Prof. Chen Song (Peking University)
Calcium ions play crucial roles in many important biological processes, such as muscle contraction, signal transduction in nervous system, cell signaling regulation and cell fate determination. However, the existing Ca2+ models in the classical non-polarizable force field are inaccurate in calculating the interaction energies between calcium ions and protein molecules, which has hindered the computational studies of Ca2+-interacting proteins. We developed a new multi-site Ca2+ model in the framework of the classical non-polarizable force field, which can not only reproduce the energetical and dynamical properties of calcium ions in solution, but also describe the interaction energies between calcium ions and proteins more accurately. We utilized this new Ca2+ model to simulate ion permeation through the RyR channels, and revealed the detailed Ca2+ permeation behavior. We hope this Ca2+ model can find more applications soon.
Prof. Daisuke Kihara (Purdue University)
The significant progress of the cryo-EM poses a pressing need for software for structural interpretation of EM maps. Particularly, protein structure modeling tools are needed for EM maps determined at a resolution around 4 Å or lower, where finding main-chain structure and assigning the amino acid sequence into EM map is challenging. In this seminar, we discuss computational protein structure modeling tools we have been developing and future directions, opportunities, and challenges.
We have developed a de novo modeling tool named MAINMAST (MAINchain Model trAcing from Spanning Tree) for EM maps for this resolution range (Nature Communications, 2018). MAINMAST builds main-chain traces of a protein in an EM map from a tree structure constructed by connecting high-density points without referring to known protein structures or fragments. The method has substantial advantages over the existing methods. MAINMAST showed better modeling performance than existing methods. The method is further enhanced recently to be able to model symmetric protein complexes and ligand (drug) molecules that bind to a protein in a map. Moreover, to provide structure information for maps determined at a lower resolution (5~10 Å), we have recently developed a new tool, Emap2sec, which uses convolutional deep learning for detecting secondary structures of proteins (accepted and to appear, 2019). Emap2sec scans an EM map with a voxel and assigns a secondary structure, i.e. alpha helix, beta strand, or coil, from density patterns of the voxel and its neighbors.
Prof. Ove Christiansen (Aarhus University)
I will describe methods we have developed through the years aiming at incorporating the effects of nuclear motions and states in theoretical computations, ranging from MD and QM/MM in the prediction of electronic spectra to full quantum treatment of many-mode nuclear motion. I will discuss why and how we have changed from a QM/MM approach for classical simulations of electronic spectra to so-called incremental type approaches for constructing quantum computed potential energy surfaces for many mode vibrational computations. In the context of many-mode dynamics progress in the vibrational coupled cluster approach developed in our group through the years will be discussed. Along the way I will also discuss the perspectives of boosting computations using techniques such as machine-learning and tensor decomposition. I will finally also discuss the computation of Franck-Condon factors using anharmonic wave functions with thiophenes as an example.
Prof. Toru Shiozaki (Northwestern University)
I will present recent advances in quantum chemistry with an emphasis on software development in the BAGEL program package. First, I will show that, with BAGEL, one can routinely perform first principles all-electron DFT simulations of 1000-2000 atoms in ∼15 min using a computer cluster with a few dozen of nodes, which (we hope) will allow the users to replace some of the computational protocols conventionally performed by classical force fields. Second, I will review several high-accuracy wave function methods in BAGEL, which would replace some of the simulations currently done by DFT. Finally, I will discuss how parallel software, like BAGEL, can accelerate the users' workflow and change how quantum chemistry is used in academia and in industry.
Prof. Wonpil Im (Lehigh University)
In this talk, I would like to share our ongoing efforts toward computational (structural) glycobiology in terms of (1) glycan structure and dynamics in glycoproteins, (2) roles of glycans as ligands in protein-glycan and protein-protein interactions, (3) glycolipid structure and dynamics, and (4) bacterial outer membranes containing lipopolysaccharides and their interactions with membrane proteins. We have developed various tools available in GlycanStructure.ORG (http://www.glycanstructure.org): Glycan Reader for automatic detection and annotation of carbohydrates, their chemical modifications, and glycosidic linkages in PDB files, Glycan Fragment DB for finding carbohydrate fragment structures in the PDB and torsion angle distributions of specific glycosidic linkages of a query glycan structure, Glycan Modeler for modeling glycan structures from its sequence, and GS-align for glycan structure alignment and similarity measurement. In addition, we are in the process of building Glycan Binding Structure Database and Glycan Microarray Database. A PDB survey study of N-glycan structures and protein-glycan interactions is also presented for modeling glycan structures and protein-glycan interactions.
