Evaluation of Selected Classical Force Fields for Alchemical Binding Free Energy Calculations of Protein-Carbohydrate Complexes

Protein–carbohydrate recognition is crucial in many vital biological processes including host–pathogen recognition, cell-signaling, and catalysis. Accordingly, computational prediction of protein–carbohydrate binding free energies is of enormous interest for drug design. However, the accuracy of current force fields (FFs) for predicting binding free energies of protein–carbohydrate complexes is not well understood owing to technical challenges such as the highly polar nature of the complexes, anomerization, and conformational flexibility of carbohydrates. The present study evaluated the performance of alchemical predictions of binding free energies with the GAFF1.7/AM1-BCC and GLYCAM06j force fields for modeling protein–carbohydrate complexes. Mean unsigned errors of 1.1 ± 0.06 (GLYCAM06j) and 2.6 ± 0.08 (GAFF1.7/AM1-BCC) kcal·mol^–1 are achieved for a large data set of monosaccharide ligands for <i>Ralstonia solanacearum</i> lectin (RSL). The level of accuracy provided by GLYCAM06j is sufficient to discriminate potent, moderate, and weak binders, a goal that has been difficult to achieve through other scoring approaches. Accordingly, the protocols presented here could find useful applications in carbohydrate-based drug and vaccine developments

Tartaric acid synthetic derivatives effect on phytopathogenic bacteria

Received: February 19th, 2022 ; Accepted: May 25th, 2022 ; Published: June 27th, 2022 ; Correspondence: [email protected] scientific goals of current research were devoted to targeted derivatization of natural tartaric acid (TA) for the enhancement of antimicrobial properties of it such as like the effects of them on multi-drug resistant phytopathogenic bacteria, depends to their structure features and the genetic parameters of studied microorganisms. The main utilitarian goal is to develop new class of antimicrobial biodegradable compounds with possible prospective application as moresafe alternative to traditional antibiotics applicable for: agriculture, horticulture, food industry as well as in medicine. These compounds were developed in basic research laboratory: ‘Agrarian Pesticide Creation and The Quality Control’ at National Polytechnic University of Armenia (NPUA). TA and tartrates are safe antimicrobial food additives. According to the results of in vitro studies, the synthesized cyclohexyl-, benzyl- and phenyl- derivatives of it in the form of amine complex salts (correspondently CAS, BAS and PhAS) and cyclic imides (correspondently CI, BI and PhI) are effective against the model multidrug resistant strains of Gram-negative microorganisms. Bactericidal effects of TA derivatives were demonstrated on 19 model native soil strains of phytopathogenic Xanthomonas beticola (6 strains), X. vesicatoria and Pseudomonas syringae (13 strains) representatives, which are differing in antibiotic resistance. Regarding the transformation results, the absence of transfer of resistance to TA imides and amine complex salts by plasmids, makes them promising objects for further research. Primary studies have not shown any antibacterial effect on various nonpathogenic soil bacteria (Pseudomonas chlororaphis, P. taetrolens, etc.). The described compounds are recommended for further more detailed toxicological studies

Facettes de glycobioinformatique (applications à l'étude des interactions protéines-sucres)

Le travail décrit dans ce manuscrit rassemble les résultats obtenus au cours de ma thèse de doctorat. Ils s'inscrivent dans le domaine de la glycobioinformatique. Ils ont impliqué des développements de bases de données structurales et des applications en modélisation moléculaire des interactions protéines-sucres. Les méthodes de modélisation moléculaire ont été utilisées dans la reconstruction et dans la prédiction des structures tridimensionnelles de polysaccharides et d'oligosaccharides, ces dernières étant également établies par une approche de type haut-débit par application d'un algorithme génétique à des fins de minimisation énergétique. Les données ainsi générées ont été organisées sous la forme de bases de données relationnelles, proprement annotées (PolySca3DB et BiOligo) qui sont en libre accès pour consultation sur internet. Ces méthodes de modélisation moléculaire ont été appliquées à la caractérisation, par RMN en solution, des conformations de basse énergie d'une souche pathogène d'un polysaccharide de la bactérie E. coli. D'autres bactéries pathogènes de type gram négatif, interagissent avec des oligosaccharides par l'intermédiaire de protéines secrétées, telles que des lectines. Nous avons testé, au travers de l'utilisation de méthodes d'amarrage moléculaire, la possibilité d'identifier de manière automatique, la nature de ces interactions, en prenant comme cibles des épitopes oligosaccharidiques fucosylés. Les résultats de ces recherches ont été comparés, de manière critique, à ceux issus de l'application de bio-puces à sucres et de calorimétrie isotherme de titration. Les conclusions et perspectives de ces travaux sont présentées dans un article de revue consacré à l'application des méthodes de chimie computationnelle dans l'étude des interactions protéines-glucides qui viennent compléter l'arsenal des outils dédiés au champs de recherche couvert par la glycobiologie structurale et moléculaire.This thesis presents an account of two important facets of glycobioinformatics, comprising database development and molecular modeling of 3D structures of carbohydrates alongside the simulation of protein-carbohydrate interactions. Classical molecular modeling techniques were used to reconstruct 3D polysaccharide structures from experimentally determined atomic coordinates, or known starting points about their structures were used as guidelines to model them. A genetic algorithm search was employed as a high-throughput technique to characterize low energy conformers of bioactive oligosaccharides. The data generated were organized into two open-access relational databases, namely, PolySac3DB and BiOligo, for use by the scientific community. The validation of the molecular techniques used were performed using solution phase NMR experiments on four entero aggregative pathogenic E. coli strains, and were found to be robust and realistic. Further, the impact of the presentation of human fucosylated oligosaccharide epitopes to lectins from opportunistic gram negative bacteria, was investigated in a screening study using molecular docking studies, which could help in evaluating the feasibility of using automated docking procedures in such instances as well as deciphering binding data from glycan array experiments and also correlated to isothermal calorimetry data. On comparison with high-resolution experimental crystal complexes, automated docking was found to delineate the present level of applicability, while emphasizing the need of constant monitoring and possible filtering of the results obtained. Finally, a review of the present status of the computational aspects of protein-carbohydrate interaction studies is discussed in the perspectives of using molecular modeling and simulation studies to probe this aspect of molecular and structural glycobiology.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Analysis and prediction of cancerlectins using evolutionary and domain information

