Julian Mintseris

A Protein Complex Network of Drosophila melanogaster

Determining the composition of protein complexes is an essential step toward understanding the cell as an integrated system. Using coaffinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly 5,000 individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a statistical framework designed to define individual protein-protein interactions, led to the generation of a Drosophila protein interaction map (DPiM) encompassing 556 protein complexes. The high quality of the DPiM and its usefulness as a paradigm for metazoan proteomes are apparent from the recovery of many known complexes, significant enrichment for shared functional attributes, and validation in human cells. The DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. The DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.

The BioPlex Network: A Systematic Exploration of the Human Interactome

Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.

Protein–protein docking benchmark version 3.0

We present version 3.0 of our publicly available protein–protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, Structural Classification of Proteins (Murzin et al., J Mol Biol 1995;247:536–540) was used to remove redundant test cases. The 124 unbound‐unbound test cases in Benchmark 3.0 are classified into 88 rigid‐body cases, 19 medium‐difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at http://zlab.bu.edu/benchmark. Proteins 2008.

A protein-protein docking benchmark.

We have developed a nonredundant benchmark for testing protein–protein docking algorithms. Currently it contains 59 test cases: 22 enzyme‐inhibitor complexes, 19 antibody‐antigen complexes, 11 other complexes, and 7 difficult test cases. Thirty‐one of the test cases, for which the unbound structures of both the receptor and ligand are available, are classified as follows: 16 enzyme‐inhibitor, 5 antibody‐antigen, 5 others, and 5 difficult. Such a centralized resource should benefit the docking community not only as a large curated test set but also as a common ground for comparing different algorithms. The benchmark is available at (http://zlab.bu.edu/∼rong/dock/benchmark.shtml). Proteins 2003;52:88–91.

Are protein–protein interfaces more conserved in sequence than the rest of the protein surface?

Protein interfaces are thought to be distinguishable from the rest of the protein surface by their greater degree of residue conservation. We test the validity of this approach on an expanded set of 64 protein–protein interfaces using conservation scores derived from two multiple sequence alignment types, one of close homologs/orthologs and one of diverse homologs/paralogs. Overall, we find that the interface is slightly more conserved than the rest of the protein surface when using either alignment type, with alignments of diverse homologs showing marginally better discrimination. However, using a novel surface‐patch definition, we find that the interface is rarely significantly more conserved than other surface patches when using either alignment type. When an interface is among the most conserved surface patches, it tends to be part of an enzyme active site. The most conserved surface patch overlaps with 39% (± 28%) and 36% (± 28%) of the actual interface for diverse and close homologs, respectively. Contrary to results obtained from smaller data sets, this work indicates that residue conservation is rarely sufficient for complete and accurate prediction of protein interfaces. Finally, we find that obligate interfaces differ from transient interfaces in that the former have significantly fewer alignment gaps at the interface than the rest of the protein surface, as well as having buried interface residues that are more conserved than partially buried interface residues.

Protein–protein docking benchmark 2.0: An update

We present a new version of the Protein–Protein Docking Benchmark, reconstructed from the bottom up to include more complexes, particularly focusing on more unbound–unbound test cases. SCOP (Structural Classification of Proteins) was used to assess redundancy between the complexes in this version. The new benchmark consists of 72 unbound–unbound cases, with 52 rigid‐body cases, 13 medium‐difficulty cases, and 7 high‐difficulty cases with substantial conformational change. In addition, we retained 12 antibody–antigen test cases with the antibody structure in the bound form. The new benchmark provides a platform for evaluating the progress of docking methods on a wide variety of targets. The new version of the benchmark is available to the public at http://zlab.bu.edu/benchmark2. Proteins 2005;60:214–216.

Ubiquitin-Like Protein Involved in the Proteasome Pathway of Mycobacterium tuberculosis

The protein modifier ubiquitin is a signal for proteasome-mediated degradation in eukaryotes. Proteasome-bearing prokaryotes have been thought to degrade proteins via a ubiquitin-independent pathway. We have identified a prokaryotic ubiquitin-like protein, Pup (Rv2111c), which was specifically conjugated to proteasome substrates in the pathogen Mycobacterium tuberculosis. Pupylation occurred on lysines and required proteasome accessory factor A (PafA). In a pafA mutant, pupylated proteins were absent and substrates accumulated, thereby connecting pupylation with degradation. Although analogous to ubiquitylation, pupylation appears to proceed by a different chemistry. Thus, like eukaryotes, bacteria may use a small-protein modifier to control protein stability.

Phosphoproteome Analysis of Drosophila melanogaster Embryos

Protein phosphorylation is a key regulatory event in most cellular processes and development. Mass spectrometry-based proteomics provides a framework for the large-scale identification and characterization of phosphorylation sites. Here, we used a well-established phosphopeptide enrichment and identification strategy including the combination of strong cation exchange chromatography, immobilized metal affinity chromatography, and high-accuracy mass spectrometry instrumentation to study phosphorylation in developing Drosophila embryos. In total, 13,720 different phosphorylation sites were discovered from 2702 proteins with an estimated false-discovery rate (FDR) of 0.63% at the peptide level. Because of the large size of the data set, both novel and known phosphorylation motifs were extracted using the Motif-X algorithm, including those representative of potential ordered phosphorylation events.

Integrating statistical pair potentials into protein complex prediction

The biophysical study of protein–protein interactions and docking has important implications in our understanding of most complex cellular signaling processes. Most computational approaches to protein docking involve a tradeoff between the level of detail incorporated into the model and computational power required to properly handle that level of detail. In this work, we seek to optimize that balance by showing that we can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance. We also introduce a pair‐wise statistical potential suitable for docking that builds on previous work and show that this potential can be incorporated into our fast fourier transform‐based docking algorithm ZDOCK. We use the Protein Docking Benchmark to illustrate the improved performance of this potential compared with less detailed other scoring functions. Furthermore, we show that the new potential performs well on antibody‐antigen complexes, with most predictions clustering around the Complementarity Determining Regions of antibodies without any manual intervention. Proteins 2007.

Predictome: a database of putative functional links between proteins

The current deluge of genomic sequences has spawned the creation of tools capable of making sense of the data. Computational and high-throughput experimental methods for generating links between proteins have recently been emerging. These methods effectively act as hypothesis machines, allowing researchers to screen large sets of data to detect interesting patterns that can then be studied in greater detail. Although the potential use of these putative links in predicting gene function has been demonstrated, a central repository for all such links for many genomes would maximize their usefulness. Here we present Predictome, a database of predicted links between the proteins of 44 genomes based on the implementation of three computational methods--chromosomal proximity, phylogenetic profiling and domain fusion--and large-scale experimental screenings of protein-protein interaction data. The combination of data from various predictive methods in one database allows for their comparison with each other, as well as visualization of their correlation with known pathway information. As a repository for such data, Predictome is an ongoing resource for the community, providing functional relationships among proteins as new genomic data emerges. Predictome is available at http://predictome.bu.edu.