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Background: Locating small molecule binding sites in target proteins, in the resolution of either pocket or residue, is critical in many drug-discovery scenarios. Since it is not always easy to find such binding sites using conventional methods, different deep learning methods to predict binding sites out of protein structures have been developed in recent years. The existing deep learning based methods have several limitations, including (1) the inefficiency of the CNN-only architecture, (2) loss of information due to excessive post-processing, and (3) the under-utilization of available data sources. Methods: We present a new model architecture and training method that resolves the aforementioned problems. First, by layering geometric self-attention units on top of residue-level 3D CNN outputs, our model overcomes the problems of CNN-only architectures. Second, by configuring the fundamental units of computation as residues and pockets instead of voxels, our method reduced the information loss from post-processing. Lastly, by employing inter-resolution transfer learning and homology-based augmentation, our method maximizes the utilization of available data sources to a significant extent. Results: The proposed method significantly outperformed all state-of-the-art baselines regarding both resolutions—pocket and residue. An ablation study demonstrated the indispensability of our proposed architecture, as well as transfer learning and homology-based augmentation, for achieving optimal performance. We further scrutinized our model's performance through a case study involving human serum albumin, which demonstrated our model's superior capability in identifying multiple binding sites of the protein, outperforming the existing methods. Conclusions: We believe that our contribution to the literature is twofold. Firstly, we introduce a novel computational method for binding site prediction with practical applications, substantiated by its strong performance across diverse benchmarks and case studies. Secondly, the innovative aspects in our method— specifically, the design of the model architecture, inter-resolution transfer learning, and homology-based augmentation—would serve as useful components for future work. [ABSTRACT FROM AUTHOR]

Abstract Background Locating small molecule binding sites in target proteins, in the resolution of either pocket or residue, is critical in many drug-discovery scenarios. Since it is not always easy to find such binding sites using conventional methods, different deep learning methods to predict binding sites out of protein structures have been developed in recent years. The existing deep learning based methods have several limitations, including (1) the inefficiency of the CNN-only architecture, (2) loss of information due to excessive post-processing, and (3) the under-utilization of available data sources. Methods We present a new model architecture and training method that resolves the aforementioned problems. First, by layering geometric self-attention units on top of residue-level 3D CNN outputs, our model overcomes the problems of CNN-only architectures. Second, by configuring the fundamental units of computation as residues and pockets instead of voxels, our method reduced the information loss from post-processing. Lastly, by employing inter-resolution transfer learning and homology-based augmentation, our method maximizes the utilization of available data sources to a significant extent. Results The proposed method significantly outperformed all state-of-the-art baselines regarding both resolutions—pocket and residue. An ablation study demonstrated the indispensability of our proposed architecture, as well as transfer learning and homology-based augmentation, for achieving optimal performance. We further scrutinized our model’s performance through a case study involving human serum albumin, which demonstrated our model’s superior capability in identifying multiple binding sites of the protein, outperforming the existing methods. Conclusions We believe that our contribution to the literature is twofold. Firstly, we introduce a novel computational method for binding site prediction with practical applications, substantiated by its strong performance across diverse benchmarks and case studies. Secondly, the innovative aspects in our method— specifically, the design of the model architecture, inter-resolution transfer learning, and homology-based augmentation—would serve as useful components for future work.

Abstract Background The annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings. Results Central to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect. Conclusion The embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.

Background: The annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings. Results: Central to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect. Conclusion: The embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology. [ABSTRACT FROM AUTHOR]

The Elongator complex plays a pivotal role in the wobble uridine modification of the tRNA anticodon. Comprising two sets of six distinct subunits, namely, Elongator proteins (ELP1-ELP6) and associated proteins, the holo-Elongator complex demonstrates remarkable functional and structural conservation across eukaryotes. However, the precise details of the evolutionary conservation of the holo-Elongator complex and its individual sub-complexes (i.e., ELP123; ELP456) in plants remain limited. In this study, we conducted an in vivo analysis of protein–protein interactions among Arabidopsis ELP4, ELP5, and ELP6 proteins. Additionally, we predicted their structural configurations and performed a comparative analysis with the structure of the yeast Elp456 sub-complex. Protein–protein interaction analysis revealed that AtELP4 interacts with AtELP6 but not directly with AtELP5. Furthermore, we found that the Arabidopsis Elongator-associated protein, Deformed Roots and Leaves 1 (DRL1), did not directly bind to AtELP proteins. The structural comparison of the ELP456 sub-complex between Arabidopsis and yeast demonstrated high similarity, encompassing the RecA-ATPase fold and the positions of hydrogen bonds, despite their relatively low sequence homology. Our findings suggest that Arabidopsis ELP4, ELP5, and ELP6 proteins form a heterotrimer, with ELP6 serving as a bridge, indicating high structural conservation between the ELP456 sub-complexes from Arabidopsis and yeast. [ABSTRACT FROM AUTHOR]

Abstract Background BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. Results We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. Conclusion TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

Strategies targeting nucleolin have enabled a significant improvement in intracellular bioavailability of their encapsulated payloads. In this respect, assessment of the impact of target cell heterogeneity and nucleolin homology across species (structurally and functionally) is of major importance. This work also aimed at mathematically modelling the nucleolin expression levels at the cell membrane, binding and internalization of pH-sensitive pegylated liposomes encapsulating doxorubicin and functionalized with the nucleolin-binding F3 peptide (PEGASEMP), and resulting cytotoxicity against cancer cells from mouse, rat, canine, and human origin. Herein, it was shown that nucleolin expression levels were not a limitation on the continuous internalization of F3 peptide-targeted liposomes, despite the saturable nature of the binding mechanism. Modeling enabled the prediction of nucleolin-mediated total doxorubicin exposure provided by the experimental settings of the assessment of PEGASEMP's impact on cell death. The former increased proportionally with nucleolin-binding sites, a measure relevant for patient stratification. This pattern of variation was observed for the resulting cell death in nonsaturating conditions, depending on the cancer cell sensitivity to doxorubicin. This approach differs from standard determination of cytotoxic concentrations, which normally report values of incubation doses rather than the actual intracellular bioactive drug exposure. Importantly, in the context of development of nucleolin-based targeted drug delivery, the structural nucleolin homology (higher than 84%) and functional similarity across species presented herein, emphasized the potential to use toxicological data and other metrics from lower species to infer the dose for a first-in-human trial. [ABSTRACT FROM AUTHOR]

Background: BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. Results: We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. Conclusion: TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence. [ABSTRACT FROM AUTHOR]

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

Abstract Background In bioinformatics community, many tasks associate with matching a set of protein query sequences in large sequence datasets. To conduct multiple queries in the database, a common used method is to run BLAST on each original querey or on the concatenated queries. It is inefficient since it doesn’t exploit the common subsequences shared by queries. Results We propose a compression and cluster based BLASTP (C2-BLASTP) algorithm to further exploit the joint information among the query sequences and the database. Firstly, the queries and database are compressed in turn by procedures of redundancy analysis, redundancy removal and distinction record. Secondly, the database is clustered according to Hamming distance among the subsequences. To improve the sensitivity and selectivity of sequence alignments, ten groups of reduced amino acid alphabets are used. Following this, the hits finding operator is implemented on the clustered database. Furthermore, an execution database is constructed based on the found potential hits, with the objective of mitigating the effect of increasing scale of the sequence database. Finally, the homology search is performed in the execution database. Experiments on NCBI NR database demonstrate the effectiveness of the proposed C2-BLASTP for batch searching of homology in sequence database. The results are evaluated in terms of homology accuracy, search speed and memory usage. Conclusions It can be seen that the C2-BLASTP achieves competitive results as compared with some state-of-the-art methods.

