Modeling the Structure, Dynamics, and Transformations of Proteins with the UNRES Force Field

Sieradzan, Adam K.; Czaplewski, Cezary; Krupa, Paweł; Mozolewska, M. A.; Karczyńska, Agnieszka; Lipska, Agnieszka G.; Lubecka, Emilia A.; Gołaś, Ewa; Wirecki, Tomasz; Makowski, Mariusz; Ołdziej, Stanisław; Liwo, Adam

doi:10.1007/978-1-0716-1716-8_23

Cited by 19 publications

(50 citation statements)

References 65 publications

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“…The coarse-grained simulations were conducted with the UNRES model of polypeptide chains [ 23 , 26 ], using the scale-consistent NEWCT-9P variant of the UNRES force field developed and parameterized in our recent work [ 24 , 25 ]. Briefly, UNRES is a highly coarse-grained model, in which a polypeptide chain is represented as a sequence of

-carbon (C

) atoms, with united sidechains attached by virtual bonds to the respective C

atoms and the united peptide groups located in the middle between two consecutive C

atoms.…”

Section: Methodsmentioning

confidence: 99%

“…The details of the model and the recent variant of the force field can be found in the references cited [ 23 , 24 , 25 , 26 ]. We used the parallelized UNRES program [ 48 ], which was recently heavily upgraded and optimized [ 49 ].…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we used the physics-based UNRES coarse-grained model [ 23 , 24 , 25 , 26 ] developed in our laboratory to investigate the association of 16 peptides, whose aggregation has already been studied experimentally [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], as well as of four new peptides. These peptides have sizes ranging from 6 to 32 amino-acid residues and varying contents of hydrophobic, polar and charged residues.…”

Section: Introductionmentioning

confidence: 99%

“…The other ones contain potentially aggregating sequences merged with charged sequences with the intent of making functionalized gels or films or are signaling peptides [ 31 , 32 ]. Even though the aggressive coarse-graining has been implemented in UNRES (only two sites per residue), it is capable of ab initio modeling the structure, dynamics, thermodynamics and free-energy landscapes of small and medium-size proteins and bioninformatics- and data-assisted modeling of large proteins, including oligomeric proteins [ 23 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

et al. 2022

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The UNited RESidue (UNRES) model of polypeptide chains was applied to study the association of 20 peptides with sizes ranging from 6 to 32 amino-acid residues. Twelve of those were potentially aggregating hexa- or heptapeptides excised from larger proteins, while the remaining eight contained potentially aggregating sequences, functionalized by attaching larger ends rich in charged residues. For 13 peptides, the experimental data of aggregation were used. The remaining seven were synthesized, and their properties were measured in this work. Multiplexed replica-exchange simulations of eight-chain systems were conducted at 12 temperatures from 260 to 370 K at concentrations from 0.421 to 5.78 mM, corresponding to the experimental conditions. The temperature profiles of the fractions of monomers and octamers showed a clear transition corresponding to aggregate dissociation. Low simulated transition temperatures were obtained for the peptides, which did not precipitate after incubation, as well as for the H-GNNQQNY-NH2 prion–protein fragment, which forms small fibrils. A substantial amount of inter-strand β-sheets was found in most of the systems. The results suggest that UNRES simulations can be used to assess peptide aggregation except for glutamine- and asparagine-rich peptides, for which a revision of the UNRES sidechain–sidechain interaction potentials appears necessary.

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-carbon (C

) atoms, with united sidechains attached by virtual bonds to the respective C

atoms and the united peptide groups located in the middle between two consecutive C

atoms.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

et al. 2022

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“…The very recent introduction of ML approaches such as AlfaFold2 [ 20 ] and RosettaFold [ 21 ] has colossally influenced and dramatically changed the field of protein structure prediction. However, to date, multiscale algorithms remain widely used methods to study long-time protein dynamics and aggregation [ 22 ]. The original Levitt–Warshel model has been continuously used, and its refined version has been recently published [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

2022

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The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.

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Optimization of parallel implementation ofUNRESpackage for coarse‐grained simulations to treat large proteins

et al. 2022

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We report major algorithmic improvements of the UNRES package for physics-based coarse-grained simulations of proteins. These include (i) introduction of interaction lists to optimize computations, (ii) transforming the inertia matrix to a pentadiagonal form to reduce computing and memory requirements, (iii) removing explicit angles and dihedral angles from energy expressions and recoding the most time-consuming energy/force terms to minimize the number of operations and to improve numerical stability, (iv) using OpenMP to parallelize those sections of the code for which distributed-memory parallelization involves unfavorable computing/communication time ratio, and (v) careful memory management to minimize simultaneous access of distant memory sections. The new code enables us to run molecular dynamics simulations of protein systems with size exceeding 100,000 amino-acid residues, reaching over 1 ns/day (1 μs/day in all-atom timescale) with 24 cores for proteins of this size.

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Modeling the Structure, Dynamics, and Transformations of Proteins with the UNRES Force Field

Cited by 19 publications

References 65 publications

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

Optimization of parallel implementation ofUNRESpackage for coarse‐grained simulations to treat large proteins

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