The analysis of correlation in alignments generates a matrix of predicted contacts between positions in the structure and while these can arise for many reasons, the simplest explanation is that the pair of residues are in contact in a three-dimensional structure and are affecting each others selection pressure. To analyse these data, A dynamic programming algorithm was developed for parsing secondary structure interactions in predicted contact maps.The non-local nature of the constraints required an iterated approach (using a “frozen approximation”) but with good starting definitions, a single pass was usually sufficient. The method was shown to be effective when applied to the transmembrane class of protein and error tolerant even when the signal becomes degraded. In the globular class of protein, where the extent of interactions are more limited and more complex, the algorithm still behaved well, classifying most of the important interactions correctly in both a small and a large test case. For the larger protein, this involved examples of the algorithm apportioning parts of a single large secondary structure element between two different interactions.It is expected that the method will be useful as a pre-processor to coarse-grained modelling methods to extend the range of protein tertiary structure prediction to larger proteins or to data that is currently too ’noisy’ to be used by current residue-based methods. In recent years, the huge increase in the number of sequences, combined with a number of new methods for the analysis of correlated mutations (or substitutions) in multiple protein sequence alignments has led to a revival in the interest and ability to predict protein structure from sequence data alone.  has been used in the analysis of protein structure for many years and is still often referred to as a Phillips or Ooi plot from its original use in the early 1970s , 3. The These early attempts to construct protein 3D structures from predicted distances were hampered by the poor quality and specificity of the predicted distance estimates (which were based largely on just the preference of hydrophobic amino acids to lie closer together than average). With data from correlated mutations, specificity has been greatly improved but in many situations, the quality of the data is too poor to directly construct a model using the distance-geometry methods mentioned above.An approach to improve the success of these methods is to use a more coarse-grained approach and to group the predicted pairwise residue interactions into groups that are linked in a common secondary structure which can then be treated as a rigid body. Early attempts at this tended to focus on pattern-matching combined with combinatorial enumeration –15, howeBy contrast, the method of correlated mutations predicts residue contacts that are specific and so can in principle distinguish the packing orientation of secondary structure segments. This produces a pattern of contact strips that run parallel to or orthogonal to the diagonal of the plot as shown in Fig. The aim of this work is to automatically identify these interaction patterns  in a contact matrix and to assign them as deriving either from the parallel or antiparallel packing of secondary structure elements (SSEs). In addition as the predicted end-points of the SSE are error prone, the definition of their packing can also be used to refine their end-points. The method is thus a general parser for secondary structure packing in protein contact maps.An inter-residue distance or contact map is a square matrix with a size equal to the length of the protein in which each element holds a value for each distance between two residues or, a sometimes binary, assignment where a contact is defined between them.  defined by two secondary structure elements in a contact map, The diagonal/orthogonal trend can be quantified by the strength of fit of a regression line in each direction. Neglecting the angle at which the pair of segments pack (which will affect the gradient of the line), if exact parallel and antiparallel packing is assumed, then the fit to a line passing through a point R) of the parallel moment  and s was assigned as 10 and the results indicated no reason to change this.In its current general formulation, the ratio score, p, q} on each diagonal line to be tested, between the limits of {b/4, 3b/4} which excludes two opposing corners of a box having sides of length a and b  was then weighted by a Gaussian function giving: r is a damping term.  By alternately incrementing the values of p and q across the range defined above, the diagonal line  was then found that gave a minimum weighted sum of squares of the predicted packing values (w). The moment (M) of the predicted packing scores about this best line is thus:The perpendicular distance to the line  that define the interaction box used in the calculation. This would not be an unreasonable approach as there is generally a substantial error associated with the prediction of SSEs and by optimizing the degree of predicted packing between them, the definition of the elements themselves might even be improved.This approach is similar to the applications to transmembrane segment prediction  and parsDespite clear similarity to the latter application, the current problem involves, as a minimum two SSEs and, in general, for every end-point that is altered, all interactions involving that element will need to be recalculated. This non-local aspect to the problem is therefore more equivalent to the comparison of structural environments between two protein structures  or the pAlthough the number of SSEs is generally small in a protein of moderate size, each end can be displaced up and down independently giving a large combinatorial space to be explored so stochastic search methods were avoided. Unfortunately, the DDP algorithm does not map easily to the current problem so it too was not tried. However, given that there are reasonable predicted estimates for the SSE end-points and a deterministic dynamic programming algorithm to find a local solution, the FAA was adopted as the most practical alternative.A, diagonal packing assignments (M) can be found as described above for each pair of SSEs (Eq. i and j specify a pair of SSEs with end-points i), with the full set of N end-points, A = {aaFor a set of given SSE end-points SSEs Eq.  and summk, its partial score will be:T) was constructed with the protein sequence (of length L) along one axis , if position ak  of different secondary structure types. Fortunately, in proteins, there are only two dominant secondary types: the See ref. ; Fig. 8 If two SSEs of the same type with the same length pack with aligned axes, then their interaction stripe in the contact plot will lie on a 1:1 diagonal line. However, if one of the elements has double the number of residues over the same length, then the slope of the diagonal packing stripe will run between the corners of a rectangle with sides in the ratio 1:2 and this factor will need to be accommodated in the calculation of the best fit.To do this, the simple method of calculating the moment of inertia about the 1:1 diagonal was retained but when one SSE was an http://bioinf.cs.ucl.ac.uk/MetaPSICOV/.Sequences were extracted from the non-redundant NCBI sequence databank using the jackHmmer program  and reduEach of the co-evolution analysis methods described below perform their own sequence search using either jackHmmer or a similar program. In each server we accepted the default search parameters. .PSICOV: Sequences were submitted to the PSICOV server [http://bioinf.cs.ucl.ac.uk/MetaPSICOV/.V server  at: httpGREMLIN: Sequences were submitted to the Gremlin server [http://gremlin.bakerlab.org/submit.php.n server  at: httphttp://topcons.cbr.su.se/pred/.TM helices were predicted as a consensus of the methods calculated by the TOPCONS server  at: httpAn integral transmembrane (TM) protein was selected for initial analysis as the helices in this class of protein tend to be longer than those found in a globular protein of a similar size, giving a clearer signal for testing and development purposes. The well characterised family of rhodopsins was chosen Fig. , using bThe basic algorithm was tested initially without iteration using the TM segment endpoints predicted by TOPCONS and the results for both rhodopsin families using the GREMLIN contacts are shown in Fig. For the eukaryotic family, it is clear that the algorithm has made the obvious choice of packing line assignment in every box. Given the clarity of the signal with this family, any mis-assignment would have been easily identified Fig. a.For the bacterial family Fig. b, most oAlthough most of the fixed TM endpoints corresponded well with the extent of the predicted packing interactions, a clear example of a truncated interaction can be seen in the top-left corner involving the packing of TM segments 1 and 7. To see if this could be corrected, the program was then run with the activation of the full iterated dynamic programming algorithm, taking five cycles to allow progressive refinement of the “frozen” endpoint approximations provided by the TOPCONS prediction.The results from this run extended most helix endpoints to varying degrees and incorporated the “lost” pairs between segments 1 and 7 in the eukaryotic protein Fig. a. Only oInterestingly, from an algorithmic viewpoint, the refined end-points attained their final assignments after just one pass of the dynamic programming algorithm. This is perhaps not unexpected as the predicted starting definitions were reasonable accurate. (Discussed further below).From a structural viewpoint, it is worth noting that the extension of these segments does not imply that there is a larger portion of the TM-helices in the membrane as it is quite common for TM-helices to continue unbroken out of the membrane and still continue to pack together.The attainment of a perfect criss-cross packing pattern can generally be taken as a sign that the interactions are meaningful and that there are no helices that have been completely missed by the prediction. However, this