The CD98 heavy chain (CD98hc) constitutes both a promising cell surface target for the treatment of cancers and a transcytosis receptor potentially useful for the brain delivery of therapeutics. However, pharmacokinetic studies and safety assessment of cognate antibodies or nonimmunoglobulin binding proteins in rodents is hampered by cross‐species variability of both amino acid sequence and glycosylation pattern. Here, we report the crystal structure of the murine CD98hc extracellular domain and a comprehensive comparison with its human ortholog, revealing only one conserved surface patch that is neither shielded by glycosylation nor by the cell membrane with an accessible surface area typical for an antibody epitope. Our results imply the necessity of a surrogate approach for CD98hc‐specific binding proteins with predictive power for clinical investigations. [ABSTRACT FROM AUTHOR]

The Dynameomics project contains native state and unfolding simulations of 807 protein domains, where each domain is representative of a different metafold; these metafolds encompass ~97% of protein fold space. There is a long-standing question in structural biology as to whether proteins in the same fold family share the same folding/unfolding characteristics. Using molecular dynamics simulations from the Dynameomics project, we conducted a detailed study of protein unfolding/folding pathways for 5 protein domains from the immunoglobulin (Ig)-like β-sandwich metafold (the highest ranked metafold in our database). The domains have sequence similarities ranging from 4 to 15% and are all from different SCOP superfamilies, yet they share the same overall Ig-like topology. Despite having very different amino acid sequences, the dominant unfolding pathway is very similar for the 5 proteins, and the secondary structures that are peripheral to the aligned, shared core domain add variability to the unfolding pathway. Aligned residues in the core domain display consensus structure in the transition state primarily through conservation of hydrophobic positions. Commonalities in the obligate folding nucleus indicate that insights into the major events in the folding/unfolding of other domains from this metafold may be obtainable from unfolding simulations of a few representative proteins. [ABSTRACT FROM AUTHOR]

Two chitinase (CTS)-encoding genes (cts) from Coccidioides immitis (Ci), a respiratory fungal pathogen of humans, were cloned and sequenced. Both the genomic and cDNA sequences are presented. The transcription start points and poly(A)-addition sites were confirmed. The cts1 gene contains five introns and a 1281-bp ORF which translates a 427-amino-acid (aa) protein of 47.4 kDa. The cts2 gene contains two introns and a 2580-bp ORF which translates a 860-aa protein of 91.4 kDa. The deduced CTS1 protein showed highest homology to the Aphanocladium album and Trichoderma harzianum CTS (74% and 76%, respectively), while CTS2 showed highest homology to the CTS of Saccharomyces cerevisiae (Sc) and Candida albicans (47% and 51%, respectively). The putative N-terminal sequence of the mature CTS1 protein also showed 89% homology to the reported N-terminal sequence of a 48-kDa complement fixation antigen (CF-Ag) of Ci which has demonstrated chitinase activity. The CF-Ag is a clinically important antigen used in serodiagnosis of this fungal disease. CTS2 showed several of the conserved features of the Sc CTS, including putative catalytic and Ser/Thr-rich domains, and a C-terminal Cys-rich region. We propose that CTS1 and CTS2 of Ci are members of two distinct classes of fungal chitinases, an observation not previously reported for a single fungus.

The objective of this study was to investigate the antioxidant activity and the homology of peptides extracted from dry-cured Xuanwei and Jinhua ham. The antioxidant activity of crude peptides was assessed by ORAC and ABTS assays and the scavenging effects on DPPH and O 2− free radicals. LC-ESI-Q-TOF-MS/MS was used to analyze the peptide composition. Based on the identified peptides, homologous proteins were matched by the Peaks software. Overall, 243 and 213 peptides were identified with their parent proteins in Xuanwei and Jinhua dry-cured hams. Based on the ORAC and TEAC values, XHP showed higher antioxidant ability than JHP ( P  < 0.05). After further purification by G-15 chromatography, the results indicate that the oligopeptides with less than 1000 Da had greater antioxidant activity than the other two fractions. Homology-based proteomics showed that the majority of peptides originated from myosin which accounted for 26% in XHP and 32% in JHP respectively. [ABSTRACT FROM AUTHOR]

