Analysis of dihedral angle preferences for alanine and glycine residues in alpha and beta transmembrane regions

SARS-CoV-2 Spike Protein Interaction Space

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a +sense single-strand RNA virus. The virus has four major surface proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N), respectively. The constitutive proteins present a high grade of symmetry. Identifying a binding site is difficult. The virion is approximately 50-200 nm in diameter. Angiotensin-converting enzyme 2 (ACE2) acts as the cell receptor for the virus. SARS-CoV-2 has an increased affinity to human ACE2 compared with the original SAR strain. Topological space, and its symmetry, is a critical component in molecular interactions. By exploring this space, a suitable ligand space can be characterized accordingly. A spike protein (S) computational model in a complex with ACE 2 was generated using silica methods. Topological spaces were probed using high computational throughput screening techniques to identify and characterize the topological space of both SARS and SARS-CoV-2 spike protein and its ligand space. In order to identify the symmetry clusters, computational analysis techniques, together with statistical analysis, were utilized. The computations are based on crystallographic protein data bank PDB-based models of constitutive proteins. Cartesian coordinates of component atoms and some cluster maps were generated and analyzed. Dihedral angles were used in order to compute a topological receptor space. This computational study uses a multimodal representation of spike protein interactions with some fragment proteins. The chemical space of the receptors (a dimensional volume) suggests the relevance of the receptor as a drug target. The spike protein S of SARS and SARS-CoV-2 is analyzed and compared. The results suggest a mirror symmetry of SARS and SARS-CoV-2 spike proteins. The results show thatSARS-CoV-2 space is variable and has a distinct topology. In conclusion, surface proteins grant virion variability and symmetry in interactions with a potential complementary target (protein, antibody, ligand). The mirror symmetry of dihedral angle clusters determines a high specificity of the receptor space.

Structural analysis of human G-protein-coupled receptor 17 ligand binding sites

The human G protein coupled membrane receptor (GPR17), the sensor of brain damage, is identified as a biomarker for many neurological diseases. In human brain tissue, GPR17 exist in two isoforms, long and short. While cryo-electron microscopy technology has provided the structure of the long isoform of GPR17 with Gi complex, the structure of the short isoform and its activation mechanism remains unclear. Recently, we theoretically modeled the structure of the short isoform of GPR17 with Gi signaling protein and identified novel ligands. In the present work, we demonstrated the presence of two distinct ligand binding sites in the short isoform of GPR17. The molecular docking of GPR17 with endogenous (UDP) and synthetic ligands (T0510.3657, MDL29950) found the presence of two distinct binding pockets. Our observations revealed that endogenous ligand UDP can bind stronger in two different binding pockets as evidenced by glide and autodock vina scores, whereas the other two ligand's binding with GPR17 has less docking score. The analysis of receptor-UDP interactions shows complexes' stability in the lipid environment by 100 ns atomic molecular dynamics simulations. The amino acid residues VAL83, ARG87, and PHE111 constitute ligand binding site 1, whereas site 2 constitutes ASN67, ARG129, and LYS232. Root mean square fluctuation analysis showed the residues 83, 87, and 232 with higher fluctuations during molecular dynamics simulation in both binding pockets. Our findings imply that the residues of GPR17's two binding sites are crucial, and their interaction with UDP reveals the protein's hidden signaling and communication properties. Furthermore, this finding may assist in the development of targeted therapies for the treatment of neurological diseases.

Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions

More than 60 prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the World Wide Web. Nearly, all of these predictors give balanced accuracies in the ~65%-~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.

