Predicting Protein Stability Research Articles

A Two Stage Model on Prediction of Protein Stability Changes in Case of Uncertainty using Fuzzy K-Means Clustering and Fuzzy Artificial Neural Networks

In both industrial applications and basic research the manipulation of protein stability is essential for knowing the principles which govern protein thermostability. This leads to hotspot in data mining based protein engineering and stability prediction. There are so many works related to the prediction of protein stability but they all lack in data preprocessing, presence of duplicates in the dataset and ability to handle uncertainty present in them. The main aim of this paper is to enhance the quality of the protein stability dataset and to increase the accuracy rate of prediction system. For deduplication process fuzzy K-means (FKM)based clustering is applied to cluster and match the duplicate records and eradicate them. To handle the uncertainty Fuzzy Artificial Neural Network (FANN) is used to perform prediction on protein stability. Simulation results proved the efficiency of FKM-FANN which yields excellent results comparing the existing methods

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.

Effects of ionic interactions on protein stability prediction using solid-state hydrogen deuterium exchange with mass spectrometry (ssHDX-MS).

Deuterium incorporation in solid-state hydrogen deuterium exchange with mass spectrometry (ssHDX-MS) has been correlated with protein aggregation on storage in sugar-based solid matrices. Here, the effects of sucrose, arginine and histidine buffer on the rate of aggregation of a lyophilized monoclonal antibody (mAb) were assessed using design of experiments (DoE) and response surface methodology. Lyophilized formulations were characterized using ssHDX-MS and Fourier transform infrared spectroscopy (ssFTIR) to assess potential correlation with stability in solid state. The samples were subjected to storage stability at 5 °C and stressed stability at 40 °C/75% RH for 6 months, and the aggregation rate was measured using size exclusion chromatography (SEC). Different levels of arginine had no significant effect on deuterium uptake in ssHDX-MS, although stability studies showed that aggregation rate decreased with increasing arginine concentration. Similarly, when histidine buffer was replaced with phosphate buffer at the same pH and molarity, ssHDX-MS showed no differences in deuterium uptake, but storage stability studies showed a significant increase in aggregation rate. The results suggest that proteins can be stabilized in amorphous solids by ionic interactions which ssHDX-MS does not detect, an important indication of the limitations of the method.

A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative algorithms and found all of them suffer from the problem of overfitting. This approach is based on the fact that if a wild-type protein is more stable than a mutant protein, then the same mutant is less stable than the wild-type protein. We analyzed the underlying issues and suggest that the main causes of the overfitting problem include that the numbers of training cases were too small, and the features used in the models were not sufficiently informative for the task. We make recommendations on how to avoid overfitting in this important research area and improve the reliability and robustness of ML-based algorithms in general.

Large-scale analysis of B-cell epitopes of envelope: Implications for Zika vaccine and immunotherapeutic development.

Background: Cases of the re-emergence of Zika virus in 2015 were associated with severe neurologic complications, including Gillien-Barre syndrome in adults and congenital Zika syndrome in newborns. The major structural determinant of immunity to the Zika virus is the E protein. Although B-cell epitopes of Zika E protein were recently identified, data regarding epitope variations among Zika strains in pre-epidemic and epidemic periods are lacking. Methods: Here, we conducted systematic bioinformatics analyses of Zika strains isolated between 1968 and 2017. Multiple sequence alignment of E protein as well as B-cell epitopes annotations were performed. In addition, homology-based approach was utilized to construct three-dimensional structures of monomeric E glycoproteins to annotate epitope variations. Lastly, prediction of of N-glycosylation patterns and prediction of protein stability upon mutations were also investigated. Results: Our analyses indicates that epitopes recognized by human mAbs ZIKV-117, ZIKV-15, and ZIKV-19 were highly conserved, suggesting as attractive targets for the development of vaccines and immunotherapeutics directed against diverse Zika strains. In addition, the epitope recognized by ZIKV-E-2A10G6 mAb derived from immunized mice was mostly conserved across Zika strains. Conclusions: Our data provide new insights regarding antigenic similarities between Zika strains circulating worldwide. These data are essential for understanding the impact of evolution on antigenic cross-reactivity between Zika lineages and strains. Further in-vitro analyses are needed to determine how mutationsat predefined epitopescould impact the development of vaccines that can effectively neutralize Zika viruses.

