Analysis and Application of Potential Energy Smoothing and Search Methods for Global Optimization

Impact of an Ionic Liquid Solution on Horseradish Peroxidase Activity

Horseradish peroxidase (HRP) is an enzyme that oxidizes pollutants from wastewater. A previous report indicated that peroxidases can have an enhancement in initial enzymatic activity in an aqueous solution of 0.26 M 1-ethyl-3-methylimidazolium ethyl sulfate ([EMIm][EtSO4]) at neutral pH. However, the atomistic details remain elusive. In the enzymatic landscape of HRP, compound II (Cpd II) plays a key role and involves a histidine (H42) residue. Cpd II exists as oxoferryl (2a) or hydroxoferryl (2b(FeIV)) forms, where 2a is the predominantly observed form in experimental studies. Intriguingly, the ferric 2b(FeIII) form seen in synthetic complexes has not been observed in HRP. Here, we have investigated the structure and dynamics of HRP in pure water and aqueous [EMIm][EtSO4] (0.26 M), as well as the reaction mechanism of 2a to 2b conversion using polarizable molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations. When HRP is solvated in aq [EMIm][EtSO4], the catalytic water displaces, and H42 directly orients over the ferryl moiety, allowing a direct proton transfer (PT) with a significant energy barrier reduction. Conversely, in neat water, the reaction of 2a to 2b follows the previously reported mechanism. We further investigated the deprotonated form of H42. Analysis of the electric fields at the active site indicates that the aq [EMIm][EtSO4] medium facilitates the reaction by providing a more favorable environment compared with the system solvated in neat water. Overall, the atomic level supports the previous experimental observations and underscores the importance of favorable electric fields in the active site to promote catalysis.

ABEEM/MM OH- Models for OH-(H2O)n Clusters and Aqueous OH-: Structures, Charge Distributions, and Binding Energies

Based on the atom-bond electronegativity equalization method fused into molecular mechanics (ABEEM/MM), two fluctuating charge models of OH^--water system were proposed. The difference between these two models is whether there is charge transfer between OH^- and its first-shell water molecules. The structures, charge distributions, charge transfer, and binding energies of the OH^-(H₂O)_n (n = 1-8, 10, 15, 23) clusters were studied by these two ABEEM/MM models, the OPLS/AA force field, the OPLS-SMOOTH/AA force field, and the QM methods. The results demonstrate that two ABEEM/MM models can search out all stable structures just as the QM methods, and the structures and charge distributions agree well with those from the QM calculations. The structures, the charge transfer, and the strength of hydrogen bonds in the first hydration shell are closely related to the coordination number of OH^-. Molecular dynamics simulations on the aqueous OH^- solution are performed at 298 and 278 K using ABEEM/MM-I model. The MD results show that the populations of three-, four-, and five-coordinated OH^- are 29.6%, 67.1%, and 3.4% at 298 K, respectively, and those of two-, three-, four-, and five-coordinated OH^- are 10.8%, 44.9%, 39.2%, and 4.9% at 278 K, respectively; the average hydrogen bond lengths and the hydrogen bond angle in the first shell increase with the temperature decreasing.

POLARIS: Path of Least Action Analysis on Energy Landscapes

Free-energy landscapes are a powerful tool for analyzing dynamical processes - capable of providing a complete mapping of a system's configurations in state space while articulating its energetics topologically in the form of sprawling hills and valleys. Within this mapping, the path of least action can be derived - representing the most probable sequence of transitions taken between any two states in the landscape. In this article, POLARIS (Path of Least Action Recursive Survey) is presented as a dynamic, global approach that efficiently automates the discovery of the least action path on previously determined 2D energy landscapes. Important built-in features of this program include plotting of landscape trajectories and transition state theory diagrams, generation of text files with least action coordinates and respective energies, and bifurcation analysis tools that provide downstream versatility for comparing most probable paths and reaction rates.