cannot be guaranteed as exceptions exist where a “re-entrant” segment can dip in then out of the membrane on the same side or simply run along the membrane surface.In the better quality data for the eukaryotic protein, there was no indication that anything other than a purely parallel/antiparallel packing model was required. From this it might be assumed that discriminating deviations from aligned packing is beyond the resolution of the predicted contacts, given that there is an apparent twist between pairs of helices in the structure Fig. . HoweverThe contact maps of globular proteins, whether predicted or derived from a known structure, tend to be more complex that those for helical transmembrane proteins. As the name implies, they are more globular in nature which means that for the same sized protein, fewer residues are required to cross from one side of the protein to the other and, without the constraint of the membrane, chain segments are not constrained to make a complete transversal. For the same reason, SSEs are also less restricted in their packing angles which leads to fewer simple patterns to recognise.In addition, the two types of secondary structure can co-exist in the same protein and although this does not, in principle, affect the analysis of their packing, With this complication in mind, two proteins of the ediction . The firSEs Fig. . For botThe basic dynamic programming algorithm used by the current method has the equivalent of a gap-penalty in sequence alignment to penalise large segments  and if one of these is deleted then the neighbouring segments can claim its space on the next iteration. If a starting SSE is an erroneous prediction then this is desirable behaviour, however, it also allows potentially large segments to “swallow” their weaker neighbours even when they are correct. A way to prevent this is to increase the gap-penalty.For the two proteins considered in this section, the gap-penalty was increased from 1 in unit steps until every predicted segment retained an interaction box. This occurred at a value of 4 for both proteins and further increases over this produced no apparent change, even at high values. A ’default’ value of 5 was used in the results below.The parsed contact for the smaller protein PDB:3chyA, is shown in Fig. Of algorithmic interest is the treatment of the numerous interactions between L-fuculose-1-phosphate aldolase (2opiA) contains a number of more complex interactions, including long helices that interact with more than one other SSE along their length and adjacent elements that continue along the same direction. In general, the interactions between SSE were well predicted and even using an older data set, were sufficient to allow the correct fold of the protein to be predicted [Compared to rhodopsin and the smaller chemotaxis-Y protein, the structure of redicted .In the current data, a few interactions were missed by the parsing algorithm: one was between a minor helix  terminal part packing with the first The second occurrence involved a complex set of packing between The simple algorithm developed above for parsing secondary structure interactions in predicted contact maps has been shown to be effective in the transmembrane class of protein and error tolerant even when the signal becomes degraded.In the globular class of protein, where the extent of interactions are more limited and more complex, the algorithm still behaved well, classifying most of the important interactions correctly in both a small and a large test case.For the larger protein, this involved examples of the algorithm apportioning parts of a single secondary structure between two different interactions.The method was initially designed to use an iterated frozen approximation algorithm (FAA), simply on the theoretical grounds that the starting end-points should not be optimal. In the application of the algorithm, despite the end-points being redefined after the first pass of the dynamic programming algorithm, they were not seen to move again except when using a low gap-penalty with the globular proteins that allowed large segments to ’steal’ their neighbours space. With hind-sight, this may be the expected behaviour as the dynamic programming algorithm will make an optimal segment assignment on the first pass almost certainly by expanding each segment to include more interactions. There will therefore be no scope for further expansion unless a segment is removed. As there can exist situations were weak predictions may be better removed, the iterated algorithm was retained but with just three cycles.A drawback of the frozen approximation is that initial values need to be provided and these can sometimes only be obtained from unreliable predictions, especially when there is a paucity of sequence data. The current algorithm has a degree of robustness to errors in the starting definitions as we have seen that it can divide long SSEs into multiple interaction regions or, as discussed above, delete weak SSEs. Two methods have recently been developed to recognise patterns in predicted contact maps , 30 thatAsumming, even with improved starting points, that some error might still remain, random end-points could be assigned and refined using a stochastic optimisation process (the Genetic Algorithm would be an attractive possibility). An alternative might be to use a recursive divide-and-conquer algorithm. Choosing any element in the score matrix divides the sequence into three pieces: the segment itself and the regions above and below, one of which may have zero length (but not both or there is nothing to pack). Similarly, points can then be selected in these flanking regions, again dividing them each into three pieces, and so on until no unassigned parts of the sequence remain. At this stage, the value of the original starting element can be evaluated and the algorithm back-tracked. If a reasonably large limit can be imposed on the smallest segment, the approach might be practical.However, the most effective approach would probably be to make use of variation in the biological data found in the multiple sequences . Variations in the secondary structure predictions, or random perturbations to them, could all be used to generate initial end-points which could also be combined with variation of the ’gap’-penalty value to allow segment deletion to occur. As the basic algorithm is very fast, taking a fraction of a second to execute, many trials could be made and a selection of the best retained.The method described in this work will provide a useful pre-processing analysis of predicted contact data that can then be used in the subsequent prediction of a 3D structure using coarse grained methods. It is hoped that this strategy will help extend the range of protein tertiary structure prediction to larger proteins or to data that is currently too ’noisy’ to be used by current residue-based methods.


M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.Emergence of multiple drug resistant strains of M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis interacts with its host.This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users. Mycobacterium tuberculosis (MTB), is a major global health concern [Mycobacterium has threatened to derail global efforts for reigning in this pathogen [M. tuberculosis.Tuberculosis (TB), caused by  concern . Accordi concern , there w concern . Further concern ,4. Drug pathogen . TherefoM. tuberculosis primarily infects the respiratory system where it encounters alveolar macrophages and dendritic cells patrolling the lungs. However, the bacterium has an uncanny ability to survive the onslaught and in fact it uses the host macrophages for replication [UreC gene that prevents acidification of phagosomes, and the ability of the pathogen to neutralize reactive nitrogen and oxygen intermediates using reductases helps the bacterium evade the host immune system. In addition to macrophages, T-cells have been shown to participate in host cell response against mycobacterium [M. tuberculosis and the host to learn how these bacteria cause disease [M. tuberculosis [Homo sapiens [lication . Virulenacterium ,7. Howev disease . The avarculosis  and the  sapiens  providesHost-pathogen protein interactions (HPIs) are often involved in the pathogen’s strategy to invade the host organism, breach the host’s immune defenses, as well as replicate and persist within the organism ,11. ExpeM. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs between M. tuberculosis and Homo sapiens by ‘interolog’ method. The HPIs were further filtered by domain-domain interactions (DDIs) prediction. Then, protein functional annotations and existing experiments results were applied to remaining HPIs. As a result, 118 pairs of HPI were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. Intra-species PPIs were further predicted for the proteins from M. tuberculosis and proteins from Homo sapiens using VisANT [M. tuberculosis and Homo sapiens was then constructed by the predicted inter- and intra-species interactions. Finally, a database named PATH (Protein interactions of M.tuberculosis and Human) was constructed to store these predicted interactions and proteins.In this work, we developed a systematic flow to predict the HPIs between g VisANT , Reactomg VisANT , InteroPg VisANT , IntAct g VisANT , DIP [32g VisANT , MINT [3g VisANT , and HPRg VisANT . A bioloFigure Homo sapiens (taxid:9606). The query protein sequences were obtained from TB database [To predict homologs, the basic local alignment search tool BLAST   was empldatabase .Homo sapiens and Mycobacterium tuberculosis were assigned to families or domains; 2) a whole domain-domain interaction network was drawn; 3) mapping the domain ‘a’ from protein ‘A’ in Homo sapiens and domain ‘b’ from protein ‘B’ in Mycobacterium tuberculosis to the whole network. If the domain ‘a’ interacted with domain ‘b’, the protein ‘A’ was predicted to interact with the protein ‘B’ . If the functional annotation of the pair of interactors in the quasi-credible HPI was found to correspond with at least one of the defined terms, the quasi-credible HPI was selected and upgraded as credible HPIs. The terms were selected from previously published studies on the infection and pathology of MTB [The information of each protein in the HPI pairs  was obtained from the Uniprot website  were predicted based on sequence motifs, using HPIDB and BIPS. By performing a BLAST search of the two databases, 3219 HPIs were obtained between Homo sapiens and Mycobacterium tuberculosis from HPIDB, and 136664 HPIs from BIPS, with 1041 overlapping HPIs between the two databases. In total, there were 138842 non-redundant HPIs involving 1168 MTB proteins and 20987 human proteins  specific DDIs were extracted , which contains not only all the predicted host-pathogen interactions, but also the intraspecific interactions predicted from 7 external databases. Using the web-interface, users can acquire protein-specific interaction information by searching MTB’s gene locus (eg. Rv0001) or Uniprot ID (eg.P49993) and the human protein’s Ensembl identifier (eg.ENSP00000349142) or Uniprot ID (eg.P36542) , which is publicly available and incorporates the predicted interspecific and intraspecific interactions between