Prof. Kenneth M. Merz, Jr. (Michigan State University)
The 12−6 Lennard-Jones (LJ) nonbonded model is routinely used to represent metal ions in the simulation of the structure and thermodynamics of a range of coordination compounds. However this model often fails to simultaneously reproduce these properties, which limits its applicability. Our 12−6−4 LJ-type nonbonded model, that includes a 1/r4 term to incorporate charge-induced dipole interactions, reproduces multiple experimental properties of highly charged metal ions [1-3]. We recently optimized 12−6−4 LJ parameters for Cd2+, Ni2+, Fe2+, and Zn2+ binding to ethylenediamine (en) in water in order to capture detailed mechanistic insights into the chelate effect [4]. This study highlighted the role of water molecules in the first solvation shell of the metal ion in facilitating chelate ring formation. In this talk, we’ll present recent efforts in furthering the application of our model to simulate the structural and thermodynamic properties of the self-assembly process involving metal ions with organic and biological ligands.
References:
[1] Li, P. and Merz, K. M., Jr. J. Chem. Theory Comput., 2014, 10, 289−297.
[2] Li, P.; Song, L. F. and Merz, K. M., Jr. J. Phys. Chem. B, 2014, 119, 883−895.
[3] Li, P.; Song, L. F. and Merz, K. M., Jr. J. Chem. Theory Comput., 2015, 11, 1645−1657.
[4] Sengupta, A. Seitz, A.; Merz, K. M., Jr. J. Am. Chem. Soc., 2018, 140, 15166–15169.
Prof. Daniel M. Zuckerman (Oregon Health & Science University)
The basic outcome of an ordinary molecular simulation is a trajectory, or sequence of molecular configurations, and any equilibrium or non-equilibrium observable can be derived from a sufficiently long trajectory. Likewise, arbitrary observables can be derived from an appropriate ensemble of trajectories. The talk will explain how the basic physics of trajectory ensembles can be exploited for enhanced sampling and for improved analysis of ordinary trajectories via Markov models. In particular, the “weighted ensemble” enhanced sampling approach has been employed to estimate protein folding times up to the second timescale. Further, the related trajectory theory enables construction of Markov models of protein folding that remain unbiased at very short lag times, greatly enhancing their capability to describe fundamental aspects of mechanism (pathways).
Prof. Yuko Okamoto (Nagoya University)
In this talk I will present the results of our analyses of amyloid formations of peptide fragments by replica-exchange molecular dynamics simulations. Two peptide fragments were studied. For the former system, we found that there is a clear phase transition temperature in which the peptides aggregate with each other. Moreover, we found by the free energy analyses that there are two major stable states: One of them is like amyloid fibrils and the other is amorphous aggregates. For the latter system, we focused on the concentration dependency. We showed that high concentration environment of fibril-forming peptides is likely to cause the protein aggregation.
Prof. Naoyuki Miyashita (Kindai University)
Prof. John E. Straub (Boston University)
Considerable progress has been made, using experiments and computations, to decipher the general principles governing the mechanism of formation of oligomers and fibrils of amyloid proteins implicated in diseases, including the amyloid-β protein (Aβ) associated with Alzheimer's disease (AD). However, the identification of the link between protein aggregation and the systems of disease at the molecular level has proved elusive. The biogenesis of Aβ starts with interaction of the Amyloid Precursor Protein (APP) with secretases in the presence of membrane. Subsequently, interactions with cholesterol and other proteins such as the cellular prion protein (PrPC) determine the route to oligomer formation and the extent of cytotoxicity. We report on theoretical and computational studies designed to systematically investigate the biogenesis of Aβ, its propensity toward aggregation, and putative mechanisms of cytotoxicity in order to highlight critical areas for future research.
Dr. Robert Best (NIH)
Intrinsically disordered proteins (IDPs) are increasingly realized to play a wide range of functional as well as pathological roles in biology. However, biophysical characterization of these proteins is experimentally challenging due to the extremely heterogeneous ensemble of structures which they populate. Computational tools, in particular molecular simulations, can therefore play a role in elucidating structure, function and dynamics in IDPs. Here, Dr. Best will show how both detailed atomistic simulations, as well as simplified coarse-grained models can be used to assist in the interpretation of experiments on IDPs. In particular, Dr. Best will describe recent work characterizing an ultra-high affinity complex between two charged IDPs which, remarkably, remain completely disordered upon binding.
Prof. Heather Kulik (Massachusetts Institute of Technology)
Computation has emerged as a powerful tool for accelerating the discovery of new materials and molecules: first through first-principles simulation in high throughput screening and very recently even further with machine learning (ML). In the first part of my talk, I will discuss the unique challenges that remain for the accelerated discovery and design of inorganic complexes, despite the highly tunable electronic structure properties that make these materials so compelling for applications in energy storage and catalysis. I will describe our recent efforts to overcome the high cost and low accuracy of electronic structure for inorganic chemistry through developing ML models (e.g., artificial neural networks and kernel ridge regression) that predict key first-principles energetic (e.g., spin-state ordering and redox potential) and geometric properties to within the accuracy of the underlying simulation method that provides the training data. I will describe our efforts to use these ML models for extrapolative applications (i.e., design) by incorporating heuristics for ML model uncertainty in a genetic algorithm to discover new inorganic compounds. In the second part of my talk, I will discuss how we leverage recent advances in stream processors to carry out large-scale electronic structure in QM and QM/MM simulation with over 1000 atoms treated quantum mechanically. I will describe how these simulations have given us new insights into enzyme mechanism, what potential challenges there are in applying conventional electronic structure methods on such large systems, and how we can understand when large scale electronic structure is needed in enzyme simulation.
Prof. Patrick Barth (Baylor College of Medicine)
Communication and transport across lipid membranes control a large variety of cellular processes but remain poorly understood, largely because membrane proteins are difficult to study experimentally. The scarcity of high-resolution membrane protein structures and mechanistic insights hinder the development of effective therapeutics, with membrane proteins estimated to represent around 60% of possible cellular drug targets. To address these challenges, we have recently developed an ensemble of integrated computational/experimental approaches to accurately model and design membrane protein structures and functions. With our methods, we can (1) predict the functional consequences of membrane protein sequence variations, (2) uncover new molecular determinants of membrane protein structure and function, and (3) rationally design membrane receptors with novel biophysical and signaling properties. We leverage these combinatorial approaches to engineer biosensors, as well as reprogram and create novel signaling pathways for applications in synthetic, systems biology and personalized medicine, including immunotherapeutic interventions.
Mr. Tomasz Pieńko and Prof. Joanna Trylska (University of Warsaw)
Dr. Karissa Sanbonmatsu (Los Alamos National Laboratory)
Chromatin architecture plays a key role in embryonic stem cell programming, human embryo development, brain function and cancer. Specifically, epigenetic methylation and acetylation marks are thought to control gene expression by dramatically altering global chromatin architecture; however the exact mechanism by which a single methyl group can induce a large scale conformation change of chromatin is not well understood. By examining histones in a dense nucleosome context, we aim to gain insight into possible scenarios by which methylation can alter chromatin conformation. Using coarse grain models of chromatin as a basis, we construct all atom chromatin models and simulate these with the GENESIS molecular dynamics code on the large-scale high performance platforms at Los Alamos National Laboratory. The multi-disciplinary effort combined computer science, high performance computing, chip design, biophysics, structural biology, and cell biology, including researchers from RIKEN, LANL, NYU, Intel and Cray. Several performance optimizations for the KNL architecture enabled scaling to large numbers of cores.
Dr. Tomotaka Oroguchi (Keio University)
The influence of lone-pair electrons on the directionality of hydrogen bonds that are formed by oxygen and nitrogen atoms in the side chains of nine hydrophilic was investigated using molecular dynamics simulations. The simulations were conducted using two types of force fields; one incorporated lone-pair electrons placed at off-atom sites and the other did not. The density distributions of the hydration water molecules around the oxygen and nitrogen atoms were calculated from the simulation trajectories, and were compared with the empirical hydration distribution functions, which were constructed from a large number of hydration water molecules found in the crystal structures of proteins. Only simulations using the force field explicitly incorporating lone-pair electrons reproduced the directionality of hydrogen bonds that is observed in the empirical distribution functions for the deprotonated oxygen and nitrogen atoms in the sp2-hybridization. The amino acids that include such atoms are functionally important glutamate, aspartate, and histidine. Therefore, a set of force field that incorporates lone-pair electrons as off-atom charge sites would be effective for considering hydrogen bond formation by these amino acids in molecular dynamics simulation studies.
Prof. Kenneth M. Merz Jr. (Michigan State University)
Docking (posing) calculations coupled with binding free energy estimates (scoring) are a mainstay of structure based drug design. Docking and scoring methods have steadily improved over the years, but remain challenging because of the extensive sampling that is required, the need for accurate scoring functions and challenges encountered in accurately estimating entropy effects. To address these issues we have been developing a number of novel strategies in our laboratory. In particular, in this presentation, we will describe the Movable Type sampling (MTS) method developed in our laboratory that estimates binding free energies, entropies and enthalpies. The utility of MTS will be explored through a series of examples that involve rigid or flexible protein-ligand docking, protein-protein docking and entropy estimation. We will show that MTS allows us to compute thermodynamic quantities associated with myriad biological processes rapidly, accurately and yields structural information at a minimal computational cost relative to currently available methods.
Prof. Huan-Xiang Zhou (Departments of Chemistry and Physics, University of Illinois at Chicago)
I will start with an overview of my research program, which includes studies on protein association, macromolecular crowding, and peptide self-assembly. The focus will then shift to our studies on ion channels. These membrane proteins, relative to water-soluble proteins, have less intrinsic stability and are more prone to influences of the solubilizing environments. Indeed, our recent assessment of helical membrane protein structures in the Protein Data Bank identified many cases of potential distortions in transmembrane domains, attributable to sample preparations used for X-ray crystallography and solution NMR spectroscopy [1]. To achieve native-like structures, we use solid-state NMR data for refinement through restrained molecular dynamics simulations in native-like environments, i.e., in lipid bilayers. For the Influenza M2 protein (an acid-activated proton-selective channel), our study further targeted its functional center, i.e., a histidine tetrad within the channel pore that acts as both the pH sensor and ion selectivity filter. Based on solid-state NMR data and quantum chemistry calculations, we developed a mechanism for acid activation and proton conductance [2]. We have also remodeled transmembrane domains from crystal structures, both for correcting distortions [3] and for generating structural models in different functional states [4]. Lastly we have used molecular dynamics simulations and structural modeling to develop mechanisms for ionotropic glutamate receptors on channel gating, partial agonism, and disease-associated mutations, and are integrating these results into electrophysiological studies [5, 6].