Computational insight into the interaction of oxaliplatin with insulin

In an organism, cisplatin and its derivatives are known to interact with proteins besides their principal DNA target. These off-target interactions have major therapeutic consequences including undesired side effects, loss of bioavailability and emergence of resistance. Insulin is one of the prototypical protein targets of platinum drugs as it has been seen to be involved in bioavailability reduction and might also determine resistance in certain cancer lines. However, despite the interest in understanding the nature of the oxaliplatin-insulin adducts, no 3D models have been achieved so far. In this study, we apply our recent computational multiscale protocol optimized for bioinorganic interactions to provide structural insights into these systems. To do so, the initial structures are predicted by blind protein-metalloligand docking calculations optimized to account for a metal-containing species, and then refined using a Molecular Dynamics (MD) and Quantum Mechanics/Molecular Mechanics (QM/MM) integrated protocol. The results are consistent with experimental information obtained from fragment analysis, and also provide novel structural information like conformational changes occurring upon binding and potential effects on the biological functions of the protein. This study opens an avenue towards applying similar strategies to a wide ensemble of metallodrug-protein/peptide systems for which no structural data are available.

Force Field Parameter Development for the Thiolate/Defective Au(111) Interface

A molecular-level understanding of the interplay between self-assembled monolayers (SAMs) of thiolates and gold surface is of great importance to a wide range of applications in surface science and nanotechnology. Despite theoretical research progress of the past decade, an atomistic model, capable of describing key features of SAMs at reconstructed gold surfaces, is still missing. In this work, periodic ab initio density functional theory (DFT) calculations were utilized to develop a new atomistic force field model for alkanethiolate (AT) SAMs on a reconstructed Au(111) surface. The new force field parameters were carefully trained to reproduce the key features, including vibrational spectra and torsion energy profiles of ethylthiolate (C₂S) in the bridge or staple motif model on the Au(111) surface, wherein, the force constants of the bond and angle terms were trained by matching the vibrational spectra, while the torsion parameters of the dihedral angles were trained via fitting the torsion energy profiles from DFT calculations. To validate the developed force field parameters, we performed classical molecular dynamics (MD) simulations for both pristine and reconstructed Au-S interface models with a (2√3 × 3) unit cell, which includes four dodecanethiolate (C₁₀S) molecules on the Au(111) surface. The simulation results showed that the geometrical features of the investigated Au-S interface models and structural properties of the C₁₀S SAMs are in good agreement with the ab initio MD studies. The newly developed atomistic force field model provides new fundamental insights into AT SAMs on the reconstructed Au(111) surface and adds advancement to the existing interface research knowledge.

A Mechanism of Modulating the Direction of Flagellar Rotation in Bacteria by Fumarate and Fumarate Reductase

Fumarate, an electron acceptor in anaerobic respiration of Escherichia coli, has an additional function of assisting the flagellar motor to shift from counterclockwise to clockwise rotation, with a consequent modulation of the bacterial swimming behavior. Fumarate transmits its effect to the motor via the fumarate reductase complex (FrdABCD), shown to bind to FliG-one of the motor's switch proteins. How binding of the FrdABCD respiratory enzyme to FliG enhances clockwise rotation and how fumarate is involved in this activity have remained puzzling. Here we show that the FrdA subunit in the presence of fumarate is sufficient for binding to FliG and for clockwise enhancement. We further demonstrate by in vitro binding assays and super-resolution microscopy in vivo that the mechanism by which fumarate-occupied FrdA enhances clockwise rotation involves its preferential binding to the clockwise state of FliG (FliG_cw). Continuum electrostatics combined with docking analysis and conformational sampling endorsed the experimental conclusions and suggested that the FrdA-FliG_cw interaction is driven by the positive electrostatic potential generated by FrdA and the negatively charged areas of FliG. They further demonstrated that fumarate changes FrdA's conformation to one that can bind to FliG_cw. These findings also show that the reason for the failure of the succinate dehydrogenase flavoprotein SdhA (an almost-identical analog of FrdA shown to bind to FliG equally well) to enhance clockwise rotation is that it has no binding preference for FliG_cw. We suggest that this mechanism is physiologically important as it can modulate the magnitude of ΔG⁰ between the clockwise and counterclockwise states of the motor to tune the motor to the growth conditions of the bacteria.