<p>Abstract</p> <p>Background</p> <p>Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins.</p> <p>Results</p> <p>All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy.</p> <p>Conclusion</p> <p>BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at <url>http://www.imtech.res.in/raghava/cancer_pred/</url></p

Toward the use of Ankyrins and Affibodies as Scaffolds for Glycan Binding Proteins: A Directed Evolution and Computational Approach

Glycans are present in all domains of life and have broad physiological functions that are implicated not only in normal, healthy biological functions, but also in various diseases such as cancer, making them diagnostic and therapeutic targets. The study of glycans is currently limited in part by a lack of specific tools to target glycans of interest, with available glycan binding proteins often having low affinity and specificity. Thus, there is a need to develop new tools that can accelerate the development of novel glycan binding proteins. Here we discuss the application of directed evolution by mRNA display using new binding protein scaffold libraries, in combination with molecular dynamics simulations, as tools for developing novel glycan binding proteins. We designed mRNA display compatible libraries of affibodies and designed ankyrin repeat protein (DARPin) to be screened for binding against sialyl Lewis X (SLeX), a tumour associated carbohydrate antigen that is overexpressed on cell surfaces of various cancers. We developed an improved enzymatic synthesis protocol for SLeX and describe a click chemistry immobilization method coupled with a fluorescent-based lectin assay to test glycan immobilization. The affibody library was successfully created for future selection, whereas the DARPin library assembly needs to be optimized further. Future mRNA display selection should also be complemented with the molecular dynamics probing method developed here. We demonstrate that short, computationally inexpensive probing simulations were able to identify the binding site of a nucleoside sugar dTDP-Qui3N in an ankyrin domain of an N-formyltransferase. Future approaches could simulate protein variants selected by display methods to identify glycan-protein interactions that may be used to improve the protein design. In combination, the tools developed here provide a framework to accelerate the discovery and production of future glycan binding proteins with applications in cancer diagnostics and therapeutics

Tools for decoding the structure-function relationships of biopolymers in nanotechnology and glycobiology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 232-252).In this thesis, new tools have been developed for decoding structure-function relationships governing complex biopolymers that have emerged as key players in biology, biotechnology, and medicine. Specifically: (1.) The first part of this thesis addresses the structure-function relationship of synthetic biodegradable plastics that are at the forefront of nanotechnology for spatiotemporally-regulated targeting and sustained release of drugs to treat Cancer and other chronic diseases. A Voxel-based 3-D platform for accurately simulating all phases of polymeric nanoparticle erosion and drug release is introduced. Using the developed Voxel platform, the release of anti-inflammatory and anti-cancer drugs such as doxorubicin and dexamethasone from poly lactic-co-glycolic acid (PLGA) nanoparticles is precisely predicted. The Voxel platform emerges as a powerful and versatile tool for deducing the dynamics in interplay of polymer, drug, and water molecules, thus permitting the rational design of optimal nanotechnology treatments for cancer. (2.) The second part of this thesis is focused on development of tools to elucidate structure-function relationships of complex polysaccharides (glycans) that specifically interact with proteins to modulate a host of biological processes including growth, development, angiogenesis, cancer, anticoagulation, microbial pathogenesis, and viral infections. First, towards the fine structure determination of complex linear glycans (glycosaminoglycans or GAGs), enzymatic tools are developed for both depolymerizing GAGs at specific linkages and for effectively modulating their functional groups. Specifically, the development and integrated biochemical-structural characterization of the Chondroitinase ABC-II enzyme that depolymerizes dermatan sulfate and chondroitin sulfate GAGs (CSGAGs), and the 6-0- Sulfatase and N-Sulfamidase enzymes that de-sulfate functional groups on heparin and heparan sulfate GAGs (HSGAGs) are described. Second, the interaction of branched glycans with proteins is analyzed using the interplay of Influenza virus surface proteins (mainly Hemagglutinin and Neuraminidase) with host cell surface sialylated glycan receptors as a model system. For this purpose, the novel triple reassortant "Swine Flu" pandemic virus (or 2009 HINI virus) is studied. Finally, in order to overcome the challenges facing protein structure prediction in the "Twilight Zone" of low homology that is rampant in glycan-binding protein (lectin) families, a new tool is introduced for modeling the 3-D structure of proteins directly from sequence. Specifically, it is identified that protein core atomic interaction networks (PCAINs) are evolutionarily non-tinkered and fold-conserved, and this finding is utilized towards assignment of folds, structures, and potential glycan substrates to lectin sequences. It is further demonstrated that the developed tool is effective universally; thus emerging as a promising platform for generic protein sequence-to-structure and function mapping in a broad spectrum of biological applications.by Venkataramanan Soundararajan.Ph.D