Ab initio modelling of the N-terminal domain of the secretin receptors

COVID-19 Vaccines: Computational tools and Development

The 2019 coronavirus outbreak, also known as COVID-19, poses a serious threat to global health and has already had widespread, devastating effects around the world. Scientists have been working tirelessly to develop vaccines to stop the virus from spreading as much as possible, as its cure has not yet been found. As of December 2022, 651,918,402 cases and 6,656,601 deaths had been reported. Globally, over 13 billion doses of vaccine have been administered, representing 64.45% of the world's population that has received the vaccine. To expedite the vaccine development process, computational tools have been utilized. This paper aims to analyze some computational tools that aid vaccine development by presenting positive evidence for proving the efficacy of these vaccines to suppress the spread of the virus and for the use of computational tools in the development of vaccines for emerging diseases.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.

Human insulin modulates α-synuclein aggregation via DAF-2/DAF-16 signalling pathway by antagonising DAF-2 receptor in C. elegans model of Parkinson's disease

Insulin-signalling is an important pathway in multiple cellular functions and organismal ageing across the taxa. A strong association of insulin-signalling with Parkinson’s disease (PD) has been proposed but the exact nature of molecular events and genetic associations are yet to be understood. We employed transgenic C. elegans strain harboring human α-synuclein::YFP transgene, towards studying the aggregation pattern of α-synuclein, a PD-associated endpoint, under human insulin (Huminsulin®) treatment and DAF-16/DAF-2 knockdown conditions, independently and in combination. The aggregation was increased when DAF-16 was knocked-down independently or alongwith a co-treatment of Human insulin (HumINS) and decreased when DAF-2 was knocked-down independently or alongwith a co-treatment of HumINS; whereas HumINS treatment per se, reduced the aggregation. Our results depicted that HumINS decreases α-synuclein aggregation via DAF-2/DAF-16 pathway by acting as an antagonist for DAF-2 receptor. Knockdown of reported DAF-2 agonist (INS-6) and antagonists (INS-17 and INS-18) also resulted in a similar effect on α-synuclein aggregation. Further by utilizing bioinformatics tools, we compared the differences between the binding sites of probable agonists and antagonists on DAF-2 including HumINS. Our results suggest that HumINS treatment and DAF-16 expression play a protective role against α-synuclein aggregation and its associated effects.

Similarity between class A and class B G-protein-coupled receptors exemplified through calcitonin gene-related peptide receptor modelling and mutagenesis studies

Modelling class B G-protein-coupled receptors (GPCRs) using class A GPCR structural templates is difficult due to lack of homology. The plant GPCR, GCR1, has homology to both class A and class B GPCRs. We have used this to generate a class A–class B alignment, and by incorporating maximum lagged correlation of entropy and hydrophobicity into a consensus score, we have been able to align receptor transmembrane regions. We have applied this analysis to generate active and inactive homology models of the class B calcitonin gene-related peptide (CGRP) receptor, and have supported it with site-directed mutagenesis data using 122 CGRP receptor residues and 144 published mutagenesis results on other class B GPCRs. The variation of sequence variability with structure, the analysis of polarity violations, the alignment of group-conserved residues and the mutagenesis results at 27 key positions were particularly informative in distinguishing between the proposed and plausible alternative alignments. Furthermore, we have been able to associate the key molecular features of the class B GPCR signalling machinery with their class A counterparts for the first time. These include the [K/R]KLH motif in intracellular loop 1, [I/L]xxxL and KxxK at the intracellular end of TM5 and TM6, the NPXXY/VAVLY motif on TM7 and small group-conserved residues in TM1, TM2, TM3 and TM7. The equivalent of the class A DRY motif is proposed to involve Arg^2.39, His^2.43 and Glu^3.46, which makes a polar lock with T^6.37. These alignments and models provide useful tools for understanding class B GPCR function.

Protein knots and fold complexity: Some new twists

The current knowledge on topological knots in protein structure is reviewed, considering in turn, knots with three, four and five strand crossings. The latter is the most recent to be identified and has two distinct topological forms. The knot observed in the protein structure is the form that requires the least number of strand crossings to become un-knotted. The position of the chain termini must also correspond to a position that allows (un) knotting in one move. This is postulated as a general property of protein knots and other more complex knots with this property are proposed as the next most likely knots that might be found in a protein. It is also noted that the "Jelly-roll" fold found in some all-beta proteins would provide likely candidates. Alternative measures of knottedness and entanglement are reviewed, including the occurrence of slip-knots. These measures are related to the complexity of the protein fold and may provide useful filters for selecting predicted model structures.