Large-scale analysis of B-cell epitopes of envelope: Implications for Zika vaccine and immunotherapeutic development

Background: Cases of the re-emergence of Zika virus in 2015 were associated with severe neurologic complications, including Gillien-Barre syndrome in adults and congenital Zika syndrome in newborns. The major structural determinant of immunity to the Zika virus is the E protein. Although B-cell epitopes of Zika E protein were recently identified, data regarding epitope variations among Zika strains in pre-epidemic and epidemic periods are lacking. Methods: Here, we conducted systematic bioinformatics analyses of Zika strains isolated between 1968 and 2017. Multiple sequence alignment of E protein as well as B-cell epitopes annotations were performed. In addition, homology-based approach was utilized to construct three-dimensional structures of monomeric E glycoproteins to annotate epitope variations. Lastly, prediction of of N-glycosylation patterns and prediction of protein stability upon mutations were also investigated. Results: Our analyses indicates that epitopes recognized by human mAbs ZIKV-117, ZIKV-15, and ZIKV-19 were highly conserved, suggesting as attractive targets for the development of vaccines and immunotherapeutics directed against diverse Zika strains. In addition, the epitope recognized by ZIKV-E-2A10G6 mAb derived from immunized mice was mostly conserved across Zika strains. Conclusions: Our data provide new insights regarding antigenic similarities between Zika strains circulating worldwide. These data are essential for understanding the impact of evolution on antigenic cross-reactivity between Zika lineages and strains. Further in-vitro analyses are needed to determine how mutationsat predefined epitopes could impact the development of vaccines that can effectively neutralize Zika viruses.

Applicability of Instability Index for In vitro Protein Stability Prediction.

The dipeptide composition-based Instability Index (II) is one of the protein primary structure-dependent methods available for in vivo protein stability predictions. As per this method, proteins with II value below 40 are stable proteins. Intracellular protein stability principles guided the original development of the II method. However, the use of the II method for in vitro protein stability predictions raises questions about the validity of applying the II method under experimental conditions that are different from the in vivo setting. The aim of this study is to experimentally test the validity of the use of II as an in vitro protein stability predictor. A representative protein CCM (CCM - Caulobacter crescentus metalloprotein) that rapidly degrades under in vitro conditions was used to probe the dipeptide sequence-dependent degradation properties of CCM by generating CCM mutants to represent stable and unstable II values. A comparative degradation analysis was carried out under in vitro conditions using wildtype CCM, CCM mutants and two other candidate proteins: metallo-β-lactamase L1 and α -S1- casein representing stable, borderline stable/unstable, and unstable proteins as per the II predictions. The effect of temperature and a protein stabilizing agent on CCM degradation was also tested. Data support the dipeptide composition-dependent protein stability/instability in wt-CCM and mutants as predicted by the II method under in vitro conditions. However, the II failed to accurately represent the stability of other tested proteins. Data indicate the influence of protein environmental factors on the autoproteolysis of proteins. Broader application of the II method for the prediction of protein stability under in vitro conditions is questionable as the stability of the protein may be dependent not only on the intrinsic nature of the protein but also on the conditions of the protein milieu.

