CarbDisMut: database on neutral and disease-causing mutations in human carbohydrate-binding proteins.

Protein-carbohydrate interactions are involved in several cellular and biological functions. Integrating structure and function of carbohydrate-binding proteins with disease-causing mutations help to understand the molecular basis of diseases. Although databases are available for protein-carbohydrate complexes based on structure, binding affinity and function, no specific database for mutations in human carbohydrate-binding proteins is reported in the literature. We have developed a novel database, CarbDisMut, a comprehensive integrated resource for disease-causing mutations with sequence and structural features. It has 1.17 million disease-associated mutations and 38,636 neutral mutations from 7,187 human carbohydrate-binding proteins. The database is freely available at https://web.iitm.ac.in/bioinfo2/carbdismut. The web-site is implemented using HTML, PHP and JavaScript and supports recent versions of all major browsers, such as Firefox, Chrome and Opera.

PCA-MutPred: Prediction of Binding Free Energy Change Upon Missense Mutation in Protein-carbohydrate Complexes.

Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task. In this work, we have collected the experimental data for the binding affinity change of 318 unique mutants and related with sequence and structural features of amino acid residues at the mutant sites. We found that accessible surface area, secondary structure, mutation preference, conservation score, hydrophobicity and contact energies are important to understand the binding affinity change upon mutation. We have developed multiple regression equations for predicting the binding affinity change upon mutation and our method showed an average correlation of 0.74 and a mean absolute error of 0.70 kcal/mol between experimental and predicted ΔΔG on a 10-fold cross-validation. Further, we have validated our method using an independent test data set of 124 (62 unique) mutations, which showed a correlation and MAE of 0.79 and 0.56 kcal/mol, respectively. We have developed a web server PCA-MutPred, Protein-CArbohydrate complex Mutation affinity Predictor, for predicting the change in binding affinity of protein-carbohydrate complexes and it is freely accessible at https://web.iitm.ac.in/bioinfo2/pcamutpred. We suggest that the method could be a useful resource for designing protein-carbohydrate complexes with desired affinities.

Investigations on the binding specificity of ß-galactoside analogues with human galectin-1 using molecular dynamics simulations.

Galectin-1 (Gal-1) is the first member of galectin family, which has a carbohydrate recognition domain, specifically binds towards ß-galactoside containing oligosaccharides. Owing its association with carbohydrates, Gal-1 is involved in many biological processes such as cell signaling, adhesion and pathological pathways such as metastasis, apoptosis and increased tumour cell survival. The development of ß-galactoside based inhibitors would help to control the Gal-1 expression. In the current study, we carried out molecular dynamics (MD) simulations to examine the structural and dynamic behaviour Gal-1-thiodigalactoside (TDG), Gal-1-lactobionic acid (LBA) and Gal-1-beta-(1→6)-galactobiose (G16G) complexes. The analysis of glycosidic torsional angles revealed that ß-galactoside analogues TDG and LBA have a single binding mode (BM1) whereas G16G has two binding modes (BM1 and BM2) for interacting with Gal-1 protein. We have computed the binding free energies for the complexes Gal-1-TDG, Gal-1-LBA and Gal-1-G16G using MM/PBSA and are -6.45, -6.22 and -3.08 kcal/mol, respectively. This trend agrees well with experiments that the binding of Gal-1 with TDG is stronger than LBA. Further analysis revealed that the interactions due to direct and water-mediated hydrogen bonds play a significant role to the structural stability of the complexes. The result obtained from this study is useful to formulate a set of rules and derive pharmacophore-based features for designing inhibitors against galectin-1.Communicated by Ramaswamy H. Sarma.

Prediction of protein-carbohydrate complex binding affinity using structural features.

