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Alzheimer’s disease (AD) is a neurodegenerative disorder that is presented with a progressive loss of memory, a decline in cognitive abilities and multiple changes in behavior. Its pathogenicity has been linked to genetic factors in approximately 60–80% of the cases specifically APOE gene family and as well as other gene families. This study utilized advanced computational biology methods to analyze AD-associated nsSNPs extracted from the NHGRI-EBI GWAS Catalog. Ensembl Variant Effect Predictor (VEP) is used to annotate the variants associated with AD. Annotated missense variants were subjected to PolyPhen-2, SNPs&Go, PredictSNP servers which were used to predict pathogenicity of selected missense variants by protein sequence information. DynaMut and DUET servers were applied to determine protein stability due to the amino acid change by integrating protein structure information. Missense variations associated with AD were annotated to 26 proteins and further analyzed in our study. Following rigorous data filtration steps, 15 candidate variants (13 proteins) were identified and subjected to sequence and structure-based analysis. Finally in this in-silico study, five deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) were identified in ACKR2(V41A), APOE(R176C), ATP8B4(G395S), LAMB2(E987K), and TOMM40(R239W), and these findings were subsequently backed-up by existing in-vivo and in-vitro literature. This study not only provides invaluable insight into the intricate pathogenic mechanisms underlying AD but also offers a distinctive perspective that paves the way for future, more comprehensive investigations aimed at unraveling the molecular intricacies responsible for the development and progression of AD. Nonetheless, it is imperative that further rigorous in vivo and in vitro experiments are conducted to validate and expand upon the findings presented here. [ABSTRACT FROM AUTHOR]

The Protein Structure Prediction Problem is one of the most important and challenging open problems in Computer Science and Structural Bioinformatics. Accurately predicting protein conformations would significantly impact several fields, such as understanding proteinopathies and developing smart protein-based drugs. As such, this work has as its primary goal to improve the prediction power of ab initio methods by utilizing a self-adaptive evolutionary algorithm using Monte Carlo based fragment insertion and conformational clustering. A meta-heuristic is used as the core of the conformation sampling process with fragment insertion, feeding domain-specific information into the process. The online parameter control routines allow the method to adapt to a protein's structure specificity and behave dynamically in different stages of the optimization process. The results obtained by the proposed method were compared to results obtained from several other algorithms found in the literature. It is possible to conclude that the proposed method is highly competitive in terms of free-energy and RMSD for the protein set used in the experiments. [ABSTRACT FROM AUTHOR]

Respiratory tract infections (RTIs) have a significant impact on global health, especially among children and the elderly. The key bacterial pathogens Streptococcus pneumoniae, Haemophilus influenzae, Klebsiella pneumoniae, Staphylococcus aureus and non-fermenting Gram Negative bacteria such as Acinetobacter baumannii and Pseudomonas aeruginosa are most commonly associated with RTIs. These bacterial pathogens have evolved a diverse array of resistance mechanisms through horizontal gene transfer, often mediated by mobile genetic elements and environmental acquisition. Treatment failures are primarily due to antimicrobial resistance and inadequate bacterial engagement, which necessitates the development of alternative treatment strategies. To overcome this, our review mainly focuses on different virulence mechanisms and their resulting pathogenicity, highlighting different therapeutic interventions to combat resistance. To prevent the antimicrobial resistance crisis, we also focused on leveraging the application of artificial intelligence and machine learning to manage RTIs. Integrative approaches combining mechanistic insights are crucial for addressing the global challenge of antimicrobial resistance in respiratory infections. [ABSTRACT FROM AUTHOR]

KRAS belongs to a family of small GTPases that act as binary switches upstream of several signalling cascades, controlling proliferation and survival of cells. Mutations in KRAS drive oncogenesis, especially in pancreatic, lung, and colorectal cancers (CRC). Although historic attempts at targeting mutant KRAS with small molecule inhibitors have proven challenging, there are recent successes with the G12C, and G12D mutations. However, clinically important RAS mutations such as G12V, G13D, Q61L, and A146T, remain elusive drug targets, and insights to their structural landscape is of critical importance to develop novel, and effective therapeutic concepts. We present a fully open, P-loop exposing conformer of KRAS G13D by X-ray crystallography at 1.4–2.4 Å resolution in Mg2+-free phosphate and malonate buffers. The G13D conformer has the switch-I region displaced in an upright position leaving the catalytic core fully exposed. To prove that this state is druggable, we developed a P-loop-targeting monoclonal antibody (mAb). The mAb displayed high-affinity binding to G13D and was shown using high resolution fluorescence microscopy to be spontaneously taken up by G13D-mutated HCT 116 cells (human CRC derived) by macropinocytosis. The mAb inhibited KRAS signalling in phosphoproteomic and genomic studies. Taken together, the data propose novel druggable space of G13D that is reachable in the cellular context. It is our hope that these findings will stimulate attempts to drug this fully open state G13D conformer using mAbs or other modalities. [ABSTRACT FROM AUTHOR]

This work is an overview of deep machine learning methods aimed at predicting binding sites in protein structures. Several classes of methods are selected: prediction of binding sites for small molecules, proteins, and nucleic acids. For each class, various approaches to prediction are considered (prediction of binding atoms, residues, surfaces, pockets). Specifics of feature selection and neural network architectures inherent to each class and approach are highlighted, and an attempt is made to explain these specifics and foresee the further direction of their development. [ABSTRACT FROM AUTHOR]

Protein-nucleic acid (PNA) binding plays critical roles in the transcription, translation, regulation, and three-dimensional organization of the genome. Structural models of proteins bound to nucleic acids (NA) provide insights into the chemical, electrostatic, and geometric properties of the protein structure that give rise to NA binding but are scarce relative to models of unbound proteins. We developed a deep learning approach for predicting PNA binding given the unbound structure of a protein that we call PNAbind. Our method utilizes graph neural networks to encode the spatial distribution of physicochemical and geometric properties of protein structures that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein, and using local encodings, they predict the location of individual NA binding residues. Our models can discriminate between specificity for DNA or RNA binding, and we show that predictions made on computationally derived protein structures can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92–0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and showed that our model predictions are consistent with and help explain experimental RNA binding data. [ABSTRACT FROM AUTHOR]

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 – CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44–63, 85–93, 126–137, 193–205, and 208–237), (26–46, 52–71, 98–114, 142–155, and 183–200), and (29–45, 58–82, 119–142, 177–198, and 222–251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173–181, 189–197, and 202–210), (61–69, 91–99, 159–167, and 181–189), and (3–11, 24–32, 167–175, and 216–224), while T helper lymphocyte epitopes were displayed at (39–53, 57–65, 150–158, 163–171), (79–87, 95–108, 115–123, 128–142, and 189–197), and (39–47, 109–123, 216–224, and 245–253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (− 16.8744 to − 15.1922), (− 16.0424 to − 14.1645), and (− 14.7566 to − 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins. [ABSTRACT FROM AUTHOR]

Abstract The type VI secretion system (T6SS) is an important mediator of microbe–microbe and microbe–host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein–protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs.

Centrosomes and cilia are microtubule-based superstructures vital for cell division, signaling, and motility. The once thought hollow lumen of their microtubule core structures was recently found to hold a rich meshwork of microtubule inner proteins (MIPs). To address the outstanding question of how distinct MIPs evolved to recognize microtubule inner surfaces, we applied computational sequence analyses, structure predictions, and experimental validation to uncover evolutionarily conserved microtubule- and MIP-binding modules named NWE, SNYG, and ELLEn, and PYG and GFG-repeat by their signature motifs. These modules intermix with MT-binding DM10-modules and Mn-repeats in 24 Chlamydomonas and 33 human proteins. The modules molecular characteristics provided keys to identify elusive cross-species homologs, hitherto unknown human MIP candidates, and functional properties for seven protein subfamilies, including the microtubule seam-binding NWE and ELLEn families. Our work defines structural innovations that underpin centriole and axoneme assembly and demonstrates that MIPs co-evolved with centrosomes and cilia. The inside surface of microtubules contains so-called microtubule inner proteins, but little is known about their identity. Here the authors use bioinformatics to identify structural motifs within this class of proteins and potential new members. [ABSTRACT FROM AUTHOR]

Abstract Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.

Molecular dynamics simulations were performed for wild-type purine nucleoside phosphorylase in complexes with two substrates (adenosine and guanosine). The MD simulations were also performed for the mutant form of the enzyme with the same substrates. The free energy changes upon the formation of the complexes were evaluated from the molecular dynamics trajectories by the MM-GBSA method. [ABSTRACT FROM AUTHOR]

The bacteria Vibrio cholerae causes cholera, an acute diarrheal infection that can lead to dehydration and even death. Over 100,000 people die each year as a result of epidemic diseases; vaccination has emerged as a successful strategy for combating cholera. This study uses bioinformatics tools to create a multi-epitope vaccine against cholera infection using five structural polyproteins from the V. cholerae (CTB, TCPA, TCPF, OMPU, and OMPW). The antigenic retrieved protein sequence were analyzed using BCPred and IEDB bioinformatics tools to predict B cell and T cell epitopes, respectively, which were then linked with flexible linkers together with an adjuvant to boost it immunogenicity. The construct has a theoretical PI of 6.09, a molecular weight of 53.85 kDa, and an estimated half-life for mammalian reticulocytes in vitro of 4.4 h. These results demonstrate the construct's longevity. The vaccine design was docked against the human toll-like receptor (TLR) to evaluate compatibility and effectiveness; also other additional post-vaccination assessments were carried out on the designed vaccine. Through in silico cloning, its expression was determined. The results show that it has a CAI value of 0.1 and GC contents of 58.97% which established the adequate expression and downstream processing of the vaccine construct, and our research demonstrated that the multi-epitope subunit vaccine exhibits antigenic characteristics. Additionally, we carried out an in silico immunological simulation to examine the immune reaction to an injection. Our results strongly suggest that the vaccine candidate on further validation would induce immune response against the V. cholerae infection. [ABSTRACT FROM AUTHOR]

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring. [ABSTRACT FROM AUTHOR]

With different countries facing multiple waves, with some SARS-CoV-2 variants more deadly and virulent, the COVID-19 pandemic is becoming more dangerous by the day and the world is facing an even more dreadful extended pandemic with exponential positive cases and increasing death rates. There is an urgent need for more efficient and faster methods of vaccine development against SARS-CoV-2. Compared to experimental protocols, the opportunities to innovate are very high in immunoinformatics/in silico approaches, especially with the recent adoption of structural bioinformatics in peptide vaccine design. In recent times, multi-epitope-based peptide vaccine candidates (MEBPVCs) have shown extraordinarily high humoral and cellular responses to immunization. Most of the publications claim that respective reported MEBPVC(s) assembled using a set of in silico predicted epitopes, to be the computationally validated potent vaccine candidate(s) ready for experimental validation. However, in this article, for a given set of predicted epitopes, it is shown that the published MEBPVC is one among the many possible variants and there is high likelihood of finding more potent MEBPVCs than the published candidates. To test the same, a methodology is developed where novel MEBP variants are derived by changing the epitope order of the published MEBPVC. Further, to overcome the limitations of current qualitative methods of assessment of MEBPVC, to enable quantitative comparison and ranking for the discovery of more potent MEBPVCs, novel predictors, Percent Epitope Accessibility (PEA), Receptor specific MEBP vaccine potency (RMVP), MEBP vaccine potency (MVP) are introduced. The MEBP variants indeed showed varied MVP scores indicating varied immunogenicity. Further, the MEBP variants with IDs, SPVC_446 and SPVC_537, had the highest MVP scores indicating these variants to be more potent MEBPVCs than the published MEBPVC and hence should be preferred candidates for immediate experimental testing and validation. The method enables quicker selection and high throughput experimental validation of vaccine candidates. This study also opens the opportunity to develop new software tools for designing more potent MEBPVCs in less time. [ABSTRACT FROM AUTHOR]

LIMK2 inhibitors are one of the potential therapeutic modalities for treating various diseases. In the current scenario, there is a paucity of effective LIMK inhibitors that are highly specific with minimal off-target effects. To date, the conformational transitions of LIMK2 from DFGinαCin (CIDI) (active) to DFGoutαCout (CODO) (inactive) states are yet to be probed and are essential for capturing the unique, druggable conformations. Therefore, this study was intended to capture the diverse conformational states of LIMK2 for accelerating the rational identification of conformation specific inhibitors through high-end structural bioinformatics protocols. Hence, in this study, molecular modelling followed by an extensive microsecond timescale of molecular dynamics simulation was performed encompassing perturbation response scanning, metapath, and community analysis towards the conformational sampling of LIMK2. Overall this study precisely identifies the conformational ensemble of LIMK2 the intermediate inactive states namely, CIDO, CinterDinter, CIDinter, CinterDI, CinterDO, CODI, CODinter apart from CIDI and CODO. This also facilitated observing that β8 preceding XDFG, αC (F373, L374), and αD (L413) as the major effectors that may facilitate the regulation of varying conformational transitions among the states. Additionally, the conserved β sheets and the loops namely, C.l, b.l, and G/P.loop were observed to be involved in the metapath for allosteric communication among the intermediates with CIDI and CODO state. Moreover, only the CODO state was observed to have closed type A.l, while the CIDI and other intermediate states except for CIDO were observed to have open-DFG out type A.l, thereby enabling the binding of substrate. Apart from these, the druggable site analysis inferred that the CIDI and CODO states harbor prominent druggable sites spanning the conserved N-lobe, while the intermediates were observed to have unraveled allosteric druggable sites distal from the ATP binding site, majorly spanning the C-lobe of LIMK2. Thus, this study provides potential insights into the intermediate conformational druggable states of LIMK2 and also the druggable conformations, especially the inactive states of LIMK2, as a specific therapeutic targeting mode. Thus, providing a widened avenue to ponder the allosteric sites or the isoform selectivity conformations for targeting LIMK2 in various disease conditions. [ABSTRACT FROM AUTHOR]

Base pairing in RNA are significantly rich and versatile due to the potential non-canonical base pairing amongst nucleotides. Not only that, one base in RNA can pair with more than one bases simultaneously. This opens up a new dimension of research to detect such types of base-base pair networks in RNA and to analyze them. Even if a base do not form a pair, it may have significant extent of π - π stacking overlap that can stabilize the structures. In this work, we report a software tool, called BPNet, that accepts a mmCIF or PDB file and computes the base-pair/ π - π contact network components using graph formalism. The software can run on Linux platform in both serial and parallel modes. It generates several information in suitable file formats for visualization of the networks. This paper describes the BPNet software and also presents some interesting results obtained by analyzing several RNA structures by the software to show its effectiveness. [ABSTRACT FROM AUTHOR]

COVID19 is the most impactful pandemic of recent times worldwide. It is a highly infectious disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), To date there is specific drug nor vaccination against COVID19. Therefor the need for novel and pioneering anti-COVID19 is of paramount importance. In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2. In silico modelling takes advantage of the massive amounts of biological and chemical data available on the nature of the interactions between the targeted systems and molecules, as well as the rapid progress of computational tools and software. Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and design of potent anti- SARS-CoV-2 modulators. [ABSTRACT FROM AUTHOR]

Macroautophagy (hereafter referred to as autophagy) is a highly conserved catabolic eukaryotic pathway that is critical for stress responses and homeostasis. Atg18, one of the core proteins involved in autophagy, belongs to the PROPPIN family and is composed of seven WD40 repeats. Together with Atg2, Atg18 participates in the elongation of phagophores and the recycling of Atg9 in yeast. Despite extensive studies on the PROPPIN family, the structure of Atg18 from Saccharomyces cerevisiae has not been determined. Here, we report the structure of ScAtg18 at a resolution of 2.8 Å. Based on bioinformatics and structural analysis, we found that the 7AB loop of ScAtg18 is extended in Atg18, in comparison to other members of the PROPPIN family. Genetic analysis revealed that the 7AB loop of ScAtg18 is required for autophagy. Biochemical and biophysical experiments indicated that the 7AB loop of ScAtg18 is critical for interaction with ScAtg2 and the recruitment of ScAtg2 to the autophagy-initiating site. Collectively, our results show that the 7AB loop of ScAtg18 is a new binding site for Atg2 and is of functional importance to autophagy. [ABSTRACT FROM AUTHOR]

Background: Xylanase with high specific activity plays a crucial role in hemicellulose biodegradation and has important industrial application. The amino acids located in the active site determine the enzyme biological characterization. In this study, structure bioinformatics analysis and alanine screening experiments were performed to explore the roles of amino acids at each subsite of the GH11 xylanase active site. Results: There are highly conserved amino acids at − 2 to + 1 subsites, and the network of the interactions is concentrated near the catalytic sites (E86, E178). However, the amino acids at relatively distal subsites, especially at the + 2 and + 3 subsites, are few but diverse. Alanine substitution of amino acids in the active site architecture exerted different impacts on catalytic efficiency. Interestingly, mutants Y180A at the + 2 subsite and Y96A at the + 3 subsite had reduced enzymatic activities by almost 95%, which indicate that these two aromatic residues are necessary for the catalysis of substrates in addition to the highly conserved residues at the − 2 and + 1 subsites. Moreover, in these two subsites, aromatic amino acids with different side-chain properties also affected enzyme activity. The mutants Y180W and Y96W showed 6.2% and 12.8% increase in specific activities by comparison with wild-type enzyme at 50 °C, respectively. Conclusion: We elucidated the interaction between amino acids and substrates in the active site, which will aid understanding of the protein-ligand interaction in enzyme engineering. Key points: • Xylanase of GH11 family is a good industrial candidate. • The roles of residues at each subsite of GH11 xylanase active site are explored. • The two aromatic residues at the + 2 and + 3 subsites are necessary for the catalysis. • Y180W and Y96W increased the enzymatic activity by 6.2% and 12.8% at low temperature. [ABSTRACT FROM AUTHOR]

