Amyloid fibrils are locally ordered protein aggregates that self-assemble under a variety of physiological and in vitro conditions. Their formation is of fundamental interest as a physical chemistry problem and plays a central role in Alzheimer’s disease, Type II diabetes, and other human diseases. As the number of known amyloid fibril structures has grown, the need has arisen for a nomenclature for describing and classifying fibril types, as well as a theoretical description of the physics that gives rise to the self-assembly of these structures. Here, we introduce a systematic nomenclature and coarse-graining methodology for describing the topology of fibrils and other protein aggregates, along with a computational methodology for simulating protein aggregation. Both have mathematical underpinnings in graph theory and statistical mechanics and are consistent with available experimental data on the fibril structure and aggregation kinetics. Our graph representation of the fibril topology enables us to define a network Hamiltonian based on connectivity patterns among monomers rather than detailed intermolecular interactions, greatly speeding up the simulation of large ensembles. Our simulation strategy is capable of recapitulating the formation of all currently known amyloid fibril topologies found in the Protein Data Bank, as well as the formation kinetics of fibrils and oligomers.

Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer’s disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution. Many current models of amyloid deposition diseases posit that the most toxic species are oligomers that form either along the pathway to forming fibrils or in competition with their formation, making it even more critical to understand the kinetics of fibrillization. A recently introduced topological model for aggregation based on network Hamiltonians is capable of recapitulating the entire process of amyloid fibril formation, beginning with thousands of free monomers and ending with kinetically accessible and thermodynamically stable amyloid fibril structures. The model can be parameterized to match the five topological classes encompassing all amyloid fibril structures so far discovered in the PDB. This paper introduces a set of network statistical and topological metrics for quantitative analysis and characterization of the fibrillization mechanisms predicted by the network Hamiltonian model. The results not only provide insight into different mechanisms leading to similar fibril structures, but also offer targets for future experimental exploration into the mechanisms by which fibrils form.

Zika virus (ZIKV), a mosquito-borne flavivirus, was originally isolated from sentinel rhesus monkey in the Zika Forest of Uganda in 1947. The virus is transmitted to humans by Aedes mosquitoes. The recent evidences have revealed that this virus is linked to serious pathological disorders including microcephaly in newborns and Guillain-Barr e syndrome in adults. Up to now, there is no currently available vaccine or therapeutic drug for preventing or controlling ZIKV infection. One of the attractive drug-targets for ZIKV treatment is the NS2B/NS3 serine protease that is crucial for viral polyprotein during infection. Herein, we have used a hybrid Quantum Mechanics and Molecular Mechanics (QM/MM) umbrella sampling simulation at the PM6/ff14SB level of theory to investigate the acylation process of the reaction catalyzed by the ZIKV protease. The obtained results reveal that proton transfer from S135 to H51 and nucleophilic attack on the substrate by S135 occur in a concerted manner. The ratelimiting step is the formation of tetrahedral intermediate with an energy barrier of $10.0 kcal,mol À1 . In addition, single-point energy QM/MM calculations on the BH&HLYP-D3/6-31G(d)/ff144SB optimized geometries were carried out at the SCS-(L)MP2/(aug)-cc-pVTZ/ff14SB and L-CCSD(T)/(aug)-cc-pVTZ/ ff14SB to correct the potential energy surfaces. The average values of computed activation energies at the SCS-LMP2/(aug)-cc-pVTZ/ff14SB of 17.8 5 1.2 kcall,mol À1 and L-CCSD(T)/(aug)-cc-pVTZ/ff14SB of 16.3 5 1.4 kcal,mol À1 are in good agreement with the experimental data. Therefore, the ability of the QM/MM presented here could be informative and useful for further designing of NS2B/NS3 inhibitors based on transition state analogues.

This work demonstrates how computational techniques can facilitate quickly moving from raw sequence data to refined structural models and comparative analysis, and to select promising candidates for subsequent biochemical characterization. This capability is increasingly important given the large and growing body of data from high-throughput genome sequencing, which makes experimental characterization of every target impractical.

In plants, esterase/lipases perform transesterification reactions, playing an important role in the synthesis of useful molecules, such as those comprising the waxy coatings of leaf surfaces. Plant genomes and transcriptomes have provided a wealth of data about expression patterns and the circumstances under which these enzymes are upregulated, e.g. pathogen defense and response to drought; however, predicting their functional characteristics from genomic or transcriptome data is challenging due to weak sequence conservation among the diverse members of this group. Although functional sequence blocks mediating enzyme activity have been identified, progress to date has been hampered by the paucity of information on the structural relationships among these regions and how they affect substrate specificity. Here we present methodology for predicting overall protein flexibility and active site flexibility based on molecular modeling and analysis of protein structure networks (PSNs). We define two new types of specialized PSNs: sequence region networks (SRNs) and active site networks (ASNs), which provide parsimonious representations of molecular structure in reference to known features of interest. Our approach, intended as an aid to target selection for poorly characterized enzyme classes, is demonstrated for 26 previously uncharacterized esterase/lipases from the genome of the carnivorous plant Drosera capensis and validated using a case/control design. Analysis of the network relationships among functional blocks and among the chemical moieties making up the catalytic triad reveals potentially functionally significant differences that are not apparent from sequence analysis alone.