A Pareto-Optimal Refinement Method for Protein Design Scaffolds

By L. Nivón, Rocco Moretti, David Baker. 2013

298 citations recorded by the source corpus.

Computational design of protein function involves a search for amino acids with the lowest energy subject to a set of constraints specifying function. In many cases a set of natural protein backbone structures, or “scaffolds”, are searched to find regions where functional sites (an enzyme active site, ligand binding pocket, protein – protein interaction region, etc.) can be placed, and the identities of the surrounding amino acids are optimized to satisfy functional constraints. Input native protein structures almost invariably have regions that score very poorly with the design force field, and any design based on these unmodified structures may result in mutations away from the native sequence solely as a result of the energetic strain. Because the input structure is already a stable protein, it is desirable to keep the total number of mutations to a minimum and to avoid mutations resulting from poorly-scoring input structures. Here we describe a protocol using cycles of minimization with combined backbone/sidechain restraints that is Pareto-optimal with respect to RMSD to the native structure and energetic strain reduction. The protocol should be broadly useful in the preparation of scaffold libraries for functional site design.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Protein sequence design with a learned potential (2022, methodology): “Backbone relaxes for designs were done with the RosettaRelax application 61 – 64 , with the ex1 ex2 options for extra χ 1 and χ 2 rotamer sampling.”
Design of Nanoparticulate Group 2 Influenza Virus Hemagglutinin Stem Antigens That Activate Unmutated Ancestor B Cell Receptors of Broadly Neutralizing Antibody Lineages (2018, unknown): “Loops were modeled with LOOPY (62), superpositions were performed using UCSF Chimera (63) or LSQMAN (64), and energy minimization of ﬁnal models was performed using RELAX (65) from the ROSETTA suite.”
RosettaAntibodyDesign (RAbD): A general framework for computational antibody design (2017, methodology): “…and starting complexes for antibody design were first minimized into the Rosetta energy function using the Pareto-optimal protocol of Nivon et al. [62], which relaxes [60] the starting structure using restraints on the back-bone and side-chain atoms to strike a balance between deviation from the…”
Repertoire Analysis of Antibody CDR-H3 Loops Suggests Affinity Maturation Does Not Typically Result in Rigidification (2017, methodology): “Antibody structural ensembles with 10 members were generated using either the Rosetta FastRelax 187 (47, 48) or Rosetta KIC protocol (49).”
Modeling and docking antibody structures with Rosetta (2017, background): “Once the partners have been refined (usually with the coordinates constrained to the starting position as in Nivón et al. 37 ), a putative complex can be assembled and prepacked.”
Improving peptide-protein docking with AlphaFold-Multimer using forced sampling (2021, unknown): “…model should not contain severe clashes but, during sampling, it might be advantageous to allow for some structural overlap to enable the method to explore various predictions, similar to a soft repulsive energy term that is often used during re ﬁ nement (Raveh et al., 2011; Nivón et al., 2013).”
Protein sequence design with a learned potential (2020, methodology): “Backbone relaxes for designs were done with the RosettaRelax application [107, 108, 109, 110], with the ex1 and ex2 options for extra χ 1 and χ 2 rotamer sampling.”