Thermodynamics of folding and association of lattice-model proteins

Interactions between tetrahydroisoindoline-1,3-dione derivatives and human serum albumin via multiple spectroscopy techniques

Some tetrahydroisoindoline-1,3-dione derivatives (TDDs) possess potent herbicidal activity. To assess possible impacts of TDDs on humans, the interactions between TDDs and human serum albumin (HSA) were evaluated with steady-state and time-resolved fluorescence spectroscopy, synchronous fluorescence spectroscopy, Fourier transform-infrared spectroscopy, and circular dichroism spectroscopy. The thermodynamic data obtained at temperatures of 298, 307, and 316 K indicate that TDDs spontaneously bind to HSA and thus form a TDD-HSA complex. The conformation and secondary structure of HSA are changed, and the intrinsic fluorescence of HSA is statically quenched by TDDs. Moreover, the TDD-HSA complex is formed primarily through electrostatic interactions and has only one binding site on HSA. A competitive ligand-binding assay revealed that site II (subdomain IIIA) displays the greatest affinity for TDDs. In addition, an acute toxicity bioassay showed no zebrafish mortality upon exposure to 4000 μg L^-1 of TDDs. This work is helpful for understanding interactions between TDDs and HSA.

Coarse-grained simulations of protein aggregation

Protein aggregation is believed to be responsible for a number of human diseases and limits the yields of pharmaceutical proteins during production. Computer simulations can be used to develop novel experimentally testable hypotheses pertaining to aggregation. While all-atom simulations with explicit solvent are too computationally intensive to address the multitude of relevant time scales, coarse-grained models make it possible to observe the transition of monomers to an equilibrium containing aggregates. Here, we provide the reader with background information and a list of steps for setting up, performing, and analyzing computer simulations of aggregating coarse-grained (CG) proteins.

In silico protein fragmentation reveals the importance of critical nuclei on domain reassembly

Protein complementation assays (PCAs) based on split protein fragments have become powerful tools that facilitate the study and engineering of intracellular protein-protein interactions. These assays are based on the observation that a given protein can be split into two inactive fragments and these fragments can reassemble into the original properly folded and functional structure. However, one experimentally observed limitation of PCA systems is that the folding of a protein from its fragments is dramatically slower relative to that of the unsplit parent protein. This is due in part to a poor understanding of how PCA design parameters such as split site position in the primary sequence and size of the resulting fragments contribute to the efficiency of protein reassembly. We used a minimalist on-lattice model to analyze how the dynamics of the reassembly process for two model proteins was affected by the location of the split site. Our results demonstrate that the balanced distribution of the "folding nucleus," a subset of residues that are critical to the formation of the transition state leading to productive folding, between protein fragments is key to their reassembly.

Protein aggregation in silico

Protein aggregation is a challenge to the successful manufacture of protein therapeutics; it can impose severe limitations on purification yields and compromise formulation stability. Advances in computer power, and the wealth of computational studies pertaining to protein folding, have facilitated the development of molecular simulation as a tool to investigate protein misfolding and aggregation. Here, we highlight the successes of protein aggregation studies carried out in silico, with a particular emphasis on studies related to biotechnology. To conclude, we discuss future prospects for the field, and identify several biotechnology-related problems that would benefit from molecular simulation.

A coarse-grained model for the formation of α helix with a noninteger period on simple cubic lattices

Periodicity is an important parameter in the characterization of a helix in proteins. In this work, a coarse-grained model for a homopolypeptide in simple cubic lattices has been extended to build an alpha helix with a noninteger period. The lattice model is based on the bond fluctuation algorithm in which bond lengths and orientations are altered in a quasicontinuous way, and the simulation is performed via dynamic Monte Carlo simulation. Hydrogen bonds are assumed to be formed between a virtual-carbonyl group in a residue and a downstream virtual-imino group in another residue. Consequently, the period of the formed alpha helix is a noninteger. An improved spatial correlation function has been suggested to quantitatively describe the periodicity of the helical conformation, by which helical period and helical persistent length can be calculated by statistics. The resultant periods are very close to 3.6 residues; the persistent length based upon the improved definition can be larger or smaller than the chain length and reflect the inherent regularity of a chain including one or multiple helical blocks. The simulation outputs agree with the calculation of the Zimm-Bragg theory based upon the associated nucleation parameter and propagation parameter as well.

