Antibody CDR-H3 Modelling with Prime

In a blog post from last month, Konrad discussed the most recent Antibody Modelling Assessment (AMA-II), a CASP-like blind prediction study designed to test the current state-of-the-art in antibody modelling. In the second round of this assessment, participants were given the crystal structure of ten antibodies with their H3 loops missing – the loop usually found in the centre of the binding site that is largely responsible for the binding properties of the antibody. The groups of researchers were asked to model this loop in its native environment. Modelling this loop is challenging, since it is much more variable in sequence and structure than the other five loops in the binding site.

For eight out of the ten loops, the Prime software from Schrodinger (the non-commercial version of which is called PLOP) produced the most accurate predictions. Prime is an ab initio method, meaning that loop conformations are generated from scratch (unlike knowledge-based methods, which  use databases of known loop structures). In this algorithm, described here,  a  ‘full’ prediction job is made up of consecutive ‘standard’ prediction jobs. A standard prediction job involves building loops from dihedral angle libraries – for each residue in the sequence, random phi/psi angles are chosen from the libraries. Loops are built in halves – lots of conformations of the first half are generated, along with many of the second half, and then all the first halves are cross-checked against the second halves to see whether any of them meet in the middle. If so, then the two halves are melded and a full loop structure is made. All loop structures are then clash-checked using an overlap factor (a cutoff on how close two atoms can get to each other). Finally, the loops are clustered, and a representative structure has its side chain conformations predicted and its energy minimised.

A full loop prediction job is made up of a series of standard jobs, with the goal of guiding the conformational search to focus on structures with low energy. The steps are as follows:

  • Initial – five standard jobs are run, with slightly different overlap factors.
  • Ref1 – the first refinement stage. The conformational space around the top 10 loops from each standard job of the Initial stage is explored further by constraining the distance between Ca atoms.
  • Fixed – the top 10 loops of all those generated so far are passed to this series of stages. To begin with, the first and last residues of the loop are excluded from the prediction and the rest of the loop is re-modelled. The top 10 loops after this are then taken to the second Fixed stage, where two residues at each end of the loop are kept fixed. This is repeated five times, with the number of fixed residues at each end of the loop being increased by one each time.
  • Ref2 – a second refinement stage, which is the same as the first, except tighter distance constraints are used.
  • Final  – all the loop structures generated are ranked according to their energy, and the lowest energy conformation is chosen as the final prediction.

In a recent paper, Prime was used to predict the structures of 53 antibody H3 loops (using the dataset of a previous RosettaAntibody paper). 91% of the targets were predicted with sub 2-angstrom accuracy, and 81% predictions were sub-angstrom. Compared to RosettaAntibody, which achieved 53% and 17% for predictions below 2A and 1A respectively, this is very impressive. For AMA-II, however, where each group was required to give five predictions, and some poor models were included in each group’s top five, it is apparent that ranking loop conformations is still a major challenge in loop modelling.

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