Sampling of the conformational landscape of small proteins with Monte Carlo methods

Computer simulation provides an increasingly realistic picture of large-scale conformational change of proteins, but investigations remain fundamentally constrained by the femtosecond timestep of molecular dynamics simulations. For this reason, many biologically interesting questions cannot be addressed using accessible state-of-the-art computational resources. Here, we report the development of an all-atom Monte Carlo approach that permits the modelling of the large-scale conformational change of proteins using standard off-the-shelf computational hardware and standard all-atom force fields. We demonstrate extensive thermodynamic characterization of the folding process of the α-helical Trp-cage, the Villin headpiece and the β-sheet WW-domain. We fully characterize the free energy landscape, transition states, energy barriers between different states, and the per-residue stability of individual amino acids over a wide temperature range. We demonstrate that a state-of-the-art intramolecular force field can be combined with an implicit solvent model to obtain a high quality of the folded structures and also discuss limitations that still remain

Monte-Carlo Simulations of Soft Matter Using SIMONA: A Review of Recent Applications

Molecular simulations such as Molecular Dynamics (MD) and Monte Carlo (MC) have gained increasing importance in the explanation of various physicochemical and biochemical phenomena in soft matter and help elucidate processes that often cannot be understood by experimental techniques alone. While there is a large number of computational studies and developments in MD, MC simulations are less widely used, but they offer a powerful alternative approach to explore the potential energy surface of complex systems in a way that is not feasible for atomistic MD, which still remains fundamentally constrained by the femtosecond timestep, limiting investigations of many essential processes. This paper provides a review of the current developments of a MC based code, SIMONA, which is an efficient and versatile tool to perform large-scale conformational sampling of different kinds of (macro)molecules. We provide an overview of the approach, and an application to soft-matter problems, such as protocols for protein and polymer folding, physical vapor deposition of functional organic molecules and complex oligomer modeling. SIMONA offers solutions to different levels of programming expertise (basic, expert and developer level) through the usage of a designed Graphical Interface pre-processor, a convenient coding environment using XML and the development of new algorithms using Python/C++. We believe that the development of versatile codes which can be used in different fields, along with related protocols and data analysis, paves the way for wider use of MC methods

Influence of pH and Acidic Side Chain Charges on the Behavior of Designed Model Peptides in Lipid Bilayer Membranes

The molecular properties of transmembrane proteins and their interactions with lipids regulate biological function. Of particular interest are interfacial aromatic residues and charged residues in the core helix whose functions range from stabilizing the native structure to regulating ion channels. This dissertation addresses the pH dependence and influence of potentially negatively charged tyrosine, glutamic acid or aspartic acid side chains. We have employed GWALP23 (acetyl-GGALW5LALALALALALALW19LAGA-amide) as favorable host peptide framework. We have substituted W5 with Tyr (Y5GWALP23) and Leu residues with Glu (L12E, L14E or L16E) or Asp (L14D or L16D), and have incorporated specific 2H-labeled alanine residues within the core helix or near the ends of the sequence. Solid-state 2H-NMR spectra reveal a pKa of about 10.5 for bilayer incorporated Y5GWALP23. Solid-state 2H-NMR spectra of GWALP23-E12, –E14 and –E16 with core labels reveal little change to the orientation of the transmembrane helix over a pH range of 4 to 12.5 but modest changes in quadrupolar splitting magnitudes above pH 12.5 in DLPC bilayer membranes, with E12 peptides showing no change even at pH 13. The E12, E14 and E16 peptides display broad 2H NMR spectra in aligned DOPC bilayers, with individual resonances not being observed for the core labels. Labeling the ends of the helix at A3 and A21 provided insights into the pH-dependent unwinding of the E14 and E16 peptide helices in both lipid systems. An aspartic acid residue at position 14 shows contrasting behavior to that of its Glu counterpart. The 2H-NMR spectra for core 2H-alanines of GWALP23-D14 show a preference for a well oriented conformation in DOPC bilayers in comparison to DLPC lipids. While the core helix does not respond to pH, the helix terminals show changes in unwinding between pH 6 and 13 suggesting a possible pKa around 13. The polar but uncharged Gln residue at position 14 behaves similarly to Glu in DLPC and DOPC lipid bilayers. The Q14 peptide, however, does not titrate in either lipid and displays well-resolved sharper 2H-NMR resonances in DLPC bilayers. The combined results illustrate complex behavior for carboxyl and carboxamide side chains in bilayer membranes

