We use a coarse-grained protein model to characterize the critical nucleus, structural stability, and fibril elongation propensity of Aβ 1−40 oligomers for the C 2x and C 2z quaternary forms proposed by solid-state NMR. By estimating equilibrium populations of structurally stable and unstable protofibrils, we determine the shift in the dominant population from free monomer to ordered fibril at a critical nucleus of ten chains for the C 2x and C 2z forms. We find that a minimum assembly of 16 monomer chains is necessary to mimic a mature fibril, and show that its structural stability correlates with a plateau in the hydrophobic residue density and a decrease in the likelihood of losing hydrophobic interactions by rotating the fibril subunits. While Aβ 1−40 protofibrils show similar structural stability for both C 2x and C 2z quaternary structures, we find that the fibril elongation propensity is greater for the C 2z form relative to the C 2x form. We attribute the increased propensity for elongation of the C 2z form as being due to a stagger in the interdigitation of the N-terminal and C-terminal β-strands, resulting in structural asymmetry in the presented fibril ends that decreases the amount of incorrect addition to the N terminus on one end. We show that because different combinations of stagger and quaternary structure affect the structural symmetry of the fibril end, we propose that differences in quaternary structures will affect directional growth patterns and possibly different morphologies in the mature fiber.

We develop a sequence based alpha-carbon model to incorporate a mean field estimate of the orientation dependence of the polypeptide chain that gives rise to specific hydrogen bond pairing to stabilize alpha-helices and beta-sheets. We illustrate the success of the new protein model in capturing thermodynamic measures and folding mechanism of proteins L and G. Compared to our previous coarse-grained model, the new model shows greater folding cooperativity and improvements in designability of protein sequences, as well as predicting correct trends for kinetic rates and mechanism for proteins L and G. We believe the model is broadly applicable to other protein folding and protein-protein co-assembly processes, and does not require experimental input beyond the topology description of the native state. Even without tertiary topology information, it can also serve as a mid-resolution protein model for more exhaustive conformational search strategies that can bridge back down to atomic descriptions of the polypeptide chain.

Computational modeling at the atomistic and mesoscopic levels has undergone dramatic development in the past 10 years to meet the challenge of adequately accounting for the many-body nature of intermolecular interactions. At the heart of this challenge is the ability to identify the strengths and specific limitations of pairwise-additive interactions, to improve classical models to explicitly account for many-body effects, and consequently to enhance their ability to describe a wider range of reference data and build confidence in their predictive capacity. However, the corresponding computational cost of these advanced classical models increases significantly enough that statistical convergence of condensed phase observables becomes more difficult to achieve. Here we review a hierarchy of potential energy surface models used in molecular simulations for systems with many degrees of freedom that best meet the trade-off between accuracy and computational speed in order to define a sweet spot for a given scientific problem of interest.

CONSPECTUS Protein aggregation can be defined as the sacrifice of stabilizing intrachain contacts of the functional state that are replaced with interchain contacts to form non-functional states. The resulting aggregate morphologies range from amorphous structures without long-range order typical of nondisease proteins involved in inclusion bodies to highly structured fibril assemblies typical of amyloid disease proteins. In this Account, we describe the development and application of computational models for the investigation of nondisease and disease protein aggregation as illustrated for the proteins L and G and the Alzheimer’s Aβ systems. In each case, we validate the models against relevant experimental observables and then expand on the experimental window to better elucidate the link between molecular properties and aggregation outcomes. Our studies show that each class of protein exhibits distinct aggregation mechanisms that are dependent on protein sequence, protein concentration, and solution conditions. Nondisease proteins can have native structural elements in the denatured state ensemble or rapidly form early folding intermediates, which offers avenues of protection against aggregation even at relatively high concentrations. The possibility that early folding intermediates may be evolutionarily selected for their protective role against unwanted aggregation could be a useful strategy for reengineering sequences to slow aggregation and increase folding yield in industrial protein production. The observed oligomeric aggregates that we see for nondisease proteins L and G may represent the nuclei for larger aggregates, not just for large amorphous inclusion bodies, but potentially as the seeds of ordered fibrillar aggregates, since most nondisease proteins can form amyloid fibrils under conditions that destabilize the native state. By contrast, amyloidogenic protein sequences such as Aβ1–40,42 and the familial Alzheimer’s disease (FAD) mutants favor aggregation into ordered fibrils once the free-energy barrier for forming a critical nucleus is crossed. However, the structural characteristic and oligomer size of the soluble nucleation species have yet to be determined experimentally for any disease peptide sequence, and the molecular mechanism of polymerization that eventually delineates a mature fibril is unknown. This is in part due to the limited experimental access to very low peptide concentrations that are required to characterize these early aggregation events, providing an opportunity for theoretical studies to bridge the gap between the monomer and fibril end points and to develop testable hypotheses. Our model shows that Aβ1–40 requires as few as 6–10 monomer chains (depending on sequence) to begin manifesting the cross-β order that is a signature of formation of amyloid filaments or fibrils assessed in dye-binding kinetic assays. The richness of the oligomeric structures and viable filament and fibril polymorphs that we observe may offer structural clues to disease virulence variations that...

