ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

Molecular mechanisms that distinguish TFIID housekeeping from regulatable SAGA promoters

An important distinction is frequently made between constitutively expressed housekeeping genes versus regulated genes. Although generally characterized by different DNA elements, chromatin architecture and cofactors, it is not known to what degree promoter classes strictly follow regulatability rules and which molecular mechanisms dictate such differences. We show that SAGA‐dominated/TATA‐box promoters are more responsive to changes in the amount of activator, even compared to TFIID/TATA‐like promoters that depend on the same activator Hsf1. Regulatability is therefore an inherent property of promoter class. Further analyses show that SAGA/TATA‐box promoters are more dynamic because TATA‐binding protein recruitment through SAGA is susceptible to removal by Mot1. In addition, the nucleosome configuration upon activator depletion shifts on SAGA/TATA‐box promoters and seems less amenable to preinitiation complex formation. The results explain the fundamental difference between housekeeping and regulatable genes, revealing an additional facet of combinatorial control: an activator can elicit a different response dependent on core promoter class.     

Residue conservation in viral capsid assembly

To evaluate the evolutionary constraints placed on viral proteins by the structure and assembly of the capsid, we calculate Shannon entropies in the aligned sequences of 45 polypeptide chains in 32 icosahedral viruses, and relate these entropies to the residue location in the three-dimensional structure of the capsids. Three categories of residues have entropies lower than the chain average implying that they are better conserved than average: residues that are buried within a subunit (the protein core), residues that contain atoms buried at an interface between subunits (the interface core), and residues that contribute to several such interfaces. The interface core is also conserved in homomeric proteins and in transient protein-protein complexes, which have only one interface whereas capsids have many. In capsids, the subunit interfaces implicate most of the polypeptide chain: on average, 66% of the capsid residues are at an interface, 34% at more than one, and 47% at the interface core. Nevertheless, we observe that the degree of residue conservation can vary widely between interfaces within a capsid and between regions within an interface. The interfaces and regions of interfaces that show a low sequence variability are likely to play major roles in the self-assembly of the capsid, with implications on its mechanism that we discuss taking adeno-associated virus as an example.     

Systematic prediction of autophagy-related proteins using Arabidopsis thaliana interactome data

Autophagy is a self-degradative process that is crucial for maintaining cellular homeostasis by removing damaged cytoplasmic components and recycling nutrients. Such an evolutionary conserved proteolysis process is regulated by the autophagy-related (Atg) proteins. The incomplete understanding of plant autophagy proteome and the importance of a proteome-wide understanding of the autophagy pathway prompted us to predict Atg proteins and regulators in Arabidopsis. Here, we developed a systems-level algorithm to identify autophagy-related modules (ARMs) based on protein subcellular localization, protein–protein interactions, and known Atg proteins. This generates a detailed landscape of the autophagic modules in Arabidopsis. We found that the newly identified genes in each ARM tend to be upregulated and coexpressed during the senescence stage of Arabidopsis. We also demonstrated that the Golgi apparatus ARM, ARM13, functions in the autophagy process by module clustering and functional analysis. To verify the in silico analysis, the Atg candidates in ARM13 that are functionally similar to the core Atg proteins were selected for experimental validation. Interestingly, two of the previously uncharacterized proteins identified from the ARM analysis, AGD1 and Sec14, exhibited bona fide association with the autophagy protein complex in plant cells, which provides evidence for a cross-talk between intracellular pathways and autophagy. Thus, the computational framework has facilitated the identification and characterization of plant-specific autophagy-related proteins and novel autophagy proteins/regulators in higher eukaryotes.     

Selection of synthetic proteins to modulate the human frataxin function

Frataxin is a kinetic activator of the mitochondrial supercomplex for iron-sulfur cluster assembly. Low frataxin expression or a decrease in its functionality results in Friedreich's Ataxia (FRDA). With the aim of creating new molecular tools to study this metabolic pathway, and ultimately, to explore new therapeutic strategies, we have investigated the possibility of obtaining small proteins exhibiting a high affinity for frataxin. In this study, we applied the ribosome display approach, using human frataxin as the target. We focused on Affi_224, one of the proteins that we were able to select after five rounds of selection. We have studied the interaction between both proteins and discussed some applications of this specific molecular tutor, concerning the modulation of the supercomplex activity. Affi_224 and frataxin showed a K_D value in the nanomolar range, as judged by surface plasmon resonance analysis. Most likely, it binds to the frataxin acidic ridge, as suggested by the analysis of chemical shift perturbations (nuclear magnetic resonance) and computational simulations. Affi_224 was able to increase Cys NFS1 desulfurase activation exerted by the FRDA frataxin variant G130V. Importantly, Affi_224 interacts with frataxin in a human cellular model. Our results suggest quaternary addition may be a new tool to modulate frataxin function in vivo. Nevertheless, more functional experiments under physiological conditions should be carried out to evaluate Affi_224 effectiveness in FRDA cell models.     

