Keunwan Park

Two genetic variants of CD38 in subjects with autism spectrum disorder and controls.

The neurobiological basis of autism spectrum disorder (ASD) remains poorly understood. Given the role of CD38 in social recognition through oxytocin (OT) release, we hypothesized that CD38 may play a role in the etiology of ASD. Here, we first examined the immunohistochemical expression of CD38 in the hypothalamus of post-mortem brains of non-ASD subjects and found that CD38 was colocalized with OT in secretory neurons. In studies of the association between CD38 and autism, we analyzed 10 single nucleotide polymorphisms (SNPs) and mutations of CD38 by re-sequencing DNAs mainly from a case-control study in Japan, and Caucasian cases mainly recruited to the Autism Genetic Resource Exchange (AGRE). The SNPs of CD38, rs6449197 (p<0.040) and rs3796863 (p<0.005) showed significant associations with a subset of ASD (IQ>70; designated as high-functioning autism (HFA)) in the U.S. 104 AGRE family trios, but not with Japanese 188 HFA subjects. A mutation that caused tryptophan to replace arginine at amino acid residue 140 (R140W; (rs1800561, 4693C>T)) was found in 0.6-4.6% of the Japanese population and was associated with ASD in the smaller case-control study. The SNP was clustered in pedigrees in which the fathers and brothers of T-allele-carrier probands had ASD or ASD traits. In this cohort OT plasma levels were lower in subjects with the T allele than in those without. One proband with the T allele who was taking nasal OT spray showed relief of symptoms. The two variant CD38 poloymorphysms tested may be of interest with regard of the pathophysiology of ASD.

Design of a binding scaffold based on variable lymphocyte receptors of jawless vertebrates by module engineering

Repeat proteins have recently been of great interest as potential alternatives to immunoglobulin antibodies due to their unique structural and biophysical features. We here present the development of a binding scaffold based on variable lymphocyte receptors, which are nonimmunoglobulin antibodies composed of Leucine-rich repeat modules in jawless vertebrates, by module engineering. A template scaffold was first constructed by joining consensus repeat modules between the N- and C-capping motifs of variable lymphocyte receptors. The N-terminal domain of the template scaffold was redesigned based on the internalin-B cap by analyzing the modular similarity between the respective repeat units using a computational approach. The newly designed scaffold, termed “Repebody,” showed a high level of soluble expression in bacteria, displaying high thermodynamic and pH stabilities. Ease of molecular engineering was shown by designing repebodies specific for myeloid differentiation protein-2 and hen egg lysozyme, respectively, by a rational approach. The crystal structures of designed repebodies were determined to elucidate the structural features and interaction interfaces. We demonstrate general applicability of the scaffold by selecting repebodies with different binding affinities for interleukin-6 using phage display.

Analysis of the residue–residue coevolution network and the functionally important residues in proteins

It is a common belief that some residues of a protein are more important than others. In some cases, point mutations of some residues make butterfly effect on the protein structure and function, but in other cases they do not. In addition, the residues important for the protein function tend to be not only conserved but also coevolved with other interacting residues in a protein. Motivated by these observations, the authors propose that there is a network composed of the residues, the residue–residue coevolution network (RRCN), where nodes are residues and links are set when the coevolutionary interaction strengths between residues are sufficiently large. The authors build the RRCN for the 44 diverse protein families. The interaction strengths are calculated by using McBASC algorithm. After constructing the RRCN, the authors identify residues that have high degree of connectivity (hub nodes), and residues that play a central role in network flow of information (CI nodes). The authors show that these residues are likely to be functionally important residues. Moreover, the CI nodes appear to be more relevant to the function than the hub nodes. Unlike other similar methods, the method described in this study is solely based on sequences. Therefore, the method can be applied to the function annotation of a wider range of proteins. Proteins 2008.

Binding similarity network of ligand.