A Boundary-Integral Approach for the Poisson-Boltzmann Equation with Polarizable Force Fields

Implicit-solvent models are widely used to study the electrostatics in dissolved biomolecules, which are parameterized using force fields. Standard force fields treat the charge distribution with point charges; however, other force fields have emerged which offer a more realistic description by considering polarizability. In this work, we present the implementation of the polarizable and multipolar force field atomic multipole optimized energetics for biomolecular applications (AMOEBA), in the boundary integral Poisson-Boltzmann solver PyGBe. Previous work from other researchers coupled AMOEBA with the finite-difference solver APBS, and found difficulties to effectively transfer the multipolar charge description to the mesh. A boundary integral formulation treats the charge distribution analytically, overlooking such limitations. This becomes particularly important in simulations that need high accuracy, for example, when the quantity of interest is the difference between solvation energies obtained from separate calculations, like happens for binding energy. We present verification and validation results of our software, compare it with the implementation on APBS, and assess the efficiency of AMOEBA and classical point-charge force fields in a Poisson-Boltzmann solver. We found that a boundary integral approach performs similarly to a volumetric method on CPU. Also, we present a GPU implementation of our solver. Moreover, with a boundary element method, the mesh density to correctly resolve the electrostatic potential is the same for standard point-charge and multipolar force fields. Finally, we saw that for binding energy calculations, a boundary integral approach presents more consistent results than a finite difference approximation for multipolar force fields. © 2019 Wiley Periodicals, Inc.

Towards Unraveling the Histone Code by Fragment Blind Docking

Histones serve as protein spools for winding the DNA in the nucleosome. High variability of their post-translational modifications result in a unique code system often responsible for the pathomechanisms of epigenetics-based diseases. Decoding is performed by reader proteins via complex formation with the N-terminal peptide tails of histones. Determination of structures of histone-reader complexes would be a key to unravel the histone code and the design of new drugs. However, the large number of possible histone complex variations imposes a true challenge for experimental structure determination techniques. Calculation of such complexes is difficult due to considerable size and flexibility of peptides and the shallow binding surfaces of the readers. Moreover, location of the binding sites is often unknown, which requires a blind docking search over the entire surface of the target protein. To accelerate the work in this field, a new approach is presented for prediction of the structure of histone H3 peptide tails docked to their targets. Using a fragmenting protocol and a systematic blind docking method, a collection of well-positioned fragments of the H3 peptide is produced. After linking the fragments, reconstitution of anchoring regions of the target-bound H3 peptide conformations was possible. As a first attempt of combination of blind and fragment docking approaches, our new method is named fragment blind docking (FBD).

The Role of Tertiary Structure in MicroRNA Target Recognition

Translational repression and degradation of transcripts by microRNAs (miRNAs) is mediated by a ribonucleoprotein complex called the miRNA-induced silencing complex (miRISC, or RISC). Advances in experimental determination of RISC structures have enabled detailed analysis and modeling of known miRNA targets, yet a full appreciation of the structural factors influencing target recognition remains a challenge, primarily because target recognition involves a combination of RNA-RNA and RNA-protein interactions that can vary greatly among different miRNA-target pairs. In this chapter, we review progress toward understanding the role of tertiary structure in miRNA target recognition using computational approaches to assemble RISC complexes at known targets and physics-based methods for computing target interactions. Using this framework to examine RISC structures and dynamics, we describe how the conformational flexibility of Argonautes plays an important role in accommodating the diversity of miRNA-target duplexes formed at canonical and noncanonical target sites. We then discuss applications of tertiary structure-based approaches to emerging topics, including the structural effects of SNPs in miRNA targets and cooperative interactions involving Argonaute-Argonaute complexes. We conclude by assessing the prospects for genome-scale modeling of RISC structures and modeling of higher-order Argonaute complexes associated with miRNA biogenesis, mRNA regulation, and other functions.

Tinker 8: Software Tools for Molecular Design

The Tinker software, currently released as version 8, is a modular molecular mechanics and dynamics package written primarily in a standard, easily portable dialect of Fortran 95 with OpenMP extensions. It supports a wide variety of force fields, including polarizable models such as the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field. The package runs on Linux, macOS, and Windows systems. In addition to canonical Tinker, there are branches, Tinker-HP and Tinker-OpenMM, designed for use on message passing interface (MPI) parallel distributed memory supercomputers and state-of-the-art graphical processing units (GPUs), respectively. The Tinker suite also includes a tightly integrated Java-based graphical user interface called Force Field Explorer (FFE), which provides molecular visualization capabilities as well as the ability to launch and control Tinker calculations.

OptMAVEn-2.0: De novo Design of Variable Antibody Regions against Targeted Antigen Epitopes

Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45–65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section).

Investigation of the binding mode of a novel cruzain inhibitor by docking, molecular dynamics, ab initio and MM/PBSA calculations

Chagas disease remains a major health problem in South America, and throughout the world. The two drugs clinically available for its treatment have limited efficacy and cause serious adverse effects. Cruzain is an established therapeutic target of Trypanosoma cruzi, the protozoan that causes Chagas disease. Our group recently identified a competitive cruzain inhibitor (compound 1) with an IC₅₀ = 15 µM that is also more synthetically accessible than the previously reported lead, compound 2. Prior studies, however, did not propose a binding mode for compound 1, hindering understanding of the structure-activity relationship and optimization. Here, the cruzain binding mode of compound 1 was investigated using docking, molecular dynamics (MD) simulations with ab initio derived parameters, ab initio calculations, and MM/PBSA. Two ligand protonation states and four binding poses were evaluated. A careful ligand parameterization method was employed to derive more physically meaningful parameters than those obtained by automated tools. The poses of unprotonated 1 were unstable in MD, showing large conformational changes and diffusing away from the binding site, whereas the protonated form showed higher stability and interaction with negatively charged residues Asp161 and Cys25. MM/PBSA also suggested that these two residues contribute favorably to binding of compound 1. By combining results from MD, ab initio calculations, and MM/PBSA, a binding mode of 1 is proposed. The results also provide insights for further optimization of 1, an interesting lead compound for the development of new cruzain inhibitors.

Probing chirality recognition of protonated glutamic acid dimers by gas-phase vibrational spectroscopy and first-principles simulations

We characterize stereospecific aspects of homochiral and heterochiral dimers of glutamic acid by infrared spectroscopy and first-principles molecular dynamics simulations.

Machine Learning Force Field Parameters from Ab Initio Data

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor during the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local "shape" of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.

Modeling disordered protein interactions from biophysical principles

Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.

New Algorithms for Global Optimization and Reaction Path Determination

We present new schemes to improve the convergence of an important global optimization problem and to determine reaction pathways (RPs) between identified minima. Those methods have been implemented into the CAST program (Conformational Analysis and Search Tool). The first part of this chapter shows how to improve convergence of the Monte Carlo with minimization (MCM, also known as Basin Hopping) method when applied to optimize water clusters or aqueous solvation shells using a simple model. Since the random movement on the potential energy surface (PES) is an integral part of MCM, we propose to employ a hydrogen bonding-based algorithm for its improvement. We show comparisons of the results obtained for random dihedral and for the proposed random, rigid-body water molecule movement, giving evidence that a specific adaption of the distortion process greatly improves the convergence of the method. The second part is about the determination of RPs in clusters between conformational arrangements and for reactions. Besides standard approaches like the nudged elastic band method, we want to focus on a new algorithm developed especially for global reaction path search called Pathopt. We started with argon clusters, a typical benchmark system, which possess a flat PES, then stepwise increase the magnitude and directionality of interactions. Therefore, we calculated pathways for a water cluster and characterize them by frequency calculations. Within our calculations, we were able to show that beneath local pathways also additional pathways can be found which possess additional features.

Probabilistic validation of protein NMR chemical shift assignments

Data validation plays an important role in ensuring the reliability and reproducibility of studies. NMR investigations of the functional properties, dynamics, chemical kinetics, and structures of proteins depend critically on the correctness of chemical shift assignments. We present a novel probabilistic method named ARECA for validating chemical shift assignments that relies on the nuclear Overhauser effect data . ARECA has been evaluated through its application to 26 case studies and has been shown to be complementary to, and usually more reliable than, approaches based on chemical shift databases. ARECA is available online at http://areca.nmrfam.wisc.edu/.

