AWSEM-MD: Protein Structure Prediction Using Coarse-Grained Physical Potentials and Bioinformatically Based Local Structure Biasing

Davtyan, Aram; Schafer, Nicholas P.; Zheng, Weihua; Clementi, Cecilia; Wolynes, Peter G.; Papoian, Garegin A.

doi:10.1021/jp212541y

Cited by 290 publications

(387 citation statements)

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“…To overcome these problems, we use coarse-grained simulations of the aggregation of HTT exon 1-encoded fragments that use the AWSEM force field. While being efficient to simulate, the AWSEM force field can predict the structures of protein monomers (22)(23)(24) and predict details of assembly into oligomers (9,(25)(26)(27)(28). We have already used the model to explore the detailed mechanisms of aggregation of pure polyQ peptides (9) and the peptide implicated in Alzheimer's disease (28), Aβ40.…”

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confidence: 99%

Aggregation landscapes of Huntingtin exon 1 protein fragments and the critical repeat length for the onset of Huntington’s disease

Chen

Wolynes

2017

Proc. Natl. Acad. Sci. U.S.A.

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104

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Huntington's disease (HD) is a neurodegenerative disease caused by an abnormal expansion in the polyglutamine (polyQ) track of the Huntingtin (HTT) protein. The severity of the disease depends on the polyQ repeat length, arising only in patients with proteins having 36 repeats or more. Previous studies have shown that the aggregation of N-terminal fragments (encoded by HTT exon 1) underlies the disease pathology in mouse models and that the HTT exon 1 gene product can self-assemble into amyloid structures. Here, we provide detailed structural mechanisms for aggregation of several protein fragments encoded by HTT exon 1 by using the associative memory, water-mediated, structure and energy model (AWSEM) to construct their free energy landscapes. We find that the addition of the N-terminal 17-residue sequence (NT 17 ) facilitates polyQ aggregation by encouraging the formation of prefibrillar oligomers, whereas adding the C-terminal polyproline sequence (P 10 ) inhibits aggregation. The combination of both terminal additions in HTT exon 1 fragment leads to a complex aggregation mechanism with a basic core that resembles that found for the aggregation of pure polyQ repeats using AWSEM. At the extrapolated physiological concentration, although the grand canonical free energy profiles are uphill for HTT exon 1 fragments having 20 or 30 glutamines, the aggregation landscape for fragments with 40 repeats has become downhill. This computational prediction agrees with the critical length found for the onset of HD and suggests potential therapies based on blocking early binding events involving the terminal additions to the polyQ repeats.Huntington's disease | aggregation | solubility | aggregation free energy landscape | critical length H untingtin (HTT) is a huge protein (3,100 amino acid residues) that has been implicated in a variety of physiological functions (1, 2). Having an expanded polyglutamine (polyQ) region of 36 or more glutamine residues in the N terminus of HTT leads to Huntington's disease as witnessed by the deposition of large intracellular protein aggregates or inclusion bodies, which are comprised primarily of N-terminal fragments coded by the exon 1 sequence of the mutant HTT (2-5). These N-terminal fragments, each composed of an N-terminal 17-residue sequence (NT17), a polyQ sequence that has expanded in length, and a proline-rich region afterward, originate from proteolysis of HTT (2, 3). The aggregation of the HTT exon 1 product is, therefore, believed to cause Huntington's disease. Clinical studies have shown an inverse correlation between polyQ length and age of disease onset (5). By implicating polymer physics in the disease process, this regularity has intrigued biophysicists for decades.Expanded polyQ sequences, more generally, are associated with nine known neurodegenerative diseases (4). Biophysical chemists have, therefore, focused on first understanding the aggregation of pure polyQ peptides. The rate of aggregation of polyQ peptides is length-dependent, and primary nucleation is rate-limiti...

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confidence: 99%

Aggregation landscapes of Huntingtin exon 1 protein fragments and the critical repeat length for the onset of Huntington’s disease

Chen

Wolynes

2017

Proc. Natl. Acad. Sci. U.S.A.

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104

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“…Our force field extends AWSEM-a predictive, coarse-grained molecular dynamics Hamiltonian with transferable tertiary interactions and local-in-sequence interactions determined via bioinformatic pattern matching (12). This model does quite well for globular proteins.…”

Section: Ingredients Of Awsem-membranementioning

confidence: 94%

“…The ratio δE/ΔE also determines how confidently a structure can be assigned to being folded. Coarse-grained Hamiltonians that optimize δE/ΔE for a training set of proteins yield energy landscapes with transferable parameters have been shown to successfully predict structures of monomers with quite different sequences and also to predict dimer interfaces of globular proteins de novo (12,19,20). AWSEM's transferable tertiary interactions also equip the model to investigate problems such as the complex energy landscapes of designed proteins and multidomain protein misfolding as well as the mechanism of the initiation of aggregation (21)(22)(23).…”

Section: Ingredients Of Awsem-membranementioning

confidence: 99%

Predictive energy landscapes for folding α-helical transmembrane proteins

Kim

Schafer

Wolynes

2014

Proc. Natl. Acad. Sci. U.S.A.

