Towards the Idea of Molecular Brains

How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors.

Centrality of drug targets in protein networks

Background In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target’s ‘fitness’ based on its node characteristics within annotated protein functional networks to complement contingent therapeutic hypotheses. Results We observed that targets of approved, selective small molecule drugs exhibit high node centrality within protein networks relative to a broader set of investigational targets spanning various development stages. Targets of approved drugs also exhibit higher centrality than other proteins within their respective functional class. These findings expand on previous reports of drug targets’ network centrality by suggesting some centrality metrics such as low topological coefficient as inherent characteristics of a ‘good’ target, relative to other exploratory targets and regardless of its functional class. These centrality metrics could thus be indicators of an individual protein’s ‘fitness’ as potential drug target. Correlations between protein nodes’ network centrality and number of associated publications underscored the possibility of knowledge bias as an inherent limitation to such predictions. Conclusions Despite some entanglement with knowledge bias, like structure-oriented ‘druggability’ assessments of new protein targets, centrality metrics could assist early pharmaceutical discovery teams in evaluating potential targets with limited experimental proof of concept and help allocate resources for an effective drug discovery pipeline.

Regulation of ER Composition and Extent, and Putative Action in Protein Networks by ER/NE Protein TMEM147

Nuclear envelope (NE) and endoplasmic reticulum (ER) collaborate to control a multitude of nuclear and cytoplasmic actions. In this context, the transmembrane protein TMEM147 localizes to both NE and ER, and through direct and indirect interactions regulates processes as varied as production and transport of multipass membrane proteins, neuronal signaling, nuclear-shape, lamina and chromatin dynamics and cholesterol synthesis. Aiming to delineate the emerging multifunctionality of TMEM147 more comprehensively, we set as objectives, first, to assess potentially more fundamental effects of TMEM147 on the ER and, second, to identify significantly TMEM147-associated cell-wide protein networks and pathways. Quantifying curved and flat ER markers RTN4 and CLIMP63/CKAP4, respectively, we found that TMEM147 silencing causes area and intensity increases for both RTN4 and CLIMP63, and the ER in general, with a profound shift toward flat areas, concurrent with reduction in DNA condensation. Protein network and pathway analyses based on comprehensive compilation of TMEM147 interactors, targets and co-factors then served to manifest novel and established roles for TMEM147. Thus, algorithmically simplified significant pathways reflect TMEM147 function in ribosome binding, oxidoreductase activity, G protein-coupled receptor activity and transmembrane transport, while analysis of protein factors and networks identifies hub proteins and corresponding pathways as potential targets of TMEM147 action and of future functional studies.