Protein Structure Determination Using Chemical Shifts

In this PhD thesis, a novel method to determine protein structures using chemical shifts is presented.Comment: Univ Copenhagen PhD thesis (2014) in Biochemistr

Methods for the refinement of protein structure 3D models

The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge

NMR-Based Computational Studies of Membrane Proteins in Explicit Membranes

Since nuclear magnetic resonance (NMR) spectroscopy data, including solution NMR from micelles and solid-state NMR from bilayers, provide valuable structural and dynamics information of membrane proteins, they are commonly used as restraints in structural determination methods for membrane proteins. However, most of these methods determine the protein structures by fitting the single-confer model into all available NMR restraints regardless of the explicit environmental effects that are determinant in the structures of membrane proteins. To develop a reliable protocol for obtaining optimal structures of membrane proteins in their native-like environments, various NMR properties were applied in the refinement approaches using explicit molecular dynamics (MD) simulations in this research. First, solution NMR NOE based-distance measurements were used as restraints in MD simulations to refine an activating immunoreceptor complex in explicit environments. Compared to the structure determined in vacuum, the resulting structures from the explicit restrained simulations yields a more favorable and realistic side-chain arrangement of a key Asp residue, which is highly consistent with mutagenesis studies on such residue. Incorporating solid-state NMR and solution NMR, MD simulations were performed in the explicit bilayers to refine the structure of membrane-bound Pf1 coat protein. Since solid-state NMR is sparse in its N-terminal periplasmic helix, the protein structure was determined by combining solid-state NMR and solution NMR. Benefiting from the sophisticated energy function and the explicit environments in MD, the orientation of Pf1's periplasmic helix can be identified in simulations restrained by solid-state NMR alone. In the simulations restrained with both solid-state NMR and solution NMR, physically irrelevant structures were frequently observed, suggesting there are conflicts between the restraints from different sample types (e.g., bilayers and micelles). As NMR data are ensemble-averaged measures, the solid-state NMR restrained explicit ensemble dynamics (ED) simulations of fd coat protein were performed in different ensemble sizes and compared to the unrestrained MD simulations. As the ensemble size increases, the violations of resulting structures from experimental NMR data decrease, while the structural variations increase to be comparable to the unrestrained MD simulations, indicating the efficacy of restrained ED in refining structures and extracting dynamics. To investigate the influence of different environments on the structures of membrane proteins, in this research, MD simulations were performed in bilayers and micelles, respectively. Since building a preassembled protein/micelle complex for MD simulation is challenging and requires considerable experience with simulation software, a web-based graphical interface Micelle Builder in CHARMM-GUI (http://www.charmm-gui.org/input/micelle) was developed to support users to build micelle systems in a automatic and simplified process. Using this interface, Pf1 coat protein was preassembled in a protein/micelle model and simulated in explicit environment. Compared to previous simulations of Pf1 coat protein in bilayers, different protein conformations were observed in these simulations due to the distinct behavior and geometry of micelles

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Identification of a 3rd Na+ binding site of the glycine transporter, GlyT2

The Na+/Cl- dependent glycine transporters GlyT1 and GlyT2 regulate synaptic glycine concentrations. Glycine transport by GlyT2 is coupled to the co-transport of three Na+ ions, whereas transport by GlyT1 is coupled to the co-transport of only two Na+ ions. These differences in ion-flux coupling determine their respective concentrating capacities and have a direct bearing on their functional roles in synaptic transmission. The crystal structures of the closely related bacterial Na+-dependent leucine transporter, LeuTAa, and the Drosophila dopamine transporter, dDAT, have allowed prediction of two Na+ binding sites in GlyT2, but the physical location of the third Na+ site in GlyT2 is unknown. A bacterial betaine transporter, BetP, has also been crystallized and shows structural similarity to LeuTAa. Although betaine transport by BetP is coupled to the co-transport of two Na+ ions, the first Na+ site is not conserved between BetP and LeuTAa, the so called Na1' site. We hypothesized that the third Na+ binding site (Na3 site) of GlyT2 corresponds to the BetP Na1' binding site. To identify the Na3 binding site of GlyT2, we performed molecular dynamics (MD) simulations. Surprisingly, a Na+ placed at the location consistent with the Na1' site of BetP spontaneously dissociated from its initial location and bound instead to a novel Na3 site. Using a combination of MD simulations of a comparative model of GlyT2 together with an analysis of the functional properties of wild type and mutant GlyTs we have identified an electrostatically favorable novel third Na+ binding site in GlyT2 formed by Trp263 and Met276 in TM3, Ala481 in TM6 and Glu648 in TM10

A two-domain elevator mechanism for sodium/proton antiport

Sodium/proton (Na+/H+) antiporters, located at the plasma membrane in every cell, are vital for cell homeostasis1. In humans, their dysfunction has been linked to diseases, such as hypertension, heart failure and epilepsy, and they are well-established drug targets2. The best understood model system for Na+/H+ antiport is NhaA from Escherichia coli1, 3, for which both electron microscopy and crystal structures are available4, 5, 6. NhaA is made up of two distinct domains: a core domain and a dimerization domain. In the NhaA crystal structure a cavity is located between the two domains, providing access to the ion-binding site from the inward-facing surface of the protein1, 4. Like many Na+/H+ antiporters, the activity of NhaA is regulated by pH, only becoming active above pH 6.5, at which point a conformational change is thought to occur7. The only reported NhaA crystal structure so far is of the low pH inactivated form4. Here we describe the active-state structure of a Na+/H+ antiporter, NapA from Thermus thermophilus, at 3 Å resolution, solved from crystals grown at pH 7.8. In the NapA structure, the core and dimerization domains are in different positions to those seen in NhaA, and a negatively charged cavity has now opened to the outside. The extracellular cavity allows access to a strictly conserved aspartate residue thought to coordinate ion binding1, 8, 9 directly, a role supported here by molecular dynamics simulations. To alternate access to this ion-binding site, however, requires a surprisingly large rotation of the core domain, some 20° against the dimerization interface. We conclude that despite their fast transport rates of up to 1,500 ions per second3, Na+/H+ antiporters operate by a two-domain rocking bundle model, revealing themes relevant to secondary-active transporters in general