Hierarchical Coarse-Grained Strategy for Macromolecular Self-Assembly: Application to Hepatitis B Virus-Like Particles

Macromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models

Modeling of supramolecular biopolymers: Leading the <i>in silico</i> revolution of tissue engineering and nanomedicine

Abstract The field of tissue engineering is poised to be positively influenced by the advent of supramolecular biopolymers, because of their promising tailorability coming from the bottom-up approach used for their development, absence of toxic byproducts from their gelation reaction and intrinsic better mimicry of extracellular matrix nanotopography and mechanical properties. However, a deep understanding of the phenomena ruling their properties at the meso- and macroscales is still missing. In silico approaches are increasingly helping to shine a light on questions still of out of reach for almost all empirical methods. In this review, we will present the most significant and updated efforts on molecular modeling of SBP properties, and their interactions with the living counterparts, at all scales. In detail, the currently available molecular mechanic approaches will be discussed, paying attention to the pros and cons related to their representability and transferability. We will also give detailed insights for choosing different biomolecular modeling strategies at various scales. This is a systematic overview of tools and approaches yielding to advances at atomistic, molecular, and supramolecular levels, with a holistic perspective demonstrating the urgent need for theories and models connecting biomaterial design and their biological effect in vivo

Modelowanie oddziaływań białek i peptydów w zredukowanej przestrzeni konformacyjnej.

Celem pracy było opracowanie metody do modelowania oddziaływań białek i peptydów. Metodę oparto na modelu CABS służącym do modelowania dynamiki pojedynczych łańcuchów białkowych. Poza głównym programem opracowano szereg narzędzi i połączono w automatyczną procedurę CABSDock. Praca zawiera opis opracowanych narzędzi, oraz szereg przykładów zastosowania CABSDock

Secondary structure-based template selection for fragment-assembly protein structure prediction

Proteins play critical biochemical roles in all living organisms; in human beings, they are the targets of 50% of all drugs. Although the first protein structure was determined 60 years ago, experimental techniques are still time and cost consuming. Consequently, in silico protein structure prediction, which is considered a main challenge in computational biology, is fundamental to decipher conformations of protein targets. This thesis contributes to the state of the art of fragment-assembly protein structure prediction. This category has been widely and thoroughly studied due to its application to any type of targets. While the majority of research focuses on enhancing the functions that are used to score fragments by incorporating new terms and optimising their weights, another important issue is how to pick appropriate fragments from a large pool of candidate structures. Since prediction of the main structural classes, i.e. mainly-alpha, mainly-beta and alpha-beta, has recently reached quite a high level of accuracy, we have introduced a novel approach by decreasing the size of the pool of candidate structures to comprise only proteins that share the same structural class a target is likely to adopt. Picking fragments from this customised set of known structures not only has contributed in generating decoys with higher level of accuracy but also has eliminated irrelevant parts of the search space which makes the selection of first models a less complicated process, addressing the inaccuracies of energy functions. In addition to the challenge of adopting a unique template structure for all targets, another one arises whenever relying on the same amount of corrections and fine tunings; such a phase may be damaging to “easy’ targets, i.e. those that comprise a relatively significant percentage of alpha helices. Owing to the sequence-structure correlation based on which fragment-based protein structure prediction was born, we have also proposed a customised phase of correction based on the structural class prediction of the target in question. After using secondary structure prediction as a “global feature” of a target, i.e. structural classes, we have also investigated its usage as a “local feature” to customise the number of candidate fragments, which is currently the same at all positions. Relying on the known facts regarding diversity of short fragments of helices, sheets and loops, the fragment insertion process has been adjusted to make “changes” relative to the expected complexity of each region. We have proved in this thesis the extent to which secondary structure features can be used implicitly or explicitly to enhance fragment assembly protein structure prediction

