Biomedical subjects

Chien Peter Chen

Publications and source records attributed to Chien Peter Chen.

7 recordsLinked to original sources

PubMed2006

Type II cadherin ectodomain structures: implications for classical cadherin specificity.

Type I and II classical cadherins help to determine the adhesive specificities of animal cells. Crystal-structure determination of ectodomain regions from three type II cadherins reveals adhesive dimers formed by exchange of N-terminal beta strands between partner extracellular cadherin-1 (EC1) domains. These interfaces have two conserved tryptophan side chains that anchor each swapped strand, compared with one in type I cadherins, and include large hydrophobic regions unique to type II interfaces. The EC1 domains of type I and type II cadherins appear to encode cell adhesive specificity in vitro. Moreover, perturbation of motor neuron segregation with chimeric cadherins depends on EC1 domain identity, suggesting that this region, which includes the structurally defined adhesive interface, encodes type II cadherin functional specificity in vivo.

Amino Acid Sequence↗

PubMed2005

Specificity of cell-cell adhesion by classical cadherins: Critical role for low-affinity dimerization through beta-strand swapping.

Cadherins constitute a family of cell-surface proteins that mediate intercellular adhesion through the association of protomers presented from juxtaposed cells. Differential cadherin expression leads to highly specific intercellular interactions in vivo. This cell-cell specificity is difficult to understand at the molecular level because individual cadherins within a given subfamily are highly similar to each other both in sequence and structure, and they dimerize with remarkably low binding affinities. Here, we provide a molecular model that accounts for these apparently contradictory observations. The model is based in part on the fact that cadherins bind to one another by "swapping" the N-terminal beta-strands of their adhesive domains. An inherent feature of strand swapping (or, more generally, the domain swapping phenomenon) is that "closed" monomeric conformations act as competitive inhibitors of dimer formation, thus lowering affinities even when the dimer interface has the characteristics of high-affinity complexes. The model describes quantitatively how small affinity differences between low-affinity cadherin dimers are amplified by multiple cadherin interactions to establish large specificity effects at the cellular level. It is shown that cellular specificity would not be observed if cadherins bound with high affinities, thus emphasizing the crucial role of strand swapping in cell-cell adhesion. Numerical estimates demonstrate that the strength of cellular adhesion is extremely sensitive to the concentration of cadherins expressed at the cell surface. We suggest that the domain swapping mechanism is used by a variety of cell-adhesion proteins and that related mechanisms to control affinity and specificity are exploited in other systems.

Amino Acid Sequence↗

PubMed2005

Identification of a transiently exposed VE-cadherin epitope that allows for specific targeting of an antibody to the tumor neovasculature.

VE-cadherin is an adhesion molecule localized at the adherens junctions of endothelial cells. It is crucial for the proper assembly of vascular structures during angiogenesis and maintaining vascular integrity. We have studied 3 monoclonal antibodies (mAbs) against murine VE-cadherin that inhibit angiogenesis and tumor growth. Two of these, BV13 and 10G4, also disrupted normal vessels, resulting in severe vascular leakage, whereas the third, E4G10, did not. The goal of the current report was to identify the epitope of E4G10 and distinguish it from those of the disruptive mAbs. We mapped the epitope of E4G10 to within the first 10 amino acids of mature VE-cadherin and demonstrated that conserved tryptophan residues in this sequence are required for VE-cadherin-mediated trans-adhesion. The disruptive mAbs target a different epitope within amino acids 45 to 56, which structural homology modeling suggests is not involved in trans-adhesion. From our studies, we hypothesize that E4G10 can only bind the neovasculature, where VE-cadherin has not yet engaged in trans-adhesion and its epitope is fully exposed. Thus, E4G10 can inhibit junction formation and angiogenesis but is unable to target normal vasculature because its epitope is masked. In contrast, BV13 and 10G4 bind an epitope that is accessible regardless of VE-cadherin interactions, leading to the disruption of adherens junctions. Our findings establish the immediate N-terminal region of VE-cadherin as a novel target for inhibiting angiogenesis.

Angiogenesis Inhibitors↗

PubMed2003

Cadherin-mediated cell-cell adhesion: sticking together as a family.

