Recent advances in computational methods for biosensor design

Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost-effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast-growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamics simulation, quantum mechanics calculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.

Antibody-mediated biorecognition of myelin oligodendrocyte glycoprotein: computational evidence of demyelination-related epitopes

Antigen-antibody interaction is crucial in autoimmune disease pathogenesis, as multiple sclerosis and neuromyelitis optica. Given that, autoantibodies are essential biomolecules, of which the myelin oligodendrocyte glycoprotein (MOG) can figure as a target. Here we combined Molecular Dynamics (MD), Steered Molecular Dynamics (SMD), and Atomic Force Microscope (AFM) to detail MOG recognition by its specific antibody. The complex model consisted of the MOG external domain interacting with an experimental anti-MOG antibody from the Protein Data Bank (1PKQ). Computational data demonstrated thirteen MOG residues with a robust contribution to the antigen-antibody interaction. Comprising five of the thirteen anchor residues (ASP₁₀₂, HIS₁₀₃, SER₁₀₄, TYR₁₀₅, and GLN₁₀₆), the well-known MOG_92–106 peptide in complex with the anti-MOG was analysed by AFM and SMD. These analyses evidenced similar force values of 780 pN and 765 pN for computational and experimental MOG_92–106 and anti-MOG detachment, respectively. MOG_92–106 was responsible for 75% of the total force measured between MOG external domain and anti-MOG, holding the interaction with the antibody. The antigen-antibody binding was confirmed by Surface Plasmon Resonance (SPR) measurements. Combined approaches presented here can conveniently be adjusted to detail novel molecules in diseases research. This can optimize pre-clinical steps, guiding experiments, reducing costs, and animal model usage.

Resistance determination of the ACCase-inhibiting herbicide of clodinafop propargyl in Avena ludoviciana (Durieu), and study of their interaction using molecular docking and simulation

Structural mutations providing herbicide resistance may cause a modification of the three dimensional structure of a protein which will lead to a decrease in the herbicide efficacy. Wild oat (Avena ludoviciana Durieu.) is an increasingly disruptive weed in areas of intensive cereal production, thus the aim of this research was to identify mutations conferring resistance to ACCase-inhibitor herbicides at greenhouse, laboratory and in silico scales. Among the selected biotypes, No. 3 in the position 1781 (Ile1781-Leu) and No. 14 in the position 2041 (Ile2041-Asn), showed resistance to ACCase-inhibitor. The above mutations were confirmed using the specific primers and PCR-based methods. Analysis of molecular docking indicated that residues of Trp1948 and Pro2001 are important in the binding site and showed remarkable variation in the mutation types. Using molecular dynamic simulation analysis, we demonstrated that mutation types changed the conformation of the enzyme. These changes resulted in compressed conformation in the active site, which limited the availability of binding herbicide-enzyme. In present, no crystallography molecular structure and modeling reported on the ACCase of plants and this study investigated interactions of clodinafop propargyl and ACCase CT domain in A. ludoviciana by modeling, docking and simulations for the first time. Totally, bioinformatics analysis as well as PCR-based method confirmed that herbicide resistance conferred by nucleotide mutations in the gene sequence.

Unbinding pathway energy of glyphosate from the EPSPs enzyme binding site characterized by Steered Molecular Dynamics and Potential of Mean Force

The quantification of herbicides in the environment, like glyphosate, is extremely important to prevent contamination. Nanobiosensors stands out in the quantization process, because of the high selectivity, sensitivity and short response time of the method. In order to emulate the detection of glyphosate using a specific nanobiossensor through an Atomic Force Microscope (AFM), this work carried out Steered Molecular Dynamics simulations (SMD) in which the herbicide was unbinded from the active site of the enzyme 5- enolpyruvylshikimate 3 phosphate synthase (EPSPS) along three different directions.After the simulations, Potential of Mean Force calculations were carried, from a cumulant expansion of Jarzynski's equation to obtain the profile of free energy of interaction between the herbicide and the active site of the enzyme in the presence of shikimate-3 substrate phosphate (S3P). The set of values for external work, had a Gaussian distribution. The PMF values ranged according to the directions of the unbindong pahway of each simulation, displaying energy values of 10.7, 14.7 and 19.5KJmol-1. The results provide a theoretical support in order to assist the construction of a specific nanobiossensor to quantify the glyphosate herbicide.

Nanobiosensors based on chemically modified AFM probes: a useful tool for metsulfuron-methyl detection

The use of agrochemicals has increased considerably in recent years, and consequently, there has been increased exposure of ecosystems and human populations to these highly toxic compounds. The study and development of methodologies to detect these substances with greater sensitivity has become extremely relevant. This article describes, for the first time, the use of atomic force spectroscopy (AFS) in the detection of enzyme-inhibiting herbicides. A nanobiosensor based on an atomic force microscopy (AFM) tip functionalised with the acetolactate synthase (ALS) enzyme was developed and characterised. The herbicide metsulfuron-methyl, an ALS inhibitor, was successfully detected through the acquisition of force curves using this biosensor. The adhesion force values were considerably higher when the biosensor was used. An increase of ~250% was achieved relative to the adhesion force using an unfunctionalised AFM tip. This considerable increase was the result of a specific interaction between the enzyme and the herbicide, which was primarily responsible for the efficiency of the nanobiosensor. These results indicate that this methodology is promising for the detection of herbicides, pesticides, and other environmental contaminants.