There are seven β-tubulin isotypes present in distinct quantities in mammalian cells of different origin. Altered expression of β-tubulin isotypes has been reported in cancer cell lines resistant to microtubule stabilizing agents (MSAs) and in human tumors resistant to Taxol. To study the relative binding affinities of MSAs, tubulin from different sources, with distinct β-tubulin isotype content, were specifically photolabeled with a tritium-labeled Taxol analog, 2-(m-azidobenzoyl)taxol, alone or in the presence of MSAs. The inhibitory effects elicited by these MSAs on photolabeling were distinct for β-tubulin from different sources. To determine the exact amount of drug that binds to different β-tubulin isotypes, bovine brain tubulin was photolabeled and the isotypes resolved by high-resolution isoelectrofocusing. All bands were analyzed by mass spectrometry following cyanogen bromide digestion, and the identity and relative quantity of each β-tubulin isotype determined. It was found that compared with other β-tubulin isotypes, βIII-tubulin bound the least amount of 2-(m-azidobenzoyl)taxol. Analysis of the sequences of β-tubulin near the Taxol binding site indicated that, in addition to the M-loop that is known to be involved in drug binding, the leucine cluster region of βIII-tubulin contains a unique residue, alanine, at 218, compared with other isotypes that contain threonine. Molecular dynamic simulations indicated that the frequency of Taxol-accommodating conformations decreased dramatically in the T218A variant, compared with other β-tubulins. Our results indicate that the difference in residue 218 in βIII-tubulin may be responsible for inhibition of drug binding to this isotype, which could influence downstream cellular events.

Summary Members of extracellular Immunoglobulin Superfamily (IgSFs) play a key role in immune regulation through the control of the co-stimulatory pathway, and have emerged as potent drug targets in cancers, infectious diseases, and autoimmunity. Despite the difficult experimental access to this class of proteins, single structures of ectodomains of IgSF proteins are solved with regularity. However, the most biologically critical challenge for this class of proteins is the identification of their cognate ligands that communicate intercellular signals. We describe a conceptually novel method, Protein Ligand Interface Design (ProtLID), for the problem of identifying cognate ligands from a sub-proteome for a given target receptor protein. ProtLID designs an optimal protein interface for a given receptor by running extensive molecular dynamics simulations of single residue probes. The type and location of residue preferences establish a residue-based pharmacophore, which is subsequently used to find potential matches among candidate ligands within a sub-proteome.

We derive a new numerical approach to solving the linearized Poisson Boltzmann equation (PBE) by representing the protein surface as a collection of spheres in which the surface charges can then be iteratively solved by new analytical multipole methods previously introduced by us [Lotan & Head-Gordon, 2006]. We show that our Poisson Boltzmann semi-analytical method, PB-SAM, realizes better accuracy, more flexible memory management, and at reduced cost relative to either finite difference or boundary element method PBE solvers. We provide two new benchmarks of PBE solution accuracy to test the numerical PBE solutions based on (1) arrays of up to hundreds of spherical low dielectric geometries with asymmetric charges in which mutual polarization is treated exactly, and (2) two overlapping spheres with increasing charge asymmetry by solving the PB-SAM method to very high pole order. We illustrate the strength of the PB-SAM approach by computing the potential profile of an array of 60 T1-particle forming monomers of the bromine mosaic virus.

Secreted and cell surface-localized members of the immunoglobulin superfamily (IgSF) play central roles in regulating adaptive and innate immune responses, and are prime targets for the development of protein-based therapeutics. An essential activity of the ectodomains of these proteins is the specific recognition of cognate ligands, which are often other members of the IgSF. In this work we provide functional insight for this important class of proteins through the development of a clustering algorithm that groups together extracellular domains of the IgSF with similar binding preferences. Information from hidden Markov model-based sequence profiles and domain structure is calibrated against manually curated protein interaction data to define functional families of IgSF proteins. The method is able to assign 82% of the 477 extracellular IgSF protein to a functional family, while the rest are either single proteins with unique function or proteins that could not be assigned with the current technology. The functional clustering of IgSF proteins generates hypotheses regarding the identification of new cognate receptor:ligand pairs and reduces the pool of possible interacting partners to a manageable level for experimental validation.