Analysis of protein sequence/structure similarity relationships

Anthriscus sylvestris Deoxypodophyllotoxin Synthase Involved in the Podophyllotoxin Biosynthesis

Tetrahydrofuran ring formation from dibenzylbutyrolactone lignans is a key step in the biosynthesis of aryltetralin lignans including deoxypodophyllotoxin and podophyllotoxin. Previously, Fe(II)- and 2-oxoglutarate-dependent dioxygenase (2-ODD) from Podophyllum hexandrum (Himalayan mayapple, Berberidaceae) was found to catalyze the cyclization of a dibenzylbutyrolactone lignan, yatein, to give deoxypodophyllotoxin and designated as deoxypodophyllotoxin synthase (DPS). Recently, we reported that the biosynthesis of deoxypodophyllotoxin and podophyllotoxin evolved in a lineage-specific manner in phylogenetically unrelated plant species such as P. hexandrum and Anthriscus sylvestris (cow parsley, Apiaceae). Therefore, a comprehensive understanding of the characteristics of DPSs that catalyze the cyclization of yatein to deoxypodophyllotoxin in various plant species is important. However, for plant species other than P. hexandrum, the isolation of the DPS enzyme gene and the type of the enzyme, e.g. whether it is 2-ODD or another type of enzyme such as cytochrome P-450, have not been reported. In this study, we report the identification and characterization of A. sylvestris DPS (AsDPS). Phylogenetic analysis showed that AsDPS belonged to the 2-ODD superfamily and shared moderate amino acid sequence identity (40.8%) with P. hexandrum deoxypodophyllotoxin synthase (PhDPS). Recombinant protein assay indicated that AsDPS and PhDPS differ in terms of the selectivity of substrate enantiomers. Protein modeling using AlphaFold2 and site-directed mutagenesis indicated that the Tyr305 residue of AsDPS probably contributes to substrate recognition. This study advances our understanding of the podophyllotoxin biosynthetic pathway in A. sylvestris and provides new insight into 2-ODD involved in plant secondary (specialized) metabolism.

Exploring Flexibility and Folding Patterns Throughout Time in Voltage Sensors

The voltage-sensing domain (VSD) is a module capable of responding to changes in the membrane potential through conformational changes and facilitating electromechanical coupling to open a pore gate, activate proton permeation pathways, or promote enzymatic activity in some membrane-anchored phosphatases. To carry out these functions, this module acts cooperatively through conformational changes. The VSD is formed by four transmembrane segments (S1-S4) but the S4 segment is critical since it carries positively charged residues, mainly Arg or Lys, which require an aqueous environment for its proper function. The discovery of this module in voltage-gated ion channels (VGICs), proton channels (Hv1), and voltage sensor-containing phosphatases (VSPs) has expanded our understanding of the principle of modularity in the voltage-sensing mechanism of these proteins. Here, by sequence comparison and the evaluation of the relationship between sequence composition, intrinsic flexibility, and structural analysis in 14 selected representatives of these three major protein groups, we report five interesting differences in the folding patterns of the VSD both in prokaryotes and eukaryotes. Our main findings indicate that this module is highly conserved throughout the evolutionary scale, however: (1) segments S1 to S3 in eukaryotes are significantly more hydrophobic than those present in prokaryotes; (2) the S4 segment has retained its hydrophilic character; (3) in eukaryotes the extramembranous linkers are significantly larger and more flexible in comparison with those present in prokaryotes; (4) the sensors present in the kHv1 proton channel and the ciVSP phosphatase, both of eukaryotic origin, exhibit relationships of flexibility and folding patterns very close to the typical ones found in prokaryotic voltage sensors; and (5) archaeal channels KvAP and MVP have flexibility profiles which are clearly contrasting in the S3-S4 region, which could explain their divergent activation mechanisms. Finally, to elucidate the obscure origins of this module, we show further evidence for a possible connection between voltage sensors and TolQ proteins.

Bacterial Proteases as Potentially Exploitable Modulators of SARS-CoV-2 Infection: Logic from the Literature, Informatics, and Inspiration from the Dog

(1) Background: The COVID-19 pandemic left many intriguing mysteries. Retrospective vulnerability trends tie as strongly to odd demographics as to exposure profiles, genetics, health, or prior medical history. This article documents the importance of nasal microbiome profiles in distinguishing infection rate trends among differentially affected subgroups. (2) Hypothesis: From a detailed literature survey, microbiome profiling experiments, bioinformatics, and molecular simulations, we propose that specific commensal bacterial species in the Pseudomonadales genus confer protection against SARS-CoV-2 infections by expressing proteases that may interfere with the proteolytic priming of the Spike protein. (3) Evidence: Various reports have found elevated Moraxella fractions in the nasal microbiomes of subpopulations with higher resistance to COVID-19 (e.g., adolescents, COVID-19-resistant children, people with strong dietary diversity, and omnivorous canines) and less abundant ones in vulnerable subsets (the elderly, people with narrower diets, carnivorous cats and foxes), along with bioinformatic evidence that Moraxella bacteria express proteases with notable homology to human TMPRSS2. Simulations suggest that these proteases may proteolyze the SARS-CoV-2 spike protein in a manner that interferes with TMPRSS2 priming.

SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures

BACKGROUND

The relationship between the sequence of a protein, its structure, and the resulting connection between its structure and function, is a foundational principle in biological science. Only recently has the computational prediction of protein structure based only on protein sequence been addressed effectively by AlphaFold, a neural network approach that can predict the majority of protein structures with X-ray crystallographic accuracy. A question that is now of acute relevance is the "inverse protein folding problem": predicting the sequence of a protein that folds into a specified structure. This will be of immense value in protein engineering and biotechnology, and will allow the design and expression of recombinant proteins that can, for instance, fold into specified structures as a scaffold for the attachment of recombinant antigens, or enzymes with modified or novel catalytic activities. Here we describe the development of SeqPredNN, a feed-forward neural network trained with X-ray crystallographic structures from the RCSB Protein Data Bank to predict the identity of amino acids in a protein structure using only the relative positions, orientations, and backbone dihedral angles of nearby residues.