Knowing the binding site of protein–protein complexes helps understand their function and shows possible regulation sites. The ultimate goal of protein–protein docking is the prediction of the three-dimensional structure of a protein–protein complex. Docking itself only produces plausible candidate structures, which must be ranked using scoring functions to identify the structures that are most likely to occur in nature.In this work, we rescore rigid body protein–protein predictions using the optimized potential for efficient structure prediction (OPEP), which is a coarse-grained force field. Using a force field based on continuous functions rather than a grid-based scoring function allows the introduction of protein flexibility during the docking procedure. First, we produce protein–protein predictions using ZDOCK, and after energy minimization via OPEP we rank them using an OPEP-based soft rescoring function. We also train the rescoring function for different complex classes and demonstrate its improved performance for an independent dataset.The trained rescoring function produces a better ranking than ZDOCK for more than 50 % of targets, rising to over 70 % when considering only enzyme/inhibitor complexes.This study demonstrates for the first time that energy functions derived from the coarse-grained OPEP force field can be employed to rescore predictions for protein–protein complexes. One of the main goals of proteomic research is to understand the biological function of proteins. Many proteins generate their function not as monomers but as part of complexes. Thus knowledge about protein–protein interactions is fundamental and allows regulation of protein structure and function. The Protein Data Bank (PDB)  containsin silico method for predicting the structures of protein–protein complexes. One can predict possible binding sites in a complex based on the protein structures in their unbound state. The binding partners can be single proteins or smaller protein–protein complexes. To increase computing efficiency, the proteins are usually modelled as rigid bodies at the first six-dimensional (6D) global search stage. Most of these global search methods are based on the convolution of grids, where the surface of the binding partners are parametrized such that an overlap between the surfaces of the two binding partners becomes possible. The aim of this surface description is to implicitly account for conformational changes upon binding. The convolution of the grids is accelerated by fast Fourier transformation (FFT) [Protein-protein docking is an on (FFT) –5. In thensional D global on (FFT) , clusteron (FFT) , 9, or son (FFT) . As compThe global search, which is performed via ZDOCK in this study , usuallyTo investigate protein–protein complexes produced by ZDOCK, docking approaches that allow for more protein flexibility than ZDOCK with low time expenditure are needed. A coarse-grained force field should be a good choice here. Various coarse-grained force fields have already been developed for the treatment of protein–protein complexes, including the calculation of thermodynamic and structural properties of multi-protein complexes with relatively low binding affinities . Coarse-In the current study, we apply the coarse-grained ‘Optimized Potential for Efficient structure Prediction’ (OPEP)  to the pWe perform unbound docking, which starts from the binding partners in their native conformations. The methods applied for predicting and rescoring protein–protein complexes can be summarized via the following pipeline: For each of the 96 targets we produce 54,000 docking predictions with ZDOCK and retain the best 2000 of these complexes, as recommended by the ZDOCK developers. These predictions are energy minimized using the OPEP force field step 1) in Fig.  in Fig.  in Fig. We use two different benchmarks to perform unbound docking. ZDOCK benchmark 4.0 is used as training dataset, while for further evaluation we use the Dockground benchmark 2.0. We used a subset of ZDOCK benchmark 4.0 . We downThe second dataset is a subset of the Dockground benchmark . Here weZDOCK is an FFT-based rigid-body protein–protein docking algorithm. During the search procedure one protein is kept fixed, while the other is moved around it. The fixed protein is usually the larger of the two and is called the receptor, while the other protein is the ligand. ZDOCK generates grid-based representations from the full atom chains of receptor and ligand and after each ligand rotation the grids can be fast convoluted via FFT. The three rotational angles of the ligand are sampled with a 6° spacing, and the 3 translational degrees of freedom are sampled with a 1.2 Å spacing. For each set of rotational angles, only the best (based on ZDOCK score) translationally sampled prediction is retained . This le2).Some of the complex structures considered are missing certain residues in the receptor and/or ligand. Although this is no problem for a grid-based method like ZDOCK, it must be resolved for treatment with OPEP. Missing residues lead to gaps in the backbone chain and, if untreated, they would be considered overstretched bonds. In order to resolve this problem, polypeptides with missing residues are treated as separate chains. The distance between the terminal carbon and the terminal nitrogen of the gap is kept fixed via a harmonic potential with the equilibrium distance equal to the initial gap length and a force constant of 100 kcal/, (2) hydrophobic and hydrophilic side chain–side chain interactions (ESS), (3) hydrogen bond (H-bond) interactions between backbone atoms (EHB), and (4) a potential for salt bridges (ESB). Interactions between side chains ESS are modelled differently for attractive and repulsive interactions [As rescoring function we use the coarse-grained potential OPEP or variations of it. OPEP uses a six bead representation for every amino acid except proline and glycine. The amino nitrogen N, the Cce field  and sevece field . We use ce field , which fce field , 31–33. ractions :rij is the distance between interacting beads i and j, the equilibrium distance σij is correlated with where εij is the interaction strength, and σij. Equation (1) replaces the common 12-6 Lennard-Jones potential in order to limit ESS at longer distances. Figure 2α-atom, while for all other side chains the interaction center is a bead representing the center of mass of the side chain [ESS is not used for salt bridges between side chains. Instead, salt bridges are modelled with a potential, ESB, derived from all atom MD simulations [EVDW, which is modelled via a 12-6 Lennard-Jones potential. H-Bond interactions, EHB, are modelled between the backbone N-H and the backbone C’-O atoms. In addition, OPEP has special terms for stabilizing α-helices and β-sheets. The two-body term for H-bonds between residues in the same chain has different equilibrium distances for H-bonds less than five residues apart and for H-bonds further than four residues apart. For stabilizing α-helices, the intra-chain potentials also contain a 4-body H-bond term. Furthermore, 11 side chain–side chain interactions were identified to be more frequently found in  and  contacts in α-helices. Therefore, these side chain–side chain interactions with this particular separation were made more attractive [Figure 2de chain . The potulations , where ttractive .Fig. 2TESS from Eq. (1), and the 12-6 Lennard-Jones potential EVDW with an 8-6 Lennard-Jones potential. This kind of soft potential is also used in the Attract force field that was developed for protein–protein docking [ESS86 and EVDW86, and the formula for ESS86 is given as:Before rescoring the predictions, we perform an energy minimization using OPEPv5 to relax the complexes after their transformation from the grid presentation to the coarse-grained model. We perform 140 minimization steps, as we found this to be the best compromise between computational efficiency and optimization result. We tested the effect of fewer and more minimization steps. Extending the minimization beyond 140 steps does not change the outcome of the rescoring result as for ∼90 % of the structures the energy only changes marginally at this point. Moreover, it happens especially for misdocked complexes that the energy minimum has not been reached within 140 minimization steps. However, there is no need to further optimize such misdocked decoys. Reducing the number of minimization steps below 140 bears the risk that also near-native structures have not been properly minimized yet, which would lead to a poor ranking for them. For the scoring function we found that it becomes more reliable if we introduce a softer potential, which allows for more overlap between the beads than the original OPEPv5 energy function. To obtain a softer scoring function we replace both the side chain–side chain interaction potential,  docking . We callσij′=0.866σij and εij′=9.481ESS(σij), with σij given in Eq. , one can see that the repulsive-only potential is not modified. An example of the attractive ESS86 term is shown in Fig. 2ESS86 and EVDW86 have broader wells than in OPEPv5, some overlap between beads is tolerated and, in addition, imperfectly fitted contacts are more strongly attractive at larger distances. The potentials for H-bonds and salt bridges were not modified, leading to our new scoring function, E86, with the modified potentials EVDW86 and ESS86: Here, n in Eq. . The valwhich calculates the binding energy between receptor and ligand for scoring purposes. It should be noted that each binding partner can consist of several proteins (chains). We consider all chains from one binding partner as a single protein. Hence, we only consider non-bonded energies between the two binding partners, e.g., between receptor and ligand.