Theoretical model of prion propagation: a misfolded protein induces misfolding

Evaluating native-like structures of RNA-protein complexes through the deep learning method

RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes.

Advances in the understanding of protein misfolding and aggregation through molecular dynamics simulation

Aberrant protein folding known as protein misfolding is counted as one of the striking factors of neurodegenerative diseases. The extensive range of pathologies caused by protein misfolding, aggregation and subsequent accumulation are mainly classified into either gain of function diseases or loss of function diseases. In order to seek for novel strategies for treatment and diagnosis of neurodegenerative diseases, insights into the mechanism of misfolding and aggregation is essential. A comprehensive knowledge on the factors influencing misfolding and aggregation is required as well. An extensive experimental study on protein aggregation is somewhat challenging due to the insoluble and noncrystalline nature of amyloid fibrils. Thus there has been a growing use of computational approaches including Monte Carlo simulation, docking simulation, molecular dynamics simulation in the study of protein misfolding and aggregation. The review presents a discussion on molecular dynamics simulation alone as to how it has emerged as a promising tool in the understanding of protein misfolding and aggregation in general, detailing upon three different aspects considering four misfold prone proteins in particular. It is noticeable that all four proteins considered in this review i.e prion, superoxide dismutase1, huntingtin and amyloid β are linked to chronic neurodegenerative diseases with debilitating effects. Initially the review elaborates on the factors influencing the misfolding and aggregation. Next, it addresses our current understanding of the amyloid structures and the associated aggregation mechanisms, finally, summarizing the contribution of this computational tool in the search for therapeutic strategies against the respective protein-deposition diseases.

Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions

Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.

β-Methylamino-L-alanine substitution of serine in SOD1 suggests a direct role in ALS etiology

Exposure to the environmental toxin β-methylamino-L-alanine (BMAA) is linked to amyotrophic lateral sclerosis (ALS), but its disease-promoting mechanism remains unknown. We propose that incorporation of BMAA into the ALS-linked protein Cu,Zn superoxide dismutase (SOD1) upon translation promotes protein misfolding and aggregation, which has been linked to ALS onset and progression. Using molecular simulation and predictive energetic computation, we demonstrate that substituting any serine with BMAA in SOD1 results in structural destabilization and aberrant dynamics, promoting neurotoxic SOD1 aggregation. We propose that translational incorporation of BMAA into SOD1 is directly responsible for its toxicity in neurodegeneration, and BMAA modification of SOD1 may serve as a biomarker of ALS.

Reversible unfolding of infectious prion assemblies reveals the existence of an oligomeric elementary brick

Mammalian prions, the pathogens that cause transmissible spongiform encephalopathies, propagate by self-perpetuating the structural information stored in the abnormally folded, aggregated conformer (PrP^Sc) of the host-encoded prion protein (PrP^C). To date, no structural model related to prion assembly organization satisfactorily describes how strain-specified structural information is encoded and by which mechanism this information is transferred to PrP^C. To achieve progress on this issue, we correlated the PrP^Sc quaternary structural transition from three distinct prion strains during unfolding and refolding with their templating activity. We reveal the existence of a mesoscopic organization in PrP^Sc through the packing of a highly stable oligomeric elementary subunit (suPrP), in which the strain structural determinant (SSD) is encoded. Once kinetically trapped, this elementary subunit reversibly loses all replicative information. We demonstrate that acquisition of the templating interface and infectivity requires structural rearrangement of suPrP, in concert with its condensation. The existence of such an elementary brick scales down the SSD support to a small oligomer and provide a basis of reflexion for prion templating process and propagation.

Prions are self-propagating assemblies with all necessary and sufficient replicative information stored in the 3D structure of the misfolded form of PrP called PrP^Sc. Since the emergence of the prion theory in the 80s, many attempts have been done to identify prion replicative information at molecular scale. Different models have been constructed based on a broad panel of experimental observations and some of them predict the existence of periodic elements constituting prion assemblies. Here, by using partial unfolding approaches, we trapped an oligomeric conformer that we called suPrP, which could constitute the elementary brick of prion assemblies. Once isolated, this elementary brick is devoid of infectivity. However, it becomes infectious once condensated into larger assemblies. The identification of the elementary PrP building block provides a new structural basis for understanding prion replicative information storage and spreading.

