Analysis and prediction of carbohydrate binding sites

PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces

The Protein Structure Transformer (PeSTo), a geometric transformer, has exhibited exceptional performance in predicting protein-protein binding interfaces and distinguishing interfaces with nucleic acids, lipids, small molecules, and ions. In this study, we introduce PeSTo-Carbs, an extension of PeSTo specifically engineered to predict protein-carbohydrate binding interfaces. We evaluate the performance of this approach using independent test sets and compare them with those of previous methods. Furthermore, we highlight the model's capability to specialize in predicting interfaces involving cyclodextrins, a biologically and pharmaceutically significant class of carbohydrates. Our method consistently achieves remarkable accuracy despite the scarcity of available structural data for cyclodextrins.

Benchmarking Water Models in Molecular Dynamics of Protein-Glycosaminoglycan Complexes

Glycosaminoglycans (GAGs) made of repeating disaccharide units intricately engage with proteins, playing a crucial role in the spatial organization of the extracellular matrix (ECM) and the transduction of biological signals in cells to modulate a number of biochemical processes. Exploring protein-GAG interactions reveals several challenges for their analysis, namely, the highly charged and periodic nature of GAGs, their multipose binding, and the abundance of the interfacial water molecules in the protein-GAG complexes. Most of the studies on protein-GAG interactions are conducted using the TIP3P water model, and there are no data on the effect of various water models on the results obtained in molecular dynamics (MD) simulations of protein-GAG complexes. Hence, it is essential to perform a systematic analysis of different water models in MD simulations for these systems. In this work, we aim to evaluate the properties of the protein-GAG complexes in MD simulations using different explicit: TIP3P, SPC/E, TIP4P, TIP4PEw, OPC, and TIP5P and implicit: IGB = 1, 2, 5, 7, and 8 water models to find out which of them are best suited to study the dynamics of protein-GAG complexes. The FF14SB and GLYCAM06 force fields were used for the proteins and GAGs, respectively. The interactions of several GAG types, such as heparin, chondroitin sulfate, and hyaluronic acid with basic fibroblast growth factor, cathepsin K, and CD44 receptor, respectively, are investigated. The observed variations in different descriptors used to study the binding in these complexes emphasize the relevance of the choice of water models for the MD simulation of these complexes.

Structure-based neural network protein-carbohydrate interaction predictions at the residue level

Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

Emergence of Cooperative Glucose-Binding Networks in Adaptive Peptide Systems

Minimalistic peptide-based systems that bind sugars in water are challenging to design due to the weakness of interactions and required cooperative contributions from specific amino-acid side chains. Here, we used a bottom-up approach to create peptide-based adaptive glucose-binding networks by mixing glucose with selected sets of input dipeptides (up to 4) in the presence of an amidase to enable in situ reversible peptide elongation, forming mixtures of up to 16 dynamically interacting tetrapeptides. The choice of input dipeptides was based on amino-acid abundance in glucose-binding sites found in the protein data bank, with side chains that can support hydrogen bonding and CH-π interactions. Tetrapeptide sequence amplification patterns, determined through LC-MS analysis, served as a readout for collective interactions and led to the identification of optimized binding networks. Systematic variation of dipeptide input revealed the emergence of two networks of non-covalent hydrogen bonding and CH-π interactions that can co-exist, are cooperative and context-dependent. A cooperative binding mode was determined by studying the binding of the most amplified tetrapeptide (AWAD) with glucose in isolation. Overall, these results demonstrate that the bottom-up design of complex systems can recreate emergent behaviors driven by covalent and non-covalent self-organization that are not observed in reductionist designs and lead to the identification of system-level cooperative binding motifs.

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Systematic review on lectin-based electrochemical biosensors for clinically relevant carbohydrates and glycoconjugates

Carbohydrates and glycoconjugates are involved in numerous natural and pathological metabolic processes, and the precise elucidation of their biochemical functions has been supported by smart technologies assembled with lectins, i.e., ubiquitous proteins of nonimmune origin with carbohydrate-specific domains. When lectins are anchored on suitable electrochemical transducers, sensitive and innovative bioanalytical tools (lectin-based biosensors) are produced, with the ability to screen target sugars at molecular levels. In addition to the remarkable electroanalytical sensitivity, these devices associate specificity, precision, stability, besides the possibility of miniaturization and portability, which are special features required for real-time and point-of-care measurements. The mentioned attributes can be improved by combining lectins with biocompatible 0-3D semiconductors derived from carbon, metal nanoparticles, polymers and their nanocomposites, or employing labeled biomolecules. This systematic review aims to substantiate and update information on the progress made with lectin-based biosensors designed for electroanalysis of clinically relevant carbohydrates and glycoconjugates (glycoproteins, pathogens and cancer biomarkers), highlighting their main detection principles and performance in highly complex biological milieus. Moreover, particular emphasis is given to the main advantages and limitations of the reported devices, as well as the new trends for the current demands. We believe that this review will support and encourage more cutting-edge research involving lectin-based electrochemical biosensors.

