Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

Single cell sequencing technologies have rapidly advanced in the last decade and are increasingly applied to gain unprecedented insights by deconstructing complex biology to its fundamental unit, the individual cell. First developed for measurement of gene expression, single cell sequencing approaches have evolved to allow simultaneous profiling of multiple additional features, including chromatin accessibility within the nucleus and protein expression at the cell surface. These multi-omic approaches can now further be applied to cells in situ, capturing the spatial context within which their biology occurs. To extract insights from these complex datasets, new computational tools have facilitated the integration of information across different data types and the use of machine learning approaches. Here, we summarize current experimental and computational methods for generation and integration of single cell multi-omic datasets. We focus on opportunities for multi-omic single cell sequencing to augment therapeutic development for kidney disease, including applications for biomarkers, disease stratification and target identification.

An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.

High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides

MOTIVATION

The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides.

RESULTS

Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides.

AVAILABILITY AND IMPLEMENTATION

Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrating ab initio and template-based algorithms for protein-protein complex structure prediction

MOTIVATION

Template-based and template-free methods have both been widely used in predicting the structures of protein-protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein-protein complex structure prediction.

RESULTS

Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein-protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently.

AVAILABILITY AND IMPLEMENTATION

ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Performance of ZDOCK and IRAD in CAPRI rounds 39-45

We report docking performance on the six targets of Critical Assessment of PRedicted Interactions (CAPRI) rounds 39-45 that involved heteromeric protein-protein interactions and had the solved structures released since the rounds were held. Our general strategy involved protein-protein docking using ZDOCK, reranking using IRAD, and structural refinement using Rosetta. In addition, we made extensive use of experimental data to guide our docking runs. All the experimental information at the amino-acid level proved correct. However, for two targets, we also used protein-complex structures as templates for modeling interfaces. These resulted in incorrect predictions, presumably due to the low sequence identity between the targets and templates. Albeit a small number of targets, the performance described here compared somewhat less favorably with our previous CAPRI reports, which may be due to the CAPRI targets being increasingly challenging.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Computational investigation into the fluorescence of luciferin analogues

Luciferin analogues that display bioluminescence at specific wavelengths can broaden the scope of imaging and biological assays, but the need to design and synthesize many new analogues can be time-consuming. Employing a collection of previously synthesized and characterized aminoluciferin analogues, we demonstrate that computational TD-DFT methods can accurately reproduce and further explain the experimentally measured fluorescence wavelengths. The best computational approach yields a correlation with experiment of r = 0.98, which we expect to guide and accelerate the further development of luciferin analogues. © 2018 Wiley Periodicals, Inc.

Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking

Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection

MOTIVATION

In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction.

RESULTS

Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100.

AVAILABILITY AND IMPLEMENTATION

IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/∼SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request.

CONTACT

moal@ebi.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Performance of ZDOCK and IRAD in CAPRI rounds 28-34

We report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking. Proteins 2017; 85:408-416. © 2016 Wiley Periodicals, Inc.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

Combining Quantum Mechanics Methods with Molecular Mechanics Methods in ONIOM

The purpose of this paper is 2-fold. First, we present several extensions to the ONIOM(QM:MM) scheme. In its original formulation, the electrostatic interaction between the regions is included at the classical level. Here we present the extension to electronic embedding. We show how the behavior of ONIOM with electronic embedding can be more stable than QM/MM with electronic embedding. We also investigate the link atom correction, which is implicit in ONIOM but not in QM/MM. Second, we demonstrate some of the practical aspects of ONIOM(QM:MM) calculations. Specifically, we show that the potential surface can be discontinuous when there is bond breaking and forming closer than three bonds from the MM region.

Performance of ZDOCK in CAPRI rounds 20-26

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.

Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.

Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes

Background

T cell receptors (TCRs) can recognize diverse lipid and metabolite antigens presented by MHC-like molecules CD1 and MR1, and the molecular basis of many of these interactions has not been determined. Here we applied our protein docking algorithm TCRFlexDock, previously developed to perform docking of TCRs to peptide-MHC (pMHC) molecules, to predict the binding of αβ and γδ TCRs to CD1 and MR1, starting with the structures of the unbound molecules.

Results

Evaluating against TCR-CD1d complexes with crystal structures, we achieved near-native structures in the top 20 models for two out of four cases, and an acceptable-rated prediction for a third case. We also predicted the structure of an interaction between a MAIT TCR and MR1-antigen that has not been structurally characterized, yielding a top-ranked model that agreed remarkably with a characterized TCR-MR1-antigen structure that has a nearly identical TCR α chain but a different β chain, highlighting the likely dominance of the conserved α chain in MR1-antigen recognition. Docking performance was improved by re-training our scoring function with a set of TCR-pMHC complexes, and for a case with an outlier binding mode, we found that alternative docking start positions improved predictive accuracy. We then performed unbound docking with two mycolyl-lipid specific TCRs that recognize lipid-bound CD1b, which represent a class of interactions that is not structurally characterized. Highly-ranked models of these complexes showed remarkable agreement between their binding topologies, as expected based on their shared germline sequences, while differences in residue-level interactions with their respective antigens point to possible mechanisms underlying their distinct specificities.

