Ribosomal Database Project (RDP; http://rdp.cme.msu.edu/) provides the research community with aligned and annotated rRNA gene sequence data, along with tools to allow researchers to analyze their own rRNA gene sequences in the RDP framework. RDP data and tools are utilized in fields as diverse as human health, microbial ecology, environmental microbiology, nucleic acid chemistry, taxonomy and phylogenetics. In addition to aligned and annotated collections of bacterial and archaeal small subunit rRNA genes, RDP now includes a collection of fungal large subunit rRNA genes. RDP tools, including Classifier and Aligner, have been updated to work with this new fungal collection. The use of high-throughput sequencing to characterize environmental microbial populations has exploded in the past several years, and as sequence technologies have improved, the sizes of environmental datasets have increased. With release 11, RDP is providing an expanded set of tools to facilitate analysis of high-throughput data, including both single-stranded and paired-end reads. In addition, most tools are now available as open source packages for download and local use by researchers with high-volume needs or who would like to develop custom analysis pipelines.

Ribosomal Database Project: data and tools for high throughput rRNA analysis

Motivation: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. Results: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (� 7 times shorter hit list before expansion), faster (� 6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation. Availability and implementation: Web access and file download from UniProt website at http:// www.uniprot.org/uniref and ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. BLAST searches against UniRef are available at http://www.uniprot.org/blast/

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Detection of protein homology via sequence similarity has important applications in biology, from protein structure and function prediction to reconstruction of phylogenies. Although current methods for aligning protein sequences are powerful, challenges remain, including problems with homologous overextension of alignments and with regions under convergent evolution. Here, we test the ability of the profile hidden Markov model method HMMER3 to correctly assign homologous sequences to >13,000 manually curated families from the Pfam database. We identify problem families using protein regions that match two or more Pfam families not currently annotated as related in Pfam. We find that HMMER3 E-value estimates seem to be less accurate for families that feature periodic patterns of compositional bias, such as the ones typically observed in coiled-coils. These results support the continued use of manually curated inclusion thresholds in the Pfam database, especially on the subset of families that have been identified as problematic in experiments such as these. They also highlight the need for developing new methods that can correct for this particular type of compositional bias.

/pdf/challenges-in-homology-search-hmmer3-and-convergent-2xlrynesv8.pdf

Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions

A Deep Learning Approach to Antibiotic Discovery

Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.

/pdf/caver-3-0-a-tool-for-the-analysis-of-transport-pathways-in-4w4rrj5thk.pdf

CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures.

With many genomes now sequenced, computational annotation methods to characterize genes and proteins from their sequence are increasingly important. The BioSapiens Network has developed tools to address all stages of this process, and here we review progress in the automated prediction of protein function based on protein sequence and structure.

/pdf/protein-function-annotation-by-homology-based-inference-gxtxvtowuj.pdf

Protein function annotation by homology-based inference

Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets.

Application: We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without explicitly requiring all dissimilarities in memory. The algorithms are general and are applicable to any dataset. We present a data-dependent characterization of hardness and clustering efficiency. The presented concepts are applicable to any agglomerative clustering formulation.

Results: We apply our algorithm to the entire collection of protein sequences, to automatically build a comprehensive evolutionary-driven hierarchy of proteins from sequence alone. The newly created tree captures protein families better than state-of-the-art large-scale methods such as CluSTr, ProtoNet4 or single-linkage clustering. We demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data. Furthermore, we argue that non-metric constraints are an inherent complexity of the sequence space and should not be overlooked. The robustness of UPGMA allows significant improvement, especially for multidomain proteins, and for large or divergent families.

Availability: A comprehensive tree built from all UniProt sequence similarities, together with navigation and classification tools will be made available as part of the ProtoNet service. A C++ implementation of the algorithm is available on request.

Contact: lonshy@cs.huji.ac.il

Efficient algorithms for accurate hierarchical clustering of huge datasets

Motivation: Mapping of remote evolutionary links is a classic computational problem of much interest. Relating protein families allows for functional and structural inference on uncharacterized families. Since sequences have diverged beyond reliable alignment, these are too remote to identify by conventional methods.

Approach: We present a method to systematically identify remote evolutionary relations between protein families, leveraging a novel evolutionary-driven tree of all protein sequences and families. A global approach which considers the entire volume of similarities while clustering sequences, leads to a robust tree that allows tracing of very faint evolutionary links. The method systematically scans the tree for clusters which partition exceptionally well into extant protein families, thus suggesting an evolutionary breakpoint in a putative ancient superfamily. Our method does not require family pro.les (or HMMs), or multiple alignment.

Results: Considering the entire Pfam database, we are able to suggest 710 links between protein families, 125 of which are con.rmed by existence of Pfam clans. The quality of our predictions is also validated by structural assignments. We further provide an intrinsic characterization of the validity of our results and provide examples for new biological.ndings, from our systematic scan. For example, we are able to relate several bacterial pore-forming toxin families, and then link them with a novel family of eukaryotic toxins expressed in plants,.sh venom and notably also uncharacterized proteins from human pathogens.

Availability: A detailed list of putative homologous superfamilies, including 210 families of unknown function, has been made available online: http://www.protonet.cs.huji.ac.il/dots

Contact: lonshy@cs.huji.ac.il

Connect the dots

The explosive growth in the number of sequenced genomes has created a flood of protein sequences with unknown structure and function A routine protocol for functional inference on an input query sequence is based on a database search for homologues Searching a query against a non-redundant database using BLAST (or more advanced methods, eg PSI-BLAST) suffers from several drawbacks: (i) a local alignment often dominates the results; (ii) the reported statistical score (ie E-value) is often misleading; (iii) incorrect annotations may be falsely propagated Several systematic methods are commonly used to assign sequences with functions on a genomic scale In Pfam (1) and resources alike, statistical profiles (HMMs) are built from semi-manual multiple alignments of seed homologous sequences The profiles are then used to scan genomic sequences for additional family members The drawbacks of this scheme are: (i) only families with a predetermined seed are considered; (ii) the query must have a detectable sequence similarity to seed sequences; (iii) attention to internal relationships among the family members or the relations to other families is lacking; (iv) family membership is often set by pre-determined thresholdsAn alternative to profile or model based methods for functional inference relies on a hierarchical clustering of the protein space, as implemented in the ProtoNet approach (2) The fundamental principle is the creation of a tree that captures evolutionary relatedness among protein families The tree construction is fully automatic, and is based only on reported BLAST similarities among clustered sequences The tree provides protein groupings in continuous evolutionary granularities, from closely related to distant superfamilies Clusters in the ProtoNet tree show high correspondence with homologous sequence (ie Pfam and InterPro), functional (ie EC classification) and structural (ie, SCOP) families (3) A new clustering scheme (4) has provided an extensive update to the ProtoNet process, which is now based on direct clustering of all detectable sequence similarities Herein, we use the ProtoNet resource to develop a methodology for a consistent and safe functional inference for remote families We illustrate the success of our approach towards clusters of poorly characterized viral proteins Viral sequences are characterized by a rapid evolutionary rate which drives viral families to be even more remote (sequence-similarity-wise) Thus, functional inference for viral families is apparently an unsolved task Despite this inherent difficulty, the new ProtoNet tree scaffold reliably captures weak evolutionary connections for viral families, which were previously overlooked We take advantage of this, and propose new functional assignments for viral protein families

Yaniv Loewenstein

Papers

Protein function annotation by homology-based inference

Efficient algorithms for accurate hierarchical clustering of huge datasets

Connect the dots

Safe Functional Inference for Uncharacterized Viral Proteins