19F NMR viewed through two different lenses: ligand-observed and protein-observed 19F NMR applications for fragment-based drug discovery

19F NMR has emerged as a powerful tool in drug discovery, particularly in fragment-based screens. The favorable magnetic resonance properties of the fluorine-19 nucleus, the general absence of fluorine in biological settings, and its ready incorporation into both small molecules and biopolymers, has enabled multiple applications of 19F NMR using labeled small molecules and proteins in biophysical, biochemical, and cellular experiments. This review will cover developments in ligand-observed and protein-observed 19F NMR experiments tailored towards drug discovery with a focus on fragment screening. We also cover the key advances that have furthered the field in recent years, including quantitative, structural, and in-cell methodologies. Several case studies are described for each application to highlight areas for innovation and to further catalyze new NMR developments for using this versatile nucleus.

Investigation of the structure of regulatory proteins interacting with glycosaminoglycans by combining NMR spectroscopy and molecular modeling - the beginning of a wonderful friendship

The interaction of regulatory proteins with extracellular matrix or cell surface-anchored glycosaminoglycans (GAGs) plays important roles in molecular recognition, wound healing, growth, inflammation and many other processes. In spite of their high biological relevance, protein-GAG complexes are significantly underrepresented in structural databases because standard tools for structure determination experience difficulties in studying these complexes. Co-crystallization with subsequent X-ray analysis is hampered by the high flexibility of GAGs. NMR spectroscopy experiences difficulties related to the periodic nature of the GAGs and the sparse proton network between protein and GAG with distances that typically exceed the detection limit of nuclear Overhauser enhancement spectroscopy. In contrast, computer modeling tools have advanced over the last years delivering specific protein-GAG docking approaches successfully complemented with molecular dynamics (MD)-based analysis. Especially the combination of NMR spectroscopy in solution providing sparse structural constraints with molecular docking and MD simulations represents a useful synergy of forces to describe the structure of protein-GAG complexes. Here we review recent methodological progress in this field and bring up examples where the combination of new NMR methods along with cutting-edge modeling has yielded detailed structural information on complexes of highly relevant cytokines with GAGs.

Carbohydrate-Protein Interactions: Advances and Challenges

A carbohydrate, also called saccharide in biochemistry, is a biomolecule consisting of carbon (C), hydrogen (H) and oxygen (O) atoms. For example, sugars are low molecular-weight carbohydrates, and starches are high molecular-weight carbohydrates. Carbohydrates are the most abundant organic substances in nature and essential constituents of all living things. Protein-carbohydrate interactions play important roles in many biological processes, such as cell growth, differentiation, and aggregation. They also have broad applications in pharmaceutical drug design. In this review, we will summarize the characteristic features of protein-carbohydrate interactions and review the computational methods for structure prediction, energy calculations, and kinetic studies of protein-carbohydrate complexes. Finally, we will discuss the challenges in this field.

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.

Structural Analysis of Binding Determinants of Salmonella typhimurium Trehalose-6-phosphate Phosphatase Using Ground-State Complexes

Trehalose-6-phosphate phosphatase (T6PP) catalyzes the dephosphorylation of trehalose 6-phosphate (T6P) to the disaccharide trehalose. The enzyme is not present in mammals but is essential to the viability of multiple lower organisms as trehalose is a critical metabolite, and T6P accumulation is toxic. Hence, T6PP is a target for therapeutics of human pathologies caused by bacteria, fungi, and parasitic nematodes. Here, we report the X-ray crystal structures of Salmonella typhimurium T6PP (StT6PP) in its apo form and in complex with the cofactor Mg²⁺ and the substrate analogue trehalose 6-sulfate (T6S), the product trehalose, or the competitive inhibitor 4-n-octylphenyl α-d-glucopyranoside 6-sulfate (OGS). OGS replaces the substrate phosphoryl group with a sulfate group and the glucosyl ring distal to the sulfate group with an octylphenyl moiety. The structures of these substrate-analogue and product complexes with T6PP show that specificity is conferred via hydrogen bonds to the glucosyl group proximal to the phosphoryl moiety through Glu123, Lys125, and Glu167, conserved in T6PPs from multiple species. The structure of the first-generation inhibitor OGS shows that it retains the substrate-binding interactions observed for the sulfate group and the proximal glucosyl ring. The OGS octylphenyl moiety binds in a unique manner, indicating that this subsite can tolerate various chemotypes. Together, these findings show that these conserved interactions at the proximal glucosyl ring binding site could provide the basis for the development of broad-spectrum therapeutics, whereas variable interactions at the divergent distal subsite could present an opportunity for the design of potent organism-specific therapeutics.

A systematic analysis of protein-carbohydrate interactions in the Protein Data Bank

Protein-carbohydrate interactions underlie essential biological processes. Elucidating the mechanism of protein-carbohydrate recognition is a prerequisite for modeling and optimizing protein-carbohydrate interactions, which will help in discovery of carbohydrate-derived therapeutics. In this work, we present a survey of a curated database consisting of 6,402 protein-carbohydrate complexes in the Protein Data Bank (PDB). We performed an all-against-all comparison of a subset of nonredundant binding sites, and the result indicates that the interaction pattern similarity is not completely relevant to the binding site structural similarity. Investigation of both binding site and ligand promiscuities reveals that the geometry of chemical feature points is more important than local backbone structure in determining protein-carbohydrate interactions. A further analysis on the frequency and geometry of atomic interactions shows that carbohydrate functional groups are not equally involved in binding interactions. Finally, we discuss the usefulness of protein-carbohydrate complexes in the PDB with acknowledgement that the carbohydrates in many structures are incomplete.

Protein-observed 19F NMR of LecA from Pseudomonas aeruginosa

The carbohydrate-binding protein LecA (PA-IL) from Pseudomonas aeruginosa plays an important role in the formation of biofilms in chronic infections. Development of inhibitors to disrupt LecA-mediated biofilms is desired but it is limited to carbohydrate-based ligands. Moreover, discovery of drug-like ligands for LecA is challenging because of its weak affinities. Therefore, we established a protein-observed 19F (PrOF) nuclear magnetic resonance (NMR) to probe ligand binding to LecA. LecA was labeled with 5-fluoroindole to incorporate 5-fluorotryptophanes and the resonances were assigned by site-directed mutagenesis. This incorporation did not disrupt LecA preference for natural ligands, Ca2+ and d-galactose. Following NMR perturbation of W42, which is located in the carbohydrate-binding region of LecA, allowed to monitor binding of low-affinity ligands such as N-acetyl d-galactosamine (d-GalNAc, Kd = 780 ± 97 μM). Moreover, PrOF NMR titration with glycomimetic of LecA p-nitrophenyl β-d-galactoside (pNPGal, Kd = 54 ± 6 μM) demonstrated a 6-fold improved binding of d-Gal proving this approach to be valuable for ligand design in future drug discovery campaigns that aim to generate inhibitors of LecA.

Bioinformatics studies on a function of the SARS-CoV-2 spike glycoprotein as the binding of host sialic acid glycans

SARS-CoV and SARS-CoV-2 do not appear to have functions of a hemagglutinin and neuraminidase. This is a mystery, because sugar binding activities appear essential to many other viruses including influenza and even most other coronaviruses in order to bind to and escape from the glycans (sugars, oligosaccharides or polysaccharides) characteristic of cell surfaces and saliva and mucin. The S1 N terminal Domains (S1-NTD) of the spike protein, largely responsible for the bulk of the characteristic knobs at the end of the spikes of SARS-CoV and SARS-CoV-2, are here predicted to be “hiding” sites for recognizing and binding glycans containing sialic acid. This may be important for infection and the ability of the virus to locate ACE2 as its known main host cell surface receptor, and if so it becomes a pharmaceutical target. It might even open up the possibility of an alternative receptor to ACE2. The prediction method developed, which uses amino acid residue sequence alone to predict domains or proteins that bind to sialic acids, is naïve, and will be advanced in future work. Nonetheless, it was surprising that such a very simple approach was so useful, and it can easily be reproduced in a very few lines of computer program to help make quick comparisons between SARS-CoV-2 sequences and to consider the effects of viral mutations.

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This paper extends the studies of the author's previous SARS-CoV-2 papers.

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Designing vaccine and drugs must seek to avoid escape mutations.

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Strangely, SARS-CoV and SARS-CoV-2 appear to lack sialic acid binding functions.

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Sequence motifs are found, but they require a simple prediction method.