Conclusions

Together these results indicate that flexible docking simulations can provide accurate models and atomic-level insights into TCR recognition of MHC-like molecules presenting lipid and other small molecule antigens.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-319) contains supplementary material, which is available to authorized users.

The HP1 homolog rhino anchors a nuclear complex that suppresses piRNA precursor splicing

piRNAs guide an adaptive genome defense system that silences transposons during germline development. The Drosophila HP1 homolog Rhino is required for germline piRNA production. We show that Rhino binds specifically to the heterochromatic clusters that produce piRNA precursors, and that binding directly correlates with piRNA production. Rhino colocalizes to germline nuclear foci with Rai1/DXO-related protein Cuff and the DEAD box protein UAP56, which are also required for germline piRNA production. RNA sequencing indicates that most cluster transcripts are not spliced and that rhino, cuff, and uap56 mutations increase expression of spliced cluster transcripts over 100-fold. LacI::Rhino fusion protein binding suppresses splicing of a reporter transgene and is sufficient to trigger piRNA production from a trans combination of sense and antisense reporters. We therefore propose that Rhino anchors a nuclear complex that suppresses cluster transcript splicing and speculate that stalled splicing differentiates piRNA precursors from mRNAs.

Blind prediction of interfacial water positions in CAPRI

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 Å, 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.

ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers

SUMMARY

Protein-protein interactions are essential to cellular and immune function, and in many cases, because of the absence of an experimentally determined structure of the complex, these interactions must be modeled to obtain an understanding of their molecular basis. We present a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers. With a goal of providing an accessible and intuitive interface, we provide options for users to guide the scoring and the selection of output models, in addition to dynamic visualization of input structures and output docking models. This server enables the research community to easily and quickly produce structural models of protein-protein complexes and symmetric multimers for their own analysis.

AVAILABILITY

The ZDOCK server is freely available to all academic and non-profit users at: http://zdock.umassmed.edu. No registration is required.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

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.

Binding interface prediction by combining protein-protein docking results

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.

Evaluating template-based and template-free protein-protein complex structure prediction

We compared the performance of template-free (docking) and template-based methods for the prediction of protein-protein complex structures. We found similar performance for a template-based method based on threading (COTH) and another template-based method based on structural alignment (PRISM). The template-based methods showed similar performance to a docking method (ZDOCK) when the latter was allowed one prediction for each complex, but when the same number of predictions was allowed for each method, the docking approach outperformed template-based approaches. We identified strengths and weaknesses in each method. Template-based approaches were better able to handle complexes that involved conformational changes upon binding. Furthermore, the threading-based and docking methods were better than the structural-alignment-based method for enzyme-inhibitor complex prediction. Finally, we show that the near-native (correct) predictions were generally not shared by the various approaches, suggesting that integrating their results could be the superior strategy.

Exploring angular distance in protein-protein docking algorithms

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.

UAP56 couples piRNA clusters to the perinuclear transposon silencing machinery

piRNAs silence transposons during germline development. In Drosophila, transcripts from heterochromatic clusters are processed into primary piRNAs in the perinuclear nuage. The nuclear DEAD box protein UAP56 has been previously implicated in mRNA splicing and export, whereas the DEAD box protein Vasa has an established role in piRNA production and localizes to nuage with the piRNA binding PIWI proteins Ago3 and Aub. We show that UAP56 colocalizes with the cluster-associated HP1 variant Rhino, that nuage granules containing Vasa localize directly across the nuclear envelope from cluster foci containing UAP56 and Rhino, and that cluster transcripts immunoprecipitate with both Vasa and UAP56. Significantly, a charge-substitution mutation that alters a conserved surface residue in UAP56 disrupts colocalization with Rhino, germline piRNA production, transposon silencing, and perinuclear localization of Vasa. We therefore propose that UAP56 and Vasa function in a piRNA-processing compartment that spans the nuclear envelope.

Prediction of protein-protein binding free energies

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.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology

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.

Integrating atom-based and residue-based scoring functions for protein-protein docking

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.

A machine learning approach for the prediction of protein surface loop flexibility

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. 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 (SVMs). 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 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.

Protein-protein docking benchmark version 4.0

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/.

Performance of ZDOCK and ZRANK in CAPRI rounds 13-19

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.

