Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Protein motions and dynamic effects in enzyme catalysis

The role of protein motions in promoting the chemical step of enzyme catalysed reactions remains a subject of considerable debate. Here, a unified view of the role of protein dynamics in dihydrofolate reductase catalysis is described. Recently the role of such motions has been investigated by characterising the biophysical properties of isotopically substituted enzymes through a combination of experimental and computational analyses. Together with previous work, these results suggest that dynamic coupling to the chemical coordinate is detrimental to catalysis and may have been selected against during DHFR evolution. The full catalytic power of Nature's catalysts appears to depend on finely tuning protein motions in each step of the catalytic cycle

Polarizable Water Model for the Coarse-Grained MARTINI Force Field

Coarse-grained (CG) simulations have become an essential tool to study a large variety of biomolecular processes, exploring temporal and spatial scales inaccessible to traditional models of atomistic resolution. One of the major simplifications of CG models is the representation of the solvent, which is either implicit or modeled explicitly as a van der Waals particle. The effect of polarization, and thus a proper screening of interactions depending on the local environment, is absent. Given the important role of water as a ubiquitous solvent in biological systems, its treatment is crucial to the properties derived from simulation studies. Here, we parameterize a polarizable coarse-grained water model to be used in combination with the CG MARTINI force field. Using a three-bead model to represent four water molecules, we show that the orientational polarizability of real water can be effectively accounted for. This has the consequence that the dielectric screening of bulk water is reproduced. At the same time, we parameterized our new water model such that bulk water density and oil/water partitioning data remain at the same level of accuracy as for the standard MARTINI force field. We apply the new model to two cases for which current CG force fields are inadequate. First, we address the transport of ions across a lipid membrane. The computed potential of mean force shows that the ions now naturally feel the change in dielectric medium when moving from the high dielectric aqueous phase toward the low dielectric membrane interior. In the second application we consider the electroporation process of both an oil slab and a lipid bilayer. The electrostatic field drives the formation of water filled pores in both cases, following a similar mechanism as seen with atomistically detailed models

Reliable and efficient reaction path and transition state finding for surface reactions with the growing string method

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136310/1/jcc24720_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136310/2/jcc24720.pd

Identification and Characterization of Novel Mutations in the Human Gene Encoding the Catalytic Subunit Calpha of Protein Kinase A (PKA)

The genes PRKACA and PRKACB encode the principal catalytic (C) subunits of protein kinase A (PKA) Cα and Cβ, respectively. Cα is expressed in all eukaryotic tissues examined and studies of Cα knockout mice demonstrate a crucial role for Cα in normal physiology. We have sequenced exon 2 through 10 of PRKACA from the genome of 498 Norwegian donors and extracted information about PRKACA mutations from public databases. We identified four interesting nonsynonymous point mutations, Arg45Gln, Ser109Pro, Gly186Val, and Ser263Cys, in the Cα1 splice variant of the kinase. Cα variants harboring the different amino acid mutations were analyzed for kinase activity and regulatory (R) subunit binding. Whereas mutation of residues 45 and 263 did not alter catalytic activity or R subunit binding, mutation of Ser109 significantly reduced kinase activity while R subunit binding was unaltered. Mutation of Cα Gly186 completely abrogated kinase activity and PKA type I but not type II holoenzyme formation. Gly186 is located in the highly conserved DFG motif of Cα and mutation of this residue to Val was predicted to result in loss of binding of ATP and Mg2+, which may explain the kinetic inactivity. We hypothesize that individuals born with mutations of Ser109 or Gly186 may be faced with abnormal development and possibly severe disease

Free Energy Simulations of a GTPase: GTP and GDP Binding to Archaeal Initiation Factor 2

International audienceArchaeal initiation factor 2 (aIF2) is a protein involved in the initiation of protein biosynthesis. In its GTP-bound, "ON" conformation, aIF2 binds an initiator tRNA and carries it to the ribosome. In its GDP-bound, "OFF" conformation, it dissociates from tRNA. To understand the specific binding of GTP and GDP and its dependence on the ON or OFF conformational state of aIF2, molecular dynamics free energy simulations (MDFE) are a tool of choice. However, the validity of the computed free energies depends on the simulation model, including the force field and the boundary conditions, and on the extent of conformational sampling in the simulations. aIF2 and other GTPases present specific difficulties; in particular, the nucleotide ligand coordinates a divalent Mg(2+) ion, which can polarize the electronic distribution of its environment. Thus, a force field with an explicit treatment of electronic polarizability could be necessary, rather than a simpler, fixed charge force field. Here, we begin by comparing a fixed charge force field to quantum chemical calculations and experiment for Mg(2+):phosphate binding in solution, with the force field giving large errors. Next, we consider GTP and GDP bound to aIF2 and we compare two fixed charge force fields to the recent, polarizable, AMOEBA force field, extended here in a simple, approximate manner to include GTP. We focus on a quantity that approximates the free energy to change GTP into GDP. Despite the errors seen for Mg(2+):phosphate binding in solution, we observe a substantial cancellation of errors when we compare the free energy change in the protein to that in solution, or when we compare the protein ON and OFF states. Finally, we have used the fixed charge force field to perform MDFE simulations and alchemically transform GTP into GDP in the protein and in solution. With a total of about 200 ns of molecular dynamics, we obtain good convergence and a reasonable statistical uncertainty, comparable to the force field uncertainty, and somewhat lower than the predicted GTP/GDP binding free energy differences. The sign and magnitudes of the differences can thus be interpreted at a semiquantitative level, and are found to be consistent with the experimental binding preferences of ON- and OFF-aIF2