A Reappraisal of Sedimentation Nonideality Coefficients for the Analysis of Weak Interactions of Therapeutic Proteins

The study of weak or colloidal interactions of therapeutic proteins in different formulations allows prediction and optimization of protein stability. Various biophysical techniques have been applied to determine the second osmotic virial coefficient B2 as it reflects on the macromolecular distance distribution that governs solution behavior at high concentration. In the present work, we exploit a direct link predicted by hydrodynamic theory between B2 and the nonideality of sedimentation, commonly measured in sedimentation velocity analytical ultracentrifugation through the nonideality coefficient of sedimentation, kS. Using sedimentation equilibrium analytical ultracentrifugation for independent measurement of B2, we have examined the dependence of kS on B2 for model proteins in different buffers. The data exhibit the expected linear relationship and highlight the impact of protein shape on the magnitude of the nonideality coefficient kS. Recently, measurements of kS have been considerably simplified allowing higher throughput and simultaneous polydispersity assessment at higher protein concentrations. Thus, sedimentation velocity may offer a useful approach to compare the impact of formulation conditions on weak interactions and simultaneously on higher-order structure of therapeutic proteins.

Identification and characterization of a sterically robust phenylalanine ammonia-lyase among 481 natural isoforms through association of in silico and in vitro studies

The enzyme phenylalanine ammonia lyase (PAL) is of special importance for the treatment of phenylketonuria patients. The aim of this study was to find a stable recombinant PAL with suitable kinetic properties among all natural PAL producing species using in silico and experimental approaches. To find such a stable PAL among 481 natural isoforms, 48,000 of 3-D models were predicted using the Modeller 9.10 program and evaluated by Ramachandran plot. Correlation analysis between Ramachandran plot and the energy of different thermodynamic components indicated that this plot could be an appropriate tool to predict protein stability. Hence, PAL6 from Lotus japonicus (LjPAL6) was selected as a stable isoform. Molecular dynamic (MD) simulation for 50 ns and docking has been conducted for LjPAL6-phenylalanine complex. The best PAL-phenylalanine frame was selected by re-docking with l-phenylalanine (L-Phe) and root-mean-square deviation (RMSD) value. MD simulation showed that the complex has a good stability, depicted by the low RMSD value, binding free energy and hydrogen bindings. Docking results showed that LjPAL6 has a high affinity toward l-Phe according to the low level of binding free energy. By overexpressing Ljpal6 in E. coli BL21, a total of 33.5 mg/l of protein was obtained, which has been increased to 83.7 mg/l via the optimization of LjPAL6 production using response surface methodology. The optimal pH and temperature were 8.5 and 50 °C, respectively. LjPAL6 showed a specific activity of 42 nkat/mg protein, with Km, Kcat and Kcat/Km values of 0.483 mM, 7 S−1 and 14.5 S−1 mM−1 for l-phe, respectively. In conclusion, finding models with the most reasonable stereo-chemical quality and lowest numbers of steric clashes would result in easier folding. Hence, in silico analyses of bulk data from natural origin will lead one to find an optimal model for in vitro studies and drug design.

A natural upper bound to the accuracy of predicting protein stability changes upon mutations.

Accurate prediction of protein stability changes upon single-site variations (ΔΔG) is important for protein design, as well as for our understanding of the mechanisms of genetic diseases. The performance of high-throughput computational methods to this end is evaluated mostly based on the Pearson correlation coefficient between predicted and observed data, assuming that the upper bound would be 1 (perfect correlation). However, the performance of these predictors can be limited by the distribution and noise of the experimental data. Here we estimate, for the first time, a theoretical upper-bound to the ΔΔG prediction performances imposed by the intrinsic structure of currently available ΔΔG data. Given a set of measured ΔΔG protein variations, the theoretically "best predictor" is estimated based on its similarity to another set of experimentally determined ΔΔG values. We investigate the correlation between pairs of measured ΔΔG variations, where one is used as a predictor for the other. We analytically derive an upper bound to the Pearson correlation as a function of the noise and distribution of the ΔΔG data. We also evaluate the available datasets to highlight the effect of the noise in conjunction with ΔΔG distribution. We conclude that the upper bound is a function of both uncertainty and spread of the ΔΔG values, and that with current data the best performance should be between 0.7 and 0.8, depending on the dataset used; higher Pearson correlations might be indicative of overtraining. It also follows that comparisons of predictors using different datasets are inherently misleading. Supplementary data are available at Bioinformatics online.

