Identification of Active Sites of Biomolecules. 1. Methyl-α-mannopyranoside and FeIII

Challenges and limitations in the studies of glycoproteins: A computational chemist's perspective

Experimenters face challenges and limitations while analyzing glycoproteins due to their high flexibility, stereochemistry, anisotropic effects, and hydration phenomena. Computational studies complement experiments and have been used in characterization of the structural properties of glycoproteins. However, recent investigations revealed that computational studies face significant challenges as well. Here, we introduce and discuss some of these challenges and weaknesses in the investigations of glycoproteins. We also present requirements of future developments in computational biochemistry and computational biology areas that could be necessary for providing more accurate structural property analyses of glycoproteins using computational tools. Further theoretical strategies that need to be and can be developed are discussed herein.

Current Challenges and Limitations in the Studies of Intrinsically Disordered Proteins in Neurodegenerative Diseases by Computer Simulations

Experiments face challenges in the analysis of intrinsically disordered proteins in solution due to fast conformational changes and enhanced aggregation propensity. Computational studies complement experiments, being widely used in the analyses of intrinsically disordered proteins, especially those positioned at the centers of neurodegenerative diseases. However, recent investigations - including our own - revealed that computer simulations face significant challenges and limitations themselves. In this review, we introduced and discussed some of the scientific challenges and limitations of computational studies conducted on intrinsically disordered proteins. We also outlined the importance of future developments in the areas of computational chemistry and computational physics that would be needed for generating more accurate data for intrinsically disordered proteins from computer simulations. Additional theoretical strategies that can be developed are discussed herein.

Quantum Chemistry Meets Deep Learning for Complex Carbohydrate and Glycopeptide Species I

Carbohydrate complexes are crucial in many various biological and medicinal processes. The impacts of N-acetyl on the glycosidic linkage flexibility of methyl β-D-glucopyranose, and of the glycoamino acid β-D-glucopyranose-asparagine are poorly understood at the electronic level. Furthermore, the effect of D- and L-isomers of asparagine in the complexes of N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine is unknown. In this study, we performed density functional theory calculations of methyl β-D-glucopyranose, methyl N-acetyl-β-D-glucopyranose, and of glycoamino acids β-D-glucopyranose-asparagine, N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine for studying their linkage flexibilities, total solvated energies, thermochemical properties and intra-molecular hydrogen bond formations in an aqueous solution environment using the COnductor-like Screening MOdel (COSMO) for water. We linked these density functional theory calculations to deep learning via estimating the total solvated energy of each linkage torsional angle value. Our results show that deep learning methods accurately estimate the total solvated energies of complex carbohydrate and glycopeptide species and provide linkage flexibility trends for methyl β-D-glucopyranose, methyl N-acetyl-β-D-glucopyranose, and of glycoamino acids β-D-glucopyranose-asparagine, N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine in agreement with density functional theory results. To the best of our knowledge, this study represents the first application of density functional theory along with deep learning for complex carbohydrate and glycopeptide species in an aqueous solution medium. In addition, this study shows that a few thousands of optimization frames from DFT calculations are enough for accurate estimations by deep learning tools.

Molecular Mechanisms of Drug Action: X-ray Crystallography at the Basis of Structure-based and Ligand-based Drug Design

Biological systems are recognized for their complexity and diversity and yet we sometimes manage to cure disease via the administration of small chemical drug molecules. At first, active ingredients were found accidentally and at that time there did not seem a need to understand the molecular mechanism of drug functioning. However, the urge to develop new drugs, the discovery of multipurpose characteristics of some drugs, and the necessity to remove unwanted secondary drug effects, incited the pharmaceutical sector to rationalize drug design. This did not deliver success in the years directly following its conception, but it drove the evolution of biochemical and biophysical techniques to enable the characterization of molecular mechanisms of drug action. Functional and structural data generated by biochemists and structural biologists became a valuable input for computational biologists, chemists and bioinformaticians who could extrapolate in silico, based on variations in the structural aspects of the drug molecules and their target. This opened up new avenues with much improved predictive power because of a clearer perception of the role and impact of structural elements in the intrinsic affinity and specificity of the drug for its target. In this chapter, we review how crystal structures can initiate structure-based drug design in general.

Structures and free energy landscapes of aqueous zinc(II)-bound amyloid-β(1-40) and zinc(II)-bound amyloid-β(1-42) with dynamics

Binding of divalent metal ions with intrinsically disordered fibrillogenic proteins, such as amyloid-β (Aβ), influences the aggregation process and the severity of neurodegenerative diseases. The Aβ monomers and oligomers are the building blocks of the aggregates. In this work, we report the structures and free energy landscapes of the monomeric zinc(II)-bound Aβ40 (Zn:Aβ40) and zinc(II)-bound Aβ42 (Zn:Aβ42) intrinsically disordered fibrillogenic metallopeptides in an aqueous solution by utilizing an approach that employs first principles calculations and parallel tempering molecular dynamics simulations. The structural and thermodynamic properties, including the secondary and tertiary structures and conformational Gibbs free energies of these intrinsically disordered metallopeptide alloforms, are presented. The results show distinct differing characteristics for these metallopeptides. For example, prominent β-sheet formation in the N-terminal region (Asp1, Arg5, and Tyr10) of Zn:Aβ40 is significantly decreased or lacking in Zn:Aβ42. Our findings indicate that blocking multiple reactive residues forming abundant β-sheet structure located in the central hydrophobic core and C-terminal regions of Zn:Aβ42 via antibodies or small organic molecules might help to reduce the aggregation of Zn(II)-bound Aβ42. Furthermore, we find that helix formation increases but β-sheet formation decreases in the C-terminal region upon Zn(II) binding to Aβ. This depressed β-sheet formation in the C-terminal region (Gly33-Gly38) in monomeric Zn:Aβ42 might be linked to the formation of amorphous instead of fibrillar aggregates of Zn:Aβ42.