Fast and accurate protein structure search with Foldseek.

As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing tertiary amino acid interactions within proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of Dali, TM-align and CE, respectively.

DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.

The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

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.

RNA sequencing indicates widespread conservation of circadian clocks in marine zooplankton.

Zooplankton are important eukaryotic constituents of marine ecosystems characterized by limited motility in the water. These metazoans predominantly occupy intermediate trophic levels and energetically link primary producers to higher trophic levels. Through processes including diel vertical migration (DVM) and production of sinking pellets they also contribute to the biological carbon pump which regulates atmospheric CO2 levels. Despite their prominent role in marine ecosystems, and perhaps, because of their staggering diversity, much remains to be discovered about zooplankton biology. In particular, the circadian clock, which is known to affect important processes such as DVM has been characterized only in a handful of zooplankton species. We present annotated de novo assembled transcriptomes from a diverse, representative cohort of 17 marine zooplankton representing six phyla and eight classes. These transcriptomes represent the first sequencing data for a number of these species. Subsequently, using translated proteomes derived from this data, we demonstrate in silico the presence of orthologs to most core circadian clock proteins from model metazoans in all sequenced species. Our findings, bolstered by sequence searches against publicly available data, indicate that the molecular machinery underpinning endogenous circadian clocks is widespread and potentially well conserved across marine zooplankton taxa.

Cln5 represents a new type of cysteine-based S-depalmitoylase linked to neurodegeneration.

Genetic CLN5 variants are associated with childhood neurodegeneration and Alzheimer's disease; however, the molecular function of ceroid lipofuscinosis neuronal protein 5 (Cln5) is unknown. We solved the Cln5 crystal structure and identified a region homologous to the catalytic domain of members of the N1pC/P60 superfamily of papain-like enzymes. However, we observed no protease activity for Cln5; and instead, we discovered that Cln5 and structurally related PPPDE1 and PPPDE2 have efficient cysteine palmitoyl thioesterase (S-depalmitoylation) activity using fluorescent substrates. Mutational analysis revealed that the predicted catalytic residues histidine-166 and cysteine-280 are critical for Cln5 thioesterase activity, uncovering a new cysteine-based catalytic mechanism for S-depalmitoylation enzymes. Last, we found that Cln5-deficient neuronal progenitor cells showed reduced thioesterase activity, confirming live cell function of Cln5 in setting S-depalmitoylation levels. Our results provide new insight into the function of Cln5, emphasize the importance of S-depalmitoylation in neuronal homeostasis, and disclose a new, unexpected enzymatic function for the N1pC/P60 superfamily of proteins.

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.

Tejaas: reverse regression increases power for detecting trans-eQTLs.

Trans-acting expression quantitative trait loci (trans-eQTLs) account for ≥70% expression heritability and could therefore facilitate uncovering mechanisms underlying the origination of complex diseases. Identifying trans-eQTLs is challenging because of small effect sizes, tissue specificity, and a severe multiple-testing burden. Tejaas predicts trans-eQTLs by performing L2-regularized "reverse" multiple regression of each SNP on all genes, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Combined with a novel unsupervised k-nearest neighbor method to remove confounders, Tejaas predicts 18851 unique trans-eQTLs across 49 tissues from GTEx. They are enriched in open chromatin, enhancers, and other regulatory regions. Many overlap with disease-associated SNPs, pointing to tissue-specific transcriptional regulation mechanisms.

SpacePHARER: sensitive identification of phages from CRISPR spacers in prokaryotic hosts.

SUMMARY: SpacePHARER (CRISPR Spacer Phage-Host Pair Finder) is a sensitive and fast tool for de novo prediction of phage-host relationships via identifying phage genomes that match CRISPR spacers in genomic or metagenomic data. SpacePHARER gains sensitivity by comparing spacers and phages at the protein level, optimizing its scores for matching very short sequences, and combining evidence from multiple matches, while controlling for false positives. We demonstrate SpacePHARER by searching a comprehensive spacer list against all complete phage genomes. AVAILABILITY AND IMPLEMENTATION: SpacePHARER is available as an open-source (GPLv3), user-friendly command-line software for Linux and macOS: https://github.com/soedinglab/spacepharer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bayesian Markov models improve the prediction of binding motifs beyond first order.

Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy. Models that can learn dependencies between positions, for instance, induced by DNA structure preferences, have yielded markedly improved predictions for most TFs on in vivo data. However, they are more prone to overfit the data and to learn patterns merely correlated with rather than directly involved in TF binding. We present an improved, faster version of our Bayesian Markov model software, BaMMmotif2. We tested it with state-of-the-art motif discovery tools on a large collection of ChIP-seq and HT-SELEX datasets. BaMMmotif2 models of fifth-order achieved a median false-discovery-rate-averaged recall 13.6% and 12.2% higher than the next best tool on 427 ChIP-seq datasets and 164 HT-SELEX datasets, respectively, while being 8 to 1000 times faster. BaMMmotif2 models showed no signs of overtraining in cross-cell line and cross-platform tests, with similar improvements on the next-best tool. These results demonstrate that dependencies beyond first order clearly improve binding models for most TFs.

Evidence for additive and synergistic action of mammalian enhancers during cell fate determination.

Enhancer activity drives cell differentiation and cell fate determination, but it remains unclear how enhancers cooperate during these processes. Here we investigate enhancer cooperation during transdifferentiation of human leukemia B-cells to macrophages. Putative enhancers are established by binding of the pioneer factor C/EBPα followed by chromatin opening and enhancer RNA (eRNA) synthesis from H3K4-monomethylated regions. Using eRNA synthesis as a proxy for enhancer activity, we find that most putative enhancers cooperate in an additive way to regulate transcription of assigned target genes. However, transcription from 136 target genes depends exponentially on the summed activity of its putative paired enhancers, indicating that these enhancers cooperate synergistically. The target genes are cell type-specific, suggesting that enhancer synergy can contribute to cell fate determination. Enhancer synergy appears to depend on cell type-specific transcription factors, and such interacting enhancers are not predicted from occupancy or accessibility data that are used to detect superenhancers.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Protein Sequence Analysis Using the MPI Bioinformatics Toolkit.

The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) provides interactive access to a wide range of the best-performing bioinformatics tools and databases, including the state-of-the-art protein sequence comparison methods HHblits and HHpred. The Toolkit currently includes 35 external and in-house tools, covering functionalities such as sequence similarity searching, prediction of sequence features, and sequence classification. Due to this breadth of functionality, the tight interconnection of its constituent tools, and its ease of use, the Toolkit has become an important resource for biomedical research and for teaching protein sequence analysis to students in the life sciences. In this article, we provide detailed information on utilizing the three most widely accessed tools within the Toolkit: HHpred for the detection of homologs, HHpred in conjunction with MODELLER for structure prediction and homology modeling, and CLANS for the visualization of relationships in large sequence datasets. © 2020 The Authors. Basic Protocol 1: Sequence similarity searching using HHpred Alternate Protocol: Pairwise sequence comparison using HHpred Support Protocol: Building a custom multiple sequence alignment using PSI-BLAST and forwarding it as input to HHpred Basic Protocol 2: Calculation of homology models using HHpred and MODELLER Basic Protocol 3: Cluster analysis using CLANS.

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.

MetaEuk-sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics.

BACKGROUND: Metagenomics is revolutionizing the study of microorganisms and their involvement in biological, biomedical, and geochemical processes, allowing us to investigate by direct sequencing a tremendous diversity of organisms without the need for prior cultivation. Unicellular eukaryotes play essential roles in most microbial communities as chief predators, decomposers, phototrophs, bacterial hosts, symbionts, and parasites to plants and animals. Investigating their roles is therefore of great interest to ecology, biotechnology, human health, and evolution. However, the generally lower sequencing coverage, their more complex gene and genome architectures, and a lack of eukaryote-specific experimental and computational procedures have kept them on the sidelines of metagenomics. RESULTS: MetaEuk is a toolkit for high-throughput, reference-based discovery, and annotation of protein-coding genes in eukaryotic metagenomic contigs. It performs fast searches with 6-frame-translated fragments covering all possible exons and optimally combines matches into multi-exon proteins. We used a benchmark of seven diverse, annotated genomes to show that MetaEuk is highly sensitive even under conditions of low sequence similarity to the reference database. To demonstrate MetaEuk's power to discover novel eukaryotic proteins in large-scale metagenomic data, we assembled contigs from 912 samples of the Tara Oceans project. MetaEuk predicted >12,000,000 protein-coding genes in 8 days on ten 16-core servers. Most of the discovered proteins are highly diverged from known proteins and originate from very sparsely sampled eukaryotic supergroups. CONCLUSION: The open-source (GPLv3) MetaEuk software (https://github.com/soedinglab/metaeuk) enables large-scale eukaryotic metagenomics through reference-based, sensitive taxonomic and functional annotation. Video abstract.

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.

HH-suite3 for fast remote homology detection and deep protein annotation.

BACKGROUND: HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. RESULTS: We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite . CONCLUSION: The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

Lysine/RNA-interactions drive and regulate biomolecular condensation.

Cells form and use biomolecular condensates to execute biochemical reactions. The molecular properties of non-membrane-bound condensates are directly connected to the amino acid content of disordered protein regions. Lysine plays an important role in cellular function, but little is known about its role in biomolecular condensation. Here we show that protein disorder is abundant in protein/RNA granules and lysine is enriched in disordered regions of proteins in P-bodies compared to the entire human disordered proteome. Lysine-rich polypeptides phase separate into lysine/RNA-coacervates that are more dynamic and differ at the molecular level from arginine/RNA-coacervates. Consistent with the ability of lysine to drive phase separation, lysine-rich variants of the Alzheimer's disease-linked protein tau undergo coacervation with RNA in vitro and bind to stress granules in cells. Acetylation of lysine reverses liquid-liquid phase separation and reduces colocalization of tau with stress granules. Our study establishes lysine as an important regulator of cellular condensation.

Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold.

The open-source de novo protein-level assembler, Plass ( https://plass.mmseqs.com ), assembles six-frame-translated sequencing reads into protein sequences. It recovers 2-10 times more protein sequences from complex metagenomes and can assemble huge datasets. We assembled two redundancy-filtered reference protein catalogs, 2 billion sequences from 640 soil samples (soil reference protein catalog) and 292 million sequences from 775 marine eukaryotic metatranscriptomes (marine eukaryotic reference catalog), the largest free collections of protein sequences.

PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes.

SUMMARY: Cellular lineage trees can be derived from single-cell RNA sequencing snapshots of differentiating cells. Currently, only datasets with simple topologies are available. To test and further develop tools for lineage tree reconstruction, we need test datasets with known complex topologies. PROSSTT can simulate scRNA-seq datasets for differentiation processes with lineage trees of any desired complexity, noise level, noise model and size. PROSSTT also provides scripts to quantify the quality of predicted lineage trees. AVAILABILITY AND IMPLEMENTATION: https://github.com/soedinglab/prosstt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MMseqs2 desktop and local web server app for fast, interactive sequence searches.

SUMMARY: The MMseqs2 desktop and web server app facilitates interactive sequence searches through custom protein sequence and profile databases on personal workstations. By eliminating MMseqs2's runtime overhead, we reduced response times to a few seconds at sensitivities close to BLAST. AVAILABILITY AND IMPLEMENTATION: The app is easy to install for non-experts. GPLv3-licensed code, pre-built desktop app packages for Windows, MacOS and Linux, Docker images for the web server application and a demo web server are available at https://search.mmseqs.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.