Large-scale analysis of B-cell epitopes of envelope: Implications for Zika vaccine and immunotherapeutic development

Background: Cases of the re-emergence of Zika virus in 2015 were associated with severe neurologic complications, including Gillien-Barre syndrome in adults and congenital Zika syndrome in newborns. The major structural determinant of immunity to the Zika virus is the E protein. Although B-cell epitopes of Zika E protein were recently identified, data regarding epitope variations among Zika strains in pre-epidemic and epidemic periods are lacking. Methods: Here, we conducted systematic bioinformatics analyses of Zika strains isolated between 1968 and 2017. Multiple sequence alignment of E protein as well as B-cell epitopes annotations were performed. In addition, homology-based approach was utilized to construct three-dimensional structures of monomeric E glycoproteins to annotate epitope variations. Lastly, of N-glycosylation patterns and prediction of protein stability upon mutations were also investigated. Results: Our analyses indicates that epitopes recognized by human mAbs ZIKV-117, ZIKV-15, and ZIKV-119 were highly conserved, suggesting as attractive targets for the development of vaccines and immunotherapeutics directed against diverse Zika strains. In addition, the epitope recognized by ZIKV-E-2A10G6 mAb derived from immunized mice was highly conserved across Zika strains. Conclusions: Our data provide new insights regarding antigenic similarities between Zika strains circulating worldwide. These data are essential for understanding the impact of evolution on antigenic cross-reactivity between Zika lineages and strains. Further in-vitro analyses are needed to determine how mutations could impact the development of vaccines that can effectively neutralize Zika viruses.

MGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion.

MotivationProteins are commonly used by biochemical industry for numerous processes. Refining these proteins’ properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability is necessary to facilitate efficient protein design. However, accuracy of predictive models is ultimately constrained by the limited availability of experimental data.ResultsWe have developed mGPfusion, a novel Gaussian process (GP) method for predicting protein’s stability changes upon single and multiple mutations. This method complements the limited experimental data with large amounts of molecular simulation data. We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and then learns a predictive GP model from the combined data. Our protein-specific model requires experimental data only regarding the protein of interest and performs well even with few experimental measurements. The mGPfusion models proteins by contact maps and infers the stability effects caused by mutations with a mixture of graph kernels. Our results show that mGPfusion outperforms state-of-the-art methods in predicting protein stability on a dataset of 15 different proteins and that incorporating molecular simulation data improves the model learning and prediction accuracy.Availability and implementationSoftware implementation and datasets are available at github.com/emmijokinen/mgpfusion.Supplementary information Supplementary data are available at Bioinformatics online.

Quantification of biases in predictions of protein stability changes upon mutations

Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations. We thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations (ΔΔG0) and proposed some unbiased solutions. We started by constructing a dataset Ssym of experimentally measured ΔΔG0s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild-type and mutant protein is available. On this balanced dataset, we assessed the performances of 15 widely used ΔΔG0 predictors. After the astonishing observation that almost all these methods are strongly biased toward destabilizing mutations, especially those that use black-box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiCsym. This new predictor constitutes an efficient trade-off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed. Supplementary data are available at Bioinformatics online. The article 10.1093/bioinformatics/bty340/, published alongside this paper, also addresses the problem of biases in protein stability change predictions.

ProtDataTherm: A database for thermostability analysis and engineering of proteins.

Protein thermostability engineering is a powerful tool to improve resistance of proteins against high temperatures and thereafter broaden their applications. For efficient protein thermostability engineering, different thermostability-classified data sources including sequences and 3D structures are needed for different protein families. However, no data source is available providing such data easily. It is the first release of ProtDataTherm database for analysis and engineering of protein thermostability which contains more than 14 million protein sequences categorized based on their thermal stability and protein family. This database contains data needed for better understanding protein thermostability and stability engineering. Providing categorized protein sequences and structures as psychrophilic, mesophilic and thermophilic makes this database useful for the development of new tools in protein stability prediction. This database is available at http://profiles.bs.ipm.ir/softwares/protdatatherm. As a proof of concept, the thermostability that improves mutations were suggested for one sample protein belonging to one of protein families with more than 20 mesophilic and thermophilic sequences and with known experimentally measured ΔT of mutations available within ProTherm database.