MOLECULAR PHYSIOLOGY OF BLOOD-BRAIN BARRIER TIGHT JUNCTIONS

The molecular interface of the blood-brain barrier (BBB) is a highly selective physiological barrier. The BBB shields the central nervous system (CNS) for harmful agents while also preventing lifesaving drugs from entering the CNS. With the prevalence of neurogenerative disease on the rise, there is a growing interest to design therapeutic interventions that can surpass the BBB. Such efforts necessitate a thorough understanding of the BBB, requiring one to decipher: why the BBB is so selective? what governing molecular rules govern selectivity across the BBB? and how does it impact physiology. As a contribution towards this understanding the following dissertation discusses nuances of the BBB see from the perspective of its tight junctions (TJ). Tight junctions are a protein-protein adhesion structures that seal the paracellular space for small solutes. Tight junctions are a common feature in many epithelial and endothelial tissues and a crucial component of the BBB. The BBB tight junctions are shown to be regulate a size and charge selective barrier that permeates only molecules of 800 Da in size. In the following chapters a computational microscopy approach was utilized to probe different structural and biochemical features of the tight junction. Chapter 2 discusses the molecular assembly of tight junction proteins investigates for the first time under molecular dynamics simulations. The key findings included the discovery of dimeric interfaces that are seen to form tight structural contacts between conserved residues. An experimental investigation with formaldehyde as a cross-linker in HeLa cells validated the existence of such contacts. Chapter 3 investigated the tight junction assembly in the paracellular space of adjacent cells by mimicking this interface with two membranes. These simulations revealed the structural aspects of the pores that are feasible under claudin-5 tight junction assembly. We performed a mutation experiment that distinguished the dimeric interfaces between claudin-3 and claudin-5, further a biophysical investigation showed how the flexibility of the transmembrane domains affect the dimerization of claudins. Chapter 4 extends upon the discoveries from chapters 2 and 3 to other claudins that are relevant for the tight junction biology. There is an inherent need to compare different members of the claudin family of proteins to enhance the overall understanding about tight junction biology and consequently the BBB tight junctions. Major findings include the discovery of a putative trimeric receptor assembly for Clostridium perfringens enterotoxin. The pore assemblies of claudin-2 and the dynamics of ions across the pores. Chapter 5 investigates the ion selectivity of claudin-5 and claudin-2 in a greater detail. The key findings include that the barrier to charge selectivity in the claudin pores are due to charge repulsion from the pore lining residues. The electrostatic interaction dominates the pore selectivity while the steric interaction plays a role for divalent cations. These biophysical evidence reveal how the claudin-5 tight junction pores that line the BBB screen charged ions and water. These computational findings push the boundaries of current knowledge on the BBB and sets the stage for applications targeted towards drug delivery strategies. The computational methods and tools discussed herein sets precedent for its transferability to the investigation of other tight junction proteins and in wider scope other membrane proteins

Cell-free expression and molecular modeling of the γ-secretase complex and G-protein-coupled receptors