Matrix metalloproteinase 2 inhibition: combined quantum mechanics and molecular mechanics studies of the inhibition mechanism of (4-phenoxyphenylsulfonyl)methylthiirane and its oxirane analogue

The inhibition mechanism of matrix metalloproteinase 2 (MMP2) by the selective inhibitor (4-phenoxyphenylsulfonyl)methylthiirane (SB-3CT) and its oxirane analogue is investigated computationally. The inhibition mechanism involves C-H deprotonation with concomitant opening of the three-membered heterocycle. SB-3CT was docked into the active site of MMP2, followed by molecular dynamics simulation to prepare the complex for combined quantum mechanics and molecular mechanics (QM/MM) calculations. QM/MM calculations with B3LYP/6-311+G(d,p) for the QM part and the AMBER force field for the MM part were used to examine the reaction of these two inhibitors in the active site of MMP2. The calculations show that the reaction barrier for transformation of SB-3CT is 1.6 kcal/mol lower than its oxirane analogue, and the ring-opening reaction energy of SB-3CT is 8.0 kcal/mol more exothermic than that of its oxirane analogue. Calculations also show that protonation of the ring-opened product by water is thermodynamically much more favorable for the alkoxide obtained from the oxirane than for the thiolate obtained from the thiirane. A six-step partial charge fitting procedure is introduced for the QM/MM calculations to update atomic partial charges of the quantum mechanics region and to ensure consistent electrostatic energies for reactants, transition states, and products.

The application of the ONIOM hybrid method to the cycloaddition reactions of bromo-substituted 2(H)-pyran-2-ones

We investigate the performance of Our own N-layered Integrated molecular Orbital and molecular Mechanics (ONIOM) hybrid computational method applied to Diels–Alder reactions of bromo-2(H)-pyran-2-ones, combining the B3LYP/6–31G(d) method with a variety of low-level methods. We show that ONIOM is able to reproduce full B3LYP calculations, including the prediction of the stereoselectivity, which requires accurate potentials. We focus on the various ways in which the performance and potential errors of ONIOM can be analyzed, and show that the best method combination depends on the property one is interested in.

Computational investigation of irreversible inactivation of the zinc-dependent protease carboxypeptidase A

Zinc proteases are ubiquitous and the zinc ion plays a central function in the catalytic mechanism of these enzymes. A novel class of mechanism-based inhibitors takes advantage of the zinc ion chemistry in carboxypeptidase A (CPA) to promote covalent attachment of an inhibitor to the carboxylate of Glu-270, resulting in irreversible inhibition of the enzyme. The effect of the active site zinc ion on irreversible inactivation of CPA was probed by molecular orbital (MO) calculations on a series of active site models and the Cl(-) + CH(3)Cl S(N)2 reaction fragment. Point charge models representing the active site reproduced energetics from full MO calculations at 12.0 A separation between the zinc and the central carbon of the S(N)2 reaction, but at 5.0 A polarization played an important role in moderating barrier suppression. ONIOM MO/MO calculations that included the residues within 10 A of the active site zinc suggest that about 75% of the barrier suppression arises from the zinc ion and its ligands. A model of the pre-reactive complex of the 2-benzyl-3-iodopropanoate inactivator with CPA was constructed from the X-ray structure of l-phenyl lactate bound in the active site of the enzyme. The model was fully solvated and minimized by using the AMBER force field to generate the starting structure for the ONIOM QM/MM calculations. Optimization of this structure led to the barrierless S(N)2 displacement of the iodide of the inhibitor by Glu-270, assisted by interaction of the zinc ion with the leaving group. The resulting product is in good agreement with the X-ray structure of the covalently modified enzyme obtained by irreversible inhibition of CPA by 2-benzyl-3-iodopropanoate.

Transition States of Binap–Rhodium(I)-Catalyzed Asymmetric Hydrogenation: Theoretical Studies on the Origin of the Enantioselectivity

By using the hybrid IMOMM(B3LYP:MM3) method, we examined the binap-Rh(I)-catalyzed oxidative-addition and insertion steps of the asymmetric hydrogenation of the enamide 2-acetylamino-3-phenylacrylic acid. We report a path that is energetically more favorable for the major enantiomer than for the minor enantiomer. This path follows the "lock-and-key" motif and leads to the major enantiomeric product via an energetically favorable binap-dihydride-Rh(III)-enamide complex. Our theoretical results are consistent with the mechanism that takes place via Rh(III) dihydride formation, that is, oxidative addition of H2 followed by enamide insertion.

