Reassessing enzyme kinetics: Considering protease-as-substrate interactions in proteolytic networks.

Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection.

This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the prealigned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.

AESOP: A Python Library for Investigating Electrostatics in Protein Interactions.

Electric fields often play a role in guiding the association of protein complexes. Such interactions can be further engineered to accelerate complex association, resulting in protein systems with increased productivity. This is especially true for enzymes where reaction rates are typically diffusion limited. To facilitate quantitative comparisons of electrostatics in protein families and to describe electrostatic contributions of individual amino acids, we previously developed a computational framework called AESOP. We now implement this computational tool in Python with increased usability and the capability of performing calculations in parallel. AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electrostatic potential files for protein structures provided by the end user. There are methods within AESOP for quantitatively comparing sets of grid-based electrostatic potentials in terms of similarity or generating ensembles of electrostatic potential files for a library of mutants to quantify the effects of perturbations in protein structure and protein-protein association.

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during the last four CASP experiments, a majority of the methods continue to degrade models rather than improve them. Princeton_TIGRESS (Khoury et al., Proteins 2014;82:794-814) was developed previously and utilizes separate sampling and selection stages involving Monte Carlo and molecular dynamics simulations and classification using an SVM predictor. The initial implementation was shown to consistently refine protein structures 76% of the time in our own internal benchmarking on CASP 7-10 targets. In this work, we improved the sampling and selection stages and tested the method in blind predictions during CASP11. We added a decomposition of physics-based and hybrid energy functions, as well as a coordinate-free representation of the protein structure through distance-binning Cα-Cα distances to capture fine-grained movements. We performed parameter estimation to optimize the adjustable SVM parameters to maximize precision while balancing sensitivity and specificity across all cross-validated data sets, finding enrichment in our ability to select models from the populations of similar decoys generated for targets in CASPs 7-10. The MD stage was enhanced such that larger structures could be further refined. Among refinement methods that are currently implemented as web-servers, Princeton_TIGRESS 2.0 demonstrated the most consistent and most substantial net refinement in blind predictions during CASP11. The enhanced refinement protocol Princeton_TIGRESS 2.0 is freely available as a web server at http://atlas.engr.tamu.edu/refinement/. Proteins 2017; 85:1078-1098. © 2017 Wiley Periodicals, Inc.

conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure.

Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus secondary structure prediction method, conSSert, which is based on support vector machines (SVM) and provides exceptional accuracy for the prediction of beta-strands with QE accuracy of over 0.82 and a Q2-EH of 0.86. conSSert uses as input probabilities for the three types of secondary structure (helix, strand, and coil) that are predicted by four top performing methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR. conSSert was trained/tested using 4261 protein chains from PDBSelect25, and 8632 chains from PISCES. Further validation was performed using targets from CASP9, CASP10, and CASP11. Our data suggest that poor performance in strand prediction is likely a result of training bias and not solely due to the nonlocal nature of beta-sheet contacts. conSSert is freely available for noncommercial use as a webservice: http://ares.tamu.edu/conSSert/.

Princeton_TIGRESS: protein geometry refinement using simulations and support vector machines.

Protein structure refinement aims to perform a set of operations given a predicted structure to improve model quality and accuracy with respect to the native in a blind fashion. Despite the numerous computational approaches to the protein refinement problem reported in the previous three CASPs, an overwhelming majority of methods degrade models rather than improve them. We initially developed a method tested using blind predictions during CASP10 which was officially ranked in 5th place among all methods in the refinement category. Here, we present Princeton_TIGRESS, which when benchmarked on all CASP 7,8,9, and 10 refinement targets, simultaneously increased GDT_TS 76% of the time with an average improvement of 0.83 GDT_TS points per structure. The method was additionally benchmarked on models produced by top performing three-dimensional structure prediction servers during CASP10. The robustness of the Princeton_TIGRESS protocol was also tested for different random seeds. We make the Princeton_TIGRESS refinement protocol freely available as a web server at http://atlas.princeton.edu/refinement. Using this protocol, one can consistently refine a prediction to help bridge the gap between a predicted structure and the actual native structure.

Surrogate Modeling of the Relative Entropy for Inverse Design Using Smolyak Sparse Grids.

Relative entropy minimization, a statistical-mechanics approach for finding potential energy functions that produce target structural ensembles, has proven to be a powerful strategy for the inverse design of nanoparticle self-assembly. For a given target structure, the gradient of the relative entropy with respect to the adjustable parameters of the potential energy function is computed by performing a simulation, and then these parameters are updated using iterative gradient-based optimization. Small parameter updates per iteration and many iterations can be required for numerical stability, but this incurs considerable computational expense because a new simulation must be performed to reevaluate the gradient at each iteration. Here, we investigate the use of surrogate modeling to decouple the process of minimizing the relative entropy from the computationally demanding process of determining its gradient. We approximate the relative-entropy gradient using Chebyshev polynomial interpolation on Smolyak sparse grids. Our approach potentially increases the robustness and computational efficiency of using the relative entropy for inverse design, primarily for physically informed potential energy functions that have a small number of adjustable parameters.

Novel compstatin family peptides inhibit complement activation by drusen-like deposits in human retinal pigmented epithelial cell cultures.

We have used a novel human retinal pigmented epithelial (RPE) cell-based model that mimics drusen biogenesis and the pathobiology of age-related macular degeneration to evaluate the efficacy of newly designed peptide inhibitors of the complement system. The peptides belong to the compstatin family and, compared to existing compstatin analogs, have been optimized to promote binding to their target, complement protein C3, and to enhance solubility by improving their polarity/hydrophobicity ratios. Based on analysis of molecular dynamics simulation data of peptide-C3 complexes, novel binding features were designed by introducing intermolecular salt bridge-forming arginines at the N-terminus and at position -1 of N-terminal dipeptide extensions. Our study demonstrates that the RPE cell assay has discriminatory capability for measuring the efficacy and potency of inhibitory peptides in a macular disease environment.

The two sides of complement C3d: evolution of electrostatics in a link between innate and adaptive immunity.

The interaction between complement fragment C3d and complement receptor 2 (CR2) is a key aspect of complement immune system activation, and is a component in a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on Earth for the last 600 million years. However, the link between the complement system and adaptive immunity, which is formed through the association of the B-cell co-receptor complex, including the C3d-CR2 interaction, is a much more recent adaptation. Human C3d and CR2 have net charges of -1 and +7 respectively, and are believed to have evolved favoring the role of electrostatics in their functions. To investigate the role of electrostatics in the function and evolution of human C3d and CR2, we have applied electrostatic similarity methods to identify regions of evolutionarily conserved electrostatic potential based on 24 homologues of complement C3d and 4 homologues of CR2. We also examine the effects of structural perturbation, as introduced through molecular dynamics and mutations, on spatial distributions of electrostatic potential to identify perturbation resistant regions, generated by so-called electrostatic "hot-spots". Distributions of electrostatic similarity based on families of perturbed structures illustrate the presence of electrostatic "hot-spots" at the two functional sites of C3d, while the surface of CR2 lacks electrostatic "hot-spots" despite its excessively positive nature. We propose that the electrostatic "hot-spots" of C3d have evolved to optimize its dual-functionality (covalently attaching to pathogen surfaces and interaction with CR2), which are both necessary for the formation B-cell co-receptor complexes. Comparison of the perturbation resistance of the electrostatic character of the homologues of C3d suggests that there was an emergence of a new role of electrostatics, and a transition in the function of C3d, after the divergence of jawless fish.

A gain-of-function mutation of Arabidopsis lipid transfer protein 5 disturbs pollen tube tip growth and fertilization.

During compatible pollination of the angiosperms, pollen tubes grow in the pistil transmitting tract (TT) and are guided to the ovule for fertilization. Lily (Lilium longiflorum) stigma/style Cys-rich adhesin (SCA), a plant lipid transfer protein (LTP), is a small, secreted peptide involved in pollen tube adhesion-mediated guidance. Here, we used a reverse genetic approach to study biological roles of Arabidopsis thaliana LTP5, a SCA-like LTP. The T-DNA insertional gain-of-function mutant plant for LTP5 (ltp5-1) exhibited ballooned pollen tubes, delayed pollen tube growth, and decreased numbers of fertilized eggs. Our reciprocal cross-pollination study revealed that ltp5-1 results in both male and female partial sterility. RT-PCR and beta-glucuronidase analyses showed that LTP5 is present in pollen and the pistil TT in low levels. Pollen-targeted overexpression of either ltp5-1 or wild-type LTP5 resulted in defects in polar tip growth of pollen tubes and thereby decreased seed set, suggesting that mutant ltp5-1 acts as a dominant-active form of wild-type LTP5 in pollen tube growth. The ltp5-1 protein has additional hydrophobic C-terminal sequences, compared with LTP5. In our structural homology/molecular dynamics modeling, Tyr-91 in ltp5-1, replacing Val-91 in LTP5, was predicted to interact with Arg-45 and Tyr-81, which are known to interact with a lipid ligand in maize (Zea mays) LTP. Thus, Arabidopsis LTP5 plays a significant role in reproduction.

Influence of electrostatics on the complement regulatory functions of Kaposica, the complement inhibitor of Kaposi's sarcoma-associated herpesvirus.

