Examination of Tyrosine/Adenine Stacking Interactions in Protein Complexes

The π-stacking interactions between tyrosine amino acid side chains and adenine-bearing ligands are examined. Crystalline protein structures from the protein data bank (PDB) exhibiting face-to-face tyrosine/adenine arrangements were used to construct 20 unique 4-methylphenol/N9-methyladenine (<i>p</i>-cresol/9MeA) model systems. Full geometry optimization of the 20 crystal structures with the M06-2X density functional theory method identified 11 unique low-energy conformations. CCSD­(T) complete basis set (CBS) limit interaction energies were estimated for all of the structures to determine the magnitude of the interaction between the two ring systems. CCSD­(T) computations with double-ζ basis sets (e.g., 6-31G*(0.25) and aug-cc-pVDZ) indicate that the MP2 method overbinds by as much as 3.07 kcal mol^–1 for the crystal structures and 3.90 kcal mol^–1 for the optimized structures. In the 20 crystal structures, the estimated CCSD­(T) CBS limit interaction energy ranges from −4.00 to −6.83 kcal mol^–1, with an average interaction energy of −5.47 kcal mol^–1, values remarkably similar to the corresponding data for phenylalanine/adenine stacking interactions. Geometry optimization significantly increases the interaction energies of the <i>p</i>-cresol/9MeA model systems. The average estimated CCSD­(T) CBS limit interaction energy of the 11 optimized structures is 3.23 kcal mol^–1 larger than that for the 20 crystal structures

Improving Protein Expression, Stability, and Function with ProteinMPNN

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins

Improving Protein Expression, Stability, and Function with ProteinMPNN

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins