Howard J Feldman's research while affiliated with Samuel Lunenfeld Research Institute and other places

... Specifically, ensembles are: SH3-1 (PED00156, 100 confs.) an unfolded ensemble was optimized from a random pool generated by TraDES; 43,44 SH3-2 (PED00157, 100 conf.) in which the conformational pool was generated by EN-5 . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. ...

... System preparation. To prepare for membrane association simulations, WASP and N-WASP fragments consisting of residues 133-255 and 116-220, respectively, were assigned initial conformations using TraDES [38]. These fragments were first simulated in water (with 100 mM NaCl) for 500 ns in quadruplicate with different random number seeds on GPUs using pmemd.cuda ...

... Of late, several broad classes of different approaches have been developed for this purpose. 31 Methods based on pre-existing random coil library and simple volume exclusions (examples: Flexible Meccano, 32 TraDES, 33 BEGR 34 ) are often used to create an initial exhaustive pool of conformations, which are further processed to produce refined ensembles upon combining with experimental constraints. 30,35−40 These methods, though purely statistical in nature, provide a computationally efficient approach to calculate IDP conformational ensembles that are consistent with experimental data. ...

... Both could be used as tools for homology model formation and 3D structures are produced containing post translational modified amino acids. FOLDTRAJ helps in generating plausible probabilistic protein conformers [42]. ...

... To construct an initial model for tau K19, the helical segments were built using pymol (https://pymol.org/) while the linkers and disordered tails were generated using TRADES (Feldman & Hogue, 2002). The initial depth of the helices was adjusted to have complete burial of the nonpolar sidechains in the hydrophobic region of a membrane. ...

... 18 The presence of azobenzenes is shown to impact the structure and properties of nucleic acids, peptides, and antibody-antigen interactions and has found applications as biosensors. [19][20][21][22] The incorporation of azobenzenes in dental restorative materials is highly promising due to their unique antibacterial properties. 16,23 An acrylated hydroxyazobenzene (AHA) coating synthesized by the Nair lab at the University of Colorado School of Dental Medicine, Aurora, Colo., USA, completely prevented SM growth both on the surface of the coating and in the surrounding media. ...

... Amino acid composition (AAC) is the basic method to identify a protein sequence in which each kind of amino acid is represented by the percentage [12]. Several studies exist in the literature that used AAC to encode the proteins [13][14][15][16] In order to increase the information beyond amino acid composition, the concept of pseudoamino acid composition (PseAAC) was proposed by Chou [17]. There are also many studies that utilize PseAAC exists in the literature [17][18][19][20][21][22][23][24]. ...

... Consequently, several attempts have been made to combine weakly axiomatised chemical ontologies with fast cheminformatics (algorithmic) chemical graph operations in hybrid systems that are able to expose some of the chemical knowledge in ontology axioms, but ultimately perform performance-intensive operations such as graph substructure or superstructure matching algorithmically, that is, outside of the ontology. One of the first such attempts was "CO" [15], an ontology of 260 classes based on combinations of chemical functional groups generated with the cheminformatics "checkmol" software. Later, we have developed this into the more complete approach reported in [9] which used a custom general molecular fragmentation algorithm to enumerate all the parts of each molecular entity and assert those as axioms in a resulting OWL ontology. ...

... Multiple methods for the automatic assignment of atomic bonding, bond types and atom charges have been proposed over the years [2][3][4][5][6][7][8][9][10][11] with greater focus being placed on organic molecules. Furthermore, the actual implementations of the described algorithms are often not openly available thus complicating their reuse or modification. ...

... Often, researchers solve the problem of identifying the functional features of domains in ligands (peptides, proteins) that bind to nucleic acids or proteins. Thus, we pay attention to a number of works that propose machine learning and bioinformatics tools for analyzing the binding of proteins to nucleic acids and peptides [138]; proteins binding to peptides [139]; amino acid residues binding to DNA, RNA, and proteins [140]; and prediction of small molecule binding sites in proteins [141]. On the other hand, we analyzed DNA segments with which short biologically active peptides bind. ...