To assess the ubiquity of the potential for inorganic polyphosphate (polyP) synthesis in microalgae, we searched databases for algal homologues to the polyP polymerase VTC4 of Chlamydomonas reinhardtii. Homologues of this protein were found within >40 species of microalgae known to inhabit marine, freshwater, and terrestrial environments. Phylogenetic analysis demonstrated that these proteins were organized into clades aligning with their taxonomic relationships. These similarities and evolutionary relationships suggest that polyP synthesis represents an ancient ability that has evolved with species as the microalgal lineage has spread out over time. Based on these results and prior knowledge on P metabolism, C. reinhardtii , Chlorella vulgaris , Desmodesmus cf. armatus , Gonium pectorale , and Microcystis aeruginosa were further tested in bioassays known to trigger the synthesis of polyP within dense granules, by addition of P following a period of P depletion. While the cellular P content of C. reinhardtii , G. pectorale , M. aeruginosa , and D. cf. armatus increased to similar maxima, ranging from 2.6 ± 0.5 % to 3.6 ± 1.3 % 24 h after P repletion, P content only reached 1.2 ± 0.2 % in C. vulgaris , suggesting a lesser ability to accumulate polyP than the strains of the other species. Models of predicted VTC4 proteins were generated from the four eukaryotic species tested and showed that the microalgae share the conserved VTC catalytic core and SPX phosphate-sensing domains found in the yeast VTC4 proteins. This confirms the role of microalgal VTC4 as polyP polymerase and suggests a similar regulation of VTC4 proteins to the one described in yeast. Further work is now needed to uncover the assembly of the microalgal VTC complex and its regulation. A deeper study of the microalgal VTC structure could also help to understand whether differences in VTC structures can explain observed differences in P accumulation kinetics. • Homologues of Chlamydomonas reinhardtii VTC4 protein were found in 48 algal species. • These proteins were organized into clades aligning with the species' taxonomic relationships. • Modelling of algal VTC4 structures shows conservation of the VTC and SPX domains. • For the first time, Gonium pectorale was shown to synthesize polyphosphate. [ABSTRACT FROM AUTHOR]

Current sequencing technologies are making available unprecedented amounts of genetic data for a large variety of species including nonmodel organisms. Although many phylogenomic surveys spend considerable time finding orthologs from the wealth of sequence data, these results do not transcend the original study and after being processed for specific phylogenetic purposes these orthologs do not become stable orthology hypotheses. We describe a procedure to detect and document the phylogenetic distribution of orthologs allowing researchers to use this information to guide selection of loci best suited to test specific evolutionary questions. At the core of this pipeline is a new phylogenetic orthology method that is neither affected by the position of the root nor requires explicit assignment of outgroups. We discuss the properties of this new orthology assessment method and exemplify its utility for phylogenomics using a small insects dataset. In addition, we exemplify the pipeline to identify and document stable orthologs for the group of orb-weaving spiders (Araneoidea) using RNAseq data. [ABSTRACT FROM AUTHOR]

Type I and type III CRISPR-Cas effector complexes share similar architecture and have homologous key subunits. However, the relationship between the so-called small subunits of these complexes remains a contentious issue. Here, it is shown that the recently solved structure of Thermotoga maritima Csm2 represents a dimer with the extensive structure swapping between monomers. Unswapping the structure generates a compact globular monomer which shares similar structure and surface properties with Cmr5, the small subunit of a related Cmr complex. Detailed analysis of available structures of small subunits reveals that they all have a common fold suggesting their common origin. [ABSTRACT FROM AUTHOR]

BACKGROUND: The size of medical datasets is usually very large, which directly affects the computational cost of the data mining process. Instance selection is a data preprocessing step in the knowledge discovery process, which can be employed to reduce storage requirements while also maintaining the mining quality. This process aims to filter out outliers (or noisy data) from a given (training) dataset. However, when the dataset is very large in size, more time is required to accomplish the instance selection task. OBJECTIVE: In this paper, we introduce an efficient data preprocessing approach (EDP), which is composed of two steps. The first step is based on training a model over a small amount of training data after preforming instance selection. The model is then used to identify the rest of the large amount of training data. METHODS: Experiments are conducted based on two medical datasets for breast cancer and protein homology prediction problems that contain over 100000 data samples. In addition, three well-known instance selection algorithms are used, IB3, DROP3, and genetic algorithms. On the other hand, three popular classification techniques are used to construct the learning models for comparison, namely the CART decision tree, k-nearest neighbor (k-NN), and support vector machine (SVM). RESULTS: The results show that our proposed approach not only reduces the computational cost by nearly a factor of two or three over three other state-of-the-art algorithms, but also maintains the final classification accuracy. CONCLUSIONS: To perform instance selection over large scale medical datasets, it requires a large computational cost to directly execute existing instance selection algorithms. Our proposed EDP approach solves this problem by training a learning model to recognize good and noisy data. To consider both computational complexity and final classification accuracy, the proposed EDP has been demonstrated its efficiency and effectiveness in the large scale instance selection problem. [ABSTRACT FROM AUTHOR]

