Conformational States of the GDP- and GTP-Bound HRAS Affected by A59E and K117R: An Exploration from Gaussian Accelerated Molecular Dynamics.

The HRAS protein is considered a critical target for drug development in cancers. It is vital for effective drug development to understand the effects of mutations on the binding of GTP and GDP to HRAS. We conducted Gaussian accelerated molecular dynamics (GaMD) simulations and free energy landscape (FEL) calculations to investigate the impacts of two mutations (A59E and K117R) on GTP and GDP binding and the conformational states of the switch domain. Our findings demonstrate that these mutations not only modify the flexibility of the switch domains, but also affect the correlated motions of these domains. Furthermore, the mutations significantly disrupt the dynamic behavior of the switch domains, leading to a conformational change in HRAS. Additionally, these mutations significantly impact the switch domain's interactions, including their hydrogen bonding with ligands and electrostatic interactions with magnesium ions. Since the switch domains are crucial for the binding of HRAS to effectors, any alterations in their interactions or conformational states will undoubtedly disrupt the activity of HRAS. This research provides valuable information for the design of drugs targeting HRAS.

Molecular dynamics simulations to study the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides.

The hydrophobic amino acid biphenylalanine (B) plays a key role in the antibacterial activity of ultrashort peptides. In this study, the interactions of tetrapeptide BRBR-NH2 (BRBR) and pentapeptide BRBRB-NH2 (BRBRB) with dioleoylphosphatidylcholine/dioleoylphosphatidylglycerol (DOPC/DOPG) mixed model membrane were studied by molecular dynamics simulation to assess the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides. At low peptide concentrations, both peptides presented amphiphilic conformations; residues B of the pentapeptide approached the membrane faster than those of the tetrapeptide and made more contacts with the membrane; BRBRB exhibited stronger membrane affinity than BRBR. However, due to the low peptide concentrations, the effects of these two peptides on the membrane were not significantly different. At high peptide concentrations, the strong affinity of BRBRB made it have more interaction with membrane than BRBR and most residues B of BRBRB inserted into the membrane; BRBRB was more prone to aggregation and caused the membrane more disordered and thinner than BRBR. Hydrophobic residues often act as anchors in the antibacterial activity of ultrashort antimicrobial peptides. Adding a hydrophobic residue B to the C-terminal of BRBR could improve the ability of the peptide to "grasp" the membrane. At high peptide concentrations, the addition of residue B might enhance the antibacterial activity of the peptide. Thus, our results will be helpful in designing efficient antibacterial drugs.

EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments.

Long non-coding RNAs (lncRNAs) play important functional roles in many diverse biological processes. However, not all expressed lncRNAs are functional. Thus, it is necessary to manually collect all experimentally validated functional lncRNAs (EVlncRNA) with their sequences, structures, and functions annotated in a central database. The first release of such a database (EVLncRNAs) was made using the literature prior to 1 May 2016. Since then (till 15 May 2020), 19 245 articles related to lncRNAs have been published. In EVLncRNAs 2.0, these articles were manually examined for a major expansion of the data collected. Specifically, the number of annotated EVlncRNAs, associated diseases, lncRNA-disease associations, and interaction records were increased by 260%, 320%, 484% and 537%, respectively. Moreover, the database has added several new categories: 8 lncRNA structures, 33 exosomal lncRNAs, 188 circular RNAs, and 1079 drug-resistant, chemoresistant, and stress-resistant lncRNAs. All records have checked against known retraction and fake articles. This release also comes with a highly interactive visual interaction network that facilitates users to track the underlying relations among lncRNAs, miRNAs, proteins, genes and other functional elements. Furthermore, it provides links to four new bioinformatics tools with improved data browsing and searching functionality. EVLncRNAs 2.0 is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/.

Experimentally Validated Plant lncRNAs in EVLncRNAs Database.

Plant long noncoding RNAs (lncRNAs) play important functional roles in various biological processes. Most databases deposit all plant lncRNA candidates produced by high-throughput experimental and/or computational techniques. There are several databases for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases, or interactions). Thus, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to May 1, 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer, and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species, including 428 plant lncRNAs from 44 plant species. Compared to PLNIncRBase, our dataset does not contain any lncRNAs from microarray and deep sequencing. Moreover, 40% of entries contain new information (interaction and additional information from NCBI and Ensembl). The database allows users to browse, search, and download as well as to submit experimentally validated lncRNAs. The database is available at http://biophy.dzu.edu.cn/EVLncRNAs .

Molecular Dynamics Simulations of Human Antimicrobial Peptide LL-37 in Model POPC and POPG Lipid Bilayers.

Cathelicidins are a large family of cationic antimicrobial peptides (AMPs) found in mammals with broad spectrum antimicrobial activity. LL-37 is the sole amphipathic α-helical AMP from human Cathelicidins family. In addition to its bactericidal capability, LL-37 has antiviral, anti-tumor, and immunoregulatory activity. Despite many experimental studies, its molecular mechanism of action is not yet fully understood. Here, we performed three independent molecular dynamics simulations (600 ns or more) of a LL-37 peptide in the presence of 256 lipid bilayers with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG) mimicking bacterial and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) mimicking mammalian membranes. We found that LL-37 can be quickly absorbed onto the POPG bilayer without loss of its helical conformation in the core region and with the helix lying in parallel to the bilayer. The POPG bilayer was deformed. In contrast, LL-37 is slower in reaching the POPC surface and loss much of its helical conformation during the interaction with the bilayer. LL-37 only partially entered the POPC bilayer without significant deformation of the membrane. The observed difference for different bilayers is largely due to the fact that LL-37 is positively charged, POPG is negatively charged, and POPC is neutral. Our simulation results demonstrated the initial stage of disruption of the bacterial membrane by LL-37 in atomic details. Comparison to experimental results on LL-37 and simulation studies in other systems was made.

EVLncRNAs: a manually curated database for long non-coding RNAs validated by low-throughput experiments.

Long non-coding RNAs (lncRNAs) play important functional roles in various biological processes. Early databases were utilized to deposit all lncRNA candidates produced by high-throughput experimental and/or computational techniques to facilitate classification, assessment and validation. As more lncRNAs are validated by low-throughput experiments, several databases were established for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases or interactions). Thus, it is highly desirable to have a comprehensive dataset for experimentally validated lncRNAs as a central repository for all of their structures, functions and phenotypes. Here, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to 1 May 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species that is 2.9 times larger than the current largest database for experimentally validated lncRNAs. Seventy-four percent lncRNA entries are partially or completely new, comparing to all existing experimentally validated databases. The established database allows users to browse, search and download as well as to submit experimentally validated lncRNAs. The database is available at http://biophy.dzu.edu.cn/EVLncRNAs.

Revealing the binding modes and the unbinding of 14-3-3σ proteins and inhibitors by computational methods.

The 14-3-3σ proteins are a family of ubiquitous conserved eukaryotic regulatory molecules involved in the regulation of mitogenic signal transduction, apoptotic cell death, and cell cycle control. A lot of small-molecule inhibitors have been identified for 14-3-3 protein-protein interactions (PPIs). In this work, we carried out molecular dynamics (MD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method to study the binding mechanism between a 14-3-3σ protein and its eight inhibitors. The ranking order of our calculated binding free energies is in agreement with the experimental results. We found that the binding free energies are mainly from interactions between the phosphate group of the inhibitors and the hydrophilic residues. To improve the binding free energy of Rx group, we designed the inhibitor R9 with group R9 = 4-hydroxypheny. However, we also found that the binding free energy of inhibitor R9 is smaller than that of inhibitor R1. By further using the steer molecular dynamics (SMD) simulations, we identified a new hydrogen bond between the inhibitor R8 and residue Arg64 in the pulling paths. The information obtained from this study may be valuable for future rational design of novel inhibitors, and provide better structural understanding of inhibitor binding to 14-3-3σ proteins.

Turn-directed α-ß conformational transition of α-syn12 peptide at different pH revealed by unbiased molecular dynamics simulations.

The transition from α-helical to ß-hairpin conformations of α-syn12 peptide is characterized here using long timescale, unbiased molecular dynamics (MD) simulations in explicit solvent models at physiological and acidic pH values. Four independent normal MD trajectories, each 2500 ns, are performed at 300 K using the GROMOS 43A1 force field and SPC water model. The most clustered structures at both pH values are ß-hairpin but with different turns and hydrogen bonds. Turn9-6 and four hydrogen bonds (HB9-6, HB6-9, HB11-4 and HB4-11) are formed at physiological pH; turn8-5 and five hydrogen bonds (HB8-5, HB5-8, HB10-3, HB3-10 and HB12-1) are formed at acidic pH. A common folding mechanism is observed: the formation of the turn is always before the formation of the hydrogen bonds, which means the turn is always found to be the major determinant in initiating the transition process. Furthermore, two transition paths are observed at physiological pH. One of the transition paths tends to form the most-clustered turn and improper hydrogen bonds at the beginning, and then form the most-clustered hydrogen bonds. Another transition path tends to form the most-clustered turn, and turn5-2 firstly, followed by the formation of part hydrogen bonds, then turn5-2 is extended and more hydrogen bonds are formed. The transition path at acidic pH is as the same as the first path described at physiological pH.