The cadherins comprise a family of single-pass transmembrane proteins critical for cell-cell adhesion in vertebrates and invertebrates. The recently determined structure of the whole ectodomain from C-cadherin suggests that the adhesion of cadherins presented by juxtaposed cells is mediated by a strand-swapped dimer in which core hydrophobic elements are exchanged between the partner molecules. Sequence analysis suggests that several cadherin subfamilies share this adhesive mechanism. Recent work has shed new light on the molecular basis of cadherin adhesion, although understanding the specificity of these interactions remains a major challenge.

Amino Acid Sequence↗

PubMed2002

Transmembrane helix predictions revisited.

Methods that predict membrane helices have become increasingly useful in the context of analyzing entire proteomes, as well as in everyday sequence analysis. Here, we analyzed 27 advanced and simple methods in detail. To resolve contradictions in previous works and to reevaluate transmembrane helix prediction algorithms, we introduced an analysis that distinguished between performance on redundancy-reduced high- and low-resolution data sets, established thresholds for significant differences in performance, and implemented both per-segment and per-residue analysis of membrane helix predictions. Although some of the advanced methods performed better than others, we showed in a thorough bootstrapping experiment based on various measures of accuracy that no method performed consistently best. In contrast, most simple hydrophobicity scale-based methods were significantly less accurate than any advanced method as they overpredicted membrane helices and confused membrane helices with hydrophobic regions outside of membranes. In contrast, the advanced methods usually distinguished correctly between membrane-helical and other proteins. Nonetheless, few methods reliably distinguished between signal peptides and membrane helices. We could not verify a significant difference in performance between eukaryotic and prokaryotic proteins. Surprisingly, we found that proteins with more than five helices were predicted at a significantly lower accuracy than proteins with five or fewer. The important implication is that structurally unsolved multispanning membrane proteins, which are often important drug targets, will remain problematic for transmembrane helix prediction algorithms. Overall, by establishing a standardized methodology for transmembrane helix prediction evaluation, we have resolved differences among previous works and presented novel trends that may impact the analysis of entire proteomes.

Algorithms↗

PubMed2002

Long membrane helices and short loops predicted less accurately.

Low-resolution experiments suggest that most membrane helices span over 17-25 residues and that most loops between two helices are longer than 15 residues. Both constraints have been used explicitly in the development of prediction methods. Here, we compared the largest possible sequence-unique data sets from high- and low-resolution experiments. For the high-resolution data, we found that only half of the helices fall into the expected length interval and that half of the loops were shorter than 10 residues. We compared the accuracy of detecting short loops and long helices for 28 advanced and simple prediction methods: All methods predicted short loops less accurately than longer ones. In particular, loops shorter than 7 residues appeared to be very difficult to detect by current methods. Similarly, all methods tended to be more accurate for longer than for shorter helices. However, helices with more than 32 residues were predicted less accurately than all other helices. Our findings may suggest particular strategies for improving predictions of membrane helices.

Calcium-Transporting ATPases↗

PubMed2002

State-of-the-art in membrane protein prediction.

Membrane proteins are crucial for many biological functions and have become attractive targets for pharmacological agents. About 10%-30% of all proteins contain membrane-spanning helices. Despite recent successes, high-resolution structures for membrane proteins remain exceptional. The gap between known sequences and known structures calls for finding solutions through bioinformatics. While many methods predict membrane helices, very few predict membrane strands. The good news is that most methods for helical membrane proteins are available and are more often right than wrong. The best current prediction methods appear to correctly predict all membrane helices for about 50%-70% of all proteins, and to falsely predict membrane helices for about 10% of all globular proteins. The bad news is that developers have seriously overestimated the accuracy of their methods. In particular, while simple hydrophobicity scales identify many membrane helices, they frequently and incorrectly predict membrane helices in globular proteins. Additionally, all methods tend to confuse signal peptides with membrane helices. Nonetheless, wet-lab biologists can reach into an impressive toolbox for membrane protein predictions. However, the computational biologists will have to improve their methods considerably before they reach the levels of accuracy they claim.

Computational Biology↗