Alzheimer’s disease (AD), which was first reported more than a century ago by Alhzeimer, is one of the commonest forms of dementia which affects >30 million people globally (>8 million in Europe). The origin and pathogenesis of AD is poorly understood and there is no cure available for the disease. AD is characterized by the accumulation of senile plaques composed of amyloid beta peptides (Ab 37-43) which is formed by the gamma secretase (GS) complex by cleaving amyloid precursor protein. Therefore GS can be an attractive drug target. Since GS processes several other substrates like Notch, CD44 and Cadherins, nonspecific inhibition of GS has many side effects. Due to the lack of crystal structure of GS, which is attributed to the extreme difficulties in purifying it, molecular modeling can be useful to understand its architecture. So far only low resolution cryoEM structures of the complex has been solved which only provides a rough structure of the complex at low 12-15 A resolution Furthermore the activity of GS in vitro can be achieved by means of cell-free (CF) expression. GS comprises catalytic subunits namely presenilins and supporting elements containing Pen-2, Aph-1 and Nicastrin. The origin of AD is hidden in the regulated intramembrnae proteolysis (RIP) which is involved in various physiological processes and also in leukemia. So far growth factors, cytokines, receptors, viral proteins, cell adhesion proteins, signal peptides and GS has been shown to undergo RIP. During RIP, the target proteins undergo extracellular shredding and intramembrane proteolysis. This thesis is based on molecular modeling, molecular dynamics (MD) simulations, cell-free (CF) expression, mass spectrometry, NMR, crystallization, activity assay etc of the components of GS complex and G-protein coupled receptors (GPCRs). First I validated the NMR structure of PS1 CTF in detergent micelles and lipid bilayers using coarse-grained MD simulations using MARTINI forcefield implemented in Gromacs. CTF was simulated in DPC micelles, DPPC and DLPC lipid bilayer. Starting from random configuration of detergent and lipids, micelle and lipid bilyer were formed respectively in presence of CTF and it was oriented properly to the micelle and bilyer during the simulation. Around DPC molecules formed micelle around CTF in agreement of the experimental results in which 80-85 DPC molecules are required to form micelles. The structure obtained in DPC was similar to that of NMR structure but differed in bilayer simulations showed the possibility of substrate docking in the conserved PAL motif. Simulations of CTF in implicit membrane (IMM1) in CHAMM yielded similar structure to that from coarse grained MD. I performed cell-free expression optimization, crystallization and NMR spectroscopy of Pen-2 in various detergent micelles. Additionally Pen-2 was modeled by a combination of rosetta membrane ab-initio method, HHPred distant homology modeling and incorporating NMR constraints. The models were validated by all atom and coarse grained MD simulations both in detergent micelles and POPC/DPPC lipid bilayers using MARTINI forcefield. GS operon consisting of all four subunits was co-expressed in CF and purified. The presence of of GS subunits after pull-down with Aph-1 was determined by western blotting (Pen-2) and mass spectrometry (Presenilin-1 and Aph-1). I also studied interactions of especially PS1 CTF, APP and NTF by docking and MD. I also made models and interfaces of Pen-2 with PS1 NTF and checked their stability by MD simulations and compared with experimental results. The goal is to model the interfaces between GS subunits using molecular modeling approaches based on available experimental data like cross-linking, mutations and NMR structure of C-terminal fragment of PS1 and transmembrane part of APP. The obtained interfaces of GS subunits may explain its catalysis mechanism which can be exploited for novel lead design. Due to lack of crystal/NMR structure of the GS subunits except the PS1 CTF, it is not possible to predict the effect of mutations in terms of APP cleavage. So I also developed a sequence based approach based on machine learning using support vector machine to predict the effect of PS1 CTF L383 mutations in terms of Aβ40/Aβ42 ratio with 88% accuracy. Mutational data derived from the Molgen database of Presenilin 1 mutations was using for training. GPCRs (also called 7TM receptors) form a large superfamily of membrane proteins, which can be activated by small molecules, lipids, hormones, peptides, light, pain, taste and smell etc. Although 50% of the drugs in market target GPCRs , only few are targeted therapeutically. Such wide range of targets is due to involvement of GPCRs in signaling pathways related to many diseases i.e. dementia (like Alzheimer's disease), metabolic (like diabetes) including endocrinological disorders, immunological including viral infections, cardiovascular, inflammatory, senses disorders, pain and cancer. Cannabinoid and adrenergic receptors belong to the class A (similar to rhodopsin) GPCRs. Docking of agonists and antagonists to CB1 and CB2 cannabinoid receptors revealed the importance of a centrally located rotamer toggle switch, and its possible role in the mechanism of agonist/antagonist recognition. The switch is composed of two residues, F3.36 and W6.48, located on opposite transmembrane helices TM3 and TM6 in the central part of the membranous domain of cannabinoid receptors. The CB1 and CB2 receptor models were constructed based on the adenosine A2A receptor template. The two best scored conformations of each receptor were used for the docking procedure. In all poses (ligand-receptor conformations) characterized by the lowest ligand-receptor intermolecular energy and free energy of binding the ligand type matched the state of the rotamer toggle switch: antagonists maintained an inactive state of the switch, whereas agonists changed it. In case of agonists of β2AR, the (R,R) and (S,S) stereoisomers of fenoterol, the molecular dynamics simulations provided evidence of different binding modes while preserving the same average position of ligands in the binding site. The (S,S) isomer was much more labile in the binding site and only one stable hydrogen bond was created. Such dynamical binding modes may also be valid for ligands of cannabinoid receptors because of the hydrophobic nature of their ligand-receptor interactions. However, only very long molecular dynamics simulations could verify the validity of such binding modes and how they affect the process of activation. Human N-formyl peptide receptors (FPRs) are G protein-coupled receptors (GPCRs) involved in many physiological processes, including host defense against bacterial infection and resolving inflammation. The three human FPRs (FPR1, FPR2 and FPR3) share significant sequence homology and perform their action via coupling to Gi protein. Activation of FPRs induces a variety of responses, which are dependent on the agonist, cell type, receptor subtype, and also species involved. FPRs are expressed mainly by phagocytic leukocytes. Together, these receptors bind a large number of structurally diverse groups of agonistic ligands, including N-formyl and nonformyl peptides of different composition, that chemoattract and activate phagocytes. For example, N-formyl-Met-Leu-Phe (fMLF), an FPR1 agonist, activates human phagocyte inflammatory responses, such as intracellular calcium mobilization, production of cytokines, generation of reactive oxygen species, and chemotaxis. This ligand can efficiently activate the major bactericidal neutrophil functions and it was one of the first characterized bacterial chemotactic peptides. Whereas fMLF is by far the most frequently used chemotactic peptide in studies of neutrophil functions, atomistic descriptions for fMLF-FPR1 binding mode are still scarce mainly because of the absence of a crystal structure of this receptor. Elucidating the binding modes may contribute to designing novel and more efficient non-peptide FPR1 drug candidates. Molecular modeling of FPR1, on the other hand, can provide an efficient way to reveal details of ligand binding and activation of the receptor. However, recent modelings of FPRs were confined only to bovine rhodopsin as a template. To locate specific ligand-receptor interactions based on a more appropriate template than rhodopsin we generated the homology models of FPR1 using the crystal structure of the chemokine receptor CXCR4, which shares over 30% sequence identity with FPR1 and is located in the same γ branch of phylogenetic tree of GPCRs (rhodopsin is located in α branch). Docking and model refinement procedures were pursued afterward. Finally, 40 ns full-atom MD simulations were conducted for the Apo form as well as for complexes of fMLF (agonist) and tBocMLF (antagonist) with FPR1 in the membrane. Based on locations of the N- and C-termini of the ligand the FPR1 extracellular pocket can be divided into two zones, namely, the anchor and activation regions. The formylated M1 residue of fMLF bound to the activation region led to a series of conformational changes of conserved residues. Internal water molecules participating in extended hydrogen bond networks were found to play a crucial role in transmitting the agonist-receptor interactions. A mechanism of initial steps of the activation concurrent with ligand binding is proposed. I accurately predicted the structure and ligand binding pose of dopamine receptor 3 (RMSD to the crystal structure: 2.13 Å) and chemokine receptor 4 (CXCR4, RMSD to the crystal structure 3.21 Å) in GPCR-Dock 2010 competition. The homology model of the dopamine receptor 3 was 8 th best overall in the competition

