A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning.

Acidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, "fuzzy" interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.

Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins.

MOTIVATION: Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do not take multivalent binding into account. RESULTS: We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two cooperatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions. AVAILABILITY AND IMPLEMENTATION: BMF source code is available at https://github.com/soedinglab/bipartite_motif_finder under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fast and accurate phylogeny reconstruction using filtered spaced-word matches.

Motivation: Word-based or 'alignment-free' algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods. Results: We propose Filtered Spaced Word Matches (FSWM) , a fast alignment-free approach to estimate phylogenetic distances between large genomic sequences. For a pre-defined binary pattern of match and don't-care positions, FSWM rapidly identifies spaced word-matches between input sequences, i.e. gap-free local alignments with matching nucleotides at the match positions and with mismatches allowed at the don't-care positions. We then estimate the number of nucleotide substitutions per site by considering the nucleotides aligned at the don't-care positions of the identified spaced-word matches. To reduce the noise from spurious random matches, we use a filtering procedure where we discard all spaced-word matches for which the overall similarity between the aligned segments is below a threshold. We show that our approach can accurately estimate substitution frequencies even for distantly related sequences that cannot be analyzed with existing alignment-free methods; phylogenetic trees constructed with FSWM distances are of high quality. A program run on a pair of eukaryotic genomes of a few hundred Mb each takes a few minutes. Availability and Implementation: The program source code for FSWM including a documentation, as well as the software that we used to generate artificial genome sequences are freely available at http://fswm.gobics.de/. Contact: chris.leimeister@stud.uni-goettingen.de. Supplementary information: Supplementary data are available at Bioinformatics online.

A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis.

Focal Segmental Glomerulosclerosis (FSGS) is a type of nephrotic syndrome which accounts for 20 and 40 % of such cases in children and adults, respectively. The high prevalence of FSGS makes it the most common primary glomerular disorder causing end-stage renal disease. Although the pathogenesis of this disorder has been widely investigated, the exact mechanism underlying this disease is still to be discovered. Current therapies seek to stop the progression of FSGS and often fail to cure the patients since progression to end-stage renal failure is usually inevitable. In the present work, we use a kidney-specific metabolic network model to study FSGS. The model was obtained by merging two previously published kidney-specific metabolic network models. The validity of the new model was checked by comparing the inactivating reaction genes identified in silico to the list of kidney disease implicated genes. To model the disease state, we used a complete list of FSGS metabolic biomarkers extracted from transcriptome and proteome profiling of patients as well as genetic deficiencies known to cause FSGS. We observed that some specific pathways including chondroitin sulfate degradation, eicosanoid metabolism, keratan sulfate biosynthesis, vitamin B6 metabolism, and amino acid metabolism tend to show variations in FSGS model compared to healthy kidney. Furthermore, we computationally searched for the potential drug targets that can revert the diseased metabolic state to the healthy state. Interestingly, only one drug target, N-acetylgalactosaminidase, was found whose inhibition could alter cellular metabolism towards healthy state.

Cooperativity boosts affinity and specificity of proteins with multiple RNA-binding domains.

Numerous cellular processes rely on the binding of proteins with high affinity to specific sets of RNAs. Yet most RNA-binding domains display low specificity and affinity in comparison to DNA-binding domains. The best binding motif is typically only enriched by less than a factor 10 in high-throughput RNA SELEX or RNA bind-n-seq measurements. Here, we provide insight into how cooperative binding of multiple domains in RNA-binding proteins (RBPs) can boost their effective affinity and specificity orders of magnitude higher than their individual domains. We present a thermodynamic model to calculate the effective binding affinity (avidity) for idealized, sequence-specific RBPs with any number of RBDs given the affinities of their isolated domains. For seven proteins in which affinities for individual domains have been measured, the model predictions are in good agreement with measurements. The model also explains how a two-fold difference in binding site density on RNA can increase protein occupancy 10-fold. It is therefore rationalized that local clusters of binding motifs are the physiological binding targets of multi-domain RBPs.

Mechanisms for Active Regulation of Biomolecular Condensates.

Liquid-liquid phase separation is a key organizational principle in eukaryotic cells, on par with intracellular membranes. It allows cells to concentrate specific proteins into condensates, increasing reaction rates and achieving switch-like regulation. We propose two active mechanisms that can explain how cells regulate condensate formation and size. In both, the cell regulates the activity of an enzyme, often a kinase, that adds post-translational modifications to condensate proteins. In enrichment inhibition, the enzyme enriches in the condensate and weakens interactions, as seen in stress granules (SGs), Cajal bodies, and P granules. In localization-induction, condensates form around immobilized enzymes that strengthen interactions, as observed in DNA repair, transmembrane signaling, and microtubule assembly. These models can guide studies into the many emerging roles of biomolecular condensates.

Transcriptome maps of general eukaryotic RNA degradation factors.

RNA degradation pathways enable RNA processing, the regulation of RNA levels, and the surveillance of aberrant or poorly functional RNAs in cells. Here we provide transcriptome-wide RNA-binding profiles of 30 general RNA degradation factors in the yeast Saccharomyces cerevisiae. The profiles reveal the distribution of degradation factors between different RNA classes. They are consistent with the canonical degradation pathway for closed-loop forming mRNAs after deadenylation. Modeling based on mRNA half-lives suggests that most degradation factors bind intact mRNAs, whereas decapping factors are recruited only for mRNA degradation, consistent with decapping being a rate-limiting step. Decapping factors preferentially bind mRNAs with non-optimal codons, consistent with rapid degradation of inefficiently translated mRNAs. Global analysis suggests that the nuclear surveillance machinery, including the complexes Nrd1/Nab3 and TRAMP4, targets aberrant nuclear RNAs and processes snoRNAs.

Tissue engineering; strategies, tissues, and biomaterials.

Current tissue regenerative strategies rely mainly on tissue repair by transplantation of the synthetic/natural implants. However, limitations of the existing strategies have increased the demand for tissue engineering approaches. Appropriate cell source, effective cell modification, and proper supportive matrices are three bases of tissue engineering. Selection of appropriate methods for cell stimulation, scaffold synthesis, and tissue transplantation play a definitive role in successful tissue engineering. Although the variety of the players are available, but proper combination and functional synergism determine the practical efficacy. Hence, in this review, a comprehensive view of tissue engineering and its different aspects are investigated.