Narrow Results

THz time domain spectroscopy of biomolecules was performed to determine applicability of the technique for chemical and conformational identification of biomolecules. Measurements were performed on samples of DNA, bovine serum albumin, collagen, hen egg white lysozyme, myoglobin, and bacteriorhodopsin as a function of temperature, hydration and photoexcitation. The results are compared to normal mode calculations. We demonstrate a clear resemblance of the observed broad and near featureless THz absorbance spectrum to the calculated density of normal modes. While the magnitude of the absorbance and the center of the broad response depends on biomolecular species, unique chemical identification would appear challenging. The observed dependence on hydration is in agreement with mass and dielectric loading, and the dependence on temperature is in agreement with decreasing conformational flexibility with reduced temperature. Finally THz absorbance dependence on the biomolecular conformation and mutation is demonstrated for bacteriorhodopsin.

The application of the multicanonical simulation method to small proteins and peptides seems to be feasible and should be undertaken. In this work, the three-dimensional structures of five common tetrapeptide sequences (QPGQ, QSGQ, YPTS, SPQQ and QPGY, in one letter code) in the repetitive central domain of HMW glutenin subunits are investigated by using the multicanonical simulation procedure. Ramachandran plots were prepared and analyzed to predict the relative occurrence probabilities of β-turn and γ-turn structures and helical states. Structural predictions of the five tetrapeptide sequences indicated the presence of high level of β-turns and considerable level of γ-turns. It was also possible to distinguish different type of turns and their occurrence probabilities.

The three-dimensional structures of two hexapeptide repeat motifs (PGQGQQ and SGQGQQ, in one letter code) in the repetitive central domain of HMW glutenin subunits are investigated by using the multicanonical simulation procedure. Ramachandran plots were prepared and analyzed to predict the relative occurrence probabilities of β-turn and γ-turn structures and helical state. Structural predictions of PGQGQQ repeat motif indicated the presence of high level of β-turns and considerable level of γ-turns. Simulations of the repeat motifs in the repetitive central domain of HMW glutenin subunits indicated that these structures take important part in the three-dimensional structures of repeat motifs.

A formal language is called factorizable if any substring of a word in it also belongs to the language. Symbolic sequences from symbolic dynamics make factorizable languages by definition. In studying avoided and under-represented strings in bacterial genomes we have defined a factorizable language for each complete genome. Recently, in studying the problem of uniqueness of reconstruction of a protein sequence from its constituent K-peptides we encounter again factorizable language which helps to build a finite state automaton to recognize the uniqueness of reconstruction. We present a brief review of these applications of factorizable languages from dynamics to biology.

Within this study we have calculated the surface fractal dimension (D_s) and the backbone fractal dimensions associated to the local folding (D₁) and to the global folding (D₂) for two unbiased sets of 50 proteins each, one for monomer and the other for homo- multimer proteins. The mean surface fractal dimension is D_s = 2.29 ± 0.02 for monomers and D_s = 2.21 ± 0.01 for multimers, the two means being significantly different. The mean backbone fractal dimensions associated to the local folding are D₁ = 1.34 ± 0.14 for monomers and D₁ = 1.33 ± 0.11 for multimers and those associated to the global folding are D₂ = 1.33 ± 0.05 for monomers and D₂ = 1.29 ± 0.04 for multimers, respectively. There are not significant differences between the mean values of the backbone fractal dimensions corresponding to monomers and multimers. These results suggest that there are different structural characteristics between monomer and multimer proteins only concerning their surface roughness, with multimers being smoother than monomers.

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A novel, direct technique has been developed to measure the interactions of bovine serum albumin (BSA) on surfaces by using atomic force microscopy (AFM) in a liquid environment. We have been able to measure adhesion forces between proteins and substrate surfaces in phosphate-buffered saline (PBS) solution directly, without any modification to the substrate and the AFM tip. Two different surfaces have been used in the measurements: mica (hydrophilic surface) and polystyrene (hydrophobic surface). The results show that a polystyrene surface has larger adhesion forces to BSA than a mica surface. This is consistent with previous research, which demonstrated that hydrophobic surfaces enhance protein adhesion but hydrophilic surfaces do not.

Electrostatic solvation modeling based upon the Poisson–Boltzmann equation is widely used in studies of biomolecular structures and functions. This manuscript provides a thorough review of published efforts to adapt the numerical Poisson–Boltzmann methods to molecular simulations so that these methods can be extended to biomolecular studies involving conformational fluctuation and/or dynamics. We first review the fundamental works on how to define the electrostatic free energy and the Maxwell stress tensor. These topics are followed by three different strategies in developing algorithms to compute electrostatic forces and how to improve their numerical performance. Finally procedures are also presented in detail on how to discretize these algorithms for numerical calculations. Given the pioneer works reviewed here, further developmental efforts will be on how to balance efficiency and accuracy in these theoretical sound approaches — two important issues in applying any numerical algorithms for routine biomolecular applications. Even if not reviewed here, more advanced numerical solvers are certainly necessary to achieve higher accuracy than the widely used classical methods to improve the overall performance of the numerical Poisson–Boltzmann methods.