[references]
1. H.-X. Zhou and T. A. Cross (2013). Influences of membrane mimetic environments on membrane protein structures. Annu. Rev. Biophys. 42, 361-392.
2. M. Sharma, M. Yi, H. Dong, H. Qin, E. Peterson, D. D. Busath, H.-X. Zhou, and T. A. Cross (2010). Insight into the mechanism of the Influenza A proton channel from a structure in a lipid bilayer. Science 330, 509-512.
3. G. Heymann, J. Dai, M. Li, S. D. Silberberg, H.-X. Zhou, and K. J. Swartz (2013). Inter- and intrasubunit interactions between transmembrane helices in the open state of P2X receptor channels. Proc. Natl. Acad. Sci. USA 110, E4045-E4054.
4. J. Dai and H.-X. Zhou (2014). General rules for the arrangements and gating motions of pore-lining helices in homomeric ion channels. Nat. Commun. 5, 4641.
5. R. Kazi, J. Dai, C. Sweeney, H.-X. Zhou, and L. P. Wollmuth (2014). Mechanical coupling maintains the fidelity of NMDA receptor-mediated currents. Nat. Neurosci. 17, 914-922.
6. H.-X. Zhou and L. P. Wollmuth (2017). Advancing NMDA receptor physiology by integrating multiple approaches. Trends Neurosci. 40, 129-137.
Prof. Joanna Trylska (Centre of New Technologies, University of Warsaw)
Bacterial ribosomal RNA (rRNA) is a target for small molecule antibiotics whose binding inhibits protein synthesis. However, rRNA constitutes two-thirds of the ribosome by mass so it offers many other possible interaction sites. We explored bacterial rRNA as a target for complementary oligomers that would bind observing the Watson-Crick pairing rules. We analysed various properties of the rRNA regions such as accessibility, functionality, hydrogen bond patterns, easiness of opening for strand invasion and flexibility. To determine 16S rRNA flexibility in the ribosome context, we performed all-atom molecular dynamics simulations of the small ribosome subunit in explicit solvent. Based on these properties we selected rRNA targets for hybridization with complementary oligoribonucleotides. Next, we tested translation inhibition efficiencies of these ribosome-interfering oligomers in a cell-free translation system. Selected rRNA sites were targeted with peptide nucleic acid oligomers and tested for inhibition of bacterial growth.
Prof. Kang Kim (Univ. of Osaka)
Understanding the universal mechanism of glass transitions is a challenging problem for condensed phases, despite extensive efforts in theories, simulations, and experiments. A remarkable feature of glass-forming liquids is the drastic slowing down that accompanies non-exponentially and non-Gaussianity observed in various time correlation functions. On the contrary, the amorphous structures upon supercooling remain unchanged and are similar to those in liquid states. In this talk, I first provide the general review about glass transition problem and then introduce my recent simulation studies. The particular interest is related to temperature dependence of transport coefficients such as diffusivity, viscosity, and structural relaxation time in glasses. This temperature dependence is characterized by the degree of the Arrhenius property, which is referred to as fragility. It is well known that anisotropic tetrahedral network-forming liquids (SiO2) exhibit the Arrhenius behavior, while isotropic short-ranged potential liquids (metallic alloys) act as another type of glass former exhibiting super-Arrhenius temperature dependence. Here, it is demonstrated that the fragility can be controlled over a wide range by tuning the potential in a single simulation model. This model uses the short-ranged and isotropic pairwise potential. However, the reduction of the potential depth, eventually transforming from tetrahedral into isotropic structures, seamlessly changes the temperature dependence from Arrhenius to super-Arrhenius.
Prof. Toshifumi Mori (Institute for Molecular Science)
Protein folds in to a unique structure, but have some flexibility to function efficiently. The importance of flexibility, or protein dynamics such as configurational fluctuations and conformational transitions, have become evident in recent studies, yet understanding how it acts, especially at molecular level, is still a challenging task. In this talk I will discuss our recent studies on two topics, protein folding and enzymtic reactions. For the folding, we analyze multiple ∼μs long molecular dynamics trajectories from Anton to study how folding/unfolding proceed behind a seemingly two-state folding free energy profile. For the enzymatic reaction, the peptidyl-prolyl cis-trans isomerization reaction in Pin1 is studies, and the transitino mechanism is discussed in detail. These results show that the heterogeneous dynamics of the proteins found at molecular level play a fundamental role in folding into the native structure and catalyzing the reaction efficiently.
Prof. Xuhui Huang (The Hong Kong University of Science and Technology)
Transcription, the synthesis of RNA from a complementary DNA template, plays a crucial role in cellular regulation, including differentiation, development, and other fundamental processes. In this talk, I will discuss our results on modeling the RNA polymerase II (Pol II, a system with ∼400K atoms) Translocation and other functional conformational changes of this enzyme at sub-millisecond timescales. We have developed a novel algorithm, Hierarchical Nystrom Extension Graph method, to construct kinetic network models to extract long timescale dynamics from short simulations. For example, we reveal that RNA polymerase II translocation is driven purely by thermal energy and does not require the input of any additional chemical energy. Our model shows an important role for the bridge helix: Large thermal oscillations of this structural element facilitate the translocation by specific interactions that lower the free-energy barriers between four metastable states. The dynamic view of translocation presented in our study represents a substantial advance over the current understanding based on the static snapshots provided by X-ray structures of transcribing complexes. At the end of my talk, I will briefly discuss our recent progress on extending our kinetic network model to include sequence-dependent molecular dynamics of Pol II elongation to predict transcriptional accuracy in the genome-wide transcriptomic datasets. This model creates a critical link between the structural-mechanics understanding of Pol II fidelity and the genome-wide transcriptional accuracy.
Dr. Luigi D'Ascenzo (IBMC)
RNA is implied in many fundamental biological processes, such as protein translation, gene regulation and catalysis, which are accomplished thanks to its intrinsic structural plasticity, derived from RNA motifs polymorphism. During my PhD I studied a particular class of these motifs, RNA tetraloops, formed by four nucleotides that cap helices inducing a backbone U-turn. One of the overlooked structural features of tetraloops is the stacking of backbone oxygen atoms with nucleobases, originating anion-π or lone pair-π stacking interactions. These two interactions can be used to define two folds for tetraloops and are significant for the local and global RNA plasticity, as well as helping still problematic RNA folding experiments. Tetraloops and their stacking interactions are modulated by water and ions interactions. Moreover, intracellular environments are crowded by macromolecules and metabolites. Our current knowledge on the crowding phenomena is limited to macroscopic effects, and much has to be discovered about molecular details. For that, I propose to study by full-atomistic MD a system embedded inside the tRNA T-box riboswitch (essential for gene expression in bacteria) that is composed by an intramolecular stacking interactions between two base pairs. The effects of molecular and macromolecular crowding during the thermally-induced “opening” of this system will be analyzed, in order to expand our knowledge about local modification on hydration structure and more generally on how crowding affects biomolecular recognition.
Dr. Filip Leonarski (IBMC)
Assigning accurately chemical specie to solvent densities is one of the remaining challenges for crystallographers. While progress was made in refining macromolecules with workflows like Phenix or PDB_REDO, ion placement often results in errors. I will present examples of Mg2+ binding to purine N7 atoms assignment errors. As the affinity of Mg2+ for nitrogen is considerably smaller than for oxygen atoms, the former are not natural Mg2+ partners. Indeed, through a survey of small molecular assemblies from the Cambridge Structural Database (CSD) and the larger PDB macromolecular systems, we were able: (i) to define more precisely the binding patterns of Mg2+ ions towards purine N7, (ii) to assess that N7 is very rarely interacting with these ions and (iii) to establish that most of the Mg2+ ions placed in front of N7 atoms are monovalent ions, water molecules or transition metals. These results demonstrate that better ion placement methodologies need to be developed.
With that goal in mind, I started molecular dynamics (MD) simulations in crystallo. Here I present application of this method to refinement of a 0.6 å sarcin-ricin loop crystal structure. This ultra-high resolution allows to see not only detailed electron density for the RNA, but also well resolved first and second solvation shell. Although metal cations were present in the crystallization media, they were not identified among the solvent densities. Yet, ammonium sulfate was used in the crystallization media as well and it is likely that some densities considered as water conceal NH4+ since the similarities of this ion to water make it an elusive specie. The only way to differentiate between H2O and NH4+ is by analyzing their hydrogen bonding modes. However, even at such remarkable resolution, some details are missing. By modelling dynamics in crystallo of the sarcin-ricin loop, we could infer positions of hydrogens that cannot be found in experimental densities including those of NH4+ ions. We believe that such methodology can be further developed to help identifying solvent species in macromolecular structures.
Dr. Pascal Auffinger (IBMC)
I will briefly introduce past and present work that we have done in our group on RNA structure and dynamics with the help of Luigi D’Asenzo ad Filip Leonarski. I will especially show the importance of short hydrogen bonds that might arise in protein and RNA/protein or DNA/protein systems as a result of the protonation of Asp and Glu carboxylate groups. The existence of such short hydrogen bonds might challenge some of the current force fields used for molecular dynamics (MD) simulations. Further, I will emphasize the importance of correctly evaluating crystallographic structures that might sometimes lead to ambiguous or even wrong models that considerably complicate the creation of reliable structural databases that are essential for the validation of MD models.
Prof. Michael Feig (Michigan State University)
Protein structure prediction has progressed significantly over the last decade due to advances in computational methods and increased numbers of known structures that can be used as templates. It is now routinely possible to predict approximate protein structures for the majority of gene sequences. The resulting models often correctly capture the overall fold and many structural aspects are correctly reproduced but, in detail, it remains difficult to match experimental accuracy. New methods based on molecular dynamics simulations are discussed that aim at overall refinement as well as local structure refinement. These methods take advantage of extensive sampling and a combined structure selection and averaging protocol. Recent evaluation of this method in the context of CASP is discussed.
