Conformational equilibria and spectroscopy of gas-phase homologous peptides from first principles

Peptides and proteins fulfil crucial tasks enabling and maintaining life. Their function is directly correlated with their three-dimensional structure, which is in turn determined by their chemical composition, the amino-acid sequence. Predicting the structure of a peptide based only on its sequence information is of fundamental interest. A fully first-principles treatment free of empirical parameters would be ideal. However, this presents an ongoing challenge, due to the large system size and conformational space of most peptides. In the present work, we address this challenge concentrating on the example of polyalanine-based peptides in the gas phase. Such studies under isolated conditions follow a bottom-up approach that allows one to investigate the intramolecular interactions important for secondary structure separate from environmental effects. Furthermore, direct benchmarks of theoretical structure predictions against experiment are facilitated. The peptide series Ac-Alan-Lys(H+), (n > 6), forms α-helices in the gas phase due to a favorable interaction of the helix dipole with the positive charge at the C-terminal lysine residue. Using this design principle as a template, we explore the impact of increased structural flexibility on the conformational space due to (i) sequence length [Ac-Alan-Lys(H+), n = 19], (ii) charge placement [Ac-Ala19-Lys(H+) versus Ac-Lys(H+)-Ala19], and (iii) backbone elongation of the monomer units as represented by β-amino acids [Ac-β2hAla6-Lys(H+)]. To address the large conformational space, we develop a three-step structure-search strategy employing an unprecedented first-principles screening effort. After pre-sampling of the conformational space using a force field, thousands of structures are optimized employing density-functional theory (DFT). For this, the PBE functional is used, coupled with a pairwise correction for van der Waals interactions. For the best few structure candidates, ab initio replica-exchange molecular-dynamics simulations are performed in order to refine the local structural environment. It is shown that these can yield lower-energy conformations and lead to rearrangements of the hydrogen-bonding network. In order to connect to experiment, collision cross sections are calculated that link to ion mobility-mass spectrometry. Furthermore, infrared spectra are derived from ab initio Born-Oppenheimer molecular-dynamics simulations accounting for anharmonicities within the classical-nuclei approximation. As expected, the 20-residue peptide Ac-Ala19-Lys(H+) forms helical structures. In contrast, placing the charge at the N-terminus [Ac-Lys(H+)-Ala19], leads to several different compact structures, which are close in energy. Such small energy differences present a challenge to the theoretical approach. Incorporating exact exchange and many-body van der Waals effects predicts the presence of only one dominant conformer, which is compatible with both experimental datasets. In comparison to Ac-Ala6-Lys(H+), the β-peptide Ac-β2hAla6-Lys(H+) exhibits increased conformational flexibility due to an extended monomer backbone. Out of the almost 15,000 structures optimized with DFT, no helical conformers are found in the low-energy regime. This is changed when considering vibrational free energy (300K, harmonic approximation), which strongly favors helical conformations due to softer vibrational modes. One possible structure candidate is the H16-helix, which is compatible with both experiments. It is a unique structure as it exhibits a hydrogen-bonding pattern equivalent to the helix of natural peptides. The systems considered here highlight the advances of current DFT functionals to address the large conformational space of peptides, but also the need for further development

Development of Improved Torsional Potentials in Classical Force Field Models of Poly (Lactic Acid)

In this work, existing force field descriptions of poly (lactic acid), or PLA, were improved by modifying the torsional potential energy terms to more accurately model the bond rotational behavior of PLA. Extensive calculations were carried out using density functional theory (DFT), for small PLA molecules in vacuo, and also using DFT with a continuum model to approximate the electronic structure of PLA in its condensed phase. From these results, improved force field parameters were developed using a combination of the OPLS and CHARMM force fields. The new force field, PLAFF2, is an update to the previously developed PLAFF model developed in David Bruce\u27s group, and results in more realistic conformational distributions during simulation of bulk amorphous PLA. It is demonstrated that the PLAFF2 model retains the accuracy of the original PLAFF in simulating the crystalline α polymorph of PLA. The PLAFF2 model has superior performance to any other publicly available force field for use with PLA; hence, we recommend its use in future modeling studies on the material, whether in its crystalline or amorphous form

