Shiyong Liu

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.

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. Proteins 2013; 81:1980–1987.

Nonnatural protein–protein interaction-pair design by key residues grafting

Protein–protein interface design is one of the most exciting fields in protein science; however, designing nonnatural protein–protein interaction pairs remains difficult. In this article we report a de novo design of a nonnatural protein–protein interaction pair by scanning the Protein Data Bank for suitable scaffold proteins that can be used for grafting key interaction residues and can form stable complexes with the target protein after additional mutations. Using our design algorithm, an unrelated protein, rat PLCδ1-PH (pleckstrin homology domain of phospholipase C-δ1), was successfully designed to bind the human erythropoietin receptor (EPOR) after grafting the key interaction residues of human erythropoietin binding to EPOR. The designed mutants of rat PLCδ1-PH were expressed and purified to test their binding affinities with EPOR. A designed triple mutation of PLCδ1-PH (ERPH1) was found to bind EPOR with high affinity (KD of 24 nM and an IC50 of 5.7 μM) both in vitro and in a cell-based assay, respectively, although the WT PLCδ1-PH did not show any detectable binding under the assay conditions. The in vitro binding affinities of the PLCδ1-PH mutants correlate qualitatively to the computational binding affinities, validating the design and the protein–protein interaction model. The successful practice of finding a proper protein scaffold and making it bind with EPOR demonstrates a prospective application in protein engineering targeting protein–protein interfaces.

DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking

BackgroundComputational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state.ResultsThe study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results.ConclusionsA novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials.

ASPDock: protein-protein docking algorithm using atomic solvation parameters model

BackgroundAtomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes. This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. In this paper we propose an FFT-based algorithm to calculate ASP scores of protein complexes and develop an ASP-based protein-protein docking method (ASPDock).ResultsThe ASPDock is first tested on the 21 complexes whose binding free energies have been determined experimentally. The results show that the calculated ASP scores have stronger correlation (r ≈ 0.69) with the binding free energies than the pure shape complementarity scores (r ≈ 0.48). The ASPDock is further tested on a large dataset, the benchmark 3.0, which contain 124 complexes and also shows better performance than pure shape complementarity method in docking prediction. Comparisons with other state-of-the-art docking algorithms showed that ASP score indeed gives higher success rate than the pure shape complementarity score of FTDock but lower success rate than Zdock3.0. We also developed a softly restricting method to add the information of predicted binding sites into our docking algorithm. The ASP-based docking method performed well in CAPRI rounds 18 and 19.ConclusionsASP may be more accurate and physical than the pure shape complementarity in describing the feature of protein docking.

A combinatorial score to distinguish biological and nonbiological protein–protein interfaces

With the large amount of protein–protein complex structural data available, to understand the key features governing the specificity of protein–protein recognition and to define a suitable scoring function for protein–protein interaction predictions, we have analyzed the protein interfaces from geometric and energetic points of view. Atom‐based potential of mean force (PMFScore), packing density, contact size, and geometric complementarity are calculated for crystal contacts in 74 homodimers and 91 monomers, which include real biological interactions in dimers and nonbiological contacts in monomers and dimers. Simple cutoffs were developed for single and combinatorial parameters to distinguish biological and nonbiological contacts. The results show that PMFScore is a better discriminator between biological and nonbiological interfaces comparable in size. The combination of PMFScore and contact size is the most powerful pairwise discriminator. A combinatorial score (CFPScore) based on the four parameters was developed, which gives the success rate of the homodimer discrimination of 96.6% and error rate of the monomer discrimination of 6.0% and 19.8% according to Valdars and our definition, respectively. Compared with other statistical learning models, the cutoffs for the four parameters and their combinations are directly based on physical models, simple, and can be easily applied to protein–protein interface analysis and docking studies. Proteins 2006.

Quaternary Structure, Substrate Selectivity and Inhibitor Design for SARS 3C-Like Proteinase

The SARS coronavirus 3C-like proteinase is recognized as a potential drug design target for the treatment of severe acute respiratory syndrome. In the past few years, much work has been done to understand the catalytic mechanism of this target protein and to design its selective inhibitors. The protein exists as a dimer/monomer mixture in solution and the dimer was confirmed to be the active species for the enzyme reaction. Quantitative dissociation constants have been reported for the dimer by using analytic ultracentrifuge, gel filtration and enzyme assays. Though the enzyme is a cysteine protease with a chymotrypsin fold, SARS 3C-like proteinase follows the general base catalytic mechanism similar to chymotrypsin. As the enzyme can cut eleven different sites on the viral polyprotein, the substrate specificity has been studied by synthesized peptides corresponding or similar to the cleavage sites on the polyprotein. Predictive model was built for substrate structure and activity relationships and can be applied in inhibitor design. Due to the lack of potential drugs for the treatment of SARS, the discovery of inhibitors against SARS 3C-like proteinase, which can potentially be optimized as drugs appears to be highly desirable. Various groups have been working on inhibitor discovery by virtual screening, compound library screening, modification of existing compounds or natural products. High-throughput in vitro assays, auto-cleavage assays and viral replication assays have been developed for inhibition activity tests. Inhibitors with IC50 values as low as 60 nM have been reported.

Dynamic property is a key determinant for protein–protein interactions

Dynamic property is highly correlated with the biological functions of macromolecules, such as the activity and specificity of enzymes and the allosteric regulation in the signal transduction process. Applications of the dynamic property to protein function researches have been discussed and encouraging progresses have been achieved, for example, in enzyme activity and protein–protein docking studies. However, how the global dynamic property contributes to protein–protein interaction was still unclear. We have studied the dynamic property in protein–protein interactions based on Gaussian Network Model and applied it to classify biological and nonbiological protein–protein complexes in crystal structures. The global motion correlation between residues from the two protomers was found to be remarkably different for biological and nonbiological complexes. This correlation has been used to discriminate biological and nonbiological complexes in crystal and gave a classification rate of 86.9% in the cross‐validation test. The innovation of this feature is that it is a global dynamic property which does not rely directly on the interfacial properties of the complex. In addition, the correlation of the global motions was found to be weakly correlated with the dissociation rate constant of protein complexes. We suggest that the dynamic property is a key determinant for protein–protein interaction, which can be used to discriminate native and crystal complexes and potentially be applied in protein–protein dynamic rate constants estimations. Proteins 2008.

Break CDK2/Cyclin E1 interface allosterically with small peptides.

Most inhibitors of Cyclin-dependent kinase 2 (CDK2) target its ATP-binding pocket. It is difficult, however, to use this pocket to design very specific inhibitors because this catalytic pocket is highly conserved in the protein family of CDKs. Here we report some short peptides targeting a noncatalytic pocket near the interface of the CDK2/Cyclin complex. Docking and molecular dynamics simulations were used to select the peptides, and detailed dynamical network analysis revealed that these peptides weaken the complex formation via allosteric interactions. Our experiments showed that upon binding to the noncatalytic pocket, these peptides break the CDK2/Cyclin complex partially and diminish its kinase activity in vitro. The binding affinity of these peptides measured by Surface Plasmon Resonance can reach as low as 0.5 µM.

The dataset for protein–RNA binding affinity

We have developed a non‐redundant protein–RNA binding benchmark dataset derived from the available protein–RNA structures in the Protein Database Bank. It consists of 73 complexes with measured binding affinity. The experimental conditions (pH and temperature) for binding affinity measurements are also listed in our dataset. This binding affinity dataset can be used to compare and develop protein–RNA scoring functions. The predicted binding free energy of the 73 complexes from three available scoring functions for protein–RNA docking has a low correlation with the binding Gibbs free energy calculated from Kd.