Assessing annotation transfer for genomics: quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores

A large-scale assessment of sequence database search tools for homology-based protein function prediction

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

NRPS-like ATRR in Plant-Parasitic Nematodes Involved in Glycine Betaine Metabolism to Promote Parasitism

Plant-parasitic nematodes (PPNs) are among the most serious phytopathogens and cause widespread and serious damage in major crops. In this study, using a genome mining method, we identified nonribosomal peptide synthetase (NRPS)-like enzymes in genomes of plant-parasitic nematodes, which are conserved with two consecutive reducing domains at the N-terminus (A-T-R1-R2) and homologous to fungal NRPS-like ATRR. We experimentally investigated the roles of the NRPS-like enzyme (MiATRR) in nematode (Meloidogyne incognita) parasitism. Heterologous expression of Miatrr in Saccharomyces cerevisiae can overcome the growth inhibition caused by high concentrations of glycine betaine. RT-qPCR detection shows that Miatrr is significantly upregulated at the early parasitic life stage (J2s in plants) of M. incognita. Host-derived Miatrr RNA interference (RNAi) in Arabidopsis thaliana can significantly decrease the number of galls and egg masses of M. incognita, as well as retard development and reduce the body size of the nematode. Although exogenous glycine betaine and choline have no obvious impact on the survival of free-living M. incognita J2s (pre-parasitic J2s), they impact the performance of the nematode in planta, especially in Miatrr-RNAi plants. Following application of exogenous glycine betaine and choline in the rhizosphere soil of A. thaliana, the numbers of galls and egg masses were obviously reduced by glycine betaine but increased by choline. Based on the knowledge about the function of fungal NRPS-like ATRR and the roles of glycine betaine in host plants and nematodes, we suggest that MiATRR is involved in nematode-plant interaction by acting as a glycine betaine reductase, converting glycine betaine to choline. This may be a universal strategy in plant-parasitic nematodes utilizing NRPS-like ATRR to promote their parasitism on host plants.

Aon: a service to augment Alliance Genome Resource data with additional species

OBJECTIVE

Cross-species comparative genomics requires access to accurate homology data across the entire range of annotated genes. The Alliance of Genome Resources (AGR) provides an open-source and comprehensive database of homology data calculated using a wide array of algorithms at differing stringencies to elucidate orthologous relationships. However, the current AGR application program interface (API) is limited to five homology endpoints for nine species. While AGR provides a robust resource for several canonical species, its utility can be greatly enhanced by increased filtering and data processing options and incorporating additional species.

RESULTS

Here, we describe a novel API tool, AON, that expands access to the AGR orthology resource by creating a data structure that supports 50 additional endpoints. More importantly, it provides users with a framework for adding bespoke endpoints, custom species, and additional orthology data. We demonstrate AON's functionality by incorporating the service into the GeneWeaver ecosystem for supporting cross-species data analysis.

A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Identification of regenerating island-derived protein 3E in dogs

Regenerating islet-derived protein (REG) 1A (aka pancreatic stone protein) and REG3A (aka pancreatitis-associated protein) are upregulated in humans with sepsis, pancreatitis, and gastrointestinal diseases, but little is known about this protein family in dogs. Our aim was to identify REG1 and REG3 family members in dogs. REG-family genes were computationally annotated in the canine genome and proteome, with verification of gene expression using publicly available RNA-seq data. The presence of the protein in canine pancreatic tissue and plasma was investigated with Western blot and immunohistochemistry, using anti-human REG1A and REG3A antibodies. Protein identity was confirmed with mass spectrometry. Two members of the REG3 subfamily were found in the canine genome, REG3E1 and REG3E2, both encoding for the same 176 AA protein, subsequently named REG3E. Anti-human REG3A antibodies demonstrated cross-reactivity with the canine REG3E protein in pancreas homogenates. In canine plasma, a protein band of approximately 17 kDa was apparent. Mass spectrometry confirmed this protein to be the product of the two annotated REG3E genes. Strong immunoreactivity to anti-human REG3A antibodies was found in sections of canine pancreas affected with acute pancreatitis, but it was weak in healthy pancreatic tissue. Recombinant canine REG3E protein underwent a selective trypsin digestion as described in other species. No evidence for the presence of a homolog of REG1A in dogs was found in any of the investigations. In conclusion, dogs express REG3E in the pancreas, whose role as biomarker merits further investigations. Homologs to human REG1A are not likely to exist in dogs.

Roles of Species-Specific Legumains in Pathogenicity of the Pinewood Nematode Bursaphelenchus xylophilus

Peptidases are very important to parasites, which have central roles in parasite biology and pathogenesis. In this study, by comparative genome analysis, genome-wide peptidase diversities among plant-parasitic nematodes are estimated. We find that genes encoding cysteine peptidases in family C13 (legumain) are significantly abundant in pine wood nematodes Bursaphelenchus genomes, compared to those in other plant-parasitic nematodes. By phylogenetic analysis, a clade of B. xylophilus-specific legumain is identified. RT-qPCR detection shows that these genes are highly expressed at early stage during the nematode infection process. Utilizing transgene technology, cDNAs of three species-specific legumain were introduced into the Arabidopsis γvpe mutant. Functional complementation assay shows that these B. xylophilus legumains can fully complement the activity of Arabidopsis γVPE to mediate plant cell death triggered by the fungal toxin FB1. Secretory activities of these legumains are experimentally validated. By comparative transcriptome analysis, genes involved in plant cell death mediated by legumains are identified, which enrich in GO terms related to ubiquitin protein transferase activity in category molecular function, and response to stimuli in category biological process. Our results suggest that B. xylophilu-specific legumains have potential as effectors to be involved in nematode-plant interaction and can be related to host cell death.

