Monthly Archives: July 2014

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.

Sampling Conformations of Antibodies using MOSAICS

Much work has been done to study the conformational changes taking place in antibodies, particularly during the event of binding to an antigen. This has been done through comparison of crystal structures, circular dichroism, and recently with high resolution single particle electron microscopy. The ability to resolve domains within an antibody from single particles without any averaging  made it possible to show distributions of properties such as the shape of a Fab domain, measured by the ratio of width to length. Some of the variation in structure seen involves very large scale motions, but it is not known how conformational changes may be transmitted from the antigen binding region to the Fc, and therefore influence effector function. Molecular dynamics simulations have been performed on some large antibody systems, however none have been possible on a time scale which would be able to provide information on the converged distributions of large scale properties such as the angle between the Fab and Fc fragments.

In my short project with Peter Minary, I used MOSAICS to investigate the dynamics of an antibody Fab fragment, using the coarse-grained natural move Monte Carlo approach described by Sam a few weeks ago. This makes it possible to split a structure into units which are believed to move in a correlated way, and propose moves for the components of each region together. The rate of sampling is accelerated in degrees of freedom which may have functional significance, for example the movement of the domains in a Fab fragment relative to one another (separate regions shown in the diagram below). I used ABangle to analyse the output of each sampling trajectory and observe any changes in the relative orientations of The VH and VL domains.

Region definitions for MOSAICS

Fab region definitions for MOSAICS

Of particular interest would be any correlations between conformational changes in the variable and constant parts of the Fab fragment, as these could be involved in transmitting conformational changes between remote parts of the antibody. We also hoped to see in our model some effect of including the antigen in the simulation, bound to the antibody fragment as seen in the crystal structure. In the time available for the project, we was able to  set up a model representing the Fab fragment and run some relatively short simulations to explore favoured conformational states and see how the set up of regions affects distributions seen. In order to draw conclusions about the meaning of the results, a much greater number of simulations will need to be run to ensure sampling of the whole conformational space.

Computational Antibody Affinity Maturation

In this week’s journal club, we reviewed a paper by Lippow et al. in Nature Biotechnology, which features a computational pipeline that is capable of maturing antibodies (Abs) by up to 140-fold. The paper itself discusses 4 test case Abs (D44.1, cetuximab, 4-4-20, bevacizumab) and uses changes in electrostatic energy to identify favourable mutations. Up to the point when this paper was published back in 2007, computational antibody design was an (almost) unexplored field of research – except for a study by Clark et al. in 2006, no one else had done anything like the work presented in this paper.

The idea behind the paper is to identify certain positions within the Ab structure for mutation and hopefully find an Ab with a higher binding affinity.

The idea behind the paper is to identify certain positions within the Ab structure for mutation and hopefully find an Ab with a higher binding affinity.


Briefly speaking, the group generated a mutant Ab-antigen (Ag) complex using a series of algorithms (dead-end elimination and A*), which was then scored by the group’s energy function for identifying favourable mutations. Lippow et al. used the electrostatics term of their binding affinity prediction in order to estimate the effects of mutations on an Ab’s binding affinity. In other words, instead of examining their entire scoring function, which includes terms such as van der Waal’s energy, the group only used changes in the electrostatic energy term as an indicator for proposing mutations. Overall, in 2 of the 4 mentioned test cases (D44.1 & cetuximab), the proposed mutations were experimentally tested to confirm their computational design pipeline – a brief overview of these two case studies will be described.


In the case of the D44.1 anti-lysozyme Ab, the group proposed 9 single mutations by their electrostatics-based calculation method; 6/9 single mutants were confirmed to be beneficial (i.e., the mutant had an increased binding affinity). The beneficial single mutants were combined, ultimately leading to a quadruple mutant structure with a 100-fold improvement in affinity. The quadruple mutant was then subjected to a second round of computer-guided affinity maturation, leading to a new variant with six mutations (effectively a 140-fold improvement over the wild-type Ab). This case study was a solid testimony to the validity of their method; since anti-lysozyme Abs are often used as model systems, these results demonstrated that their design pipeline had taken, in principle, a suitable approach to maturing Abs in silico.

The second case study with cetuximab was arguably the more interesting result. Like the D44.1 case above, mutations were proposed to increase the Ab’s binding affinity on the basis of the changes in electrostatics. Although the newly-designed triple mutant only showed a 10-fold improvement over its wild-type counterpart, the group showed that their protocols can work for therapeutically-relevant Abs. The cetuximab example was a perfect complement to the previous case study — it demonstrated the practical implications of the method, and how this pipeline could potentially be used to mature existing Abs within the clinic today.

Effectively, the group suggested that mutations that either introduce hydrophobicity or a net charge at the binding interface tend to increase an Ab’s binding affinity. These conclusions shouldn’t come with huge surprise, but it was remarkable that the group had reached these conclusions with just one term from their energy function.


Effectively, the paper set off a whole new series of possibilities and helped us to widen our horizons. The paper was by no means perfect, especially with respect to predicting the precise binding affinities of mutants – much of this error could be bottled down to the modelling stage of their pipeline. However, the paper showed that computational affinity maturation is not just a dream – in fact, the paper showed that it’s perfectly doable, and immediately applicable. Interestingly, Lippow et al.’s manipulation of an Ab’s electrostatics seemed to be a valid approach, with recent publications on Ab maturation showing that introducing charged residues can enhance binding affinity (e.g. Kiyoshi et al., 2014).

More importantly, the paper was a beautiful showcase of how computational analyses could inform the decision making process in an in vitro framework, and I believe it exemplified how we should approach our problems in bioinformatics. We should not think of proteins as mere text files and numbers, but realise that they are living systems, and we’re not yet at a point where we fully understand how proteins behave. This shouldn’t discourage us from research; instead, it should give us the incentive to take things more slowly, and develop a method/product that could be used to solve greater, pragmatic problems.