Going clean: structure and dynamics of peptides in the gas phase and paths to solvation

The gas phase is an artificial environment for biomolecules that has gained much attention both experimentally and theoretically due to its unique characteristic of providing a clean room environment for the comparison between theory and experiment. In this review we give an overview mainly on first-principles simulations of isolated peptides and the initial steps of their interactions with ions and solvent molecules: a bottom up approach to the complexity of biological environments. We focus on the accuracy of different methods to explore the conformational space, the connections between theory and experiment regarding collision cross section evaluations and (anharmonic) vibrational spectra, and the challenges faced in this field.

Assembly and analysis of eukaryotic Argonaute-RNA complexes in microRNA-target recognition

Experimental studies have uncovered a variety of microRNA (miRNA)–target duplex structures that include perfect, imperfect and seedless duplexes. However, non-canonical binding modes from imperfect/seedless duplexes are not well predicted by computational approaches, which rely primarily on sequence and secondary structural features, nor have their tertiary structures been characterized because solved structures to date are limited to near perfect, straight duplexes in Argonautes (Agos). Here, we use structural modeling to examine the role of Ago dynamics in assembling viable eukaryotic miRNA-induced silencing complexes (miRISCs). We show that combinations of low-frequency, global modes of motion of Ago domains are required to accommodate RNA duplexes in model human and C. elegans Ago structures. Models of viable miRISCs imply that Ago adopts variable conformations at distinct target sites that generate distorted, imperfect miRNA-target duplexes. Ago's ability to accommodate a duplex is dependent on the region where structural distortions occur: distortions in solvent-exposed seed and 3′-end regions are less likely to produce steric clashes than those in the central duplex region. Energetic analyses of assembled miRISCs indicate that target recognition is also driven by favorable Ago-duplex interactions. Such structural insights into Ago loading and target recognition mechanisms may provide a more accurate assessment of miRNA function.

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

Databases of Conformations and NMR Structures of Glycan Determinants

The present study reports a comprehensive nuclear magnetic resonance (NMR) characterization and a systematic conformational sampling of the conformational preferences of 170 glycan moieties of glycosphingolipids as produced in large-scale quantities by bacterial fermentation. These glycans span across a variety of families including the blood group antigens (A, B and O), core structures (Types 1, 2 and 4), fucosylated oligosaccharides (core and lacto-series), sialylated oligosaccharides (Types 1 and 2), Lewis antigens, GPI-anchors and globosides. A complementary set of about 100 glycan determinants occurring in glycoproteins and glycosaminoglycans has also been structurally characterized using molecular mechanics-based computation. The experimental and computational data generated are organized in two relational databases that can be queried by the user through a user-friendly search engine. The NMR ((1)H and (13)C, COSY, TOCSY, HMQC, HMBC correlation) spectra and 3D structures are available for visualization and download in commonly used structure formats. Emphasis has been given to the use of a common nomenclature for the structural encoding of the carbohydrates and each glycan molecule is described by four different types of representations in order to cope with the different usages in chemistry and biology. These web-based databases were developed with non-proprietary software and are open access for the scientific community available at http://glyco3d.cermav.cnrs.fr.

Native like helices in a specially designed β peptide in the gas phase

First principles simulations and gas phase spectroscopy suggest equilibrium of helices for an oligomer of open chain β amino acids.

Solution thermodynamics, computational and relaxometric studies of ditopic DO3A-based Mn(ii) complexes

The structure, stability and magnetic properties of novel ditopic Mn(ii)-complexes were studied by molecular modeling, potentiometric and relaxometric techniques.

Structural modification by adding Li cations into Mg/Cs-TFSA molten salt facilitating Mg electrodeposition

Free volume around Mg ions in Li/Mg/Cs-TFSA by adding Li cations would facilitate the Mg electrodeposition, which has been studied by Raman spectroscopy, high-energy X-ray diffraction, and reverse Monte Carlo structural refinement using molecular mechanics.