Approaches to the molecular modeling of 7-transmembrane helical receptors

7-Transmembrane helical receptors (7TMs) represent the single most important class of target for drug therapy; therefore, a great deal of effort has gone into computational studies of their structures. Historically, these were based on low resolution electron diffraction data, together with the use of computational methods such as multiple sequence alignments, distance geometry, and molecular mechanics calculations. In the year 2000 the situation changed when the first crystal structure of a 7TM, was published. It was then possible to use the homology modeling techniques to generate more accurate models of these proteins. This unit reviews the modeling of 7TMs and describes in detail how homology modeling can be used to build a structure of the 5-HT2a receptor. Special attention is given to the initial sequence alignment, the most important step in the process. Use of automatic alignment programs often produces incorrect results, and manual intervention is necessary before proceeding further.

Dynamic domain threading

A modeling method is described that avoids the need to consider the domain structure of the template used for modeling, and automatically extracts compact fragments of structure that would be of a suitable size to build the model. This aids automation as the size or nature of the template structure can be ignored and does not have to be broken into domain (or multi-domain) units beforehand. The approach leads to the generation of a large number of models each based on slightly differing domain definitions and this variation was further increased by considering alternative secondary structure predictions. Each model, of which there may be thousands, takes the form of a complete alpha-carbon trace and some methods (including residue burial) were investigated for their power to discriminate good models from bad models using decoys. The method is also compared to an earlier retroviral capsid modeling problem for which the X-ray structure is now known. Some potential extensions of the approach to more distant modeling problems are discussed.

A simple approach for protein structure discrimination based on the network pattern of conserved hydrophobic residues

Evolutionarily conserved hydrophobic residues at the core of protein structures are generally assumed to play a structural role in protein folding and stability. Recent studies have implicated that their importance to protein structures is uneven, with a few of them being crucial and the rest of them being secondary. In this work, we explored the possibility of employing this feature of native structures for discriminating non-native structures from native ones. First, we developed a network tool to quantitatively measure the structural contributions of individual amino acid residues. We systematically applied this method to diverse fold-type sets of native proteins. It was confirmed that this method could grasp the essential structural features of native proteins. Next, we applied it to a number of decoy sets of proteins. The results indicate that such an approach indeed identified non-native structures in most test cases. This finding should be of help for the investigation of the fundamental problem of protein structure prediction.

Paired cysteine mutagenesis to establish the pattern of disulfide bonds in the functional intact secretin receptor

The amino-terminal domain of class B G protein-coupled receptors contains six conserved cysteine residues involved in structurally and functionally critical disulfide bonds. The mapping of these bonds has been unclear, with one pattern based on biochemical and NMR structural characterizations of refolded, nonglycosylated amino-terminal fragments, and another pattern derived from functional characterizations of intact receptors having paired cysteine mutations. In the present study, we determined the disulfide bonding pattern of the prototypic class B secretin receptor by applying the same paired cysteine mutagenesis approach and confirming the predicted bonding pattern with proteolytic cleavage of intact functional receptor. As expected, systematic mutation to serine of the six conserved cysteine residues within this region of the secretin receptor singly and in pairs resulted in loss of function of most constructs. Notable exceptions were single mutations of the 4th and 6th cysteine residues and paired mutations involving the 1st and 3rd, 2nd and 5th, and 4th and 6th conserved cysteines, with secretin eliciting statistically significant cAMP responses above basal levels of activation for each of these constructs. Immunofluorescence microscopy confirmed similar levels of plasma membrane expression for each of the mutated receptors. Furthermore, cyanogen bromide cleaved a series of wild type and mutant secretin receptors, yielding patterns that agreed with our paired cysteine mutagenesis results. In conclusion, these data suggest the same pattern of disulfide bonding as that predicted previously by NMR and thus support a consistent pattern of amino-terminal disulfide bonds in class B G protein-coupled receptors.

Molecular mechanisms of ligand binding, signaling, and regulation within the superfamily of G-protein-coupled receptors: molecular modeling and mutagenesis approaches to receptor structure and function

The superfamily of G-protein-coupled receptors (GPCRs) could be subclassified into 7 families (A, B, large N-terminal family B-7 transmembrane helix, C, Frizzled/Smoothened, taste 2, and vomeronasal 1 receptors) among mammalian species. Cloning and functional studies of GPCRs have revealed that the superfamily of GPCRs comprises receptors for chemically diverse native ligands including (1) endogenous compounds like amines, peptides, and Wnt proteins (i.e., secreted proteins activating Frizzled receptors); (2) endogenous cell surface adhesion molecules; and (3) photons and exogenous compounds like odorants. The combined use of site-directed mutagenesis and molecular modeling approaches have provided detailed insight into molecular mechanisms of ligand binding, receptor folding, receptor activation, G-protein coupling, and regulation of GPCRs. The vast majority of family A, B, C, vomeronasal 1, and taste 2 receptors are able to transduce signals into cells through G-protein coupling. However, G-protein-independent signaling mechanisms have also been reported for many GPCRs. Specific interaction motifs in the intracellular parts of these receptors allow them to interact with scaffold proteins. Protein engineering techniques have provided information on molecular mechanisms of GPCR-accessory protein, GPCR-GPCR, and GPCR-scaffold protein interactions. Site-directed mutagenesis and molecular dynamics simulations have revealed that the inactive state conformations are stabilized by specific interhelical and intrahelical salt bridge interactions and hydrophobic-type interactions. Constitutively activating mutations or agonist binding disrupts such constraining interactions leading to receptor conformations that associates with and activate G-proteins.

A structural pattern-based method for protein fold recognition

A method (SPREK) was developed to evaluate the register of a sequence on a structure based on the matching of structural patterns against a library derived from the protein structure databank. The scores obtained were normalized against random background distributions derived from sequence shuffling and permutation methods. 'Random' structures were also used to evaluate the effectiveness of the method. These were generated by a simple random-walk and a more sophisticated structure prediction method that produced protein-like folds. For comparison with other methods, the performance of the method was assessed using collections of models including decoys and models from the CASP-5 exercise. The performance of SPREK on the decoy models was equivalent to (and sometimes better than) those obtained with more complex approaches. An exception was the two smallest proteins, for which SPREK did not perform well due to a lack of patterns. Using the best parameter combination from trials on decoy models, the CASP models of intermediate difficulty were evaluated by SPREK and the quality of the top scoring model was evaluated by its CASP ranking. Of the 14 targets in this class, half lie in the top 10% (out of around 140 models for each target). The two worst rankings resulted from the selection by our method of a well-packed model that was based on the wrong fold. Of the other poor rankings, one was the smallest protein and the others were the four largest (all over 250 residues).

Spatial approximation between two residues in the mid-region of secretin and the amino terminus of its receptor. Incorporation of seven sets of such constraints into a three-dimensional model of the agonist-bound secretin receptor

Photoaffinity labeling of receptors by bound agonists can provide important spatial constraints for molecular modeling of activated receptor complexes. Secretin is a 27-residue peptide hormone with a diffuse pharmacophoric domain that binds to the secretin receptor, a prototypic member of the Class B family of G protein-coupled receptors. In this work, we have developed, characterized, and applied two new photolabile probes for this receptor, with sites for covalent attachment in peptide positions 12 and 14, surrounding the previously most informative site of affinity labeling of this receptor. The [Tyr10,(BzBz)Lys12]rat secretin-27 probe covalently labeled receptor residue Val6, whereas the [Tyr10,(BzBz)Lys14]rat secretin-27 probe labeled receptor residue Pro38. When combined with previous photoaffinity labeling data, there are now seven independent sets of constraints distributed throughout the peptide and receptor amino-terminal domain that can be used together to generate a new molecular model of the ligand-occupied secretin receptor. The amino-terminal domain of this receptor presented a stable platform for peptide ligand interaction, with the amino terminus of the peptide hormone extended toward the transmembrane helix domain of the receptor. This provides clear insights into the molecular basis of natural ligand binding and supplies testable hypotheses regarding the molecular basis of activation of this receptor.