RESULTS

We predict the sequence of a protein expected to fold into a specified structure and assess the accuracy of the prediction using both AlphaFold and RoseTTAFold to computationally generate the fold of the derived sequence. We show that the sequences predicted by SeqPredNN fold into a structure with a median TM-score of 0.638 when compared to the crystal structure according to AlphaFold predictions, yet these sequences are unique and only 28.4% identical to the sequence of the crystallized protein.

CONCLUSIONS

We propose that SeqPredNN will be a valuable tool to generate proteins of defined structure for the design of novel biomaterials, pharmaceuticals, catalysts, and reporter systems. The low sequence identity of its predictions compared to the native sequence could prove useful for developing proteins with modified physical properties, such as water solubility and thermal stability. The speed and ease of use of SeqPredNN offers a significant advantage over physics-based protein design methods.

Using AlphaFold Predictions in Viral Research

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

Use of an Integrated Approach Involving AlphaFold Predictions for the Evolutionary Taxonomy of Duplodnaviria Viruses

The evaluation of the evolutionary relationships is exceptionally important for the taxonomy of viruses, which is a rapidly expanding area of research. The classification of viral groups belonging to the realm Duplodnaviria, which include tailed bacteriophages, head-tailed archaeal viruses and herpesviruses, has undergone many changes in recent years and continues to improve. One of the challenging tasks of Duplodnaviria taxonomy is the classification of high-ranked taxa, including families and orders. At the moment, only 17 of 50 families have been assigned to orders. The evaluation of the evolutionary relationships between viruses is complicated by the high level of divergence of viral proteins. However, the development of structure prediction algorithms, including the award-winning AlphaFold, encourages the use of the results of structural predictions to clarify the evolutionary history of viral proteins. In this study, the evolutionary relationships of two conserved viral proteins, the major capsid protein and terminase, representing different viruses, including all classified Duplodnaviria families, have been analysed using AlphaFold modelling. This analysis has been undertaken using structural comparisons and different phylogenetic methods. The results of the analyses mainly indicated the high quality of AlphaFold modelling and the possibility of using the AlphaFold predictions, together with other methods, for the reconstruction of the evolutionary relationships between distant viral groups. Based on the results of this integrated approach, assumptions have been made about refining the taxonomic classification of bacterial and archaeal Duplodnaviria groups, and problems relating to the taxonomic classification of Duplodnaviria have been discussed.

In silico analysis of peroxidase from Luffa acutangula

Peroxidases are oxidoreductase enzymes that widely gained attention as biocatalysts for their robust catalytic activity, specificity, and regioselective functionality for phenolic compounds. The study of molecular aspects of peroxidases is as crucial as that of the physicochemical aspects. A bioinformatics approach is utilized in this study to investigate the structural aspects and functions of luffa peroxidase (LPrx) from Luffa acutangula. The evolutionary relationship of LPrx with other class III peroxidases was studied by constructing a neighbour-joining phylogenetic tree. An analysis of the phylogenetic tree revealed that plant peroxidases share a common ancestor. The gene ontology term showed that LPrx had a molecular functionality of the oxidation-reduction process, heme binding and peroxidase-like activity, and the biological function of hydrogen peroxide scavenging activity. The enzyme-ligand interactions were studied from a catalytic point of view using the molecular docking technique. The molecular docking was carried out with LPrx as a receptor and guaiacol, m-cresol, p-cresol, catechol, quinol, pyrogallol, 2,4-dimethoxyphenol, gallic acid, aniline, and o-phenylenediamine as ligands. The results presented in the current communication will have a significant implication in proteomics, biochemistry, biotechnology, and the potential applications of peroxidases in the biotransformations of organic compounds.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03432-8.

Transcription factor OsNAC055 regulates GA-mediated lignin biosynthesis in rice straw

Crop straws represent enormous biomass resource that mainly contain secondary cell walls (SCWs) consisting of cellulose, hemicelluloses and lignin. Nevertheless, the regulatory mechanism of SCW biosynthesis still needs to be well understood. In this study, we identified a rice NAC (NAM, ATAF1/2, CUC2) transcription factor OsNAC055 that regulates GA-mediated lignin biosynthesis. As a nucleus-localized transcription factor, OsNAC055 exhibits the transcriptional activation activity. Overexpression of OsNAC055 increases the lignin content in rice straw. Transcriptomic analyses showed that the expression of multiple lignin biosynthetic genes was increased in OsNAC055-overexpressing plants. Further ChIP-qPCR analysis and transient transactivation assays indicated that OsNAC055 directly activates rice lignin biosynthetic genes CINNAMOYL-CoA REDUCTASE 10 (OsCCR10) and CINNAMYL ALCOHOL DEHYDROGENASE 2 (OsCAD2) by binding to their promoters. On the other hand, phytohormone measurement showed that OsNAC055 overexpression significantly increased exogenous GA3 levels in rice plants by regulating GA biosynthetic gene OsGA20ox2. Moreover, yeast two-hybrid and bimolecular fluorescence complement (BiFC) assays indicated that OsNAC055 interacts with SLENDER RICE1 (SLR1), the repressor in GA signaling. More importantly, exogenous GA treatment markedly enhanced the transcription of OsCCR10 and OsCAD2, suggesting the role of GA in lignin biosynthesis. Together, our results provide the evidence that OsNAC055 functions as an essential transcription factor to regulate the GA-mediated lignin biosynthesis, which provides a strategy for manipulating lignin production.

NHX Gene Family in Camellia sinensis: In-silico Genome-Wide Identification, Expression Profiles, and Regulatory Network Analysis

Salt stress affects the plant growth and productivity worldwide and NHX is one of those genes that are well known to improve salt tolerance in transgenic plants. It is well characterized in several plants, such as Arabidopsis thaliana and cotton; however, not much is known about NHXs in tea plant. In the present study, NHX genes of tea were obtained through a genome-wide search using A. thaliana as reference genome. Out of the 9 NHX genes in tea, 7 genes were localized in vacuole while the remaining 2 genes were localized in the endoplasmic reticulum (ER; CsNHX8) and plasma membrane (PM; CsNHX9), respectively. Furthermore, phylogenetic relationships along with structural analysis which includes gene structure, location, and protein-conserved motifs and domains were systematically examined and further, predictions were validated by the expression analysis. The dN/dS values show that the majority of tea NHX genes is subjected to strong purifying selection under the course of evolution. Also, functional interaction was carried out in Camellia sinensis based on the orthologous genes in A. thaliana. The expression profiles linked to various stress treatments revealed wide involvement of NHX genes from tea in response to various abiotic factors. This study provides the targets for further comprehensive identification, functional study, and also contributed for a better understanding of the NHX regulatory network in C. sinensis.