Cancer remains one of the leading causes of death worldwide. Introduction of natural compounds with anticancer properties can be an effective step in the prevention and treatment of cancer. Antimicrobial peptides (AMPs) are short peptides whose anticancer activities have been proved previously. In the present study, Enterocin-P (EntP) as a bacteriocin of E. faecium was expressed in HEK expression system by pcDNA3.1 + vector. The recombinant peptide was purified from culture medium using Ni2+ affinity chromatography with an average yield of 0.6 mg/ml. The cytotoxic activity of the recombinant peptide was determined toward some cancer cell lines including: SW1353, HUH7, Huh-7.5, C26, B16F0 and NIH3T3 as a normal cell line. Our results showed that EntP peptide had selective cytotoxicity activity only against C26 (IC50: 0.32 mg/ml) and SW1353 (IC50: 1.36 mg/ml) as cancer cell and did not show cytotoxic properties against normal cell. In the second phase of our study, to better understand the mechanisms of EntP peptide, we have tried to predict the possible interaction of this peptide to predominantly negative charge molecules in the cell membrane of cancer cells. Our in-silico analysis revealed that EntP peptide has strong tendency to chondroitin sulfate (− 189 ± 6.24 kJ/mol) and heparan sulfate (− 115 ± 5.12 kJ/mol) as two well-known anionic molecules on surface cancer cells. [ABSTRACT FROM AUTHOR]

Structural information on the interactions of proteins with other molecules is plentiful, and for some proteins and protein families, there may be 100s of available structures. It can be very difficult for a scientist who is not trained in structural bioinformatics to access this information comprehensively. Previously, we developed the Protein Common Interface Database (ProtCID), which provided clusters of the interfaces of full-length protein chains as a means of identifying biological assemblies. Because proteins consist of domains that act as modular functional units, we have extended the analysis in ProtCID to the individual domain level. This has greatly increased the number of large protein-protein clusters in ProtCID, enabling the generation of hypotheses on the structures of biological assemblies of many systems. The analysis of domain families allows us to extend ProtCID to the interactions of domains with peptides, nucleic acids, and ligands. ProtCID provides complete annotations and coordinate sets for every cluster. The authors previously developed the Protein Common Interface Database (ProtCID), which compares and clusters the interfaces of pairs of full-length protein chains with defined Pfam domain architectures in different PDB entries to identify biological assemblies. Here the authors extend ProtCID to the clustering of domain-domain interactions that also allows analyzing domain interactions with peptides, nucleic acids, and ligands. [ABSTRACT FROM AUTHOR]

Thermostability plays an important role in the application of L-asparaginase in the pharmaceutical and food industries. Understanding the key residues and structures that influence thermostability in L-asparaginase is necessary to obtain suitable L-asparaginase candidates. In this study, special residues and structures that altered thermostability in thermophilic L-asparaginase and non-thermophilic L-asparaginase II were identified. Interchanging these special residues and structures of L-asparaginases from the four strains, that is, Pyrococcus yayanosii CH1 (PYA), Thermococcus gammatolerans (TGA), Bacillus subtilis (BSA II), and Escherichia coli (ECA II), revealed the 51st and 298th residues of PYA (corresponding to 57th, 305th residues of ECA II) as the key residues responsible for thermal stability of thermophilic L-asparaginase and non-thermophilic L-asparaginase II. Moreover, the C terminal tightness, loop rigidity, and low surface charge around activity sites were of great significance to the thermostability of L-asparaginase. This study therefore revealed the crucial amino acid residues and structures responsible for the difference in thermostability of the thermophilic and non-thermophilic L-asparaginase and provides a reference for engineering thermostability in L-asparaginase II. [ABSTRACT FROM AUTHOR]

Key message: Several classes of transcription factors are involved in the activation of defensins. A new type of the transcription factor responsible for the regulation of wheat grain specific defensins was characterised in this work. HD-Zip class IV transcription factors constitute a family of multidomain proteins. A full-length cDNA of HD-Zip IV, designated TaGL7 was isolated from the developing grain of bread wheat, using a specific DNA sequence as bait in the Y1H screen. 3D models of TaGL7 HD complexed with DNA cis-elements rationalised differences that underlined accommodations of binding and non-binding DNA, while the START-like domain model predicted binding of lipidic molecules inside a concave hydrophobic cavity. The 3′-untranslated region of TaGL7 was used as a probe to isolate the genomic clone of TdGL7 from a BAC library prepared from durum wheat. The spatial and temporal activity of the TdGL7 promoter was tested in transgenic wheat, barley and rice. TdGL7 was expressed mostly in ovary at fertilisation and its promoter was active in a liquid endosperm during cellularisation and later in the endosperm transfer cells, aleurone, and starchy endosperm. The pattern of TdGL7 expression resembled that of genes that encode grain-specific lipid transfer proteins, particularly defensins. In addition, GL7 expression was upregulated by mechanical wounding, similarly to defensin genes. Co-bombardment of cultured wheat cells with TdGL7 driven by constitutive promoter and seven grain or root specific defensin promoters fused to GUS gene, revealed activation of four promoters. The data confirmed the previously proposed role of HD-Zip IV transcription factors in the regulation of genes that encode lipid transfer proteins involved in lipid transport and defence. The TdGL7 promoter could be used to engineer cereal grains with enhanced resistance to insects and fungal infections. [ABSTRACT FROM AUTHOR]

Protein fold recognition is considered as an essential step in determining the tertiary structure of proteins in bioinformatics. The most complex challenge in the protein folding problem is the high dimensionality of feature vectors and the diversity of the protein fold classes. In this paper, two frameworks are proposed to solve this problem. The main components in the first two-level framework are Deep Kernelized Extreme Learning Machine (DKELM) and linear discriminant analysis. Second framework consists of three levels. In the first level, the dataset is initialized to be used in the next level. In the second level, OVADKELM and OVODKELM are independently employed to extract four and six new features, respectively, which are added into the basic datasets in the third level. DKELM is applied in the third level as a final classifier to classify the instances into folds. The proposed frameworks are implemented on DD and TG datasets which are considered as SCOP datasets. The experimental results indicate that proposed methods could improve the classification accuracy in both datasets. [ABSTRACT FROM AUTHOR]

Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods—including those based on machine learning techniques and evolutionary methods—are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent. [ABSTRACT FROM AUTHOR]

