Nadav Rappoport

ProtoNet 6.0: organizing 10 million protein sequences in a compact hierarchical family tree

ProtoNet 6.0 (http://www.protonet.cs.huji.ac.il) is a data structure of protein families that cover the protein sequence space. These families are generated through an unsupervised bottom–up clustering algorithm. This algorithm organizes large sets of proteins in a hierarchical tree that yields high-quality protein families. The 2012 ProtoNet (Version 6.0) tree includes over 9 million proteins of which 5.5% come from UniProtKB/SwissProt and the rest from UniProtKB/TrEMBL. The hierarchical tree structure is based on an all-against-all comparison of 2.5 million representatives of UniRef50. Rigorous annotation-based quality tests prune the tree to most informative 162 088 clusters. Every high-quality cluster is assigned a ProtoName that reflects the most significant annotations of its proteins. These annotations are dominated by GO terms, UniProt/Swiss-Prot keywords and InterPro. ProtoNet 6.0 operates in a default mode. When used in the advanced mode, this data structure offers the user a view of the family tree at any desired level of resolution. Systematic comparisons with previous versions of ProtoNet are carried out. They show how our view of protein families evolves, as larger parts of the sequence space become known. ProtoNet 6.0 provides numerous tools to navigate the hierarchy of clusters.

Viral proteins acquired from a host converge to simplified domain architectures.

The infection cycle of viruses creates many opportunities for the exchange of genetic material with the host. Many viruses integrate their sequences into the genome of their host for replication. These processes may lead to the virus acquisition of host sequences. Such sequences are prone to accumulation of mutations and deletions. However, in rare instances, sequences acquired from a host become beneficial for the virus. We searched for unexpected sequence similarity among the 900,000 viral proteins and all proteins from cellular organisms. Here, we focus on viruses that infect metazoa. The high-conservation analysis yielded 187 instances of highly similar viral-host sequences. Only a small number of them represent viruses that hijacked host sequences. The low-conservation sequence analysis utilizes the Pfam family collection. About 5% of the 12,000 statistical models archived in Pfam are composed of viral-metazoan proteins. In about half of Pfam families, we provide indirect support for the directionality from the host to the virus. The other families are either wrongly annotated or reflect an extensive sequence exchange between the viruses and their hosts. In about 75% of cross-taxa Pfam families, the viral proteins are significantly shorter than their metazoan counterparts. The tendency for shorter viral proteins relative to their related host proteins accounts for the acquisition of only a fragment of the host gene, the elimination of an internal domain and shortening of the linkers between domains. We conclude that, along viral evolution, the host-originated sequences accommodate simplified domain compositions. We postulate that the trimmed proteins act by interfering with the fundamental function of the host including intracellular signaling, post-translational modification, protein-protein interaction networks and cellular trafficking. We compiled a collection of hijacked protein sequences. These sequences are attractive targets for manipulation of viral infection.

PANDORA: analysis of protein and peptide sets through the hierarchical integration of annotations

Derivation of biological meaning from large sets of proteins or genes is a frequent task in genomic and proteomic studies. Such sets often arise from experimental methods including large-scale gene expression experiments and mass spectrometry (MS) proteomics. Large sets of genes or proteins are also the outcome of computational methods such as BLAST search and homology-based classifications. We have developed the PANDORA web server, which functions as a platform for the advanced biological analysis of sets of genes, proteins, or proteolytic peptides. First, the input set is mapped to a set of corresponding proteins. Then, an analysis of the protein set produces a graph-based hierarchy which highlights intrinsic relations amongst biological subsets, in light of their different annotations from multiple annotation resources. PANDORA integrates a large collection of annotation sources (GO, UniProt Keywords, InterPro, Enzyme, SCOP, CATH, Gene-3D, NCBI taxonomy and more) that comprise ∼200 000 different annotation terms associated with ∼3.2 million sequences from UniProtKB. Statistical enrichment based on a binomial approximation of the hypergeometric distribution and corrected for multiple hypothesis tests is calculated using several background sets, including major gene-expression DNA-chip platforms. Users can also visualize either standard or user-defined binary and quantitative properties alongside the proteins. PANDORA 4.2 is available at http://www.pandora.cs.huji.ac.il.

NeuroPID: a classifier of neuropeptide precursors

Neuropeptides (NPs) are short secreted peptides produced in neurons. NPs act by activating signaling cascades governing broad functions such as metabolism, sensation and behavior throughout the animal kingdom. NPs are the products of multistep processing of longer proteins, the NP precursors (NPPs). We present NeuroPID (Neuropeptide Precursor Identifier), an online machine-learning tool that identifies metazoan NPPs. NeuroPID was trained on 1418 NPPs annotated as such by UniProtKB. A large number of sequence-based features were extracted for each sequence with the goal of capturing the biophysical and informational-statistical properties that distinguish NPPs from other proteins. Training several machine-learning models, including support vector machines and ensemble decision trees, led to high accuracy (89–94%) and precision (90–93%) in cross-validation tests. For inputs of thousands of unseen sequences, the tool provides a ranked list of high quality predictions based on the results of four machine-learning classifiers. The output reveals many uncharacterized NPPs and secreted cell modulators that are rich in potential cleavage sites. NeuroPID is a discovery and a prediction tool that can be used to identify NPPs from unannotated transcriptomes and mass spectrometry experiments. NeuroPID predicted sequences are attractive targets for investigating behavior, physiology and cell modulation. The NeuroPID web tool is available at http:// neuropid.cs.huji.ac.il.

A genome-wide association study identifies only two ancestry specific variants associated with spontaneous preterm birth

Preterm birth (PTB), or the delivery prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. Although twin studies estimate that maternal genetic contributions account for approximately 30% of the incidence of PTB, and other studies reported fetal gene polymorphism association, to date no consistent associations have been identified. In this study, we performed the largest reported genome-wide association study analysis on 1,349 cases of PTB and 12,595 ancestry-matched controls from the focusing on genomic fetal signals. We tested over 2 million single nucleotide polymorphisms (SNPs) for associations with PTB across five subpopulations: African (AFR), the Americas (AMR), European, South Asian, and East Asian. We identified only two intergenic loci associated with PTB at a genome-wide level of significance: rs17591250 (P = 4.55E-09) on chromosome 1 in the AFR population and rs1979081 (P = 3.72E-08) on chromosome 8 in the AMR group. We have queried several existing replication cohorts and found no support of these associations. We conclude that the fetal genetic contribution to PTB is unlikely due to single common genetic variant, but could be explained by interactions of multiple common variants, or of rare variants affected by environmental influences, all not detectable using a GWAS alone.

Overlooked Short Toxin-Like Proteins: A Shortcut to Drug Design

Short stable peptides have huge potential for novel therapies and biosimilars. Cysteine-rich short proteins are characterized by multiple disulfide bridges in a compact structure. Many of these metazoan proteins are processed, folded, and secreted as soluble stable folds. These properties are shared by both marine and terrestrial animal toxins. These stable short proteins are promising sources for new drug development. We developed ClanTox (classifier of animal toxins) to identify toxin-like proteins (TOLIPs) using machine learning models trained on a large-scale proteomic database. Insects proteomes provide a rich source for protein innovations. Therefore, we seek overlooked toxin-like proteins from insects (coined iTOLIPs). Out of 4180 short (<75 amino acids) secreted proteins, 379 were predicted as iTOLIPs with high confidence, with as many as 30% of the genes marked as uncharacterized. Based on bioinformatics, structure modeling, and data-mining methods, we found that the most significant group of predicted iTOLIPs carry antimicrobial activity. Among the top predicted sequences were 120 termicin genes from termites with antifungal properties. Structural variations of insect antimicrobial peptides illustrate the similarity to a short version of the defensin fold with antifungal specificity. We also identified 9 proteins that strongly resemble ion channel inhibitors from scorpion and conus toxins. Furthermore, we assigned functional fold to numerous uncharacterized iTOLIPs. We conclude that a systematic approach for finding iTOLIPs provides a rich source of peptides for drug design and innovative therapeutic discoveries.

Trends in genome dynamics among major orders of insects revealed through variations in protein families

BackgroundInsects belong to a class that accounts for the majority of animals on earth. With over one million identified species, insects display a huge diversity and occupy extreme environments. At present, there are dozens of fully sequenced insect genomes that cover a range of habitats, social behavior and morphologies. In view of such diverse collection of genomes, revealing evolutionary trends and charting functional relationships of proteins remain challenging.ResultsWe analyzed the relatedness of 17 complete proteomes representative of proteomes from insects including louse, bee, beetle, ants, flies and mosquitoes, as well as an out-group from the crustaceans. The analyzed proteomes mostly represented the orders of Hymenoptera and Diptera. The 287,405 protein sequences from the 18 proteomes were automatically clustered into 20,933 families, including 799 singletons. A comprehensive analysis based on statistical considerations identified the families that were significantly expanded or reduced in any of the studied organisms. Among all the tested species, ants are characterized by an exceptionally high rate of family gain and loss. By assigning annotations to hundreds of species-specific families, the functional diversity among species and between the major clades (Diptera and Hymenoptera) is revealed. We found that many species-specific families are associated with receptor signaling, stress-related functions and proteases. The highest variability among insects associates with the function of transposition and nucleic acids processes (collectively coined TNAP). Specifically, the wasp and ants have an order of magnitude more TNAP families and proteins relative to species that belong to Diptera (mosquitoes and flies).ConclusionsAn unsupervised clustering methodology combined with a comparative functional analysis unveiled proteomic signatures in the major clades of winged insects. We propose that the expansion of TNAP families in Hymenoptera potentially contributes to the accelerated genome dynamics that characterize the wasp and ants.