Preformed template fluctuations promote fibril formation: insights from lattice and all-atom models

Fibril formation resulting from protein misfolding and aggregation is a hallmark of several neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Despite the fact that the fibril formation process is very slow and thus poses a significant challenge for theoretical and experimental studies, a number of alternative pictures of molecular mechanisms of amyloid fibril formation have been recently proposed. What seems to be common for the majority of the proposed models is that fibril elongation involves the formation of pre-nucleus seeds prior to the creation of a critical nucleus. Once the size of the pre-nucleus seed reaches the critical nucleus size, its thermal fluctuations are expected to be small and the resulting nucleus provides a template for sequential (one-by-one) accommodation of added monomers. The effect of template fluctuations on fibril formation rates has not been explored either experimentally or theoretically so far. In this paper, we make the first attempt at solving this problem by two sets of simulations. To mimic small template fluctuations, in one set, monomers of the preformed template are kept fixed, while in the other set they are allowed to fluctuate. The kinetics of addition of a new peptide onto the template is explored using all-atom simulations with explicit water and the GROMOS96 43a1 force field and simple lattice models. Our result demonstrates that preformed template fluctuations can modulate protein aggregation rates and pathways. The association of a nascent monomer with the template obeys the kinetics partitioning mechanism where the intermediate state occurs in a fraction of routes to the protofibril. It was shown that template immobility greatly increases the time of incorporating a new peptide into the preformed template compared to the fluctuating template case. This observation has also been confirmed by simulation using lattice models and may be invoked to understand the role of template fluctuations in slowing down fibril elongation in vivo.

How Efficient Is Replica Exchange Molecular Dynamics? An Analytic Approach

Replica exchange molecular dynamics (REMD) has become a standard technique for accelerating relaxation in biosimulations. Despite its widespread use, questions remain about its efficiency compared with conventional, constant temperature molecular dynamics (MD). An analytic approach is taken to describe the relative efficiency of REMD with respect to MD. This is applied to several simple two-state models and to several real proteins-protein L and the B domain of protein A-to predict the relative efficiency of REMD with respect to MD in actual applications. In agreement with others, we find the following: as long as there is a positive activation energy for folding, REMD is more efficient than MD; the effectiveness of REMD is strongly dependent on the activation enthalpy; and the efficiency of REMD for actual proteins is a strong function of the maximum temperature. Choosing the maximum temperature too high can result in REMD becoming significantly less efficient than conventional MD. A good rule of thumb appears to be to choose the maximum temperature of the REMD simulation slightly above the temperature at which the enthalpy for folding vanishes. Additionally, we find that the number of replicas in REMD, while important for simulations shorter than one or two relaxation times, has a minimal effect on the asymptotic efficiency of the method.

Combining coarse-grained protein models with replica-exchange all-atom molecular dynamics

We describe a combination of all-atom simulations with CABS, a well-established coarse-grained protein modeling tool, into a single multiscale protocol. The simulation method has been tested on the C-terminal beta hairpin of protein G, a model system of protein folding. After reconstructing atomistic details, conformations derived from the CABS simulation were subjected to replica-exchange molecular dynamics simulations with OPLS-AA and AMBER99sb force fields in explicit solvent. Such a combination accelerates system convergence several times in comparison with all-atom simulations starting from the extended chain conformation, demonstrated by the analysis of melting curves, the number of native-like conformations as a function of time and secondary structure propagation. The results strongly suggest that the proposed multiscale method could be an efficient and accurate tool for high-resolution studies of protein folding dynamics in larger systems.

Chemical mechanism identification from frequency response to small temperature modulation

The description of interactions between biochemical species and the elucidation of the corresponding chemical mechanisms encounter an increasing interest both for the comprehension of biological pathways at the molecular scale and for the rationalization of drug design. Relying on powerful experimental tools such as thermal microfluidics and fluorescence detection, we propose a methodology to determine the chemical mechanism of a reaction without fitting parameters. A mechanism consistent with the accessible knowledge is assumed, and the assumption is checked through an iterative protocol. The test is based on the frequency analysis of the response of a targeted reactive species to temperature modulation. We build specific functions of the frequency that are constant for the assumed mechanism and show that the graph of these functions can be drawn from appropriate data analysis. The method is general and can be applied to any complex mechanism. It is here illustrated in detail in the case of single relaxation time mechanisms.