Discovering the Ultimate Limits of Protein Secondary Structure Prediction

Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81-86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4-5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84-87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

Genomic Analysis of Prophages from Klebsiella pneumoniae Clinical Isolates

Klebsiella pneumoniae is an increasing threat to public health and represents one of the most concerning pathogens involved in life-threatening infections. The resistant and virulence determinants are coded by mobile genetic elements which can easily spread between bacteria populations and co-evolve with its genomic host. In this study, we present the full genomic sequences, insertion sites and phylogenetic analysis of 150 prophages found in 40 K. pneumoniae clinical isolates obtained from an outbreak in a Portuguese hospital. All strains harbored at least one prophage and we identified 104 intact prophages (69.3%). The prophage size ranges from 29.7 to 50.6 kbp, coding between 32 and 78 putative genes. The prophage GC content is 51.2%, lower than the average GC content of 57.1% in K. pneumoniae. Complete prophages were classified into three families in the order Caudolovirales: Myoviridae (59.6%), Siphoviridae (38.5%) and Podoviridae (1.9%). In addition, an alignment and phylogenetic analysis revealed nine distinct clusters. Evidence of recombination was detected within the genome of some prophages but, in most cases, proteins involved in viral structure, transcription, replication and regulation (lysogenic/lysis) were maintained. These results support the knowledge that prophages are diverse and widely disseminated in K. pneumoniae genomes, contributing to the evolution of this species and conferring additional phenotypes. Moreover, we identified K. pneumoniae prophages in a set of endolysin genes, which were found to code for proteins with lysozyme activity, cleaving the β-1,4 linkages between N-acetylmuramic acid and N-acetyl-D-glucosamine residues in the peptidoglycan network and thus representing genes with the potential for lysin phage therapy.

Phylogenetic and Structural Analysis of NIN-Like Proteins With a Type I/II PB1 Domain That Regulates Oligomerization for Nitrate Response

Absorption of macronutrients such as nitrogen is a critical process for land plants. There is little information available on the correlation between the root evolution of land plants and the protein regulation of nitrogen absorption and responses. NIN-like protein (NLP) transcription factors contain a Phox and Bem1 (PB1) domain, which may regulate nitrate-response genes and seem to be involved in the adaptation to growing on land in terms of plant root development. In this report, we reveal the NLP phylogeny in land plants and the origin of NLP genes that may be involved in the nitrate-signaling pathway. Our NLP phylogeny showed that duplication of NLP genes occurred before divergence of chlorophyte and land plants. Duplicated NLP genes may lost in most chlorophyte lineages. The NLP genes of bryophytes were initially monophyletic, but this was followed by divergence of lycophyte NLP genes and then angiosperm NLP genes. Among those identified NLP genes, PB1, a protein-protein interaction domain was identified across our phylogeny. To understand how protein-protein interaction mediate via PB1 domain, we examined the PB1 domain of Arabidopsis thaliana NLP7 (AtNLP7) in terms of its molecular oligomerization and function as representative. Based on the structure of the PB1 domain, determined using small-angle x-ray scattering (SAXS) and site-directed mutagenesis, we found that the NLP7 PB1 protein forms oligomers and that several key residues (K867 and D909/D911/E913/D922 in the OPCA motif) play a pivotal role in the oligomerization of NLP7 proteins. The fact that these residues are all conserved across land plant lineages means that this oligomerization may have evolved after the common ancestor of extant land plants colonized the land. It would then have rapidly become established across land-plant lineages in order to mediate protein-protein interactions in the nitrate-signaling pathway.

Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank

BACKGROUND

In gut microbiome studies, the cultured gut microbial resource plays essential roles, such as helping to unravel gut microbial functions and host-microbe interactions. Although several major studies have been performed to elucidate the cultured human gut microbiota, up to 70% of the Unified Human Gastrointestinal Genome species have not been cultured to date. Large-scale gut microbial isolation and identification as well as availability to the public are imperative for gut microbial studies and further characterizing human gut microbial functions.

RESULTS

In this study, we constructed a human Gut Microbial Biobank (hGMB; homepage: hgmb.nmdc.cn ) through the cultivation of 10,558 isolates from 31 sample mixtures of 239 fresh fecal samples from healthy Chinese volunteers, and deposited 1170 strains representing 400 different species in culture collections of the International Depository Authority for long-term preservation and public access worldwide. Following the rules of the International Code of Nomenclature of Prokaryotes, 102 new species were characterized and denominated, while 28 new genera and 3 new families were proposed. hGMB represented over 80% of the common and dominant human gut microbial genera and species characterized from global human gut 16S rRNA gene amplicon data (n = 11,647) and cultured 24 "most-wanted" and "medium priority" taxa proposed by the Human Microbiome Project. We in total sequenced 115 genomes representing 102 novel taxa and 13 previously known species. Further in silico analysis revealed that the newly sequenced hGMB genomes represented 22 previously uncultured species in the Unified Human Gastrointestinal Genome (UHGG) and contributed 24 representatives of potentially "dark taxa" that had not been discovered by UHGG. The nonredundant gene catalogs generated from the hGMB genomes covered over 50% of the functionally known genes (KEGG orthologs) in the largest global human gut gene catalogs and approximately 10% of the "most wanted" functionally unknown proteins in the FUnkFams database.

CONCLUSIONS

A publicly accessible human Gut Microbial Biobank (hGMB) was established that contained 1170 strains and represents 400 human gut microbial species. hGMB expands the gut microbial resources and genomic repository by adding 102 novel species, 28 new genera, 3 new families, and 115 new genomes of human gut microbes. Video abstract.

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

MOTIVATION

Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available.

RESULTS

We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining.

AVAILABILITY AND IMPLEMENTATION

Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs

Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

MicroScope-an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data

The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources.

Whole genome sequence analysis reveals the broad distribution of the RtxA type 1 secretion system and four novel putative type 1 secretion systems throughout the Legionella genus

Type 1 secretion systems (T1SSs) are broadly distributed among bacteria and translocate effectors with diverse function across the bacterial cell membrane. Legionella pneumophila, the species most commonly associated with Legionellosis, encodes a T1SS at the lssXYZABD locus which is responsible for the secretion of the virulence factor RtxA. Many investigations have failed to detect lssD, the gene encoding the membrane fusion protein of the RtxA T1SS, in non-pneumophila Legionella, which has led to the assumption that this system is a virulence factor exclusively possessed by L. pneumophila. Here we discovered RtxA and its associated T1SS in a novel Legionella taurinensis strain, leading us to question whether this system may be more widespread than previously thought. Through a bioinformatic analysis of publicly available data, we classified and determined the distribution of four T1SSs including the RtxA T1SS and four novel T1SSs among diverse Legionella spp. The ABC transporter of the novel Legionella T1SS Legionella repeat protein secretion system shares structural similarity to those of diverse T1SS families, including the alkaline protease T1SS in Pseudomonas aeruginosa. The Legionella bacteriocin (1-3) secretion systems T1SSs are novel putative bacteriocin transporting T1SSs as their ABC transporters include C-39 peptidase domains in their N-terminal regions, with LB2SS and LB3SS likely constituting a nitrile hydratase leader peptide transport T1SSs. The LB1SS is more closely related to the colicin V T1SS in Escherichia coli. Of 45 Legionella spp. whole genomes examined, 19 (42%) were determined to possess lssB and lssD homologs. Of these 19, only 7 (37%) are known pathogens. There was no difference in the proportions of disease associated and non-disease associated species that possessed the RtxA T1SS (p = 0.4), contrary to the current consensus regarding the RtxA T1SS. These results draw into question the nature of RtxA and its T1SS as a singular virulence factor. Future studies should investigate mechanistic explanations for the association of RtxA with virulence.

The Mouse Gut Microbial Biobank expands the coverage of cultured bacteria

Mice are widely used as experimental models for gut microbiome (GM) studies, yet the majority of mouse GM members remain uncharacterized. Here, we report the construction of a mouse gut microbial biobank (mGMB) that contains 126 species, represented by 244 strains that have been deposited in the China General Microorganism Culture Collection. We sequence and phenotypically characterize 77 potential new species and propose their nomenclatures. The mGMB includes 22 and 17 species that are significantly enriched in ob/ob and wild-type C57BL/6J mouse cecal samples, respectively. The genomes of the 126 species in the mGMB cover 52% of the metagenomic nonredundant gene catalog (sequence identity ≥ 60%) and represent 93-95% of the KEGG-Orthology-annotated functions of the sampled mouse GMs. The microbial and genome data assembled in the mGMB enlarges the taxonomic characterization of mouse GMs and represents a useful resource for studies of host-microbe interactions and of GM functions associated with host health and diseases.

Molecular function limits divergent protein evolution on planetary timescales

Functional conservation is known to constrain protein evolution. Nevertheless, the long-term divergence patterns of proteins maintaining the same molecular function and the possible limits of this divergence have not been explored in detail. We investigate these fundamental questions by characterizing the divergence between ancient protein orthologs with conserved molecular function. Our results demonstrate that the decline of sequence and structural similarities between such orthologs significantly slows down after ~1-2 billion years of independent evolution. As a result, the sequence and structural similarities between ancient orthologs have not substantially decreased for the past billion years. The effective divergence limit (>25% sequence identity) is not primarily due to protein sites universally conserved in all linages. Instead, less than four amino acid types are accepted, on average, per site across orthologous protein sequences. Our analysis also reveals different divergence patterns for protein sites with experimentally determined small and large fitness effects of mutations.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Transcriptional and physiological responses to inorganic nutrition in a tropical Pacific strain of Alexandrium minutum: Implications for nutrient uptakes and assimilation

