Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis.

The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability indirectly. Here, we develop an automated method to generate thermodynamic stability data for nearly every single mutant in a small 56-residue protein. Analysis reveals that most single mutants have a neutral effect on stability, mutational sensitivity is largely governed by residue burial, and unexpectedly, hydrophobics are the best tolerated amino acid type. Correlating the output of various stability-prediction algorithms against our data shows that nearly all perform better on boundary and surface positions than for those in the core and are better at predicting large-to-small mutations than small-to-large ones. We show that the most stable variants in the single-mutant landscape are better identified using combinations of 2 prediction algorithms and including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analyzed and the datasets used to train the algorithm. Finally, we find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and that data produced by these methods may be applicable toward training future stability-prediction tools.

Structurally detailed coarse-grained model for Sec-facilitated co-translational protein translocation and membrane integration.

We present a coarse-grained simulation model that is capable of simulating the minute-timescale dynamics of protein translocation and membrane integration via the Sec translocon, while retaining sufficient chemical and structural detail to capture many of the sequence-specific interactions that drive these processes. The model includes accurate geometric representations of the ribosome and Sec translocon, obtained directly from experimental structures, and interactions parameterized from nearly 200 µs of residue-based coarse-grained molecular dynamics simulations. A protocol for mapping amino-acid sequences to coarse-grained beads enables the direct simulation of trajectories for the co-translational insertion of arbitrary polypeptide sequences into the Sec translocon. The model reproduces experimentally observed features of membrane protein integration, including the efficiency with which polypeptide domains integrate into the membrane, the variation in integration efficiency upon single amino-acid mutations, and the orientation of transmembrane domains. The central advantage of the model is that it connects sequence-level protein features to biological observables and timescales, enabling direct simulation for the mechanistic analysis of co-translational integration and for the engineering of membrane proteins with enhanced membrane integration efficiency.

Allosteric response and substrate sensitivity in peptide binding of the signal recognition particle.

We characterize the conformational dynamics and substrate selectivity of the signal recognition particle (SRP) using a thermodynamic free energy cycle approach and microsecond timescale molecular dynamics simulations. The SRP is a central component of the co-translational protein targeting machinery that binds to the N-terminal signal peptide (SP) of nascent proteins. We determined the shift in relative conformational stability of the SRP upon substrate binding to quantify allosteric coupling between SRP domains. In particular, for dipeptidyl aminopeptidase, an SP that is recognized by the SRP for co-translational targeting, it is found that substrate binding induces substantial changes in the SRP toward configurations associated with targeting of the nascent protein, and it is found that the changes are modestly enhanced by a mutation that increases the hydrophobicity of the SP. However, for alkaline phosphatase, an SP that is recognized for post-translational targeting, substrate binding induces the reverse change in the SRP conformational distribution away from targeting configurations. Microsecond timescale trajectories reveal the intrinsic flexibility of the SRP conformational landscape and provide insight into recent single molecule studies by illustrating that 10-nm lengthscale changes between FRET pairs occur via the rigid-body movement of SRP domains connected by the flexible linker region. In combination, these results provide direct evidence for the hypothesis that substrate-controlled conformational switching in the SRP provides a mechanism for discriminating between different SPs and for connecting substrate binding to downstream steps in the protein targeting pathway.

ProtaBank: A repository for protein design and engineering data.

We present ProtaBank, a repository for storing, querying, analyzing, and sharing protein design and engineering data in an actively maintained and updated database. ProtaBank provides a format to describe and compare all types of protein mutational data, spanning a wide range of properties and techniques. It features a user-friendly web interface and programming layer that streamlines data deposition and allows for batch input and queries. The database schema design incorporates a standard format for reporting protein sequences and experimental data that facilitates comparison of results across different data sets. A suite of analysis and visualization tools are provided to facilitate discovery, to guide future designs, and to benchmark and train new predictive tools and algorithms. ProtaBank will provide a valuable resource to the protein engineering community by storing and safeguarding newly generated data, allowing for fast searching and identification of relevant data from the existing literature, and exploring correlations between disparate data sets. ProtaBank invites researchers to contribute data to the database to make it accessible for search and analysis. ProtaBank is available at https://protabank.org.