Kaposica, the complement regulator of Kaposi's sarcoma-associated herpesvirus, inhibits complement by supporting factor I-mediated inactivation of the proteolytically activated form of C3 (C3b) and C4 (C4b) (cofactor activity [CFA]) and by accelerating the decay of classical and alternative pathway C3-convertases (decay-accelerating activity [DAA]). Previous data suggested that electrostatic interactions play a critical role in the binding of viral complement regulators to their targets, C3b and C4b. We therefore investigated how electrostatic potential on Kaposica influences its activities. We built a homology structure of Kaposica and calculated the electrostatic potential of the molecule, using the Poisson-Boltzmann equation. Mutants were then designed to alter the overall positive potential of the molecule or of each of its domains and linkers by mutating Lys/Arg to Glu/Gln, and the functional activities of the expressed mutants were analyzed. Our data indicate that 1) positive potential at specific sites and not the overall positive potential on the molecule guides the CFAs and classical pathway DAA; 2) positive potential around the linkers between complement control protein domains (CCPs) 1-2 and 2-3 is more important for DAAs than for CFAs; 3) positive potential in CCP1 is crucial for binding to C3b and C4b, and thereby its functional activities; 4) conversion to negative or enhancement of negative potential for CCPs 2-4 has a marked effect on C3b-linked activities as opposed to C4b-linked activities; and 5) reversal of the electrostatic potential of CCP4 to negative has a differential effect on classical and alternative pathway DAAs. Together, our data provide functional relevance to conservation of positive potential in CCPs 1 and 4 and the linkers of viral complement regulators.

Role of Electrostatic Hotspots in the Selectivity of Complement Control Proteins Toward Human and Bovine Complement Inhibition.

Poxviruses are dangerous pathogens, which can cause fatal infection in unvaccinated individuals. The causative agent of smallpox in humans, variola virus, is closely related to the bovine vaccinia virus, yet the molecular basis of their selectivity is currently incompletely understood. Here, we examine the role of the electrostatics in the selectivity of the smallpox protein SPICE and vaccinia protein VCP toward the human and bovine complement protein C3b, a key component of the complement immune response. Electrostatic calculations, in-silico alanine-scan and electrostatic hotspot analysis, as introduced by Kieslich and Morikis (PLoS Comput. Biol. 2012), are used to assess the electrostatic complementarity and to identify sites resistant to local perturbation where the electrostatic potential is likely to be evolutionary conserved. The calculations suggest that the bovine C3b is electrostatically prone to selectively bind its VCP ligand. On the other hand, the human isoform of C3b exhibits a lower electrostatic complementarity toward its SPICE ligand. Yet, the human C3b displays a highly preserved electrostatic core, which suggests that this isoform could be less selective in binding different ligands like SPICE and the human Factor H. This is supported by experimental cofactor activity assays revealing that the human C3b is prone to bind both SPICE and Factor H, which exhibit diverse electrostatic properties. Additional investigations considering mutants of SPICE and VCP that revert their selectivity reveal an "electrostatic switch" into the central modules of the ligands, supporting the critical role of the electrostatics in the selectivity. Taken together, these evidences provide insights into the selectivity mechanism of the complement regulator proteins encoded by the variola and vaccinia viruses to circumvent the complement immunity and exert their pathogenic action. These fundamental aspects are valuable for the development of novel vaccines and therapeutic strategies.

A multifaceted study of stigma/style cysteine-rich adhesin (SCA)-like Arabidopsis lipid transfer proteins (LTPs) suggests diversified roles for these LTPs in plant growth and reproduction.

Lily stigma/style cysteine-rich adhesin (SCA), a plant lipid transfer protein (LTP) which is secreted into the extracellular matrix, functions in pollen tube guidance in fertilization. A gain-of-function mutant (ltp5-1) for Arabidopsis LTP5, an SCA-like molecule, was recently shown to display defects in sexual reproduction. In the current study, it is reported that ltp5-1 plants have dwarfed primary shoots, delayed hypocotyl elongation, various abnormal tissue fusions, and display multibranching. These mutant phenotypes in vegetative growth are recessive. No abnormality was found in ltp5-1/+ plants. In a phylogenetic analysis of plant LTPs, SCA-like Arabidopsis LTPs were classified with conventional plant LTPs. Homology modelling-based electrostatic similarity index (ESI) clustering was used to show diversity in spatial distributions of electrostatic potentials of SCA-like LTPs, suggestive of their various roles in interaction in the extracellular matrix space. ß-Glucuronidase (GUS) analysis showed that SCA-like Arabidopsis LTP genes are diversely present in various tissues. LTP4 was found specifically in the guard cells and LTP6 in trichomes as well as in other tissues. LTP1 levels were specifically abundant in the stigma, and both LTP3 and LTP6 in the ovules. LTP2 and LTP4 gene levels were up-regulated in whole seedlings with 20% polyethylene glycol (PEG) and 300 mM NaCl treatments, respectively. LTP5 was up-regulated in the hypocotyl with 3 d dark growth conditions. LTP6 was specifically expressed in the tip of the cotyledon under drought stress conditions. The results suggest that SCA-like Arabidopsis LTPs are multifunctional, with diversified roles in plant growth and reproduction.

