Tag Archives: Docking

Watch out when using PDBbind!

Now that PDBbind 2020 has been released, I want to draw some attention to an issue with using the SDF files that are supplied in the PDBbind refined set 2020.

Normally, SDF files save the chirality information of compounds in the atom block of the file which is shown belowas a snipped of the full sdf file for the ligand of PDB entry 4qsv. The column that defines chirality is marked in red.

As you can see, all columns shown here are 0. The SDF files supplied by PDBbind for some reason do NOT encode chirality information explicitly. This will be a problem when using RDKit to read the molecule and transform it into a smiles string. By using the following commands to read the ligand for 4qsv from PDBBind 2020 and write a SMILES string, we get:

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Constrained docking for bump and hole methodology

Selectivity is an important trait to consider when designing small molecule probes for chemical biology. If you wish to use a small molecule to study a particular protein, but that small molecule is fairly promiscuous in its binding habits, there are risks that any effects you observe may be due to it binding other proteins with similarly shaped binding pockets, instead of your protein of interest.

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Experimental Binding Modes of Small Molecules in Protein-Ligand Docking

Protein-ligand docking tends to be very good at generating binding modes that resemble experimental binding modes from X-ray crystallography and other methods (assuming we have a high quality structure…); but it is also very good at generating plausible models for ligands that don’t bind. These so-called “false positives” lead to reduced accuracy in structure-based virtual screening campaigns.

Structure-based methods are not the only way of approaching virtual screening: when all we know is the chemical structure of an active molecule, but nothing about its target (or targets), we can use ligand-based virtual screening methods, which operate on the principle of molecular similarity (Maggiora et al., 2014).

But what if we combine both methods?

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Journal Club: Can Linear Progamming (LP) be useful to us?

Linear programming (LP) is known as a fast and powerful computational technique. It has been applied to a large range of problems in finances and economics, but it is not very popular among us bioinformaticians, computational biologists, and the likes.

Source: http://hotmath.com/hotmath_help/topics/linear-programming.html

Linear Programming is all about find feasible solutions that satisfy a series of constraints (usually represented by inequalities). Does it sound like a familiar problem to bioinformaticians and computational biologists out there?

Source: http://hotmath.com/hotmath_help/topics/linear-programming.html

This leaves room for some questioning: can biological phenomena be modelled or simplified under the assumption of linearity? Furthermore, can LP be used to tackle the many difficult problems posed in our field? Perhaps an even bigger question: why would any of us use Linear Programming instead of another type of linear modelling? What are the advantages of it?

I will not incur in explaining the particulars of LP here. There is a plethora of materials available online (Wikipedia and Wolfram are accessible starting points) that detail Linear Programming. For those eager for something more substantial, V. Chvatal’s Linear Programming and Dantzig’s Linear Programming and Extensions are two good texts on the subject.

During this week’s journal club, I discussed an article that attempted to use Linear Programming to devise knowledge-based Docking Potentials (DP) tailored for transient protein-protein complexes. Transient complexes tend to be under-represented on the PDB, mostly due to the inherent difficulties of crystallizing such complexes. Hence, the development of knowledge-based potentials for these special cases of protein interaction is drastically hindered by a sample size limitation.

Source: Bizzarri AR, Brunori E, Bonanni B, Cannistraro S. Docking and molecular dynamics simulation of the Azurin–Cytochrome c551 electron transfer complex. J. Mol. Recognit. 2007; 20: 122–131

A cartoon representation of a transient complex between Azurin (cyan) and its partner Cytochrome C551 (dark blue) from Pseudomonas aeruginosa. Transient protein complexes are hard to crystallize, hence, under-represented on the PDB.

Source: Bizzarri AR, Brunori E, Bonanni B, Cannistraro S. Docking and molecular dynamics simulation of the Azurin–Cytochrome c551 electron transfer complex. J. Mol. Recognit. 2007; 20: 122–131

To offset such limitation, it would be ideal if one could extrapolate information from decoys (non-native conformations obtained from computational docking tools) in order to improve the Docking potentials. Furthermore, in an ideal world, one would also address the bias introduced by homology/sequence similarity between the existing proteins in the available structures of transient complexes.

The author of the article “Designing coarse grained-and atom based-potentials for protein-protein docking – Tobi D. – BMC Structural Biology 2010, 10:40 doi:10.1186/1472-6807-10-40 ” claims that LP can address such issues by incorporating information from the decoys as linear constraints to the model. The article describes a linear problem, in which the aim is to minimize the variance of how much the non-native energy potentials differ from the native ones. Also, they impose the constraints that native structures must have a lower energy than all of the non-native structures for a given complex (lower in this case is good).

The energy is defined as a weighted sum of the counts of specific interaction types on the complex interface. In their work, the author employed two models: an atom-based model and a side chain-based model. These models are used to classify atoms into groups and to simplify calculations. Initially, they define boolean (one-step) interactions: two atoms interact if they are within a cutoff distance of each other. This cutoff varies according to the type of atoms involved. The initial model led to a state of infeasibility, and it was then replaced by a two-step model, where you have strong and weak interactions and two sets of cutoff (this leads to twice as many unknowns in the LP model).

Well, does it work? How does it fair against other existing knowledge-based DPs?

Source: Designing coarse grained-and atom based-potentials for protein-protein docking. - Tobi D. - BMC Structural Biology 2010, 10:40 doi:10.1186/1472-6807-10-40Source: Designing coarse grained-and atom based-potentials for protein-protein docking. – Tobi D. – BMC Structural Biology 2010, 10:40 doi:10.1186/1472-6807-10-40

Despite the lack of brilliant results or any apparent improvement compared to the state-of-art, the potentials described in the article seem to slightly outperform ZDOCK2.3’s scoring functions.

This may actually speak in favour of the applicability of LP to problems in our area. In the case presented during the journal club, an LP approach produced comparable results to more conventional techniques.

Perhaps the best answer to “why should I use LP?” is that it is an unconventional, creative solution. It is significantly fast and, therefore, easy to try out depending on your problem. Science is all about experimentation, after all. Why would you not try a different technique if you have the means to?

Image Source: http://www.designthenewbusiness.com/blog/documenting/thinking-inside-the-box.html

The moral of the story: it is good to think outside the box, as long as you keep your feet on the ground.

Image Source: http://www.designthenewbusiness.com/blog/documenting/thinking-inside-the-box.html

Check the article discussed in the post here.