Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.

Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

Evidence demonstrates that brain-derived neurotrophic factor (BDNF) has a pivotal role in the pathogenesis of major depressive disorder (MDD). Precursor-BDNF (proBDNF) and mature BDNF (mBDNF) have opposing biological effects in neuroplasticity, and the tissue-type plasminogen activator (tPA)/plasmin system is crucial in the cleavage processing of proBDNF to mBDNF. However, very little is known about the role of the tPA–BDNF pathway in MDD. We examined serum protein concentrations in the tPA–BDNF pathway, including tPA, BDNF, tropomyosin receptor kinase B (TrkB), proBDNF and p75NTR, obtained from 35 drug-free depressed patients before and after 8 weeks of escitalopram (mean 12.5 mg per day) or duloxetine (mean 64 mg per day) treatment and 35 healthy controls using sandwich ELISA (enzyme-linked immunosorbent assay) methods. Serum tPA and BDNF and the ratio of BDNF/proBDNF were significantly lower in the MDD patients than in controls, whereas TrkB, proBDNF and its receptor p75NTR were higher. After 8 weeks of treatment, tPA, BDNF and proBDNF and the BDNF/proBDNF ratio were reversed, but p75NTR was higher than baseline, and TrkB was not significantly changed. tPA, BDNF, TrkB, proBDNF and p75NTR all yielded fairly good or excellent diagnostic performance (area under the receiver operating characteristic curve (AUC) >0.8 or 0.9). Combination of these five proteins demonstrated much better diagnostic effectiveness (AUC: 0.977) and adequate sensitivity and specificity of 88.1% and 92.7%, respectively. Our results suggest that the tPA–BDNF lysis pathway may be implicated in the pathogenesis of MDD and the mechanisms underlying antidepressant therapeutic action. The combination of tPA, BDNF, TrkB, proBDNF and p75NTR may provide a diagnostic biomarker panel for MDD.