Predicting changes in protein thermostability brought about by single- or multi-site mutations

<p>Abstract</p> <p>Background</p> <p>An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔ<it>G</it>) of thermal denaturation.</p> <p>Results</p> <p>We have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations.</p> <p>Conclusions</p> <p>The performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.</p

Machine learning-guided directed evolution for protein engineering

Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio

Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site

Motivation: Accurate prediction of protein stability is important for understanding the molecular underpinnings of diseases and for the design of new proteins. We introduce a novel approach for the prediction of changes in protein stability that arise from a single-site amino acid substitution; the approach uses available data on mutations occurring in the same position and in other positions. Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features. Pro-Maya predicts the stability free energy difference of mutant versus wild type, denoted as ΔΔG

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of \u394\u394G values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of \u394\u394G values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases

Engineering stabilized enzymes via computational design and immobilization

Includes bibliographical references.2016 Fall.The realm of biocatalysis has significantly matured beyond ancient fermentation techniques to accommodate the demand for modern day products. Enzymatically produced goods already influence our daily lives, from sweeteners and laundry detergent to blood pressure medication and antibiotics. Protein engineering has been a major driving force behind this biorevolution, yielding catalysts that can transform non-native substrates and withstand harsh industrial conditions. Although successful in many regards, computational design efforts are still limited by the crude approximations employed in searching a complex energy landscape. Advancements in protein engineering methods will be necessary to develop our understanding of biomolecules and accelerate the next generation of biotechnology applications. Our work employs a combination of computational design and simulation to achieve improved enzyme stability. In the first example, an enzyme used in the production of cellulosic biofuels was redesigned to remain active at high temperature. An initial approach involving consensus sequence analysis, predicted point mutation energy, and combinatorial optimization resulted in a sequence with reduced stability and activity. However, by using recombination methods and molecular dynamics simulations, we were able to identify specific mutations that had a stabilizing or destabilizing effect, and we successfully isolated mutations that benefited enzyme stability. Our iterative approach demonstrated how common design failures could be overcome by careful interpretation and suggested methods for improving future computational design efforts. In the second example, a cellulase was designed to have a high net charge via selected surface mutagenesis. “Supercharged” cellulases were experimentally characterized in various ionic liquids to assess the effect of high ion concentration on enzyme stability and activity. The designed enzymes also provided an opportunity to systematically probe the protein-solvent interface. Molecular dynamics simulations showed how ions influenced protein behavior by inducing minor unfolding events or by physically blocking the active site. Contradictory to previous reports, charged mutations only appeared to alter the affinity of anions and did not significantly change the binding of cations at the protein surface. Understanding the different modes of enzyme inactivation could motivate targeted design strategies for engineering protein resilience in ionic solvents. In addition to the discussed computational design methods, immobilization strategies were identified for capturing enzymes within porous protein crystals. Immobilization offers a generic approach for improving enzyme stability and activity. Our preliminary studies involving horseradish peroxidase and other enzymes suggested protein scaffolds could be employed as an effective immobilization material. Co-immobilizing multiple enzymes within the porous material led to improved product yield via exclusion of off-pathway reactions. Although future studies will be required to assess the potential capabilities of this immobilization strategy in comparison to other materials, preliminary results suggest protein crystals offer a favorable, controlled environment for immobilizing enzymes. The diversity of approaches presented in this thesis emphasizes that there are many options for engineering enzyme stability. Extending the lessons learned from our cellulase engineering to the greater field of rational protein design promotes the concept of biomolecules as designable entities. By establishing the shortcomings of our designs and suggesting routes for improvement, we anticipate our design methods and immobilization strategies will procure continued interest from the biotechnology community. The toolsets we developed for cellulases can be directly transferred to other enzymes and have the potential to impact a range of protein engineering applications

FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed