scholarly journals ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution

2019 ◽  
Vol 16 (1) ◽  
pp. 528-552 ◽  
Author(s):  
Chuan Tian ◽  
Koushik Kasavajhala ◽  
Kellon A. A. Belfon ◽  
Lauren Raguette ◽  
He Huang ◽  
...  
Keyword(s):  
Quantum Mechanics ◽  
Amino Acid ◽  
Specific Protein ◽  
Protein Backbone ◽  
Energy Surfaces

scholarly journals ff19SB: Amino-Acid Specific Protein Backbone Parameters Trained Against Quantum Mechanics Energy Surfaces in Solution

2019 ◽  
Author(s):  
Chuan Tian ◽  
Koushik Kasavajhala ◽  
Kellon Belfon ◽  
Lauren Raguette ◽  
He Huang ◽  
...  
Keyword(s):  
Amino Acids ◽  
Quantum Mechanics ◽  
Amino Acid ◽  
Protein Design ◽  
Md Simulations ◽  
Data Bank ◽  
Specific Protein ◽  
Water Model ◽  
Solvent Polarization ◽  
Energy Surfaces

<p>Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled ϕ/ψ parameters using 2D ϕ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, we have performed a total of ~5 milliseconds MD simulations in explicit solvent. Our results show that after amino-acid specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino acid specific Protein Data Bank (PDB) Ramachandran maps better, but also shows significantly improved capability to differentiate amino acid dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. </p>

scholarly journals ff19SB: Amino-Acid Specific Protein Backbone Parameters Trained Against Quantum Mechanics Energy Surfaces in Solution

2019 ◽  
Author(s):  
Chuan Tian ◽  
Koushik Kasavajhala ◽  
Kellon Belfon ◽  
Lauren Raguette ◽  
He Huang ◽  
...  
Keyword(s):  
Amino Acids ◽  
Quantum Mechanics ◽  
Amino Acid ◽  
Protein Design ◽  
Md Simulations ◽  
Data Bank ◽  
Specific Protein ◽  
Water Model ◽  
Solvent Polarization ◽  
Energy Surfaces

<p>Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled ϕ/ψ parameters using 2D ϕ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, we have performed a total of ~5 milliseconds MD simulations in explicit solvent. Our results show that after amino-acid specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino acid specific Protein Data Bank (PDB) Ramachandran maps better, but also shows significantly improved capability to differentiate amino acid dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. </p>

scholarly journals Combining statistical and neural network approaches to derive energy functions for completely flexible protein backbone design

2019 ◽  
Author(s):  
Bin Huang ◽  
Yang Xu ◽  
Haiyan Liu
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Conformational Space ◽  
Sequence Information ◽  
Protein Backbone ◽  
Energy Functions ◽  
Non Local ◽  
Energy Surfaces

AbstractA designable protein backbone is one for which amino acid sequences that stably fold into it exist. To design such backbones, a general method is much needed for continuous sampling and optimization in the backbone conformational space without specific amino acid sequence information. The energy functions driving such sampling and optimization must faithfully recapitulate the characteristically coupled distributions of multiplexes of local and non-local conformational variables in designable backbones. It is also desired that the energy surfaces are continuous and smooth, with easily computable gradients. We combine statistical and neural network (NN) approaches to derive a model named SCUBA, standing for Side-Chain-Unspecialized-Backbone-Arrangement. In this approach, high-dimensional statistical energy surfaces learned from known protein structures are analytically represented as NNs. SCUBA is composed as a sum of NN terms describing local and non-local conformational energies, each NN term derived by first estimating the statistical energies in the corresponding multi-variable space via neighbor-counting (NC) with adaptive cutoffs, and then training the NN with the NC-estimated energies. To determine the relative weights of different energy terms, SCUBA-driven stochastic dynamics (SD) simulations of natural proteins are considered. As initial computational tests of SCUBA, we apply SD simulated annealing to automatically optimize artificially constructed polypeptide backbones of different fold classes. For a majority of the resulting backbones, structurally matching native backbones can be found with Dali Z-scores above 6 and less than 2 Å displacements of main chain atoms in aligned secondary structures. The results suggest that SCUBA-driven sampling and optimization can be a general tool for protein backbone design with complete conformational flexibility. In addition, the NC-NN approach can be generally applied to develop continuous, noise-filtered multi-variable statistical models from structural data.Linux executables to setup and run SCUBA SD simulations are publicly available (http://biocomp.ustc.edu.cn/servers/download_scuba.php). Interested readers may contact the authors for source code availability.

scholarly journals Identification of critical amino acid residues in the regulatory N-terminal domain of PMEL

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Susan M. Mitchell ◽  
Morven Graham ◽  
Xinran Liu ◽  
Ralf M. Leonhardt
Keyword(s):  
Amino Acid ◽  
Pigment Cell ◽  
Specific Protein ◽  
Single Amino Acid ◽  
Amyloid Formation ◽  
Amino Acid Residues ◽  
Proteolytic Fragment ◽  
The Core ◽  
N Terminus ◽  
In Cis

AbstractThe pigment cell-specific protein PMEL forms a functional amyloid matrix in melanosomes onto which the pigment melanin is deposited. The amyloid core consists of a short proteolytic fragment, which we have termed the core-amyloid fragment (CAF) and perhaps additional parts of the protein, such as the PKD domain. A highly O-glycosylated repeat (RPT) domain also derived from PMEL proteolysis associates with the amyloid and is necessary to establish the sheet-like morphology of the assemblies. Excluded from the aggregate is the regulatory N-terminus, which nevertheless must be linked in cis to the CAF in order to drive amyloid formation. The domain is then likely cleaved away immediately before, during, or immediately after the incorporation of a new CAF subunit into the nascent amyloid. We had previously identified a 21 amino acid long region, which mediates the regulatory activity of the N-terminus towards the CAF. However, many mutations in the respective segment caused misfolding and/or blocked PMEL export from the endoplasmic reticulum, leaving their phenotype hard to interpret. Here, we employ a saturating mutagenesis approach targeting the motif at single amino acid resolution. Our results confirm the critical nature of the PMEL N-terminal region and identify several residues essential for PMEL amyloidogenesis.

scholarly journals Mechanism and kinetics for both thermal and electrochemical reduction of N2 catalysed by Ru(0001) based on quantum mechanics

2019 ◽  
Vol 21 (32) ◽  
pp. 17605-17612 ◽  
Author(s):  
Liang-Yu Chen ◽  
Tung-Chun Kuo ◽  
Zih-Siang Hong ◽  
Mu-Jeng Cheng ◽  
William A. Goddard
Keyword(s):  
Free Energy ◽  
Quantum Mechanics ◽  
Electrochemical Reduction ◽  
Mechanism And Kinetics ◽  
Energy Surfaces

QM calculations were used to predict the free energy surfaces for N2 thermal and electrochemical reduction (N2TR and N2ER) on Ru(0001), to find the detailed atomistic mechanism and kinetics, and provide the basis for improving the efficiency of N2ER.

scholarly journals An Overview of the Intrinsic Role of Citrullination in Autoimmune Disorders

2019 ◽  
Vol 2019 ◽  
pp. 1-39 ◽  
Author(s):  
Mohammed Alghamdi ◽  
Doaa Alasmari ◽  
Amjad Assiri ◽  
Ehab Mattar ◽  
Abdullah A. Aljaddawi ◽  
...  
Keyword(s):  
Cell Death ◽  
Amino Acid ◽  
Protein Interactions ◽  
Protein Denaturation ◽  
Immunological Tolerance ◽  
Specific Protein ◽  
Autoimmune Disorders ◽  
Autoantibody Production ◽  
Hydrogen Bond Formation ◽  
Wide Range

A protein undergoes many types of posttranslation modification. Citrullination is one of these modifications, where an arginine amino acid is converted to a citrulline amino acid. This process depends on catalytic enzymes such as peptidylarginine deiminase enzymes (PADs). This modification leads to a charge shift, which affects the protein structure, protein-protein interactions, and hydrogen bond formation, and it may cause protein denaturation. The irreversible citrullination reaction is not limited to a specific protein, cell, or tissue. It can target a wide range of proteins in the cell membrane, cytoplasm, nucleus, and mitochondria. Citrullination is a normal reaction during cell death. Apoptosis is normally accompanied with a clearance process via scavenger cells. A defect in the clearance system either in terms of efficiency or capacity may occur due to massive cell death, which may result in the accumulation and leakage of PAD enzymes and the citrullinated peptide from the necrotized cell which could be recognized by the immune system, where the immunological tolerance will be avoided and the autoimmune disorders will be subsequently triggered. The induction of autoimmune responses, autoantibody production, and cytokines involved in the major autoimmune diseases will be discussed.

scholarly journals Primary Structure of Human Platelet Factor 4.

1977 ◽  
Author(s):  
F.J. Morgan ◽  
G.S. Begg ◽  
C.N. Chesterman
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Structural Information ◽  
Human Platelet ◽  
Specific Protein ◽  
Platelet Factor 4 ◽  
Platelet Factor ◽  
Tryptic Peptides

The amino acid sequence of human platelet factor 4 (PF4) has been studied. PF4 is a platelet specific protein with antiheparin activity, released from platelets as a proteoglycan complex, whose measurement may provide an important index of platelet activation both in vivo and in vitro. These studies were undertaken to characterize fully the PF4 molecule. PF4 is a stable tetramer, composed of identical subunits, each with a molecular weight based on the sequence studies of approx. 7,770. Each PF4 subunit contains 69 amino acids, including 4 half-cystine (# 10, 12, 36, 37), one tyrosine (# 59), 3 arginine and 8 lysine, but no methionine, phenylalanine or tryptophan residues. The basic residues are predominantly in the C-terminal region. The tryptic peptides were aligned after studies which included tryptic digestion of citraconylated RCM-PF4, and automated Edman degradation of RCM-PF4 and citraconylated tryptic peptides. No glycopeptides were detected. This structural information should enable clear distinction to be made between PF4 and other platelet proteins such as β thromboglobulin. The provisional amino acid sequence of each subunit is:Glu-Ala-Glu-Glu-Asp-Gly-Asp-Leu-Gln-Cys-Leu-Cys-Val-Lys-Thr-Thr-Ser-Gln-Val-Arg-Pro-Arg-His-Ile-Thr-Ser-Leu-Glu-Val-Ile-Lys-Ala-Gly-Pro-His-Cys-Cys-Pro-Thr-Ala-Gln-Ile-Leu-Ala-Thr-Leu-Lys-Asn-Gly-Arg-Lys-Ile-Pro-Leu-Asp-Leu-Gln-Ala-Tyr-Leu-Lys-Ile-Lys(Lys, Lys, Ser, Glx, Leu, Leu)

scholarly journals Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis

2019 ◽  
Vol 116 (36) ◽  
pp. 17825-17830 ◽  
Author(s):  
John M. Nicoludis ◽  
Anna G. Green ◽  
Sanket Walujkar ◽  
Elizabeth J. May ◽  
Marcos Sotomayor ◽  
...  
Keyword(s):  
Amino Acid ◽  
Interaction Energy ◽  
Protein Interactions ◽  
Neuronal Development ◽  
Large Family ◽  
Specific Protein ◽  
Protein Family ◽  
Stable Complex ◽  
Residue Interaction ◽  
Clustered Protocadherins

Clustered protocadherins, a large family of paralogous proteins that play important roles in neuronal development, provide an important case study of interaction specificity in a large eukaryotic protein family. A mammalian genome has more than 50 clustered protocadherin isoforms, which have remarkable homophilic specificity for interactions between cellular surfaces. A large antiparallel dimer interface formed by the first 4 extracellular cadherin (EC) domains controls this interaction. To understand how specificity is achieved between the numerous paralogs, we used a combination of structural and computational approaches. Molecular dynamics simulations revealed that individual EC interactions are weak and undergo binding and unbinding events, but together they form a stable complex through polyvalency. Strongly evolutionarily coupled residue pairs interacted more frequently in our simulations, suggesting that sequence coevolution can inform the frequency of interaction and biochemical nature of a residue interaction. With these simulations and sequence coevolution, we generated a statistical model of interaction energy for the clustered protocadherin family that measures the contributions of all amino acid pairs at the interface. Our interaction energy model assesses specificity for all possible pairs of isoforms, recapitulating known pairings and predicting the effects of experimental changes in isoform specificity that are consistent with literature results. Our results show that sequence coevolution can be used to understand specificity determinants in a protein family and prioritize interface amino acid substitutions to reprogram specific protein–protein interactions.

Nuclear basic protein transition during sperm differentiation. Amino acid sequence of a spermatid-specific protein from the dog-fish Scylliorhinus caniculus

1987 ◽  
Vol 169 (1) ◽  
pp. 105-111 ◽  
Author(s):  
Muriel CHAUVIERE ◽  
Arlette MARTINAGE ◽  
Gilbert BRIAND ◽  
Pierre SAUTIERE ◽  
Philippe CHEVAILLIER
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Basic Protein ◽  
Specific Protein
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