Increasing the Affinity of an O-Antigen Polysaccharide Binding Site in Shigella flexneri Bacteriophage Sf6 Tailspike Protein

Broad and unspecific use of antibiotics accelerates spread of resistances. Sensitive and robust pathogen detection is thus important for a more targeted application. Bacteriophages contain a large repertoire of pathogen-binding proteins. These tailspike proteins (TSP) often bind surface glycans and represent a promising design platform for specific pathogen sensors. We analysed bacteriophage Sf6 TSP that recognizes the O-polysaccharide of dysentery-causing Shigella flexneri to develop variants with increased sensitivity for sensor applications. Ligand polyrhamnose backbone conformations were obtained from 2D 1 H,1 H-trNOESY NMR utilizing methine-methine and methine-methyl correlations. They agreed well with conformations obtained from molecular dynamics (MD), validating the method for further predictions. In a set of mutants, MD predicted ligand flexibilities that were in good correlation with binding strength as confirmed on immobilized S. flexneri O-polysaccharide (PS) with surface plasmon resonance. In silico approaches combined with rapid screening on PS surfaces hence provide valuable strategies for TSP-based pathogen sensor design.

Lactosyl-sepharose binding proteins from pancreatic cancer cells show differential expression in primary and metastatic organs

In normal cells, glycan binding proteins mediate various cellular processes upon recognition and binding to respective ligands. In tumor cells, these proteins have been associated with metastasis. Lactosyl-sepharose binding proteins (LSBPs) were isolated and identified in a workflow involving lactosyl affinity chromatography and label-free quantification mass spectrometry (LFQ MS). A binding study with monosaccharides was performed by microscale thermophoresis and nuclear magnetic resonance spectroscopy. Influence of galactose on LSBPs’ binding to the lactosyl resin was investigated by competitive affinity chromatography followed by LFQ MS. An analysis of amino acids with sugar binding motifs was searched using bioinformatics tools. The expression profiles of these proteins at the mRNA level, as determined by a chip array from a pancreatic ductal adenocarcinoma (PDAC) liver metastasis model, were used for evaluating their potential role in cancer progression. Proteomics data and their respective genes were analyzed by MaxQuant and Ingenuity Pathway Analysis. In total, 1295 LSBPs were isolated and identified from Suit2-007 human pancreatic adenocarcinoma cells. Interaction studies revealed that these proteins exhibit low to moderate affinity for monosaccharide sugars. Some of these LSBPs even showed reduced affinity after calcium depletion. Among the isolated proteins were annexins and galectins in addition to other families, with no history of binding lactosyl residues. A subset of LSBPs exhibited differential profiles in the pancreas, liver, and lung environments. These modulations may be related to tumor progression. In conclusion, we show that PDAC cells contain LSBPs, a subset of which binds galactose with calcium dependency. The differential expression of these proteins in a rat model highlights their value for diagnosis and as potential drug targets for PDAC therapy. Future work will be required to validate these findings in patient samples.

Impact statement

Interaction of glycan binding proteins with aberrantly expressed glycans in tumor environment is crucial for metastasis. Here, we established a work flow for investigating the presence of a subset of these proteins in PDAC cells, which bind to a lactosyl-sepharose resin. The resin had been designed to isolate proteins with lectin-like properties. The corresponding lactosyl-sepharose binding proteins (LSBPs) show affinity for galactose and other monosaccharides. A subset of the LSBPs shows also calcium dependency. The importance of these proteins is highlighted by their differential expression profiles in PDAC cells growing in primary (pancreas) and metastatic (liver and lung) organ sites. Based on their affinity for the lactosyl-resin and monosaccharides, LSBPs hold potential for PDAC diagnosis and as drug targets. This work has set the stage for further investigation of the occurrence and the role of LSBPs in patient samples using the newly established workflow.

Docking software performance in protein-glycosaminoglycan systems

We present a benchmarking study for protein-glycosaminoglycan systems with eight docking programs: Dock, rDock, ClusPro, PLANTS, HADDOCK, Hex, SwissDock and ATTRACT. We used a non-redundant representative dataset of 28 protein-glycosaminoglycan complexes with experimentally available structures, where a glycosaminoglycan ligand was longer than a trimer. Overall, the ligand binding poses could be correctly predicted in many cases by the tested docking programs, however the ranks of the docking poses are often poorly assigned. Our results suggest that Dock program performs best in terms of the pose placement, has the most suitable scoring function, and its performance did not depend on the ligand size. This suggests that the implementation of the electrostatics as well as the shape complementarity procedure in Dock are the most suitable for docking glycosaminoglycan ligands. We also analyzed how free energy patterns of the benchmarking complexes affect the performance of the evaluated docking software.

Computational Prediction of Carbohydrate-Binding Proteins and Binding Sites

Protein-carbohydrate interaction is essential for biological systems, and carbohydrate-binding proteins (CBPs) are important targets when designing antiviral and anticancer drugs. Due to the high cost and difficulty associated with experimental approaches, many computational methods have been developed as complementary approaches to predict CBPs or carbohydrate-binding sites. However, most of these computational methods are not publicly available. Here, we provide a comprehensive review of related studies and demonstrate our two recently developed bioinformatics methods. The method SPOT-CBP is a template-based method for detecting CBPs based on structure through structural homology search combined with a knowledge-based scoring function. This method can yield model complex structure in addition to accurate prediction of CBPs. Furthermore, it has been observed that similarly accurate predictions can be made using structures from homology modeling, which has significantly expanded its applicability. The other method, SPRINT-CBH, is a de novo approach that predicts binding residues directly from protein sequences by using sequence information and predicted structural properties. This approach does not need structurally similar templates and thus is not limited by the current database of known protein-carbohydrate complex structures. These two complementary methods are available at https://sparks-lab.org. © 2018 by John Wiley & Sons, Inc.

Probing binding specificity of the sucrose transporter AtSUC2 with fluorescent coumarin glucosides

The phloem sucrose transporter, AtSUC2, is promiscuous with respect to substrate recognition, transporting a range of glucosides in addition to sucrose, including naturally occurring coumarin glucosides. We used the inherent fluorescence of coumarin glucosides to probe the specificity of AtSUC2 for its substrates, and determined the structure-activity relationships that confer phloem transport in vivo using Arabidopsis seedlings. In addition to natural coumarin glucosides, we synthesized new compounds to identify key structural features that specify recognition by AtSUC2. Our analysis of the structure-activity relationship revealed that the presence of a free hydroxyl group on the coumarin moiety is essential for binding by AtSUC2 and subsequent phloem mobility. Structural modeling of the AtSUC2 substrate-binding pocket explains some important structural requirements for the interaction of coumarin glucosides with the AtSUC2 transporter.

Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines

Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew's correlation coefficient of 0.34 and 0.29, respectively for 10-fold cross validation and independent test without balancing binding and nonbinding residues. The quality of the method is further demonstrated by having statistically significantly more binding residues predicted for carbohydrate-binding proteins than presumptive nonbinding proteins in the human proteome, and by the bias of rare alleles toward predicted carbohydrate-binding sites for nonsynonymous mutations from the 1000 genome project. SPRINT-CBH is available as an online server at http://sparks-lab.org/server/SPRINT-CBH .

Computational analysis of interactions in structurally available protein-glycosaminoglycan complexes

Glycosaminoglycans represent a class of linear anionic periodic polysaccharides, which play a key role in a variety of biological processes in the extracellular matrix via interactions with their protein targets. Computationally, glycosaminoglycans are very challenging due to their high flexibility, periodicity and electrostatics-driven nature of the interactions with their protein counterparts. In this work, we carry out a detailed computational characterization of the interactions in protein-glycosaminoglycan complexes from the Protein Data Bank (PDB), which are split into two subsets accounting for their intrinsic nature: non-enzymatic-protein-glycosaminoglycan and enzyme-glycosaminoglycan complexes. We apply molecular dynamics to analyze the differences in these two subsets in terms of flexibility, retainment of the native interactions in the simulations, free energy components of binding and contributions of protein residue types to glycosaminoglycan binding. Furthermore, we systematically demonstrate that protein electrostatic potential calculations, previously found to be successful for glycosaminoglycan binding sites prediction for individual systems, are in general very useful for proposing protein surface regions as putative glycosaminoglycan binding sites, which can be further used for local docking calculations with these particular polysaccharides. Finally, the performance of six different docking programs (Autodock 3, Autodock Vina, MOE, eHiTS, FlexX and Glide), some of which proved to perform well for particular protein-glycosaminoglycan complexes in previous work, is evaluated on the complete protein-glycosaminoglycan data set from the PDB. This work contributes to widen our knowledge of protein-glycosaminoglycan molecular recognition and could be useful to steer a choice of the strategies to be applied in theoretical studies of these systems.

MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein-mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.

The effect of glycosaminoglycans (GAGs) on amyloid aggregation and toxicity

Amyloidosis is a protein folding disorder in which normally soluble proteins are deposited extracellularly as insoluble fibrils, impairing tissue structure and function. Charged polyelectrolytes such as glycosaminoglycans (GAGs) are frequently found associated with the proteinaceous deposits in tissues of patients affected by amyloid diseases. Experimental evidence indicate that they can play an active role in favoring amyloid fibril formation and stabilization. Binding of GAGs to amyloid fibrils occurs mainly through electrostatic interactions involving the negative polyelectrolyte charges and positively charged side chains residues of aggregating protein. Similarly to catalyst for reactions, GAGs favor aggregation, nucleation and amyloid fibril formation functioning as a structural templates for the self-assembly of highly cytotoxic oligomeric precursors, rich in β-sheets, into harmless amyloid fibrils. Moreover, the GAGs amyloid promoting activity can be facilitated through specific interactions via consensus binding sites between amyloid polypeptide and GAGs molecules. We review the effect of GAGs on amyloid deposition as well as proteins not strictly related to diseases. In addition, we consider the potential of the GAGs therapy in amyloidosis.

Development of a machine learning method to predict membrane protein-ligand binding residues using basic sequence information

Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.

Carbohydrate-binding protein identification by coupling structural similarity searching with binding affinity prediction

Carbohydrate-binding proteins (CBPs) are potential biomarkers and drug targets. However, the interactions between carbohydrates and proteins are challenging to study experimentally and computationally because of their low binding affinity, high flexibility, and the lack of a linear sequence in carbohydrates as exists in RNA, DNA, and proteins. Here, we describe a structure-based function-prediction technique called SPOT-Struc that identifies carbohydrate-recognizing proteins and their binding amino acid residues by structural alignment program SPalign and binding affinity scoring according to a knowledge-based statistical potential based on the distance-scaled finite-ideal gas reference state (DFIRE). The leave-one-out cross-validation of the method on 113 carbohydrate-binding domains and 3442 noncarbohydrate binding proteins yields a Matthews correlation coefficient of 0.56 for SPalign alone and 0.63 for SPOT-Struc (SPalign + binding affinity scoring) for CBP prediction. SPOT-Struc is a technique with high positive predictive value (79% correct predictions in all positive CBP predictions) with a reasonable sensitivity (52% positive predictions in all CBPs). The sensitivity of the method was changed slightly when applied to 31 APO (unbound) structures found in the protein databank (14/31 for APO versus 15/31 for HOLO). The result of SPOT-Struc will not change significantly if highly homologous templates were used. SPOT-Struc predicted 19 out of 2076 structural genome targets as CBPs. In particular, one uncharacterized protein in Bacillus subtilis (1oq1A) was matched to galectin-9 from Mus musculus. Thus, SPOT-Struc is useful for uncovering novel carbohydrate-binding proteins. SPOT-Struc is available at http://sparks-lab.org.

Prediction of fatty acid-binding residues on protein surfaces with three-dimensional probability distributions of interacting atoms

Protein-fatty acid interaction is vital for many cellular processes and understanding this interaction is important for functional annotation as well as drug discovery. In this work, we present a method for predicting the fatty acid (FA)-binding residues by using three-dimensional probability density distributions of interacting atoms of FAs on protein surfaces which are derived from the known protein-FA complex structures. A machine learning algorithm was established to learn the characteristic patterns of the probability density maps specific to the FA-binding sites. The predictor was trained with five-fold cross validation on a non-redundant training set and then evaluated with an independent test set as well as on holo-apo pair's dataset. The results showed good accuracy in predicting the FA-binding residues. Further, the predictor developed in this study is implemented as an online server which is freely accessible at the following website, http://ismblab.genomics.sinica.edu.tw/.

Community-based network study of protein-carbohydrate interactions in plant lectins using glycan array data

Lectins play major roles in biological processes such as immune recognition and regulation, inflammatory responses, cytokine signaling, and cell adhesion. Recently, glycan microarrays have shown to play key roles in understanding glycobiology, allowing us to study the relationship between the specificities of glycan binding proteins and their natural ligands at the omics scale. However, one of the drawbacks in utilizing glycan microarray data is the lack of systematic analysis tools to extract information. In this work, we attempt to group various lectins and their interacting carbohydrates by using community-based analysis of a lectin-carbohydrate network. The network consists of 1119 nodes and 16769 edges and we have identified 3 lectins having large degrees of connectivity playing the roles of hubs. The community based network analysis provides an easy way to obtain a general picture of the lectin-glycan interaction and many statistically significant functional groups.

A guide into glycosciences: How chemistry, biochemistry and biology cooperate to crack the sugar code

BACKGROUND

The most demanding challenge in research on molecular aspects within the flow of biological information is posed by the complex carbohydrates (glycan part of cellular glycoconjugates). How the 'message' encoded in carbohydrate 'letters' is 'read' and 'translated' can only be unraveled by interdisciplinary efforts.

SCOPE OF REVIEW

This review provides a didactic step-by-step survey of the concept of the sugar code and the way strategic combination of experimental approaches characterizes structure-function relationships, with resources for teaching.