Roles of cosolvents on protein stability

The function of a protein is determined by its three-dimensional structure which emerges from the delicate balance of forces involving atoms of the protein and the solvent. This balance can be perturbed by changing temperature, pressure, pH and by adding organic molecules known as cosolvents to the solution. Despite the wide use of cosolvents to perturb protein structures in the lab and in living systems, their molecular mechanisms are still not well established. Understanding these mechanisms is a problem of substantial interest, with potential application to the design of new drugs to target proteins. In this dissertation, we probe the role of two major cosolvents, urea and trimethylamine N-oxide (TMAO) at atomic level. Urea is widely used as a denaturant in the lab to destabilize native protein conformations. However, the atomic mechanism of this molecule remains a question of debate. To unravel its molecular mechanism, explicit all-atom molecular dynamics simulations of unrestrained and extended poly-alanine and poly-leucine dimers are performed. Consistent with experimental results, we find that the large non-polar side chain of leucine is affected by urea whereas backbone atoms and alanine’s side chain are not. Urea is found to occupy positions between leucine’s side chains that are not accessible to water. This accounts for extra Lennard-Jones bonds between urea and side chains that favors the unfolded state. These bonds compete with urea-solvent interactions that favor the folded state. The sum of these two energetic terms provides the enthalpic driving force for unfolding. It is shown here that this enthalpy correlates with the potential of mean force of poly-leucine dimers. To provide insights into the stabilizing mechanisms TMAO on protein structures, microsecond all-atom molecular dynamics simulations of peptides and replica exchange molecular dynamics simulations (REMD) of the Trp-cage miniprotein are performed. Most previous studies have focused on the effect of this osmolyte on protein backbone. Our results are consistent with these studies as we show that TMAO induces the backbone to adopt compact conformations. However, it is shown that effects of TMAO on the backbone are not dominant. In particular, TMAO\u27s effect on the backbone is overcompensated by its destabilizing effect on the hydrophobic core: non-polar peptides and residues forming the hydrophobic core of the Trp-cage protein adopt more extended conformations in solutions containing TMAO. It is found that a main interaction that can stabilize folded proteins are charge-charge interactions. In light of these results, we propose that competing effects of TMAO on hydrophobic and charge-charge interactions account for its net stabilizing role on proteins

Systematic conformational search with constraint satisfaction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 170-177).Determining the conformations of biological molecules is a high scientific priority for biochemists and for the pharmaceutical industry. This thesis describes a systematic method for conformational search, an application of the method to determining the structure of the formyl-Met-Leu-Phe-OH (fMLF)peptide by solid-state NMR spectroscopy, and a separate project to determine the structure of a protein-DNA complex by X-ray crystallography. The purpose of the systematic search method is to enumerate all conformations of a molecule (at a given level of torsion angle resolution) that satisfy a set of local geometric constraints. Constraints would typically come from NMR experiments, but applications such as docking or homology modelling could also give rise to similar constraints. The molecule to be searched is partitioned into small subchains so that the set of possible conformations for the whole molecule may be constructed by merging the feasible conformations for the parts. However, instead of using a binary tree for straightforward divide-and-conquer, four innovations are introduced: (1) OMNIMERGE searches a subproblem for every possible subchain of the molecule. Searching every subchain provides the advantage that every possible merge is available; by choosing the most favorable merge for each subchain, the bottleneck subchain(s) and therefore the whole search may be completed more efficiently. (2) A cost function evaluates alternative divide-and-conquer trees, provided that a preliminary OMNIMERGE search of the molecule has been completed. Then dynamic programming determines the optimal partitioning or "merge-tree" for the molecule; this merge-tree can be used to improve the efficiency of future searches.(cont.) (3) PROPAGATION shares information by enforcing arc consistency between the solution sets of overlapping subchains. By filtering the solution set of each subchain, infeasible conformations are discarded rapidly. (4) An A* function prioritizes each subchain based on estimated future costs. Subchains with sufficiently low priority can be skipped, which improves efficiency. A common theme of these four ideas is to make good choices about how to break the large search problem into lower-dimensional subproblems. These novel algorithms were implemented and the effectiveness of each is demonstrated on a well-constrained peptide with 40 degrees of freedom.by Lisa Tucker-Kellogg.Ph.D

