11 research outputs found
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Deep learning methods have revolutionized protein structure prediction and design but are currently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA) which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications given their sequences and chemical structures. By fine tuning on denoising tasks we obtain RFdiffusionAA, which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we design and experimentally validate, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light harvesting molecule bilin
Mild and selective base-free CâH arylation of heteroarenes: experiment and computation
A mild and selective CâH arylation strategy for indoles, benzofurans and benzothiophenes is described. The arylation method engages aryldiazonium salts as arylating reagents in equimolar amounts. The protocol is operationally simple, base free, moisture tolerant and air tolerant. It utilizes low palladium loadings (0.5 to 2.0 mol% Pd), short reaction times, green solvents (EtOAc/2-MeTHF or MeOH) and is carried out at room temperature, providing a broad substrate scope (47 examples) and excellent selectivity (C-2 arylation for indoles and benzofurans, C-3 arylation for benzothiophenes). Mechanistic experiments and DFT calculations support a HeckâMatsuda type coupling mechanism.Hannes P.L. Gemoets, Indrek Kalvet, Alexander V. Nyuchev, Nico Erdmann, Volker Hessel, Franziska Schoenebeck and Timothy NoĂ«
Mild and selective base-free CâH arylation of heteroarenes:experiment and computation
A mild and selective CâH arylation strategy for indoles, benzofurans and benzothiophenes is described. The arylation method engages aryldiazonium salts as arylating reagents in equimolar amounts. The protocol is operationally simple, base free, moisture tolerant and air tolerant. It utilizes low palladium loadings (0.5 to 2.0 mol% Pd), short reaction times, green solvents (EtOAc/2-MeTHF or MeOH) and is carried out at room temperature, providing a broad substrate scope (47 examples) and excellent selectivity (C-2 arylation for indoles and benzofurans, C-3 arylation for benzothiophenes). Mechanistic experiments and DFT calculations support a HeckâMatsuda type coupling mechanism
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