Computational Analysis for the Rational Design of Anti-Amyloid Beta (Aβ) Antibodies

Glycosides and Their Corresponding Small Molecules Inhibit Aggregation and Alleviate Cytotoxicity of Aβ40

Polyphenols are the class of naturally synthesized compounds in the secondary metabolism of plants, which are widely distributed in fruits and vegetables. Their potential health treatment strategies have attracted wide attention in the scientific community. The abnormal aggregation of Aβ to form mature fibrils is pathologically related to Alzheimer's disease (AD). Therefore, inhibiting Aβ40 fibrillogenesis was considered to be the major method for the intervention and therapy of AD. Glycosides, as a cluster of natural phenolic compounds, are widely distributed in Chinese herbs, fruits, and vegetables. The inhibitory effect of glycosides (phloridzin, salidroside, polydatin, geniposide, and gastrodin) and their corresponding small molecules (phloretin, 4-hydroxyphenyl ethanol, resveratrol, genipin, and 4-hydroxybenzyl alcohol) on Aβ40 aggregation and fibrils prolongation, disaggregation against mature fibrils, and the resulting cytotoxicity were studied by systematical biochemical, cell biology and molecular docking techniques, respectively. As a result, all inhibitors were observed against Aβ40 aggregation and fibrils prolongation and disaggregated mature Aβ40 fibrils in a dose-dependent manner. Besides, the cell validity experiments also showed that all inhibitors could effectively alleviate the cytotoxicity induced by Aβ40 aggregates, and the glycoside groups played important roles in this inhibiting process. Finally, molecular docking was performed to study the interactions between these inhibitors and Aβ40. Docking showed that all inhibitors were bound to the similar region of Aβ40, and glycoside group formed hydrogen bonds with the pivotal residues Lys16. These results indicated that the glycoside groups could increase the inhibitory effects and reduce cytotoxicity. Glycosides have tremendous potential to be developed as an innovative type of aggregation inhibitor to control and treat neurodegenerative diseases.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Molecular interactions between monoclonal oligomer-specific antibody 5E3 and its amyloid beta cognates

Oligomeric amyloid β (Aβ) is currently considered the most neurotoxic form of the Aβ peptide implicated in Alzheimer’s disease (AD). The molecular structures of the oligomers have remained mostly unknown due to their transient nature. As a result, the molecular mechanisms of interactions between conformation-specific antibodies and their Aβ oligomer (AβO) cognates are not well understood. A monoclonal conformation-specific antibody, m5E3, was raised against a structural epitope of Aβ oligomers. m5E3 binds to AβOs with high affinity, but not to Aβ monomers or fibrils. In this study, a computational model of the variable fragment (Fv) of the m5E3 antibody (Fv5E3) is introduced. We further employ docking and molecular dynamics simulations to determine the molecular details of the antibody-oligomer interactions, and to classify the AβOs as Fv5E3-positives and negatives, and to provide a rationale for the low affinity of Fv5E3 for fibrils. This information will help us to perform site-directed mutagenesis on the m5E3 antibody to improve its specificity and affinity toward oligomeric Aβ species. We also provide evidence for the possible capability of the m5E3 antibody to disaggregate AβOs and to fragment protofilaments.

Molecular dynamics simulations of copper binding to N-terminus mutants of amyloid-β

We report results of molecular dynamic (MD) simulations on N-terminus mutants of the copper-bound, amyloid-β (Aβ) peptide. Eight structures of Aβ were modelled, including seven mutant peptides in addition to the unaltered wild-type (WT). Trajectories analysed for each individual system were all approximately 1.4 μs in length, yielding a total of over 11 μs in total. The impact of these mutations are marked and varied compared to the wild-type peptide, including effects on secondary structure, stability and conformational changes. Each system showed differing levels of stability with some showing consistent, compact conformations whereas others displayed more flexible structures. Contrasts between comparable mutations at similar sites, such as A2T/A2V and D7H/D7N, show the location as well as the type of mutation have effects on protein structure observed in Ramachandran plots. We also report notable changes in peptide structure at residues remote to the site of substitution showing these mutations influence the entirety of Aβ. Salt-bridge profiles show this most clearly: addition or removal of charged residues affecting all salt-bridge interactions present in WT, even those remote from the site of mutation. Effects on secondary structure differ between mutations, most notably a change in incidence of β-strand, which has been linked to enhanced aggregational properties for the peptide. GFN2-xTB semi-empirical calculations show clear differences in binding energies of the copper-centre for each system.Communicated by Ramaswamy H. Sarma.

Rational modulator design by exploitation of protein-protein complex structures

The horizon of drug discovery is currently expanding to target and modulate protein-protein interactions (PPIs) in globular proteins and intrinsically disordered proteins that are involved in various diseases. To either interrupt or stabilize PPIs, the 3D structure of target protein-protein (or protein-peptide) complexes can be exploited to rationally design PPI modulators (inhibitors or stabilizers) through structure-based molecular design. In this review, we present an overview of experimental and computational methods that can be used to determine 3D structures of protein-protein complexes. Several approaches including rational and in silico methods that can be applied to design peptides, peptidomimetics and small compounds by utilization of determined 3D protein-protein/peptide complexes are summarized and illustrated.

Industrially produced pullulanases with thermostability: Discovery, engineering, and heterologous expression

Pullulanases (EC 3.2.1.41) are well-known starch-debranching enzymes widely used to hydrolyze α-1,6-glucosidic linkages in starch, pullulan, amylopectin, and other oligosaccharides, with application potentials in food, brewing, and pharmaceutical industries. Although extensive studies are done to discover and express pullulanases, only few are available with desirable characteristics for industrial applications. This raises the challenge to mine new enzyme sources, engineer proteins based on sequence/structure, and regulate expressions. We review here the identification of extremophilic and mesophilic microbes as sources of industrial pullulanases with desirable characteristics, including acid-resistance, thermostability, and psychrotrophism. We present current advances in site-directed mutagenesis and sequence/structure-guided protein engineering of pullulanases. In addition, we discuss heterologous expression of pullulanases in prokaryotic and eukaryotic microbial systems, and address the effectiveness of the expression elements and their regulation of enzyme production. Finally, we indicate future research needs to develop desired industrial pullulanases.

Robustness and Efficiency of Poisson-Boltzmann Modeling on Graphics Processing Units

Poisson-Boltzmann equation (PBE) based continuum electrostatics models have been widely used in modeling electrostatic interactions in biochemical processes, particularly in estimating protein-ligand binding affinities. Fast convergence of PBE solvers is crucial in binding affinity computations as numerous snapshots need to be processed. Efforts have been reported to develop PBE solvers on graphics processing units (GPUs) for efficient modeling of biomolecules, though only relatively simple successive over-relaxation and conjugate gradient methods were implemented. However, neither convergence nor scaling properties of the two methods are optimal for large biomolecules. On the other hand, geometric multigrid (MG) has been shown to be an optimal solver on CPUs, though no MG have been reported for biomolecular applications on GPUs. This is not a surprise as it is a more complex method and depends on simpler but limited iterative methods such as Gauss-Seidel in its core relaxation procedure. The robustness and efficiency of MG on GPUs are also unclear. Here we present an implementation and a thorough analysis of MG on GPUs. Our analysis shows that robustness is a more pronounced issue than efficiency for both MG and other tested solvers when the single precision is used for complex biomolecules. We further show how to balance robustness and efficiency utilizing MG's overall efficiency and conjugate gradient's robustness, pointing to a hybrid GPU solver with a good balance of efficiency and accuracy. The new PBE solver will significantly improve the computational throughput for a range of biomolecular applications on the GPU platforms.

Mutational Basin-Hopping: Combined Structure and Sequence Optimization for Biomolecules

The study of energy landscapes has led to a good understanding of how and why proteins and nucleic acids adopt their native structure. Through evolution, sequences have adapted until they exhibit a strongly funneled energy landscape, stabilizing the native fold. Design of artificial biomolecules faces the challenge of creating similar stable, minimally frustrated, and functional sequences. Here we present a biminimization approach, mutational basin-hopping, in which we simultaneously use global optimization to optimize the energy and a target function describing a desired property of the system. This optimization of structure and sequence is a generalized basin-hopping method and produces an efficient design process, which can target properties such as binding affinity or solubility.