α interface atoms of the co-crystallized model and the prediction after superposition. Interface Cα atoms are all atoms within 10 Å distance of the binding partner in the co-crystallized complex [The interface RMSD (IRMSD) is defined as the RMSD between C complex . For the complex .As is standard , 39, we Npred, that we consider from the sorted prediction list. This is averaged over the number of targets, Ntarget, and is calculated according to following equation: The performance is evaluated by ranking the predictions according to their (re)scoring energy in increasing order. From this list, the best ranked prediction with an IRMSD lower than 4 Å is reported. Furthermore, we calculate the success rate, which is a function of the number of predictions, Si(Npred)=1 when the subset of Npred=1,2,…,2,000 predictions contains at least one hit, otherwise Si(Npred)=0. Thus, the success rate corresponds to the probability of finding the native complex among the Npred first models based on the (re)scoring energy.where E86, one can classify the predictions for each complex into one of the four groups: true positive (TP), false negative (FN), false positive (FP), and true negative (TN). TPs have an IRMSD <4 Å and rank lower than or equal to 20, while the TN predictions have IRMSD ≥4 Å and a rank higher than 20. All other predictions are either FNs or FPs depending on whether their IRMSD is < or ≥ 4 Å and their ranking is > or ≤20. We only consider the first N=20 TPs or, if N<20 hits are found, we consider only those, because ideally one wants the correct predictions within the top hits. Twenty complexes is a small enough number for further processing by computationally more expensive approaches and visual inspection. We further limit the number of FNs and FPs to 20−N for training purposes. Thus, we do not consider FN and FP predictions if ≥20 hits are found for a target, as for such targets E86 already produces satisfying results. For each TP, FN, FP, and TN prediction considered, we calculate the frequency map for residue-residue contacts and average them over all targets for the enzyme/inhibitor, antigen/antibody, and other complexes. Next, we select residue-residue contacts where the frequency is higher in the maps for TP and FN than for the FP and TN maps. We assume these contacts need to be strengthened, so current FN predictions become TP without further favoring FP predictions. Therefore, we decrease the energy value ESS86 or ESB for this contact. The other contacts, for which we modify the potential, are those where the frequency of TPs and FNs is lower than FPs and TNs. It appears these contacts are not important for the complex class in question and should thus be disfavored, with the aim of transforming a current FP prediction into a TN prediction. Therefore, we increase ESS86 or ESB for such contacts. Figure After minimization, a residue-residue contact map between receptor and ligand is produced for each prediction. A contact is present if any of the beads of two residues are closer than 8 Å. Depending on the ranking with i and j is determined by the ratio between the corresponding FN ij and FP ij frequencies. A value greater than one means this interaction energy has to be decreased, while the opposite indicates this interaction energy has to be increased. We do this by changing the interaction potentials ESS86 and ESB according to The amount of change for the selected interaction between residues EX=ESB or EX=ESS86 depending on the residue contact . For the parameter k, values between 0.1 and 0.6 were tested, and k=0.2 was found to be optimal. Equation  increase our chances of identifying the right prediction as the increase of the average rank for E86 originates mainly from further deterioration of the already high ranks obtained with E86 scoring function for its application to antigen/antibody complexes.Comparison of columns three and four of Table periment  one can E86 energy for predictions sorted by their IRMSD using a bin size of 1 Å. We show the averaged values of ESS86, ESB, and EHB for the three complex classes. For the enzyme/inhibitor complexes, a minimum in ESS86 is present for predictions up to 5 Å. However, for IRMSD values above 25 Å ESS86 becomes small again, in some cases even smaller than for the hits. This is more than counterbalanced by the H-bond energy, as only near-native hits have more and better oriented H-bonds, leading to EHB values more than 10 kcal/mol smaller than for all other predictions. Salt bridges seem to be of minor importance for the protein binding in enzyme/inhibitor complexes, as there is no correlation between the ESB values and the IRMSD, and the contribution of ESB to E86 is generally small, with all values fluctuating around −5 kcal/mol. Thus, the sum of ESS86 and EHB is mainly responsible for distinguishing between correct and incorrect complex predictions. This partly agrees with previous findings that protease-inhibitor complexes interact predominantly through main chain–main chain interactions [E86 function.Figure ractions , which aESS86 and EHB adopt their smallest values for IRMSD ≈20 Å, which explains why E86 does not perform well for this complex class. Compared to enzyme/inhibitor complexes, backbone H-bonds are less important for the native complex. This agrees with the previous observation that antigen and antibody complexes predominantly bind through side chain–side chain or side chain–main chain interactions [E86 but not by EHB. For antigen/antibody complexes, the formation of salt bridges is also of minor importance. There is only one exception, at IRMSD ≈34 Å, where with ESB≈−13 kcal/mol the smallest salt bridge energy is observed, also taking the other two complex classes into account.For antigen/antibody complexes, none of the three energy contributions clearly decreases with decreasing IRMSD. Instead, both ractions , which aESS86 are found for the complexes with IRMSD <4 Å. H-bonds seem to be of minor importance for binding receptor and ligand in this complex category, as all EHB values are >−1 kcal/mol, an order of magnitude higher than those in enzyme/inhibitor and antigen/antibody complexes. On the other hand, other complexes are the only ones where salt bridges contribute to stabilizing the complexes, as for IRMSD >5 Å, ESB increases. This trend only breaks for IRMSD ≤5 Å as ESB does not further decrease for the near-native predictions. This means that either ESS86 dominates these binding modes or the E86 potential can be further improved in this range.The hits for other complexes are stabilized by side chain–side chain interactions, as the lowest values for E86 can be enhanced by training it according to Eq. , which is not the case with ZDOCK. The performance of E86 is weak for Npred<10, but when considering more than 10 predictions the results improve, and E86 performs then better than ZDOCK and similar to E86 function near the native complex structure is not high enough; this is improved by training the scoring function, yielding Npred<10, the performance of Both OPEP-based rescoring functions can significantly improve the average ranking compared to ZDOCK, and E86 performs less well, only improving the ranks of three targets and worsening them for the other nine targets. Figure 7E86 has as many top-1 hits as ZDOCK has, but its success rate dwindles when more predictions are taken into account. Npred>12 ZDOCK is still most successful for antigen/antibody complexes. Nonetheless, training E86 was worthwhile, as for Npred>1 the trained potential always performs better than or equal to E86, improving the average rank by more than 120 places , the RMSD of the ligand molecules in the predicted versus the target complexes (LRMSD), and the IRMSD. A detailed description of these parameters and the corresponding thresholds used in classifying predictions can be found in previous CAPRI reports [fnat requires an atomisitic representation of the predicted complexes as it is defined based on contacts between any atoms of interacting residues. Therefore, a transformation from the coarse-grained OPEP to an atomistic representation would be first required for the calculation of fnat. This would probably entail an optimization of the side chain positions so that the correct residue-residue contacts can form. While desirable, this is, however, would be beyond the scope of current study, which focuses on testing of OPEP as rescoring function for protein-protein docking. Therefore, only the IRMSD is used to classify the accuracy of predictions as high if IRMSD≤1 Å, medium if IRMSD≤2 Å, and acceptable if IRMSD<4 Å [E86, we determined the best ranks using these thresholds for the predictions obtained for the Dockground 2.0 test set. The results are listed in Table In the CAPRI evaluation , where t reports , 46. In RMSD<4 Å , 46. As E86 and E86 and E86 predicts for only three complexes top-10 ranks, however, for two of them they are on the first place. For antigen/antibody complexes, ZDOCK finds for two complexes models in the top-10 rank list, while E86 and E86 is slightly better than ZDOCK and Table ettaDock , would bE86 and E86 and E86 and/or E86 and In summary, E86, based on the interprotein OPEP interactions. This approach produces a better rank for the best prediction than ZDOCK for 54 % of targets. However, the results differ significantly across the three