Rocco Moretti

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community‐wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community‐wide assessment of methods to predict the effects of mutations on protein–protein interactions. Twenty‐two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side‐chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large‐scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies. Proteins 2013; 81:1980–1987.

Engineering an allosteric transcription factor to respond to new ligands.

Genetic regulatory proteins inducible by small molecules are useful synthetic biology tools as sensors and switches. Bacterial allosteric transcription factors (aTFs) are a major class of regulatory proteins, but few aTFs have been redesigned to respond to new effectors beyond natural aTF-inducer pairs. Altering inducer specificity in these proteins is difficult because substitutions that affect inducer binding may also disrupt allostery. We engineered an aTF, the Escherichia coli lac repressor, LacI, to respond to one of four new inducer molecules: fucose, gentiobiose, lactitol and sucralose. Using computational protein design, single-residue saturation mutagenesis or random mutagenesis, along with multiplex assembly, we identified new variants comparable in specificity and induction to wild-type LacI with its inducer, isopropyl β-D-1-thiogalactopyranoside (IPTG). The ability to create designer aTFs will enable applications including dynamic control of cell metabolism, cell biology and synthetic gene circuits.

Scoring functions for protein-protein interactions.

The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning.

A Pareto-Optimal Refinement Method for Protein Design Scaffolds

Computational design of protein function involves a search for amino acids with the lowest energy subject to a set of constraints specifying function. In many cases a set of natural protein backbone structures, or “scaffolds”, are searched to find regions where functional sites (an enzyme active site, ligand binding pocket, protein – protein interaction region, etc.) can be placed, and the identities of the surrounding amino acids are optimized to satisfy functional constraints. Input native protein structures almost invariably have regions that score very poorly with the design force field, and any design based on these unmodified structures may result in mutations away from the native sequence solely as a result of the energetic strain. Because the input structure is already a stable protein, it is desirable to keep the total number of mutations to a minimum and to avoid mutations resulting from poorly-scoring input structures. Here we describe a protocol using cycles of minimization with combined backbone/sidechain restraints that is Pareto-optimal with respect to RMSD to the native structure and energetic strain reduction. The protocol should be broadly useful in the preparation of scaffold libraries for functional site design.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Rearrangement of the extracellular domain/extracellular loop 1 interface is critical for thyrotropin receptor activation

The thyroid stimulating hormone receptor (TSHR) is a G protein-coupled receptor (GPCR) with a characteristic large extracellular domain (ECD). TSHR activation is initiated by binding of the hormone ligand TSH to the ECD. How the extracellular binding event triggers the conformational changes in the transmembrane domain (TMD) necessary for intracellular G protein activation is poorly understood. To gain insight in this process, the knowledge on the relative positioning of ECD and TMD and the conformation of the linker region at the interface of ECD and TMD are of particular importance. To generate a structural model for the TSHR we applied an integrated structural biology approach combining computational techniques with experimental data. Chemical cross-linking followed by mass spectrometry yielded 17 unique distance restraints within the ECD of the TSHR, its ligand TSH, and the hormone-receptor complex. These structural restraints generally confirm the expected binding mode of TSH to the ECD as well as the general fold of the domains and were used to guide homology modeling of the ECD. Functional characterization of TSHR mutants confirms the previously suggested close proximity of Ser-281 and Ile-486 within the TSHR. Rigidifying this contact permanently with a disulfide bridge disrupts ligand-induced receptor activation and indicates that rearrangement of the ECD/extracellular loop 1 (ECL1) interface is a critical step in receptor activation. The experimentally verified contact of Ser-281 (ECD) and Ile-486 (TMD) was subsequently utilized in docking homology models of the ECD and the TMD to create a full-length model of a glycoprotein hormone receptor.

Rosetta and the Design of Ligand Binding Sites

Proteins that bind small molecules (ligands) can be used as biosensors, signal modulators, and sequestering agents. When naturally occurring proteins for a particular target ligand are not available, artificial proteins can be computationally designed. We present a protocol based on RosettaLigand to redesign an existing protein pocket to bind a target ligand. Starting with a protein structure and the structure of the ligand, Rosetta can optimize both the placement of the ligand in the pocket and the identity and conformation of the surrounding sidechains, yielding proteins that bind the target compound.

Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web‐accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pKa determination, lipid accessibility calculation, ribonucleic acid redesign, protein‐protein docking, protein‐small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.

Identification of a ubiquitin-binding interface using Rosetta and DEER

Significance Structure–function aspects of dynamic, membrane-associated proteins are difficult to mechanistically define. In this study, computational and biophysical methods are applied to identify a ubiquitin-interaction domain essential for the activity of a bacterial cytotoxin, ExoU. Introduction of mutations that diminish or improve ubiquitin interaction alters the biochemical and biological activities of the toxin. This strategy and these verification procedures may be useful where traditional NMR or crystallographic technologies to analyze protein–protein interactions are limited. ExoU is a member of a family of newly discovered ubiquitin-activated enzymes encoded by several bacterial pathogens. Understanding the details of intracellular enzyme activation will be critical to the development of inhibitors aimed at reducing tissue damage during infection by a variety of organisms. ExoU is a type III-secreted cytotoxin expressing A2 phospholipase activity when injected into eukaryotic target cells by the bacterium Pseudomonas aeruginosa. The enzymatic activity of ExoU is undetectable in vitro unless ubiquitin, a required cofactor, is added to the reaction. The role of ubiquitin in facilitating ExoU enzymatic activity is poorly understood but of significance for designing inhibitors to prevent tissue injury during infections with strains of P. aeruginosa producing this toxin. Most ubiquitin-binding proteins, including ExoU, demonstrate a low (micromolar) affinity for monoubiquitin (monoUb). Additionally, ExoU is a large and dynamic protein, limiting the applicability of traditional structural techniques such as NMR and X-ray crystallography to define this protein–protein interaction. Recent advancements in computational methods, however, have allowed high-resolution protein modeling using sparse data. In this study, we combine double electron–electron resonance (DEER) spectroscopy and Rosetta modeling to identify potential binding interfaces of ExoU and monoUb. The lowest-energy scoring model was tested using biochemical, biophysical, and biological techniques. To verify the binding interface, Rosetta was used to design a panel of mutations to modulate binding, including one variant with enhanced binding affinity. Our analyses show the utility of computational modeling when combined with sensitive biological assays and biophysical approaches that are exquisitely suited for large dynamic proteins.

MEHP and MEOHP but not DEHP bind productively to the peroxisome proliferator-activated receptor γ

RATIONALE The most frequently occurring phthalate, di(2-ethylhexyl) phthalate (DEHP), causes adverse effects on glucose homeostasis and insulin sensitivity in several cell models and epidemiological studies. However, thus far, there is no information available on the molecular interaction of phthalates and one of the key regulators of the metabolism, the peroxisome proliferator-activated receptor gamma (PPARγ). Since the endogenous ligand of PPARγ, 15-deoxy-delta-12,14-prostaglandin J2 (15Δ-PGJ2 ), features structural similarity to DEHP and its main metabolites produced in human hepatic metabolism, mono(2-ethylhexyl) phthalate (MEHP) and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), we tested the hypothesis of direct interactions between PPARγ and DEHP or its transformation products. METHODS Hydrogen/deuterium exchange mass spectrometry (HDX-MS) and docking were conducted to obtain structural insights into the interactions and surface plasmon resonance (SPR) analysis to reveal information about binding levels. To confirm the activation of PPARγ upon ligand binding on the cellular level, the GeneBLAzer® bioassay was performed. RESULTS HDX-MS and SPR analyses demonstrated that the metabolites MEHP and MEOHP, but not DEHP itself, bind to the ligand binding pocket of PPARγ. This binding leads to typical activation-associated conformational changes, as observed with its endogenous ligand 15Δ-PGJ2 . Furthermore, the reporter gene assay confirmed productive interaction. DEHP was inactive up to a concentration of 14 μM, while the metabolites MEHP and MEOHP were active at low micromolar concentrations. CONCLUSIONS In summary, this study gives structural insights into the direct interaction of PPARγ with MEHP and MEOHP and shows that the DEHP transformation products may modulate the lipid metabolism through PPARγ pathways.