MAJOR CONCLUSIONS

The unsurpassed coding capacity of glycans is an ideal platform for generating a broad range of molecular 'messages'. Structural and functional analyses of complex carbohydrates have been made possible by advances in chemical synthesis, rendering production of oligosaccharides, glycoclusters and neoglycoconjugates possible. This availability facilitates to test the glycans as ligands for natural sugar receptors (lectins). Their interaction is a means to turn sugar-encoded information into cellular effects. Glycan/lectin structures and their spatial modes of presentation underlie the exquisite specificity of the endogenous lectins in counterreceptor selection, that is, to home in on certain cellular glycoproteins or glycolipids.

GENERAL SIGNIFICANCE

Understanding how sugar-encoded 'messages' are 'read' and 'translated' by lectins provides insights into fundamental mechanisms of life, with potential for medical applications.

Binding mechanism of inositol stereoisomers to monomers and aggregates of Aβ(16-22)

Alzheimer's disease (AD) is a severe neurodegenerative disease with no cure. A potential therapeutic approach is to prevent or reverse the amyloid formation of Aβ42, a key pathological hallmark of AD. We examine the molecular basis for stereochemistry-dependent inhibition of the formation of Aβ fibrils in vitro by a polyol, scyllo-inositol. We present molecular dynamics simulations of the monomeric, disordered aggregate, and protofibrillar states of Aβ(16-22), an amyloid-forming peptide fragment of full-length Aβ, successively with and without scyllo-inositol and its inactive stereoisomer chiro-inositol. Both stereoisomers bind monomers and disordered aggregates with similar affinities of 10-120 mM, whereas binding to β-sheet-containing protofibrils yields affinities of 0.2-0.5 mM commensurate with in vitro inhibitory concentrations of scyllo-inositol. Moreover, scyllo-inositol displays a higher binding specificity for phenylalanine-lined grooves on the protofibril surface, suggesting that scyllo-inositol coats the surface of Aβ protofibrils and disrupts their lateral stacking into amyloid fibrils.

Nucleos: a web server for the identification of nucleotide-binding sites in protein structures

Nucleos is a web server for the identification of nucleotide-binding sites in protein structures. Nucleos compares the structure of a query protein against a set of known template 3D binding sites representing nucleotide modules, namely the nucleobase, carbohydrate and phosphate. Structural features, clustering and conservation are used to filter and score the predictions. The predicted nucleotide modules are then joined to build whole nucleotide-binding sites, which are ranked by their score. The server takes as input either the PDB code of the query protein structure or a user-submitted structure in PDB format. The output of Nucleos is composed of ranked lists of predicted nucleotide-binding sites divided by nucleotide type (e.g. ATP-like). For each ranked prediction, Nucleos provides detailed information about the score, the template structure and the structural match for each nucleotide module composing the nucleotide-binding site. The predictions on the query structure and the template-binding sites can be viewed directly on the web through a graphical applet. In 98% of the cases, the modules composing correct predictions belong to proteins with no homology relationship between each other, meaning that the identification of brand-new nucleotide-binding sites is possible using information from non-homologous proteins. Nucleos is available at http://nucleos.bio.uniroma2.it/nucleos/.

Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

BACKGROUND

The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure.

RESULTS

In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets.

CONCLUSIONS

This study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).

Prediction of protein-mannose binding sites using random forest

Mannose is an abundant cell surface monosaccharide and has an important role in many biochemical processes. It binds to a great diversity of receptor proteins. In this study we have employed Random Forest for prediction of mannose binding sites. Mannosebinding site is taken to be a sphere around the centroid of the ligand and the sphere is subdivided into different layers and atom wise and residue wise features were extracted for each layer. The method achieves 95.59 % of accuracy using Random Forest with 10 fold cross validation. Prediction of mannose binding site analysis will be quite useful in drug design.

Amino acid substitutions of MagA in Klebsiella pneumoniae affect the biosynthesis of the capsular polysaccharide

Mucoviscosity-associated gene A (magA) of Klebsiella pneumoniae contributes to K1 capsular polysaccharide (CPS) biosynthesis. Based on sequence homology and gene alignment, the magA gene has been predicted to encode a Wzy-type CPS polymerase. Sequence alignment with the Wzy_C and RfaL protein families (which catalyze CPS or lipopolysaccharide (LPS) biosynthesis) and topological analysis has suggested that eight highly conserved residues, including G308, G310, G334, G337, R290, P305, H323, and N324, were located in a hypothetical loop region. Therefore, we used site-directed mutagenesis to study the role of these residues in CPS production, and to observe the consequent phenotypes such as mucoviscosity, serum and phagocytosis resistance, and virulence (as assessed in mice) in pyogenic liver abscess strain NTUH-K2044. Alanine substitutions at R290 or H323 abolished all of these properties. The G308A mutant was severely impaired for these functions. The G334A mutant remained mucoid with decreased CPS production, but its virulence was significantly reduced in vivo. No phenotypic change was observed for strains harboring magA G310A, G337A, P305A, or N324A mutations. Therefore, R290, G308, H323, and G334 are functionally important residues of the MagA (Wzy) protein of K. pneumoniae NTUH-K2044, capsular type K1. These amino acids are also likely to be important for the function of Wzy in other capsular types in K. pneumoniae and other species bearing Wzy_C family proteins.

Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study

BACKGROUND

There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions.

RESULTS

The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature.

CONCLUSIONS

In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

[Development and validation of programs for ligand-binding-pocket search]

Searching for the ligand-binding pockets of proteins plays an important role in structure-based drug design (SBDD), which is based on knowledge of the three-dimensional structures of target proteins. In SBDD, small molecules that can interact with the target protein are designed. SBDD methods require the identification of ligand-binding pockets, in which ligand molecules interact with protein atoms. The computer programs for the detection of ligand-binding pockets are categorized into two types: one is programs using only geometric properties; and the other is programs using the physicochemical properties of proteins as well as geometry. This paper describes the development and evaluation of a program for ligand-binding pocket search. The program HBOP (Hydropho Bicity On a Protein) searches for ligand-binding pockets using hydrophobic potentials derived from experimentally determined functions. This is based on the fact that hydrophobicity plays a significant role in protein-ligand binding. The results of evaluation indicate that programs using physicochemical properties can discover actual ligand-binding pockets more efficiently than those using only geometric properties.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

Identification of mannose interacting residues using local composition

Background

Mannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs.

Results

This paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM) based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1) main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2) realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/).

Conclusions

Compositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function of protein as well as in understanding the role of mannose in the immune system.

Biology of human sodium glucose transporters

There are two classes of glucose transporters involved in glucose homeostasis in the body, the facilitated transporters or uniporters (GLUTs) and the active transporters or symporters (SGLTs). The energy for active glucose transport is provided by the sodium gradient across the cell membrane, the Na(+) glucose cotransport hypothesis first proposed in 1960 by Crane. Since the cloning of SGLT1 in 1987, there have been advances in the genetics, molecular biology, biochemistry, biophysics, and structure of SGLTs. There are 12 members of the human SGLT (SLC5) gene family, including cotransporters for sugars, anions, vitamins, and short-chain fatty acids. Here we give a personal review of these advances. The SGLTs belong to a structural class of membrane proteins from unrelated gene families of antiporters and Na(+) and H(+) symporters. This class shares a common atomic architecture and a common transport mechanism. SGLTs also function as water and urea channels, glucose sensors, and coupled-water and urea transporters. We also discuss the physiology and pathophysiology of SGLTs, e.g., glucose galactose malabsorption and familial renal glycosuria, and briefly report on targeting of SGLTs for new therapies for diabetes.

Docking glycosaminoglycans to proteins: analysis of solvent inclusion

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein-protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.

From lectin structure to functional glycomics: principles of the sugar code

Lectins are carbohydrate-binding proteins which lack enzymatic activity on their ligand and are distinct from antibodies and free mono- and oligosaccharide sensor/transport proteins. Emerging insights into the functional dimension of lectin binding to cellular glycans have strongly contributed to the shaping of the 'sugar code'. Fittingly, over a dozen folds and a broad spectrum of binding site architecture, ranging from shallow grooves to deep pockets, have developed sugar-binding capacity. A central question is how the exquisite target specificity of endogenous lectins for certain cellular glycans can be explained. In this regard, affinity regulation is first systematically dissected into six levels. Experimentally, the strategic combination of methods to monitor distinct aspects of the lectin-glycan interplay offers a promising perspective to answer this question.

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches.

Phosphate binding sites identification in protein structures

Nearly half of known protein structures interact with phosphate-containing ligands, such as nucleotides and other cofactors. Many methods have been developed for the identification of metal ions-binding sites and some for bigger ligands such as carbohydrates, but none is yet available for the prediction of phosphate-binding sites. Here we describe Pfinder, a method that predicts binding sites for phosphate groups, both in the form of ions or as parts of other non-peptide ligands, in proteins of known structure. Pfinder uses the Query3D local structural comparison algorithm to scan a protein structure for the presence of a number of structural motifs identified for their ability to bind the phosphate chemical group. Pfinder has been tested on a data set of 52 proteins for which both the apo and holo forms were available. We obtained at least one correct prediction in 63% of the holo structures and in 62% of the apo. The ability of Pfinder to recognize a phosphate-binding site in unbound protein structures makes it an ideal tool for functional annotation and for complementing docking and drug design methods. The Pfinder program is available at http://pdbfun.uniroma2.it/pfinder.