VTR: A Web Tool for Identifying Analogous Contacts on Protein Structures and Their Complexes

Evolutionarily related proteins can present similar structures but very dissimilar sequences. Hence, understanding the role of the inter-residues contacts for the protein structure has been the target of many studies. Contacts comprise non-covalent interactions, which are essential to stabilize macromolecular structures such as proteins. Here we show VTR, a new method for the detection of analogous contacts in protein pairs. The VTR web tool performs structural alignment between proteins and detects interactions that occur in similar regions. To evaluate our tool, we proposed three case studies: we 1) compared vertebrate myoglobin and truncated invertebrate hemoglobin; 2) analyzed interactions between the spike protein RBD of SARS-CoV-2 and the cell receptor ACE2; and 3) compared a glucose-tolerant and a non-tolerant β-glucosidase enzyme used for biofuel production. The case studies demonstrate the potential of VTR for the understanding of functional similarities between distantly sequence-related proteins, as well as the exploration of important drug targets and rational design of enzymes for industrial applications. We envision VTR as a promising tool for understanding differences and similarities between homologous proteins with similar 3D structures but different sequences. VTR is available at http://bioinfo.dcc.ufmg.br/vtr.

In-silico genome wide analysis of Mitogen activated protein kinase kinase kinase gene family in C. sinensis

Mitogen activated protein kinase kinase kinase (MAPKKK) form the upstream component of MAPK cascade. It is well characterized in several plants such as Arabidopsis and rice however the knowledge about MAPKKKs in tea plant is largely unknown. In the present study, MAPKKK genes of tea were obtained through a genome wide search using Arabidopsis thaliana as the reference genome. Among 59 candidate MAPKKK genes in tea, 17 genes were MEKK-like, 31 genes were Raf-like and 11 genes were ZIK- like. Additionally, phylogenetic relationships were established along with structural analysis, which includes gene structure, its location as well as conserved motifs, cis-acting regulatory elements and functional domain signatures that were systematically examined. Also, on the basis of one orthologous gene found between tea and Arabidopsis, functional interaction was carried out in C. sinensis based on an Arabidopsis association model. The expressional profiles indicated major involvement of MAPKKK genes from tea in response to various abiotic stress factors. Taken together, this study provides the targets for additional inclusive identification, functional study, and provides comprehensive knowledge for a better understanding of the MAPKKK cascade regulatory network in C. sinensis.

In silico investigation of the viroporin E as a vaccine target against SARS-CoV-2

Viroporins, integral viral membrane ion channel proteins, interact with host-cell proteins deregulating physiological processes and activating inflammasomes. Severity of COVID-19 might be associated with hyperinflammation, thus we aimed at the complete immunoinformatic analysis of the SARS-CoV-2 viroporin E, P0DTC4. We also identified the human proteins interacting with P0DTC4 and the enriched molecular functions of the corresponding genes. The complete sequence of P0DTC4 in FASTA format was processed in 10 databases relative to secondary and tertiary protein structure analyses and prediction of optimal vaccine epitopes. Three more databases were accessed for the retrieval and the molecular functional characterization of the P0DTC4 human interactors. The immunoinformatics analysis resulted in the identification of 4 discontinuous B-cell epitopes along with 1 linear B-cell epitope and 11 T-cell epitopes which were found to be antigenic, immunogenic, nonallergen, nontoxin, and unable to induce autoimmunity thus fulfilling prerequisites for vaccine design. The functional enrichment analysis showed that the predicted host interactors of P0DTC4 target the cellular acetylation network. Two of the identified host-cell proteins – BRD2 and BRD4 – have been shown to be promising targets for antiviral therapy. Thus, our findings have implications for COVID-19 therapy and indicate that viroporin E could serve as a promising vaccine target against SARS-CoV-2. Validation experiments are required to complement these in silico results.

Including residual contact information into replica-exchange MD simulations significantly enriches native-like conformations

Proteins are complex biomolecules which perform critical tasks in living organisms. Knowledge of a protein's structure is essential for understanding its physiological function in detail. Despite the incredible progress in experimental techniques, protein structure determination is still expensive, time-consuming, and arduous. That is why computer simulations are often used to complement or interpret experimental data. Here, we explore how in silico protein structure determination based on replica-exchange molecular dynamics (REMD) can benefit from including contact information derived from theoretical and experimental sources, such as direct coupling analysis or NMR spectroscopy. To reflect the influence from erroneous and noisy data we probe how false-positive contacts influence the simulated ensemble. Specifically, we integrate varying numbers of randomly selected native and non-native contacts and explore how such a bias can guide simulations towards the native state. We investigate the number of contacts needed for a significant enrichment of native-like conformations and show the capabilities and limitations of this method. Adhering to a threshold of approximately 75% true-positive contacts within a simulation, we obtain an ensemble with native-like conformations of high quality. We find that contact-guided REMD is capable of delivering physically reasonable models of a protein's structure.

Topology-independent and global protein structure alignment through an FFT-based algorithm

MOTIVATION

Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods may suffer from suboptimal alignment and/or over-fragmentation and thus lead to a biologically wrong alignment in some cases. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign.

RESULTS

Our FTAlign algorithm was extensively tested on six commonly used datasets and compared with seven state-of-the-art structure alignment approaches, TMalign, DeepAlign, Kpax, 3DCOMB, MICAN, SPalignNS and CLICK. It was shown that FTAlign outperformed the other methods in reproducing manually curated alignments and obtained a high success rate of 96.7 and 90.0% on two gold-standard benchmarks, MALIDUP and MALISAM, respectively. Moreover, FTAlign also achieved the overall best performance in terms of biologically meaningful structure overlap (SO) and TMscore on both the sequential alignment test sets including MALIDUP, MALISAM and 64 difficult cases from HOMSTRAD, and the non-sequential sets including MALIDUP-NS, MALISAM-NS, 199 topology-different cases, where FTAlign especially showed more advantage for non-sequential alignment. Despite its global search feature, FTAlign is also computationally efficient and can normally complete a pairwise alignment within one second.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/ftalign/.

MAPK cascade gene family in Camellia sinensis: In-silico identification, expression profiles and regulatory network analysis

BACKGROUND

Mitogen Activated Protein Kinase (MAPK) cascade is a fundamental pathway in organisms for signal transduction. Though it is well characterized in various plants, there is no systematic study of this cascade in tea.

RESULT

In this study, 5 genes of Mitogen Activated Protein Kinase Kinase (MKK) and 16 genes of Mitogen Activated Protein Kinase (MPK) in Camellia sinensis were found through a genome-wide search taking Arabidopsis thaliana as the reference genome. Also, phylogenetic relationships along with structural analysis which includes gene structure, location as well as protein conserved motifs and domains, were systematically examined and further, predictions were validated by the results. The plant species taken for comparative study clearly displayed segmental duplication, which was a significant candidate for MAPK cascade expansion. Also, functional interaction was carried out in C. sinensis based on the orthologous genes in Arabidopsis. The expression profiles linked to various stress treatments revealed wide involvement of MAPK and MAPKK genes from Tea in response to various abiotic factors. In addition, the expression of these genes was analysed in various tissues.

CONCLUSION

This study provides the targets for further comprehensive identification, functional study, and also contributed for a better understanding of the MAPK cascade regulatory network in C. sinensis.

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.

Structural variations within proteins can be as large as variations observed across their homologues

Understanding the structural plasticity of proteins is key to understanding the intricacies of their functions and mechanistic basis. In the current study, we analyzed the available multiple crystal structures of the same protein for the structural differences. For this purpose we used an abstraction of protein structures referred as Protein Blocks (PBs) that was previously established. We also characterized the nature of the structural variations for a few proteins using molecular dynamics simulations. In both the cases, the structural variations were summarized in the form of substitution matrices of PBs. We show that certain conformational states are preferably replaced by other specific conformational states. Interestingly, these structural variations are highly similar to those previously observed across structures of homologous proteins (r² = 0.923) or across the ensemble of conformations from NMR data (r² = 0.919). Thus our study quantitatively shows that overall trends of structural changes in a given protein are nearly identical to the trends of structural differences that occur in the topologically equivalent positions in homologous proteins. Specific case studies are used to illustrate the nature of these structural variations.

Combined transcriptomic and proteomic analysis reveals a diversity of venom-related and toxin-like peptides expressed in the mat anemone Zoanthus natalensis (Cnidaria, Hexacorallia)

Venoms from marine animals have been recognized as a new emerging source of peptide-based therapeutics. Several peptide toxins from sea anemone have been investigated as therapeutic leads or pharmacological tools. Venom complexity should be further highlighted using combined strategies of large-scale sequencing and data analysis which integrated transcriptomics and proteomics to elucidate new proteins or peptides to be compared among species. In this work, transcriptomic and proteomic analyses were combined to identify six groups of expressed peptide toxins in Zoanthus natalensis. These include neurotoxin, hemostatic and hemorrhagic toxin, protease inhibitor, mixed function enzymes, venom auxiliary proteins, allergen peptides, and peptides related to the innate immunity. Molecular docking analysis indicated that one expressed Zoanthus Kunitz-like peptide, ZoaKuz1, could be a voltage-gated potassium channels blocker and, hence, it was selected for functional studies. Functional bioassays revealed that ZoaKuz1 has an intrinsic neuroprotective activity in zebrafish model of Parkinson's disease. Since pharmacological blockade of K_V channels is known to induce neuroprotective effects, ZoaKuz1 holds the potential to be developed in a therapeutic tool to control neural dysfunction by slowing or even halting neurodegeneration mediated by ion-channel hyperactivity.

A new widespread subclass of carbonic anhydrase in marine phytoplankton

Most aquatic photoautotrophs depend on CO₂-concentrating mechanisms (CCMs) to maintain productivity at ambient concentrations of CO₂, and carbonic anhydrase (CA) plays a key role in these processes. Here we present different lines of evidence showing that the protein LCIP63, identified in the marine diatom Thalassiosira pseudonana, is a CA. However, sequence analysis showed that it has a low identity with any known CA and therefore belongs to a new subclass that we designate as iota-CA. Moreover, LCIP63 unusually prefers Mn²⁺ to Zn²⁺ as a cofactor, which is potentially of ecological relevance since Mn²⁺ is more abundant than Zn²⁺ in the ocean. LCIP63 is located in the chloroplast and only expressed at low concentrations of CO₂. When overexpressed using biolistic transformation, the rate of photosynthesis at limiting concentrations of dissolved inorganic carbon increased, confirming its role in the CCM. LCIP63 homologs are present in the five other sequenced diatoms and in other algae, bacteria, and archaea. Thus LCIP63 is phylogenetically widespread but overlooked. Analysis of the Tara Oceans database confirmed this and showed that LCIP63 is widely distributed in marine environments and is therefore likely to play an important role in global biogeochemical carbon cycling.

Stress-immune responses and DNA protection function of thioredoxin domain containing 12 in zebrafish (Danio rerio)

Proteins with dithiol-disulfide oxidoreductase catalytic domain are well known for their capacity in the cellular redox homeostasis. In this study, we characterized the zebrafish thioredoxin domain containing 12 (Zftxndc12) gene, analyzed the transcriptional responses and studied the functional properties of its recombinant protein. Full-length cDNA of Zftxndc12 consists 519 bp coding region encoding 172 amino acids (AA) including the signal peptide. Highly consensus active motif (⁶⁵WCGAC⁶⁹) and probable ER retrieval motif (¹⁶⁹GDEL¹⁷²) were identified. Ubiquitous expression of Zftxndc12 mRNA was observed from one cell to juvenile stage as well as different organs of adult zebrafish. Moreover, whole mount in situ hybridization (WISH) results showed a higher expression of Zftxndc12 in primordial gills, central nerves system and eye. The tissue specific expression analysis (by qRT-PCR) also showed the highest expression in gills followed by brain in adult zebrafish. In larvae, up-regulated Zftxndc12 mRNA expression upon exposure to H₂O_2,Edwardsiella tarda and Saprolegnia parasitica suggests that it may involve in both stress and immune responses. Moreover, transcriptional expression of Zftxndc12 was up-regulated upon Streptococcus iniae challenge in gills of adult zebrafish. The recombinant ZfTxndc12 (rZfTxndc12) was overexpressed, purified and tested for its biological activities. Results revealed that rZfTxndc12 is able to reduce the DNA damage and detoxify the H₂O₂ toxicity in concentration dependent manner. Overall results suggest that Zftxndc12 is important antioxidant and immune functional member of the host defense system in zebrafish.

