TASK DEFINITION: In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
PROBLEM: Background: Nonadditivity in fitness effects from two or more mutations, termed epistasis, can result in compensation of deleterious mutations or negation of beneficial mutations. Recent evidence shows the importance of epistasis in individual evolutionary pathways. However, an unresolved question in molecular evolution is how often and how significantly fitness effects change in alternative genetic backgrounds. Results: To answer this question, we quantified the effects of all single mutations and double mutations between all positions in the IgG-binding domain of protein G (GB1). By observing the first two steps of all possible evolutionary pathways using this fitness profile, we were able to characterize the extent and magnitude of pairwise epistasis throughout an entire protein molecule. Furthermore, we developed a novel approach to quantitatively determine the effects of single mutations on structural stability (DDG U ). This enabled determination of the importance of stability effects in functional epistasis. Conclusions: Our results illustrate common biophysical mechanisms for occurrences of positive and negative epistasis. Our results show pervasive positive epistasis within a conformationally dynamic network of residues. The stability analysis shows that significant negative epistasis, which is more common than positive epistasis, mostly occurs between combinations of destabilizing mutations. Furthermore, we show that although significant positive epistasis is rare, many deleterious mutations are beneficial in at least one alternative mutational background. The distribution of conditionally beneficial mutations throughout the domain demonstrates that the functional portion of sequence space can be significantly expanded by epistasis.

SOLUTION: Article A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain

PROBLEM: Attenuated viral vaccines can be generated by targeting essential pathogenicity factors. We report here the rational design of an attenuated recombinant coronavirus vaccine based on a deletion in the coding sequence of the nonstructural protein 1 (nsp1). In cell culture, nsp1 of mouse hepatitis virus (MHV), like its SARS-coronavirus homolog, strongly reduced cellular gene expression. The effect of nsp1 on MHV replication in vitro and in vivo was analyzed using a recombinant MHV encoding a deletion in the nsp1-coding sequence. The recombinant MHV nsp1 mutant grew normally in tissue culture, but was severely attenuated in vivo. Replication and spread of the nsp1 mutant virus was restored almost to wild-type levels in type I interferon (IFN) receptor-deficient mice, indicating that nsp1 interferes efficiently with the type I IFN system. Importantly, replication of nsp1 mutant virus in professional antigen-presenting cells such as conventional dendritic cells and macrophages, and induction of type I IFN in plasmacytoid dendritic cells, was not impaired. Furthermore, even low doses of nsp1 mutant MHV elicited potent cytotoxic T cell responses and protected mice against homologous and heterologous virus challenge. Taken together, the presented attenuation strategy provides a paradigm for the development of highly efficient coronavirus vaccines.

SOLUTION: Coronavirus Non-Structural Protein 1 Is a Major Pathogenicity Factor: Implications for the Rational Design of Coronavirus Vaccines

PROBLEM: Quickly detecting harmful cascades in networks can allow us to analyze the causes and prevent further spreading of destructive influence. Since it is often impossible to observe the state of all nodes in a network, a common method is to detect harmful cascades from sparsely placed sensors. However, the harmful cascades are usually dynamic (e.g., the cascade initiators and diffusion trajectories can change over the time), which can severely destroy the robustness of selected sensors. Meanwhile the large scale of current networks greatly increases the time complexity of sensor selection. Motivated by the observation, in this paper we investigate the scalable sensor selection problem for early detection of dynamic harmful cascades in networks. Specifically, we first put forward a dynamic susceptible-infected model to describe harmful cascades, and formally define a detection time minimization (DTM) problem which focuses on effective sensors placement for early detection of dynamic cascades. We prove that it is #P-hard to calculate the objective function exactly and propose two Monte-Carlo methods to estimate it efficiently. We prove the NP-hardness of DTM problem and design a corresponding greedy algorithm. Based on that, we propose an efficient upper bound based greedy (UBG) algorithm with the theoretical performance guarantee reserved. To further meet different types of large-scale networks, we propose two accelerations of UBG: Quickest-Path-UBG for sparse networks and Local-Reduction-UBG for dense networks to improve the time complexity. The experimental results on synthetic and real-world social networks demonstrate the practicality of our approaches.

SOLUTION:
Early detection of dynamic harmful cascades in large-scale networks