Prof. Wonpil Im (University of Kansas)
The outer membrane of gram-negative bacteria is a unique asymmetric membrane bilayer that is composed of phospholipids in the inner leaflet and lipopolysaccharides (LPS) in the outer leaflet. Its function as a selective barrier is crucial for the survival of bacteria in many distinct environments, and it also renders gram-negative bacteria more resistant to antibiotics than their gram-positive counterparts. LPS comprises three regions: lipid A, core oligosaccharide, and O-antigen polysaccharide. In this talk, I will present our ongoing efforts on understanding various bacterial outer membranes and their interactions with outer membrane proteins, including (1) construction of a model of an E. coli R1 (core) O6 (antigen) LPS molecule using the CHARMM36 lipid and carbohydrate force fields and simulations of various E. coli R1.O6 LPS bilayers; (2) modeling of E. coli R2, R3, R4, and K12 cores and other O-antigens and their bilayer simulations; (3) development of LPS Modeler in CHARMM-GUI; (4) modeling and simulation of E. coli outer membranes with phospholipids in the inner leaflet and LPS in the outer leaflet as well as OmpLA in the outer membrane; (5) modeling and simulation of BamA in the E. coli outer membrane; (6) other ongoing outer membrane - protein simulations.
Prof. Masayoshi Nakasako (Department of Physics, Faculty of Science and Technology, Keio University)
Coherent X-ray Diffraction Imaging (CXDI) is an imaging technique suitable for the whole structure analyses of non-crystalline and micrometer-size specimens without staining, sectioning or chemical labeling, due to the large penetration depth of short-wavelength X-rays. We applied the CXDI technique to the structure analysis of cells and cellular organelles at the XFEL facility SACLA. Here I would like to introduce the theory, experimental techniques and current application of the technique for biological specimens.
Prof. Joanna Trylska (Centre of New Technologies, University of Warsaw, Poland)
Internal dynamics of RNA is required for its proper biological function. For example, flexibility of ribosomal RNA and mRNA is essential for efficient translation of mRNA into polypeptides. In our laboratory we apply molecular dynamics simulations, absorbance and fluorescence spectroscopy to investigate the RNA dynamics. I will speak about the importance of RNA flexibility in two biologically-relevant RNA motifs. One is the mRNA decoding site in the ribosome, which is also the binding site of aminoglycoside antibiotics. I will present the dynamical properties of this site in the context of thermodynamics of aminoglycoside binding to bacterial and human ribosomes. The other one is an RNA hairpin that acts as a thermosensor responsible for translation initiation in bacteria upon heat shock. These short mRNA sequences respond to temperature changes and their local melting allows mRNA binding to the ribosome. I will present the mechanism of thermal unwinding of a fourU thermometer.
Prof. James C. Gumbart (School of Physics, Georgia Institute of Technology, Atlanta, USA)
The ability of atomistic molecular dynamics simulations to model biological systems has increased dramatically in the past few years. This ability has come about through advances in hardware, software, and methodology. Recent examples include millisecond simulations of individual proteins, representations of membranes with intricate compositions, and even modeling of entire viruses at atomistic resolution. In this talk, I will describe the application of MD simulations to a specialized sub-cellular region, the bacterial periplasm. Situated between two membranes in Gram-negative bacteria, the periplasm is home to a number of unique component and systems. I will specifically address simulations of three outer-membrane proteins in their native environment: BtuB, a vitamin B12 transporter; BamA, responsible for outer-membrane-protein insertion; and LptD/E, a protein that inserts lipopolysaccharides into the outer leaflet of the outer membrane. I will also demonstrate that simulations can capture the mechanical properties of the bacterial cell wall, which is anchored to the outer membrane. Finally, I will describe our current efforts to unify all of these aspects into a model of the entire periplasmic space.
Prof. Naresh Patwari (Department of Chemistry, Indian Institute of Technology Bombay, India)
In general intermolecular interactions between pair of closed shell molecules can be represented by [-1, -6, +12] potential. Various intermolecular interaction varies significantly due to the differences in the weightage for each of the three terms. Spectroscopy and ab-initio calculations provide the reasonable understanding of many intermolecular interactions. However, each method has specific shortfalls. Physically meaningful models can only be constructed by adequately addressing these shortfalls while interpreting the data. The importance of each of the components of [-1, -6, +12] potential in understating hydrogen bonding, π–π stacking and in foldamers will be highlighted.
