Structure and stability of the designer protein WRAP-T and its permutants

Abstract$$\beta $$ β -Propeller proteins are common natural disc-like pseudo-symmetric proteins that contain multiple repeats (‘blades’) each consisting of a 4-stranded anti-parallel $$\beta $$ β -sheet. So far, 4- to 12-bladed $$\beta $$ β -propellers have been discovered in nature showing large functional and sequential variation. Using computational design approaches, we created perfectly symmetric $$\beta $$ β -propellers out of natural pseudo-symmetric templates. These proteins are useful tools to study protein evolution of this very diverse fold. While the 7-bladed architecture is the most common, no symmetric 7-bladed monomer has been created and characterized so far. Here we describe such a engineered protein, based on a highly symmetric natural template, and test the effects of circular permutation on its stability. Geometrical analysis of this protein and other artificial symmetrical proteins reveals no systematic constraint that could be used to help in engineering of this fold, and suggests sequence constraints unique to each $$\beta $$ β -propeller sub-family.

Structure and Stability of the designer protein WRAP-T and its permutants

β-propeller proteins are common natural disc-like pseudo-symmetric proteins that contain multiple repeats ('blades') each consisting of a 4-stranded anti-parallel beta-sheet. So far, 4- to 12-bladed β-propellers have been discovered in nature showing large functional and sequential variation. Using computational design approaches, we created perfectly symmetric β-propellers out of natural pseudo-symmetric templates. These proteins are useful tools to study protein evolution of this very diverse fold. While the 7-bladed architecture is the most common, no symmetric 7-bladed monomer has been created and characterized so far. Here we describe such a engineered protein, based on a highly symmetric natural template, and test the effects of circular permutation on its stability. Geometrical analysis of this protein and other artificial symmetrical proteins reveals no systematic constraint that could be used to help in engineering of this fold, and suggests sequence constraints unique to each β-propeller sub-family.

Neutral components show a hierarchical community structure in the genotype–phenotype map of RNA secondary structure

Genotype–phenotype (GP) maps describe the relationship between biological sequences and structural or functional outcomes. They can be represented as networks in which genotypes are the nodes, and one-point mutations between them are the edges. The genotypes that map to the same phenotype form subnetworks consisting of one or multiple disjoint connected components–so-called neutral components (NCs). For the GP map of RNA secondary structure, the NCs have been found to exhibit distinctive network features that can affect the dynamical processes taking place on them. Here, we focus on the community structure of RNA secondary structure NCs. Building on previous findings, we introduce a method to reveal the hierarchical community structure solely from the sequence constraints and composition of the genotypes that form a given NC. Thereby, we obtain modularity values similar to common community detection algorithms, which are much more complex. From this knowledge, we endorse a sampling method that allows a fast exploration of the different communities of a given NC. Furthermore, we introduce a way to estimate the community structure from genotype samples, which is useful when an exhaustive analysis of the NC is not feasible, as is the case for longer sequence lengths.

HEDGES error-correcting code for DNA storage corrects indels and allows sequence constraints

Synthetic DNA is rapidly emerging as a durable, high-density information storage platform. A major challenge for DNA-based information encoding strategies is the high rate of errors that arise during DNA synthesis and sequencing. Here, we describe the HEDGES (Hash Encoded, Decoded by Greedy Exhaustive Search) error-correcting code that repairs all three basic types of DNA errors: insertions, deletions, and substitutions. HEDGES also converts unresolved or compound errors into substitutions, restoring synchronization for correction via a standard Reed–Solomon outer code that is interleaved across strands. Moreover, HEDGES can incorporate a broad class of user-defined sequence constraints, such as avoiding excess repeats, or too high or too low windowed guanine–cytosine (GC) content. We test our code both via in silico simulations and with synthesized DNA. From its measured performance, we develop a statistical model applicable to much larger datasets. Predicted performance indicates the possibility of error-free recovery of petabyte- and exabyte-scale data from DNA degraded with as much as 10% errors. As the cost of DNA synthesis and sequencing continues to drop, we anticipate that HEDGES will find applications in large-scale error-free information encoding.

Synthetic biology open language (SBOL) version 3.0.0

Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both wet bench scientists and dry scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.0.0, which condenses and simplifies previous versions of SBOL based on experiences in deployment across a variety of scientific and industrial settings. In particular, SBOL 3.0.0, (1) separates sequence features from part/sub-part relationships, (2) renames Component Definition/Component to Component/Sub-Component, (3) merges Component and Module classes, (4) ensures consistency between data model and ontology terms, (5) extends the means to define and reference Sub-Components, (6) refines requirements on object URIs, (7) enables graph-based serialization, (8) moves Systems Biology Ontology (SBO) for Component types, (9) makes all sequence associations explicit, (10) makes interfaces explicit, (11) generalizes Sequence Constraints into a general structural Constraint class, and (12) expands the set of allowed constraints.

Probing biophysical sequence constraints within the transmembrane domains of rhodopsin by deep mutational scanning

Membrane proteins must balance the sequence constraints associated with folding and function against the hydrophobicity required for solvation within the bilayer. We recently found the expression and maturation of rhodopsin are limited by the hydrophobicity of its seventh transmembrane domain (TM7), which contains polar residues that are essential for function. On the basis of these observations, we hypothesized that rhodopsin’s expression should be less tolerant of mutations in TM7 relative to those within hydrophobic TM domains. To test this hypothesis, we used deep mutational scanning to compare the effects of 808 missense mutations on the plasma membrane expression of rhodopsin in HEK293T cells. Our results confirm that a higher proportion of mutations within TM7 (37%) decrease rhodopsin’s plasma membrane expression relative to those within a hydrophobic TM domain (TM2, 25%). These results in conjunction with an evolutionary analysis suggest solvation energetics likely restricts the evolutionary sequence space of polar TM domains.