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We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naïve Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.

The growing structural coverage of proteomes is making structural comparison a powerful tool for function annotation. Such template-based approaches are based on the observation that structural similarity is often sufficient to infer similar function. However, it seems clear that, in addition to structural similarity, the specific characteristics of a given protein should also be taken into account in predicting function. Here we describe PredUs 2.0, a method to predict regions on a protein surface likely to bind other proteins, that is, interfacial residues. PredUs 2.0 is based on the PredUs method that is entirely template-based and uses known binding sites in structurally similar proteins to predict interfacial residues. PredUs 2.0 uses a Bayesian approach to combine the template-based scoring of PredUs with a score that reflects the propensities of individual amino acids to be in interfaces. PredUs 2.0 includes a novel protein size dependent metric to determine the number of residues that should be reported as interfacial. PredUs 2.0 significantly outperforms PredUs as well as other published interface prediction methods.

We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.

Epigenetic dysregulation is a hallmark of germinal center derived B cell lymphomas. In particular, the monoallelic mutations in histone acetyltransferases (HATs), p300 and CBP are present in approximately 40% of DLBCL and FL patients. The haploinsufficiency of CBP leads to loss of MHC which contributes to both immune escape and lymphomagenesis. The intact wild-type allele offers an opportunity to leverage the normal enzyme to overcome the pathogenic impact of the mutated allele. We reported our lead compound YF2 out of a library (n=70) of HAT activators led to significant cytotoxic effect in lymphoma cell lines compared to its parent compound CTPB, which is not cell membrane permeable. YF2 induced p300-mediated histone acetylation as well as p53 acetylation in cell-free acetylation assay. YF2 also induced the acetylation of H3K14 and H3K27 at the cellular level. Since CREBBP mutations are more frequent than EP300 and are mutually exclusive in patients, it is of high insterest to determine if YF2 affects CBP-mediated acetylation. We measured CBP mediated histone acetylation in a cell free assay by combining the catalytic core of CBP, histone H3 and acetyl-CoA. YF2 increased the level of CBP-mediated Ac-H3K27 (EC50=175.4nM) and Ac-H3K18 (EC50=1330 nM). Studies have shown that in mice lacking Crebbp there is reduced MHC II surface expression on GC cells impairing antigen presentation. To determine if YF2 affects the expression of MHC class I, II and related genes in B cell lymphoma cells, qPCR based gene expression profiling was performed pre and post YF2 exposure. YF2 significantly induces mRNA expression level of HLA-DMA (2.9 fold), PSMB10 (2 fold), and reduced the expression of HLA-G (10 fold). Encode ChIP-seq data confirms enrichement of CBP and p300 in the promotor regions of HLA-DMA and PSMB10 in germinal center B cells, indicating a regulatory effect of YF2 on these genes via HAT modulation. HLA‐G is known to inhibit NK‐cell- and CTL‐mediated cytotoxicity, therefore a reduction in HLA-G may release the inhibition of NK-cell and CTL recognition. Taken together, YF2 may modulate p300 and CBP mediated HLA and related gene expression to induce improved immune surveillance. YF2 is selectively cytotoxic in EP300 mutated B cell lymphoma lines. Since HDAC inhibitors have been approved in the clinic for patients with T cell lymphoma (TCL), we further tested the cytotoxic effect of YF2 in 6 TCL cell lines. Interestingly, YF2 is equivalently cytotoxic in TCL cell lines with IC50 ranging from 4.4 to 14 uM compared to the IC50 values in EP300 mutated BCL lines (5 to 11 uM). Additionally, YF2 induces apoptosis and G1 arrest in a dose dependent manner demonstrated by flow cytometry. We collected three primary lymphoma patient samples including one FL sample carrying CREBBP and p53 mutations and observed that YF2 induced cell death in patient samples, but not in PBMCs from 3 healthy donors, indicating a good therapeutic window. Robust synergy with combination of YF2 and romidepsin was observed in 100% (6/6) EP300 mutated BCL, 40% (2/5) EP300 wild-type BCL and 60% (3/5) TCL cells (EOB = 28). In vivo, SCID‐beige mice were xenografted with SUDHL6 and treated with either vehicle, YF2 40mg/kg, romidepsin 2mg/kg or the combination. Single agent YF2 led to equivalent tumor growth delay as FDA-approved drug romidepsin. The YF2 plus romidepsin combination led to significant tumor growth delay compared to the single agents and control. The efficacy of YF2 and romidepsin in PDX model will be determined in an ongoing study. Given the fact that the RING domain of p300 functions as an inhibitory domain to regulate p300 activity, we hypothesized that YF2 may bind to the RING domain, change the confirmation to facilitate access to the HAT substrate-binding pocket. As predicted by LBias, small molecule binding sites on p300 are on the RING domain and bromodomain. We further checked the potential binding site by docking YF2-like small molecules on the RING domain. We discovered the potential binding site of YF2 in the pocket of RING domain. Kinome screening showed YF2 has no off-target effects on 97 kinases. Ongoing experiments will determine the binding of YF2 to the p300 and CBP catalytic cores. In summary, our lead compound YF2 induces HAT enzymes by activating not only p300 but also CBP mediated acetylation of histone. Modulating HLA genes by YF2 may potentially offer an opportunity to reverse immune evasion adopted by lymphoma cells as a mechanism for survival. Disclosures O'Connor: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; ADCT Therapeutics, Affimed, Agensys, Merck, Seattle Genetics, Spectrum, Trillium, and Verastem Oncology.: Research Funding; TG Therapeutics: Other: Travel Support, Research Funding. Arancio:Appia Pharmaceuticals: Research Funding; Neurokine Therapeutics: Other: Founder. Amengual:Epizyme: Speakers Bureau; Appia Pharmaceuticals: Research Funding.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 A, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

We report the performance of our approaches for protein-protein docking and interface analysis in CAPRI rounds 20-26. At the core of our pipeline was the ZDOCK program for rigid-body protein-protein docking. We then reranked the ZDOCK predictions using the ZRANK or IRAD scoring functions, pruned and analyzed energy landscapes using clustering, and analyzed the docking results using our interface prediction approach RCF. When possible, we used biological information from the literature to apply constraints to the search space during or after the ZDOCK runs. For approximately half of the standard docking challenges we made at least one prediction that was acceptable or better. For the scoring challenges we made acceptable or better predictions for all but one target. This indicates that our scoring functions are generally able to select the correct binding mode.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Most scoring functions for protein–protein docking algorithms are either atom-based or residue-based, with the former being able to produce higher quality structures and latter more tolerant to conformational changes upon binding. Earlier, we developed the ZRANK algorithm for reranking docking predictions, with a scoring function that contained only atom-based terms. Here we combine ZRANK's atom-based potentials with five residue-based potentials published by other labs, as well as an atom-based potential IFACE that we published after ZRANK. We simultaneously optimized the weights for selected combinations of terms in the scoring function, using decoys generated with the protein–protein docking algorithm ZDOCK. We performed rigorous cross validation of the combinations using 96 test cases from a docking benchmark. Judged by the integrative success rate of making 1000 predictions per complex, addition of IFACE and the best residue-based pair potential reduced the number of cases without a correct prediction by 38 and 27% relative to ZDOCK and ZRANK, respectively. Thus combination of residue-based and atom-based potentials into a scoring function can improve performance for protein–protein docking. The resulting scoring function is called IRAD (integration of residue- and atom-based potentials for docking) and is available at http://zlab.umassmed.edu.

Proteins often undergo conformational changes when binding to each other. A major fraction of backbone conformational changes involves motion on the protein surface, particularly in loops. Proper accounting for the motion of protein surface loops represents a challenge for protein-protein docking algorithms. A first step in addressing this challenge is to distinguish protein surface loops that are likely to undergo backbone conformational changes upon protein-protein binding (mobile loops) from those that are not (stationary loops). In this study we developed a machine learning strategy based on support vector machines (SVM). Our SVM uses three features of loop residues in the unbound protein structures — Ramachandran angles, crystallographic B-factors and relative accessible surface area — to distinguish mobile loops from stationary ones. This method yields an average prediction accuracy of 75.3% compared with a random prediction accuracy of 50%, and an average of 0.79 area under the receiver operating characteristic (ROC) curve, using 4-fold cross-validation. Testing the method on an independent dataset, we obtained a prediction accuracy of 70.5%. Finally, we applied the method to 11 complexes that involve members from the Ras superfamily and achieved prediction accuracy of 92.8% for the Ras superfamily proteins and 74.4% for their binding partners.

We have assembled a nonredundant set of 144 protein–protein complexes that have high-resolution structures available for both the complexes and their unbound components, and for which dissociation constants have been measured by biophysical methods. The set is diverse in terms of the biological functions it represents, with complexes that involve G-proteins and receptor extracellular domains, as well as antigen/antibody, enzyme/inhibitor, and enzyme/substrate complexes. It is also diverse in terms of the partners' affinity for each other, with Kd ranging between 10−5 and 10−14M. Nine pairs of entries represent closely related complexes that have a similar structure, but a very different affinity, each pair comprising a cognate and a noncognate assembly. The unbound structures of the component proteins being available, conformation changes can be assessed. They are significant in most of the complexes, and large movements or disorder-to-order transitions are frequently observed. The set may be used to benchmark biophysical models aiming to relate affinity to structure in protein–protein interactions, taking into account the reactants and the conformation changes that accompany the association reaction, instead of just the final product.

We updated our protein–protein docking benchmark to include complexes that became available since our previous release. As before, we only considered high-resolution complex structures that are nonredundant at the family–family pair level, for which the X-ray or NMR unbound structures of the constituent proteins are also available. Benchmark 4.0 adds 52 new complexes to the 124 cases of Benchmark 3.0, representing an increase of 42%. Thus, benchmark 4.0 provides 176 unbound–unbound cases that can be used for protein–protein docking method development and assessment. Seventeen of the newly added cases are enzyme-inhibitor complexes, and we found no new antigen-antibody complexes. Classifying the new cases according to expected difficulty for protein–protein docking algorithms gives 33 rigid body cases, 11 cases of medium difficulty, and 8 cases that are difficult. Benchmark 4.0 listings and processed structure files are publicly accessible at http://zlab.umassmed.edu/benchmark/ Proteins 2010. © 2010 Wiley-Liss, Inc.

We report the performance of the ZDOCK and ZRANK algorithms in CAPRI rounds 13-19 and introduce a novel measure atom contact frequency (ACF). To compute ACF, we identify the residues that most often make contact with the binding partner in the complete set of ZDOCK predictions for each target. We used ACF to predict the interface of the proteins, which, in combination with the biological data available in the literature, is a valuable addition to our docking pipeline. Furthermore, we incorporated a straightforward and efficient clustering algorithm with two purposes: (1) to determine clusters of similar docking poses (corresponding to energy funnels) and (2) to remove redundancies from the final set of predictions. With these new developments, we achieved at least one acceptable prediction for targets 29 and 36, at least one medium-quality prediction for targets 41 and 42, and at least one high-quality prediction for targets 37 and 40; thus, we succeeded for six out of a total of 12 targets.

We present an evaluation of our protein-protein docking approach using the ZDOCK and ZRANK algorithms, in combination with structural clustering and filtering, utilizing biological data in Rounds 6-11 of the CAPRI docking experiment. We achieved at least one prediction of acceptable accuracy for five of six targets submitted. In addition, two targets resulted in medium-accuracy predictions. In the new scoring portion of the CAPRI exercise, we were able to attain at least one acceptable prediction for the three targets submitted and achieved three medium-accuracy predictions for Target 26. Scoring was performed using ZRANK, a new algorithm for reranking initial-stage docking predictions using a weighted energy function and no structural refinement. Here we outline a practical and successful docking strategy, given limited prior biological knowledge of the complex to be predicted.

We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein-protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision-recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta-PPISP (AUC = 0.43), which is one of the best monomer-based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta-PPISP and another monomer-based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta-PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues.

We present a two-stage hybrid-resolution approach for rigid-body protein-protein docking. The first stage is carried out at low-resolution (15°) angular sampling. In the second stage, we sample promising regions from the first stage at a higher resolution of 6°. The hybrid-resolution approach produces the same results as a 6° uniform sampling docking run, but uses only 17% of the computational time. We also show that the angular distance can be used successfully in clustering and pruning algorithms, as well as the characterization of energy funnels. Traditionally the root-mean-square-distance is used in these algorithms, but the evaluation is computationally expensive as it depends on both the rotational and translational parameters of the docking solutions. In contrast, the angular distances only depend on the rotational parameters, which are generally fixed for all docking runs. Hence the angular distances can be pre-computed, and do not add computational time to the post-processing of rigid-body docking results.

We present an energy function for predicting binding free energies of protein–protein complexes, using the three-dimensional structures of the complex and unbound proteins as input. Our function is a linear combination of nine terms and achieves a correlation coefficient of 0.63 with experimental measurements when tested on a benchmark of 144 complexes using leave-one-out cross validation. Although we systematically tested both atomic and residue-based scoring functions, the selected function is dominated by residue-based terms. Our function is stable for subsets of the benchmark stratified by experimental pH and extent of conformational change upon complex formation, with correlation coefficients ranging from 0.61 to 0.66.

International audience; The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

We have assembled a nonredundant set of 144 protein-protein complexes that have high-resolution structures available for both the complexes and their unbound components, and for which dissociation constants have been measured by biophysical methods. The set is diverse in terms of the biological functions it represents, with complexes that involve G-proteins and receptor extracellular domains, as well as antigen/antibody, enzyme/inhibitor, and enzyme/substrate complexes. It is also diverse in terms of the partners' affinity for each other, with K(d) ranging between 10(-5) and 10(-14) M. Nine pairs of entries represent closely related complexes that have a similar structure, but a very different affinity, each pair comprising a cognate and a noncognate assembly. The unbound structures of the component proteins being available, conformation changes can be assessed. They are significant in most of the complexes, and large movements or disorder-to-order transitions are frequently observed. The set may be used to benchmark biophysical models aiming to relate affinity to structure in protein-protein interactions, taking into account the reactants and the conformation changes that accompany the association reaction, instead of just the final product.

We present version 3.0 of our publicly available protein-protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, Structural Classification of Proteins (Murzin et al., J Mol Biol 1995;247:536-540) was used to remove redundant test cases. The 124 unbound-unbound test cases in Benchmark 3.0 are classified into 88 rigid-body cases, 19 medium-difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at http://zlab.bu.edu/benchmark.