Monte Carlo simulation studies of DNA hybridization and DNA-directed nanoparticle assembly

A coarse-grained lattice model of DNA oligonucleotides is proposed to investigate how fundamental thermodynamic processes are encoded by the nucleobase sequence at the microscopic level, and to elucidate the general mechanisms by which single-stranded oligonucleotides hybridize to their complements either in solution or when tethered to nanoparticles. Molecular simulations based on a high-coordination cubic lattice are performed using the Monte Carlo method. The dependence of the model's thermal stability on sequence complementarity is shown to be qualitatively consistent with experiment and statistical mechanical models. From the analysis of the statistical distribution of base-paired states and of the associated free-energy landscapes, two general hybridization scenarios are found. For sequences that do not follow a two-state process, hybridization is weakly cooperative and proceeds in multiple sequential steps involving stable intermediates with increasing number of paired bases. In contrast, sequences that conform to two-state thermodynamics exhibit moderately rough landscapes, in which multiple metastable intermediates appear over broad free-energy barriers. These intermediates correspond to duplex species that bridge the configurational and energetic gaps between duplex and denatured states with minimal loss of conformational entropy, and lead to a strongly cooperative hybridization. Remarkably, two-state thermodynamic signatures are generally observed in both scenarios. The role of cooperativity in the assembly of nanoparticles tethered with model DNA oligonucleotides is similarly addressed with the Monte Carlo method, where nanoparticles are represented as finely discretized hard-core spheres on a cubic lattice. The energetic and structural mechanisms of self-assembling are investigated by simulating the aggregation of small "satellite" particles from the bulk onto a large "core" particle. A remarkable enhancement of the system's thermal stability is attained by increasing the number of strands per satellite particle available to hybridize with those on the core particle. This cooperative process is driven by the formation of multiple bridging duplexes under favorable conditions of reduced translational entropy and the resultant energetic compensation; this behavior rapidly weakens above a certain threshold of linker strands per satellite particle. Cooperativity also enhances the structural organization of the assemblies by systematically narrowing the radial distribution of the satellite particles bound the core