Computational modeling of the sugar-lectin interaction

The future of plant lectinology: Advanced technologies and computational tools

Lectins play crucial roles in many biological processes and serve as tools in fields ranging from agriculture to biomedicine. While classical methods for lectin discovery and characterization were foundational for the field, they often lack sensitivity and throughput, limiting the detection of less abundant or weakly binding lectins, such as the stress-inducible or monovalent lectins. This review focuses on recent advancements in plant lectin research, particularly novel technologies that complement traditional approaches. Techniques such as glycan microarrays allow rapid assessment of lectin specificity across a diverse range of glycans by evaluating interactions with immobilized glycans on solid surfaces. Phage display libraries enable the identification of carbohydrate-mimetic peptides and the development of ligands for lectins by presenting diverse peptide libraries on bacteriophages. Genomic and transcriptomic analyses facilitate the exploration of the lectome in various plant species by scanning entire datasets to identify genes that contain lectin motifs-specific conserved amino acid sequences involved in carbohydrate recognition-and lectin domains, the larger structural regions that facilitate and stabilize these interactions. Additionally, computational methods-including molecular docking, molecular dynamics simulations, and machine learning pipelines-support predictions of lectin structures and binding properties, underpinning experimental efforts. These advanced techniques bring increased efficiency, accuracy, and a broader scope to lectin studies, with potential impacts across multiple fields. However, challenges such as data complexity and the need for experimental validation for computational methods remain. The future of lectin research will depend on the integration of these methods and the strengthening of interdisciplinarity to unlock the full potential of lectins.

Ultrasensitive colorimetric detection of Staphylococcus aureus using wheat germ agglutinin and IgY as a dual-recognition strategy

A novel colorimetric platform was designed for the determination of S. aureus by utilizing a dual-recognition strategy, where wheat germ agglutinin (WGA)-functionalized magnetic beads were served as separation elements to capture and enrich S. aureus efficiently from the matrix. Horseradish peroxidase (HRP) labeled chicken anti-protein A IgY (HRP-IgY) was used to label the captured S. aureus. A chicken IgY was introduced as a signal tracer to bind with staphylococcal protein A (SPA) on the surface of S. aureus, which can circumvent the interference from protein G-producing Streptococcus. Subsequently, the colorimetric signal was achieved by an HRP-catalyzed reaction, which was amplified by HRP-IgY bound by approximately 80,000 SPA molecules on one S. aureus. The entire detection process could be accomplished within 90 min. Under optimal conditions, the linear response of different S. aureus concentrations ranged from 7.8 × 102 to 2.0 × 105 CFU/mL and the limit of detection reached down to 3.9 × 102 CFU/mL. Some common non-target bacteria yielded negative results, indicating the excellent specificity of the method. The developed strategy was successfully applied to the determination of S. aureus in various types of samples with satisfactory recoveries. Therefore, the novel dual-recognition strategy possessed the advantages of high sensitivity, specificity, and low cost and exhibited considerable potential as a promising tool to defend public health.

An efficient SERS platform for the ultrasensitive detection of Staphylococcus aureus and Listeria monocytogenes via wheat germ agglutinin-modified magnetic SERS substrate and streptavidin/aptamer co-functionalized SERS tags

A novel surface-enhanced Raman scattering (SERS)-based analytical technique was proposed to simultaneously detect two highly pathogenic bacteria, namely, Staphylococcus aureus (S. aureus) and Listeria monocytogenes (L. mono) by using a dual-recognition pattern with wheat germ agglutinin (WGA) and nucleic acid aptamers. WGA was modified onto Fe₃O₄@Au magnetic nanoparticles (MNPs) for the efficient capture of S. aureus and L. mono in complex samples (orange juice, extracts of lettuce, and human urine) within 15 min. The streptavidin (SA)/aptamers co-functionalized SERS tags were fabricated by covalent attaching two different Raman reporters and SA molecules onto 45 nm Au NPs and then conjugated with two biotin-aptamers that specifically bind to their target bacteria with high affinity and stability. The combined use of high-sensitive SERS tags, WGA-mediated magnetic enrichment, and SA-mediated aptamer conjugation remarkably improved the assay sensitivity. Under optimized conditions, the developed SERS biosensor can simultaneously detect the two target bacteria with high detection sensitivity (<6 cells/mL), favorable linear relation (10-10⁷ cells/mL), and high accuracy (recovery rate <7.03%). Therefore, the proposed SERS platform is rapid, sensitive, easy to use, and thus show potential as a tool for the timely identification of pathogenic bacteria in real samples.

