of low frequency motion. SGLD accelerates the conformational change while preserves the conformational distribution well. Therefore, this method is applied to study a SAT - lactose permease of E.coli. (LacY), which belongs to major facilitator superfamily (MFS). LacY structures of the cytoplasmic-open and occluded-like structure have been determined. To search the periplasmic-open LacY, SGLD simulations are performed with inward-facing LacY as initial structure, with different residues protonated, with and without ββ-(Galp)2, in the implicit membrane and in an explicit membrane in the NVT ensemble (available recently). The SGLD parameters adjusted are guiding factor and friction factor to control guiding force, and also local average time to set the boundary of low frequency motion to enhance. The preliminary broad SGLD parameters sweep are applied to find reasonable parameter set range for sampling protein structures. Then, a fine parameter sweep is applied to obtain the optimized structure. The effect of each parameter set, the protonation states, and presence of sugar on LacY residue distances and pore radii will be presented. These optimal SGLD parameter values may be applied to study conformational changes of other similar SAT proteins.

Replica exchanging self-guided Langevin dynamics for efficient and accurate conformational sampling

This work presents a replica exchanging self-guided Langevin dynamics (RXSGLD) simulation method for efficient conformational searching and sampling. Unlike temperature-based replica exchanging simulations, which use high temperatures to accelerate conformational motion, this method uses self-guided Langevin dynamics (SGLD) to enhance conformational searching without the need to elevate temperatures. A RXSGLD simulation includes a series of SGLD simulations, with simulation conditions differing in the guiding effect and/or temperature. These simulation conditions are called stages and the base stage is one with no guiding effect. Replicas of a simulation system are simulated at the stages and are exchanged according to the replica exchanging probability derived from the SGLD partition function. Because SGLD causes less perturbation on conformational distribution than high temperatures, exchanges between SGLD stages have much higher probabilities than those between different temperatures. Therefore, RXSGLD simulations have higher conformational searching ability than temperature based replica exchange simulations. Through three example systems, we demonstrate that RXSGLD can generate target canonical ensemble distribution at the base stage and achieve accelerated conformational searching. Especially for large systems, RXSGLD has remarkable advantages in terms of replica exchange efficiency, conformational searching ability, and system size extensiveness.

Replica Exchange of Self-Guided Langevin Dynamics Simulation

Based on the self-guided Langevine dynamics (SGLD) simulation method, this work presents a guiding effect based replica exchange method (RXSGLD). The key characteristic of this method is that it can be performed at constant temperature to achieve high replica exchange efficiency. By avoiding temperature elevation, this method has a higher replica exchange efficiency for large systems and uses relatively fewer replicas compared with temperature based replica exchange methods. This method is made possible through recent progress in the understanding of the conformational distribution in SGLD simulations. The partition function of the SGLD ensemble provides a quantitative relation for the calculation of exchange probabilities between replicas. RXSGLD can be utilized as an alternative to the force-momentum based self-guided Langevin dynamics (SGLDfp) to directly sample canonical ensemble without reweighting. This method can be combined with the temperature-based replica exchange method to further improve conformational search efficiency. Using skewed double well systems, we demonstrate that RXSGLD produces correct ensemble distributions while accelerating statistical convergence. Through β-hairpin folding simulations with implicit and explicit solvent, we demonstrate that RXSGLD has excellent conformational search efficiency and can be applied to large systems, while preserving the ensemble distribution.

Force-momentum-based self-guided Langevin dynamics: a rapid sampling method that approaches the canonical ensemble.

The self-guided Langevin dynamics (SGLD) is a method to accelerate conformational searching. This method is unique in the way that it selectively enhances and suppresses molecular motions based on their frequency to accelerate conformational searching without modifying energy surfaces or raising temperatures. It has been applied to studies of many long time scale events, such as protein folding. Recent progress in the understanding of the conformational distribution in SGLD simulations makes SGLD also an accurate method for quantitative studies. The SGLD partition function provides a way to convert the SGLD conformational distribution to the canonical ensemble distribution and to calculate ensemble average properties through reweighting. Based on the SGLD partition function, this work presents a force-momentum-based self-guided Langevin dynamics (SGLDfp) simulation method to directly sample the canonical ensemble. This method includes interaction forces in its guiding force to compensate the perturbation caused by the momentum-based guiding force so that it can approximately sample the canonical ensemble. Using several example systems, we demonstrate that SGLDfp simulations can approximately maintain the canonical ensemble distribution and significantly accelerate conformational searching. With optimal parameters, SGLDfp and SGLD simulations can cross energy barriers of more than 15 kT and 20 kT, respectively, at similar rates for LD simulations to cross energy barriers of 10 kT. The SGLDfp method is size extensive and works well for large systems. For studies where preserving accessible conformational space is critical, such as free energy calculations and protein folding studies, SGLDfp is an efficient approach to search and sample the conformational space.