Antibody Cross-Reactivity in Antivenom Research

Antivenom cross-reactivity has been investigated for decades to determine which antivenoms can be used to treat snakebite envenomings from different snake species. Traditionally, the methods used for analyzing cross-reactivity have been immunodiffusion, immunoblotting, enzyme-linked immunosorbent assay (ELISA), enzymatic assays, and in vivo neutralization studies. In recent years, new methods for determination of cross-reactivity have emerged, including surface plasmon resonance, antivenomics, and high-density peptide microarray technology. Antivenomics involves a top-down assessment of the toxin-binding capacities of antivenoms, whereas high-density peptide microarray technology may be harnessed to provide in-depth knowledge on which toxin epitopes are recognized by antivenoms. This review provides an overview of both the classical and new methods used to investigate antivenom cross-reactivity, the advantages and disadvantages of each method, and examples of studies using the methods. A special focus is given to antivenomics and high-density peptide microarray technology as these high-throughput methods have recently been introduced in this field and may enable more detailed assessments of antivenom cross-reactivity.

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues

Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM's weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol's ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner's interacting scaffold is more flexible and adaptable.

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.

Threading: A novel insilico indagation method for genetic characterization of some diplostomoid metacercariae (Digenea:Diplostomidae Poirier, 1886)

The protein encoding zone of Mitochondrial DNA region (inherited from single lineage) seems most suitable and effective for taxonomic, systematic, ecological, evolutionary, DNA barcoding, cryptic species and population studies, exploiting nucleotide/amino acid datasets (1D/2D/3D conformational level). Nowadays, expeditious computerized methods are in trend for analyzing genetic material to demonstrate variations at various levels of protein structures. Structural proteomics have implemented here for genetic identification, differentiation and relationship of species from information rich data of mt COI gene of the family Diplostomidae with inclusion of molecular tools. Various aspects have been utilized herein for re-validation and infallible discrimination of Trematode diplostomoid metacercariae (Tetracotyle lucknowensis Pandey, 1971; T. xenentodoni Chakrabarti, 1970; T. fausti Rai and Pande, 1969; T. muscularius Chakrabarti, 1970 and Diplostomulum minutum Pandey, 1968), the infective stage in the life cycle, causing severe damage to fish host, whose adults are found mainly in fish eating birds and mammals.

Improving the efficiency of molecular replacement by utilizing a new iterative transform phasing algorithm

An iterative transform method proposed previously for direct phasing of high-solvent-content protein crystals is employed for enhancing the molecular-replacement (MR) algorithm in protein crystallography. Target structures that are resistant to conventional MR due to insufficient similarity between the template and target structures might be tractable with this modified phasing method. Trial calculations involving three different structures are described to test and illustrate the methodology. The relationship of the approach to PHENIX Phaser-MR and MR-Rosetta is discussed.

Structural and evolutionary relationships among RuBisCOs inferred from their large and small subunits

Ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is the key enzyme to assimilate CO(2) into the biosphere. The nonredundant structural data sets for three RuBisCO domain superfamilies, i.e. large subunit C-terminal domain (LSC), large subunit N-terminal domain (LSN) and small subunit domain (SS), were selected using QR factorization based on the structural alignment with QH as the similarity measure. The structural phylogenies were then constructed to investigate a possible functional significance of the evolutionary diversification. The LSC could have occurred in both bacteria and archaea, and has evolved towards increased complexity in both bacteria and eukaryotes with a 4-helix-2-helix-2-helix bundle being extended into a 5-helix-3-helix-3-helix one at the LSC carboxyl-terminus. The structural variations of LSN could have originated not only in bacteria with a short coil, but also in eukaryotes with a long one. Meanwhile, the SS dendrogram can be contributed to the structural variations at the βA-βB-loop region. All the structural variations observed in the coil regions have influence on catalytic performance or CO(2)/O(2) selectivities of RuBisCOs from different species. Such findings provide insights on RuBisCO improvements.

A bacterial homologue of the human iron exporter ferroportin

A bacterial homologue of the human iron exporter ferroportin found in the predatory Gram-negative bacterium Bdellovibrio bacteriovorus has been investigated. Molecular modelling, expression in recombinant form and iron binding and transport assays demonstrate that B. bacteriovorus ferroportin (bdFpn) is indeed an orthologue of human ferroportin. Key residues corresponding to those essential for iron binding and transport in human ferroportin are conserved in the bacterial homologue and are predicted to be correctly clustered in the central cavity of the protein. Mutation of these residues grossly affects the iron binding and transport ability of bdFpn.

Substitute sweeteners: diverse bacterial oligosaccharyltransferases with unique N-glycosylation site preferences

The central enzyme in the Campylobacter jejuni asparagine-linked glycosylation pathway is the oligosaccharyltransferase (OST), PglB, which transfers preassembled glycans to specific asparagine residues in target proteins. While C. jejuni PglB (CjPglB) can transfer many diverse glycan structures, the acceptor sites that it recognizes are restricted predominantly to those having a negatively charged residue in the -2 position relative to the asparagine. Here, we investigated the acceptor-site preferences for 23 homologs with natural sequence variation compared to CjPglB. Using an ectopic trans-complementation assay for CjPglB function in glycosylation-competent Escherichia coli, we demonstrated in vivo activity for 16 of the candidate OSTs. Interestingly, the OSTs from Campylobacter coli, Campylobacter upsaliensis, Desulfovibrio desulfuricans, Desulfovibrio gigas, and Desulfovibrio vulgaris, exhibited significantly relaxed specificity towards the -2 position compared to CjPglB. These enzymes glycosylated minimal N-X-T motifs in multiple targets and each followed unique, as yet unknown, rules governing acceptor-site preferences. One notable example is D. gigas PglB, which was the only bacterial OST to glycosylate the Fc domain of human immunoglobulin G at its native 'QYNST' sequon. Overall, we find that a subset of bacterial OSTs follow their own rules for acceptor-site specificity, thereby expanding the glycoengineering toolbox with previously unavailable biocatalytic diversity.