This study aimed to clarify the evolution of antioxidant peptides and their proteomic homology during a continuous period of Jinhua ham processing. The peptide content increased during the process, reaching a value of 1.92% (weight %, based on meat weight) in ham at the end of dry-ripening, which was approximately 8-fold higher than that prior to salting. From scavenging effects on 1,1-diphenyl-2-picrylhydrazyl radical (DPPH•) and hydroxyl radical (•OH) and chelating ability on Fe2+, it was suggested that peptides generated at the end of dry-ripening had the highest level of antioxidant activity. Size-exclusion chromatography indicated that the antioxidation of peptides might be related to their MWs, where more fractions with small MWs result in higher antioxidant activity. The results of endogenous protease activities showed 22.33% of the initial activities of Cathepsin B were retained, suggesting that Cathepsin B plays a key role in the formation of antioxidant peptides. Homology-based proteomics showed that peptides primarily arose from myosin (32.37%), followed by actin (16.91%), troponin T (12.08%) and others. • The antioxidant peptides at different processing stages of Jinhua ham were evaluated. • More peptides fractions with small MWs result in higher antioxidant activity. • Cathepsin B plays a key role in the formation of antioxidant peptides. • Antioxidant peptides primarily arise from myosin, actin and troponin T. [ABSTRACT FROM AUTHOR]

The subcellular localization of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned subcellular localizations. This and other problems in subcellular localization prediction, such as the relatively high false positive and false negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing subcellular localization prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false positive and false negative predictions. ClubSub-P can assign the subcellular localization of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the subcellular localization prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/

Bcl-2 family proteins regulate apoptosis in animals. This protein family includes several homologous proteins and a collection of other proteins lacking sequence similarity except for a Bcl-2 homology (BH)3 motif. Thus, membership in the Bcl-2 family requires only one of the four BH motifs. On this basis, a growing number of diverse BH3-only proteins are being reported. Although compelling cell biological and biophysical evidence validates many BH3-only proteins, claims of significant BH3 sequence similarity are often unfounded. Computational and phylogenetic analyses suggest that only some BH3 motifs arose by divergent evolution from a common ancestor (homology), whereas others arose by convergent evolution or random coincidence (homoplasy), challenging current assumptions about which proteins constitute the extended Bcl-2 family. [ABSTRACT FROM AUTHOR]

Candidatus Liberibacter asiaticus ( Ca. L. asiaticus) is a Gram-negative bacterium and the pathogen of Citrus Greening disease (Huanglongbing, HLB). As a parasitic bacterium, Ca. L. asiaticus harbors ABC transporters that play important roles in exchanging chemical compounds between Ca. L. asiaticus and its host. Here, we analyzed all the ABC transporter-related proteins in Ca. L. asiaticus. We identified 14 ABC transporter systems and predicted their structures and substrate specificities. In-depth sequence and structure analysis including multiple sequence alignment, phylogenetic tree reconstruction, and structure comparison further support their function predictions. Our study shows that this bacterium could use these ABC transporters to import metabolites (amino acids and phosphates) and enzyme cofactors (choline, thiamine, iron, manganese, and zinc), resist to organic solvent, heavy metal, and lipid-like drugs, maintain the composition of the outer membrane (OM), and secrete virulence factors. Although the features of most ABC systems could be deduced from the abundant experimental data on their orthologs, we reported several novel observations within ABC system proteins. Moreover, we identified seven nontransport ABC systems that are likely involved in virulence gene expression regulation, transposon excision regulation, and DNA repair. Our analysis reveals several candidates for further studies to understand and control the disease, including the type I virulence factor secretion system and its substrate that are likely related to Ca. L. asiaticus pathogenicity and the ABC transporter systems responsible for bacterial OM biosynthesis that are good drug targets. Proteins 2012. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: P-glycoprotein (P-gp) is an ATP-dependent transport protein. It is selectively expressed at entry points of xenobiotics where, acting as an efflux pumps which prevents the entry into sensitive organs. This protein also plays a key role in the absorption and blood-brain barrier penetration of many drugs, while it''s over expression in cancer cells has been linked to multidrug resistance (MDR) in tumors. In P-gp, the MDR is the principal mechanism by which many tumor cells develop resistance to chemotherapy drugs. The tumor begins to grow again; chemotherapy may fail because the remaining tumor cells are now resistant. Flexible receptor docking has been used to develop new prediction algorithm for P-gp binding specificity. Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction and evolutionary analysis. Hidden Markov Models (HMMs) are usually used for statistical modeling, database searching, and multiple alignments of protein families and protein domains. In this paper, a detailed survey on P-gp (its function and structure), HMM (its function and structure with respect to P-gp) and MDR has been given. [Copyright &y& Elsevier]

