RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction .     

Community-Wide Experimental Evaluation of the PROSS Stability-Design Method

Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.     

利用机器学习提高M. jannaschii酪氨酰tRNA合成酶底物特异性分子建模预测的准确度

设计结合不同化学结构底物的酶结合袋是一个巨大的挑战. 传统的湿实验要筛选成千上万甚至上百万个突变体来寻找对特定配体结合的突变体,此过程需要耗费大量的时间和资源. 为了加快筛选过程,我们提出了一种新的工作流程,将分子建模和数据驱动的机器学习方法相结合,生成具有高富集率的突变文库,用于高效筛选能识别特定底物的蛋白质突变体. M. jannaschii酪氨酰tRNA合成酶（Mj. TyrRS）能识别特定的非天然氨基酸并催化形成氨酰tRNA,其不同的突变体能够识别不同结构的非天然氨基酸,并且已经有了许多报道和数据的积累,因此我们使用TyrRS作为一个例子来进行此筛选流程的概念验证. 基于已知的多个Mj. TyrRS的晶体结构及分子建模的结果,我们发现D158G/P是影响残基158~163位α螺旋蛋白骨架变化的关键突变. 我们的模拟结果表明,在含有687个突变体的测试数据中,与随机突变相比,分子建模和打分函数计算排序可以将目标突变体的富集率提高2倍,而使用已知突变体和对应的非天然氨基酸数据训练的机器学习模型进行校准后,筛选富集率可提高11倍. 这种分子建模和机器学习相结合的计算和筛选流程非常有助于Mj.TyrRS的底物特异性设计,可以大大减少湿实验的时间和成本. 此外,这种新方法在蛋白质计算设计领域具有广泛的应用前景.     

Intranasal administration of an Escherichia coli-expressed codon-optimized rotavirus VP6 protein induces protection in mice

We are developing rotavirus vaccines based on the VP6 protein of the human G1P[8] [corrected] [J. Virol. 73 (1999) 7574] CJN strain of rotavirus. One prototype candidate consisting of MBP::VP6::His6, a chimeric protein of maltose-binding protein, VP6 and hexahistidine, was expressed mainly as truncated polypeptides in Escherichia coli BL21(DE3) cells. A possible reason for this extensive truncation is the high frequencies of rare bacterial codons within the rotavirus VP6 gene. Expression of truncated recombinant VP6 was found to be reduced, and expression of complete VP6 protein was simultaneously increased, when the protein was expressed in Rosetta(DE3)pLacI E. coli cells that contain increased amounts of transfer RNAs for a selection of rare codons. The same observation was made when a synthetic codon-optimized CJN-VP6 gene was expressed in E. coli BL21 or Rosetta cells. To increase protein recovery, recombinant E. coli cells were treated with 8M urea. Denatured, full-length MBP::VP6::His6 protein was then purified and used for intranasal vaccination of BALB/c mice (2 doses administered with E. coli heat-labile toxin LT(R192G) as adjuvant). Following oral challenge with the G3P[16] [corrected] [J. Virol. 76 (2002) 560] EDIM strain of murine rotavirus, protection levels against fecal rotavirus shedding were comparable (P>0.05) between groups of mice immunized with denatured codon-optimized or native (not codon-optimized) immunogen with values ranging from 87 to 99%. These protection levels were also comparable to those found after immunization with non-denatured CJN VP6. Thus, expression of complete rotavirus VP6 protein was greatly enhanced by codon optimization, and the protection elicited was not affected by denaturation of recombinant VP6.     

Distributions of beta sheets in proteins with application to structure prediction

We recently developed the Rosetta algorithm for ab initio protein structure prediction, which generates protein structures from fragment libraries using simulated annealing. The scoring function in this algorithm favors the assembly of strands into sheets. However, it does not discriminate between different sheet motifs. After generating many structures using Rosetta, we found that the folding algorithm predominantly generates very local structures. We surveyed the distribution of beta-sheet motifs with two edge strands (open sheets) in a large set of non-homologous proteins. We investigated how much of that distribution can be accounted for by rules previously published in the literature, and developed a filter and a scoring method that enables us to improve protein structure prediction for beta-sheet proteins. Proteins 2002;48:85-97.     

Structural adaptation of tooth enamel protein amelogenin in the presence of SDS micelles

Amelogenin, the major extracellular matrix protein of developing tooth enamel is intrinsically disordered. Through its interaction with other proteins and mineral, amelogenin assists enamel biomineralization by controlling the formation of highly organized enamel crystal arrays. We used circular dichroism (CD), dynamic light scattering (DLS), fluorescence, and NMR spectroscopy to investigate the folding propensity of recombinant porcine amelogenin rP172 following its interaction with SDS, at levels above critical micelle concentration. The rP172‐SDS complex formation was confirmed by DLS, while an increase in the structure moiety of rP172 was noted through CD and fluorescence experiments. Fluorescence quenching analyses performed on several rP172 mutants where all but one Trp was replaced by Tyr at different sequence regions confirmed that the interaction of amelogenin with SDS micelles occurs via the N‐terminal region close to Trp25 where helical segments can be detected by NMR. NMR spectroscopy and structural refinement calculations using CS‐Rosetta modeling confirm that the highly conserved N‐terminal domain is prone to form helical structure when bound to SDS micelles. Our findings reported here reveal interactions leading to significant changes in the secondary structure of rP172 upon treatment with SDS. These interactions may reflect the physiological relevance of the flexible nature of amelogenin and its sequence specific helical propensity that might enable it to structurally adapt with charged and potential targets such as cell surface, mineral, and other proteins during enamel biomineralization. © 2013 Wiley Periodicals, Inc. Biopolymers 101: 525–535, 2014.     