Intrinsically disorder proteins (IDPs) constitute a significant part of proteins that exist and act in cells of living organisms. IDPs play key roles in central cellular processes and some of them are closely related to various human diseases, like cancer or neurodegenerative disorders. Identification of IDPs and studying their structural characteristics have become an important part of structural bioinformatics and structural genomics. However, growing amount of genomic and protein sequences in public repositories pose a pressure on existing methods for identification of IDPs. Large volumes of protein amino acid sequences need to be analyzed in terms of propensity to form disordered regions, and this task requires novel tools and scalable platforms to cope with this big biological data challenge. In this paper, we show how the identification of disordered regions of 3D protein structures can be efficiently accelerated with the use of Apache Spark cluster established and scaled on the public Cloud. For this purpose, we propose Spark-based meta-predictor (Spark-IDPP), which enables efficient prediction of disordered regions of proteins on a large-scale. Results of our performance tests show that, for large data sets, our method achieves almost linear speedup, when scaling out the computations on the 32-node Spark cluster located in the Azure cloud. This proves that through appropriate partitioning of data and by increasing the degree of parallelism, we can significantly improve efficiency of IDP predictions. Additionally, by using several basic predictors, aggregating their ranks in various consensus modes, and filtering the final outcome with a dedicated fuzzy filter, the Spark-IDPP increases the quality of predictions. [ABSTRACT FROM AUTHOR]

Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and resources. Inability to properly measure druggability has accounted for a significant portion of failures in drug discovery. This problem is only further exacerbated by the large sample space of proteins involved in human diseases. With these barriers, the druggability space within the human proteome remains unexplored and has made it difficult to develop drugs for numerous diseases. Hence, we present a new feature developed in eFindSite that employs supervised machine learning to predict the druggability of a given protein. Benchmarking calculations against the Non-Redundant data set of Druggable and Less Druggable binding sites demonstrate that an AUC for druggability prediction with eFindSite is as high as 0.88. With eFindSite, we elucidated the human druggability space to be 10,191 proteins. Considering the disease space from the Open Targets Platform and excluding already known targets from the predicted data set reveal 2731 potentially novel therapeutic targets. eFindSite is freely available as a stand-alone software at https://github.com/michal-brylinski/efindsite. [ABSTRACT FROM AUTHOR]

Genetic variants in low-density lipoprotein receptor (LDLR) are known to cause familial hypercholesterolemia (FH), occurring in up to 1 in 200 people (Youngblom E. et al. 1993 and Nordestgaard BG et al. 34:3478–3490a, 2013) and leading to significant risk for heart disease. Clinical genomics testing using high-throughput sequencing is identifying novel genomic variants of uncertain significance (VUS) in individuals suspected of having FH, but for whom the causal link to the disease remains to be established (Nordestgaard BG et al. 34:3478–3490a, 2013). Unfortunately, experimental data about the atomic structure of the LDL binding domains of LDLR at extracellular pH does not exist. This leads to an inability to apply protein structure-based methods for assessing novel variants identified through genetic testing. Thus, the ambiguities in interpretation of LDLR variants are a barrier to achieving the expected clinical value for personalized genomics assays for management of FH. In this study, we integrated data from the literature and related cellular receptors to develop high-resolution models of full-length LDLR at extracellular conditions and use them to predict which VUS alter LDL binding. We believe that the functional effects of LDLR variants can be resolved using a combination of structural bioinformatics and functional assays, leading to a better correlation with clinical presentation. We have completed modeling of LDLR in two major physiologic conditions, generating detailed hypotheses for how each of the 1007 reported protein variants may affect function. Key messages: • Hundreds of variants are observed in the LDLR, but most lack interpretation. • Molecular modeling is aided by biochemical knowledge. • We generated context-specific 3D protein models of LDLR. • Our models allowed mechanistic interpretation of many variants. • We interpreted both rare and common genomic variants in their physiologic context. • Effects of genomic variants are often context-specific. [ABSTRACT FROM AUTHOR]

Multimodal and asymmetric bivariate circular data arise in several different disciplines and fitting appropriate distribution plays an important role in the analysis of such data. In this paper, we propose a new bivariate circular distribution which can be used to model both asymmetric and multimodal bivariate circular data simultaneously. In fact the proposed density covers unimodality as well as multimodality, symmetry as well as asymmetry of circular bivariate data. A number of properties of the proposed density are presented. A Bayesian approach with MCMC scheme is employed for statistical inference. Three real datasets and a simulation study are provided to illustrate the performance of the proposed model in comparison with alternative models such as finite mixture Cosine model. [ABSTRACT FROM AUTHOR]

The number of fatalities and economic losses caused by the Ebola virus infection across the planet culminated in the havoc that occurred between August and November 2014. However, little is known about the molecular protein profile of this devastating virus. This work represents a thorough bioinformatics analysis of the regularities of charge distribution (polar profiles) in two groups of proteins and their functional domains associated with Ebola virus disease: Ebola virus proteins and Human proteins interacting with Ebola virus. Our analysis reveals that a fragment exists in each of these proteins—one named the “functional domain”—with the polar profile similar to the polar profile of the protein that contains it. Each protein is formed by a group of short sub-sequences, where each fragment has a different and distinctive polar profile and where the polar profile between adjacent short sub-sequences changes orderly and gradually to coincide with the polar profile of the whole protein. When using the charge distribution as a metric, it was observed that it effectively discriminates the proteins from their functional domains. As a counterexample, the same test was applied to a set of synthetic proteins built for that purpose, revealing that any of the regularities reported here for the Ebola virus proteins and human proteins interacting with Ebola virus were not present in the synthetic proteins. Our results indicate that the polar profile of each protein studied and its corresponding functional domain are similar. Thus, when building each protein from its functional domai—adding one amino acid at a time and plotting each time its polar profile—it was observed that the resulting graphs can be divided into groups with similar polar profiles. [ABSTRACT FROM AUTHOR]