Abstract: Human perception of sweetness, behind the felt pleasure, is thought to play a role as an indicator of energy density of foods. For humans, only a small number of plant proteins taste sweet. As non-caloric sweeteners, these plant proteins have attracted attention as candidates for the control of obesity, oral health and diabetic management. Significant advances have been made in the characterization of the sweet-tasting plant proteins, as well as their binding interactions with the appropriate receptors. The elucidation of sweet-taste receptor gene sequences represents an important step towards the understanding of sweet taste perception. However, many questions on the molecular basis of sweet-taste elicitation by plant proteins remain unanswered. In particular, why homologues of these proteins do not elicit similar responses? This question is discussed in this report, on the basis of available sequences and structures of sweet-tasting proteins, as well as of sweetness-sensing receptors. A simple procedure based on sequence comparisons between sweet-tasting protein and its homologous counterparts was proposed to identify critical residues for sweetness elicitation. The open question on the physiological function of sweet-tasting plant proteins is also considered. In particular, this review leads us to suggest that sweet-tasting proteins may interact with taste receptor in a serendipity manner. [Copyright &y& Elsevier]

Abstract: Thyroid Hormone Receptor β (THRB) defects, typically transmitted as autosomal dominant traits, cause Resistance to Thyroid Hormone (RTH). We analyzed the THRB gene in thirteen South American patients with clinical evidence RTH from eleven unrelated families. Sequence analysis revealed seven novel missense mutations. Four novel mutations were identified in exon 9. The first, a c.991A>G transition which originates a substitution of asparagine by aspartic acid (p.N331D). The second nucleotide alteration consists of a guanine to cytosine transversion at position 1003 (c.1003G>C) and results in substitution of the alanine at codon 335 by proline (p.A335P). The third mutation, a c.1022T>C transition produces a change of leucine by proline (p.L341P). The fourth mutation detected in exon 9 was a c.1036C>T transition which replaces the leucine at codon 346 by phenylalanine (p.L346F). The sequencing of the exon 10 detected three novel missense mutations. The first, a c.1293A>G transition changing isoleucine 431 for methionine (p.I431M). The second, the cytosine at position 1339 was replaced by adenine (c.1339C>A) resulting in the replacement of proline by threonine (p.P447T). The third mutation detected in exon 10 was a c.1358C>T transition resulting in the substitution of proline at codon 453 by leucine (p.P453L). Finally, sequencing analysis of the THRB gene revealed three substitutions previously described (p.A268G, p.P453T and p.F459C). The p.P453T was found in two patients. In conclusion, we report thirteen patients with RTH caused by heterozygous mutations of the THRB gene. Seven of the identified mutations correspond to novel substitutions. [Copyright &y& Elsevier]

