- Methodology Article
- Open Access
AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis
© Greener and Sternberg. 2015
Received: 20 June 2015
Accepted: 13 October 2015
Published: 23 October 2015
Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery.
AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site.
Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.
Allostery is a process where one site on a molecule is perturbed by an effector, causing a functional change at another site: it is regulation at a distance . Allostery can arise from non-covalent interactions (e.g. drug binding), covalent interactions (e.g. phosphorylation) and light absorption. This intrinsic and widespread property of proteins  is important in processes such as cellular signalling and disease, yet most allosteric mechanisms remain an enigma and a universal mechanism has proved elusive [3, 4].
Allosteric drugs have hardly been explored and are a major avenue of research for the pharmaceutical industry [5–7]. They hold many potential benefits over orthosteric (non-allosteric) drugs: they do not bind to active sites that are often conserved in protein families, and are hence highly specific; they can activate as well as inhibit a protein; they can have a ceiling to their effect; and they can be used effectively in combination with orthosteric drugs. However, discovery of allosteric drugs presents challenges beyond those encountered in orthosteric drug discovery. Whether the drug will activate or inhibit the protein is difficult to predict and in many cases the location of allosteric sites is unknown.
Allosteric drug discovery by virtual screening is an exciting prospect furthered by the elucidation of previously-unknown allosteric sites found on solved protein structures . Development of allosteric prediction methods is therefore of pressing concern and has been approached using various methods: changes in flexibility on ligand binding [9, 10]; machine-learning using pocket features ; structural conservation ; two-state Gō models ; and molecular dynamics . Methods investigating the allosteric mechanism have also been developed [14–17], giving insight into which residues propagate the allosteric signal and how it is transmitted. Many of the above approaches have been made available to the community as web servers [11, 18–20].
Several studies have used normal mode analysis (NMA) to model allosteric regulation [9, 10, 16, 21, 22]. In NMA, the structural fluctuations of a protein around an equilibrium conformation are decomposed into harmonic orthogonal modes . NMA is effective at describing protein dynamics, despite ignoring the complex nature of the protein energy landscape . Even considering the C-alpha atoms alone can be sufficient. The long-range nature of allosteric communication is often well-described by low-frequency modes that involve the motion of many atoms, though allostery does involve local effects so higher-frequency modes should also be taken into account .
We developed a novel procedure, AlloPred, which uses NMA to predict the allosteric pockets on a protein. AlloPred models how the dynamics of a protein would be altered in the presence of a modulator at a specific pocket. Pockets on the protein were first predicted using the Fpocket algorithm , which locates pockets using Voronoi tessellation and alpha spheres. The normal modes of the protein were then calculated using the elastic network model, except the spring constant of any atom pair including a residue in a chosen pocket was set to be a higher value. The effect of this perturbation was measured at the active site. These results were combined with output from Fpocket in a support vector machine (SVM) to predict allosteric pockets on proteins.
ASBench , a benchmarking set for allosteric discovery, was used as a source of known allosteric proteins. The ‘Core-Diversity set’ contains 147 structurally-diverse allosteric sites on 127 proteins from a variety of protein classes such as transferases, hydrolases and transcription factors. The PDB files, allosteric site data and active site data were obtained for each protein from ASBench. UniProt  and the Catalytic Site Atlas  were used to find active site data when it was not available from ASBench. In each PDB file, only the chain(s) containing the active and allosteric sites, and any chains linking them, were considered. This was in order to keep the size of the proteins manageable, as using entire protein assemblies would lead to a large number of pockets. It also allowed comparison with existing methods, which use similar criteria. In practice the use of larger assemblies was tried during development and did not have a large effect on the results. Seven proteins were removed from the set as the PDB file did not contain the active site, i.e. the PDB file represented the allosteric section of a larger protein. One protein was removed as Fpocket did not run successfully. This left 119 proteins in the dataset. The dataset was randomly split into a training set of 79 proteins and a test set of 40 proteins.
Potential binding pockets on the proteins were calculated using the open-source Fpocket v2.0 algorithm, which has been shown to be effective in comparison to other methods . The default parameters used in the Fpocket calculation produced pockets that were large enough to place most (average 86 %) allosteric binding residues in pockets but not so large that identifying a pocket as having allosteric effect was of little use. Sometimes multiple allosteric pockets on the same protein represented different and physically-separated allosteric sites, and sometimes adjacent calculated pockets covered a single allosteric binding site. The pockets also covered much of the protein surface, which allowed the method to detect allosteric sites that could be found anywhere on the surface. On average 41 % of residues in each protein appeared in a pocket.
Fpocket output 2,201 pockets for the 119 proteins (average 18.5 per protein), of which 389 (18 % of pockets, average 3.3 per protein) contained at least one residue identified as binding to an allosteric modulator and were hence labelled as allosteric pockets. Although being defined as an allosteric pocket in this manner does not necessarily mean that binding to that pocket causes the allosteric effect, the average number of allosteric binding residues in an allosteric pocket was 4.3, indicating the utility of locating such pockets. All but 5 proteins in the dataset had at least one allosteric binding residue placed in a pocket. We treated pockets without known allosteric binding residues as negative examples during machine learning. It should be noted that these pockets may not correspond directly to the actual pockets on the protein, or may have latent allosteric character yet to be discovered.