The biomedical significance of multifunctional nanobiomaterials: The key components for site-specific delivery of therapeutics

The emergence of nanotechnology has provided the possibilities to overcome the potential problems associated with the development of pharmaceuticals including the low solubility, non-specific cellular uptake or action, and rapid clearance. Regarding the biomaterials (BMs), huge efforts have been made for improving their multi-functionalities via incorporation of various nanomaterials (NMs). Nanocomposite hydrogels with suitable properties could exhibit a variety of beneficial effects in biomedicine particularly in the delivery of therapeutics or tissue engineering. NMs including the silica- or carbon-based ones are capable of integration into various BMs that might be due to their special compositions or properties such as the hydrophilicity, hydrophobicity, magnetic or electrical characteristics, and responsiveness to various stimuli. This might provide multi-functional nanobiomaterials against a wide variety of disorders. Meanwhile, inappropriate distribution or penetration into the cells or tissues, bio-nano interface complexity, targeting ability loss, or any other unpredicted phenomena are the serious challenging issues. Computational simulations and models enable development of NMs with optimal characteristics and provide a deeper knowledge of NM interaction with biosystems. This review highlights the biomedical significance of the multifunctional NMs particularly those applied for the development of 2-D or 3-D BMs for a variety of applications including the site-specific delivery of therapeutics. The powerful impacts of the computational techniques on the design process of NMs, quantitation and prediction of protein corona formation, risk assessment, and individualized therapy for improved therapeutic outcomes have also been discussed.

Nanoinformatics and biomolecular nanomodeling: a novel move en route for effective cancer treatment

Empowering role of nanoinformatics in design and elucidation of nanoparticles for effective cancer treatment has made this field a fascinating area for researchers, inspiring them to enhance up the quality and efficacy of existing anticancer medicines. Theoretical and computational modeling is being seen as a forefront solution for problems related to surface chemistry, optimized geometry, or other properties in nanoparticle designing and drug delivery. The current review aims to acquaint with the insight story of the incubation of in silico tools and techniques in nanotechnology to develop better anticancer nanomedicines. The review also recapitulates the assets and liabilities of this field and present an outline of existing inventiveness and endeavors of nanoinformatics. We propose how nanoinformatics could hasten up the advancements in anticancer nanomedicines through use of computational tools, nanoparticles repositories & various modeling and simulation methods.

Streptavidin-exposed magnetic nanoparticles for lectin magnetic separation (LMS) of Staphylococcus aureus prior to three quantification strategies

A lectin magnetic separation (LMS) method for Staphylococcus aureus (S. aureus) was developed with the aim to improve the efficiency of magnetic nanoparticles and to expand the scope of bacterial recognition. Poly(ethylene glycol) (PEG)-mediated magnetic nanoparticles modified with streptavidin (MNP-PEG-SA) were synthesized and then applied to a two-step LMS based on the use of wheat germ agglutinin (WGA). Three specific methods for S. aureus detection (suitable for different requirements including detection time and sensitivity) were designed. The new LMS has improved anchoring efficiency (compared to two-step LMS methods) and requires a reduced number of magnetic particles. The Baird-Parker (B-P) method can detect S. aureus with a detection limit of 3 × 10⁰ CFU·mL^-1 within 15 h; the polymerase chain reaction (PCR) method can be finished within 4 h, with the lowest detection limit (LOD) of 3 × 10² CFU·mL^-1. The LOD of HRP-pig IgG-based colorimetric method is 3 × 10⁵ CFU·mL^-1, and the method only lasts for 2 h. If combined with specific detection methods, it meets different needs for rapid detection of S. aureus. Graphical abstractSchematic representation of lectin magnetic separation (LMS) based on biotin-wheat germ agglutinin (WGA) and poly (ethylene glycol) (PEG)-mediated streptavidin-modified magnetic nanoparticles (MNP-PEG-SA) and three different quantification strategies (including B-P culture assay, PCR assay, and colorimetric assay) for S. aureus.

