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)

Structural Dynamics and Allosteric Signaling in Ionotropic Glutamate Receptors

Ionotropic glutamate receptors (iGluRs) are ligand-gated ion channels that mediate excitatory neurotransmission events in the central nervous system. All distinct classes of iGluRs (AMPA, NMDA, Kainate) are composed of an N-terminal domain (NTD) and a ligand-binding domain (LBD) in their extracellular domain, a transmembrane domain (TMD) and an intracellular carboxy-terminal domain (CTD). Ligand binding to the LBD facilitates ion channel activation. The NTDs modulate channel gating allosterically in NMDA receptors (NMDARs). A similar function of the NTD in AMPA receptors (AMPARs) is still a matter of debate. Taking advantage of recently resolved structures of the NTD and the intact AMPAR, the main focus of this dissertation is a comprehensive examination of iGluR NTD structural dynamics, ligand binding and allosteric potential of AMPARs. We use a multiscale, multi-dimensional approach using coarse-grained network models and all-atom simulations for structural analyses and information theoretic approaches for examination of evolutionary correlations. Our major contribution has been the characterization of the global motions favored by iGluR NTD architecture. These intrinsic motions favor ligand binding in NMDAR NTDs and are also shared by other iGluR NTDs. We also identified structural determinants of flexibility in AMPARs and confirmed their role through in silico mutants. The overall similarity in collective dynamics among iGluRs hints at a putative allosteric capacity of non-NMDARs and has propelled the elucidation of interdomain and intersubunit coupling in the intact AMPAR. To this end, we identified “effector” and “sensor” regions in AMPARs using a perturbation-response technique. We identified potentially functional residues that enable information propagation between effector regions and proposed an efficient mechanism of allosteric communication based on a combination of tools including network models, graph theoretical methods and sequence analyses. Finally, we assessed the “druggability” of iGluR NTDs using molecular dynamics simulations in the presence of probe molecules containing fragments shared by drug-like molecules. Based on our study, we offer key insights into the ligand-binding landscape of iGluR NTD monomers and dimers, and we also identify a novel ligand-binding site in AMPAR dimers. These findings open an avenue of searching for molecules able to bind to iGluR NTDs and allosterically modulate receptor activity

Elucidating the Energetics of Bacterial Signal Transduction: Insights From Phoq

Bacteria transduce signals across the membrane using two-component systems, consisting of a membrane-spanning sensor histidine kinase and a cytoplasmic response regulator. The histidine kinase, PhoQ, serves as a master regulator of virulence response in S. typhimurium and E. coli. It also is inhibited by divalent cations, particularly Mg2+. While the periplasmic sensor domain of this protein has a unique function, the cytoplasmic portion of this modular protein is made of structurally conserved domains found in many other bacterial sensor kinases. Signal transduction through these conserved domains is thought to be universal; however, the structural and energetic rearrangements that occur during signaling have generated numerous models. Through Bayesian inference we constructed a two-state model based on cysteine crosslinking data and homologous crystal structures. These two signaling states differ in membrane depth of the periplasmic acidic patch as well as the reciprocal displacement of diagonal helices along the dimer interface. Comparative studies of multiple histidine kinases suggest that diagonal displacement of helices is a common mode of signal transduction. A similar scissor-like model was previously ruled out in CheA-linked chemoreceptors; therefore, this new evidence suggests that sensor His-kinase and CheA-linked receptors possess different signaling mechanisms. To unify the various signaling mechanisms that exist for the different protein domains, we built a thermodynamic model based on Linked Equilibrating Domains (LED). We used this model to quantitatively interpret functional data of single-point Ala, Phe and Cys mutants throughout the signal transducing regions of PhoQ. Data from 35 mutants, including both activating and deactivating phenotypes, were globally fit using LED, and gross features such as Vmax and Kd were related to more nuanced population distributions and thermodynamic coupling. LED analysis highlights the principles by which individual signaling domains can be connected to create a functional signal transducer. These principles allow us to quantitatively explain signaling in histidine kinases and are likely to be broadly applicable to many other signal transduction proteins

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification