Rampant False Detection of Adaptive Phenotypic Optimization by ParTI-Based Pareto Front Inference.
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ABSTRACT:
SUBMITTER: Sun M
PROVIDER: S-EPMC8042732 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In order to increase the hit rate of discovering diverse, beneficial protein variants via high-throughput screening, we have developed a computational method to optimize combinatorial mutagenesis libraries for overall enrichment in two distinct properties of interest. Given scoring functions for evaluating individual variants, POCoM (Pareto Optimal Combinatorial Mutagenesis) scores entire libraries in terms of averages over their constituent members, and designs optimal libraries as sets of mutations whose combinations make the best trade-offs between average scores. This represents the first general-purpose method to directly design combinatorial libraries for multiple objectives characterizing their constituent members. Despite being rigorous in mapping out the Pareto frontier, it is also very fast even for very large libraries (e.g., designing 30 mutation, billion-member libraries in only hours). We here instantiate POCoM with scores based on a target's protein structure and its homologs' sequences, enabling the design of libraries containing variants balancing these two important yet quite different types of information. We demonstrate POCoM's generality and power in case study applications to green fluorescent protein, cytochrome P450, and β-lactamase. Analysis of the POCoM library designs provides insights into the trade-offs between structure- and sequence-based scores, as well as the impacts of experimental constraints on library designs. POCoM libraries incorporate mutations that have previously been found favorable experimentally, while diversifying the contexts in which these mutations are situated and maintaining overall variant quality.
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Project description:When organisms perform a single task, selection leads to phenotypes that maximize performance at that task. When organisms need to perform multiple tasks, a trade-off arises because no phenotype can optimize all tasks. Recent work addressed this question, and assumed that the performance at each task decays with distance in trait space from the best phenotype at that task. Under this assumption, the best-fitness solutions (termed the Pareto front) lie on simple low-dimensional shapes in trait space: line segments, triangles and other polygons. The vertices of these polygons are specialists at a single task. Here, we generalize this finding, by considering performance functions of general form, not necessarily functions that decay monotonically with distance from their peak. We find that, except for performance functions with highly eccentric contours, simple shapes in phenotype space are still found, but with mildly curving edges instead of straight ones. In a wide range of systems, complex data on multiple quantitative traits, which might be expected to fill a high-dimensional phenotype space, is predicted instead to collapse onto low-dimensional shapes; phenotypes near the vertices of these shapes are predicted to be specialists, and can thus suggest which tasks may be at play.
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Project description:Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m-1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m-1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m-1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.
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Project description:BackgroundAs a rule, peptides are more flexible and unstructured than proteins with their substantial stabilizing hydrophobic cores. Nevertheless, a few stably folding peptides have been discovered. This raises the question whether there may be more such peptides that are unknown as yet. These molecules could be helpful in basic research and medicine.ResultsAs a method to explore the space of conformationally stable peptides, we have developed an evolutionary algorithm that allows optimization of sequences with respect to several criteria simultaneously, for instance stability, accessibility of arbitrary parts of the peptide, etc. In a proof-of-concept experiment we have perturbed the sequence of the peptide Villin Headpiece, known to be stable in vitro. Starting from the perturbed sequence we applied our algorithm to optimize peptide stability and accessibility of a loop. Unexpectedly, two clusters of sequences were generated in this way that, according to our criteria, should form structures with higher stability than the wild-type. The structures in one of the clusters possess a fold that markedly differs from the native fold of Villin Headpiece. One of the mutants predicted to be stable was selected for synthesis, its molecular 3D-structure was characterized by nuclear magnetic resonance spectroscopy, and its stability was measured by circular dichroism. Predicted structure and stability were in good agreement with experiment. Eight other sequences and structures, including five with a non-native fold are provided as bona fide predictions.ConclusionThe results suggest that much more conformationally stable peptides may exist than are known so far, and that small fold classes could comprise well-separated sub-folds.
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Project description:Understanding the tradeoffs faced by organisms is a major goal of evolutionary biology. One of the main approaches for identifying these tradeoffs is Pareto task inference (ParTI). Two recent papers claim that results obtained in ParTI studies are spurious due to phylogenetic dependence (Mikami T, Iwasaki W. 2021. The flipping t-ratio test: phylogenetically informed assessment of the Pareto theory for phenotypic evolution. Methods Ecol Evol. 12(4):696-706) or hypothetical p-hacking and population-structure concerns (Sun M, Zhang J. 2021. Rampant false detection of adaptive phenotypic optimization by ParTI-based Pareto front inference. Mol Biol Evol. 38(4):1653-1664). Here, we show that these claims are baseless. We present a new method to control for phylogenetic dependence, called SibSwap, and show that published ParTI inference is robust to phylogenetic dependence. We show how researchers avoided p-hacking by testing for the robustness of preprocessing choices. We also provide new methods to control for population structure and detail the experimental tests of ParTI in systems ranging from ammonites to cancer gene expression. The methods presented here may help to improve future ParTI studies.
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Project description:Proteins have coevolved with cellular environments to improve or preserve their functions, maintaining at the same time the degree of hydrophobicity necessary to fold correctly and enough solubility to perform their biological roles. Here, we study the Escherichia coli proteome using a Pareto front analysis in the solubility-hydrophobicity space. The results indicate the existence of a Pareto optimal front, a triangle whose vertices correspond to archetypal proteins specialized in distinct tasks, such as regulatory processes, membrane transport, outer-membrane pore formation, catalysis, and binding. The vertices are further enriched with proteins that occupy different subcellular compartments, namely, cytoplasmic, inner membrane, outer membrane, and outer membrane bounded periplasmic space. The combination of various enriching features offers an interpretation of how bacteria use the physico-chemical properties of proteins, both to drive them into their final destination in the cell and to have their tasks accomplished.
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Project description:MotivationA widely applicable strategy to create cell factories is to knock out (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesise at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow.ResultsTo address this multi-objective optimization problem, we developed a software tool named gcFront - using a genetic algorithm it explores KOs that maximise cell growth, product synthesis, and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run - granting users flexibility in selecting designs to take to the lab.AvailabilitygcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755 (Legon et al., 2022).Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:In the process of software development, regression testing is one of the major activities that is done after making modifications in the current system or whenever a software system evolves. But, the test suite size increases with the addition of new test cases and it becomes in-efficient because of the occurrence of redundant, broken, and obsolete test cases. For that reason, it results in additional time and budget to run all these test cases. Many researchers have proposed computational intelligence and conventional approaches for dealing with this problem and they have achieved an optimized test suite by selecting, minimizing or reducing, and prioritizing test cases. Currently, most of these optimization approaches are single objective and static in nature. But, it is mandatory to use multi-objective dynamic approaches for optimization due to the advancements in information technology and associated market challenges. Therefore, we have proposed three variants of self-tunable Adaptive Neuro-fuzzy Inference System i.e. TLBO-ANFIS, FA-ANFIS, and HS-ANFIS, for multi-objective regression test suites optimization. Two benchmark test suites are used for evaluating the proposed ANFIS variants. The performance of proposed ANFIS variants is measured using Standard Deviation and Root Mean Square Error. A comparison of experimental results is also done with six existing methods i.e. GA-ANFIS, PSO-ANFIS, MOGA, NSGA-II, MOPSO, and TOPSIS and it is concluded that the proposed method effectively reduces the size of regression test suite without a reduction in the fault detection rate.
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Project description:Prior research suggests that episodic memory can guide value-based decisions when single episodes are encoded in relation to the specific reward-context in which they were experienced. The current experiments examine the role that a flexible recombination-related retrieval mechanism that allows one to link together distinct events plays in the misattribution of specific reward-contexts across distinct episodes. To determine whether the same recombination-related retrieval mechanism supports both successful inference and transfer of reward-context across episodes, we developed a modified version of an associative inference paradigm in which participants encoded overlapping associations (AB, BC) that could later be linked to support inferential retrieval (AC), where one element ("A") was tied to reward. Our key experimental manipulation concerned whether value memory (Experiments 1 and 2) or decision bias tests (Experiment 3) were probed before or after the associative inference test, thereby allowing us to assess whether false value transfer and decision bias scores increased after as compared to before successful versus unsuccessful inference. Results revealed that participants more frequently misattributed the specific reward-context ("A") to unrewarded items ("C;" Experiments 1 and 2) and showed higher decision bias scores when asked to choose between two previously unrewarded items ("C;" Experiment 3) for successful compared with unsuccessful inference, but only when the value memory and decision bias tests were given after the associative inference test. These results suggest that a recombination-related retrieval mechanism that supports successful inference also contributes to the misattribution of reward-context in memory and further biases participants' novel value-based decisions. (PsycINFO Database Record
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