Coupled folding-binding in a hydrophobic/polar protein model: impact of synergistic folding and disordered flanks

Coupled folding-binding is central to the function of many intrinsically disordered proteins, yet not fully understood. With a continuous three-letter protein model, we explore the free-energy landscape of pairs of interacting sequences and how it is impacted by 1), variations in the binding mechanism; and 2), the addition of disordered flanks to the binding region. In particular, we focus on two sequences, one with 16 and one with 35 amino acids, which make a stable dimeric three-helix bundle at low temperatures. Three distinct binding mechanisms are realized by altering the stabilities of the individual monomers: docking, coupled folding-binding of a single α-helix, and synergistic folding and binding. Compared to docking, the free-energy barrier for binding is reduced when the single α-helix is allowed to fold upon binding, but only marginally. A greater reduction is found for synergistic folding, which in addition results in a binding transition state characterized by very few interchain contacts. Disordered flanking chain segments attached to the α-helix sequence can, despite a negligible impact on the dimer stability, lead to a downhill free-energy surface in which the barrier for binding is eliminated.

DARS-RNP and QUASI-RNP: new statistical potentials for protein-RNA docking

Background

Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.

Results

We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.

Conclusions

In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/

Mechanism of fiber assembly: treatment of Aβ peptide aggregation with a coarse-grained united-residue force field

The growth mechanism of β-amyloid (Aβ) peptide fibrils was studied by a physics-based coarse-grained united-residue model and molecular dynamics (MD) simulations. To identify the mechanism of monomer addition to an Aβ(1-40) fibril, we placed an unstructured monomer at a distance of 20 Å from a fibril template and allowed it to interact freely with the latter. The monomer was not biased towards fibril conformation by either the force field or the MD algorithm. With the use of a coarse-grained model with replica-exchange molecular dynamics, a longer timescale was accessible, making it possible to observe how the monomers probe different binding modes during their search for the fibril conformation. Although different assembly pathways were seen, they all follow a dock-lock mechanism with two distinct locking stages, consistent with experimental data on fibril elongation. Whereas these experiments have not been able to characterize the conformations populating the different stages, we have been able to describe these different stages explicitly by following free monomers as they dock onto a fibril template and to adopt the fibril conformation (i.e., we describe fibril elongation step by step at the molecular level). During the first stage of the assembly ("docking"), the monomer tries different conformations. After docking, the monomer is locked into the fibril through two different locking stages. In the first stage, the monomer forms hydrogen bonds with the fibril template along one of the strands in a two-stranded β-hairpin; in the second stage, hydrogen bonds are formed along the second strand, locking the monomer into the fibril structure. The data reveal a free-energy barrier separating the two locking stages. The importance of hydrophobic interactions and hydrogen bonds in the stability of the Aβ fibril structure was examined by carrying out additional canonical MD simulations of oligomers with different numbers of chains (4-16 chains), with the fibril structure as the initial conformation. The data confirm that the structures are stabilized largely by hydrophobic interactions and show that intermolecular hydrogen bonds are highly stable and contribute to the stability of the oligomers as well.

Alzheimer's disease peptide beta-amyloid interacts with fibrinogen and induces its oligomerization

Increasing evidence supports a vascular contribution to Alzheimer's disease (AD), but a direct connection between AD and the circulatory system has not been established. Previous work has shown that blood clots formed in the presence of the β-amyloid peptide (Aβ), which has been implicated in AD, have an abnormal structure and are resistant to degradation in vitro and in vivo. In the present study, we show that Aβ specifically interacts with fibrinogen with a K(d) of 26.3 ± 6.7 nM, that the binding site is located near the C terminus of the fibrinogen β-chain, and that the binding causes fibrinogen to oligomerize. These results suggest that the interaction between Aβ and fibrinogen modifies fibrinogen's structure, which may then lead to abnormal fibrin clot formation. Overall, our study indicates that the interaction between Aβ and fibrinogen may be an important contributor to the vascular abnormalities found in AD.