Ionic Interactions in Biological and Physical Systems: a Variational Treatment

Chemistry is about chemical reactions. Chemistry is about electrons changing their configurations as atoms and molecules react. Chemistry studies reactions as if they occurred in ideal infinitely dilute solutions. But most reactions occur in nonideal solutions. Then everything (charged) interacts with everything else (charged) through the electric field, which is short and long range extending to boundaries of the system. Mathematics has recently been developed to deal with interacting systems of this sort. The variational theory of complex fluids has spawned the theory of liquid crystals. In my view, ionic solutions should be viewed as complex fluids. In both biology and electrochemistry ionic solutions are mixtures highly concentrated (~10M) where they are most important, near electrodes, nucleic acids, enzymes, and ion channels. Calcium is always involved in biological solutions because its concentration in a particular location is the signal that controls many biological functions. Such interacting systems are not simple fluids, and it is no wonder that analysis of interactions, such as the Hofmeister series, rooted in that tradition, has not succeeded as one would hope. We present a variational treatment of hard spheres in a frictional dielectric. The theory automatically extends to spatially nonuniform boundary conditions and the nonequilibrium systems and flows they produce. The theory is unavoidably self-consistent since differential equations are derived (not assumed) from models of (Helmholtz free) energy and dissipation of the electrolyte. The origin of the Hofmeister series is (in my view) an inverse problem that becomes well posed when enough data from disjoint experimental traditions are interpreted with a self-consistent theory.Comment: As prepared for Faraday Discussion, Pavel Jungwirth Organizer, 3 - 5 September 2012, Queens College Oxford, UK on Ion Specific Hofmeister Effects. Version 2 has significant typo corrections in eq. 1 and eq. 4, and has been reformatted to be easier to rea

Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations

Alkali (Li+, Na+, K+, Rb+, and Cs+) and halide (F−, Cl−, Br−, and I−) ions play an important role in many biological phenomena, roles that range from stabilization of biomolecular structure, to influence on biomolecular dynamics, to key physiological influence on homeostasis and signaling. To properly model ionic interaction and stability in atomistic simulations of biomolecular structure, dynamics, folding, catalysis, and function, an accurate model or representation of the monovalent ions is critically necessary. A good model needs to simultaneously reproduce many properties of ions, including their structure, dynamics, solvation, and moreover both the interactions of these ions with each other in the crystal and in solution and the interactions of ions with other molecules. At present, the best force fields for biomolecules employ a simple additive, nonpolarizable, and pairwise potential for atomic interaction. In this work, we describe our efforts to build better models of the monovalent ions within the pairwise Coulombic and 6-12 Lennard-Jones framework, where the models are tuned to balance crystal and solution properties in Ewald simulations with specific choices of well-known water models. Although it has been clearly demonstrated that truly accurate treatments of ions will require inclusion of nonadditivity and polarizability (particularly with the anions) and ultimately even a quantum mechanical treatment, our goal was to simply push the limits of the additive treatments to see if a balanced model could be created. The applied methodology is general and can be extended to other ions and to polarizable force-field models. Our starting point centered on observations from long simulations of biomolecules in salt solution with the AMBER force fields where salt crystals formed well below their solubility limit. The likely cause of the artifact in the AMBER parameters relates to the naive mixing of the Smith and Dang chloride parameters with AMBER-adapted Åqvist cation parameters. To provide a more appropriate balance, we reoptimized the parameters of the Lennard-Jones potential for the ions and specific choices of water models. To validate and optimize the parameters, we calculated hydration free energies of the solvated ions and also lattice energies (LE) and lattice constants (LC) of alkali halide salt crystals. This is the first effort that systematically scans across the Lennard-Jones space (well depth and radius) while balancing ion properties like LE and LC across all pair combinations of the alkali ions and halide ions. The optimization across the entire monovalent series avoids systematic deviations. The ion parameters developed, optimized, and characterized were targeted for use with some of the most commonly used rigid and nonpolarizable water models, specifically TIP3P, TIP4PEW, and SPC/E. In addition to well reproducing the solution and crystal properties, the new ion parameters well reproduce binding energies of the ions to water and the radii of the first hydration shells

Tools to map target genes of bacterial two‐component system response regulators

Studies on bacterial physiology are incomplete without knowledge of the signalling and regulatory systems that a bacterium uses to sense and respond to its environment. Two-component systems (TCSs) are among the most prevalent bacterial signalling systems, and they control essential and secondary physiological processes; however, even in model organisms, we lack a complete understanding of the signals sensed, the phosphotransfer partners and the functions regulated by these systems. In this review, we discuss several tools to map the genes targeted by transcriptionally acting TCSs. Many of these tools have been used for studying individual TCSs across diverse species, but systematic approaches to delineate entire signalling networks have been very few. Since genome sequences and high-throughput technologies are now readily available, the methods presented here can be applied to characterize the entire DNA-binding TCS signalling network in any bacterial species and are especially useful for non-model environmental bacteria