Impact of orientation and flexibility of peptide linkers on T. maritima lipase Tm1350 displayed on Bacillus subtilis spores surface using CotB as fusion partner

Fusion protein construction often requires peptide linkers for prolonged conformation, extended stability and enzyme activity. In this study a series of fusion between Thermotoga maritima lipase Tm1350 and Bacillus subtillis coat protein CotB, comprising of several peptide linkers, with different length, flexibility and orientations were constructed. Effects of temperature, pH and chemicals were examined, on the activity of displayed enzyme. The fusion protein with longer flexible linkers L9 [(GGGGS)₄] and L7 (GGGGS-GGGGS-EAAAK-EAAAK-GGGGS-GGGGS) possess 1.29 and 1.16-fold higher activity than the original, under optimum temperature and pH respectively. Moreover, spore surface displaying Tm1350 with L3 (EAAAK-GGGGS) and L9 ((GGGGS)₄) showed extended thermostably, maintaining 1.40 and 1.35-fold higher activity than the original respectively, at 80 °C after 5 h of incubation. The enzyme activity of linkers with different orientation, including L5, L6 and L7 was determined, where L7 maintained 1.05 and 1.27-fold higher activity than L5 and L6. Effect of 0.1% proteinase K, bromelain, 20% ethanol and 30% methanol was investigated. Linkers with appropriate Glycine residues (flexible) showed higher activity than Alanine residues (rigid). The activity of the displayed enzyme can be improved by maintaining orientation and flexibility of peptide linkers, to evaluate high activity and stability in industrial processes.

Dihedral angle preferences of amino acid residues forming various non-local interactions in proteins

In theory, a polypeptide chain can adopt a vast number of conformations, each corresponding to a set of backbone rotation angles. Many of these conformations are excluded due to steric overlaps. Ramachandran and coworkers were the first to look into this problem by plotting backbone dihedral angles in a two-dimensional plot. The conformational space in the Ramachandran map is further refined by considering the energetic contributions of various non-bonded interactions. Alternatively, the conformation adopted by a polypeptide chain may also be examined by investigating interactions between the residues. Since the Ramachandran map essentially focuses on local interactions (residues closer in sequence), out of interest, we have analyzed the dihedral angle preferences of residues that make non-local interactions (residues far away in sequence and closer in space) in the folded structures of proteins. The non-local interactions have been grouped into different types such as hydrogen bond, van der Waals interactions between hydrophobic groups, ion pairs (salt bridges), and ππ-stacking interactions. The results show the propensity of amino acid residues in proteins forming local and non-local interactions. Our results point to the vital role of different types of non-local interactions and their effect on dihedral angles in forming secondary and tertiary structural elements to adopt their native fold.

Dihedral angle preferences of DNA and RNA binding amino acid residues in proteins

A protein can interact with DNA or RNA molecules to perform various cellular processes. Identifying or analyzing DNA/RNA binding site amino acid residues is important to understand molecular recognition process. It is quite possible to accurately model DNA/RNA binding amino acid residues in experimental protein-DNA/RNA complex by using the electron density map whereas, locating/modeling the binding site amino acid residues in the predicted three dimensional structures of DNA/RNA binding proteins is still a difficult task. Considering the above facts, in the present work, we have carried out a comprehensive analysis of dihedral angle preferences of DNA and RNA binding site amino acid residues by using a classical Ramachandran map. We have computed backbone dihedral angles of non-DNA/RNA binding residues and used as control dataset to make a comparative study. The dihedral angle preference of DNA and RNA binding site residues of twenty amino acid type is presented. Our analysis clearly revealed that the dihedral angles (φ, ψ) of DNA/RNA binding amino acid residues prefer to occupy (-89° to -60°, -59° to -30°) bins. The results presented in this paper will help to model/locate DNA/RNA binding amino acid residues with better accuracy.

Backbone Dihedral Angle Prediction

More than two decades of research have enabled dihedral angle predictions at an accuracy that makes them an interesting alternative or supplement to secondary structure prediction that provides detailed local structure information for every residue of a protein. The evolution of dihedral angle prediction methods is closely linked to advancements in machine learning and other relevant technologies. Consequently recent improvements in large-scale training of deep neural networks have led to the best method currently available, which achieves a mean absolute error of 19° for phi, and 30° for psi. This performance opens interesting perspectives for the application of dihedral angle prediction in the comparison, prediction, and design of protein structures.