A3D 2.0 update for the prediction and optimization of protein solubility

Protein aggregation propensity is a property imprinted in protein sequences and structures, being associated with the onset of human diseases and limiting the implementation of protein-based biotherapies. Computational approaches stand as cost-effective alternatives for reducing protein aggregation and increasing protein solubility. AGGRESCAN 3D (A3D) is a structure-based predictor of aggregation that takes into account the conformational context of a protein, aiming to identify aggregation-prone regions exposed in protein surfaces. Here we inspect the updated 2.0 version of the algorithm, which extends the application of A3D to previously inaccessible proteins and incorporates new modules to assist protein redesign. Among these features, the new server includes stability calculations and the possibility to optimize protein solubility using an experimentally validated computational pipeline. Finally, we employ defined examples to navigate the A3D RESTful service, a routine to handle extensive protein collections. Altogether, this work is conceived to train and assist A3D non-experts in the study of aggregation-prone regions and protein solubility redesign.

Engineering sensitivity and specificity of AraC-based biosensors responsive to triacetic acid lactone and orsellinic acid

We previously described the design of triacetic acid lactone (TAL) biosensor ‘AraC-TAL1’, based on the AraC regulatory protein. Although useful as a tool to screen for enhanced TAL biosynthesis, this variant shows elevated background (leaky) expression, poor sensitivity and relaxed inducer specificity, including responsiveness to orsellinic acid (OA). More sensitive biosensors specific to either TAL or OA can aid in the study and engineering of polyketide synthases that produce these and similar compounds. In this work, we employed a TetA-based dual-selection to isolate new TAL-responsive AraC variants showing reduced background expression and improved TAL sensitivity. To improve TAL specificity, OA was included as a ‘decoy’ ligand during negative selection, resulting in the isolation of a TAL biosensor that is inhibited by OA. Finally, to engineer OA-specific AraC variants, the iterative protein redesign and optimization computational framework was employed, followed by 2 rounds of directed evolution, resulting in a biosensor with 24-fold improved OA/TAL specificity, relative to AraC-TAL1.

Positive multistate protein design

Motivation Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. Results We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. Availability and implementation https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. Supplementary information Supplementary data are available at Bioinformatics online.

OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features

We present OSPREY 3.0, a new and greatly improved release of the OSPREY protein design software. OSPREY 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of OSPREY when running the same algorithms on the same hardware. Moreover, OSPREY 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of OSPREY, OSPREY 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that OSPREY 3.0 accurately predicts the effect of mutations on protein-protein binding. OSPREY 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software.