Comparison between self‐guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

This article presents a comparative analysis of two replica-exchange simulation methods for the structure refinement of protein loop conformations, starting from low-resolution predictions. The methods are self-guided Langevin dynamics (SGLD) and molecular dynamics (MD) with a Nosé-Hoover thermostat. We investigated a small dataset of 8- and 12-residue loops, with the shorter loops placed initially from a coarse-grained lattice model and the longer loops from an enumeration assembly method (the Loopy program). The CHARMM22 + CMAP force field with a generalized Born implicit solvent model (molecular-surface parameterized GBSW2) was used to explore conformational space. We also assessed two empirical scoring methods to detect nativelike conformations from decoys: the all-atom distance-scaled ideal-gas reference state (DFIRE-AA) statistical potential and the Rosetta energy function. Among the eight-residue loop targets, SGLD out performed MD in all cases, with a median of 0.48 Å reduction in global root-mean-square deviation (RMSD) of the loop backbone coordinates from the native structure. Among the more challenging 12-residue loop targets, SGLD improved the prediction accuracy over MD by a median of 1.31 Å, representing a substantial improvement. The overall median RMSD for SGLD simulations of 12-residue loops was 0.91 Å, yielding refinement of a median 2.70 Å from initial loop placement. Results from DFIRE-AA and the Rosetta model applied to rescoring conformations failed to improve the overall detection calculated from the CHARMM force field. We illustrate the advantage of SGLD over the MD simulation model by presenting potential-energy landscapes for several loop predictions. Our results demonstrate that SGLD significantly outperforms traditional MD in the generation and populating of nativelike loop conformations and that the CHARMM force field performs comparably to other empirical force fields in identifying these conformations from the resulting ensembles.

Toward canonical ensemble distribution from self-guided Langevin dynamics simulation

This work derives a quantitative description of the conformational distribution in self-guided Langevin dynamics (SGLD) simulations. SGLD simulations employ guiding forces calculated from local average momentums to enhance low-frequency motion. This enhancement in low-frequency motion dramatically accelerates conformational search efficiency, but also induces certain perturbations in conformational distribution. Through the local averaging, we separate properties of molecular systems into low-frequency and high-frequency portions. The guiding force effect on the conformational distribution is quantitatively described using these low-frequency and high-frequency properties. This quantitative relation provides a way to convert between a canonical ensemble and a self-guided ensemble. Using example systems, we demonstrated how to utilize the relation to obtain canonical ensemble properties and conformational distributions from SGLD simulations. This development makes SGLD not only an efficient approach for conformational searching, but also an accurate means for conformational sampling.

Conformational Relaxation and Water Penetration Triggered by the Ionization of Internal Groups in Proteins

Internal ionizable groups in proteins play essential functional roles in biochemical processes related to bioenergetics, including catalysis, proton transport, and electron transfer reactions. Many proteins harness the coupling between the ionization of internal groups and structural reorganization for functional purposes. To elucidate the mechanisms and the structural basis of function in these proteins, it is necessary to understand how the ionization of internal groups is coupled to structural reorganization of the protein and how the protein environment influences the pKa values of internal groups. Through molecular dynamics simulations and Self-guided Langevin dynamics simulations we are studying the types of structural responses that can be triggered by ionization of internal groups. Water penetration, side-chain rotation and backbone relaxation are among the structural changes that have been detected with molecular dynamics simulations. A large family of variants of staphylococcal nuclease with internal ionizable groups (Lys, Arg, Asp and Glu) are being used to examine these issues systematically. Some of the ionizable groups exhibit pKa values that are shifted significantly compared to the normal values in bulk water. Most computational methods for pKa calculations cannot reproduce accurately the pKa shifts observed in such variants, primarily because they cannot reproduce correctly the structural response to the ionization of internal groups. The physical and structural insight into structural changes promoted by internal charges will be useful to guide the development of more accurate methods for structure-based pKa calculations.

Characterizing the denatured state of human prion 121–230

Misfolding and aggregation of the prion protein (PrP) are responsible for the development of fatal transmissible neurodegenerative diseases. PrP undergoes structural conversion from a natively folded state into a misfolded state, resulting in insoluble amyloid fibrils. Partial unfolding has been recognized as an essential step in fibrillation. The strong correlation of unfolding and fibrillation emphasizes the importance of denatured states. To gain insight into possible aggregation-prone denatured states, we characterized the denatured state of human prion (huPrP) 121–230 near extended conformation by self-guided Langevin dynamics simulations. Our results revealed that denatured huPrP is partially folded with α-helical structure.

Self‐guided Langevin dynamics study of regulatory interactions in NtrC

Multiple self-guided Langevin dynamics (SGLD) simulations were performed to examine structural and dynamical properties of the receiver domain of nitrogen regulatory protein C (NtrC(r)). SGLD and MD simulations of the phosphorylated active form structure suggest a mostly stable but broad structural ensemble of this protein. The finite difference Poisson-Boltzmann calculations of the pK(a) values of the active site residues suggest an increase in the pK(a) of His-84 on phosphorylation of Asp-54. In SGLD simulations of the phosphorylated active form with charged His-84, the average position of the regulatory helix alpha4 is found closer to the starting structure than in simulations with the neutral His-84. To model the transition pathway, the phosphate group was removed from the simulations. After 7 ns of simulations, the regulatory helix alpha4 was found approximately halfway between positions in the NMR structures of the active and inactive forms. Removal of the phosphate group stimulated loss of helix alpha4, suggesting that the pathway of conformational transition may involve partial unfolding mechanism. The study illustrates the potential utility of the SGLD method in studies of the coupling between ligand binding and conformational transitions.

Self-guided Langevin dynamics simulation method

This work presents a self-guided Langevin dynamics simulation method. The guiding force is calculated as a local average of the friction forces during a self-guided Langevin dynamics simulation. Three parameters, the local average time, t L , the guiding factor, λ, and the collision frequency, γ, control a self-guided Langevin dynamics simulation. It is demonstrated through three model systems that this simulation method has an enhanced conformational search ability while has little alteration in conformational distribution when the guiding factor is chosen within the limit of λγ<1 ps −1. This method is well suited for simulation studies where extensive conformational searching is required.