The protein and ligand interaction takes an important part in protein function. Both ligand and its binding site are essential components for understanding how the protein–ligand complex functions. Until now, there have been many studies about protein function and evolution, but they usually lacked ligand information. Accordingly, in this study, we tried to answer the following questions: how much ligand and binding site are associated with protein function, and how ligands themselves are related to each other in terms of binding site. To answer the questions, we presented binding similarity network of ligand. Through the network analysis, we attempted to reveal systematic relationship between the ligand and binding site. The results showed that ligand binding site and function were closely related (conservation ratio, 81%). We also showed conservative tendency of function in line with ligand structure similarity with some exceptional cases. In addition, the binding similarity network of ligand revealed scale‐free property to some degree like other biological networks. Since most nodes formed highly connected cluster, a clustering coefficient was very high compared with random. All the highly connected ligands (hubs) were involved in various functions forming large cluster and tended to act as a bridge between modular clusters in the network. Proteins 2008.

A High-Affinity Protein Binder that Blocks the IL-6/STAT3 Signaling Pathway Effectively Suppresses Non–Small Cell Lung Cancer

Interleukin-6 (IL-6) is a multifunctional cytokine that regulates immune responses for host defense and tumorigenic process. Upregulation of IL-6 is known to constitutively phosphorylate signal transducer and activator of transcription 3 (STAT3), leading to activation of multiple oncogene pathways and inflammatory cascade. Here, we present the development of a high-affinity protein binder, termed repebody, which effectively suppresses non-small cell lung cancer in vivo by blocking the IL-6/STAT3 signaling. We selected a repebody that prevents human IL-6 (hIL-6) from binding to its receptor by a competitive immunoassay, and modulated its binding affinity for hIL-6 up to a picomolar range by a modular approach that mimics the combinatorial assembly of diverse modules to form antigen-specific receptors in nature. The resulting repebody was highly specific for hIL-6, effectively inhibiting the STAT3 phosphorylation in a dose- and binding affinity-response manner in vitro. The repebody was shown to have a remarkable suppression effect on the growth of tumors and STAT3 phosphorylation in xenograft mice with non-small cell lung cancer by blocking the hIL-6/STAT3 signaling. Structural analysis of the repebody and IL-6 complex revealed that the repebody binds the site 2a of hIL-6, overlapping a number of epitope residues at site 2a with gp130, and consequently causes a steric hindrance to the formation of IL-6/IL-6Rα complex. Our results suggest that high-affinity repebody targeting the IL-6/STAT3 pathway can be developed as therapeutics for non-small cell lung cancer.

Localized network centrality and essentiality in the yeast–protein interaction network

It has been suggested that a close relationship exists between gene essentiality and network centrality in protein–protein interaction networks. However, recent studies have reported somewhat conflicting results on this relationship. In this study, we investigated whether essential proteins could be inferred from network centrality alone. In addition, we determined which centrality measures describe the essentiality well. For this analysis, we devised new local centrality measures based on several well‐known centrality measures to more precisely describe the connection between network topology and essentiality. We examined two recent yeast protein–protein interaction networks using 40 different centrality measures. We discovered a close relationship between the path‐based localized information centrality and gene essentiality, which suggested underlying topological features that represent essentiality. We propose that two important features of the localized information centrality (proper representation of environmental complexity and the consideration of local sub‐networks) are the key factors that reveal essentiality. In addition, a random forest classifier showed reasonable performance at classifying essential proteins. Finally, the results of clustering analysis using centrality measures indicate that some network clusters are closely related with both particular biological processes and essentiality, suggesting that functionally related proteins tend to share similar network properties.

Modeling allosteric signal propagation using protein structure networks

Allosteric communication in proteins can be induced by the binding of effective ligands, mutations or covalent modifications that regulate a site distant from the perturbed region. To understand allosteric regulation, it is important to identify the remote sites that are affected by the perturbation-induced signals and how these allosteric perturbations are transmitted within the protein structure. In this study, by constructing a protein structure network and modeling signal transmission with a Markov random walk, we developed a method to estimate the signal propagation and the resulting effects. In our model, the global perturbation effects from a particular signal initiation site were estimated by calculating the expected visiting time (EVT), which describes the signal-induced effects caused by signal transmission through all possible routes. We hypothesized that the residues with high EVT values play important roles in allosteric signaling. We applied our model to two protein structures as examples, and verified the validity of our model using various types of experimental data. We also found that the hot spots in protein binding interfaces have significantly high EVT values, which suggests that they play roles in mediating signal communication between protein domains.