Entropy-driven partitioning of the hierarchical protein space

Motivation: Modern protein sequencing techniques have led to the determination of >50 million protein sequences. ProtoNet is a clustering system that provides a continuous hierarchical agglomerative clustering tree for all proteins. While ProtoNet performs unsupervised classification of all included proteins, finding an optimal level of granularity for the purpose of focusing on protein functional groups remain elusive. Here, we ask whether knowledge-based annotations on protein families can support the automatic unsupervised methods for identifying high-quality protein families. We present a method that yields within the ProtoNet hierarchy an optimal partition of clusters, relative to manual annotation schemes. The method’s principle is to minimize the entropy-derived distance between annotation-based partitions and all available hierarchical partitions. We describe the best front (BF) partition of 2 478 328 proteins from UniRef50. Of 4 929 553 ProtoNet tree clusters, BF based on Pfam annotations contain 26 891 clusters. The high quality of the partition is validated by the close correspondence with the set of clusters that best describe thousands of keywords of Pfam. The BF is shown to be superior to naïve cut in the ProtoNet tree that yields a similar number of clusters. Finally, we used parameters intrinsic to the clustering process to enrich a priori the BF’s clusters. We present the entropy-based method’s benefit in overcoming the unavoidable limitations of nested clusters in ProtoNet. We suggest that this automatic information-based cluster selection can be useful for other large-scale annotation schemes, as well as for systematically testing and comparing putative families derived from alternative clustering methods. Availability and implementation: A catalog of BF clusters for thousands of Pfam keywords is provided at http://protonet.cs.huji.ac.il/bestFront/ Contact: michall@cc.huji.ac.il

Functional inference by ProtoNet family tree: the uncharacterized proteome of Daphnia pulex

BackgroundDaphnia pulex (Water flea) is the first fully sequenced crustacean genome. The crustaceans and insects have diverged from a common ancestor. It is a model organism for studying the molecular makeup for coping with the environmental challenges. In the complete proteome, there are 30,550 putative proteins. However, about 10,000 of them have no known homologues. Currently, the UniProtoKB reports on 95% of the Daphnias proteins as putative and uncharacterized proteins.ResultsWe have applied ProtoNet, an unsupervised hierarchical protein clustering method that covers about 10 million sequences, for automatic annotation of the Daphnias proteome. 98.7% (26,625) of the Daphnia full-length proteins were successfully mapped to 13,880 ProtoNet stable clusters, and only 1.3% remained unmapped. We compared the properties of the Daphnias protein families with those of the mouse and the fruitfly proteomes. Functional annotations were successfully assigned for 86% of the proteins. Most proteins (61%) were mapped to only 2953 clusters that contain Daphnias duplicated genes. We focused on the functionality of maximally amplified paralogs. Cuticle structure components and a variety of ion channels protein families were associated with a maximal level of gene amplification. We focused on gene amplification as a leading strategy of the Daphnia in coping with environmental toxicity.ConclusionsAutomatic inference is achieved through mapping of sequences to the protein family tree of ProtoNet 6.0. Applying a careful inference protocol resulted in functional assignments for over 86% of the complete proteome. We conclude that the scaffold of ProtoNet can be used as an alignment-free protocol for large-scale annotation task of uncharacterized proteomes.

Creating ethnicity-specific reference intervals for lab tests from EHR data

The results of clinical lab tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate local population and instruments variations. This approach is costly and can be biased. We describe a novel data-driven method for using electronic health record data to extract healthy patients’ information to define reference intervals. We found that the distributions of many clinical lab tests differ among self-identified racial and ethnic groups (SIREs) in healthy patients. Finally, we derived SIRE-specific reference intervals and provide evidence that these intervals have clinical prognostic value. Specifically, we show that for two lab tests, serum creatinine level and hemoglobin A1C, SIRE-specific reference intervals are more predictive for need for dialysis and development type 2 diabetes than existing reference intervals. One Sentence Summary A novel method for defining population-specific reference intervals of common clinical laboratory tests from electronical health records has better prognostic value than existing reference intervals.