Principles for the organization of gene-sets

A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs.

Effect of duplicate genes on mouse genetic robustness: an update

In contrast to S. cerevisiae and C. elegans, analyses based on the current knockout (KO) mouse phenotypes led to the conclusion that duplicate genes had almost no role in mouse genetic robustness. It has been suggested that the bias of mouse KO database toward ancient duplicates may possibly cause this knockout duplicate puzzle, that is, a very similar proportion of essential genes (P_E) between duplicate genes and singletons. In this paper, we conducted an extensive and careful analysis for the mouse KO phenotype data and corroborated a strong effect of duplicate genes on mouse genetics robustness. Moreover, the effect of duplicate genes on mouse genetic robustness is duplication-age dependent, which holds after ruling out the potential confounding effect from coding-sequence conservation, protein-protein connectivity, functional bias, or the bias of duplicates generated by whole genome duplication (WGD). Our findings suggest that two factors, the sampling bias toward ancient duplicates and very ancient duplicates with a proportion of essential genes higher than that of singletons, have caused the mouse knockout duplicate puzzle; meanwhile, the effect of genetic buffering may be correlated with sequence conservation as well as protein-protein interactivity.

Evolutionary dynamics on protein bi-stability landscapes can potentially resolve adaptive conflicts

Experimental studies have shown that some proteins exist in two alternative native-state conformations. It has been proposed that such bi-stable proteins can potentially function as evolutionary bridges at the interface between two neutral networks of protein sequences that fold uniquely into the two different native conformations. Under adaptive conflict scenarios, bi-stable proteins may be of particular advantage if they simultaneously provide two beneficial biological functions. However, computational models that simulate protein structure evolution do not yet recognize the importance of bi-stability. Here we use a biophysical model to analyze sequence space to identify bi-stable or multi-stable proteins with two or more equally stable native-state structures. The inclusion of such proteins enhances phenotype connectivity between neutral networks in sequence space. Consideration of the sequence space neighborhood of bridge proteins revealed that bi-stability decreases gradually with each mutation that takes the sequence further away from an exactly bi-stable protein. With relaxed selection pressures, we found that bi-stable proteins in our model are highly successful under simulated adaptive conflict. Inspired by these model predictions, we developed a method to identify real proteins in the PDB with bridge-like properties, and have verified a clear bi-stability gradient for a series of mutants studied by Alexander et al. (Proc Nat Acad Sci USA 2009, 106:21149–21154) that connect two sequences that fold uniquely into two different native structures via a bridge-like intermediate mutant sequence. Based on these findings, new testable predictions for future studies on protein bi-stability and evolution are discussed.

Proteins are essential molecules for performing a majority of functions in all biological systems. These functions often depend on the three-dimensional structures of proteins. Here, we investigate a fundamental question in molecular evolution: how can proteins acquire new advantageous structures via mutations while not sacrificing their existing structures that are still needed? Some authors have suggested that the same protein may adopt two or more alternative structures, switch between them and thus perform different functions with each of the alternative structures. Intuitively, such a protein could provide an evolutionary compromise between conflicting demands for existing and new protein structures. Yet no theoretical study has systematically tackled the biophysical basis of such compromises during evolutionary processes. Here we devise a model of evolution that specifically recognizes protein molecules that can exist in several different stable structures. Our model demonstrates that proteins can indeed utilize multiple structures to satisfy conflicting evolutionary requirements. In light of these results, we identify data from known protein structures that are consistent with our predictions and suggest novel directions for future investigation.

Dynamic factors controlling targeting nanocarriers to vascular endothelium

Endothelium lining the luminal surface of blood vessels is the key target and barrier for vascular drug delivery. Nanocarriers coated with antibodies or affinity peptides that bind specifically to endothelial surface determinants provide targeted delivery of therapeutic cargoes to these cells. Endothelial targeting consists of several phases including circulation in the bloodstream, anchoring on the endothelial surface and, in some cases, intracellular uptake and trafficking of the internalized materials. Dynamic parameters of the vasculature including the blood hydrodynamics as well as surface density, accessibility, membrane mobility and clustering of target determinants modulate these phases of the targeting, especially anchoring to endothelium. Further, such controlled parameters of design of drug nanocarriers such as affinity, surface density and epitope specificity of targeting antibodies, carrier size and shape also modulate endothelial targeting and resultant sub-cellular addressing. This article reviews experimental and computational approaches for analysis of factors modulating targeting nanocarriers to the endothelial cells.

PocketAnnotate: towards site-based function annotation

A computational pipeline PocketAnnotate for functional annotation of proteins at the level of binding sites has been proposed in this study. The pipeline integrates three in-house algorithms for site-based function annotation: PocketDepth, for prediction of binding sites in protein structures; PocketMatch, for rapid comparison of binding sites and PocketAlign, to obtain detailed alignment between pair of binding sites. A novel scheme has been developed to rapidly generate a database of non-redundant binding sites. For a given input protein structure, putative ligand-binding sites are identified, matched in real time against the database and the query substructure aligned with the promising hits, to obtain a set of possible ligands that the given protein could bind to. The input can be either whole protein structures or merely the substructures corresponding to possible binding sites. Structure-based function annotation at the level of binding sites thus achieved could prove very useful for cases where no obvious functional inference can be obtained based purely on sequence or fold-level analyses. An attempt has also been made to analyse proteins of no known function from Protein Data Bank. PocketAnnotate would be a valuable tool for the scientific community and contribute towards structure-based functional inference. The web server can be freely accessed at http://proline.biochem.iisc.ernet.in/pocketannotate/.

