Temporal and dimensional effects in evolutionary graph theory

The spread in time of a mutation through a population is studied analytically and computationally in fully-connected networks and on spatial lattices. The time, t_*, for a favourable mutation to dominate scales with population size N as N^{(D+1)/D} in D-dimensional hypercubic lattices and as N ln N in fully-connected graphs. It is shown that the surface of the interface between mutants and non-mutants is crucial in predicting the dynamics of the system. Network topology has a significant effect on the equilibrium fitness of a simple population model incorporating multiple mutations and sexual reproduction. Includes supplementary information.Comment: 6 pages, 4 figures Replaced after final round of peer revie

Modulation of the slow/common gating of CLC channels by intracellular cadmium.

Members of the CLC family of Cl(-) channels and transporters are homodimeric integral membrane proteins. Two gating mechanisms control the opening and closing of Cl(-) channels in this family: fast gating, which regulates opening and closing of the individual pores in each subunit, and slow (or common) gating, which simultaneously controls gating of both subunits. Here, we found that intracellularly applied Cd(2+) reduces the current of CLC-0 because of its inhibition on the slow gating. We identified CLC-0 residues C229 and H231, located at the intracellular end of the transmembrane domain near the dimer interface, as the Cd(2+)-coordinating residues. The inhibition of the current of CLC-0 by Cd(2+) was greatly enhanced by mutation of I225W and V490W at the dimer interface. Biochemical experiments revealed that formation of a disulfide bond within this Cd(2+)-binding site is also affected by mutation of I225W and V490W, indicating that these two mutations alter the structure of the Cd(2+)-binding site. Kinetic studies showed that Cd(2+) inhibition appears to be state dependent, suggesting that structural rearrangements may occur in the CLC dimer interface during Cd(2+) modulation. Mutations of I290 and I556 of CLC-1, which correspond to I225 and V490 of CLC-0, respectively, have been shown previously to cause malfunction of CLC-1 Cl(-) channel by altering the common gating. Our experimental results suggest that mutations of the corresponding residues in CLC-0 change the subunit interaction and alter the slow gating of CLC-0. The effect of these mutations on modulations of slow gating of CLC channels by intracellular Cd(2+) likely depends on their alteration of subunit interactions

MoKCa database - mutations of kinases in cancer

Members of the protein kinase family are amongst the most commonly mutated genes in human cancer, and both mutated and activated protein kinases have proved to be tractable targets for the development of new anticancer therapies The MoKCa database (Mutations of Kinases in Cancer, http://strubiol.icr.ac.uk/extra/mokca) has been developed to structurally and functionally annotate, and where possible predict, the phenotypic consequences of mutations in protein kinases implicated in cancer. Somatic mutation data from tumours and tumour cell lines have been mapped onto the crystal structures of the affected protein domains. Positions of the mutated amino-acids are highlighted on a sequence-based domain pictogram, as well as a 3D-image of the protein structure, and in a molecular graphics package, integrated for interactive viewing. The data associated with each mutation is presented in the Web interface, along with expert annotation of the detailed molecular functional implications of the mutation. Proteins are linked to functional annotation resources and are annotated with structural and functional features such as domains and phosphorylation sites. MoKCa aims to provide assessments available from multiple sources and algorithms for each potential cancer-associated mutation, and present these together in a consistent and coherent fashion to facilitate authoritative annotation by cancer biologists and structural biologists, directly involved in the generation and analysis of new mutational data

Frequency-dependent fitness induces multistability in coevolutionary dynamics

Evolution is simultaneously driven by a number of processes such as mutation, competition and random sampling. Understanding which of these processes is dominating the collective evolutionary dynamics in dependence on system properties is a fundamental aim of theoretical research. Recent works quantitatively studied coevolutionary dynamics of competing species with a focus on linearly frequency-dependent interactions, derived from a game-theoretic viewpoint. However, several aspects of evolutionary dynamics, e.g. limited resources, may induce effectively nonlinear frequency dependencies. Here we study the impact of nonlinear frequency dependence on evolutionary dynamics in a model class that covers linear frequency dependence as a special case. We focus on the simplest non-trivial setting of two genotypes and analyze the co-action of nonlinear frequency dependence with asymmetric mutation rates. We find that their co-action may induce novel metastable states as well as stochastic switching dynamics between them. Our results reveal how the different mechanisms of mutation, selection and genetic drift contribute to the dynamics and the emergence of metastable states, suggesting that multistability is a generic feature in systems with frequency-dependent fitness.Comment: 12 pages, 6 figures; J. R. Soc. Interface (2012

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Coexistence in a One-Dimensional Cyclic Dominance Process

Cyclic (rock-paper-scissors-type) population models serve to mimic complex species interactions. Focusing on a paradigmatic three-species model with mutations in one dimension, we observe an interplay between equilibrium and non-equilibrium processes in the stationary state. We exploit these insights to obtain asymptotically exact descriptions of the emerging reactive steady state in the regimes of high and low mutation rates. The results are compared to stochastic lattice simulations. Our methods and findings are potentially relevant for the spatio-temporal evolution of other non-equilibrium stochastic processes.Comment: 4 pages, 4 figures and 2 pages of Supplementary Material. To appear in Physical Review

Elevated neoantigen levels in tumors with somatic mutations in the HLA-A, HLA-B, HLA-C and B2M genes.

BackgroundThe major histocompatibility complex class I (MHC-I) molecule is a protein complex that displays intracellular peptides to T cells, allowing the immune system to recognize and destroy infected or cancerous cells. MHC-I is composed of a highly polymorphic HLA-encoded alpha chain that binds the peptide and a Beta-2-microglobulin (B2M) protein that acts as a stabilizing scaffold. HLA mutations have been implicated as a mechanism of immune evasion during tumorigenesis, and B2M is considered a tumor suppressor gene. However, the implications of somatic HLA and B2M mutations have not been fully explored in the context of antigen presentation via the MHC-I molecule during tumor development. To understand the effect that B2M and HLA MHC-I molecule mutations have on mutagenesis, we analyzed the accumulation of mutations in patients from The Cancer Genome Atlas according to their MHC-I molecule mutation status.ResultsSomatic B2M and HLA mutations in microsatellite stable tumors were associated with higher overall mutation burden and a larger fraction of HLA-binding neoantigens when compared to B2M and HLA wild type tumors. B2M and HLA mutations were highly enriched in patients with microsatellite instability. B2M mutations tended to occur relatively early during patients' respective tumor development, whereas HLA mutations were either early or late events. In addition, B2M and HLA mutated patients had higher levels of immune infiltration by natural killer and CD8+ T cells and higher levels of cytotoxicity.ConclusionsOur findings add to a growing body of evidence that somatic B2M and HLA mutations are a mechanism of immune evasion by demonstrating that such mutations are associated with a higher load of neoantigens that should be presented via MHC-I

Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft