A Machine Learning Study on the Thermostability Prediction of (R)-ω-Selective Amine Transaminase from Aspergillus terreus

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein engineering.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

NMR Structure and Biophysical Characterization of Thermophilic Single-Stranded DNA Binding Protein from Sulfolobus Solfataricus

Proteins from Sulfolobus solfataricus (S. solfataricus), an extremophile, are active even at high temperatures. The single-stranded DNA (ssDNA) binding protein of S. solfataricus (SsoSSB) is overexpressed to protect ssDNA during DNA metabolism. Although SsoSSB has the potential to be applied in various areas, its structural and ssDNA binding properties at high temperatures have not been studied. We present the solution structure, backbone dynamics, and ssDNA binding properties of SsoSSB at 50 °C. The overall structure is consistent with the structures previously studied at room temperature. However, the loop between the first two β sheets, which is flexible and is expected to undergo conformational change upon ssDNA binding, shows a difference from the ssDNA bound structure. The ssDNA binding ability was maintained at high temperature, but different interactions were observed depending on the temperature. Backbone dynamics at high temperature showed that the rigidity of the structured region was well maintained. The investigation of an N-terminal deletion mutant revealed that it is important for maintaining thermostability, structure, and ssDNA binding ability. The structural and dynamic properties of SsoSSB observed at high temperature can provide information on the behavior of proteins in thermophiles at the molecular level and guide the development of new experimental techniques.