The marine dinoflagellate Alexandrium minutum is known to produce saxitoxins that cause paralytic shellfish poisoning in human worldwide through consumption of the contaminated shellfish mollusks. Despite numerous studies on the growth physiology and saxitoxin production of this species, the knowledge on the molecular basis of nutrient uptakes in relation to toxin production in this species is limited. In this study, relative expressions of the high-affinity transporter genes of nitrate, ammonium, and phosphate (AmNrt2, AmAmt1 and AmPiPT1) and the assimilation genes, nitrate reductase (AmNas), glutamine synthase (AmGSIII) and carbamoyl phosphate synthase (AmCPSII) from A. minutum were studied in batch clonal culture condition with two nitrogen sources (nitrate: NO₃^- or ammonium: NH₄⁺) under different N:P ratios (high-P: N:P of 14 and 16, and low-P: N:P of 155). The expression of AmAmt1 was suppressed in excess NH₄⁺-grown condition but was not observed in AmNrt2 and AmNas. Expressions of AmAmt1, AmNrt2, AmNas, AmGSIII, AmCPSII, and AmPiPT1 were high in P-deficient condition, showing that A. minutum is likely to take up nutrients for growth under P-stress condition. Conversely, relative expression of AmCPSII was incongruent with cell growth, but was well correlated with toxin quota, suggesting that the gene might involve in arginine metabolism and related toxin production pathway. The expression of AmGSIII is found coincided with higher toxin production and is believed to involve in mechanism to detoxify the cells from excess ammonium stress. The gene regulation observed in this study has provided better insights into the ecophysiology of A. minutum in relation to its adaptive strategies in unfavorable environments.

The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs

Gene duplication is a driver of the evolution of new functions. The duplication of genes encoding homomeric proteins leads to the formation of homomers and heteromers of paralogs, creating new complexes after a single duplication event. The loss of these heteromers may be required for the two paralogs to evolve independent functions. Using yeast as a model, we find that heteromerization is frequent among duplicated homomers and correlates with functional similarity between paralogs. Using in silico evolution, we show that for homomers and heteromers sharing binding interfaces, mutations in one paralog can have structural pleiotropic effects on both interactions, resulting in highly correlated responses of the complexes to selection. Therefore, heteromerization could be preserved indirectly due to selection for the maintenance of homomers, thus slowing down functional divergence between paralogs. We suggest that paralogs can overcome the obstacle of structural pleiotropy by regulatory evolution at the transcriptional and post-translational levels.

A blue light receptor that mediates RNA binding and translational regulation

Sensory photoreceptor proteins underpin light-dependent adaptations in nature and enable the optogenetic control of organismal behavior and physiology. We identified the bacterial light-oxygen-voltage (LOV) photoreceptor PAL that sequence-specifically binds short RNA stem loops with around 20 nM affinity in blue light and weaker than 1 μM in darkness. A crystal structure rationalizes the unusual receptor architecture of PAL with C-terminal LOV photosensor and N-terminal effector units. The light-activated PAL:RNA interaction can be harnessed to regulate gene expression at the RNA level as a function of light in both bacteria and mammalian cells. The present results elucidate a new signal-transduction paradigm in LOV receptors and conjoin RNA biology with optogenetic regulation, thereby paving the way towards hitherto inaccessible optoribogenetic modalities.

Characterization of a class III peroxidase from Artemisia annua: relevance to artemisinin metabolism and beyond

A class III peroxidase from Artemisia annua has been shown to indicate the possibility of cellular localization-based role diversity, which may have implications in artemisinin catabolism as well as lignification. Artemisia annua derives its importance from the antimalarial artemisinin. The -O-O- linkage in artemisinin makes peroxidases relevant to its metabolism. Earlier, we identified three peroxidase-coding genes from A. annua, whereby Aa547 showed higher expression in the low-artemisinin plant stage whereas Aa528 and Aa540 showed higher expression in the artemisinin-rich plant stage. Here we carried out tertiary structure homology modelling of the peroxidases for docking studies. Maximum binding affinity for artemisinin was shown by Aa547. Further, Aa547 showed greater binding affinity for post-artemisinin metabolite, deoxyartemisinin, as compared to pre-artemisinin metabolites (dihydroartemisinic hydroperoxide, artemisinic acid, dihydroartemisinic acid). It also showed significant binding affinity for the monolignol, coniferyl alcohol. Moreover, Aa547 expression was related inversely to artemisinin content and directly to total lignin content as indicated by its transient silencing and overexpression in A. annua. Artemisinin reduction assay also indicated inverse relationship between Aa547 expression and artemisinin content. Subcellular localization using GFP fusion suggested that Aa547 is peroxisomal. Nevertheless, dual localization (intracellular/extracellular) of Aa547 could not be ruled out due to its effect on both, artemisinin and lignin. Taken together, this indicates possibility of localization-based role diversity for Aa547, which may have implications in artemisinin catabolism as well as lignification in A. annua.

New Drosophila Long-Term Memory Genes Revealed by Assessing Computational Function Prediction Methods

A major bottleneck to our understanding of the genetic and molecular foundation of life lies in the ability to assign function to a gene and, subsequently, a protein. Traditional molecular and genetic experiments can provide the most reliable forms of identification, but are generally low-throughput, making such discovery and assignment a daunting task. The bottleneck has led to an increasing role for computational approaches. The Critical Assessment of Functional Annotation (CAFA) effort seeks to measure the performance of computational methods. In CAFA3, we performed selected screens, including an effort focused on long-term memory. We used homology and previous CAFA predictions to identify 29 key Drosophila genes, which we tested via a long-term memory screen. We identify 11 novel genes that are involved in long-term memory formation and show a high level of connectivity with previously identified learning and memory genes. Our study provides first higher-order behavioral assay and organism screen used for CAFA assessments and revealed previously uncharacterized roles of multiple genes as possible regulators of neuronal plasticity at the boundary of information acquisition and memory formation.

Contributions of substitutions and indels to the structural variations in ancient protein superfamilies

Background

Quantitative evaluation of protein structural evolution is important for our understanding of protein biological functions and their evolutionary adaptation, and is useful in guiding protein engineering. However, compared to the models for sequence evolution, the quantitative models for protein structural evolution received less attention. Ancient protein superfamilies are often considered versatile, allowing genetic and functional diversifications during long-term evolution. In this study, we investigated the quantitative impacts of sequence variations on the structural evolution of homologues in 68 ancient protein superfamilies that exist widely in sequenced eukaryotic, bacterial and archaeal genomes.

Results

We found that the accumulated structural variations within ancient superfamilies could be explained largely by a bilinear model that simultaneously considers amino acid substitution and insertion/deletion (indel). Both substitutions and indels are essential for explaining the structural variations within ancient superfamilies. For those ancient superfamilies with high bilinear multiple correlation coefficients, the influence of each unit of substitution or indel on structural variations is almost constant within each superfamily, but varies greatly among different superfamilies. The influence of each unit indel on structural variations is always larger than that of each unit substitution within each superfamily, but the accumulated contributions of indels to structural variations are lower than those of substitutions in most superfamilies. The total contributions of sequence indels and substitutions (46% and 54%, respectively) to the structural variations that result from sequence variations are slightly different in ancient superfamilies.

Conclusions

Structural variations within ancient protein superfamilies accumulated under the significantly bilinear influence of amino acid substitutions and indels in sequences. Both substitutions and indels are essential for explaining the structural variations within ancient superfamilies. For those structural variations resulting from sequence variations, the total contribution of indels is slightly lower than that of amino acid substitutions. The regular clock exists not only in protein sequences, but also probably in protein structures.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5178-8) contains supplementary material, which is available to authorized users.

Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER

With the rapid growth in the number of solved protein structures stored in the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB), it is essential to develop tools to perform real-time structure similarity searches against the entire structure database. Since conventional structure alignment methods need to sample different orientations of proteins in the three-dimensional space, they are time consuming and unsuitable for rapid, real-time database searches. To this end, we have developed 3D-SURFER and EM-SURFER, which utilize 3D Zernike descriptors (3DZD) to conduct high-throughput protein structure comparison, visualization, and analysis. Taking an atomic structure or an electron microscopy map of a protein or a protein complex as input, the 3DZD of a query protein is computed and compared with the 3DZD of all other proteins in PDB or EMDB. In addition, local geometrical characteristics of a query protein can be analyzed using VisGrid and LIGSITE^CSC in 3D-SURFER. This article describes how to use 3D-SURFER and EM-SURFER to carry out protein surface shape similarity searches, local geometric feature analysis, and interpretation of the search results. © 2017 by John Wiley & Sons, Inc.

Homology modeling in a dynamical world

A key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.

The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution

Homology modeling is a powerful tool for predicting a protein's structure. This approach is successful because proteins whose sequences are only 30% identical still adopt the same structure, while structure similarity rapidly deteriorates beyond the 30% threshold. By studying the divergence of protein structure as sequence evolves in real proteins and in evolutionary simulations, we show that this nonlinear sequence-structure relationship emerges as a result of selection for protein folding stability in divergent evolution. Fitness constraints prevent the emergence of unstable protein evolutionary intermediates, thereby enforcing evolutionary paths that preserve protein structure despite broad sequence divergence. However, on longer timescales, evolution is punctuated by rare events where the fitness barriers obstructing structure evolution are overcome and discovery of new structures occurs. We outline biophysical and evolutionary rationale for broad variation in protein family sizes, prevalence of compact structures among ancient proteins, and more rapid structure evolution of proteins with lower packing density.

