Chengfei Yan

A chemical genetic approach demonstrates that MPK3/MPK6 activation and NADPH oxidase-mediated oxidative burst are two independent signaling events in plant immunity

Plant recognition of pathogen-associated molecular patterns (PAMPs) such as bacterial flagellin-derived flg22 triggers rapid activation of mitogen-activated protein kinases (MAPKs) and generation of reactive oxygen species (ROS). Arabidopsis has at least four PAMP/pathogen-responsive MAPKs: MPK3, MPK6, MPK4 and MPK11. It was speculated that these MAPKs may function downstream of ROS in plant immunity because of their activation by exogenously added H2 O2 . MPK3/MPK6 or their orthologs in other plant species have also been reported to be involved in the ROS burst from the plant respiratory burst oxidase homolog (Rboh) of the human neutrophil gp91phox. However, detailed genetic analysis is lacking. Using a chemical genetic approach, we generated a conditional loss-of-function mpk3 mpk6 double mutant. Consistent with results obtained using a conditionally rescued mpk3 mpk6 double mutant generated previously, the results obtained using the new conditional loss-of-function mpk3 mpk6 double mutant demonstrate that the flg22-triggered ROS burst is independent of MPK3/MPK6. In Arabidopsis mutants lacking a functional AtRbohD, the flg22-induced ROS burst was completely blocked. However, activation of MPK3/MPK6 was not affected. Based on these results, we conclude that the rapid ROS burst and MPK3/MPK6 activation are two independent early signaling events in plant immunity, downstream of FLS2. We also found that MPK4 negatively affects the flg22-induced ROS burst. In addition, salicylic acid pre-treatment enhances the AtRbohD-mediated ROS burst, which is again independent of MPK3/MPK6 based on analysis of the mpk3 mpk6 double mutant. The establishment of an mpk3 mpk6 double mutant system using a chemical genetic approach provides a powerful tool to investigate the function of MPK3/MPK6 in the plant defense signaling pathway.

Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction

Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.

Predicting peptide binding sites on protein surfaces by clustering chemical interactions

Short peptides play important roles in cellular processes including signal transduction, immune response, and transcription regulation. Correct identification of the peptide binding site on a given protein surface is of great importance not only for mechanistic investigation of these biological processes but also for therapeutic development. In this study, we developed a novel computational approach, referred to as ACCLUSTER, for predicting the peptide binding sites on protein surfaces. Specifically, we use the 20 standard amino acids as probes to globally scan the protein surface. The poses forming good chemical interactions with the protein are identified, followed by clustering with the density‐based spatial clustering of applications with noise technique. Finally, these clusters are ranked based on their sizes. The cluster with the largest size is predicted as the putative binding site. Assessment of ACCLUSTER was performed on a diverse test set of 251 nonredundant protein–peptide complexes. The results were compared with the performance of POCASA, a pocket detection method for ligand binding site prediction. Peptidb, another protein–peptide database that contains both bound structures and unbound or homologous structures was used to test the robustness of ACCLUSTER. The performance of ACCLUSTER was also compared with PepSite2 and PeptiMap, two recently developed methods developed for identifying peptide binding sites. The results showed that ACCLUSTER is a promising method for peptide binding site prediction. Additionally, ACCLUSTER was also shown to be applicable to nonpeptide ligand binding site prediction.

Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark.

In this study, we use the recently released 2012 Community Structure-Activity Resource (CSAR) data set to evaluate two knowledge-based scoring functions, ITScore and STScore, and a simple force-field-based potential (VDWScore). The CSAR data set contains 757 compounds, most with known affinities, and 57 crystal structures. With the help of the script files for docking preparation, we use the full CSAR data set to evaluate the performances of the scoring functions on binding affinity prediction and active/inactive compound discrimination. The CSAR subset that includes crystal structures is used as well, to evaluate the performances of the scoring functions on binding mode and affinity predictions. Within this structure subset, we investigate the importance of accurate ligand and protein conformational sampling and find that the binding affinity predictions are less sensitive to non-native ligand and protein conformations than the binding mode predictions. We also find the full CSAR data set to be more challenging in making binding mode predictions than the subset with structures. The script files used for preparing the CSAR data set for docking, including scripts for canonicalization of the ligand atoms, are offered freely to the academic community.