Genotyping and clinical characteristics screening of children with intellectual disability in Western India

Background: The RhoGTPases (ARHGEF6, OPHN1 and PAK3) are key signaling proteins, and can inter-relate extracellular and intracellular signals to sort out changes in the actin cytoskeleton of neuronal network connectivity. IL1RAPL2 is a member of Interleukin 1 receptor family that is expressed at high level in post-natal brain structures involved in the hippocampal memory system. The mutations of these genes have been reported to cause intellectual disability (ID). Therefore, this study is conducted to scrutinize distinctive distributions of genomic variations on West Indian population. Subjects and Methods: The genotyping for determination of SNPs associated with X-linked genes (rs35747426 in ARHGEF6, rs121434612 in PAK3, rs199985543 in OPHN1 and rs9887672 in IL1RAPL2) was performed in phenotypically screened 116 intellectually disabled and 100 healthy children by PCRRFLP method. Results: It has identified that allelic frequencies for rs199985543 and rs9887672 are seen progressively present in the disease group, indicating a significant level of association with ID in Gujarat population. The multifactor dimensionality reduction (MDR), SNP-SNP genotype models are resulted with a relationship of ID if there should be an occurrence of the OPHN1 and IL1RAPL2 gene variants. Additionally, rs199985543 was analyzed for OPHN1 protein structure and stability prediction, which results damaging effect to the protein. Surprisingly, no variation was found for rs35747426 ARHGEF6 and rs121434612 PAK3 polymorphisms in the present study population. Conclusion: This phenotype-genotype relationship has a solid and measurable connection according to the carrier genotypes of the variations and different types of ID, that confirm the mutation in OPHN1 and IL1RAPL2 genes permit serious attention for disease risk.

Identification of Conserved Water Sites in Protein Structures for Drug Design.

Identification of conserved waters in protein structures is a challenging task with applications in molecular docking and protein stability prediction. As an alternative to computationally demanding simulations of proteins in water, experimental cocrystallized waters in the Protein Data Bank (PDB) in combination with a local structure alignment algorithm can be used for reliable prediction of conserved water sites. We developed the ProBiS H2O approach based on the previously developed ProBiS algorithm, which enables identification of conserved water sites in proteins using experimental protein structures from the PDB or a set of custom protein structures available to the user. With a protein structure, a binding site, or an individual water molecule as a query, ProBiS H2O collects similar proteins from the PDB and performs local or binding site-specific superimpositions of the query structure with similar proteins using the ProBiS algorithm. It collects the experimental water molecules from the similar proteins and transposes them to the query protein. Transposed waters are clustered by their mutual proximity, which enables identification of discrete sites in the query protein with high water conservation. ProBiS H2O is a robust and fast new approach that uses existing experimental structural data to identify conserved water sites on the interfaces of protein complexes, for example protein-small molecule interfaces, and elsewhere on the protein structures. It has been successfully validated in several reported proteins in which conserved water molecules were found to play an important role in ligand binding with applications in drug design.

Examining the Feasibility of Using Free Energy Perturbation (FEP+) in Predicting Protein Stability.

The importance of engineering protein stability is well-known and has the potential to impact many fields ranging from pharmaceuticals to food sciences. Engineering proteins can be both a time-consuming and expensive experimental process. The use of computation is a potential solution to mitigating some of the time and expenses required to engineer a protein. This process has been previously hindered by inaccurate force fields or energy equations and slow computational processors; however, improved software and hardware have made this goal much more attainable. Here we find that Schrödinger's new FEP+, although still imperfect, proves more successful in predicting protein stability than other simpler methods of investigation. This increased accuracy comes at a cost of computational time and resources when compared to simpler methods. This work adds to the initial testing of FEP+ by offering options for more accurately predicting protein stability in an efficient manner.

