Expanding synthetic capabilities to routinely employ enzymes in organic solvents (OSs) is a dream for protein engineers and synthetic chemists. Despite significant advances in the field of protein engineering, general and transferable design principles to improve the OS resistance of enzymes are poorly understood. Herein, we report a combined computational and directed evolution study of Bacillus subtlis lipase A (BSLA) in three OSs (i. e., 1,4‐dioxane, dimethyl sulfoxide, 2,2,2‐trifluoroethanol) to devise a rational strategy to guide engineering OS resistant enzymes. Molecular dynamics simulations showed that OSs reduce BSLA activity and resistance in OSs by (i) stripping off essential water molecules from the BLSA surface mainly through H‐bonds binding; and (ii) penetrating the substrate binding cleft leading to inhibition and conformational change. Interestingly, integration of computational results with “BSLA‐SSM” variant library (3439 variants; all natural diversity with amino acid exchange) revealed two complementary rational design strategies: (i) surface charge engineering, and (ii) substrate binding cleft engineering. These strategies are most likely applicable to stabilize other lipases and enzymes and assist experimentalists to design organic solvent resistant enzymes with reduced time and screening effort in lab experiments.

Am ain remaining challengei np rotein engineering is how to recombine beneficial substitutions. Systematic recombinations tudies show that poorly performing variants are usually obtained after recombination of 3t o4beneficial substitutions. This limits researchers in exploiting nature's potential in generatingb etter enzymes. TheC omputer-assisted Recombination (CompassR)s trategy provides as election guide for beneficial substitutionst hat can be recombined to gradually improvee nzyme performance by analysis of the relative free energy of folding (DDG fold ). The performance of CompassR was evaluated by analysis of 84 recombinantsl ocated on 13 positions of Bacillus subtilis lipase A. The finally obtainedv ariant F17S/V54K/D64N/D91Eh ad a 2.7-fold improved specific activity in 18.3 %( v/v) 1-butyl-3methylimidazolium chloride ([BMIM][Cl]). In essence,t he de-ductedC ompassR rule allows recombination of beneficial substitutions in an iterative manner and empowers researchers to generate bettere nzymes in atime-efficient manner.

Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning–enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers—functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis.

Biocatalysis in organic solvents (OSs) has found various important applications, particularly in organic synthesis and for the production of pharmaceuticals, flavors, and fragrances. However, the use of enzymes in OSs often results in enzyme deactivation or a dramatic drop in catalytic activity. Herein, we have developed a comprehensive understanding of the interactions between enzymes and OSs based on numerous observables obtained from molecular dynamics simulation of 32 variants of Bacillus subtilis lipase A (BSLA). We have tested the wild-type enzymes and variants carrying single and multiple substitutions toward the organic cosolvent 2,2,2-trifluoroethanol (TFE, 12% (v/v)). After analyzing the distribution of 35 structural and dynamic observables, we uncovered that increased enzyme surface hydration of substituted sites is the predominant factor to drive the improved resistance in OS. The iterative recombination of four surface substitutions revealed that the extent of hydration in BSLA variants correlates strongly with its OS resistance (R 2 = 0.91). Remarkably, the substitutions recombination led to a highly resistant BSLA variant (I12R/M137H/N166E) with a 7.8-fold improved resistance in 12% (v/v) TFE, while retaining comparable catalytic activity (∼92%) compared to the wild-type enzyme. Our findings prove that strengthening protein surface hydration via surface charge engineering is an effective and efficient rational strategy for tailoring enzyme stability in OSs.