Fibrillation of β-lactoglobulin at low pH in the presence of a complexing anionic polysaccharide

The influence of electrostatic complexation with κ-carrageenan was tested on the fibrillation process of β-lactoglobulin at pH 2.0. Morphology and structural development were monitored through cross correlation dynamic light scattering, transmission electron microscopy, and atomic force microscopy. Scattering indicated that noncomplexed β-lactoglobulin monomers aggregated to form fibrils after 15-90 min of heating at 90 °C. However, electrostatic protein-carrageenan complexes found in the unheated system were unchanged by the thermal process. Images and scattering results showed that carrageenan complexes slowed fibrillation kinetics, possibly through reduction in available monomer concentration. Complexes adhered to fibrils at ends and junctions in TEM images, indicating interactive affinity with the fibers, presumably as heterogeneous nucleation sites.

Computational approaches for the design of peptides with anti-breast cancer properties

Background: Breast cancer is the most common cancer among women. Tamoxifen is the preferred drug for estrogen receptor-positive breast cancer treatment, yet many of these cancers are intrinsically resistant to tamoxifen or acquire resistance during treatment. Therefore, scientists are searching for breast cancer drugs that have different molecular targets. Methodology: Recently, a computational approach was used to successfully design peptides that are new lead compounds against breast cancer. We used replica exchange molecular dynamics to predict the structure and dynamics of active peptides, leading to the discovery of smaller bioactive peptides. Conclusions: These analogs inhibit estrogen-dependent cell growth in a mouse uterine growth assay, a test showing reliable correlation with human breast cancer inhibition. We outline the computational methods that were tried and used along with the experimental information that led to the successful completion of this research.

The Alzheimer beta-amyloid (Abeta(1-39)) dimer in an implicit solvent

Oligomers of Abeta peptides are suspected as the underlying cause of Alzheimer disease. Knowledge of their structural properties could therefore lead to a deeper understanding of the mechanism behind the outbreak of this disease. As a step in this direction we have studied Abeta dimers by all-atom molecular dynamics simulations. Equilibrated structures at 300 K were clustered into different families with similar structural features. The dominant cluster has parallel N-terminals and a well defined segment Leu17-Ala21 that are stabilized by salt bridges between Lys28 of one chain and either Glu22 or Asp23 of the other chain. The formation of these salt bridges may be the limiting step in oligomerization and fibrillogenesis.

Modeling of protein misfolding in disease

A short review of the results of molecular modeling of prion disease is presented in this chapter. According to the "one-protein theory" proposed by Prusiner, prion proteins are misfolded naturally occurring proteins, which, on interaction with correctly folded proteins may induce misfolding and propagate the disease, resulting in insoluble amyloid aggregates in cells of affected specimens. Because of experimental difficulties in measurements of origin and growth of insoluble amyloid aggregations in cells, theoretical modeling is often the only one source of information regarding the molecular mechanism of the disease. Replica exchange Monte Carlo simulations presented in this chapter indicate that proteins in the native state, N, on interaction with an energetically higher structure, R, can change their conformation into R and form a dimer, R(2). The addition of another protein in the N state to R(2) may lead to spontaneous formation of a trimer, R(3). These results reveal the molecular basis for a model of prion disease propagation or conformational diseases in general.

Computational design and experimental discovery of an antiestrogenic peptide derived from alpha-fetoprotein

Breast cancer is the most common cancer among women, and tamoxifen is the preferred drug for estrogen receptor-positive breast cancer treatment. Many of these cancers are intrinsically resistant to tamoxifen or acquire resistance during treatment. Consequently, there is an ongoing need for breast cancer drugs that have different molecular targets. Previous work has shown that 8-mer and cyclic 9-mer peptides inhibit breast cancer in mouse and rat models, interacting with an unsolved receptor, while peptides smaller than eight amino acids did not. We show that the use of replica exchange molecular dynamics predicts the structure and dynamics of active peptides, leading to the discovery of smaller peptides with full biological activity. Simulations identified smaller peptide analogues with the same conserved reverse turn demonstrated in the larger peptides. These analogues were synthesized and shown to inhibit estrogen-dependent cell growth in a mouse uterine growth assay, a test showing reliable correlation with human breast cancer inhibition.

Minimum model for the alpha-helix-beta-hairpin transition in proteins

We present a minimal model for proteins, which is able to capture the structural conversion between the alpha-helix and beta-hairpin. In most regimes of the parameter space, the model produces a stable structure at a low temperature; in a few limited regimes of the parameter space, the model displays an beta-hairpin transition as the physical conditions vary. These variations include a perturbation on hydrogen bonding propensity at the middle of the modeled chain, or the change of the hydrophobicity of a designated pair along the chain. Using Monte Carlo simulations, we demonstrate the structural conversion by means of state diagrams, heat capacity maps, and free energy maps.

Probing structural differences in prion protein isoforms by tyrosine nitration

Two conformational isomers of recombinant hamster prion protein (residues 90-232) have been probed by reaction with two tyrosine nitration reagents, peroxynitrite and tetranitromethane. Two conserved tyrosine residues (tyrosines 149 and 150) are not labeled by either reagent in the normal cellular form of the prion protein. These residues become reactive after the protein has been converted to the beta-oligomeric isoform, which is used as a model of the fibrillar form that causes disease. After conversion, a decrease in reactivity is noted for two other conserved residues, tyrosine 225 and tyrosine 226, whereas little to no effect was observed for other tyrosines. Thus, tyrosine nitration has identified two specific regions of the normal prion protein isoform that undergo a change in chemical environment upon conversion to a structure that is enriched in beta-sheet.

Aggregation of beta-amyloid fragments

The authors study the folding and aggregation of six chains of the beta-amyloid fragment 16-22 using Monte Carlo simulations. While the isolated fragment prefers a helical form at room temperature, in the system of six interacting fragments one observes both parallel and antiparallel beta sheets below a crossover temperature T(x) approximately equal to 420 K. The antiparallel sheets have lower energy and are therefore more stable. Above the nucleation temperature the aggregate quickly dissolves into widely separated, weakly interacting chains.

The folding mechanics of a knotted protein

An increasing number of proteins are being discovered with a remarkable and somewhat surprising feature, a knot in their native structures. How the polypeptide chain is able to "knot" itself during the folding process to form these highly intricate protein topologies is not known. Here we perform a computational study on the 160-amino-acid homodimeric protein YibK, which, like other proteins in the SpoU family of MTases, contains a deep trefoil knot in its C-terminal region. In this study, we use a coarse-grained C(alpha)-chain representation and Langevin dynamics to study folding kinetics. We find that specific, attractive nonnative interactions are critical for knot formation. In the absence of these interactions, i.e., in an energetics driven entirely by native interactions, knot formation is exceedingly unlikely. Further, we find, in concert with recent experimental data on YibK, two parallel folding pathways that we attribute to an early and a late formation of the trefoil knot, respectively. For both pathways, knot formation occurs before dimerization. A bioinformatics analysis of the SpoU family of proteins reveals further that the critical nonnative interactions may originate from evolutionary conserved hydrophobic segments around the knotted region.

Contact-induced structure transformation in transmembrane prion propagation

Based on recent experimental evidences of the transmission of prion diseases due to a particular transmembrane form (termed (Ctm)PrP), we propose a theoretical model for the molecular mechanism of such conformational diseases, in which a misfolded (Ctm)PrP induces a similar misfolding of another (Ctm)PrP. Computer simulations are performed to investigate the correlation between folding time and the concentration of misfolded PrP in various processes, including dimerization, trimerization, and cooperative dimerization. By comparing with the experimental correlation curve between incubation time and injected dose of scrapie prions, we conclude that cooperative dimerization may play an important role in the pathological mechanism of prion diseases.

Molecular dynamics simulation suggests possible interaction patterns at early steps of beta2-microglobulin aggregation

Early events in aggregation of proteins are not easily accessible by experiments. In this work, we perform a 5-ns molecular dynamics simulation of an ensemble of 27 copies of beta(2)-microglobulin in explicit solvent. During the simulation, the formation of intermolecular contacts is observed. The simulation highlights the importance of apical residues and, in particular, of those at the N-terminus end of the molecule. The most frequently found pattern of interaction involves a head-to-head contact arrangement of molecules. Hydrophobic contacts appear to be important for the establishment of long-lived (on the simulation timescale) contacts. Although early events on the pathway to aggregation and fibril formation are not directly related to the end-state of the process, which is reached on a much longer timescale, simulation results are consistent with experimental data and in general with a parallel arrangement of intermolecular beta-strand pairs.

One protein, two enzymes revisited: a structural entropy switch interconverts the two isoforms of acireductone dioxygenase

Acireductone dioxygenase (ARD) catalyzes different reactions between O2 and 1,2-dihydroxy-3-oxo-5-(methylthio)pent-1-ene (acireductone) depending upon the metal bound in the active site. Ni2+ -ARD cleaves acireductone to formate, CO and methylthiopropionate. If Fe2+ is bound (ARD'), the same substrates yield methylthioketobutyrate and formate. The two forms differ in structure, and are chromatographically separable. Paramagnetism of Fe2+ renders the active site of ARD' inaccessible to standard NMR methods. The structure of ARD' has been determined using Fe2+ binding parameters determined by X-ray absorption spectroscopy and NMR restraints from H98S ARD, a metal-free diamagnetic protein that is isostructural with ARD'. ARD' retains the beta-sandwich fold of ARD, but a structural entropy switch increases order at one end of a two-helix system that bisects the beta-sandwich and decreases order at the other upon interconversion of ARD and ARD', causing loss of the C-terminal helix in ARD' and rearrangements of residues involved in substrate orientation in the active site.

Functional clustering of yeast proteins from the protein-protein interaction network

BACKGROUND

The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins.

RESULTS

In the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the protein-protein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new protein-protein interactions that indeed have been reported recently.

CONCLUSION

Eigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions.

Interpreting the Aggregation Kinetics of Amyloid Peptides

Amyloid fibrils are insoluble mainly beta-sheet aggregates of proteins or peptides. The multi-step process of amyloid aggregation is one of the major research topics in structural biology and biophysics because of its relevance in protein misfolding diseases like Alzheimer's, Parkinson's, Creutzfeld-Jacob's, and type II diabetes. Yet, the detailed mechanism of oligomer formation and the influence of protein stability on the aggregation kinetics are still matters of debate. Here a coarse-grained model of an amphipathic polypeptide, characterized by a free energy profile with distinct amyloid-competent (i.e. beta-prone) and amyloid-protected states, is used to investigate the kinetics of aggregation and the pathways of fibril formation. The simulation results suggest that by simply increasing the relative stability of the beta-prone state of the polypeptide, disordered aggregation changes into fibrillogenesis with the presence of oligomeric on-pathway intermediates, and finally without intermediates in the case of a very stable beta-prone state. The minimal-size aggregate able to form a fibril is generated by collisions of oligomers or monomers for polypeptides with unstable or stable beta-prone state, respectively. The simulation results provide a basis for understanding the wide range of amyloid-aggregation mechanisms observed in peptides and proteins. Moreover, they allow us to interpret at a molecular level the much faster kinetics of assembly of a recently discovered functional amyloid with respect to the very slow pathological aggregation.

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).

Direct Observation of Protein Folding, Aggregation, and a Prion-like Conformational Conversion

Protein conformational transition from alpha-helices to beta-sheets precedes aggregation of proteins implicated in many diseases, including Alzheimer and prion diseases. Direct characterization of such transitions is often hindered by the complicated nature of the interaction network among amino acids. A recently engineered small protein-like peptide with a simple amino acid composition features a temperature-driven alpha-helix to beta-sheet conformational change. Here we studied the conformational transition of this peptide by molecular dynamics simulations. We observed a critical temperature, below which the peptide folds into an alpha-helical coiled-coil state and above which the peptide misfolds into beta-rich structures with a high propensity to aggregate. The structures adopted by this peptide during low temperature simulations have a backbone root mean square deviation less than 2 A from the crystal structure. At high temperatures, this peptide adopts an amyloid-like structure, which is mainly composed of coiled anti-parallel beta-sheets with the cross-beta-signature of amyloid fibrils. Most strikingly, we observed conformational conversions in which an alpha-helix is converted into a beta-strand by proximate stable beta-sheets with exposed hydrophobic surfaces and unsaturated hydrogen bonds. Our study suggested a possible generic molecular mechanism of the template-mediated aggregation process, originally proposed by Prusiner (Prusiner, S. B. (1998) Proc. Natl. Acad. Sci. U. S. A. 95, 13363-13383) to account for prion infectivity.