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We explore the hypothesis that the folding landscapes of membrane proteins are funneled once the proteins' topology within the membrane is established. We extend a protein folding model, the associative memory, water-mediated, structure, and energy model (AWSEM) by adding an implicit membrane potential and reoptimizing the force field to account for the differing nature of the interactions that stabilize proteins within lipid membranes, yielding a model that we call AWSEM-membrane. Once the protein topology is set in the membrane, hydrophobic attractions play a lesser role in finding the native structure, whereas polar-polar attractions are more important than for globular proteins. We examine both the quality of predictions made with AWSEM-membrane when accurate knowledge of the topology and secondary structure is available and the quality of predictions made without such knowledge, instead using bioinformatically inferred topology and secondary structure based on sequence alone. When no major errors are made by the bioinformatic methods used to assign the topology of the transmembrane helices, these two types of structure predictions yield roughly equivalent quality structures. Although the predictive energy landscape is transferable and not structure based, within the correct topological sector we find the landscape is indeed very funneled: Thermodynamic landscape analysis indicates that both the total potential energy and the contact energy decrease as native contacts are formed. Nevertheless the near symmetry of different helical packings with respect to native contact formation can result in multiple packings with nearly equal thermodynamic occupancy, especially at temperatures just below collapse.energy landscape theory | molecular dynamics T he folding of globular proteins has come to be well understood starting from Anfinsen's thermodynamic hypothesis (1), by means of statistical energy landscape theory (2-5) and its principle of minimal frustration. Evolution selects the sequences of most globular proteins so that folding is, by and large, thermodynamically controlled and the landscape is dominated by the interactions between residues that are close together in the folded state, i.e., the native contacts. In vivo folding of α-helical transmembrane proteins differs from the usually autonomous folding of globular proteins in that, during translation, another actor, the translocon, generally assists the nascent chain in either translocating across or integrating peptides into the lipid membrane. Topology, by which we mean the "specification of the number of transmembrane helices and their in and/or out orientations across the membrane" (ref. 6, p. 909), in vivo is thus initially established cotranslationally with few exceptions. Large barriers between alternate topologies once the protein is folded, along with the involvement of the translocon catalyst, suggest a role for kinetic control in folding of α-helical transmembrane proteins. In light of these differences, what aspects of energy landscape theory,...

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“…20 All the umbrella sampling simulations for multiple peptide chains were performed at 300K for 20 million steps. 20 million steps corresponds to roughly 0.1 ms in laboratory time in the AWSEM force field.…”

Section: : Simulation Details Using the Physics-based Awsem Force Fimentioning

confidence: 99%

“…To overcome this difficulty, here we introduce a method for detecting amyloidogenic segments that is based on the Associative Memory, Water mediated, Structure and Energy Model (AWSEM), an optimized, coarse-grained, protein folding simulation model. 20 AWSEM has been fruitfully applied in recent years to many different problems of protein structure prediction, [20][21][22] protein association, 23 allosteric mechanism 24 and protein aggregation. [25][26][27][28][29] The AWSEM-Amylometer is based on the same energy model that is used in AWSEM molecular dynamics simulations but is able to detect amyloidogenic segments using a simple and efficient threading scheme over multiple fiber template structures.…”

Section: Introductionmentioning

confidence: 99%

The AWSEM-Amylometer: predicting amyloid propensity and fibril topology using an optimized folding landscape model

Chen

Schafer

Zheng

et al. 2017

Preprint

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Amyloids are fibrillar protein aggregates with simple repeated structural motifs in their cores, usually β-strands but sometimes α-helices. Identifying the amyloid-prone regions within protein sequences is important both for understanding the mechanisms of amyloid-associated diseases and for understanding functional amyloids. Based on the crystal structures of seven cross-β amyloidogenic peptides with different topologies and one recently solved cross-α fiber structure, we have developed a computational approach for identifying amyloidogenic segments in protein sequences using the 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/138842 doi: bioRxiv preprint first posted online May. 17, 2017; aggregation-prone sequences in multiple datasets. The method also predicts the specific topologies (the relative arrangement of β-strands in the core) of the amyloid fibrilswell. An important advantage of the AWSEM-Amylometer over other existing methods is its direct connection with an efficient, optimized protein folding simulation model, AWSEM. This connection allows one to combine efficient and accurate search of protein sequences for amyloidogenic segments with the detailed study of the thermodynamic and kinetic roles that these segments play in folding and aggregation in the context of the entire protein sequence. We present new simulation results that highlight the free energy landscapes of peptides that can take on multiple fibril topologies. We also demonstrate how the Amylometer methodology can be straightforwardly extended to the study of functional amyloids that have the recently discovered cross-α fibril architecture.

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AWSEM-MD: Protein Structure Prediction Using Coarse-Grained Physical Potentials and Bioinformatically Based Local Structure Biasing

Cited by 290 publications

References 39 publications

Aggregation landscapes of Huntingtin exon 1 protein fragments and the critical repeat length for the onset of Huntington’s disease

Aggregation landscapes of Huntingtin exon 1 protein fragments and the critical repeat length for the onset of Huntington’s disease

Predictive energy landscapes for folding α-helical transmembrane proteins

The AWSEM-Amylometer: predicting amyloid propensity and fibril topology using an optimized folding landscape model

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