Inclusion of the orientational entropic effect and low‐resolution experimental information for protein–protein docking in Critical Assessment of PRedicted Interactions (CAPRI)

Inclusion of entropy is important and challenging for protein–protein binding prediction. Here, we present a statistical mechanics‐based approach to empirically consider the effect of orientational entropy. Specifically, we globally sample the possible binding orientations based on a simple shape‐complementarity scoring function using an FFT‐type docking method. Then, for each generated orientation, we calculate the probability through the partition function of the ensemble of accessible states, which are assumed to be represented by the set of nearby binding modes. For each mode, the interaction energy is calculated using our ITScorePP scoring function that was developed in our laboratory based on principles of statistical mechanics. Using the above protocol, we present the results of our participation in Rounds 22‐27 of the Critical Assessment of PRedicted Interactions (CAPRI) experiment for 10 targets (T46–T58). Additional experimental information, such as low‐resolution small‐angle X‐ray scattering data, was used when available. In the prediction (or docking) experiments of the 10 target complexes, we achieved correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T48 and T57), and three with acceptable accuracy (T49, T50, and T58). In the scoring experiments of seven target complexes, we obtained correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T49 and T50), and three with acceptable accuracy (T46, T51, and T53). Proteins 2013; 81:2183–2191.

Iterative Knowledge-Based Scoring Functions Derived from Rigid and Flexible Decoy Structures: Evaluation with the 2013 and 2014 CSAR Benchmarks.

In this study, we developed two iterative knowledge-based scoring functions, ITScore_pdbbind(rigid) and ITScore_pdbbind(flex), using rigid decoy structures and flexible decoy structures, respectively, that were generated from the protein-ligand complexes in the refined set of PDBbind 2012. These two scoring functions were evaluated using the 2013 and 2014 CSAR benchmarks. The results were compared with the results of two other scoring functions, the Vina scoring function and ITScore, the scoring function that we previously developed from rigid decoy structures for a smaller set of protein-ligand complexes. A graph-based method was developed to evaluate the root-mean-square deviation between two conformations of the same ligand with different atom names and orders due to different file preparations, and the program is freely available. Our study showed that the two new scoring functions developed from the larger training set yielded significantly improved performance in binding mode predictions. For binding affinity predictions, all four scoring functions showed protein-dependent performance. We suggest the development of protein-family-dependent scoring functions for accurate binding affinity prediction.

Inclusion of the orientational entropic effect and low-resolution experimental information for protein-protein docking in CAPRI

Inclusion of entropy is important and challenging for protein–protein binding prediction. Here, we present a statistical mechanics‐based approach to empirically consider the effect of orientational entropy. Specifically, we globally sample the possible binding orientations based on a simple shape‐complementarity scoring function using an FFT‐type docking method. Then, for each generated orientation, we calculate the probability through the partition function of the ensemble of accessible states, which are assumed to be represented by the set of nearby binding modes. For each mode, the interaction energy is calculated using our ITScorePP scoring function that was developed in our laboratory based on principles of statistical mechanics. Using the above protocol, we present the results of our participation in Rounds 22‐27 of the Critical Assessment of PRedicted Interactions (CAPRI) experiment for 10 targets (T46–T58). Additional experimental information, such as low‐resolution small‐angle X‐ray scattering data, was used when available. In the prediction (or docking) experiments of the 10 target complexes, we achieved correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T48 and T57), and three with acceptable accuracy (T49, T50, and T58). In the scoring experiments of seven target complexes, we obtained correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T49 and T50), and three with acceptable accuracy (T46, T51, and T53). Proteins 2013; 81:2183–2191.

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein–ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein–ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein–ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein–ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein–ligand complex structures available to improve predictions on binding.

The Usage of ACCLUSTER for Peptide Binding Site Prediction

Peptides mediate up to 40 % of protein-protein interactions in a variety of cellular processes and are also attractive drug candidates. Thus, predicting peptide binding sites on the given protein structure is of great importance for mechanistic investigation of protein-peptide interactions and peptide therapeutics development. In this chapter, we describe the usage of our web server, referred to as ACCLUSTER, for peptide binding site prediction for a given protein structure. ACCLUSTER is freely available for users without registration at http://zougrouptoolkit.missouri.edu/accluster .

MDockPeP: An ab-initio protein-peptide docking server: MDockPeP: An Ab-Initio Protein-Peptide Docking Server

Protein–peptide interactions play a crucial role in a variety of cellular processes. The protein–peptide complex structure is a key to understand the mechanisms underlying protein–peptide interactions and is critical for peptide therapeutic development. We present a user‐friendly protein–peptide docking server, MDockPeP. Starting from a peptide sequence and a protein receptor structure, the MDockPeP Server globally docks the all‐atom, flexible peptide to the protein receptor. The produced modes are then evaluated with a statistical potential‐based scoring function, ITScorePeP. This method was systematically validated using the peptiDB benchmarking database. At least one near‐native peptide binding mode was ranked among top 10 (or top 500) in 59% (85%) of the bound cases, and in 40.6% (71.9%) of the challenging unbound cases. The server can be used for both protein–peptide complex structure prediction and initial‐stage sampling of the protein–peptide binding modes for other docking or simulation methods. MDockPeP Server is freely available at http://zougrouptoolkit.missouri.edu/mdockpep.