Lectins and Nanostructured Drug Delivery Systems

The advances and the impact of nanostructured systems on therapeutics constitute a constantly evolving reality. New strategies have been developed for drug delivery control and for directing these systems to the targeted site improving the therapy. In this commentary, the lectins are briefly reviewed; their fundamentals and the proposed applications as ligands in nanostructured drug delivery systems are discussed.

Characterization of surface binding sites in glycoside hydrolases: A computational study

Structural properties of carbohydrate surface binding sites (SBSs) were investigated with computational methods. Eighty-five SBSs of 44 enzymes in 119 Protein Data Bank (PDB) files were collected as a dataset. On the basis of SBSs shape, they were divided into 3 categories: flat surfaces, clefts, and cavities (types A, B, and C, respectively). Ligand varieties showed the correlation between shape of SBSs and ligands size. To reduce cut-off differences in each SBSs with different ligand size, molecular docking were performed. Molecular docking results were used to refine SBSs classification and binding sites cut-off. Docking results predicted putative ligands positions and displayed dependence of the ligands binding mode to the structural type of SBSs. Physicochemical properties of SBSs were calculated for all docking results with YASARA Structure. The results showed that all SBSs are hydrophilic, while their charges could vary and depended to ligand size and defined cut-off. Surface binding sites type B had highest average values of solvent accessible surface area. Analysis of interactions showed that hydrophobic interactions occur more than hydrogen bonds, which is related to the presence of aromatic residues and carbohydrates interactions.

Design of Nanoparticle-Based Carriers for Targeted Drug Delivery

Nanoparticles have shown promise as both drug delivery vehicles and direct antitumor systems, but they must be properly designed in order to maximize efficacy. Computational modeling is often used both to design new nanoparticles and to better understand existing ones. Modeled processes include the release of drugs at the tumor site and the physical interaction between the nanoparticle and cancer cells. In this article, we provide an overview of three different targeted drug delivery methods (passive targeting, active targeting and physical targeting), compare methods of action, advantages, limitations, and the current stage of research. For the most commonly used nanoparticle carriers, fabrication methods are also reviewed. This is followed by a review of computational simulations and models on nanoparticle-based drug delivery.

Carbohydrate-protein interactions: molecular modeling insights

The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses.

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r(2) of 0.71 and root-mean-squared-error of 1.25 kcal/mol (N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

Understanding selective molecular recognition in integrated carbon nanotube-polymer sensors by simulating physical analyte binding on carbon nanotube-polymer scaffolds

Macromolecular scaffolds made of polymer-wrapped single-walled carbon nanotubes (SWCNTs) have been explored recently (Zhang et al., Nature Nanotechnology, 2013) as a new class of molecular-recognition motifs. However, selective analyte recognition is still challenging and lacks the underlying fundamental understanding needed for its practical implementation in biological sensors. In this report, we combine coarse-grained molecular dynamics (CGMD) simulations, physical adsorption/binding theories, and photoluminescence (PL) experiments to provide molecular insight into the selectivity of such sensors towards a large set of biologically important analytes. We find that the physical binding affinities of the analytes on a bare SWCNT partially correlate with their distribution coefficients in a bulk water/octanol system, suggesting that the analyte hydrophobicity plays a key role in determining the binding affinities of the analytes considered, along with the various specific interactions between the analytes and the polymer anchor groups. Two distinct categories of analytes are identified to demonstrate a complex picture for the correlation between optical sensor signals and the simulated binding affinities. Specifically, a good correlation was found between the sensor signals and the physical binding affinities of the three hormones (estradiol, melatonin, and thyroxine), the neurotransmitter (dopamine), and the vitamin (riboflavin) to the SWCNT-polymer scaffold. The four amino acids (aspartate, glycine, histidine, and tryptophan) and the two monosaccharides (fructose and glucose) considered were identified as blank analytes which are unable to induce sensor signals. The results indicate great success of our physical adsorption-based model in explaining the ranking in sensor selectivities. The combined framework presented here can be used to screen and select polymers that can potentially be used for creating synthetic molecular recognition motifs.

Hydrophobic noncovalent interactions of inosine-phenylalanine: a theoretical model for investigating the molecular recognition of nucleobases

Understanding the molecular recognition process of nucleobases is one of the greatest challenges for both computational chemistry and biophysics fields. In fact, our results point out that it is a hard task to take into account the hydrophobic interactions, such as π-π and T-stacking interactions, by theoretical calculations using conventional force fields due to quantum effects of hyperconjugation and electronic correlation. In this line, our findings put in evidence that simple modifications in the Lennard-Jones potential can improve theoretical predictions in scenarios where hydrophobic interactions can drive the molecular recognition.

Triggering effect of N-acetylglucosamine on retarded drug release from a lectin-anchored chitosan nanoparticles-in-microparticles system

The aim of this study was to investigate the use of N-acetylglucosamine (NAG) to accelerate drug release from a lectin-modified carrier. A wheat germ agglutinin (WGA)-anchored salmeterol xinafoate (SalX)-loaded nanoparticles-in-microparticles system (NiMS) was prepared with an ionotropic gelation technique combined with a spray drying method. The formulated microparticles were spherical, with diameters ranging mainly from 2 to 8 μm; the drug entrapment efficiency was >70% (w/w), and the loading capacity was approximately 8% (w/w). Drug release from WGA-SalX-NiMS, within the first 4h, was approximately 30% less than that from SalX-NiMS, indicating an effect of lectin-modification to retard drug release from the NiMS. Due to "sugar-lectin" interactions, drug release from WGA-SalX-NiMS was substantially increased after the addition of NAG to the release medium. However, no significant influence of NAG was observed on the drug release profile of SalX-NiMS without WGA anchorage. The characteristics of NAG-WGA interaction may provide valuable insights into the "triggering-effects" of specific sugars on drug release from lectin-anchored carriers. These results suggest that it is possible to control drug release from a lectin-anchored drug delivery system using a specific sugar, and that the designed novel WGA-SalX-NiMS may be a suitable formulation for chronotherapy of asthma.

In silico mutagenesis and docking study of Ralstonia solanacearum RSL lectin: performance of docking software to predict saccharide binding

In this study, in silico mutagenesis and docking in Ralstonia solanacearum lectin (RSL) were carried out, and the ability of several docking software programs to calculate binding affinity was evaluated. In silico mutation of six amino acid residues (Agr17, Glu28, Gly39, Ala40, Trp76, and Trp81) was done, and a total of 114 in silico mutants of RSL were docked with Me-α-L-fucoside. Our results show that polar residues Arg17 and Glu28, as well as nonpolar amino acids Trp76 and Trp81, are crucial for binding. Gly39 may also influence ligand binding because any mutations at this position lead to a change in the binding pocket shape. The Ala40 residue was found to be the most interesting residue for mutagenesis and can affect the selectivity and/or affinity. In general, the docking software used performs better for high affinity binders and fails to place the binding affinities in the correct order.

Lectin from Crataeva tapia bark exerts antitumor, anti-inflammtory and analgesic activities

Crataeva tapia bark lectin was evaluated for its antitumor activity against sarcoma 180 in Swiss albino mice. The anti-inflammatory and analgesic properties were investigated in models of inflammation and nociception. The anti-inflammatory assay was induced by carrageenan induced peritonitis and the analgesic activity was induced by acetic acid-induced writhing response. The lectin presents low toxicity with a LD₅₀ of 2,500 mg/kg body weight and significant antitumor activity causing inhibition of tumor growth. The lectin also promoted significant reduction (35.4%) in the number of neutrophil migration induced by carrageenan. Concerning its analgesic property, the lectin inhibits abdominal contractions induced by acetic acid. The current results revealed a lectin with significant antitumoral, anti-inflammatory and antinociceptive activities. Further investigations to unveil the exact mechanisms are needed.

Biomolecular conjugation inside synthetic polymer nanopores via glycoprotein-lectin interactions

We demonstrate the supramolecular bioconjugation of concanavalin A (Con A) protein with glycoenzyme horseradish peroxidase (HRP) inside single nanopores, fabricated in heavy ion tracked polymer membranes. Firstly, the HRP-enzyme was covalently immobilized on the inner wall of the pores using carbodiimide coupling chemistry. The immobilized HRP-enzyme molecules bear sugar (mannose) groups available for the binding of Con A protein. Secondly, the bioconjugation of Con A on the pore wall was achieved through its biospecific interactions with the mannose residues of the HRP enzyme. The immobilization of biomolecules inside the nanopore leads to the reduction of the available area for ionic transport, and this blocking effect can be exploited to tune the conductance and selectivity of the nanopore in aqueous solution. Both cylindrical and conical nanopores were used in the experiments. The possibility of obtaining two or more conductance states (output), dictated by the degree of nanopore blocking resulted from the different biomolecules in solution (input), as well as the current rectification properties obtained with the conical nanopore, could also allow implementing information processing at the nanometre scale. Model simulations based on the transport equations further verify the feasibility of the sensing procedure that involves concepts from supramolecular chemistry, molecular imprinting, recognition, and nanotechnology.

NMR and MD investigations of human galectin-1/oligosaccharide complexes

The specific recognition of carbohydrates by lectins plays a major role in many cellular processes. Galectin-1 belongs to a family of 15 structurally related beta-galactoside binding proteins that are able to control a variety of cellular events, including cell cycle regulation, adhesion, proliferation, and apoptosis. The three-dimensional structure of galectin-1 has been solved by x-ray crystallography in the free form and in complex with various carbohydrate ligands. In this work, we used a combination of two-dimensional NMR titration experiments and molecular-dynamics simulations with explicit solvent to study the mode of interaction between human galectin-1 and five galactose-containing ligands. Isothermal titration calorimetry measurements were performed to determine their affinities for galectin-1. The contribution of the different hexopyranose units in the protein-carbohydrate interaction was given particular consideration. Although the galactose moiety of each oligosaccharide is necessary for binding, it is not sufficient by itself. The nature of both the reducing sugar in the disaccharide and the interglycosidic linkage play essential roles in the binding to human galectin-1.

PLecDom: a program for identification and analysis of plant lectin domains

PLecDom is a program for detection of Plant Lectin Domains in a polypeptide or EST sequence, followed by a classification of the identified domains into known families. The web server is a collection of plant lectin domain families represented by alignments and profile Hidden Markov Models. PLecDom was developed after a rigorous analysis of evolutionary relationships between available sequences of lectin domains with known specificities. Users can test their sequences for potential lectin domains, catalog the identified domains into broad substrate classes, estimate the extent of divergence of new domains with existing homologs, extract domain boundaries and examine flanking sequences for further analysis. The high prediction accuracy of PLecDom combined with the ease with which it handles large scale input, enabled us to apply the program to protein and EST data from 48 plant genome-sequencing projects in various stages of completion. Our results represent a significant enrichment of the currently annotated plant lectins, and highlight potential targets for biochemical characterization. The search algorithm requires input in fasta format and is designed to process simultaneous connection requests from multiple users, such that huge sets of input sequences can be scanned in a matter of seconds. PLecDom is available at http://www.nipgr.res.in/plecdom.html.

Aromatic-carbohydrate interactions: an NMR and computational study of model systems

The interactions of simple carbohydrates with aromatic moieties have been investigated experimentally by NMR spectroscopy. The analysis of the changes in the chemical shifts of the sugar proton signals induced upon addition of aromatic entities has been interpreted in terms of interaction geometries. Phenol and aromatic amino acids (phenylalanine, tyrosine, tryptophan) have been used. The observed sugar-aromatic interactions depend on the chemical nature of the sugar, and thus on the stereochemistries of the different carbon atoms, and also on the solvent. A preliminary study of the solvation state of a model monosaccharide (methyl beta-galactopyranoside) in aqueous solution, both alone and in the presence of benzene and phenol, has also been carried out by monitoring of intermolecular homonuclear solvent-sugar and aromatic-sugar NOEs. These experimental results have been compared with those obtained by density functional theory methods and molecular mechanics calculations.