Why the OPLS-AA force field cannot produce the ß-hairpin structure of H1 peptide in solution when comparing with the GROMOS 43A1 force field?

The optimal combination of force field and water model is an essential problem that is able to increase molecular dynamics simulation quality for different types of proteins and peptides. In this work, an attempt has been made to explore the problem by studying H1 peptide using four different models based on different force fields, water models and electrostatic schemes. The driving force for H1 peptide conformation transition and the reason why the OPLS-AA force field cannot produce the ß-hairpin structure of H1 peptide in solution while the GROMOS 43A1 force field can do were investigated by temperature replica exchange molecular dynamics simulation (T-REMD). The simulation using the GROMOS 43A1 force field preferred to adopt a ß-hairpin structure, which was in good agreement with the several other simulations and the experimental evidences. However, the simulation using the OPLS-AA force field has a significant difference from the simulations with the GROMOS 43A1 force field simulation. The results show that the driving force in H1 peptide conformation transition is solvent exposure of its hydrophobic residues. However, the subtle balances between residue-residue interactions and residue-solvent interaction are disrupted by using the OPLS-AA force field, which induced the reduction in the number of residue-residue contact. Similar solvent exposure of the hydrophobic residues is observed for all the conformations sampled using the OPLS-AA force field. For H1 peptide which exhibits large solvent exposure of the hydrophobic residues, the GROMOS 43A1 force field with the SPC water model can provide more accurate results.

Novel strategies for drug discovery based on Intrinsically Disordered Proteins (IDPs).

Intrinsically disordered proteins (IDPs) are proteins that usually do not adopt well-defined native structures when isolated in solution under physiological conditions. Numerous IDPs have close relationships with human diseases such as tumor, Parkinson disease, Alzheimer disease, diabetes, and so on. These disease-associated IDPs commonly play principal roles in the disease-associated protein-protein interaction networks. Most of them in the disease datasets have more interactants and hence the size of the disease-associated IDPs interaction network is simultaneously increased. For example, the tumor suppressor protein p53 is an intrinsically disordered protein and also a hub protein in the p53 interaction network; α-synuclein, an intrinsically disordered protein involved in Parkinson diseases, is also a hub of the protein network. The disease-associated IDPs may provide potential targets for drugs modulating protein-protein interaction networks. Therefore, novel strategies for drug discovery based on IDPs are in the ascendant. It is dependent on the features of IDPs to develop the novel strategies. It is found out that IDPs have unique structural features such as high flexibility and random coil-like conformations which enable them to participate in both the "one to many" and "many to one" interaction. Accordingly, in order to promote novel strategies for drug discovery, it is essential that more and more features of IDPs are revealed by experimental and computing methods.

Structural and thermodynamics characters of isolated α-syn12 peptide: long-time temperature replica-exchange molecular dynamics in aqueous solution.

The structural and thermodynamics characters of α-syn12 (residues 1-12 of the human α-synuclein protein) peptide in aqueous solution were investigated through temperature replica-exchange molecular dynamics (T-REMD) simulations with the GROMOS 43A1 force field. The two independent T-REMD simulations were completed starting from an initial conformational α-helix and an irregular structure, respectively. Each replica was run for 300 ns. The structural and thermodynamics characters were studied based on parameters such as distributions of backbone dihedral angles, free energy surface, stability of folded ß-hairpin structure, and favorite conformations. The results showed that the isolated α-syn12 peptide in water adopted four different conformational states: the first state was a ß-hairpin ensemble with Turn(9-6) and four hydrogen bonds, the second state was a ß-hairpin ensemble with two turns (Turn(9-6) and Turn(5-2)) and three hydrogen bonds, the third state was a disordered structure with both Turn(8-5) and Turn(5-2), and the last state was a π-helix ensemble. Meanwhile, we studied the free energy change of α-syn12 peptide from the unfolded state to the ß-hairpin state, which was in good agreement with the experiments and molecular dynamics simulations for some other peptides. We also analyzed the driving force of the peptide transition. The results indicated that the driving forces were high solvent exposure of hydrophobic Leu8 and hydrophobic residues in secondary structure. To our knowledge, this was the first report to study the isolated α-syn12 peptide in water by T-REMD.

Effects of pH and temperature on the structural and thermodynamic character of alpha-syn12 peptide in aqueous solution.

The structural and thermodynamic characters of alpha-syn12 peptide in aqueous solution at different pH and temperatures have been investigated through temperature replica exchange molecular dynamics (T-REMD) simulations with GROMOS 43A1 force field. The two independent T-REMD simulations were completed at pH = 7.0 and 10.0, respectively. Each replica was run for 300 ns. The structural and thermodynamic characters of alpha syn12 peptide were studied based on the distributions of backbone dihedral angles, the free energy surface, and the stability of different type structure and the favorite conformations of the peptide. The results showed that the simulation at pH = 10.0 produced more sampling in alpha region than the simulation at pH = 7.0. The temperature changes from 283 K to 308 K result in negligible effects on the distributions of backbone dihedral angle. The beta hairpin conformation with Turn(9-6) and four hydrogen bonds (HB(4-11), HB(6-9), HB(9-6) and HB(11-4)) is the lowest free energy state in the simulation at pH = 7.0. However, for the simulation at pH = 10.0, the lowest free energy state corresponds to a structure with Turn(9-6) and two hydrogen bonds (HB(6-10) and HB(10-6)) induced by an overly strong residue-residue interaction effect between lysine residues. For the simulation at pH = 7.0, the free energy change of the alpha-syn12 peptide from the unfolded state to the beta hairpin state was in good agreement with the experiments and molecular dynamics simulation results for the other beta-peptides, the beta hairpin state of the alpha-syn12 peptide included the conformations that not only the Turn(9-6) is formed, but also the terminus are closed together in space. However, the subtle balances between lysine-lysine interactions and lysine-solvent interaction are disrupted in the simulation at pH = 10.0, which induced the assembly of lysine residues, the beta hairpin conformation is destabilized by the deprotonation of the Lys side chain. This study can help us to understand the conformation changes and the thermodynamic character of alpha;-syn12 peptide at atomic level induced by changing pH and temperature, which is propitious to reveal the nosogenesis of Parkinson disease. In our knowledge, this is the first report to study the influence of pH and temperature on isolated alpha-syn12 peptide in water by T-REMD.

A comparative study of two different force fields on structural and thermodynamics character of H1 peptide via molecular dynamics simulations.

The dynamics and thermodynamics character of H1 peptide in aqueous solution has been investigated through temperature replica exchange molecular dynamics (T-REMD) simulations using two different force fields (OPLS-AA and GROMOS 43A1). The two independent T-REMD simulations were completed starting from initial conformations alpha-helix and beta-sheet, respectively. Each replica was run for 300 ns. The performance of each force field was assessed from the parameters such as the distributions of backbone dihedral angles, the number of native hydrogen bond, root mean square deviations (RMSD) of C(alpha) atoms and all heavy atoms, formation of beta-turn, the stability of folded beta-hairpin structure and the favorite conformations of different force fields. The simulation using GROMOS 43A1 force field starting from alpha-helix structure sampled the conformation cluster which C(alpha) RMSD was 0.05 nm from beta-sheet structure and the cluster contains 39% of all conformations. The simulation using OPLS-AA force field produced more sampling in P(II)region than in GROMOS 43A1 force field. The both force field simulations produced some sampling in the alpha region, but the probabilities of the conformations including any helical content were only 1-2%. Under the both force fields, the beta-turn structures exhibited higher stability than alpha-helix structures and the folded beta-hairpin structures. In the GROMOS 43A1 force field, the free energy change from the unfolded state to the hairpin state was in good agreement with the results of several experiments about some beta-peptides (not the H1 peptide) and the other molecular dynamics simulations of H1 peptide. However, the folded beta-hairpin structure was more destabilized in the OPLS-AA force field than in the GROMOS 43A1 force field and experiments.

Using free energy perturbation to predict effects of changing force field parameters on computed conformational equilibriums of peptides.

We propose a method that may allow data about the conformational equilibriums of peptides to enter the parameter calibration phase in force field developments. The method combines free energy perturbation with techniques for extensive sampling in the conformational space. It predicts shifts in computed conformational equilibriums in response to separate or combined perturbations of force field parameters. As an example we considered a force field associated with an implicit solvent model. We considered two different approaches to define conformational states of four peptides. One is based on reaction coordinates and two-dimensional free energy surfaces. The other is based on the clustering analysis of sampled conformations. Effects of perturbing various model parameters on the equilibriums between nativelike states with other conformational states were considered. For one type of perturbation predicted to have consistent effects on different peptides, the predictions have been verified by actual simulations using a perturbed model.