Normal mode analysis
Number of alpha spheres
Mean local hydrophobic density
Mean alpha sphere radius
Mean alpha sphere solvent accessibility
Apolar alpha sphere proportion
Proportion of polar atoms
Alpha sphere density
Centre of mass - alpha sphere max distance
See the Fpocket documentation for more details on each of these measures. Distance to the active site, number of residues in the pocket and number of pockets in the protein were also used as features. The distance to the active site for each pocket was calculated as the distance between the geometric centre of the active site residues and the geometric centre of the residues in the pocket. Each feature (apart from number of pockets) was utilised in two different ways: the feature value normalised across all proteins (raw); and the ranking of the feature value within the values for that protein, where the ranks were scaled between 0 and 1 (ranked).
Number of alpha spheres (raw)
E 200 (ranked)
E all (ranked)
Distance to active site (raw)
Pocket size (raw)
Fpocket rank (raw)
The SVM-Light package  was used to run the SVM. The Gaussian kernel was selected, containing internal parameters C and γ. The cost factor by which training errors on positive examples outweigh errors on negative examples was set as the ratio of negative to positive examples in the training set (6.19). A leave-one-out parameterisation procedure was carried out over a grid of parameters with C equal to 0.01, 0.1, 1 or 10 and γ equal to 10−3, 10−4 or 10−5. The procedure consisted of training the SVM on pockets from 78 of the 79 proteins in the training set and testing on pockets from the one left out. The process was repeated for each protein in the set. Performance was similar across the parameter range, with the parameters C=1 and γ=10−4 being selected for the final SVM. Due to the low number of allosteric pockets on each protein, only the top prediction was chosen as being allosteric.
In order to reduce the effects of bias during the split of the dataset into training and test sets, the dataset of 119 proteins was additionally split randomly 20 times into training and tests sets of 79 and 40 proteins respectively. The SVM was then trained on the training set, using the previous parameters, and tested on the test set. The average number of correct predictions across the 20 runs was 23.6 out of 40. This shows that the above results used for comparison to other methods are indicative of the performance of the method.
Over the last few years a renewed interest in allostery, perhaps due to the potential benefits of allosteric drugs, has led to the development of a number of computational approaches to understanding allostery . Some of these are directly associated with predicting allosteric sites on proteins from structure alone.
The AlloSite server is similar to the method presented here in that it uses the Fpocket algorithm and attempts to elucidate allosteric pockets . Whereas AlloSite solely uses the Fpocket output, our method uses an approach that combines flexibility with the Fpocket output. A combination of methods may give better predictions than either method individually, as indicated by the unique predictions made by both methods during testing. In fact the AlloSite predictions were found in every case to correspond to the pocket ranked top by Fpocket. The complete ranking of pockets provided by AlloPred may also be useful, as pockets ranked second were often found to be allosteric in the test set.
An approach that combines flexibility analysis using normal modes and structural conservation scores  is also similar to the method presented here and was recently turned into a web server, PARS . Although direct comparison is difficult due to the differences in site calculation, definition of allosteric sites and datasets used, the method presented here again may be used well in combination as shown by Fig. 3.
The lack of input about the shape of the ligand and the large coverage of the protein in terms of pockets (average 18.5 pockets per protein) used by our method mean that it may be able to predict novel or unusual sites that methods which explicitly model the modulator might not. This is important, for example when searching for allosteric sites on proteins believed to be non-allosteric. The lack of conservation-based approaches in our method also facilitates discovery of sites not currently preserved by evolution. This is useful due to the large variety of allosteric modulators  and mechanisms , suggesting potential novel modulators for proteins with known allosteric pathways.
Other promising approaches [15, 17, 19] investigate the allosteric pathway and are not directly comparable with this method, which is only concerned with how the pathways transmit the effects of perturbations to the normal modes and does not directly reveal any information about the pathways themselves. Again, a combination of our method with these approaches may be useful, as pockets predicted using our or other methods can be further investigated to reveal information about the underlying allosteric communication.
The main limitation of our method is related to the diversity found in allosteric systems. Rigid-body motions of oligomers, side-chain dynamics, backbone motions and local unfolding are all mechanisms of allostery, with allosteric effects even present in intrinsically-disordered proteins . A method based around the changes in dynamics on ligand binding is likely to miss many allosteric effects, and this can go some way to explaining the predictions of our method that were incorrect. In particular, classic examples of allostery such as haemoglobin that involve oligomeric re-organisation to affect ligand cooperativity are not suitable for use with this method. However, the results shown here and in other studies are encouraging and indicate a future where we can pick modulating sites on proteins with reasonable confidence. Our method, for example, successfully predicts allosteric sites on proteins with a variety of sizes and functions.
A machine learning approach that utilises normal mode analysis and pocket descriptors to predict allosteric pockets on proteins was developed and tested on a set of known allosteric proteins. The method was able to pick out pockets containing one or more allosteric residues. The new approach presented here is comparable in performance to existing methods and has the potential to find novel allosteric sites due to its high coverage of the protein surface and lack of information about the ligand shape. It also exhibits complementarity with existing methods. The web server provides features for visualisation and analysis that allow exploration of the results in a manner that other servers do not.
The generalisation of allosteric site prediction methods from individual proteins to the whole of protein space has only begun in earnest in recent years but is the first step on the path to effective virtual screening for allosteric drugs. Without such site prediction methods, the vast potential of allosteric drugs as therapeutics will remain untapped.
We would like to thank Dr Suhail Islam for his invaluable help with deploying the web server and Dr Ioannis Filippis for useful discussions. This work was supported by the Biotechnology and Biological Sciences Research Council.
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