complex classes: There is an improvement for 65 % of the enzyme/inhibitor complexes, for 55 % of other complexes, but for only 32 % of the antigen/antibody complexes. Furthermore, the average rank with E86 is for antigen/antibody complexes considerably higher than that obtained with ZDOCK. To improve these results, we developed a training scheme for the OPEP-based rescoring function based on false positive and false negative predictions. The resulting trained rescoring function callesd In this work we examined the applicability of the coarse-grained OPEP force field  for refienchmark , producePerformance analysis for different complex classes Training the OPEP-based rescoring function revealed that the complexes from different classes are stabilized by different protein–protein interactions. For enzyme/inhibitor and other complexes, interactions between hydrophobic residues are of general importance, and for enzyme/inhibitor complexes backbone–backbone hydrogen bonds are also important. For antigen/antibody complexes we found that training strengthens the interactions between residues, which have been previoulsy shown to be prevalent at the paratope of the antibody and the epitope of the antigen [ antigen , 45. The antigen . Thus, iComparison to other rescoring approaches In summary, this study demonstrates for the first time that energy functions derived from the coarse-grained OPEP force field can be employed to rescore predictions for protein–protein complexes. This expands the applicability of OPEP to new problems. While the performance of OPEP is already very good for enzyme/inhibitor complexes and better than ZDOCK, for the other complexes and especially for antigen/antibody complexes, ZDOCK is still better suited. The comparison to RDOCK results [E86 and Npred=10  from 38Npred=10. This shows that while OPEP is still not a perfect scoring function for protein-protein docking, it is clearly better suited than other coarse-grained force fields.Comparison to other coarse-grained force fields shows that OPEP is better suited as scoring function for protein-protein docking than these. In addition to OPEP we also tested the coarse-grained force field developed by Bereau and Deserno (BD)  on a decOutlook In our future work we strive to further improve the performance of the OPEP rescoring functions. Here, special attention will be devoted to antigen/antibody complexes, where improvement is most needed. In addition, we will not only rescore the decoys generated by ZDOCK but also refine them by performing Monte Carlo simulations with OPEP. One advantage of OPEP is that it is a physics-motivated force field defined based on continuous functions and is therefore ideally suited for flexible docking. Our aim is to produce a reliable refinement and rescoring protocol based on OPEP that only needs docking decoys generated by ZDOCK or another global search algorithm as additional input. For the participation in the CAPRI experiment, however, a final transformation from the coarse-grained to the atomistic level for the top-10 decoys will become necessary as only atomistic decoys can be submitted.All decoy structures as obtained from ZDOCK and after their transformation to the OPEP coarse-grained level are available upon request from the authors.


Protein-protein interaction (PPI) is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we assessed the potential of using the support vector machine (SVM) model with selected features centered on a particular protein for PPI prediction. As a proof-of-concept study, we applied this method to identify the interactome of progesterone receptor (PR), a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. We achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Our method is generally applicable to any other proteins and therefore may be of help in guiding biomedical experiments. Proteins determine the phenotype of all organisms. Typically, proteins are not functional in isolated forms; it has been estimated that over 80% of all proteins do not operate alone but in complexes through protein–protein interaction (PPI) . UncoverVarious experimental techniques have been developed for large scale PPI analysis, such as yeast-two-hybrid (Y2H) and affinity purification combined with mass spectrometry (APMS). The limitation of these approaches is that they have experienced high rates of noise and false positives . In addiSo far, most computational studies are intended for pair-wise prediction of PPI in the whole genome. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we developed a machine learning method based on a support vector machine (SVM) with selected features centered on a particular protein. As a proof-of-concept study, we applied this method to predict the interactome of progesterone receptor (PR), a protein which is essential for coordinating female reproduction in mammals by mediating the actions of the ovarian progesterone . Our metn-dimensional vector, an SVM learns a boundary between positive and negative samples with maximum margin. The remaining uncharacterized samples in the data set are then classified according to the decision boundary.The goal of the present study was to assess the potential of efficient prediction of PPI partners for a particular protein using a machine learning approach. Our method was developed using a support vector machine (SVM), which has been widely adopted in predicting PPI . Given aAs a proof-of-principle study, the progesterone receptor (PR) protein was chosen. PR is a ligand-activated transcription factor. It interacts with many associated factors, forming a typical protein complex . To consFor predicting PPI by sequences, one of the main computational challenges is to find a suitable way to fully describe the important information of PPI. To solve this problem, we proposed a descriptor which considered both amino acid composition and functional domain annotation. Finally, a 433-dimension feature vector  was built to represent each protein. We employed an SVM to generate computational models incorporating these features. The radial basis function (RBF) was selected as the kernel function. To validate the prediction performance in a self-consistent way, 5-fold cross validation was carried out. We evaluated the performance of the SVM model in terms of sensitivity, specificity and accuracy.True positives (TP) were actual interacting proteins that were predicted correctly. True negatives (TN) were non-interacting proteins that were predicted correctly. False positives (FP) were non-interacting proteins that were predicted as interacting proteins. False negatives (FN) were interacting proteins that were missed.C and the kernel parameter γ of the SVM were tried using a grid search method in the range of C = 2−4, 2−3, …, 24 and γ = 2−10, 2−9, …, 210, respectively. Meanwhile, feature selection was performed during parameter optimization. The accuracy profile of the 5-fold cross validation on the training set versus the variations of parameters C and γ was shown in C, γ) =  with 234 selected features, indicating that the optimal values of C and γ for constructing SVM model were 2 and 0.5, respectively. With these parameters, we used the receiver operating characteristic (ROC) curve to present the inter-relationship between specificity and sensitivity  C, suggesAndrogen receptor (AR) and estrogen receptor (ER), together with PR, are members of steroid receptors . They arp ≥ 0.96  is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, several computational tools have been developed for genome-wide prediction of PPI using general pair-wise features in various model organisms. However, for biologists, they are usually interested in getting a list of potential PPI partners for a single protein. Theoretically, predicting protein partners for a particular protein is likely a simpler problem and the results can be more accurate than general PPI predictors, as protein-specific information can be used. As a proof-of-concept study, we applied this method to predict the interactome of progesterone receptor (PR), a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. Previously, Li et al.  reportedC-terminal were structurally determined [N-terminal which is believed to be the main interface of PPI has not been determined yet. Structure-based models are apparently not suitable for PR. In the present study, we extracted 433 features for each protein . In order to reduce the number of features, feature selection was performed. Finally, 234 features were selected. The importance of these features was ranked by F-score. Among the top 10 features, 4 were amino acids, namely cysteine, threonine, glycine, and isoleucine. Their ranks were 1, 2, 3 and 9, indicating that amino acid composition was the most discriminative features in our model. The other six were functional domians, including IPR011009 (protein kinase-like domain), IPR024736 , IPR011011 (zinc finger FYVE/PHD-type), IPR001452 (src homology-3 domain), IPR018359 (bromodomain) and IPR004367 . According to cross validation, the performance of SVM model was slightly improved by feature selection, from 90.5% to 91.9%. To further improve the predictive accuracy of our model, we also employed an upsampling procedure. This procedure results in a more accurate representation of the negative set. As expected, when an upsampling factor of 4 or 5 was used, the accuracy was as high as 98%.Our model was built merely on protein sequences. In fact, the majority of existing PPI prediction methods are based on protein sequences but not protein structures . Althougtermined ,18, wherPR is a member of the nuclear receptor superfamily of ligand-activated transcription factors . Ligand-In conclusion, our work demonstrated that it is possible to reliably prediction of PPI partners of a particular protein by using the SVM model. When applying to PR, we achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Notably, our method requires simple input data and could be used in a wide variety of applications. We believe that our method will be useful in guiding biomedical experiments.Known progesterone receptor -interacting