The rational in silico design of interface mutations within protein complexes is a synthetic biology tool that enables—when introduced into biological systems—the artificial rewiring of biological pathways. Here we describe the three-dimensional structure-based design of “rewiring” mutations using the FoldX force field. Specifically, we provide the protocol for the design and selection of interface mutations in three Ras-effector complex structures (PDB entries 3KUD, 4K81, and 6AMB). Ras mutations that impair binding to some but not all interacting partners are selected.

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras–effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)–mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants (“genetic contexts”) in the human Caco-2 cell line, each exposed to 11 different culture media (“culture contexts”) that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts (“context-general effectors”). Seven effectors are found in KRAS complexes in only some contexts (“context-specific effectors”). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS–effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.

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We consider approximations formed by the sum of a linear combination of given functions enhanced by ridge functions—a Linear/Ridge expansion. For an explicitly or implicitly given objective function, we reformulate finding a best Linear/Ridge expansion in terms of an optimization problem. We introduce a particle grid algorithm for its solution. Several numerical results underline the flexibility, robustness and efficiency of the algorithm. One particular source of motivation is model reduction of parameterized transport or wave equations. We show that the particle grid algorithm is able to find a Linear/Ridge expansion as an efficient nonlinear model reduction.

Summary Homology modelling, the technique of generating models of 3D protein structures based on experimental structures from related proteins, has become increasingly popular over the years. An abundance of different tools for model generation and model evaluation is available from various research groups. We present HOMELETTE, an interface which implements a unified programmatic access to these tools. This allows for the assemble of custom pipelines from pre- or self-implemented building blocks. Availability and Implementation HOMELETTE is implemented in Python, compatible with version 3.6 and newer. It is distributed under the MIT license. Documentation and tutorials are available at Read the Docs (https://homelette.readthedocs.io/). The latest version of HOMELETTE is available on PyPI (https://pypi.org/project/homelette/) and GitHub (https://github.com/PhilippJunk/homelette). A full installation of the latest version of HOMELETTE with all dependencies is also available as a Docker container (https://hub.docker.com/r/philippjunk/homelette_template). Supplementary information Supplementary data are available at Bioinformatics online.