Prof. Sihyun Ham (Department of Chemistry, Sookmyung Women's University, Korea)
Because biomolecular processes are largely under thermodynamic control, dynamic extension of thermodynamics is necessary to uncover the mechanisms and driving factors of fluctuating processes. The fluctuating thermodynamics technology presented in this talk offers a practical means for the thermodynamic characterization of conformational dynamics in biomolecules. The use of fluctuating thermodynamics has the potential to provide a comprehensive picture of fluctuating phenomena in diverse biological processes. Through the application of fluctuating thermodynamics, we provide a thermodynamic perspective on the misfolding and aggregation of the various proteins associated with human diseases. In this talk, I will present the detailed concepts and applications of the fluctuating thermodynamics technology for elucidating biological processes.
Dr. Steven Hayward (School of Computing Sciences, University of East Anglia, UK)
Molecular graphics involves visualisation of molecules but rarely allows the user to engage their sense of touch to help learn about biomolecules.
We have developed three software tools that use a haptic (force-feedback) device: "Haptimol-ISAS", "Haptimol-ENM" and "Haptimol-RD"(See: http://www.haptimol.com). Haptimol-ISAS allows the user to explore the solvent accessible surface of a biomolecule using a haptic device, Haptimol-ENM allows the user to apply forces to an elastic network model of a biomolecule and our latest software tool, Haptimol-RD allows the user to dock molecules rigidly. For Haptimol-RD methods will be described that enable the calculation interaction forces within the time constraint required for smooth perception of forces (1-2 milliseconds). These methods allow us to calculate interaction forces between very large biomolecules when implemented on the GPU.
Future developments will also be discussed, in particular tools that model protein flexibility for drug-protein and protein-protein interactions.
Dr. Makoto Taiji (RIKEN Quantitative Biology Center)
We are developing the special-purpose computer system for MD simulations, MDGRAPE-4. MDGRAPE-4 has a similar architecture as ANTON by D. E. Shaw research - it utilizes a system-on-chip architecture that integrates general-purpose processors, specialized pipelines, memories, and network interfaces.
The MDGRAPE-4 hardware has been completed on August 2014, and we are currently developing the software on it.
We will report the current status of the system and discuss future directions.
Prof. John E. Straub (Department of Chemistry, Boston University, USA)
Amyloid fibrils are naturally occurring, self-assembled, supramolecular systems. Quantitative understanding of the kinetics of fibril formation and the molecular mechanism of transition from monomers to fibrils holds the key to describing the functions of amyloid fibrils. Significant advances using computations of protein aggregation in a number of systems have established generic and sequence specific aspects of the early steps in oligomer formation, as well as the ultimate formation of protofibrils and amyloid fibrils.
Theoretical considerations, that view oligomer and fibril growth as diffusion in a complex energy landscape, and computational studies, involving minimal lattice and coarse-grained models, have revealed general principles governing the transition from monomeric protein to ordered fibrillar aggregates. Detailed atomistic calculations have explored the early stages of the protein aggregation pathway for a number of amyloidogenic proteins, most notably amyloid β (Aβ) protein and protein fragments. These computational studies have provided insights into the role of sequence, role of water, and specific interatomic interactions underlying the thermodynamics and dynamics of elementary kinetic steps in the aggregation pathway. More recently, studies have provided insight into the structural basis for the production of Aβ-peptides through interactions with secretases in the presence of membranes.
Recent results will be discussed, with an emphasis on theory and computation acting as a complement to experimental studies probing the principles governing protein aggregation.
(1) D. Thirumalai, G. Reddy, and J. E. Straub, Acc. Chem. Res. 45, 83-92 (2012).
(2) J.E. Straub and D. Thirumalai, Ann. Rev. Phys. Chem. 62, 437-463 (2011).
(3) J. E. Straub and D. Thirumalai, Curr. Opin. Struc. Bio. 20, 187-195 (2010).
Prof. Wonpil Im (Center for Bioinformatics, Department of Molecular Sciences, The University of Kansas, USA)
The outer membrane of Gram-negative bacteria is a unique asymmetric lipid bilayer that is composed of phospholipids (PL) in the inner leaflet and lipopolysaccharides (LPS) in the outer leaflet. Its function as a selective barrier is crucial for the survival of bacteria in many distinct environments, and it also renders Gram-negative bacteria more resistant to antibiotics than their Gram-positive counterparts. LPS comprises three regions: lipid A, core oligosaccharide, and O-antigen polysaccharide. Utilizing the CHARMM36 lipid and carbohydrate force fields, we have constructed a model of an E. coli R1 (core) O6 (antigen) LPS molecule. Several all-atom bilayers are built and simulated with lipid A only (LIPA) and varying lengths of 0 (LPS0), 5 (LPS5), and 10 (LPS10) O6 antigen repeating units to investigate the impact of the molecular length on LPS bilayer structures. We also studied the structural properties of a model of the E. coli outer membrane and its interaction with various outer membrane proteins, including OmpLA, OmpF, and BamA, utilizing molecular dynamics simulations.
Dr. Karissa Sanbonmatsu (Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, USA)
Prof. Kenneth M. Merz Jr. (Director, Institute for Cyber Enabled Research (iCER), Joseph Zichis Chair in Chemistry Department of Chemistry, Department of Biochemistry and Molecular Biology, Michigan State University, USA)
Docking (posing) calculations coupled with binding free energy estimates (scoring) are a mainstay of structure-based drug design. Docking and scoring methods have steadily improved over the years, but remain challenging because of the extensive sampling that is required, the need for accurate scoring functions and challenges encountered in accurately estimating entropy effects. This talk addresses the use of ensemble principles to directly address these issues and, thereby, accurately estimate protein-ligand binding free energies. In particular, we analytically demonstrate that sampling reduces computed binding free energy uncertainties and then highlight several methods that incorporate these concepts. For example, the moveable type method, employs an elegant approach to generate the necessary ensembles by using a "binned" pairwise knowledge-based potential combined with atom pair probabilities extracted from known protein-ligand complexes. This allows us to rapidly compute the ligand, protein and protein-ligand (inclusive of solvation effects) ensembles which then can be used to directly estimate protein-ligand binding free energies using basic statistical mechanical principles. This approach improves the quality of the potential (scoring) function by reducing computational uncertainty, sampling phase space in one shot and accurately incorporating entropy effects. This allows us to compute binding free energies rapidly, accurately and yields molecular poses at a minimal computational cost relative to currently available methods based on statistical mechanics.
Dr. Wataru Mizukami (University of Bristol, UK)
This talk concerns the complex structures in the electronic states of π-conjugated molecules and the molecular dynamics using quantum chemical methods. Here, the word "complex" structure means that it cannot be reduced into effective one-body problems. At first, I'll show several examples of such multireference (in other words, strongly-correlated) phenomena in organic π-conjugated systems. Then, I'll describe how the state-of-the-art electronic structure theory, such as ab initio density matrix renormalization group, have been addressed to the following intriguing phenomena: Fluorescence spectra from the dark state of polyenes; instability of high spin states of polycarbenes; emergence of multi radical electrons on finite graphene nanoribbons. Finally, I'll outline a new method for multireference quantum molecular dynamics. This method is designed for large-amplitude motions (and chemical reactions) where several degrees of freedom may strongly couple to each other. The key idea is to apply different methods to different coordinates: Strongly-correlated coordinates are treated by an expensive variational method; the remaining couplings are considered perturbatively. This scheme, which can be seen as an analogous vibrational wave function model for the MRMP method in electronic structure theory, allow us to treat a large syste ms with strongly coupled motions efficiently.
Dr. Karissa Sanbonmatsu (Los Alamos National Laboratory)
The ribosome is the universally conserved molecular machine responsible for protein synthesis. Over the past decade, we have focused on the mechanism by which the ribosome decodes genetic information ('the decoding problem', or 'tRNA selection'). By performing large-scale molecular dynamics simulations of the ribosome, we are able to examine the inner workings of this molecular machine. A key rearrangement of the parts of this machine is called 'accommodation'. Here, transfer RNAs (tRNAs) carrying protein building blocks (amino acids) move into the ribosome. We identified a new functional region of the ribosome ('the accommodation corridor') and predicted that certain parts of this corridor are important for ribosome function. Our predictions were recently validated in studies by three experimental groups. In an additional separate set of studies that combined our simulations with single molecule experiments, a new picture of ribosome function has emerged. Rather than the ribosome machine parts moving in lock-step, both simulations and single molecule experiments show the tRNAs making large-scale reversible excursions in a trial-and-error fashion. This picture is consistent with a dynamic energy landscape view of the ribosome. After studying the relatively tractable problem of 'accommodation', we are now investigating the mechanism of translocation, where a large conformational change involving the entire ribosome occurs. This motion allows the ribosome to move exactly 3 nucleotides along the messenger RNA to the next amino acid codon. Using microsecond sampling in explicit solvent for the full ribosome, in combination with experimentally measured rates, we are able to estimate barrier heights for various motions important for translocation. We have also used coarse-grained methods to simulate the various sub-steps of translocation. Our future goal is to use simulations of the ribosome to produce detailed energy landscapes of translocation.
Dr. Karl N. Kirschner (Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI))
The thiazole antibiotic thiostrepton inhibits bacterial protein synthesis by binding to a cleft formed by the ribosomal protein L11 and helix 43-44 of the 23S ribosomal RNA, a part of the GTPase associated region (GAR) on the 70S ribosome. It was proposed from ribosomal crystal structures that the ligand restricts the N-terminal movement of L11 and thus prevents proper binding of translation factors. An exact understanding of this mechanism at atomic resolution is, however, still missing. I will present our results from all-atom molecular dynamics simulations of the binary L11-23S complex and the ternary L11-23S-thiostrepton complex, which provides some new insights into this mechanism at atomic resolution. We demonstrate that thiostrepton has an impact on the protein and rRNA dynamics. Specifically, it restricts the conformational flexibility of the nearby N-terminal domain, and has a weak dynamic coupling to the distant C-terminal domain. We identified distinct conformations of the far more flexible 'apo' form of N-terminal domain that may reflect distinct interaction states with translation factors. If time permits, I will also introduce our new tool for the optimization of force field parameters.
Prof. Sihyun Ham (Department of Chemistry, Sookmyung Women's Univ., Korea)