proteins were collected from PINA2 database , which iFor independent testing, we collected another two data sets for androgen receptor (AR UniProt: P10275) and estrogen receptor . The same criteria were employed as described above. There were 299 interacting protein and 296 non-interacting proteins in final testing data set for AR. The final testing data set for ER contained 547 interacting protein and 531 non-interacting proteins.ix, i = 1, 2, …, n}. The vector ix has 20 elements corresponding to the occurrences of 20 amino acids normalized with the total number of residues in the protein. We also considered amino acid dipeptide composition with 400 elements specified the occurrences of 400 amino acid dipeptides normalized with the total number of dipeptides in the protein. We finally excluded dipeptide composition in the feature vector, because it did not improve the performance of the SVM classifier.For amino acid composition, each protein sequence was represented using a vector {http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/fselect/fselect.py).The interacting domain between proteins is indicative of PPI. Previous studies have shown the feasibility of using functional domain information to predict PPI ,40. In thttp://www.csie.ntu.edu.tw/cjlin/libsvm/) was employed in this work. The radial basis function (RBF) was chosen as the kernel function, which is defined as:u and v are two data vectors and γ is the kernel width parameter. We evaluated the predictive performance of the constructed model by 5-fold cross validation. During this process, the training data set was divided into five equal or nearly equal groups. In one round of cross validation, one subgroup was regarded as the test set, and the remaining four subgroups were treated as the training set. The cross-validation process was repeated five rounds, with each of the five subgroups used as the test set in turn. Then, all the results were combined to produce a single estimation. During the process of 5-fold cross validation, the regularization parameter C and the kernel width parameter γ were optimized to maximize predictive accuracy using a grid search approach. Finally, the parameters that yielded the highest accuracy were employed to construct the predictive model. ROC curve and AUC were calculated during 5-fold cross validation using a MATLAB script (http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/roc/plotroc.m).The software libsvm 3.20 (http://www.informatics.jax.org/gotools/MGI_GO_Slim.html). The ontology covers three categories: biological process, cellular component and molecular function. Known PR-interacting proteins and newly predicted PR-interacting proteins were mapped to GOslim terms under the molecular function category.For functional clustering analysis, we adopted the gene ontology terms defined by MGI GOslim (


In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation . Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable  Adverse drug reactions (ADRs) are detrimental, rare and complex perturbations of biological pathways by pharmacologically active small molecules. Each year ADRs cause 100,000 fatalities in the US e.g. genetic polymorphisms and medical history of the patient, treatment dosages, environmental exposures, dynamics and kinetics of the relevant systems biology, etc.), all ADRs are initiated by the binding of a drug molecule to a target, whether these binding events are intended, on-target binding or promiscuous binding to one or more off-target proteins. Currently, pharmaceutical companies commonly employ experimental in vitro toxicity panels to assay small molecule binding to potentially critical protein receptors Although many ADRs are multi-factorial and depend on patient- and treatment-specific factors  to a similar feature representation to that considered in in vitro studies or clinical trials. In all of the cases listed above, only global quality-of-performance metrics, aggregated across all considered side effects, are reported, making it difficult to assess how the models performed on individual side effects or classes of side effects.A popular approach is to data-mine the publicly available databases for experimentally elucidated interrelationships between the chemical structures of drugs, their known interactions with proteins (most often their intended targets), and their known ADR profiles. An early study by Fliri and co-workers There is another group of studies that more fully exploit the network structure of drug, protein, and ADR entity relationships. A network-oriented approach by Cami i.e. little representation of serious ADRs) and on-target or intended effects. It is difficult to see how analysis of drug-intended target binding data could be applied to explore correlations between off-target drug-protein binding and possibly rare ADRs.Indeed, the main weakness of these QSAR-like studies is their reliance on what is present in experimental data, which will tend to feature a strong bias towards approved drugs  and epidemiological data on side effect-related health outcomes  to train statistical models to predict ADRs with various degrees for success.In all of these cited cases, the efforts to solve the ADR prediction problem have focused on integrating publicly available and (in some cases proprietary) biological  would indicate the presence (absence) of an association. This matrix has been used for some of the previous efforts, as noted above, and is highly sparse with ‘0’s indicating both negative results of assays and unperformed assays. ADR-protein associations derived from these data limit us to patterns in known, intended “on-target” associations and limit the ability to find novel off-target associations. Also, data on lead compounds that have failed in the development pipeline are typically regarded as proprietary information and are generally unavailable for inclusion in analysis. Clearly, the majority of publicly available data is biased in ways which are difficult to correct.et al.et al.in vitro safety panel using the similarity ensemble approach (SEA). This was not a true docking study per se, in that SEA calculates the chemical similarity of each drug with each of the native ligands of the 73 targets.An alternative approach is to leverage ever-growing databases of high-resolution, experimentally-solved, protein structures, such as the Protein Data Bank (PDB) i.e. PubMed). Second, Xie et al.Two previous efforts, in particular, are similar to our current study. First, Wallach and co-workers These studies used the “first principles” approach to circumvent the bias issues in experimental data outlined above, but none of these previous efforts describe computational frameworks scalable to the data sizes required for a high-accuracy, high-throughput ADR screening panel for nascent compounds.More recently, Reardon i.e. N ≈ 1060 possible drug compounds Our working hypothesis is that it is valuable to predict ADRs as early in the lead identification phase as possible. Structure-based, high throughput, virtual screening is already widely applied in the early stages of drug discovery because of its low cost and high efficiency in identifying putative drug-candidate/drug-target interactions. Molecular docking-based screening studies involve fitting a large library of N small molecules into the active sites of M target protein structures, to calculate estimates of binding affinities. M and N can be quite large. Currently, the PDB has M>90 K protein structures, increasing at a rate of over 7500 per year i.e. “pose”) in the target, and the computational cost of the scoring function itself, make high-throughput ADR screening ideal for high-performance computing.These numbers, combined with the complexities of conformational sampling to find the best fit of the small molecule . VinaLC identified 64.4% of the top scoring poses with an RMSD under the 2.0 Å cutoff, while that for the best poses is 70.0%. For the best poses, all the targets have RMSD values within 10 Å and about half of the targets have RMSD values less than 1 Å. Overall, the VinaLC docking program performed well for re-docking the X-ray ligands back into the active site of the X-ray structures with the default setting for the grid sizes and exhaustiveness = 8. To improve the enrichment of the docking results, Zhang et al.Zhang e.g. drugs approved by regulatory agencies like the U.S. Food and Drug Administration) are annotated with known associated ADRs in public databases, such as SIDER. As in the prior work we cited, machine learning methods can identify statistical associations between these ADR outcomes and patterns in drug-protein binding as revealed by our VinaLC docking scores. The results can be used to build predictive models so the probabilities of certain ADRs can be predicted for a nascent or theoretical small molecule drug candidate that may not have undergone in vitro or clinical trial testing.A significant fraction of these molecules  may provide reliable, cost-effective, comprehensive high-throughput screening of a drug candidate for binding across many known on- and off-targets to predict clinically important ADRs.This study potentially provides a technological and methodological path forward to large-scale, high-throughput, http://www.uniprot.org/) for proteins that were identified as drug targets in DrugBank as of October 12, 2012 (http://www.drugbank.com/). Mappings to 587 experimental structures in the Protein Data Bank (http://www.rcsb.org/pdb/) (PDB) were obtained using the pdbtosp.txt file  from http://www.uniprot.org/docs/pdbtosp which links PDB ID numbers to UniProt IDs. A set of quality control rules were then applied  because they were associated with high morbidity, high case fatality ratio, and/or the need for extended hospitalization. Individual side effects were grouped into higher-level health outcome groupings to reduce noise and provide signals at the organ or system level. Individual side effects were identified as lowest level terms in the medical dictionary for regulatory activities (MedDRA) A set of 85 side effects were selected from the SIDER database , 409 