A Role for Widely Interspaced Zinc Finger (WIZ) in Retention of the G9a Methyltransferase on Chromatin

G9a and GLP lysine methyltransferases form a heterodimeric complex that is responsible for the majority of histone H3 lysine 9 mono- and di-methylation (H3K9me1/me2). Widely interspaced zinc finger (WIZ) associates with the G9a-GLP protein complex, but its role in mediating lysine methylation is poorly defined. Here, we show that WIZ regulates global H3K9me2 levels by facilitating the interaction of G9a with chromatin. Disrupting the association of G9a-GLP with chromatin by depleting WIZ resulted in altered gene expression and protein-protein interactions that were distinguishable from that of small molecule-based inhibition of G9a/GLP, supporting discrete functions of the G9a-GLP-WIZ chromatin complex in addition to H3K9me2 methylation.     

Human dihydrofolate reductase and thymidylate synthase form a complex in vitro and co-localize in normal and cancer cells

Enzymes involved in thymidylate biosynthesis, thymidylate synthase (TS), and dihydrofolate reductase (DHFR) are well-known targets in cancer chemotherapy. In this study, we demonstrated for the first time, that human TS and DHFR form a strong complex in vitro and co-localize in human normal and colon cancer cell cytoplasm and nucleus. Treatment of cancer cells with methotrexate or 5-fluorouracil did not affect the distribution of either enzyme within the cells. However, 5-FU, but not MTX, lowered the presence of DHFR-TS complex in the nucleus by 2.5-fold. The results may suggest the sequestering of TS by FdUMP in the cytoplasm and thereby affecting the translocation of DHFR-TS complex to the nucleus. Providing a strong likelihood of DHFR-TS complex formation in vivo, the latter complex is a potential new drug target in cancer therapy. In this paper, known 3D structures of human TS and human DHFR, and some protozoan bifunctional DHFR-TS structures as templates, are used to build an in silico model of human DHFR–TS complex structure, consisting of one TS dimer and two DHFR monomers. This complex structure may serve as an initial 3D drug target model for prospective inhibitors targeting interfaces between the DHFR and TS enzymes.     

真核基因转录与转录调节相关复合物

真核细胞核中转录因子与染色质模板如何相互作用调节基因转录是基因表达调控研究的一个中心问题.近来的研究表明,参与基因转录的各种调节因子在核内形成多种复合物,如RNA聚合酶Ⅱ全酶、染色质重塑复合物、核小体以及增强小体等.这些复合物之间相互作用,调节染色质结构,在染色质模板上进一步组装成转录复合物,参与转录调节的各个环节,调节转录复合物活性.这些复合物的形成,整合了转录调节的各种信息,提高了转录调节效率,是真核基因有效、严格、有序表达的基础.另一方面,这些复合物的存在给基因表达调控的研究提出了新问题,发展新的研究思路和新的研究技术具有重要意义.     

Coevolutionary constraints in the sequence‐space of macromolecular complexes reflect their self‐assembly pathways

Is the order in which biomolecular subunits self‐assemble into functional macromolecular complexes imprinted in their sequence‐space? Here, we demonstrate that the temporal order of macromolecular complex self‐assembly can be efficiently captured using the landscape of residue‐level coevolutionary constraints. This predictive power of coevolutionary constraints is irrespective of the structural, functional, and phylogenetic classification of the complex and of the stoichiometry and quaternary arrangement of the constituent monomers. Combining this result with a number of structural attributes estimated from the crystal structure data, we find indications that stronger coevolutionary constraints at interfaces formed early in the assembly hierarchy probably promotes coordinated fixation of mutations that leads to high‐affinity binding with higher surface area, increased surface complementarity and elevated number of molecular contacts, compared to those that form late in the assembly. Proteins 2017; 85:1183–1189. © 2017 Wiley Periodicals, Inc.     

The CW domain, a new histone recognition module in chromatin proteins

Post-translational modifications of the N-terminal histone tails, including lysine methylation, have key roles in regulation of chromatin and gene expression. A number of protein modules have been identified that recognize differentially modified histone tails and provide their proteins with the capacity to sense such modifications. Here, we identify the CW domain of plant and animal chromatin-related proteins as a novel module that recognizes different methylated states of lysine 4 on histone H3 (H3K4me). The solution structure of the CW domain of the Arabidopsis ASH1 HOMOLOG2 (ASHH2) histone methyltransferase provides insight into how different CW domains can distinguish different methylated histone tails. We provide evidence that ASHH2 is acting on H3K4me-marked genes, allowing for ASHH2-dependent H3K36 tri-methylation, which contributes to sustained expression of tissue-specific and developmentally regulated genes. This suggests that ASHH2 is a combined 'reader' and 'writer' of the histone code. We propose that different CW domains, dependent on their specificity for different H3K4 methylations, are important for epigenetic memory or participate in switching between permissive and repressive chromatin states.     

Structure and Assembly of the Yeast V-ATPase

The yeast V-ATPase belongs to a family of V-type ATPases present in all eucaryotic organisms. In Saccharomyces cerevisiae the V-ATPase is localized to the membrane of the vacuole as well as the Golgi complex and endosomes. The V-ATPase brings about the acidification of these organelles by the transport of protons coupled to the hydrolysis of ATP. In yeast, the V-ATPase is composed of 13 subunits consisting of a catalytic V₁ domain of peripherally associated proteins and a proton-translocating V₀ domain of integral membrane proteins. The regulatory subunit, Vma13p, was the first V-ATPase subunit to have its crystal structure determined. In addition to proteins forming the functional V-ATPase complex, three ER-localized proteins facilitate the assembly of the V₀ subunits following their translation and insertion into the membrane of the ER. Homologues of the Vma21p assembly factor have been identified in many higher eukaryotes supporting a ubiquitous assembly pathway for this important enzyme complex.     

Protein–protein interaction networks improve the proteomics data interpretation in induced apoptosis

Evaluation of: Deighton RF, Kerr LE, Short DM et al. Network generation enhances interpretation of proteomics data from induced apoptosis. Proteomics DOI: 10.1002/pmic.200900112 (2010) (Epub ahead of print).

The huge ongoing improvements in proteomics technologies, including the development of high-throughput mass spectrometry, are resulting in ever increasing information on protein behavior during cellular processes. The exponential accumulation of proteomics data has the promise to advance biomedical sciences by shedding light on the most important events that regulate mammalian cells under normal and pathophysiological conditions. This may provide practical insights that will impact medical practice and therapy, and may permit the development of a new generation of personalized therapeutics. Proteomics, as a powerful tool, creates numerous opportunities as well as challenges. At the different stages, data interpretation requires proteomics analysis, various tools to help deal with large proteomics data banks and the extraction of more functional information. Network analysis tools facilitate proteomics data interpretation and predict protein functions, functional interactions and in silica identification of intracellular pathways. The work reported by Deighton and colleagues illustrates an example of improving proteomics data interpretation by network generation. The authors used ingenuity pathway analysis to generate a protein network predicting direct and indirect interaction between 13 proteins found to be affected by staurosporine treatment. Importantly, the authors highlight the caution required when interpreting the results from a small number of proteins analyzed using network analysis tools.     

A gas phase fractionation acquisition scheme integrating ion mobility for rapid diaPASEF library generation

Data independent acquisition (DIA/SWATH) MS is a primary strategy in quantitative proteomics. diaPASEF is a recent adaptation using trapped ion mobility spectrometry (TIMS) to improve selectivity/sensitivity. Complex DIA spectra are typically analyzed with reference to spectral libraries. The best-established method for generating libraries uses offline fractionation to increase depth of coverage. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow DIA windows that cover different mass ranges of the complete precursor space, have been introduced that performed comparably to deep offline fractionation-based libraries. We investigated whether an analogous GPF-based approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data. We developed a rapid library generation approach using an IM-GPF acquisition scheme in the m/z versus 1/K0 space requiring seven injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation. We found that library generation by IM-GPF outperformed direct library generation from diaPASEF and had performance approaching that of the deep library. This establishes the IM-GPF scheme as a pragmatic approach to rapid library generation for analysis of diaPASEF data.