Tradeoff Between Stability and Multispecificity in the Design of Promiscuous Proteins

Natural proteins often partake in several highly specific protein-protein interactions. They are thus subject to multiple opposing forces during evolutionary selection. To be functional, such multispecific proteins need to be stable in complex with each interaction partner, and, at the same time, to maintain affinity toward all partners. How is this multispecificity acquired through natural evolution? To answer this compelling question, we study a prototypical multispecific protein, calmodulin (CaM), which has evolved to interact with hundreds of target proteins. Starting from high-resolution structures of sixteen CaM-target complexes, we employ state-of-the-art computational methods to predict a hundred CaM sequences best suited for interaction with each individual CaM target. Then, we design CaM sequences most compatible with each possible combination of two, three, and all sixteen targets simultaneously, producing almost 70,000 low energy CaM sequences. By comparing these sequences and their energies, we gain insight into how nature has managed to find the compromise between the need for favorable interaction energies and the need for multispecificity. We observe that designing for more partners simultaneously yields CaM sequences that better match natural sequence profiles, thus emphasizing the importance of such strategies in nature. Furthermore, we show that the CaM binding interface can be nicely partitioned into positions that are critical for the affinity of all CaM-target complexes and those that are molded to provide interaction specificity. We reveal several basic categories of sequence-level tradeoffs that enable the compromise necessary for the promiscuity of this protein. We also thoroughly quantify the tradeoff between interaction energetics and multispecificity and find that facilitating seemingly competing interactions requires only a small deviation from optimal energies. We conclude that multispecific proteins have been subjected to a rigorous optimization process that has fine-tuned their sequences for interactions with a precise set of targets, thus conferring their multiple cellular functions

α-amanitin resistance in Drosophila melanogaster: A genome-wide association approach

© This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. We investigated the mechanisms of mushroom toxin resistance in the Drosophila Genetic Reference Panel (DGRP) fly lines, using genome-wide association studies (GWAS). While Drosophila melanogaster avoids mushrooms in nature, some lines are surprisingly resistant to α-amanitin-a toxin found solely in mushrooms. This resistance may represent a preadaptation, which might enable this species to invade the mushroom niche in the future. Although our previous microarray study had strongly suggested that pesticide-metabolizing detoxification genes confer α-amanitin resistance in a Taiwanese D. melanogaster line Ama-KTT, none of the traditional detoxification genes were among the top candidate genes resulting from the GWAS in the current study. Instead, we identified Megalin, Tequila, and widerborst as candidate genes underlying the α-amanitin resistance phenotype in the North American DGRP lines, all three of which are connected to the Target of Rapamycin (TOR) pathway. Both widerborst and Tequila are upstream regulators of TOR, and TOR is a key regulator of autophagy and Megalin-mediated endocytosis. We suggest that endocytosis and autophagy of α-amanitin, followed by lysosomal degradation of the toxin, is one of the mechanisms that confer α-amanitin resistance in the DGRP lines

Combining computational and experimental screening for rapid optimization of protein properties

We present a combined computational and experimental method for the rapid optimization of proteins. Using β-lactamase as a test case, we redesigned the active site region using our Protein Design Automation technology as a computational screen to search the entire sequence space. By eliminating sequences incompatible with the protein fold, Protein Design Automation rapidly reduced the number of sequences to a size amenable to experimental screening, resulting in a library of ≈200,000 mutants. These were then constructed and experimentally screened to select for variants with improved resistance to the antibiotic cefotaxime. In a single round, we obtained variants exhibiting a 1,280-fold increase in resistance. To our knowledge, all of the mutations were novel, i.e., they have not been identified as beneficial by random mutagenesis or DNA shuffling or seen in any of the naturally occurring TEM β-lactamases, the most prevalent type of Gram-negative β-lactamases. This combined approach allows for the rapid improvement of any property that can be screened experimentally and provides a powerful broadly applicable tool for protein engineering

Impacts of Learning Standards and Testing on Gifted Learners in STEM Schools: A Multilevel Analytic Induction

This article shares findings from a multilevel analytic induction of administrators, teachers, and gifted students within specialized science, technology, engineering, and mathematics (STEM) schools about their beliefs regarding the role of standards and standardized tests in the education of gifted learners. Sharing results from a cross-case analysis of six schools, we explore the ways in which student experiences of standards-based learning differ from the perceptions of their teachers and school administrators. We found that there is no consensus across administrators, teachers, and students regarding the impact that standards have in the educational process or on outcomes within specialized STEM schools, though all community members value and seek to create deep learning opportunities for students

Candidate genes resulting from the 180-line GWAS on 2.0 μg/g and the 37-line GWAS.

<p>Candidate genes resulting from the 180-line GWAS on 2.0 μg/g and the 37-line GWAS.</p