proteins, and 10 outcome groups, comprising 85 severe side effects.In order to compare the ADR prediction capability of “off-target” effects, obtained by the molecular docking calculations, with that of experimentally derived “on-target” drug-protein associations, a 560 drug×555 target protein association matrix was extracted from DrugBank. More precisely, in order for a specific protein to be in the list of 555 proteins, it must be identified as a ‘target’ in the DrugBank database of one or more of the 560 drugs in our dataset. The matrix is boolean-valued where a ‘1’(‘0’) indicates the presence (absence) of the association in DrugBank.i.e. atoms designated as ‘HETATM’ after the TER record in the PDB file that are not part of common ions) were deleted. Molecular modeling software (Schrodinger Inc.) was used to protonate the protein structure. In those cases where a known catalytic site was identified, the centroid coordinates for the active sites/binding sites of the protein targets were determined by CatSId  i.e. all atoms that are not hydrogens) constraints so the geometry of the active site remains unchanged. The PDB files of energy-minimized protein structures were converted to PDBQT files, which are used in the docking procedure. During the conversion, the non-polar hydrogen atoms are removed from the protein target structures. Parameters for non-standard amino acids were calculated by the Antechamber program from the AMBERTOOLS suite. The set of 906 approved drugs were processed by the in-house program, preLigand i.e. fluorine, using a GB radius of 1.47 Å) were added into the Sander program. The PDB files of energy-minimized ligand structures were converted to PDBQT files, which were used in the docking procedure. As with the receptors, non-polar hydrogen atoms were removed from the ligand structures. All these steps mentioned above have been integrated into the preLigand program.The 409 target protein structures retrieved from the PDB were processed for molecular docking calculations. The raw PDB files were processed by our in-house Protein Function Prediction (PFP) pipeline The VinaLC parallel docking program http://bbs.llnl.gov/data.html. The VinaLC software package can be downloaded from http://bbs.llnl.gov/tools.html.The protein structures and docking co-complex structures are downloadable at The molecular docking calculations produced a 906×409 drug-protein docking score matrix. A 560×409 subset was extracted, where each of the 560 compounds has at least one side effect, as reported in SIDER, for the 10 ADR groups we are considering. Statistical analyses were performed on these data to train predictive models of serious ADRs and characterize putative ADR-protein associations. These analyses are outlined here.For the analyses, four separate data matrices are considered: (A) a 560×409 VinaLC drug-protein docking scores matrix (“VinaLC off-targets”) and (B) a 560×555 DrugBank drug-target protein association matrix (“DrugBank on-targets”). Matrix (A) is used to train logistic regression models that allow off-target ADR-protein correlations to be explored. Matrix (B) is used to train models on “on-target” drug-protein associations. The comparison of results between matrices (A) and (B) enables comparisons to be made between the relative predictive capabilities of intended target proteins and off-targets across the different ADR groups. The 16 toxicity panel target proteins in isolation are considered; thus, we also have (C) a 560×16 docking score matrix which is a subset of (A) and finally (D) a 560×16 boolean matrix which is analogous to (B), representing any drug-target associations reported in DrugBank between the 560 compounds and the 16 proteins of the toxicity panel. It is noted that the separate matrices (C) and (D) are constructed for the same on-target/off-target comparison purpose as matrices (A) and (B). Regarding the construction of the (C) matrix, there were 33 structures for the 16 proteins. Thus, multiple PDB structures that mapped to the same UniProt ID were averaged over, so (C) and (D) matrices are conformable. We note here that this was only done for the virtual toxicity panel. For the main VinaLC docking score matrix (A), the scores for individual PDB structures were mapped one-to-one to the relevant UniProt ID for that protein. The elements in matrices (B) and (D) also correspond to single UniProt IDs.i.e. transformed to have zero mean and unit standard deviation). Thresholds of (9) 1 standard deviation (SD) below the mean score  and (C) can be used as a heuristic for drug-protein binding. Global and protein-specific thresholds are defined. The raw docking score itself is used as a continuous feature, and (given that more negative scores correspond to stronger binding) additional thresholds are defined such that a docking score below the threshold indicates binding or, if above it, not binding. The docking score does not correspond to an actual energy, and it is difficult to set a single value for a threshold. Several thresholds are tried, letting the quality of the models (as quantified by the AUC) determine the best threshold for each ADR. For the VinaLC scores, ten feature sets are used, based on different choices of threshold: (1) raw docking scores, and then a series of global binding cutoffs: (2) -4.0, (3) -6.0, (4) -8.0, (5) -10.0, and (6) -12.0. Four additional thresholds based on protein-specific score percentiles were also defined: (7) 5th percentile, (8) 10th percentile, where the percentiles refer to the docking scores across all 560 compounds for a given protein. The last two thresholds were calculated by transforming the 560 docking scores for each protein into z-scores  applied to the ten feature sets each for the data matrices (A) and (C) and then for the Boolean matrices (B) and (D). The training samples were labeled by the 560×10 response matrix, consisting of the Boolean associations between the 560 compounds and the ten ADR groups, leading to 22 separate CV runs in all.i.e.p is the number of potential predictor variables, and n is the number of training samples). The L1 penalty term is proportional to the sum of regression coefficients The lasso penalty or L1 model regularization The ADR prediction problem considered here can be formalized as a case-control problem where a dichotomous variable The L1-regularized logistic regression was used as implemented in the glmnet package of Friedman and co-workers i.e. associated with a binding threshold, or DrugBank association). If the feature was continuous (i.e. the raw docking scores), the Wilcoxon rank sum test was used. In addition to p-values, we analyzed the false discovery rate (FDR) due to multiple hypothesis testing. For the models associated with the larger Vina off-targets matrix (A), we calculated q-values, using the ‘qvalue’ R-package of Storey After 10-fold CV, for each of the four data matrices (A)–(D), features with non-zero beta coefficients in the best median AUC model were extracted. The statistical significance of putative associations between the ADR groups and docking score matrix protein features were calculated. Statistical significance of the association for a putative ADR-protein pair was determined by the following procedure: univariate p-values for each ADR-protein pair were calculated using Fisher’s exact test if the protein feature was dichotomous  queries were used to search for evidence in the literature to support putative ADR-protein relationships identified by the statistical analyses of the VinaLC drug-protein docking matrix. The protocol for searching the PubMed database was as follows: 1) Queries for co-occurrences of the UniProt name of the protein and the MedDRA lowest-level term (LLT) of each individual side effect constituent of the ADR group were performed, 2) If the number of hits returned was substantive (∼10), or the quality of the hits was high, then the association was triaged for manual review of the PubMed results set. An example of a high-quality hit is the side effect and the protein terms co-occurring in the title or abstract of an article. ADR-protein associations that passed the manual review process were deemed significant and included in PubMed database (The 560×10 drug vs ADR group matrix (C) and the 560×409 drug vs protein docking score matrix (A) were used to train logistic regression models using L1-regularization, which allows the model to focus on high-information predictors and helps reduce over fitting. The ability of docking score data to identify potential associations between off-target drug-protein binding and individual side effects in the ADR groups was investigated. Additional statistical analysis was performed on the VinaLC drug-protein docking score matrix and the logistic regression models to derive associations between ADR groups and proteins. Only 21% (87 out of 409) of the drug-protein binding features involve known protein targets of the drug subset, providing a significant probe of off-target effects. In The associations between the ten ADR groups and a subset of the full VinaLC off-target docking score matrix (C) were investigated. Models trained on a 560×16 subset of the full VinaLC docking score matrix (C) were compared to the models trained on a 560×16 DrugBank on-target subset matrix (D). The docking calculations were refined using the more computationally expensive and more chemically accurate MM/GBSA-correction of the Vina score. The same logistic model training procedure used on the larger predictor sets to train logistic regression models was applied to these smaller matrices. The boxplots of the “best model” AUCs for the screening panel models are shown in Across the ADR groups, the MM/GBSA and DrugBank virtual panel model AUCs are similar for ‘immuneSystem’, ‘cardiacDisorders’, ‘gastroDisorders’, ‘bloodAndLymph’, and ‘hepatoDisorders’. The MM/GBSA-derived models for the ‘endocrineDisorders’, ‘psychDisorders’, and ‘renalDisorders’ ADR groups are all significantly worse than the corresponding DrugBank models.in vitro toxicity panels, it is of interest to see what specific associations they may have with specific side