Double Assembly Composed of Lectin Association with Columnar Molecular Assembly of Cyclic Tri-β-peptide Having Sugar Units

A novel double assembly was prepared by association between a columnar molecular assembly of cyclic tri-beta-peptides having sugar units and lectins. The NMR, FT-IR, and circular dichroism (CD) spectroscopy as well as computational calculations revealed that this compound took a flat and C3 symmetrical conformation and that the amide N-H and C=O groups protruded vertically to the ring plane. This disk-shaped molecule stacked one by one to form a columnar structure via intermolecular hydrogen bonds between the amide groups. WGA lectin moderately bound to this columnar assembly to form a double assembly. Another lectin (Con A) disturbed the columnar structure upon strong binding, and RCA lectin showed no binding. Fluorescence spectroscopy revealed that the association between WGA lectin and columnar assembly of cyclic glycopeptide could be achieved due to the high density of the hydroxyl groups on the assembly surface (cluster effects). Interestingly, after cross-linking the lectins bound to the columnar assembly (the double assembly) by glutaraldehyde, the core column of cyclic tri-beta-peptides could be washed away to leave the protein nanotube.

Purification and biological effects of Araucaria angustifolia (Araucariaceae) seed lectin

This paper describes the purification and characterization of a new N-acetyl-d-glucosamine-specific lectin from Araucaria angustifolia (AaL) seeds (Araucariaceae) and its anti-inflammatory and antibacterial activities. AaL was purified using a combination of affinity chromatography on a chitin column and ion exchange chromatography on Sephacel-DEAE. The pure protein has 8.0kDa (SDS-PAGE) and specifically agglutinates rabbit erythrocytes, effect that was independent of the presence of divalent cations and was inhibited after incubation with glucose and N-acetyl-d-glucosamine. AaL showed antibacterial activity against Gram-negative and Gram-positive strains, shown by scanning electron microscopy. AaL, intravenously injected into rats, showed anti-inflammatory effect, via carbohydrate site interaction, in the models of paw edema and peritonitis. This lectin can be used as a tool for studying bacterial infections and inflammatory processes.

SLICK − Scoring and Energy Functions for Protein−Carbohydrate Interactions

Protein-carbohydrate interactions are increasingly being recognized as essential for many important biomolecular recognition processes. From these, numerous biomedical applications arise in areas as diverse as drug design, immunology, or drug transport. We introduce SLICK, a package containing a scoring and an energy function, which were specifically designed to predict binding modes and free energies of sugars and sugarlike compounds to proteins. SLICK accounts for van der Waals interactions, solvation effects, electrostatics, hydrogen bonds, and CH...pi interactions, the latter being a particular feature of most protein-carbohydrate interactions. Parameters for the empirical energy function were calibrated on a set of high-resolution crystal structures of protein-sugar complexes with known experimental binding free energies. We show that SLICK predicts the binding free energies of predicted complexes (through molecular docking) with high accuracy. SLICK is available as part of our molecular modeling package BALL (www.ball-project.org).

Interactions of 5-deazapteridine derivatives with Mycobacterium tuberculosis and with human dihydrofolate reductases

There are major differences between the structures of human dihydrofolate reductase (hDHFR) and Mycobacterium tuberculosis dihydrofolate reductase (mtDHFR). These differences may allow us to design more selective mtDHFR inhibitors. In this paper we study the reactions of six different compounds derived from 5-deazapteridine with human and bacterial enzymes. Results suggest that the addition of hydrophobic groups to the aminophenyl ring would increase mtDHFR-inhibitor affinity and selectivity.

Survey of the year 2004 commercial optical biosensor literature

The year 2004 represents a milestone for the biosensor research community: in this year, over 1000 articles were published describing experiments performed using commercially available systems. The 1038 papers we found represent an approximately 10% increase over the past year and demonstrate that the implementation of biosensors continues to expand at a healthy pace. We evaluated the data presented in each paper and compiled a 'top 10' list. These 10 articles, which we recommend every biosensor user reads, describe well-performed kinetic, equilibrium and qualitative/screening studies, provide comparisons between binding parameters obtained from different biosensor users, as well as from biosensor- and solution-based interaction analyses, and summarize the cutting-edge applications of the technology. We also re-iterate some of the experimental pitfalls that lead to sub-optimal data and over-interpreted results. We are hopeful that the biosensor community, by applying the hints we outline, will obtain data on a par with that presented in the 10 spotlighted articles. This will ensure that the scientific community at large can be confident in the data we report from optical biosensors.