Protein-carbohydrate interactions play a major role in several cellular and biological processes. Elucidating the factors influencing the binding affinity of protein-carbohydrate complexes and predicting their free energy of binding provide deep insights for understanding the recognition mechanism. In this work, we have collected the experimental binding affinity data for a set of 389 protein-carbohydrate complexes and derived several structure-based features such as contact potentials, interaction energy, number of binding residues and contacts between different types of atoms. Our analysis on the relationship between binding affinity and structural features revealed that the important factors depend on the type of the complex based on number of carbohydrate and protein chains. Specifically, binding site residues, accessible surface area, interactions between various atoms and energy contributions are important to understand the binding affinity. Further, we have developed multiple regression equations for predicting the binding affinity of protein-carbohydrate complexes belonging to six categories of protein-carbohydrate complexes. Our method showed an average correlation and mean absolute error of 0.731 and 1.149 kcal/mol, respectively, between experimental and predicted binding affinities on a jackknife test. We have developed a web server PCA-Pred, Protein-Carbohydrate Affinity Predictor, for predicting the binding affinity of protein-carbohydrate complexes. The web server is freely accessible at https://web.iitm.ac.in/bioinfo2/pcapred/. The web server is implemented using HTML and Python and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera.

ProCaff: protein-carbohydrate complex binding affinity database.

MOTIVATION: Protein-carbohydrate interactions perform several cellular and biological functions and their structure and function are mainly dictated by their binding affinity. Although plenty of experimental data on binding affinity are available, there is no reliable and comprehensive database in the literature. RESULTS: We have developed a database on binding affinity of protein-carbohydrate complexes, ProCaff, which contains 3122 entries on dissociation constant (Kd), Gibbs free energy change (ΔG), experimental conditions, sequence, structure and literature information. Additional features include the options to search, display, visualization, download and upload the data. AVAILABILITY AND IMPLEMENTATION: The database is freely available at http://web.iitm.ac.in/bioinfo2/procaff/. The website is implemented using HTML and PHP and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera. CONTACT: gromiha@iitm.ac.in. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Is Synchronous Bilateral Breast Irradiation Using Flattening Filter-Free Beam-Based Volumetric-Modulated Arc Therapy Beneficial? A Dosimetric Study.

OBJECTIVE: The aim of this study is to validate the clinical use of flattening filter-free (FFF) beam-based volumetric-modulated arc therapy (VMAT) in synchronous bilateral breast carcinoma (SBBC) patient treatments and to compare with flattening filtered (FF) beam-based VMAT. MATERIALS AND METHODS: Computed tomography images of 15 SBBC patients were taken for this study. A dose of 50 Gy in 25 fractions was prescribed to planning target volume (PTV). VMAT plans were generated using both FFF and FF 6 MV X-ray beams in Eclipse treatment planning system. PTV and organs at risk (OARs) doses were analyzed quantitatively using dose-volume histograms (DVHs) to meet plan objectives. Pretreatment point and planar dosimetry were performed. RESULTS: The findings were reported as mean ± 1 standard deviation. PTV volume receiving 95% of the prescribed dose was 95.71% ± 0.65% for FF-VMAT and 95.45% ± 1.33% for FFF-VMAT (P = 0.743). Conformity index was 1.12 ± 0.31 (FF-VMAT) and 1.12 ± 0.02 (FFF-VMAT). Right lung mean dose was 10.95 ± 1.33 Gy (FF-VMAT) and 10.60 ± 98.5 (FFF-VMAT). Left lung mean dose was 9.73 ± 1.56 (FF-VMAT) and 9.61 ± 1.53 Gy (FFF-VMAT). Tumor control probability (TCP) was 99.68% ± 0.02% (FF-VMAT) and 99.67% ± 0.01% (FFF-VMAT) (P = 0.390). Uncomplicated TCP was 98.72% ± 0.02% (FF-VMAT) and 98.72% ± 0.01% (FFF-VMAT) (P = 0.508). CONCLUSION: The planning objective parameters achieved using FFF-based VMAT showed that FFF can also be used clinically to treat bilateral breast carcinomas and the low-dose lung volumes were still lesser with FFF-VMAT plans than FF-VMAT.

Design of fluorinated sialic acid analog inhibitor to H5 hemagglutinin of H5N1 influenza virus through molecular dynamics simulation study.

Influenza epidemics and pandemics are caused by influenza A virus. The cell surface protein of hemagglutinin and neuraminidase is responsible for viral infection and release of progeny virus on the host cell membrane. Now 18 hemagglutinin and 11 neuraminidase subtypes are identified. The avian influenza virus of H5N1 is an emergent threat to public health issues. To control the influenza viral infection it is necessary to develop antiviral inhibitors and vaccination. In the present investigation we carried out 50 ns Molecular Dynamics simulation on H5 hemagglutinin of Influenza A virus H5N1 complexed with fluorinated sialic acid by substituting fluorine atoms at any two hydroxyls of sialic acid by considering combinatorial combination. The binding affinity between the protein-ligand complex system is investigated by calculating pair interaction energy and MM-PBSA binding free energy. All the complex structures are stabilized by hydrogen bonding interactions between the H5 protein and the ligand fluorinated sialic acid. It is concluded from all the analyses that the fluorinated complexes enhance the inhibiting potency against H5 hemagglutinin and the order of inhibiting potency is SIA-F9 ≫ SIA-F2 ≈ SIA-F7 ≈ SIA-F2F4 ≈ SIA-F2F9 ≈ SIA-F7F9 > SIA-F7F8 ≈ SIA-F2F8 ≈ SIA-F8F9 > SIA-F4 ≈ SIA-F4F7 ≈ SIA-F4F8 ≈ SIA-F8 ≈ SIA-F2F7 ≈ SIA > SIA-F4F9. This study suggests that one can design the inhibitor by using the mono fluorinated models SIA-F9, SIA-F2 and SIA-F7 and difluorinated models SIA-F2F4, SIA-F2F9 and SIA-F7F9 to inhibit H5 of H5N1 to avoid Influenza A viral infection.Communicated by Ramaswamy H. Sarma.

Molecular dynamics simulation studies on influenza A virus H5N1 complexed with sialic acid and fluorinated sialic acid.

Abbreviations HA Hemagglutinin MD Molecular Dynamics MM-PBSA Molecular Mechanics Poisson-Boltzmann Surface Area NA Neuraminidase NAMD Nanoscale Molecular Dynamic Simulation PMEMD Particle Mesh Ewald Molecular Dynamics RMSD Root-Mean-Square Deviation RMSF Root-Mean-Square Fluctuation SIA sialic acid VMD Visual Molecular Dynamics Communicated by Ramaswamy H. Sarma.

Identification and Analysis of Key Residues Involved in Folding and Binding of Protein-carbohydrate Complexes.

BACKGROUND: Protein-carbohydrate interactions play vital roles in several biological processes in living organisms. The comparative analysis of binding site residues along with stabilizing residues in protein-carbohydrate complexes provides ample insights to understand the structure, function and recognition mechanism. OBJECTIVE: The main objective of this study is to identify and analyze the residues, which are involved in both folding and binding of the protein-carbohydrate complexes. METHODS: We have identified the stabilizing residues using the knowledge of hydrophobicity, longrange interactions and conservation, as well as binding site residues using a distance cutoff of 3.5Å between any heavy atoms in protein and ligand. Residues, which are common in stabilizing and binding, are termed as key residues. These key resides are analyzed with various sequence and structure based parameters such as frequency of occurrence, surrounding hydrophobicity, longrange order and conservation score. RESULTS: In this work, we have identified 2.45% binding site residues in a non-redundant dataset of 1130 complexes using distance-based criteria and 7.07% stabilizing residues using the concepts of hydrophobicity, long-range interactions and conservation of residues. Further, 5.9% of binding and 2.04% of stabilizing residues are common to each other, which are termed as key residues. The key residues have been analysed based on protein classes, carbohydrate types, gene ontology functional classifications, amino acid preference and structure-based parameters. We found that all-ß, α+ß and α/ß have more key residues than other protein classes and most of the KRs are present in ß-strands, which shows their importance in stability and binding of complexes. On the ligand side, Lsaccharide has the highest number of key residues and it has a high percentage of KRs in SRs and BRs than other carbohydrate types. Further, polar and charged residues have a high tendency to serve as key residues. Classifications based on gene ontology terms revealed that Lys is preferred in all the three groups: molecular functions, biological processes and cellular components. Key residues have 6 to 9 contacts within the protein and make only one contact with the carbohydrate ligand. These contacts are dominant to form polar-nonpolar contacts followed by the contacts between charged atoms. Further, the influence of sequence and structural parameters such as surrounding hydrophobicity, solvent accessibility, secondary structure, long-range order and conservation score has been discussed. CONCLUSION: The results obtained in the present work provide deep insights for understanding the interplay between stability and binding in protein-carbohydrate complexes.