effects. Potential ADR-protein associations are shown in Given the role of these 16 proteins in i.e. drug-protein binding) all the way up to prediction of clinical ADR phenotypes. We show, for our particular set of 560 drugs, that using molecular docking scores yields ADR prediction models comparable in quality  to models developed using publicly available, experimentally-derived drug-protein associations. However, the AUCs, for both docking scores and experimental data, are not of sufficient quality for clinical prediction, and it is interesting to note the quality is poorer for highly multi-factorial disorders (e.g. cardiac disorders). As an example, for the virtual toxicity panel model quality results shown in i.e. AUC = 0.5).The major contribution of this work is a demonstration of the feasibility to holistically treat the ADR prediction problem for nascent drug compounds. Our methods treat the problem from atomistic levels  perform better than their DrugBank-derived counterparts (B).We identify several potential off-target ADR-protein associations that would be impossible to find using only binding data between a drug and its intended protein targets see . Some ofIncreased MMP-1 gene expression appears to be a biomarker for cancer metastasis. Specifically, we find evidence for separate constituents of the ‘neoplasms’ group: breast neoplasms A possible mechanism of interaction may be a role in suppression of breast cancer metastasis to lymph nodes An analysis A case study Profilin-1 expression is markedly elevated in the atherosclerotic plaques of diabetics, showing a potential role in mediating diabetic-related vascular endothelial cell dysfunction A meta-analysis Some papers hypothesize that enhanced cellular apoptosis is a disease mechanism in neurodegenerative diseases. A postmortem study on bipolar disorder patients shows significant increases in pro-apoptotic factors  Studies have shown integrin and monocyte migration are associated with ischemic myocardium. A study in vitro screening of ADRs for drugs in the development pipeline. Our results provide a rationale, founded on independent calculations, for their inclusion in the panel based on side effect phenotypes for which they probe. Potential ADR-protein associations, supported by some level of evidence in PubMed, are listed in We also find ADR-protein associations for the 16-protein consensus panel. The protein targets are included in panels used by major pharmaceutical companies for i.e. there are large areas of the drug × protein matrix that are unexplored by in vitro assays or clinical trials. In contrast, the docking calculations enable an exhaustive probing of binding associations through the entire drug × protein matrix, allowing the exploration of unintended (i.e. off-target) interactions that may not have been previously experimentally investigated during drug development. Thus, docking scores provide a direct way to probe off-target effects.Using molecular docking scores for drug-protein matrices has advantages over other approaches to predict association of off-target effects. Molecular docking is an approach based on a physics-derived force field, such that only the structure of the drug and the protein are necessary. Not surprisingly, the docking approach does not have as strong a dependence on the availability of drug-protein correlations in manually curated biological or chemical databases , though these data can be integrated into our type of analyses as well. Experimental drug-protein association matrices are extremely sparse, et al.et al., 4) compare model performance across ADRs (thru AUC), where the work in et al. also use L1-regularization to mitigate over-fitting, our lambda parameter is chosen through 10-fold cross-validation, while their lambda parameter seems to have been arbitrarily chosen to be one-half the value needed to suppress all beta coefficients to zero. They do not appear to discuss the associations they produce in quantitative terms . Also, their study treats each side effect individually, which may lead to bad class imbalances with more rare ADRs, a common problem in QSAR studies. We mitigate this issue by classifying ADR phenotypes into groups.Here we compare our work to previous efforts that have applied molecular docking to study ADR-protein correlations. A recent large-scale drug-protein docking exercise was described by Reardon i.e. CETP inhibitors). Candidates for off-targets were limited to those proteins that had binding sites with a high-degree of similarity to the CETP binding site. The focus for our study was not a careful, detailed study of a particular system, but rather the development of a general tool that can look across multiple drug classes and a heterogeneous mixture of off-target proteins. Our methods provide a way to obtain correlations between the molecular details of docking and several clinical phenotype groups of ADRs going across several organ systems. The work of Xie et al. also provides linkages to ADR phenotypes, but spends much more effort at understanding the mechanistic details of “meso-scale” biological pathways, and how they bridge the binding event to downstream signaling and gene regulation events that may be the actual causative factors that give rise to CETP inhibitor-relevant ADRs. This detailed work must be done to achieve true understanding of how a drug causes an ADR. Their approach may offer a possible template for how to integrate docking studies with systems biology approaches, but it is difficult to see how one could scale-up their approach to a more general-purpose ADR-protein correlation tool such as ours. Combining multiple off-target effects at the pathway level would be a worthwhile improvement to our methodology.Another study similar to ours is the work of Xie and co-workers e.g. metalloproteins) et al.i.e. docking profile of a drug. The benefit of using an HPC platform is that the effects of non-linear scaling can be addressed by the allocation of additional compute nodes and processors. Investigation of the actual scaling behavior with increasing data set size and increasing number of CPUs remains to be done as future work.The limitations of our method can be categorized into two areas: 1) molecular docking and 2) ADR phenotypes. For molecular docking to be a feasible method for predicting “off-target” associations, the execution of the docking needs to be fast and reliable. Our implementation of the well-vetted Vina docking program, VinaLC, has been optimized for HPC and has been benchmarked with known limitations . Results of the toxicity panel analysis indicate that even at the MM/GBSA level, we need to improve the drug-target interaction estimates, as shown by the poor performing median AUCs for the ADR groups endocrine, psychiatric, and renal. Also, the minimal "comprehensive" set of proteins needed to obtain high-quality ADR prediction models is unknown. As more proteins and pathways are associated with ADR phenotypes, the minimal comprehensive set will be soon be obtained.For ADR phenotypes, we are currently limited by the availability of clinical data on ADR phenotypes linked to drugs. In addition, publicly available ADR outcomes data will always be biased toward approved drugs. Our results point to the importance of the target proteins, which might not be known for nascent compounds. The known or intended targets appear to be important for ADRs associated with major organ systems (i.e. UniProt names for proteins and MedDRA lowest-level terms for side effects), standardized way to see a preponderance of papers (e.g. more than 10) in the literature, where a sample could be obtained and examined manually for the quality of the correlation. In no way are we reporting exhaustive numbers of papers that contain a particular putative ADR-protein correlation in PubMed. Any other approach (e.g. stemming the terms) would also have some ambiguity associated with it.Limitations associated with the way putative ADR-protein associations are corroborated with literature studies may also exist. Biological terms are used ambiguously in the literature. Our intent was to find a well-defined  of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. The median AUCs obtained during 10-fold cross-validation were comparable between the VinaLC off-target models (AUC = 0.60–0.69) and the DrugBank on-target models (AUC = 0.61–0.74) across the ten ADR groups. Most importantly, the VinaLC off-target model out performed the DrugBank on-target model for predicting two ADR groups, neoplasms and vascularDisorders. We further investigated the associations between the ten ADR groups and a consensus subset of 16 proteins used in early-stage in vitro toxicity screening panels. The analysis identified several putative ADR-protein associations. Successful PubMed queries found published results in support of these putative ADR-protein associations. For example, several associations between neoplasm-related ADRs and known tumor suppressor (Syk) and tumor invasiveness marker (MMP-1 and C3) proteins are found. Many of these associations involve off-target proteins and would not have been found using only the available drug-target data. Thus, increasing the reliability of the drug-protein binding calculations and increasing the protein target set to include more proteins outside the known protein targets in DrugBank should identify additional off-target proteins that are associated with possible ADRs. This predictive computational platform would be advantageous during drug development to predict ADRs of drug candidates such that candidates could be dropped or redesigned at an earlier stage.We have shown in this study that molecular docking may enable reliable, cost-effective, comprehensive, high-throughput screening of a drug candidate for binding across many known targets to provide predictions of clinically important ADRs. We introduce a first principles approach to File S1Figure S1, Protein structure quality control workflow used in preparation for docking calculations. Table S1, ADR groupings and their MedDRA LLT side effect components.(DOCX)Click here for additional data file.