Computational Studies of the Effect of Shock Waves on the Binding of Model Complexes

We have simulated effects of a shock wave in water that would result from the collapse of a cavitation bubble on binding in model complexes. We have considered a benzene dimer, a pair of uracil molecules, a complex of fragments of the X-linked inhibitor of apoptosis and caspase-9, and a fragment of c-Myc oncoprotein in binding with its dimerization partner Max. The effect of the shock waves was simulated by adding a momentum to a slab of solvent water molecules and observing the system as the slab moved and caused changes. In the cases of the small molecular pairs, the passage of the shock waves lead to dissociation of the complexes. The behavior of the protein systems was more complex, yet significant disruption of the binding and geometry was also observed. In all the cases, the effects did not occur during the immediate impact of the high-momentum solvent molecules, but rather during the expansion of the compressed system that followed the passage of the waves. The rationale of the studies was in attempting to understand the strong effects that irradiation with a low-intensity ultrasound can have on biomolecular systems, because such ultrasound irradiation can cause cavitation bubbles to be produced and collapse, thus leading to local shock wave generation. The long-term objective is to contribute to future design of synergetic ultrasound and chemical drug strategy of protein inhibition.

Tunable Coarse Graining for Monte Carlo Simulations of Proteins via Smoothed Energy Tables: Direct and Exchange Simulations

Many commonly used coarse-grained models for proteins are based on simplified interaction sites and consequently may suffer from significant limitations, such as the inability to properly model protein secondary structure without the addition of restraints. Recent work on a benzene fluid (Lettieri S.; Zuckerman D. M.J. Comput. Chem.2012, 33, 268-275) suggested an alternative strategy of tabulating and smoothing fully atomistic orientation-dependent interactions among rigid molecules or fragments. Here we report our initial efforts to apply this approach to the polar and covalent interactions intrinsic to polypeptides. We divide proteins into nearly rigid fragments, construct distance and orientation-dependent tables of the atomistic interaction energies between those fragments, and apply potential energy smoothing techniques to those tables. The amount of smoothing can be adjusted to give coarse-grained models that range from the underlying atomistic force field all the way to a bead-like coarse-grained model. For a moderate amount of smoothing, the method is able to preserve about 70-90% of the α-helical structure while providing a factor of 3-10 improvement in sampling per unit computation time (depending on how sampling is measured). For a greater amount of smoothing, multiple folding-unfolding transitions of the peptide were observed, along with a factor of 10-100 improvement in sampling per unit computation time, although the time spent in the unfolded state was increased compared with less smoothed simulations. For a β hairpin, secondary structure is also preserved, albeit for a narrower range of the smoothing parameter and, consequently, for a more modest improvement in sampling. We have also applied the new method in a "resolution exchange" setting, in which each replica runs a Monte Carlo simulation with a different degree of smoothing. We obtain exchange rates that compare favorably to our previous efforts at resolution exchange (Lyman E.; Zuckerman D. M.J. Chem. Theory Comput.2006, 2, 656-666).

Transferable Force Field for Metal-Organic Frameworks from First-Principles: BTW-FF

We present an ab-initio derived force field to describe the structural and mechanical properties of metal-organic frameworks (or coordination polymers). The aim is a transferable interatomic potential that can be applied to MOFs regardless of metal or ligand identity. The initial parametrization set includes MOF-5, IRMOF-10, IRMOF-14, UiO-66, UiO-67, and HKUST-1. The force field describes the periodic crystal and considers effective atomic charges based on topological analysis of the Bloch states of the extended materials. Transferable potentials were developed for the four organic ligands comprising the test set and for the associated Cu, Zn, and Zr metal nodes. The predicted materials properties, including bulk moduli and vibrational frequencies, are in agreement with explicit density functional theory calculations. The modal heat capacity and lattice thermal expansion are also predicted.