Structure-based rational design of a Toll-like receptor 4 (TLR4) decoy receptor with high binding affinity for a target protein.

Repeat proteins are increasingly attracting much attention as alternative scaffolds to immunoglobulin antibodies due to their unique structural features. Nonetheless, engineering interaction interface and understanding molecular basis for affinity maturation of repeat proteins still remain a challenge. Here, we present a structure-based rational design of a repeat protein with high binding affinity for a target protein. As a model repeat protein, a Toll-like receptor4 (TLR4) decoy receptor composed of leucine-rich repeat (LRR) modules was used, and its interaction interface was rationally engineered to increase the binding affinity for myeloid differentiation protein 2 (MD2). Based on the complex crystal structure of the decoy receptor with MD2, we first designed single amino acid substitutions in the decoy receptor, and obtained three variants showing a binding affinity (KD) one-order of magnitude higher than the wild-type decoy receptor. The interacting modes and contributions of individual residues were elucidated by analyzing the crystal structures of the single variants. To further increase the binding affinity, single positive mutations were combined, and two double mutants were shown to have about 3000- and 565-fold higher binding affinities than the wild-type decoy receptor. Molecular dynamics simulations and energetic analysis indicate that an additive effect by two mutations occurring at nearby modules was the major contributor to the remarkable increase in the binding affinities.

Building a drug–target network and its applications

Background: One of the most recent and important developments in drug discovery is a new drug development approach of building and analyzing networks that contain relationships among drugs and targets, diseases, genes and other components. These networks and their integrations provide useful information for finding new targets as well as new drugs. Objective: This review article aims to review recent developments in various types of networks and suggest the future direction of these network studies for drug discovery. Methods: Databases and networks are integrated into a more complete network to better present the relationships among drugs, targets, genes, phenotypes and diseases. After discussing the limitations and obstacles of the recent research, we suggest several strategies to build a successful and practical drug–target network. Results/conclusion: A useful, integrated network can be built from various databases and networks by resolving several issues, such as limited coverage and inconsistency. This integrated network can be completed by the prediction of missing links, biological network comparison and drug target identification. Possible applications are multi-target drug development, drug repurposing, estimation of drug effect on target perturbations in the whole system and extraction of the suitable purpose of the drug–target sub-network.

Predicting the multi-modal binding propensity of small molecules: towards an understanding of drug promiscuity.

Drug promiscuity is one of the key issues in current drug development. Many famous drugs have turned out to behave unexpectedly due to their propensity to bind to multiple targets. One of the primary reasons for this promiscuity is that drugs bind to multiple distinctive target environments, a feature that we call multi-modal binding. Accordingly, investigations into whether multi-modal binding propensities can be predicted, and if so, whether the features determining this behavior can be found, would be an important advance. In this study, we have developed a structure-based classifier that predicts whether small molecules will bind to multiple distinct binding sites. The binding sites for all ligands in the Protein Data Bank (PDB) were clustered by binding site similarity, and the ligands that bind to many dissimilar binding sites were identified as multi-modal binding ligands. The mono-binding ligands were also collected, and the classifiers were built using various machine-learning algorithms. A 10-fold cross-validation procedure showed 70-85% accuracy depending on the choice of machine-learning algorithm, and the different definitions used to identify multi-modal binding ligands. In addition, a quantified importance measurement for global and local descriptors was also provided, which suggests that the local features are more likely to have an effect on multi-modal binding than the global ones. The interpretable global and local descriptors were also ranked by their importance. To test the classifier on real examples, several test sets including well-known promiscuous drugs were collected by a literature and database search. Despite the difficulty in constructing appropriate testable sets, the classifier showed reasonable results that were consistent with existing information on drug behavior. Finally, a test on natural enzyme substrates and artificial drugs suggests that the natural compounds tend to exhibit a broader range of multi-modal binding than the drugs.