Per-residue optimisation of protein structures: Rapid alternative to optimisation with constrained alpha carbons
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In recent years, the number of known protein structures has increased significantly. Predictive algorithms and experimental methods provide the positions of protein residues relative to each other with high accuracy. However, the local quality of the protein structure, including bond lengths, angles, and positions of individual atoms, often lacks the same level of precision. For this reason, protein structures are usually optimised by a force field prior to their application in further research sensitive to structural quality. Protein structure optimisation, however, is computationally challenging.
Abstract Figure
In this paper, we introduce a general method Per-residue optimisation of protein structures: Rapid alternative to optimisation with constrained alpha carbons (PROPTIMUS RAPHAN). Rather than optimising the entire protein structure at once, PROPTIMUS RAPHAN divides the structure into overlapping residual substructures and optimises each substructure individually. This approach results in computational time that scales linearly with the size of the structure. Additionally, we present PROPTIMUS RAPHAN GFN-FF , a reference implementation of our method employing a generic, almost QM-accurate force field, GFN-FF.
We tested PROPTIMUS RAPHAN GFN-FF on 461 AlphaFold DB structures and demonstrated that our approach achieves results comparable to the optimisation of the structure with constrained alpha carbons in significantly less time.
Scientific Contribution
The main contribution of this work is the PROPTI-MUS RAPHAN method and its reference parallelisable implementation PROP-TIMUS RAPHAN GFN-FF . Because the time requirement increases linearly with the size of the structure, PROPTIMUS RAPHAN GFN-FF optimises on average 5 000 atoms per hour and a common CPU. Therefore, prior to any research sensitive to protein structure quality, our method can be employed to obtain protein structures closer to QM-accuracy.