Thermal stability and kinetic constants for 129 variants of a family 1 glycoside hydrolase reveal that enzyme activity and stability can be separately designed.

Accurate modeling of enzyme activity and stability is an important goal of the protein engineering community. However, studies seeking to evaluate current progress are limited by small data sets of quantitative kinetic constants and thermal stability measurements. Here, we report quantitative measurements of soluble protein expression in E. coli, thermal stability, and Michaelis-Menten constants (kcat, KM, and kcat/KM) for 129 designed mutants of a glycoside hydrolase. Statistical analyses reveal that functional Tm is independent of kcat, KM, and kcat/KM in this system, illustrating that an individual mutation can modulate these functional parameters independently. In addition, this data set is used to evaluate computational predictions of protein stability using the established Rosetta and FoldX algorithms. Predictions for both are found to correlate only weakly with experimental measurements, suggesting improvements are needed in the underlying algorithms.

Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning

Proteins are marginally stable, and an understanding of the sequence determinants for improved protein solubility is highly desired. For enzymes, it is well known that many mutations that increase protein solubility decrease catalytic activity. These competing effects frustrate efforts to design and engineer stable, active enzymes without laborious high-throughput activity screens. To address the trade-off between enzyme solubility and activity, we performed deep mutational scanning using two different screens/selections that purport to gauge protein solubility for two full-length enzymes. We assayed a TEM-1 beta-lactamase variant and levoglucosan kinase (LGK) using yeast surface display (YSD) screening and a twin-arginine translocation pathway selection. We then compared these scans with published experimental fitness landscapes. Results from the YSD screen could explain 37% of the variance in the fitness landscapes for one enzyme. Five percent to 10% of all single missense mutations improve solubility, matching theoretical predictions of global protein stability. For a given solubility-enhancing mutation, the probability that it would retain wild-type fitness was correlated with evolutionary conservation and distance to active site, and anticorrelated with contact number. Hybrid classification models were developed that could predict solubility-enhancing mutations that maintain wild-type fitness with an accuracy of 90%. The downside of using such classification models is the removal of rare mutations that improve both fitness and solubility. To reveal the biophysical basis of enhanced protein solubility and function, we determined the crystallographic structure of one such LGK mutant. Beyond fundamental insights into trade-offs between stability and activity, these results have potential biotechnological applications.

Effect of R119G Mutation on Human P5CR1 Dynamic Property and Enzymatic Activity.

Pyrroline-5-carboxylate reductase (P5CR1) is a universal housekeeping enzyme that catalyzes the reduction of Δ1-pyrroline-5-carboxylate (P5C) to proline with concomitant oxidation of NAD(P)H to NAD(P)+. The enzymatic cycle between P5C and proline is important for function in amino acid metabolism, apoptosis, and intracellular redox potential balance in mitochondria. Autosomal recessive cutis laxa (ARCL) results from a mutation in P5CR1 encoded by PYCR1. Specifically, the R119G mutation is reported to be linked to ARCL although it has not yet been characterized. We synthesized R119G P5CR1 and compared it to WT P5CR1. Foldx prediction of WT and R119G mutant P5CR1 protein stability suggests that the R119G mutation could significantly reduce protein stability. We also performed enzymatic activity assays to determine how the mutation impacts P5CR1 enzymatic function. The results of these experiments show that mutagenesis of R119 to G decreases P5CR1 catalytic efficiency for 3,4-dehydro-L-proline relative to WT. Mutagenesis and kinetic studies reveal that the activity of the mutant decreases as temperature increases from 5°C to 37°C, with almost no activity at 37°C, indicating that this mutation impairs P5CR1 function in vivo. Conversely, WT P5CR1 retains its activity after incubation at 37°C and has essentially no remaining activity at 75°C. Taken together, our experimental results indicate the R119G mutation could be an involving pathomechanism for ARCL.