AWE-WQ: fast-forwarding molecular dynamics using the accelerated weighted ensemble

A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family of methods have emerged which can flexibly and efficiently sample conformational space without being trapped and allow calculation of unbiased rates. However, while WE can sample correctly and efficiently, a scalable implementation applicable to interesting biomolecular systems is not available. We provide here a GPLv2 implementation called AWE-WQ of a WE algorithm using the master/worker distributed computing WorkQueue (WQ) framework. AWE-WQ is scalable to thousands of nodes and supports dynamic allocation of computer resources, heterogeneous resource usage (such as central processing units (CPU) and graphical processing units (GPUs) concurrently), seamless heterogeneous cluster usage (i.e., campus grids and cloud providers), and support for arbitrary MD codes such as GROMACS, while ensuring that all statistics are unbiased. We applied AWE-WQ to a 34 residue protein which simulated 1.5 ms over 8 months with peak aggregate performance of 1000 ns/h. Comparison was done with a 200 μs simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy.

Plasma mutant α-galactosidase A protein and globotriaosylsphingosine level in Fabry disease

Fabry disease is an X-linked genetic disorder characterized by deficient activity of α-galactosidase A (GLA) and accumulation of glycolipids, and various GLA gene mutations lead to a wide range of clinical phenotypes from the classic form to the later-onset one. To investigate the biochemical heterogeneity and elucidate the basis of the disease using available clinical samples, we measured GLA activity, GLA protein and accumulated globotriaosylsphingosine (Lyso-Gb3), a biomarker of this disease, in plasma samples from Fabry patients. The analysis revealed that both the enzyme activity and the protein level were apparently decreased, and the enzyme activity was well correlated with the protein level in many Fabry patients. In these cases, a defect of biosynthesis or excessive degradation of mutant GLAs should be involved in the pathogenesis, and the residual protein level would determine the accumulation of Lyso-Gb3 and the severity of the disease. However, there are some exceptional cases, i.e., ones harboring p.C142Y, p.R112H and p.M296I, who exhibit a considerable amount of GLA protein. Especially, a subset of Fabry patients with p.R112H or p.M296I has been attracted interest because the patients exhibit almost normal plasma Lyso-Gb3 concentration. Structural analysis revealed that C142Y causes a structural change at the entrance of the active site. It will lead to a complete enzyme activity deficiency, resulting in a high level of plasma Lyso-Gb3 and the classic Fabry disease. On the other hand, it is thought that R112H causes a relatively large structural change on the molecular surface, and M296I a small one in a restricted region from the core to the surface, both the structural changes being far from the active site. These changes will cause not only partial degradation but also degeneration of the mutant GLA proteins, and the degenerated enzymes exhibiting small and residual activity remain and probably facilitate degradation of Lyso-Gb3 in plasma, leading to the later-onset phenotype. The results of this comprehensive analysis will be useful for elucidation of the basis of Fabry disease.

Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics

DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules.

The complex and specific pMHC interactions with diverse HIV-1 TCR clonotypes reveal a structural basis for alterations in CTL function

Immune control of viral infections is modulated by diverse T cell receptor (TCR) clonotypes engaging peptide-MHC class I complexes on infected cells, but the relationship between TCR structure and antiviral function is unclear. Here we apply in silico molecular modeling with in vivo mutagenesis studies to investigate TCR-pMHC interactions from multiple CTL clonotypes specific for a well-defined HIV-1 epitope. Our molecular dynamics simulations of viral peptide-HLA-TCR complexes, based on two independent co-crystal structure templates, reveal that effective and ineffective clonotypes bind to the terminal portions of the peptide-MHC through similar salt bridges, but their hydrophobic side-chain packings can be very different, which accounts for the major part of the differences among these clonotypes. Non-specific hydrogen bonding to viral peptide also accommodates greater epitope variants. Furthermore, free energy perturbation calculations for point mutations on the viral peptide KK10 show excellent agreement with in vivo mutagenesis assays, with new predictions confirmed by additional experiments. These findings indicate a direct structural basis for heterogeneous CTL antiviral function.

