Added README section on advice for binder design

Author: nrbennet

README.md

@@ -40,6 +40,7 @@ RFdiffusion is an open source method for structure generation, with or without c
     - [A note on `diffuser.T`](#a-note-on-diffusert)
     - [Partial diffusion](#partial-diffusion)
     - [Binder Design](#binder-design)
+    - [Practical Considerations for Binder Design](#practical-considerations-for-binder-design)
     - [Fold Conditioning](#fold-conditioning)
     - [Generation of Symmetric Oligomers](#generation-of-symmetric-oligomers)
     - [Using Auxiliary Potentials](#using-auxiliary-potentials)
@@ -267,6 +268,32 @@ An example of binder design with RFdiffusion can be found in `./examples/design_
 
 ---
 
+## Practical Considerations for Binder Design
+
+RFdiffusion is an extremely powerful binder design tool but it is not magic. In this section we will walk through some common pitfalls in RFdiffusion binder design and offer advice on how to get the most out of this method.
+
+### Selecting a Target Site
+Not every site on a target protein is a good candidate for binder design. For a site to be an attractive candidate for binding it should have >~3 hydrophobic residues for the binder to interact with. Binding to charged polar sites is still quite hard. Binding to sites with glycans close to them is also hard since they often become ordered upon binding and you will take an energetic hit for that. Historically, binder design has also avoided unstructured loops, it is not clear if this is still a requirement as RFdiffusion has been used to bind unstructured peptides which share a lot in common with unstructured loops.
+
+### Truncating your Target Protein
+RFdiffusion scales in runtime as O(N^2) where N is the number of residues in your system. As such, it is a very good idea to truncate large targets so that your computations are not unnecessarily	 expensive. RFdiffusion and all downstream steps (including AF2) are designed to allow for a truncated target. Truncating a target is an art. For some targets, such as multidomain extracellular membranes, a natural truncation point is where two domains are joined by a flexible linker. For other proteins, such as virus spike proteins, this truncation point is less obvious. Generally you want to preserve secondary structure and introduce as few chain breaks as possible. You should also try to leave ~10A of target protein on each side of your intended target site. We recommend using PyMol to truncate your target protein.
+
+### Picking Hotspots
+Hotspots are a feature that we integrated into the model to allow for the control of the site on the target which the binder will interact with. In the paper we define a hotspot as a residue on the target protein which is within 10A Cbeta distance of the binder. Of all of the hotspots which are identified on the target 0-20% of these hotspots are actually provided to the model and the rest are masked. This is important for understanding how you should pick hotspots at inference time.; the model is expecting to have to make more contacts than you specify. We normally recommend between 3-6 hotspots, you should run a few pilot runs before generating thousands of designs to make sure the number of hotspots you are providing will give results you like.
+
+If you have run the previous PatchDock RifDock binder design pipeline, for the RFdiffusion paper we chose our hotspots to be the PatchDock residues of the target.
+
+### Binder Design Scale
+In the paper, we generated ~10,000 RFdiffusion binder backbones for each target. From this set of backbones we then generated two sequences per backbone using ProteinMPNN-FastRelax. (described below) We screened these ~20,000 designs using AF2 with initial guess and target templating (described below). The larger the scale that you can go to, the more designs will pass your filters and the better your odds of identifying a binder experimentally.
+
+### Sequence Design for Binders
+You may have noticed that the binders designed by RFdiffusion come out with a poly-Glycine sequence. This is not a bug. RFdiffusion is a backbone-generation model and does not generate sequence for the designed region, therefore, another method must be used to assign a sequence to the binders. In the paper we use the ProteinMPNN-FastRelax protocol to do sequence design. We recommend that you do this as well.  The code for this protocol can be found in [this GitHub repo](https://github.com/nrbennet/dl_binder_design). While we did not find the FastRelax part of the protocol to yield the large in silico success rate improvements that it yielded with the RifDock-generated docks, it is still a good way to increase your number of shots-on-goal for each (computationally expensive) RFdiffusion backbone. If you would prefer to simply run ProteinMPNN on your binders without the FastRelax step, that will work fine but will be more computationally expensive.
+
+### Binder Design Filtering
+One of the most important parts of the binder design pipeline is a filtering step to evaluate if your binders are actually predicted to work. In the paper we filtered using AF2 with an initial guess and target templating, scripts for this protocol are available [here](https://github.com/nrbennet/dl_binder_design). We have found that filtering at pae_interaction < 10 is a good predictor of a binder working experimentally.
+
+---
+
 ### Fold Conditioning 
 Something that works really well is conditioning binder design (or monomer generation) on particular topologies. This is achieved by providing (partial) secondary structure and block adjacency information (to a model that has been trained to condition on this). 
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