Protein surface characterization using an invariant descriptor

Aim. To develop a new invariant descriptor for the characterization of protein surfaces, suitable for various analysis tasks, such as protein functional classification, and search and retrieval of protein surfaces over a large database. Methods. We start with a local descriptor of selected circular patches on the protein surface. The descriptor records the distance distribution between the central residue and the residues within the patch, keeping track of the number of particular pairwise residue cooccurrences in the patch. A global descriptor for the entire protein surface is then constructed by combining information from the local descriptors. Our method is novel in its focus on residue-specific distance distributions, and the use of residue-distance co-occurrences as the basis for the proposed protein surface descriptors. Results. Results are presented for protein classification and for retrieval for three protein families. For the three families, we obtained an area under the curve for precision and recall ranging from 0.6494 (without residue co-occurrences) to 0.6683 (with residue co-occurrences). Large-scale screening using two other protein families placed related family members at the top of the rank, with a number of uncharacterized proteins also retrieved. Comparative results with other proposed methods are included.

Biomolecularmodeling and simulation: a field coming of age

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field’s history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right.

Retro-MoRFs: identifying protein binding sites by normal and reverse alignment and intrinsic disorder prediction

Many cell functions in all living organisms rely on protein-based molecular recognition involving disorder-to-order transitions upon binding by molecular recognition features (MoRFs). A well accepted computational tool for identifying likely protein-protein interactions is sequence alignment. In this paper, we propose the combination of sequence alignment and disorder prediction as a tool to improve the confidence of identifying MoRF-based protein-protein interactions. The method of reverse sequence alignment is also rationalized here as a novel approach for finding additional interaction regions, leading to the concept of a retro-MoRF, which has the reversed sequence of an identified MoRF. The set of retro-MoRF binding partners likely overlap the partner-sets of the originally identified MoRFs. The high abundance of MoRF-containing intrinsically disordered proteins in nature suggests the possibility that the number of retro-MoRFs could likewise be very high. This hypothesis provides new grounds for exploring the mysteries of protein-protein interaction networks at the genome level.

ALADYN: a web server for aligning proteins by matching their large-scale motion

The ALADYN web server aligns pairs of protein structures by comparing their internal dynamics and detecting regions that sustain similar large-scale movements. The latter often accompany functional conformational changes in proteins and enzymes. The ALADYN dynamics-based alignment can therefore highlight functionally-oriented correspondences that could be more elusive to sequence- or structure-based comparisons. The ALADYN server takes the structure files of the two proteins as input. The optimal relative positioning of the molecules is found by maximizing the similarity of the pattern of structural fluctuations which are calculated via an elastic network model. The resulting alignment is presented via an interactive graphical Java applet and is accompanied by a number of quantitative indicators and downloadable data files. The ALADYN web server is freely accessible at the http://aladyn.escience-lab.org address.

Crystal structure of Pyrococcus horikoshii tryptophanyl-tRNA synthetase and structure-based phylogenetic analysis suggest an archaeal origin of tryptophanyl-tRNA synthetase

The ancient and ubiquitous aminoacyl-tRNA synthetases constitute a valuable model system for studying early evolutionary events. So far, the evolutionary relationship of tryptophanyl- and tyrosyl-tRNA synthetase (TrpRS and TyrRS) remains controversial. As TrpRS and TyrRS share low sequence homology but high structural similarity, a structure-based method would be advantageous for phylogenetic analysis of the enzymes. Here, we present the first crystal structure of an archaeal TrpRS, the structure of Pyrococcus horikoshii TrpRS (pTrpRS) in complex with tryptophanyl-5′ AMP (TrpAMP) at 3.0 Å resolution which demonstrates more similarities to its eukaryotic counterparts. With the pTrpRS structure, we perform a more complete structure-based phylogenetic study of TrpRS and TyrRS, which for the first time includes representatives from all three domains of life. Individually, each enzyme shows a similar evolutionary profile as observed in the sequence-based phylogenetic studies. However, TyrRSs from Archaea/Eucarya cluster with TrpRSs rather than their bacterial counterparts, and the root of TrpRS locates in the archaeal branch of TyrRS, indicating the archaeal origin of TrpRS. Moreover, the short distance between TrpRS and archaeal TyrRS and that between bacterial and archaeal TrpRS, together with the wide distribution of TrpRS, suggest that the emergence of TrpRS and subsequent acquisition by Bacteria occurred at early stages of evolution.

Diversity of protein structures and difficulties in fold recognition: the curious case of protein G

We examine the ability of current state-of-the-art methods in protein structure prediction to discriminate topologically distant folds encoded by highly similar (>90% sequence identity) designed proteins in blind protein structure prediction experiments. We detail the corresponding prognosis for the protein fold recognition field and highlight the features of the methodologies that successfully deciphered this folding riddle.

MISTRAL: a tool for energy-based multiple structural alignment of proteins

MOTIVATION

The steady growth of the number of available protein structures has constantly motivated the development of new algorithms for detecting structural correspondences in proteins. Detecting structural equivalences in two or more proteins is computationally demanding as it typically entails the exploration of the combinatorial space of all possible amino acid pairings in the parent proteins. The search is often aided by the introduction of various constraints such as considering protein fragments, rather than single amino acids, and/or seeking only sequential correspondences in the given proteins. An additional challenge is represented by the difficulty of associating to a given alignment, a reliable a priori measure of its statistical significance.

RESULTS

Here, we present and discuss MISTRAL (Multiple STRuctural ALignment), a novel strategy for multiple protein alignment based on the minimization of an energy function over the low-dimensional space of the relative rotations and translations of the molecules. The energy minimization avoids combinatorial searches and returns pairwise alignment scores for which a reliable a priori statistical significance can be given.

AVAILABILITY

MISTRAL is freely available for academic users as a standalone program and as a web service at http://ipht.cea.fr/protein.php.