Structural and clinical implications of amino acid substitutions in α-L-iduronidase: insight into the basis of mucopolysaccharidosis type I

Allelic mutations, predominantly missense ones, of the α-l-iduronidase (IDUA) gene cause mucopolysaccharidosis type I (MPS I), which exhibits heterogeneous phenotypes. These phenotypes are basically classified into severe, intermediate, and attenuated types. We previously examined the structural changes in IDUA due to MPS I by homology modeling, but the reliability was limited because of the low sequence identity. In this study, we built new structural models of mutant IDUAs due to 57 amino acid substitutions that had been identified in 27 severe, 1 severe-intermediate, 13 intermediate, 1 attenuated-intermediate and 15 attenuated type MPS I patients based on the crystal structure of human IDUA, which was recently determined by us. The structural changes were examined by calculating the root-mean-square distances (RMSD) and the number of atoms influenced by the amino acid replacements. The results revealed that the structural changes of the enzyme protein tended to be correlated with the severity of the disease. Then we focused on the structural changes resulting from amino acid replacements in the immunoglobulin-like domain and adjacent region, of which the structure had been missing in the IDUA model previously built. Coloring of atoms influenced by an amino acid substitution was performed in each case and the results revealed that the structural changes occurred in a region far from the active site of IDUA, suggesting that they affected protein folding. Structural analysis is thus useful for elucidation of the basis of MPS I.

Comparative study of structural changes caused by different substitutions at the same residue on α-galactosidase A

Missense mutations in the α-galactosidase A (GLA) gene comprising the majority of mutations responsible for Fabry disease result in heterogeneous phenotypes ranging from the early onset severe "classic" form to the "later-onset" milder form. To elucidate the molecular basis of Fabry disease from the viewpoint of structural biology, we comprehensively examined the effects of different substitutions at the same residue in the amino acid sequence of GLA on the structural change in the enzyme molecule and the clinical phenotype by calculating the number of atoms affected and the root-mean-square-distance value, and by coloring of the atoms influenced by the amino acid replacements. The results revealed that the severity of the structural change influences the disease progression, i.e., a small structural change tends to lead to the later-onset form and a large one to the classic form. Furthermore, the study revealed the residues important for expression of the GLA activity, i.e., residues involved in construction of the active site, a disulfide bond or a dimer. Structural study from such a viewpoint is useful for elucidating the basis of Fabry disease.

Tertiary structure-based analysis of microRNA-target interactions

Current computational analysis of microRNA interactions is based largely on primary and secondary structure analysis. Computationally efficient tertiary structure-based methods are needed to enable more realistic modeling of the molecular interactions underlying miRNA-mediated translational repression. We incorporate algorithms for predicting duplex RNA structures, ionic strength effects, duplex entropy and free energy, and docking of duplex-Argonaute protein complexes into a pipeline to model and predict miRNA-target duplex binding energies. To ensure modeling accuracy and computational efficiency, we use an all-atom description of RNA and a continuum description of ionic interactions using the Poisson-Boltzmann equation. Our method predicts the conformations of two constructs of Caenorhabditis elegans let-7 miRNA-target duplexes to an accuracy of ∼3.8 Å root mean square distance of their NMR structures. We also show that the computed duplex formation enthalpies, entropies, and free energies for eight miRNA-target duplexes agree with titration calorimetry data. Analysis of duplex-Argonaute docking shows that structural distortions arising from single-base-pair mismatches in the seed region influence the activity of the complex by destabilizing both duplex hybridization and its association with Argonaute. Collectively, these results demonstrate that tertiary structure-based modeling of miRNA interactions can reveal structural mechanisms not accessible with current secondary structure-based methods.

ICSM: An order N method for calculating electrostatic interactions added to TINKER

We present an order N method for calculating electrostatic interactions that has been integrated into the molecular dynamics portion of the TINKER Molecular Modeling package. This method, introduced in a previous paper [J. Chem. Phys. 131 (2009) 154103] and termed the Image-Charge Solvation Model (ICSM), is a hybrid electrostatic approach that combines the strengths of both explicit and implicit representations of the solvent. A multiple-image method is used to calculate reaction fields due to the implicit part while the Fast Multipole Method (FMM) is used to calculate the Coulomb interactions for all charges, including the explicit part. The integrated package is validated through test simulations of liquid water. The results are compared with those obtained by the Particle Mesh Ewald (PME) method that is built in the TINKER package. Timing performance of TINKER with the integrated ICSM is benchmarked on bulk water as a function of the size of the system. In particular, timing analysis results show that the ICSM outperforms the PME for sufficiently large systems with the break-even point at around 30,000 particles in the simulated system.

Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules

Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.