Campylobacter, Helicobacter and Wolinella are genera of the order Campylobacterales, belonging to the class Epsilonproteobacteria. Their habitats are various niches in the gastrointestinal tract of higher animals, where they may come into contact with bile. Microorganisms in these environments require mechanisms of resistance to the surface-active amphipathic molecules with potent antimicrobial activities present in bile. This review summarizes current knowledge on the molecular responses to bile by Campylobacterales and other bacterial species that inhabit the intestinal tract and belong to the phyla Proteobacteria, Bacteriodetes, Firmicutes and Actinobacteria. To date, 125 specific genes have been implicated in bile responses, of which 10 are found in Campylobacterales. Genome database searches, analyses of protein sequence and domain similarities, and gene ontology data integration were performed to compare the responses to bile of these bacteria. The results showed that 33 proteins of bacteria belonging to the four phyla had similarities equal to or greater than 50–46% proteins of Campylobacterales. Domain architecture analyses revealed that 151 Campylobacterales proteins had similar domain composition and organization to 60 proteins known to participate in the tolerance to bile in other bacteria. The proteins CmeB, CmeF and CbrR of Campylobacter jejuni involved in bile tolerance were homologous to 42 proteins identified in the Proteobacteria, Bacteriodetes and Firmicutes. On the other hand, the proteins CiaB, CmeA, CmeC, CmeD, CmeE and FlaAσ28 also involved in the response to bile of C. jejuni, did not have homologues in other bacteria. Among the bacteria inhabiting the gastrointestinal tract, the Campylobacterales seem to have evolved some mechanisms of bile resistance similar to those of other bacteria, as well as other mechanisms that appear to be characteristic of this order. [ABSTRACT FROM AUTHOR]

Perhaps the most widely used applications of bioinformatics are tools such as psi- blast for searching sequence databases. We describe a recently developed protein database search algorithm called rankprop. rankprop relies upon a precomputed network of pairwise protein similarities. The algorithm performs a diffusion operation from a specified query protein across the protein similarity network. The resulting activation scores, assigned to each database protein, encode information about the global structure of the protein similarity network. This type of algorithm has a rich history in associationist psychology, artificial intelligence and web search. We describe the rankprop algorithm and its relatives, and we provide evidence that the algorithm successfully improves upon the rankings produced by psi- blast. [ABSTRACT FROM AUTHOR]

Abstract: Novosphingobium aromaticivorans, a unique ubiquitous bacterium that metabolizes xenobiotics and activates environmental estrogens, has been suggested as a pathogenic factor in the development of primary biliary cirrhosis (PBC). To define the molecular basis of PBC sera reactivity, we investigated the characteristic of the bacterial antigens involved. We cloned and sequenced four genes from N. aromaticivorans coding for immunoreactive proteins, arbitrarily named Novo 1 through Novo 4. We subsequently analyzed these proteins for their homology to known mitochondrial proteins and defined their reactivity using monoclonal antibodies (mAbs), rabbit anti-lipoic acid antibody, and PBC/control sera. Moreover, we studied their phylogenetic relation with the known PBC autoantigens. Novo proteins have an extraordinary degree of amino acid homology with all of the major human mitochondrial autoantigens PDC-E2 (Novo 1 and 2), OGDC-E2 (Novo 3), and BCOADC-E2 (Novo 4). Moreover, Novo 1–4 contain a lipoylated domain, are recognized by AMA-positive sera, and react with specific mAbs to mitochondrial antigens. Interestingly, the phylogenetic relation of the proteins emphasizes the conservation of the lipoylated domain. In conclusion, our data provide a high degree of confidence that N. aromaticivorans may potentiate the breakdown of self tolerance in genetically susceptible individuals. [Copyright &y& Elsevier]

We developed a variant of the intermediate sequence search method (ISSnew) for detection and alignment of weakly similar pairs of protein sequences. ISSnew relates two query sequences by an intermediate sequence that is potentially homologous to both queries. The improvement was achieved by a more robust overlap score for a match between the queries through an intermediate. The approach was benchmarked on a data set of 2369 sequences of known structure with insignificant sequence similarity to each other (BLAST E-value larger than 0.001); 2050 of these sequences had a related structure in the set. ISSnew performed significantly better than both PSI-BLAST and a previously described intermediate sequence search method. PSI-BLAST could not detect correct homologs for 1619 of the 2369 sequences. In contrast, ISSnew assigned a correct homolog as the top hit for 121 of these 1619 sequences, while incorrectly assigning homologs for only nine targets; it did not assign homologs for the remainder of the sequences. By estimate, ISSnew may be able to assign the folds of domains in ∼29,000 of the ∼500,000 sequences unassigned by PSI-BLAST, with 90% specificity (1 − false positives fraction). In addition, we show that the 15 alignments with the most significant BLAST E-values include the nearly best alignments constructed by ISSnew. [ABSTRACT FROM AUTHOR]