A Monte Carlo Simulation of the Aggregation, Phase-Separation, and Gelation of Model Globular Molecules

Monte Carlo computer simulation on a square 3-D lattice is used to model state behavior of globular copolymers. Two types of globular molecules were defined. One consisted of a single type of subunit (a homopolymer) while the second contained a core of strongly attractive subunits and an outer layer of less strongly attractive subunits (a heteropolymer). Systems of globules were simulated at varied volume fraction (V(F)) and reduced temperature (T(R)), and state diagrams were constructed. These state diagrams contained state boundaries defined by the V(F)/T(R) combinations at which the system formed a percolating network and at which the various component subunits in the globule unfolded. Simulated systems could exist in a number of states (between 4 and 7), depending on the V(F), T(R), whether the molecule was a homo- or heteroglobule and whether the globules were allowed to interact with each other or not. All systems exhibited a gelation/crossover line that resembled a lower critical solution temperature. All systems also exhibited a critical gelation concentration, above which a continuous network was formed. The critical gelation concentration varied between about 2-4% V(F) depending on the type of system. This is comparable to experimental critical gelation concentrations of in the region of 4% (w/w) for a range of associating polymers and biopolymers such as globular proteins and polysaccharides. Other states were formed which included one where elongated, fibril-like aggregated strands were formed, and a micelle-like aggregated state. The results are discussed in terms of the known state behavior of associating polymers and biopolymers (proteins and polysaccharides).

Effect of single-point sequence alterations on the aggregation propensity of a model protein

Sequences of contemporary proteins are believed to have evolved through a process that optimized their overall fitness, including their resistance to deleterious aggregation. Biotechnological processing may expose therapeutic proteins to conditions that are much more conducive to aggregation than those encountered in a cellular environment. An important task of protein engineering is to identify alternative sequences that would protect proteins when processed at high concentrations without altering their native structure associated with specific biological function. Our computational studies exploit parallel tempering simulations of coarse-grained model proteins to demonstrate that isolated amino acid residue substitutions can result in significant changes in the aggregation resistance of the protein in a crowded environment while retaining protein structure in isolation. A thermodynamic analysis of protein clusters subject to competing processes of folding and association shows that moderate mutations can produce effects similar to those caused by changes in system conditions, including temperature, concentration, and solvent composition, that affect the aggregation propensity. The range of conditions where a protein can resist aggregation can therefore be tuned by sequence alterations, although the protein generally may retain its generic ability for aggregation.

Molecular simulation of protein aggregation

Computer simulation offers unique possibilities for investigating molecular-level phenomena difficult to probe experimentally. Drawing from a wealth of studies concerning protein folding, computational studies of protein aggregation are emerging. These studies have been successful in capturing aspects of aggregation known from experiment and are being used to refine experimental methods aimed at abating aggregation. Here we review molecular-simulation studies of protein aggregation conducted in our laboratory. Specific attention is devoted to issues with implications for biotechnology.

Predicting alternate structure attainment and amyloidogenesis: a nonlinear signal analysis approach

Chain hydrophobicity values have been used in prediction of alternate structure attainment by a polypeptide. Nonlinear signal analysis on the hydrophobicity values gives important clues about the propensities of particular stretches of a protein to form local or nonlocal contacts. These contacts determine the folding behavior of a polypeptide and helps in predicting the final structure that can be attained. A nonlinear signal analysis called the recurrent quantification analysis has been carried out using the hydrophobicity values on a wide range of proteins obtained from human, plant, and fungal sources. Here, we show that such an analysis gives us an easy handle in determining sequences within the proteins that may be important in beta-sheet formation leading to amyloidosis.

Protein-folding landscapes in multichain systems

Computational studies of proteins have significantly improved our understanding of protein folding. These studies are normally carried out by using chains in isolation. However, in many systems of practical interest, proteins fold in the presence of other molecules. To obtain insight into folding in such situations, we compare the thermodynamics of folding for a Miyazawa-Jernigan model 64-mer in isolation to results obtained in the presence of additional chains. The melting temperature falls as the chain concentration increases. In multichain systems, free-energy landscapes for folding show an increased preference for misfolded states. Misfolding is accompanied by an increase in interprotein interactions; however, near the folding temperature, the transition from folded chains to misfolded and associated chains is entropically driven. A majority of the most probable interprotein contacts are also native contacts, suggesting that native topology plays a role in early stages of aggregation.