Myosin B of Plasmodium falciparum (PfMyoB): in silico prediction of its three-dimensional structure and its possible interaction with MTIP

The mobility and invasion strategy of Plasmodium falciparum is governed by a protein complex known as the glideosome, which contains an actin-myosin motor. It has been shown that myosin A of the parasite (PfMyoA) is the myosin of the glideosome, and the interaction of PfMyoA with myosin tail domain interacting protein (MTIP) determines its correct location and its ability to function in the complex. Because PfMyoA and myosin B of P. falciparum (PfMyoB) share high sequence identity, are both small proteins without a tail domain, belong to the class XIV myosins, and are expressed in late schizonts and merozoites, we suspect that these myosins may have similar or redundant functions. Therefore, this work examined the structural similarity between PfMyoA and PfMyoB and performed a molecular docking between PfMyoB and MTIP. Three-dimensional (3D) models obtained for PfMyoA and PfMyoB achieved high scores in the structural validation programs used, and their superimposition revealed high structural similarity, supporting the hypothesis of possible similar functions for these two proteins. The 3D interaction models obtained and energy values found suggested that interaction between PfMyoB and MTIP is possible. Given the apparent abundance of PfMyoA relative to PfMyoB in the parasite, we believe that the interaction between PfMyoB and MTIP would only be detectable in specific cellular environments because under normal circumstances, it would be masked by the interaction between PfMyoA and MTIP.

Computational Methods for Annotation Transfers from Sequence

Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.

Fused Regression for Multi-source Gene Regulatory Network Inference

Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms.

Gene regulatory networks describing related biological processes are thought to share conserved interaction structure. This assumption motivates a great deal of work in model systems–where discovery of gene regulation may be more experimentally tractable–but is difficult to directly evaluate using existing methods. The presence of shared structure in a well studied model system or process should make the problem of network inference in a related process easier, but this information is not often applied to the discovery of global gene regulatory networks. Further, to be able to successfully translate findings between different organisms, it is important to be able to identify where regulatory structure is different. We provide a method based on penalized fused regression for inferring gene regulatory networks given prior knowledge about the similarity of interactions in each network. This method is demonstrated on synthetic data, and applied to the problem of inferring networks in distantly related bacterial organisms. We then introduce an extension of the method to deal with the condition of uncertainty over the degree of regulatory conservation by simultaneously inferring gene conservation and interaction weights.

Xenobiotic Metabolism and Gut Microbiomes

Humans are exposed to numerous xenobiotics, a majority of which are in the form of pharmaceuticals. Apart from human enzymes, recent studies have indicated the role of the gut bacterial community (microbiome) in metabolizing xenobiotics. However, little is known about the contribution of the plethora of gut microbiome in xenobiotic metabolism. The present study reports the results of analyses on xenobiotic metabolizing enzymes in various human gut microbiomes. A total of 397 available gut metagenomes from individuals of varying age groups from 8 nationalities were analyzed. Based on the diversities and abundances of the xenobiotic metabolizing enzymes, various bacterial taxa were classified into three groups, namely, least versatile, intermediately versatile and highly versatile xenobiotic metabolizers. Most interestingly, specific relationships were observed between the overall drug consumption profile and the abundance and diversity of the xenobiotic metabolizing repertoire in various geographies. The obtained differential abundance patterns of xenobiotic metabolizing enzymes and bacterial genera harboring them, suggest their links to pharmacokinetic variations among individuals. Additional analyses of a few well studied classes of drug modifying enzymes (DMEs) also indicate geographic as well as age specific trends.

Computational Identification of the Paralogs and Orthologs of Human Cytochrome P450 Superfamily and the Implication in Drug Discovery

The human cytochrome P450 (CYP) superfamily consisting of 57 functional genes is the most important group of Phase I drug metabolizing enzymes that oxidize a large number of xenobiotics and endogenous compounds, including therapeutic drugs and environmental toxicants. The CYP superfamily has been shown to expand itself through gene duplication, and some of them become pseudogenes due to gene mutations. Orthologs and paralogs are homologous genes resulting from speciation or duplication, respectively. To explore the evolutionary and functional relationships of human CYPs, we conducted this bioinformatic study to identify their corresponding paralogs, homologs, and orthologs. The functional implications and implications in drug discovery and evolutionary biology were then discussed. GeneCards and Ensembl were used to identify the paralogs of human CYPs. We have used a panel of online databases to identify the orthologs of human CYP genes: NCBI, Ensembl Compara, GeneCards, OMA ("Orthologous MAtrix") Browser, PATHER, TreeFam, EggNOG, and Roundup. The results show that each human CYP has various numbers of paralogs and orthologs using GeneCards and Ensembl. For example, the paralogs of CYP2A6 include CYP2A7, 2A13, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, 2E1, 2F1, 2J2, 2R1, 2S1, 2U1, and 2W1; CYP11A1 has 6 paralogs including CYP11B1, 11B2, 24A1, 27A1, 27B1, and 27C1; CYP51A1 has only three paralogs: CYP26A1, 26B1, and 26C1; while CYP20A1 has no paralog. The majority of human CYPs are well conserved from plants, amphibians, fishes, or mammals to humans due to their important functions in physiology and xenobiotic disposition. The data from different approaches are also cross-validated and validated when experimental data are available. These findings facilitate our understanding of the evolutionary relationships and functional implications of the human CYP superfamily in drug discovery.

PatchSurfers: Two methods for local molecular property-based binding ligand prediction

Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided.

Alignment behaviors of short peptides provide a roadmap for functional profiling of metagenomic data

Background

Functional assignments for short-read metagenomic data pose a significant computational challenge due to perceived unpredictability of alignment behavior and the inability to infer useful functional information from translated protein-fragments/peptides. To address this problem, we have examined the predictability of short peptide alignments by systematically studying alignment behavior of large sets of short peptides generated from well-characterized proteins as well as hypothetical proteins in the KEGG database.

Results

Using test sets of peptides modeling the length and phylogenetic distributions of short-read metagenomic data, we observed that peptides from well-characterized proteins had indistinguishable alignments to proteins from the same orthologous family and proteins from different families. Nonetheless, the patterns contained remarkable phylogenetic and structural signals, with alignments of even very short peptides naturally restricted to their orthologous family and/or proteins having similar structural folds. In stark contrast, peptides from “hypothetical proteins” had only sparse hit patterns with low frequencies and much lower identities. By weighting the structure-driven alignments and filtering peptides with behaviors similar to those derived from “hypothetical proteins”, we demonstrate that the accuracy of abundance predictions of protein families is dramatically improved.

Conclusions

Evolutionary processes have dispersed protein folds across multiple protein families, precluding accurate functional assignment to short peptides, whose alignment behavior is non-random and driven by structure. Algorithms that filter sparse peptides and weight hit patterns of peptides from “known space” dramatically improve quantification of functions from diverse mixtures of peptides and should substantially improve applications of metagenomic analyses requiring accurate quantitative measures of functional families.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2272-z) contains supplementary material, which is available to authorized users.

orthoFind Facilitates the Discovery of Homologous and Orthologous Proteins

Finding homologous and orthologous protein sequences is often the first step in evolutionary studies, annotation projects, and experiments of functional complementation. Despite all currently available computational tools, there is a requirement for easy-to-use tools that provide functional information. Here, a new web application called orthoFind is presented, which allows a quick search for homologous and orthologous proteins given one or more query sequences, allowing a recurrent and exhaustive search against reference proteomes, and being able to include user databases. It addresses the protein multidomain problem, searching for homologs with the same domain architecture, and gives a simple functional analysis of the results to help in the annotation process. orthoFind is easy to use and has been proven to provide accurate results with different datasets. Availability: http://www.bioinfocabd.upo.es/orthofind/.

Protein structure refinement with adaptively restrained homologous replicas

A novel protein refinement protocol is presented which utilizes molecular dynamics (MD) simulations of an ensemble of adaptively restrained homologous replicas. This approach adds evolutionary information to the force field and reduces random conformational fluctuations by coupling of several replicas. It is shown that this protocol refines the majority of models from the CASP11 refinement category and that larger conformational changes of the starting structure are possible than with current state of the art methods. The performance of this protocol in the CASP11 experiment is discussed. We found that the quality of the refined model is correlated with the structural variance of the coupled replicas, which therefore provides a good estimator of model quality. Furthermore, some remarkable refinement results are discussed in detail. Proteins 2016; 84(Suppl 1):302-313. © 2015 Wiley Periodicals, Inc.

Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction

Protein sorting is an important mechanism for transporting proteins to their target subcellular locations after their synthesis. Mutations on genes may disrupt the well regulated protein sorting process, leading to a variety of mislocation related diseases. This paper proposes a methodology to discover such disease genes based on gene expression data and computational protein localization prediction. A kernel logistic regression based algorithm is used to successfully identify several candidate cancer genes which may cause cancers due to their mislocation within the cell. Our results also showed that compared to the gene co-expression network defined on Pearson correlation coefficients, the nonlinear Maximum Correlation Coefficients (MIC) based co-expression network give better results for subcellular localization prediction.

Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach

DNA-binding proteins are crucial for various cellular processes and hence have become an important target for both basic research and drug development. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to establish an automated method for rapidly and accurately identifying DNA-binding proteins based on their sequence information alone. Owing to the fact that all biological species have developed beginning from a very limited number of ancestral species, it is important to take into account the evolutionary information in developing such a high-throughput tool. In view of this, a new predictor was proposed by incorporating the evolutionary information into the general form of pseudo amino acid composition via the top-n-gram approach. It was observed by comparing the new predictor with the existing methods via both jackknife test and independent data-set test that the new predictor outperformed its counterparts. It is anticipated that the new predictor may become a useful vehicle for identifying DNA-binding proteins. It has not escaped our notice that the novel approach to extract evolutionary information into the formulation of statistical samples can be used to identify many other protein attributes as well.

An orthology-based analysis of pathogenic protozoa impacting global health: an improved comparative genomics approach with prokaryotes and model eukaryote orthologs

