Protein engineering of Pseudomonas fluorescens peroxidase Dyp1B for oxidation of phenolic and polymeric lignin substrates

Directed evolution was applied to dye-decolourizing peroxidase Dyp1B from Pseudomonas fluorescens Pf-5, in order to enhance the activity for oxidation of phenolic and lignin substrates. Saturation mutagenesis was used to generate focused libraries at 7 active site residues in the vicinity of the heme cofactor, and the libraries were screened for activity towards 2,6-dichlorophenol. Mutants N193 L and H169 L were found to show 7–8 fold enhanced kcat/KM towards DCP, and replacements at Val205 and Ala209 also showed enhanced activity towards alkali Kraft lignin. Residues near the predicted Mn(II) binding site were also investigated by site-directed mutagenesis, and mutants S223 N and H127R showed 4-7-fold increased kcat/KM for Mn(II) oxidation. Mutant F128R also showed enhanced thermostability, compared to wild-type Dyp1B. Testing of mutants for low molecular weight product release from Protobind alkali lignin revealed that mutant H169 L showed enhanced product release, compared with WT enzyme, and the formation of three low molecular weight metabolites by this mutant was detected by reverse phase HPLC analysis

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

Different Approaches For Protein Engineering In Industrial Biotechnology

Protein engineering is the novel field which has wide applications from pharmaceutics, industry, commercial, laundry and research. It may apply rational design or non rational design or both. Site directed mutagenesis is a classical approach involving the protein folding principles and as such different techniques involving multidisciplinary research and broad knowledge is required involving biocomputing of complex data obtained from various sequencing projects and prediction of the future protein structure either chemically or genetically modified. Non rational mutagenesis or directed evolution involves random mutations in the gene encoding protein or shuffling the genes encoding different domains producing a random set of numerous large libraries of mutant proteins, using advanced technology the desired protein can be selected but the exact structure or changes may remain unnoticed

Protein-based materials, toward a new level of structural control

Through billions of years of evolution nature has created and refined structural proteins for a wide variety of specific purposes. Amino acid sequences and their associated folding patterns combine to create elastic, rigid or tough materials. In many respects, nature’s intricately designed products provide challenging examples for materials scientists, but translation of natural structural concepts into bio-inspired materials requires a level of control of macromolecular architecture far higher than that afforded by conventional polymerization processes. An increasingly important approach to this problem has been to use biological systems for production of materials. Through protein engineering, artificial genes can be developed that encode protein-based materials with desired features. Structural elements found in nature, such as β-sheets and α-helices, can be combined with great flexibility, and can be outfitted with functional elements such as cell binding sites or enzymatic domains. The possibility of incorporating non-natural amino acids increases the versatility of protein engineering still further. It is expected that such methods will have large impact in the field of materials science, and especially in biomedical materials science, in the future

3D Molecular Structures: Patentable Subject Matter Under 35 U.S.C. §101?

With the advent of protein engineering, the determination of a protein’s 3D structure has taken on a whole new importance. This has prompted some to call for the United States Patent and Trademark Office [USPTO] to break with tradition and allow patents on the three-dimensional structural information of proteins. This iBrief will discuss whether such information would constitute patentable subject matter under 35 U.S.C. §101, and how much protection patents on this information could actually confer

Hypocrea jecorina CEL6A protein engineering

The complex technology of converting lignocellulose to fuels such as ethanol has advanced rapidly over the past few years, and enzymes are a critical component of this technology. The production of effective enzyme systems at cost structures that facilitate commercial processes has been the focus of research for many years. Towards this end, the H. jecorina cellobiohydrolases, CEL7A and CEL6A, have been the subject of protein engineering at Genencor. Our first rounds of cellobiohydrolase engineering were directed towards improving the thermostability of both of these enzymes and produced variants of CEL7A and CEL6A with apparent melting temperatures above 70°C, placing their stability on par with that of H. jecorina CEL5A (EG2) and CEL3A (BGL1). We have now moved towards improving CEL6A- and CEL7A-specific performance in the context of a complete enzyme system under industrially relevant conditions. Achievement of these goals required development of new screening strategies and tools. We discuss these advances along with some results, focusing mainly on engineering of CEL6A