Is the Protein Surrounding the Active Site Critical for Hydrogen Peroxide Reduction by Selenoprotein Glutathione Peroxidase? An ONIOM Study

In this ONIOM(QM:MM) study, we evaluate the role of the protein surroundings in the mechanism of H2O2 reduction catalyzed by the glutathione peroxidase enzyme, using the whole monomer (3113 atoms in 196 amino acid residues) as a model. A new optimization scheme that allows the full optimization of transition states for large systems has been utilized. It was found that in the presence of the surrounding protein the optimized active site structure bears a closer resemblance to the one in the X-ray structure than that without the surrounding protein. H2O2 reduction occurs through a two-step mechanism. In the first step, the selenolate anion (E-Se(-)) formation occurs with a barrier of 16.4 kcal/mol and is endothermic by 12.0 kcal/mol. The Gln83 residue plays the key role of the proton abstractor, which is in line with the experimental suggestion. In the second step, the O-O bond is cleaved, and selenenic acid (R-Se-OH) and a water molecule are formed. The calculated barrier for this process is 6.0 kcal/mol, and it is exothermic by 80.9 kcal/mol. The overall barrier of 18.0 kcal/mol for H2O2 reduction is in reasonable agreement with the experimentally measured barrier of 14.9 kcal/mol. The protein surroundings has been calculated to exert a net effect of only 0.70 kcal/mol (in comparison to the "active site only" model including solvent effects) on the overall barrier, which is most likely due to the active site being located at the enzyme surface.

Lysine carboxylation in proteins: OXA-10 β-lactamase

An increasing number of proteins are being shown to have an N(zeta)-carboxylated lysine in their structures, a posttranslational modification of proteins that proceeds without the intervention of a specific enzyme. The role of the carboxylated lysine in these proteins is typically structural (hydrogen bonding or metal coordination). However, carboxylated lysines in the active sites of OXA-10 and OXA-1 beta-lactamases and the sensor domain of BlaR signal-transducer protein serve in proton transfer events required for the functions of these proteins. These examples demonstrate the utility of this unusual amino acid in acid-base chemistry, in expansion of function beyond those of the 20 standard amino acids. In this study, the ONIOM quantum-mechanical/molecular-mechanical (QM/MM) method is used to study the carboxylation of lysine in the OXA-10 beta-lactamase. Lys-70 and the active site of the OXA-10 beta-lactamase were treated with B3LYP/6-31G(d,p) density functional calculations and the remainder of the enzyme with the AMBER molecular mechanics force field. The barriers for unassisted carboxylation of neutral lysine by carbon dioxide or bicarbonate are high. However, when the reaction with CO2 is catalyzed by a molecule of water in the active site, it is exothermic by about 13 kcal/mol, with a barrier of approximately 14 kcal/mol. The calculations show that the carboxylation and decarboxylation of Lys-70 are likely to be accompanied by deprotonation and protonation of the carbamate, respectively. The analysis may also be relevant for other proteins with carboxylated lysines, a feature that may be more common in nature than previously appreciated.

Elucidation of the Mechanism of Selenoprotein Glutathione Peroxidase (GPx)-Catalyzed Hydrogen Peroxide Reduction by Two Glutathione Molecules: A Density Functional Study

The mechanism of the hydrogen peroxide reduction by two molecules of glutathione catalyzed by the selenoprotein glutatione peroxidase (GPx) has been computationally studied. It has been shown that the first elementary reaction of this process, (E-SeH) + H(2)O(2) --> (E-SeOH) + H(2)O (1), proceeds via a stepwise pathway with the overall barrier of 17.1 kcal/mol, which is in good agreement with the experimental barrier of 14.9 kcal/mol. During reaction 1, the Gln83 residue has been found to play a key role as a proton acceptor, which is consistent with experiments. The second elementary reaction, (E-SeOH) + GSH --> (E-Se-SG) + HOH (2), proceeds with the barrier of 17.9 kcal/mol. The last elementary reaction, (E-Se-SG) + GSH --> (E-SeH) + GS-SG (3), is initiated with the coordination of the second glutathione molecule. The calculations clearly suggest that the amide backbone of the Gly50 residue directly participates in this reaction and the presence of two water molecules is absolutely vital for the reaction to occur. This reaction proceeds with the barrier of 21.5 kcal/mol and is suggested to be a rate-determining step of the entire GPx-catalyzed reaction H(2)O(2) + 2GSH --> GS-SG + 2H(2)O. The results discussed in the present study provide intricate details of every step of the catalytic mechanism of the GPx enzyme and are in good general agreement with experimental findings and suggestions.

Density Functional Study of the Optical Rotation of Glucose in Aqueous Solution

Optical rotation values were calculated for the eight most abundant structures of glucose in aqueous solution, following the TD-DFT/GIAO approach for the property and the PCM description for the solvent. The results show that all alpha structures give a large positive contribution to the OR property, while the beta structures give both positive and negative contributions. The good agreement of the calculated OR, obtained as a Boltzmann average of the property of the eight conformers, with experimental data proves the validity of the quantum-mechanical approach and of the solvent modelization.