A key focus in 21(st) century integrative biology and drug discovery for neglected tropical and other diseases has been the use of BLAST-based computational methods for identification of orthologous groups in pathogenic organisms to discern orthologs, with a view to evaluate similarities and differences among species, and thus allow the transfer of annotation from known/curated proteins to new/non-annotated ones. We used here a profile-based sensitive methodology to identify distant homologs, coupled to the NCBI's COG (Unicellular orthologs) and KOG (Eukaryote orthologs), permitting us to perform comparative genomics analyses on five protozoan genomes. OrthoSearch was used in five protozoan proteomes showing that 3901 and 7473 orthologs can be identified by comparison with COG and KOG proteomes, respectively. The core protozoa proteome inferred was 418 Protozoa-COG orthologous groups and 704 Protozoa-KOG orthologous groups: (i) 31.58% (132/418) belongs to the category J (translation, ribosomal structure, and biogenesis), and 9.81% (41/418) to the category O (post-translational modification, protein turnover, chaperones) using COG; (ii) 21.45% (151/704) belongs to the categories J, and 13.92% (98/704) to the O using KOG. The phylogenomic analysis showed four well-supported clades for Eukarya, discriminating Multicellular [(i) human, fly, plant and worm] and Unicellular [(ii) yeast, (iii) fungi, and (iv) protozoa] species. These encouraging results attest to the usefulness of the profile-based methodology for comparative genomics to accelerate semi-automatic re-annotation, especially of the protozoan proteomes. This approach may also lend itself for applications in global health, for example, in the case of novel drug target discovery against pathogenic organisms previously considered difficult to research with traditional drug discovery tools.

eFindSite: Enhanced Fingerprint-Based Virtual Screening Against Predicted Ligand Binding Sites in Protein Models

A standard practice for lead identification in drug discovery is ligand virtual screening, which utilizes computing technologies to detect small compounds that likely bind to target proteins prior to experimental screens. A high accuracy is often achieved when the target protein has a resolved crystal structure; however, using protein models still renders significant challenges. Towards this goal, we recently developed eFindSite that predicts ligand binding sites using a collection of effective algorithms, including meta-threading, machine learning and reliable confidence estimation systems. Here, we incorporate fingerprint-based virtual screening capabilities in eFindSite in addition to its flagship role as a ligand binding pocket predictor. Virtual screening benchmarks using the enhanced Directory of Useful Decoys demonstrate that eFindSite significantly outperforms AutoDock Vina as assessed by several evaluation metrics. Importantly, this holds true regardless of the quality of target protein structures. As a first genome-wide application of eFindSite, we conduct large-scale virtual screening of the entire proteome of Escherichia coli with encouraging results. In the new approach to fingerprint-based virtual screening using remote protein homology, eFindSite demonstrates its compelling proficiency offering a high ranking accuracy and low susceptibility to target structure deformations. The enhanced version of eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite.

Sma3s: a three-step modular annotator for large sequence datasets

Automatic sequence annotation is an essential component of modern 'omics' studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ~85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes.

Improved estimates of coordinate error for molecular replacement

The estimate of the root-mean-square deviation (r.m.s.d.) in coordinates between the model and the target is an essential parameter for calibrating likelihood functions for molecular replacement (MR). Good estimates of the r.m.s.d. lead to good estimates of the variance term in the likelihood functions, which increases signal to noise and hence success rates in the MR search. Phaser has hitherto used an estimate of the r.m.s.d. that only depends on the sequence identity between the model and target and which was not optimized for the MR likelihood functions. Variance-refinement functionality was added to Phaser to enable determination of the effective r.m.s.d. that optimized the log-likelihood gain (LLG) for a correct MR solution. Variance refinement was subsequently performed on a database of over 21,000 MR problems that sampled a range of sequence identities, protein sizes and protein fold classes. Success was monitored using the translation-function Z-score (TFZ), where a TFZ of 8 or over for the top peak was found to be a reliable indicator that MR had succeeded for these cases with one molecule in the asymmetric unit. Good estimates of the r.m.s.d. are correlated with the sequence identity and the protein size. A new estimate of the r.m.s.d. that uses these two parameters in a function optimized to fit the mean of the refined variance is implemented in Phaser and improves MR outcomes. Perturbing the initial estimate of the r.m.s.d. from the mean of the distribution in steps of standard deviations of the distribution further increases MR success rates.

Cell-penetrating fusion peptides OD1 and OD2 interact with Bcr-Abl and influence the growth and apoptosis of K562 cells

The Bcr-Abl oncoprotein is the cause of chronic myelogenous leukemia (CML). Crystal structure analysis suggests that Bcr30-63 is the core of the Bcr-Abl oligomerization interface for aberrant kinase activity; however, the precise role of other residues of Bcr1-72 excluding Bcr30-63 have not been evaluated. In this study, Bcr30-63 was named OD2 and other residues of Bcr1-72 were named OD1. Cytoplasmic transduction peptide (CTP) was used to carry molecules into cytoplasm. CTP-OD1 and CTP-OD2 fusion peptides were expressed from a cold-inducible expression system. Our results demonstrated that both fusion peptides could localize into the cytoplasm, specifically interact with the Bcr-Abl protein and further inhibit growth, induce apoptosis, and decrease the phosphorylation of Bcr-Abl in K562 cell lines. However, the viability of THP-1, a Bcr-Abl negative cell line, was unaffected. These results suggested that CTP-OD1 and CTP-OD2 may be an attractive therapeutic option to inhibit the activation of Bcr-Abl kinase in CML.

Exhaustive comparison and classification of ligand-binding surfaces in proteins

Many proteins function by interacting with other small molecules (ligands). Identification of ligand-binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand-binding protein sequences and functions. Consequently, we classified the patches into ∼2000 well-characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross-fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.