Multiscale Modelling of CTCF and its Complexes

More and more experiments show that the CCCTC-binding factor (CTCF), a multi-Cys2His2 (mC2H2) zinc finger protein, plays a key role in the spatial organization of chromatin and gene regulation in the nucleus of eukaryotic cells. In this context an important problem is to uncover the underlying mechanism of how CTCF shapes the chromatin structure. In this thesis, models on different scales, from atomistic scale to coarse-grained scale, are studied to better understand the conformational and dynamical properties of both the unbound CTCF and CTCF-DNA complexes. Using homology modeling, an atomistic model of CTCF is constructed to study the conformational properties of unbound mC2H2 zinc finger proteins. To enhance the computing and sampling efficiency an atomistic pivoting algorithm and a mesoscale model for mC2H2 proteins is developed. It is shown that the conformations of unbound mC2H2 proteins, like CTCF, can be explained with a worm-like chain model. For proteins of a few zinc finger, an effective bending constraint favors an extended conformation, which is consistent with experimental findings. A self-avoiding chain model applies only to proteins of more than nine zinc fingers. As a subsequent step, a mesoscale model is designed to study how a mC2H2 zinc finger protein binds to and searches for its target DNA loci. Statistical sequence-dependent interactions between the proteins and DNA are derived. Molecular dynamics simulations of this model reproduce several kinetic properties of mC2H2 zinc finger proteins, such as the rotation coupled sliding, the asymmetrical roles of different zinc fingers and the partial binding partial dangling mode. An application to CTCF in complexes with one of its target DNA duplex shows that CTCF binds to DNA only by using its central zinc fingers. It asymmetrically bends the DNA duplex but does not form DNA loops. Other CTCF-assisted DNA looping mechanisms, like a bridged DNA loop organized by a CTCF homodimer, could be further studied with this model. Motivated by the non-covalent binding of polypeptides to DNA, I study the adsorption of a flexible polymer to a rigid polymer with periodic binding sites, both in 2d and in 3d. Analysis of Monte Carlo simulation results show that the phase transition, from non-adsorbed to adsorbed with increasing adsorbing strength, is a second order transition in 2d, and higher order transition in 3d. Compared to the adsorbed monomers, successive non-adsorbed monomers contribute more to the winding of the flexible polymer around a rigid polymer, showing the importance of the linkers in mC2H2 zinc finger proteins to wrap around DNA

Supramolecular assembly and mechanical properties of dermis

The present work is a part of a wider research project which aims at the in vitro tissues and biohybrid generation. The process of generating biological tissues requires benchmarks in order to define the optimal set of design and performance parameters for the tissue of interest. As a consequence of that, my efforts have been devoted to the study of natural tissue. In particular I have focused my attention to their composition, microstructure and macroscopic properties. The first part of the thesis reviews recent studies concerning the assembly and spatial arrangement of some biological macromolecules of interest, which compose the extracellular matrix. The extracellular matrix is indeed largely responsible for the macroscopic physical properties of connective tissues. Skin has been chosen as model of connective tissue to study. This choice is motivated by the fact that skin is a more general model rather then tendons, which are mainly subjected to uniaxal tension, and the osmosis-supported cartilage. An experimental campaign has been designed in order to gather information on dermal composition and structure, and how these characteristics can affect the macroscopic behaviour of the tissue. The results of this experimental campaign are shown in the second part of the work. At last two constitutive equations are presented. Both of them are developed within the framework of continuum mechanics. The first one is a full three dimensional model able to capture the elastic behaviour of dermis at large deformations. The second model is able to predict the viscoelastic behaviour. Both model accounts for the anisotropy of the native tissue and are structural model, since they contain parameters on the underlying histology. The development of these models provide noteworthy information on the performance of tissue-engineered constructs whose properties have been designed ab initio. In particular, since the mechanical properties of biohybrids can be on-line monitored during culturing in bioreactors. Thus constitutive models can provide cues on the evolution of the mechanical properties, giving the chance to investigate on the complex relationship between mechanical stimulus and tissue remodelling

Rigidity analysis of protein structures and rapid simulations of protein motion

It is a common goal in biophysics to understand protein structural properties and their relationship to protein function. I investigated protein structural properties using three coarse graining methods: a rigidity analysis method First, a geometric simulation method Froda and normal mode analysis as implemented in Elnemo to identify the protein directions of motion. Furthermore, I also compared the results between the coarse graining methods with the results from molecular dynamics and from experiments that I carried out. The results from the rigidity analysis across a set of protein families presented in chapter 3 highlighted two different patterns of protein rigidity loss, i.e. "sudden" and "gradual". It was found that theses characteristic patterns were in line with the rigidity distribution of glassy networks. The simulations of protein motion by merging flexibility, rigidity and normal mode analyses presented in chapter 4 were able to identify large conformational changes of proteins using minimal computational resources. I investigated the use of RMSD as a measure to characterise protein motion and showed that, despite it is a good measure to identify structural differences when comparing the same protein, the use of extensive RMSD better captures the extend of motion of a protein structure. The in-depth investigation of yeast PDI mobility presented in chapter 5 confirmed former experimental results that predicted a large conformational change for this enzyme. Furthermore, the results predicted: a characteristic rigidity distribution for yeast PDI, a minimum and a maximum active site distance and a relationship between the energy cutoff, i.e. the number of hydrogen bonds part of the network of bonds, and protein mobility. The results obtained were tested against molecular dynamics simulations in chapter 6. The MD simulation also showed a large conformational change for yeast PDI but with a slightly different minimum and maximum inter-cysteine distance. Furthermore, MD was able to reveal new data, i.e. the most likely inter-cysteine distance. In order to test the accuracy of the coarse graining and MD simulations I carried out cross-linking experiments to test the minimum inter-cysteine distance predictions. The results presented in chapter 7 show that human PDI minimum distance is below 12Å whereas the yeast PDI minimum distance must be above 12Å as no cross-linking structures where found with the available (12Å long) cross-linkers

Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning.

Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported