BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain.

Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain. We defined a global "interactome" comprising over one thousand multi-protein complexes. These include hundreds of brain-selective assemblies that have distinct physical and functional attributes, show regional and cell-type specificity, and have links to core neurological processes and disorders. Using reciprocal pull-downs and a transgenic model, we validated a putative 28-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, suggesting a coordinated function in alternative splicing in disease progression. This brain interaction map (BraInMap) resource facilitates mechanistic exploration of the unique molecular machinery driving core cellular processes of the central nervous system. It is publicly available and can be explored here https://www.bu.edu/dbin/cnsb/mousebrain/.

A reference map of the human binary protein interactome.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.

Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis.

Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps of human pluripotent embryonal carcinoma stem cells and differentiated neuronal-like cells, which presented as two discrete cell populations by single-cell RNA sequencing. The resulting networks, encompassing 6,442 high-quality associations among 600 MPs, revealed widespread changes in mitochondrial interactions and site-specific phosphorylation during neuronal differentiation. By leveraging the networks, we show the orphan C20orf24 as a respirasome assembly factor whose disruption markedly reduces respiratory chain activity in patients deficient in complex IV. We also find that a heme-containing neurotrophic factor, neuron-derived neurotrophic factor [NENF], couples with Parkinson disease-related proteins to promote neurotrophic activity. Our results provide insights into the dynamic reorganization of mitochondrial networks during neuronal differentiation and highlights mechanisms for MPs in respirasome, neuronal function, and mitochondrial diseases.

EPIC: software toolkit for elution profile-based inference of protein complexes.

Protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We and others previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (chromatographic fractionation-mass spectrometry, CF-MS), but the resulting data are challenging to process and interpret. Here, we describe EPIC (elution profile-based inference of complexes), a software toolkit for automated scoring of large-scale CF-MS data to define high-confidence multi-component macromolecules from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 612 putative worm protein complexes linked to diverse biological processes. These included novel subunits and assemblies unique to nematodes that we validated using orthogonal methods. The open source EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/).

Automated Computational Inference of Multi-protein Assemblies from Biochemical Co-purification Data.

Biology has amassed a wealth of information about the function of a multitude of protein-coding genes across species. The challenge now is to understand how all these proteins work together to form a living organism, and a crucial step for gaining this knowledge is a complete description of the molecular "wiring circuits" that underlie cellular processes. In this chapter, we describe a general computational framework for predicting multi-protein assemblies from biochemical co-fractionation data.

A Map of Human Mitochondrial Protein Interactions Linked to Neurodegeneration Reveals New Mechanisms of Redox Homeostasis and NF-κB Signaling.

Mitochondrial protein (MP) dysfunction has been linked to neurodegenerative disorders (NDs); however, the discovery of the molecular mechanisms underlying NDs has been impeded by the limited characterization of interactions governing MP function. Here, using mass spectrometry (MS)-based analysis of 210 affinity-purified mitochondrial (mt) fractions isolated from 27 epitope-tagged human ND-linked MPs in HEK293 cells, we report a high-confidence MP network including 1,964 interactions among 772 proteins (>90% previously unreported). Nearly three-fourths of these interactions were confirmed in mouse brain and multiple human differentiated neuronal cell lines by primary antibody immunoprecipitation and MS, with many linked to NDs and autism. We show that the SOD1-PRDX5 interaction, critical for mt redox homeostasis, can be perturbed by amyotrophic lateral sclerosis-linked SOD1 allelic variants and establish a functional role for ND-linked factors coupled with IκBɛ in NF-κB activation. Our results identify mechanisms for ND-linked MPs and expand the human mt interaction landscape.

Estimation of relative effectiveness of phylogenetic programs by machine learning.

Reconstruction of phylogeny of a protein family from a sequence alignment can produce results of different quality. Our goal is to predict the quality of phylogeny reconstruction basing on features that can be extracted from the input alignment. We used Fitch-Margoliash (FM) method of phylogeny reconstruction and random forest as a predictor. For training and testing the predictor, alignments of orthologous series (OS) were used, for which the result of phylogeny reconstruction can be evaluated by comparison with trees of corresponding organisms. Our results show that the quality of phylogeny reconstruction can be predicted with more than 80% precision. Also, we tried to predict which phylogeny reconstruction method, FM or UPGMA, is better for a particular alignment. With the used set of features, among alignments for which the obtained predictor predicts a better performance of UPGMA, 56% really give a better result with UPGMA. Taking into account that in our testing set only for 34% alignments UPGMA performs better, this result shows a principal possibility to predict the better phylogeny reconstruction method basing on features of a sequence alignment.

Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis.

Knowledge about non-interacting proteins (NIPs) is important for training the algorithms to predict protein-protein interactions (PPIs) and for assessing the false positive rates of PPI detection efforts. We present the second version of Negatome, a database of proteins and protein domains that are unlikely to engage in physical interactions (available online at http://mips.helmholtz-muenchen.de/proj/ppi/negatome). Negatome is derived by manual curation of literature and by analyzing three-dimensional structures of protein complexes. The main methodological innovation in Negatome 2.0 is the utilization of an advanced text mining procedure to guide the manual annotation process. Potential non-interactions were identified by a modified version of Excerbt, a text mining tool based on semantic sentence analysis. Manual verification shows that nearly a half of the text mining results with the highest confidence values correspond to NIP pairs. Compared to the first version the contents of the database have grown by over 300%.

Prediction of protein interaction types based on sequence and network features.

BACKGROUND: Protein interactions mediate a wide spectrum of functions in various cellular contexts. Functional versatility of protein complexes is due to a broad range of structural adaptations that determine their binding affinity, the number of interaction sites, and the lifetime. In terms of stability it has become customary to distinguish between obligate and non-obligate interactions dependent on whether or not the protomers can exist independently. In terms of spatio-temporal control protein interactions can be either simultaneously possible (SP) or mutually exclusive (ME). In the former case a network hub interacts with several proteins at the same time, offering each of them a separate interface, while in the latter case the hub interacts with its partners one at a time via the same binding site. So far different types of interactions were distinguished based on the properties of the corresponding binding interfaces derived from known three-dimensional structures of protein complexes. RESULTS: Here we present PiType, an accurate 3D structure-independent computational method for classifying protein interactions into simultaneously possible (SP) and mutually exclusive (ME) as well as into obligate and non-obligate. Our classifier exploits features of the binding partners predicted from amino acid sequence, their functional similarity, and network topology. We find that the constituents of non-obligate complexes possess a higher degree of structural disorder, more short linear motifs, and lower functional similarity compared to obligate interaction partners while SP and ME interactions are characterized by significant differences in network topology. Each interaction type is associated with a distinct set of biological functions. Moreover, interactions within multi-protein complexes tend to be enriched in one type of interactions. CONCLUSION: PiType does not rely on atomic structures and is thus suitable for characterizing proteome-wide interaction datasets. It can also be used to identify sub-modules within protein complexes. PiType is available for download as a self-installing package from http://webclu.bio.wzw.tum.de/PiType/PiType.zip.