We investigate the influence of three common definitions of the solute/solvent dielectric boundary (DB) on the accuracy of the electrostatic solvation energy ΔG_el computed within the Poisson–Boltzmann (PB) and the generalized Born (GB) models of implicit solvation. The test structures include small molecules, peptides and small proteins; explicit solvent ΔG_el are used as accuracy reference. For common atomic radii sets BONDI, PARSE (and ZAP9 for small molecules) the use of van der Waals (vdW) DB results, on average, in considerably larger errors in ΔG_el than the molecular surface (MS) DB. The optimal probe radius ρ_w for which the MS DB yields the most accurate ΔG_el varies considerably between structure types. The solvent accessible surface (SAS) DB becomes optimal at ρ_w ~ 0.2Å(exact value is sensitive to the structure and atomic radii), at which point the average accuracy of ΔG_el is comparable to that of the MS-based boundary. The geometric equivalence of SAS to vdW surface based on the same atomic radii uniformly increased by ρ_w gives the corresponding optimal vdW DB. For small molecules, the optimal vdW DB based on BONDI +0.2 Å radii can yield ΔG_el estimates at least as accurate as those based on the optimal MS DB. Also, in small molecules, pairwise charge–charge interactions computed with the optimal vdW DB are virtually equal to those computed with the MS DB, suggesting that in this case the two boundaries are practically equivalent by the electrostatic energy criteria. In structures other than small molecules, the optimal vdW and MS dielectric boundaries are not equivalent: the respective pairwise electrostatic interactions in the presence of solvent can differ by upto 5 kcal/mol for individual atomic pairs in small proteins, even when the total ΔG_el are equal. For small proteins, the average decrease in pairwise electrostatic interactions resulting from the switch from optimal MS to optimal vdW DB definition can be mimicked within the MS DB definition by doubling of the solute dielectric constant. However, the use of the higher interior dielectric does not eliminate the large individual deviations between pairwise interactions computed within the two DB definitions. It is argued that while the MS-based definition of the DB is more physically correct in some types of practical calculations, the choice is not so clear in some other common scenarios.

As a protein evolves, not every part of the amino acid sequence has an equal probability of being deleted or for allowing insertions, because not every amino acid plays an equally important role in maintaining the protein structure. However, the most prevalent models in fold recognition methods treat every amino acid deletion and insertion as equally probable events. We have analyzed the alignment patterns for homologous and analogous sequences to determine patterns of insertion and deletion, and used that information to determine the statistics of insertions and deletions for different amino acids of a target sequence. We define these patterns as insertion/deletion (indel) frequency arrays (IFAs). By applying IFAs to the protein threading problem, we have been able to improve the alignment accuracy, especially for proteins with low sequence identity. We have also demonstrated that the application of this information can lead to an improvement in fold recognition.

Empirical potentials for interaction of proteins with intracellular ions are presented. We derive the potentials using a training dataset of the protein 3D structure bank, PDB, based on the statistical analysis of contacts between ions and protein atoms of different types. The potentials are derived using Monte Carlo Reference State, simulating non-interacting structure elements as random 3D points in the structure space. The resulting potentials are detailed, continuous, and cover a wide range of contact distances. The obtained potentials were tested for prediction of ion-binding sites in proteins and are shown to reproduce locations and specificities of ion-binding sites with a high accuracy. A web server is created for predictions of ion-binding sites in proteins.

The secondary and tertiary structure of a protein has a primary role in determining its function. Even though many folding prediction algorithms have been developed in the past decades — mainly based on the assumption that folding instructions are encoded within the protein sequence — experimental techniques remain the most reliable to establish protein structures. In this paper, we searched for signals related to the formation of $\alpha$-helices. We carried out a statistical analysis on a large dataset of experimentally characterized secondary structure elements to find over- or under-occurrences of specific amino acids defining the boundaries of helical moieties. To validate our hypothesis, we trained various Machine Learning models, each equipped with an attention mechanism, to predict the occurrence of $\alpha$-helices. The attention mechanism allows to interpret the model’s decision, weighing the importance the predictor gives to each part of the input. The experimental results show that different models focus on the same subsequences, which can be seen as codes driving the secondary structure formation.

Blood cells are the most integral part of the body, which are made up of erythrocytes, platelets and white blood cells. The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual, accurate diagnosis, and efficient treatment strategies for diseases. Super-resolution microscopy (SRM) has recently emerged as a cutting-edge tool for the study of blood cells, providing numerous advantages over traditional methods for examining subcellular structures and proteins. In this paper, we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells. Furthermore, future prospects of SRM techniques in the analysis of blood cells are also discussed.