Exploring alternate states and oligomerization preferences of coiled‐coils by de novo structure modeling

Homomeric coiled‐coils can self‐assemble into a wide range of structural states with different helix topologies and oligomeric states. In this study, we have combined de novo structure modeling with stability calculations to simultaneously predict structure and oligomeric states of homomeric coiled‐coils. For dimers an asymmetric modeling protocol was developed. Modeling without symmetry constraints showed that backbone asymmetry is important for the formation of parallel dimeric coiled‐coils. Collectively, our results demonstrate that high‐resolution structure of coiled‐coils, as well as parallel and antiparallel orientations of dimers and tetramers, can be accurately predicted from sequence. De novo modeling was also used to generate models of competing oligomeric states, which were used to compare stabilities and thus predict the native stoichiometry from sequence. In a benchmark set of 33 coiled‐coil sequences, forming dimers to pentamers, up to 70% of the oligomeric states could be correctly predicted. The calculations demonstrated that the free energy of helix folding could be an important factor for determining stability and oligomeric state of homomeric coiled‐coils. The computational methods developed here should be broadly applicable to studies of sequence‐structure relationships in coiled‐coils and the design of higher order assemblies with improved oligomerization specificity. Proteins 2015; 83:235–247. © 2014 Wiley Periodicals, Inc.     

Computational Design of a PAK1 Binding Protein

We describe a computational protocol, called DDMI, for redesigning scaffold proteins to bind to a specified region on a target protein. The DDMI protocol is implemented within the Rosetta molecular modeling program and uses rigid-body docking, sequence design, and gradient-based minimization of backbone and side-chain torsion angles to design low-energy interfaces between the scaffold and target protein. Iterative rounds of sequence design and conformational optimization were needed to produce models that have calculated binding energies that are similar to binding energies calculated for native complexes. We also show that additional conformation sampling with molecular dynamics can be iterated with sequence design to further lower the computed energy of the designed complexes. To experimentally test the DDMI protocol, we redesigned the human hyperplastic discs protein to bind to the kinase domain of p21-activated kinase 1 (PAK1). Six designs were experimentally characterized. Two of the designs aggregated and were not characterized further. Of the remaining four designs, three bound to the PAK1 with affinities tighter than 350 μM. The tightest binding design, named Spider Roll, bound with an affinity of 100 μM. NMR-based structure prediction of Spider Roll based on backbone and ¹³C^β chemical shifts using the program CS-ROSETTA indicated that the architecture of human hyperplastic discs protein is preserved. Mutagenesis studies confirmed that Spider Roll binds the target patch on PAK1. Additionally, Spider Roll binds to full-length PAK1 in its activated state but does not bind PAK1 when it forms an auto-inhibited conformation that blocks the Spider Roll target site. Subsequent NMR characterization of the binding of Spider Roll to PAK1 revealed a comparably small binding ‘on-rate’ constant (? 10⁵ M^− 1 s^− 1). The ability to rationally design the site of novel protein-protein interactions is an important step towards creating new proteins that are useful as therapeutics or molecular probes.     

Solution Conformation and Dynamics of the HIV-1 Integrase Core Domain

The human immunodeficiency virus type 1 (HIV-1) integrase (IN) is a critical enzyme involved in infection. It catalyzes two reactions to integrate the viral cDNA into the host genome, 3′ processing and strand transfer, but the dynamic behavior of the active site during catalysis of these two processes remains poorly characterized. NMR spectroscopy can reveal important structural details about enzyme mechanisms, but to date the IN catalytic core domain has proven resistant to such an analysis. Here, we present the first NMR studies of a soluble variant of the catalytic core domain. The NMR chemical shifts are found to corroborate structures observed in crystals, and confirm prior studies suggesting that the α4 helix extends toward the active site. We also observe a dramatic improvement in NMR spectra with increasing MgCl₂ concentration. This improvement suggests a structural transition not only near the active site residues but also throughout the entire molecule as IN binds Mg²⁺. In particular, the stability of the core domain is linked to the conformation of its C-terminal helix, which has implications for relative domain orientation in the full-length enzyme. ¹⁵N relaxation experiments further show that, although conformationally flexible, the catalytic loop of IN is not fully disordered in the absence of DNA. Indeed, automated chemical shift-based modeling of the active site loop reveals several stable clusters that show striking similarity to a recent crystal structure of prototype foamy virus IN bound to DNA.     

Boosting protein stability with the computational design of β‐sheet surfaces

β‐sheets often have one face packed against the core of the protein and the other facing solvent. Mutational studies have indicated that the solvent‐facing residues can contribute significantly to protein stability, and that the preferred amino acid at each sequence position is dependent on the precise structure of the protein backbone and the identity of the neighboring amino acids. This suggests that the most advantageous methods for designing β‐sheet surfaces will be approaches that take into account the multiple energetic factors at play including side chain rotamer preferences, van der Waals forces, electrostatics, and desolvation effects. Here, we show that the protein design software Rosetta, which models these energetic factors, can be used to dramatically increase protein stability by optimizing interactions on the surfaces of small β‐sheet proteins. Two design variants of the β‐sandwich protein from tenascin were made with 7 and 14 mutations respectively on its β‐sheet surfaces. These changes raised the thermal midpoint for unfolding from 45°C to 64°C and 74°C. Additionally, we tested an empirical approach based on increasing the number of potential salt bridges on the surfaces of the β‐sheets. This was not a robust strategy for increasing stability, as three of the four variants tested were unfolded.