Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks

Enzyme miniaturization: Revolutionizing future biocatalysts

Enzyme miniaturization offers a transformative approach to overcome limitations posed by the large size of conventional enzymes in industrial, therapeutic, and diagnostic applications. However, the evolutionary optimization of enzymes for activity and stability has not inherently favored compact structures, creating challenges for modern applications requiring smaller and more efficient catalysts. In this review, we surveyed the advantages of miniature enzymes, including enhanced expressivity, folding efficiency, thermostability, and resistance to proteolysis. We described the applications of miniature enzymes as biosensors, therapeutic agents, and industrial catalysts. We highlighted strategies such as genome mining, rational design, random deletion, and de novo design for achieving enzyme miniaturization, integrating both computational and experimental techniques. By investigating these approaches, we aim to provide a framework for advancing enzyme engineering, emphasizing the unique potential of smaller enzymes to revolutionize biocatalysis, gene therapy, and biosensing technologies.

Biophysics-based protein language models for protein engineering

Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose Mutational Effect Transfer Learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure, and energetics. We finetune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity, and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.

AAindexNC: Estimating the Physicochemical Properties of Non-Canonical Amino Acids, Including Those Derived from the PDB and PDBeChem Databank

The physicochemical properties of amino acid residues from the AAindex database are widely used as predictors in building models for predicting both protein structures and properties. It should be noted, however, that the AAindex database contains data only for the 20 canonical amino acids. Non-canonical amino acids, while less common, are not rare; the Protein Data Bank includes proteins with more than 1000 distinct non-canonical amino acids. In this study, we propose a method to evaluate the physicochemical properties from the AAindex database for non-canonical amino acids and assess the prediction quality. We implemented our method as a bioinformatics tool and estimated the physicochemical properties of non-canonical amino acids from the PDB with the chemical composition presentation using SMILES encoding obtained from the PDBechem databank. The bioinformatics tool and resulting database of the estimated properties are freely available on the author's website and available for download via GitHub.