Abstract: For the initial activation of short-chain alkanes over Zn/HZSM-5 catalyst, the impact of branching degree and carbon numbers of reactants on the competition between dehydrogenation and cracking was systematically studied by experiment and calculation. The experiments were carried out on fixed-bed flow micro-reactor over HZSM-5 and Zn/HZSM-5 catalysts, by using n-butane/i-butane and propane/n-hexane as reactants with different branching degree and carbon numbers. Compared with HZSM-5, Zn/HZSM-5 obviously accelerated the cracking of C-H bond of short-chain alkanes and increased the selectivity of BTX aromatics. The selectivity to hydrogen produced from n-butane and n-hexane was higher than i-butane and propane, respectively. On the contrary, the selectivity to methane was correspondingly lower, i-butane and propane with high percentage of terminal carbons effectively suppressed the dehydrogenation. The key point to decide the reaction process through dehydrogenation or cracking is the initially activated sites of reactant. To verify this conclusion, theoretical calculations were carried out. The results showed that the (Zn-O-Zn)2+ Lewis acid sites of Zn/HZSM-5 accelerated the cracking of C-H and C-C bond simultaneously. Namely, if the initial activation occurred on the terminal carbons of reactant, the subsequent reaction would be kinetically competitive between cracking and dehydrogenation. Cracking would inevitably occur (thermodynamically favorable), and then the by-products of methane and ethane were produced in large amount. If the initial activation occurred on the internal carbons, the dehydrogenation reaction is kinetically favorable, which is beneficial to reducing the dry gas production. Therefore, cracking is more favorable than dehydrogenation for smaller alkanes and branched alkanes with high percentage of terminal carbons.Graphical Abstract: The percentage of terminal carbons of short-chain alkanes determines the probability of dehydrogenation route over Zn/HZSM-5. The activation of the internal carbon via the dehydrogenation route is more favorable for the normal alkane with higher carbon number during the first stage of conversion suppressing the formation of the by-products of methane and ethane. [ABSTRACT FROM AUTHOR]

Atomic displacement parameters (ADPs, also known as B-factors), which depend on structural heterogeneity, provide a wide spectrum of information on protein structure and dynamics and find several applications, from protein conformational disorder prediction to protein thermostabilization, and from protein folding kinetics prediction to protein binding sites prediction. A crucial aspect is the standardization of the ADPs when comparisons between two or more protein crystal structures are made, since ADPs are differently affected by several factors, from crystallographic resolution to refinement protocols. A potential limitation to ADP analysis is the modern tendency to let ADPs to inflate up to extremely large values that have little physico-chemical meaning. [ABSTRACT FROM AUTHOR]

Fungal endo-1,4-β-xylanases (EC3.2.1.8), because of their widespread industrial applications have become one of the most researched industrial enzymes in recent times. Despite its significance, the role of conserved water molecules in the catalytic activities and structural stability of these enzymes from the fungi have not been studied to a great extent. Our computational structural bioinformatics and MD simulation studies have identified the existence of seven invariant water molecules (IW1-IW7) and reveals the stereo-chemical and electronic consequences of those conserved water molecules in G-xylanase enzyme from eight different fungi. The buried water molecules IW1 and IW2 may have decisive roles in catalysis and may also be associated with ligand binding process of the enzyme, whereas IW3, IW4, IW5, IW6 and IW7 may be involved in stabilizing the important (H144/R145) residues through H-bonds. Possibly they are also involved in the stabilization of secondary structures and anchor to maintain its stereo-chemical architecture. Moreover, a distorted ‘W’ shaped signature geometry that is observed at the surface of the enzyme can be used to identify the hydrophilic centers in the electron density map of other unknown members of the family G xylanases. The results from this computational investigation could be of interest to a large number of researchers working with the xylanases. [ABSTRACT FROM AUTHOR]

Xylooligosaccharides (XOS) and arabinoxylooligosaccharides (AXOS) were produced from the insoluble arabinoxylan fraction of pretreated wheat bran by endoxylanases. The glycoside hydrolase (GH) family 10 xylanases GsXyn10A from Geobacillus stearothermophilus and RmXyn10A-CM from Rhodothermus marinus produced the AXOS A3X, A2XX and A2 + 3XX in addition to XOS. RmXyn10A-CM also produced XA2 + 3XX due to its non-conserved aglycone region accommodating additional arabinose substitutions in subsite +2. The GH11 enzymes, Pentopan from Thermomyces lanuginosus and NpXyn11A from Neocallimastix patriciarum had minor structural differences affecting hydrogen bonds in subsites −3 and +3, with similar hydrolysis profiles producing XA3XX as major AXOS and minor amounts of XA2XX but different ratios of X3/X2. In vitro analysis of the prebiotic properties of (A)XOS produced by Pentopan revealed nearly complete uptake of X2 and X3 by the probiotic bacteria Lactobacillus brevis and Bifidobacterium adolescentis. In contrast to previous reports, the GH43 arabinofuranosidase BaAXHd-3 from B. adolescentis cleaved α-1,3-linked arabinose on some single substituted AXOS. [ABSTRACT FROM AUTHOR]

The virion escaping of hepatitis B virus from human hepatocyte is mediated by the recognition and interaction of viral large envelope protein with human hepatocyte receptor γ2-adaptin. In this work, a structure-based rational strategy was described to design peptide inhibitors serving as the interaction disruptors between the preS1 domain of hepatitis B virus large envelope protein and the EAR domain of γ2-adaptin. The binding site of preS1 to γ2-EAR was extracted from the crystal structure of preS1 in complex with EAR as a short preS1 phage peptide that can extendedly bind on the β-sheet surface of γ2-EAR domain. Computational modeling revealed a systematic amino acid preference profile for the domain-peptide interaction, which was then employed to guide an optimization protocol for improving the biological activity of preS1 peptide. A number of potential hits were obtained from the evolution, and four were measured in vitro to have a comparable or higher affinity as compared to the native preS1 peptide. Potent peptide ligands were structurally optimized according to alanine-scanning mutagenesis and complex structural analysis, resulting in the strongest binder with domain-binding affinity at micromolar level. It is revealed that a potent γ2-EAR-binding peptide (8-mer sequence XXXXXXXX) can be divided into three regions; the region 1 contains residues X and X that should be occupied by negatively charged amino acids such as Glu and Asp, the region 2 covers residues X, X, and X that directly contact a hydrophobic pocket on γ2-EAR surface, and the region 3 corresponds to residue X that prefers polar amino acids and is able to form hydrogen bond. In addition, the N-terminal and C-terminal residues X and X only play a marginal role in the domain-peptide binding. [ABSTRACT FROM AUTHOR]