Identification and analysis of binding site residues in protein-carbohydrate complexes using energy based approach.

Protein-carbohydrate interactions play important roles in several biological processes in living organisms. Understanding the recognition mechanism of protein-carbohydrate complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-carbohydrate complexes. Our method identified 3.3% of residues as binding sites in protein- carbohydrate complexes whereas the binding site residues in protein-protein, protein-RNA and protein-DNA complexes are 10.8%, 7.6% and 8.7%, respectively. In all these complexes, binding site residues are accommodated in singleresidue segments so that the neighboring residues are not involved in binding. Binding propensity analysis indicates the dominance of Trp to interact with carbohydrates through aromatic-aromatic interactions. Further, the preference of residue pairs and tripeptides interacting with carbohydrates has been delineated. The information gained in the present study will be beneficial for understanding the recognition mechanism of protein-carbohydrate complexes and for predicting the binding sites in carbohydrate binding proteins.

Structural investigation of porcine stomach mucin by X-ray fiber diffraction and homology modeling.

The basic understanding of the three dimensional structure of mucin is essential to understand its physiological function. Technology has been developed to achieve orientated porcine stomach mucin molecules. X-ray fiber diffraction of partially orientated porcine stomach mucin molecules show d-spacing signals at 2.99, 4.06, 4.22, 4.7, 5.37 and 6.5 Å. The high intense d-spacing signal at 4.22 Å is attributed to the antiparallel ß-sheet structure identified in the fraction of the homology modeled mucin molecule (amino acid residues 800-980) using Nidogen-Laminin complex structure as a template. The X-ray fiber diffraction signal at 6.5 Å reveals partial organization of oligosaccharides in porcine stomach mucin. This partial structure of mucin will be helpful in establishing a three dimensional structure for the whole mucin molecule.

Dynamics of sialyl Lewis(a) in aqueous solution and prediction of the structure of the sialyl Lewis(a)-SelectinE complex.

In this article we investigate all possible three-dimensional structures for sialyl Lewis(a) (SLe(a)) in aqueous solution and we predict without a priori experimental information its conformation when bound to SelectinE by using a combination of long molecular dynamics (MD) simulations. Based on 10ns MD studies, three structures differing in glycosidic conformations are proposed for SLe(a) in aqueous solution. Based on a 4ns MD study of the SLe(a)-SelectinE complex with initial structures derived from our prediction tools, we find that, fucose and N-acetyl neuraminic acid are in close contact with SelectinE and therefore expect interactions of the protein with these two sugar rings to be significantly more important than in the case of galactose and N-acetyl glucosamine. Our predictions indicate that the N-acetyl glucosamine of SLe(a) is positioned primarily in the aqueous phase. In order to be able to interact with SLe(a) the side chains of amino acid residues Lys99 and Lys111 in SelectinE appear to undergo large conformational changes when contrasted with the positions of these residues in the X-ray crystal structure. Furthermore, amino acid residues Arg97, Glu98 and Lys99 are acting as a holding arm to position the NeuNAc of SLe(a) in the binding pocket.

Conformations of higher gangliosides and their binding with cholera toxin - investigation by molecular modeling, molecular mechanics, and molecular dynamics.

Molecular mechanics and molecular dynamics studies are performed to investigate the conformational preference of cell surface higher gangliosides (GT1A and GT1B) and their interaction with Cholera Toxin. The water mediated hydrogen bonding network exists between sugar residues in gangliosides. An integrated molecular modeling, molecular mechanics, and molecular dynamics calculation of cholera toxin complexed with GT1A and GT1B reveal that, the active site of cholera toxin can accommodate these higher gangliosides. Direct and water mediated hydrogen bonding interactions stabilize these binding modes and play an essential role in defining the order of specificity for different higher ganglioside towards cholera toxin. This study identifies that the binding site of cholera toxin is shallow and can accommodate a maximum of two NeuNAc residues. The NeuNAc binding site of cholera toxin may be crucial for the design of inhibitors that can prevent the infection of cholera.

Disialogangliosides and their interaction with cholera toxin - investigation by molecular modeling, molecular mechanics and molecular dynamics.

Molecular mechanics and molecular dynamics studies are performed to investigate the conformational preference of cell surface disialogangliosides (GD1A, GD1B and GD3) in aqueous environment. The molecular mechanics calculation reveals that water mediated hydrogen bonding network plays a significant role in the structural stabilization of GD1A, GD1B and GD3. These water mediated hydrogen bonds not only exist between neighboring residues but also exist between residues that are separated by 2 to 3 residues in between. The conformational energy difference between different conformational states of gangliosides correlates very well with the number of water mediated and direct hydrogen bonds. The spatial flexibility of NeuNAc of gangliosides at the binding site of cholera toxin is worked out. The NeuNAc has a limited allowed eulerian space at the binding site of Cholera Toxin (2.4%). The molecular modeling, molecular mechanics and molecular dynamics of disialoganglioside-cholera toxin complex reveal that cholera toxin can accommodate the disialoganglioside GD1A in three different modes. A single mode of binding is permissible for GD1B and GD3. Direct and water mediated hydrogen bonding interactions stabilizes these binding modes and play an essential role in defining the order of specificity for different disialogangliosides towards cholera toxin. This study not only provides models for the disialoganglioside-cholera toxin complexes but also identifies the NeuNAc binding site as a site for design of inhibitors that can restrict the pathogenic activity of cholera toxin.

Monosialogangliosides and their interaction with cholera toxin - investigation by molecular modeling and molecular mechanics.

Molecular mechanics and molecular dynamics studies are performed to investigate the conformational preference of cell surface monosialogangliosides (GM3, GM2 and GM1) in aqueous environment. Water mediated hydrogen bonding network plays a significant role in the structural stabilization of GM3, GM2 and GM1. The spatial flexibility of NeuNAc of gangliosides at the binding site of cholera toxin reveals a limited allowed eulerian space of 2.4% with a much less allowed eulerian space (1.4%) for external galactose of GM1. The molecular mechanics of monosialoganglioside-cholera toxin complex reveals that cholera toxin can accommodate the monosialogangliosides in three different modes. Direct and water mediated hydrogen bonding interactions stabilize these binding modes and play an essential role in defining the order of specificity for different monosialogangliosides towards cholera toxin. This study identifies the NeuNAc binding site as a site for design of inhibitors that can restrict the pathogenic activity of cholera toxin.

Conformations of terminal sialyloligosaccharide fragments--a molecular dynamics study.

Molecular dynamics simulations have been performed to understand the conformational features of the terminal sialyloligosaccharide fragments NeuNAc alpha(2-3)Gal, NeuNAc alpha(2-6)Gal, NeuNAc alpha(2-8)NeuNAc and NeuNAc alpha(2-9)NeuNAc. The conformational regions A(i), B(i) and C(i) were identified in the Ramachandran plot. Analysis of the 1000 ps trajectories collected through simulation (2000 ps in the case of NeuNAc alpha (2-9)NeuNAc) revealed that these molecules have conformational propensity in region B(i). The occurrence of these molecules in the common conformational space leads to a structural similarity between them. This structural similarity may be an essential requirement for the neuraminidase activity towards sialyloligosaccharides. The local change in the conformation of the active site residues of neuraminidases may contribute for the specificity differences between different linkages of sialyloligosaccharides. A highly conserved water-mediated hydrogen bond observed in these structures between the sugar residues, acts as an additional stabilizing force.

Molecular modeling of sialyloligosaccharide fragments into the active site of influenza virus N9 neuraminidase.

Molecular modeling studies have been carried out to investigate the interactions between substrate sialyloligosaccharide (SOS) fragments bearing different glycosidic linkages and influenza virus N9 neuraminidase, a surface glycoprotein of influenza virus subtype N9. The studies revealed that the allowed orientation for sialic acid (SA) is less than 1% in the Eulerian space at the active site. The active site of this enzyme has enough space to accommodate various SOS fragments, NeuNAcalpha(2-3)Gal, NeuNAcalpha(2-6)Gal, NeuNAcalpha(2-8)NeuNAc and NeuNAcalpha(2-9)NeuNAc, but on specific conformations. In the bound conformation, among these substrates there exists a conformational similarity leading to a structural similarity, which may be an essential requirement for the cleavage activity of the neuraminidases irrespective of the type of glycosidic linkage.

Database analysis of O-glycosylation sites in proteins.

Statistical analysis was carried out to study the sequential aspects of amino acids around the O-glycosylated Ser/Thr. 992 sequences containing O-glycosylated Ser/Thr were selected from the O-GLYCBASE database of O-glycosylated proteins. The frequency of occurrence of amino acid residues around the glycosylated Ser/Thr revealed that there is an increased number of proline residues around the O-glycosylation sites in comparison with the nonglycosylated serine and threonine residues. The deviation parameter calculated as a measure of preferential and nonpreferential occurrence of amino acid residues around the glycosylation site shows that Pro has the maximum preference around the O-glycosylation site. Pro at +3 and/or -1 positions strongly favors glycosylation irrespective of single and multiple glycosylation sites. In addition, serine and threonine are preferred around the multiple glycosylation sites due to the effect of clusters of closely spaced glycosylated Ser/Thr. The preference of amino acids around the sites of mucin-type glycosylation is found likely to be similar to that of the O-glycosylation sites when taken together, but the acidic amino acids are more preferred around Ser/Thr in mucin-type glycosylation when compared totally. Aromatic amino acids hinder O-glycosylation in contrast to N-glycosylation. Cysteine and amino acids with bulky side chains inhibit O-glycosylation. The preference of certain potential sequence motifs of glycosylation has been discussed.

Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimentional structures of proteins and its implications for secondary structure prediction from amino acid sequences.

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on the dictionary of secondary structure prediction (DSSP)-generated secondary structure for 408 selected non-homologous proteins. To the amino acid triplets which are not found in the selected dataset, a DP value of zero is assigned with respect to the secondary structural elements of proteins. The total number of parameters generated is 15,432, in the possible parameters of 25,260. Deviation parameter is complete with respect to amino acid singlets, doublets, and partially complete with respect to amino acid triplets. These generated parameters were used to predict secondary structural elements from amino acid sequence. The secondary structure predicted by our method (SSPDP) was compared with that of single sequence (NNPREDICT) and multiple sequence (PHD) methods. The average value of the percentage of prediction accuracy for a helix by SSPDP, NNPREDICT and PHD methods was found to be 57%, 44% and 69% respectively for the proteins in the selected dataset. For b-strand the prediction accuracy is found to be 69%, 21% and 53% respectively by SSPDP, NNPREDICT and PHD methods. This clearly indicates that the secondary structure prediction by our method is as good as PHD method but much better than NNPREDICT method.

A database analysis of potential glycosylating Asn-X-Ser/Thr consensus sequences.

An analysis of the frequency of occurrence of various residues at position X was carried out on the consensus glycosylating sequence Asn-X-Ser/Thr using the PDB three-dimensional database. 488 non-homologous proteins bearing 696 Asn-X-Ser/Thr (X not equal Pro) sequences were analysed. More than 65% of Asn residues, when they occur as part of the consensus sequence, lie on the surface of the protein, implying a potentiality for glycosylation. A deviation parameter (DP) was calculated as a measure of preferential (positive) or non-preferential (negative) selection. At the X position in the consensus-sequence segment, the amino acids Gly, Asn and Phe have statistically significant positive DP values. The high value of DP for Asn is a consequence of the preferential occurrence of homodoublets, while for Phe it may be a consequence of the stacking interaction of the aromatic ring with the glycan. Gly at the X position in the consensus glycosylating sequence may be functionally significant owing to its preference and its high percentage of occurrence in proteins. The Ramachandran (Phi,Psi) angles around Gly in the consensus sequence show clustering in the region which is disallowed for non-glycyl residues. In this region, a hydrogen bond between the side chain of Asn and the peptide backbone/side chain of Ser/Thr is possible, reflecting a positional as well as a conformational role in the consensus glycosylating sequence. For the 44 confirmed N-glycosylating sequences, an in-depth analysis of the (Psi(N), Phi(X), Psi(X), Phi(S/T)) dihedral angles, which position the side chains of Asn and Ser/Thr, shows that these can be grouped into nine conformational states. In most cases, a direct or water-mediated hydrogen bond between OD1 of Asn and OG of Ser/Thr is possible, reflecting the possible importance of this hydrogen bonding in the glycosylation process.