A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process.

In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dual C-SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results.

Simultaneous Fault Detection and Identification in Continuous Processes via nonlinear Support Vector Machine based Feature Selection.

Rapid detection and identification of process faults in industrial applications is crucial to sustain a safe and profitable operation. Today, the advances in sensor technologies have facilitated large amounts of chemical process data collection in real time which subsequently broadened the use of data-driven process monitoring techniques via machine learning and multivariate statistical analysis. One of the well-known machine learning techniques is Support Vector Machines (SVM) which allows the use of high dimensional feature sets for learning problems such as classification and regression. In this paper, we present the application of a novel nonlinear (kernel-dependent) SVM-based feature selection algorithm to process monitoring and fault detection of continuous processes. The developed methodology is derived from sensitivity analysis of the dual SVM objective and utilizes existing and novel greedy algorithms to rank features that also guides fault diagnosis. Specifically, we train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy of the fault detection models and perform fault diagnosis. We present results for the Tennessee Eastman process as a case study and compare our approach to existing approaches for fault detection, diagnosis and identification.

Virtual Screening of Chemical Compounds for Discovery of Complement C3 Ligands.

The complement system is our first line of defense against foreign pathogens, but when it is not properly regulated, complement is implicated in the pathology of several autoimmune and inflammatory disorders. Compstatin is a peptidic complement inhibitor that acts by blocking the cleavage of complement protein C3 to the proinflammatory fragment C3a and opsonin fragment C3b. In this study, we aim to identify druglike small-molecule complement inhibitors with physicochemical, geometric, and binding properties similar to those of compstatin. We employed two approaches using various high-throughput virtual screening methods, which incorporate molecular dynamics (MD) simulations, pharmacophore model design, energy calculations, and molecular docking and scoring. We have generated a library of 274 chemical compounds with computationally predicted binding affinities for the compstatin binding site of C3. We have tested subsets of these chemical compounds experimentally for complement inhibitory activity, using hemolytic assays, and for binding affinity, using microscale thermophoresis. As a result, although none of the compounds showed inhibitory activity, compound 29 was identified to exhibit weak competitive binding against a potent compstatin analogue, therefore validating our computational approaches. Additional docking and MD simulation studies suggest that compound 29 interacts with C3 residues, which have been shown to be important in binding of compstatin to the C3c fragment of C3. Compound 29 is amenable to physicochemical optimization to acquire inhibitory properties. Additionally, it is possible that some of the untested compounds will demonstrate binding and inhibition in future experimental studies.

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12.

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism.

HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/.

Protein folding and de novo protein design for biotechnological applications.

In the postgenomic era, the medical/biological fields are advancing faster than ever. However, before the power of full-genome sequencing can be fully realized, the connection between amino acid sequence and protein structure, known as the protein folding problem, needs to be elucidated. The protein folding problem remains elusive, with significant difficulties still arising when modeling amino acid sequences lacking an identifiable template. Understanding protein folding will allow for unforeseen advances in protein design; often referred to as the inverse protein folding problem. Despite challenges in protein folding, de novo protein design has recently demonstrated significant success via computational techniques. We review advances and challenges in protein structure prediction and de novo protein design, and highlight their interplay in successful biotechnological applications.

Engineering pre-SUMO4 as efficient substrate of SENP2.

SUMOylation, one of the most important protein post-translational modifications, plays critical roles in a variety of physiological and pathological processes. SENP (Sentrin/SUMO-specific protease), a family of SUMO-specific proteases, is responsible for the processing of pre-SUMO and removal of SUMO from conjugated substrates. SUMO4, the latest discovered member in the SUMO family, has been found as a type 1 diabetes susceptibility gene and its maturation is not understood so far. Despite the 14 amino acid differences between pre-SUMO4 and SUMO2, pre-SUMO4 is not processed by SENP2 but pre-SUMO2 does. A novel interdisciplinary approach involving computational modeling and a FRET-based protease assay was taken to engineer pre-SUMO4 as a substrate of SENP2. Given the difference in net charge between pre-SUMO4 and pre-SUMO2, the computational framework analysis of electrostatic similarities of proteins was applied to determine the contribution of each ionizable amino acid in a model of SENP2-(pre-SUMO4) binding, and to propose pre-SUMO4 mutations. The specificities of the SENP2 toward different pre-SUMO4 mutants were determined using a quantitative FRET assay by characterizing the catalytic efficiencies (kcat/KM). A single amino acid mutation made pre-SUMO4 amenable to SENP2 processing and a combination of two amino acid mutations made it highly accessible as SENP2 substrate. The combination of the two approaches provides a powerful protein engineering tool for future SUMOylation studies.