Bioconjugation and functionalization of polymer surfaces are two major tasks in materials chemistry which are accomplished using a variety of coupling agents. Immobilization of biomolecules onto polymer surfaces and the construction of bioconjugates are essential requirements of many biochemical assays and chemical syntheses. Different linkers with a variety of functional groups are used for these purposes. Among them, the benzophenones, aryldiazirines, and arylazides represent the most commonly used photolinker to produce the desired chemical linkage upon their photo-irradiation. In this review, we describe the versatile applications of 4-fluoro-3-nitrophenyl azide, one of the oldest photolinkers used for photoaffinity labeling in the late 1960s. Surprisingly, this photolinker, historically known as 1-fluoro-2-nitro-4-azidobenzene (FNAB), has remained unexplored for a long time because of apprehension that FNAB forms ring-expanded dehydroazepine as a major product and hence cannot activate an inert polymer. The first evidence of photochemical activation of an inert surface by FNAB through nitrene insertion reaction was reported in 2001, and the FNAB-activated surface was found to conjugate a biomolecule without any catalyst, reagent, or modification. FNAB has distinct advantages over perfluorophenyl azide derivatives, which are contemporary nitrene-generating photolinkers, because of its simple, single-step preparation and ease of thermochemical and photochemical reactions with versatile polymers and biomolecules. Covering these aspects, the present review highlights the flexible chemistry of FNAB and its applications in the field of surface engineering, immobilization of biomolecules such as antibodies, enzymes, cells, carbohydrates, oligonucleotides, and DNA aptamers, and rapid diagnostics. [ABSTRACT FROM AUTHOR]

Glycans are key molecules in many physiological and pathological processes. As with other molecules, like proteins, visualization of the 3D structures of glycans adds valuable information for understanding their biological function. Hence, here we introduce Azahar, a computing environment for the creation, visualization and analysis of glycan molecules. Azahar is implemented in Python and works as a plugin for the well known PyMOL package (Schrodinger in The PyMOL molecular graphics system, version 1.3r1, 2010). Besides the already available visualization and analysis options provided by PyMOL, Azahar includes 3 cartoon-like representations and tools for 3D structure caracterization such as a comformational search using a Monte Carlo with minimization routine and also tools to analyse single glycans or trajectories/ensembles including the calculation of radius of gyration, Ramachandran plots and hydrogen bonds. Azahar is freely available to download from and the source code is available at . [ABSTRACT FROM AUTHOR]

Comparison of structures of homological proteins often helps to understand functionally significant features of these structures. This concerns not only structures of separate protein chains, but also structures of macromolecular complexes. In particular, a comparison of complexes of homologous proteins with DNA is significant for analysis of the recognition of DNA by proteins. We present program LCore for detecting geometrical cores of a family of structures; a geometrical core is a set of amino acid residues and nucleotides that disposed similarly in all structures of the family. We describe the algorithm of the program, its web interface, and an example of its application to analysis of complexes of homeodomains with DNA. [ABSTRACT FROM AUTHOR]

Estrogens influence multiple physiological processes and are implicated in many diseases as well. Cellular responses to estrogens are mainly mediated by the estrogen receptors (ER)α and ERβ, which act as ligand-activated transcription factors. Recently, a member of the G protein-coupled receptor (GPCR) superfamily, namely GPER/GPR30, has been identified as a further mediator of estrogen signalling in different pathophysiological conditions, including cancer. Today, computational methods are commonly used in all areas of health science research. Among these methods, virtual ligand screening has become an established technique for hit discovery and optimization. The absence of an established three-dimensional structure of GPER promoted studies of structure-based drug design in order to build reliable molecular models of this receptor. Here, we discuss the results obtained through the structure-based virtual ligand screening for GPER, which allowed the identification and synthesis of different selective agonist and antagonist moieties. These compounds led significant advances in our understanding of the GPER function at the cellular, tissue, and organismal levels. In particular, selective GPER ligands were critical toward the evaluation of the role elicited by this receptor in several pathophysiological conditions, including cancer. Considering that structure-based approaches are fundamental in drug discovery, future research breakthroughs with the aid of computer-aided molecular design and chemo-bioinformatics could generate a new class of drugs that, acting through GPER, would be useful in a variety of diseases as well as in innovative anticancer strategies. [ABSTRACT FROM AUTHOR]

ProMOL, a plugin for the PyMOL molecular graphics system, is a structure-based protein function prediction tool. ProMOL includes a set of routines for building motif templates that are used for screening query structures for enzyme active sites. Previously, each motif template was generated manually and required supervision in the optimization of parameters for sensitivity and selectivity. We developed an algorithm and workflow for the automation of motif building and testing routines in ProMOL. The algorithm uses a set of empirically derived parameters for optimization and requires little user intervention. The automated motif generation algorithm was first tested in a performance comparison with a set of manually generated motifs based on identical active sites from the same 112 PDB entries. The two sets of motifs were equally effective in identifying alignments with homologs and in rejecting alignments with unrelated structures. A second set of 296 active site motifs were generated automatically, based on Catalytic Site Atlas entries with literature citations, as an expansion of the library of existing manually generated motif templates. The new motif templates exhibited comparable performance to the existing ones in terms of hit rates against native structures, homologs with the same EC and Pfam designations, and randomly selected unrelated structures with a different EC designation at the first EC digit, as well as in terms of RMSD values obtained from local structural alignments of motifs and query structures. This research is supported by NIH grant GM078077. [ABSTRACT FROM AUTHOR]

Comparative analysis of structures of complexes of homologous proteins with DNA is important in the analysis of DNA-protein recognition. Alignment is a necessary stage of the analysis. An alignment is a matching of amino acid residues and nucleotides of one complex to residues and nucleotides of the other. Currently, there are no programs available for aligning structures of DNA-protein complexes. We present the program StructAlign, which should fill this gap. The program inputs a pair of complexes of DNA double helix with proteins and outputs an alignment of DNA chains corresponding to the best spatial fit of the protein chains. [ABSTRACT FROM AUTHOR]

5-Phenyl-1 H-pyrrole-2,3-diones reacted with malononitrile and 1 H-pyrazole-5(4 H)-ones to give substituted 6-amino-5-cyano-2′-oxo-5′-phenyl-1′,2′-dihydro-1 H-spiro[pyrano[2,3- c]pyrazole-4,3′-pyrroles]. The crystal and molecular structures of ethyl 6-amino-1,1′-dibenzyl-5-cyano-3-methyl-2′-oxo-5′-phenyl-1′,2′-dihydro-1 H-spiro[pyrano[2,3- c]pyrazole-4,3′-pyrrole]-4′-carboxylate were determined by X-ray analysis. [ABSTRACT FROM AUTHOR]

Molecules are often characterized by sparse binary fingerprints, where 1s represent the presence of substructures and 0s represent their absence. Fingerprints are especially useful for similarity calculations, such as database searching or clustering, generally measuring similarity as the Tanimoto coefficient. In other cases, such as visualization, design of experiments, or latent variable regression, a low-dimensional Euclidian 'chemical space' is more useful, where proximity between points reflects chemical similarity. A temptation is to apply principal components analysis (PCA) directly to these fingerprints to obtain a low dimensional continuous chemical space. However, Gower has shown that distances from PCA on bit vectors are proportional to the square root of Hamming distance. Unlike Tanimoto similarity, Hamming similarity (HS) gives equal weight to shared 0s as to shared 1s, that is, HS gives as much weight to substructures that neither molecule contains, as to substructures which both molecules contain. Illustrative examples show that proximity in the corresponding chemical space reflects mainly similar size and complexity rather than shared chemical substructures. These spaces are ill-suited for visualizing and optimizing coverage of chemical space, or as latent variables for regression. A more suitable alternative is shown to be Multi-dimensional scaling on the Tanimoto distance matrix, which produces a space where proximity does reflect structural similarity. [ABSTRACT FROM AUTHOR]

Comparison of molecular structures in order to identify their similarity is an important step in solving various problems derived from computational biology, like structure alignment and modelling, motif search or clustering. Thus, there is a constant need for the development of good measures to determine distances between the structures and tools to display these distances in an easily interpretable form. In the paper we present MCQ4Structures, a new method and tool for structural similarity computation based on molecule tertiary structure representation in torsional angle space. We discuss its unique features as compared with the other measures, including RMSD and LGA, and we show its experimental use in comparison of a number of 3D structures as well as evaluation of models predicted within RNA-Puzzles contest. MCQ4Structures software is available as a free Java WebStart application at: . The source code licensed under BSD can be downloaded from the same website. [ABSTRACT FROM AUTHOR]

The principal stages of the realization of the instrumental and methodical approaches for the solution of the problems of structural biology of tissues are given with the use of synchrotron radiation (SR) at the operating channels of VEPP-3 (Siberian Center of Synchrotron and Terahertz Radiations, SCSTR, Novosibirsk) and Siberia-2 (Kurchatov Center of Converging of Nano-, Bio-, Information-, and Cognitive Sciences and Technologies, NBIC, Moscow) storage rings. Based on the obtained data, the opportunities of the designed instrumentation and methods, as well as the experimental results of the study of tissues with various degrees of ordering, have been demonstrated. [ABSTRACT FROM AUTHOR]

Protein synthesis requires accurate charging of tRNA with cognate amino acid as catalyzed by aminoacyl-tRNA synthetases. Crystal structures of tyrosyl-tRNA synthetase (YRSs) show remarkably diverse conformations for the KMSKS loop, hitherto classified as 'open' and 'closed'. This traditional classification implied that the KMSKS loop adopts different conformations depending on occupancy of active site pocket. Our structural analyses of evolutionarily derived ensemble of differentially ligated YRSs using quantitative structural criterion demonstrate intrinsic conformational heterogeneity in KMSKS loop that is independent of occupancy of active site. Differential centroid distance analyses between KMSKS motif and Rossmann fold domain reveal an intriguing bimodal distribution. These insights have been used for a more consistent re-classification of YRS conformations as either compact or extended. Our data not only reflect inherent dynamics within the conformational landscape of KMSKS loops, but also have implications for structure-based drug design efforts. [ABSTRACT FROM AUTHOR]

The prediction and analysis of the three- dimensional (3D) structure of proteins is a key research problem in Structural Bioinformatics. The 1990's Genome Projects resulted in a large increase in the number of available protein sequences. However, the number of identified 3D protein structures have not followed the same growth trend. Currently, the number of available protein sequences greatly exceeds the number of known 3D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. The most significant progress in the last Critical Assessment of protein Structure Prediction was achieved by methods that use database information. Nevertheless, a major challenge remains in the development of better strategies for template identification and representation. This article describes a computational strategy to acquire and represent structural information of experimentally determined 3D protein structures. A clustering strategy was combined with artificial neural networks in order to extract structural information from experimental protein structure templates. In the proposed strategy, the main efforts focus on the acquisition of useful and accurate structural information from 3D protein templates stored in the Protein Data Bank (PDB). The proposed method was tested in twenty protein sequences whose sizes vary from 14 to 70 amino acid residues. Our results show that the proposed method is a good way to extract and represent valuable information obtained from the PDB and also significantly reduce the 3D protein conformational search space. [ABSTRACT FROM AUTHOR]

The Ca-binding protein calmodulin (CaM) is a well-known regulator of ion-channel activity. Consequently, the Protein Data Bank contains many structures of CaM in complex with different fragments of ion channels that together display a variety of binding modes. In addition to the canonical interaction, in which CaM engages its target with both its domains, many of the ion-channel-CaM complexes demonstrate alternative non-canonical binding modes that depend on the target and experimental conditions. Based on these findings, several mechanisms of ion-channel regulation by CaM have been proposed, all exploiting its plasticity and flexibility in interacting with its targets. In this review, we focus on complexes of CaM with either the voltage-gated calcium channels; the voltage-gated sodium channels or the small conductance calcium-activated potassium channels, for which both structural and functional data are available. For each channel, the functional relevance of these structural data and possible mechanism of calcium-dependent (in)activation and/or facilitation are discussed in detail. [ABSTRACT FROM AUTHOR]

The limited sequence similarity of protein sequences with known structures has led to an indispensable need for computational technology to predict their structures. Structural bioinformatics (SB) has become integral in elucidating the sequence-structure-function relationship of a protein. This report focuses on the applications of SB within the context of protein engineering including its limitation and future challenges. [ABSTRACT FROM AUTHOR]