C4-dicarboxylates sensing mechanism revealed by the crystal structures of DctB sensor domain

C(4)-dicarboxylates are the major carbon and energy sources during the symbiotic growth of rhizobia. Responses to C(4)-dicarboxylates depend on typical two-component systems (TCS) consisting of a transmembrane sensor histidine kinase and a cytoplasmic response regulator. The DctB-DctD system is the first identified TCS for C(4)-dicarboxylates sensing. Direct ligand binding to the sensor domain of DctB is believed to be the first step of the sensing events. In this report, the water-soluble periplasmic sensor domain of Sinorhizobium meliloti DctB (DctBp) was studied, and three crystal structures were solved: the apo protein, a complex with C(4) succinate, and a complex with C(3) malonate. Different from the two structurally known CitA family of carboxylate sensor proteins CitA and DcuS, the structure of DctBp consists of two tandem Per-Arnt-Sim (PAS) domains and one N-terminal helical region. Only the membrane-distal PAS domain was found to bind the ligands, whereas the proximal PAS domain was empty. Comparison of DctB, CitA, and DcuS suggests a detailed stereochemistry of C(4)-dicarboxylates ligand perception. The structures of the different ligand binding states of DctBp also revealed a series of conformational changes initiated upon ligand binding and propagated to the N-terminal domain responsible for dimerization, providing insights into understanding the detailed mechanism of the signal transduction of TCS histidine kinases.

Sequence-similar, structure-dissimilar protein pairs in the PDB

It is often assumed that in the Protein Data Bank (PDB), two proteins with similar sequences will also have similar structures. Accordingly, it has proved useful to develop subsets of the PDB from which “redundant” structures have been removed, based on a sequence-based criterion for similarity. Similarly, when predicting protein structure using homology modeling, if a template structure for modeling a target sequence is selected by sequence alone, this implicitly assumes that all sequence-similar templates are equivalent. Here, we show that this assumption is often not correct and that standard approaches to create subsets of the PDB can lead to the loss of structurally and functionally important information. We have carried out sequence-based structural superpositions and geometry-based structural alignments of a large number of protein pairs to determine the extent to which sequence similarity ensures structural similarity. We find many examples where two proteins that are similar in sequence have structures that differ significantly from one another. The source of the structural differences usually has a functional basis. The number of such proteins pairs that are identified and the magnitude of the dissimilarity depend on the approach that is used to calculate the differences; in particular sequence-based structure superpositioning will identify a larger number of structurally dissimilar pairs than geometry-based structural alignments. When two sequences can be aligned in a statistically meaningful way, sequence-based structural superpositioning provides a meaningful measure of structural differences. This approach and geometry-based structure alignments reveal somewhat different information and one or the other might be preferable in a given application. Our results suggest that in some cases, notably homology modeling, the common use of nonredundant datasets, culled from the PDB based on sequence, may mask important structural and functional information. We have established a data base of sequence-similar, structurally dissimilar protein pairs that will help address this problem (http://luna.bioc.columbia.edu/rachel/seqsimstrdiff.htm).

Common occurrence of internal repeat symmetry in membrane proteins

Symmetry plays significant roles in protein structure and function. Particularly, symmetric interfaces are known to act as switches for two-state conformational change. Membrane proteins often undergo two-state conformational change during the transport process of ion channels or the active/inactive transitions in receptors. Here, we provide the first comprehensive analyses of internal repeat symmetry in membrane proteins. We examined the known membrane protein structures and found that, remarkably, nearly half of them have internal repeat symmetry. Moreover, we found that the conserved cores of these internal repeats are positioned at the interface of symmetric units when they are mapped on structures. Because of the large sequence divergence that occurs between internal repeats, the inherent symmetry present in protein sequences often has only been detected after structure determination. We therefore developed a sensitive procedure to predict the internal repeat symmetry from sequence information and identified 4653 proteins that are likely to have internal repeat symmetry.

Probabilistic analysis of the frequencies of amino acid pairs within characterized protein sequences

Here, we describe a unique probabilistic evaluation of the 20, naturally occurring, amino acids and their distributions within the Swiss-Prot and Complete Human Genebank databases. We have developed a computational technique that imparts both directionality and length constraints into searches for unique combinations of amino acids within protein sequences. Using statistical approaches, we have carried out searches of all possible two- and three-residue motifs contained within these databases. This technique is based on the unusually high occurrence of a small number of these motifs when compared to the expected probability of finding a specific residue grouping within a given database. Subsequent filtering of this search to identify such unique combinations has provided several examples that can be used as markers to identify particular proteins within or across databases. We focus on three of these motifs, which were found to be of greatest interest to us. The CC, CM and a combination of the two, CCM motifs all occur either more or less frequently than would be predicted based on standard amino acid distributions within the entire human proteome.

An information theoretic approach to macromolecular modeling: I. Sequence alignments

We are interested in applying the principles of information theory to structural biology calculations. In this article, we explore the information content of an important computational procedure: sequence alignment. Using a reference state developed from exhaustive sequences, we measure alignment statistics and evaluate gap penalties based on first-principle considerations and gap distributions. We show that there are different gap penalties for different alphabet sizes and that the gap penalties can depend on the length of the sequences being aligned. In a companion article, we examine the information content of molecular force fields.

A novel approach to structural alignment using realistic structural and environmental information

In the era of structural genomics, it is necessary to generate accurate structural alignments in order to build good templates for homology modeling. Although a great number of structural alignment algorithms have been developed, most of them ignore intermolecular interactions during the alignment procedure. Therefore, structures in different oligomeric states are barely distinguishable, and it is very challenging to find correct alignment in coil regions. Here we present a novel approach to structural alignment using a clique finding algorithm and environmental information (SAUCE). In this approach, we build the alignment based on not only structural coordinate information but also realistic environmental information extracted from biological unit files provided by the Protein Data Bank (PDB). At first, we eliminate all environmentally unfavorable pairings of residues. Then we identify alignments in core regions via a maximal clique finding algorithm. Two extreme value distribution (EVD) form statistics have been developed to evaluate core region alignments. With an optional extension step, global alignment can be derived based on environment-based dynamic programming linking. We show that our method is able to differentiate three-dimensional structures in different oligomeric states, and is able to find flexible alignments between multidomain structures without predetermined hinge regions. The overall performance is also evaluated on a large scale by comparisons to current structural classification databases as well as to other alignment methods.