ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network

Decoding the conformational binding of drug mixtures on ovalbumin: An integrated multimodal network

This research addresses the crucial necessity for a deeper understanding of the binding interactions between surfactants and proteins, with a specific focus on ovalbumin. Considering ovalbumin's role in diverse biochemical processes, it remains a subject of significant interest for drug discovery and design. To fill existing knowledge gaps, we investigated the binding interaction between dicloxacillin and cetyltrimethylammonium bromide (CTAB) on ovalbumin, employing a comprehensive approach that combines computational modeling with experimental validations. Using the ezPocket tool, the computational phase predicted ten relevant binding sites on ovalbumin's surface. The isobologram combination index (CI) heatmap strongly suggested a complex interplay of antagonistic and synergistic effects. Besides, a conformational drug-drug interaction network was proposed to explore the stability of the surfactant mixture within specific binding sites of ovalbumin, revealing a dynamic landscape of suggested antagonist effects. Experimental validations through UV-vis, Fluorescence, and circular dichroism (CD) spectroscopy further corroborated the computational findings, confirming the formation of stable complexes. Finally, this study not only advances our comprehension of ovalbumin's interactions with surfactants but also offers a multidimensional perspective and an advanced methodological framework for efficient therapeutic strategies, opening new avenues for future applications in drug development and applied biochemistry.

A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network

Metabolism results from enzymatic- and non-enzymatic interactions of several molecules, is easily parameterized with the dissociation constant and occurs via biochemical networks. The dissociation constant is an empirically determined parameter and cannot be used directly to investigate in silico models of biochemical networks. Here, we develop and present an algorithm to define, compute and assess the relevance of the probable dissociation constant for every reaction of a biochemical network. The reactants and reactions of this network are modelled by a stoichiometry number matrix. The algorithm computes the null space and then serially generates subspaces by combinatorially summing the spanning vectors that are non-trivial and unique. This is done until the terms of each row either monotonically diverge or form an alternating sequence whose terms can be partitioned into subsets with almost the same number of oppositely signed terms. For a selected null space-generated subspace the algorithm utilizes several statistical and mathematical descriptors to select and bin terms from each row into distinct outcome-specific subsets. The terms of each subset are summed, mapped to the real-valued open interval [Formula: see text] and used to populate a reaction-specific outcome vector. The p1-norm for this vector is then the probable dissociation constant for this reaction. These steps are continued until every reaction of a modelled network is unambiguously annotated. The assertions presented are complemented by computational studies of a biochemical network for aerobic glycolysis. The fundamental premise of this work is that every row of a null space-generated subspace is a valid reaction and can therefore, be modelled as a reaction-specific sequence vector with a dimension that corresponds to the cardinality of the subspace after excluding all trivial- and redundant-vectors. A major finding of this study is that the row-wise sum or the sum of the terms contained in each reaction-specific sequence vector is mapped unambiguously to a positive real number. This means that the probable dissociation constants, for all reactions, can be directly computed from the stoichiometry number matrix and are suitable indicators of outcome for every reaction of the modelled biochemical network. Additionally, we find that the unambiguous annotation for a biochemical network will require a minimum number of iterations and will determine computational complexity.