Difference between Proteins and Nucleic Acids
Former examples of nucleic acid (NA)-binding proteins include such as MEME Suite , MEMERIS , GLAM2 , mFold , and Aptamotif , etc. . the use of spherical polar Fourier correlation method, implemented. The tool accepts DNA or protein sequences, given in FASTA-format, and .. The MEME Suite- Motif-based sequence analysis tools (National Biomedical. Proteins are made up of a series of amino acids. The center of an amino acid is the carbon bonded to four different groups. The fourth group, R, is different for.
The in silico 3D structure prediction of CaSUN1 is particularly helpful in view of the assumption that structure is more conserved than sequence between homologous proteins. Model deviation MD structure of CaSUN1 describes not only its shape, but also its characteristics, which affect the hydrophobicity and hence its function. These approaches provide primary assessment for the functional role of previously uncharacterized proteins.
Primary structure prediction Primary structure prediction was done using ExPASy [ 8 ], and ProtParam server which analyses physiochemical characters of CaSUN1 such as theoretical isoelectric point pImolecular mass, molecular formula, instability index, total number of positive and negative residues, aliphatic index and grand average of hydropathicity GRAVY.
The conserved residues were used for further analysis. The gap penalties alignment parameters was to -1, end gap penalties was -5 to -1, and e-value was 0.
The parameters taken were: A phylogenetic tree was constructed by neighbor-joining method using the default parameters. The redundant sequences were removed after identifying potential target mRNA sequences for functional analysis.
The templates were used to identify the existence of similar regions from different models. The 3D structure of all the identified homologs were downloaded from PDB database and used for structure prediction. The predicted model was further validated using PSVS server [ 13 ]. Sequence-structure-function relationship The identified conserved patterns in the predicted structure were used to find out patterns and identify their domain families using Pfam analysis [ 17 ].
In CaSUN1, number of amino acids istotal number of atoms ismolecular weight is The instability index is computed to be Prediction of secondary structure revealed the favoured structural property.
The secondary structure patterns of CaSUN1. Pink indicates alpha helix, yellow indicates beta sheets and black indicates the coils of CaSUN1.
Subcellular localization of chickpea SUN proteins SUN-domain proteins are majorly a component of nuclear pore complexes. Although these proteins have been reported to be involved in membrane anchorage, their presence as TMD in various subcellular structures suggests their diverse functions.
Comparative Analysis of Sequence-Structure Function Relationship of the SUN-Domain Protein CaSUN1
In chickpea, the three SUN proteins were analysed for their putative subcellular localisation with different localisation prediction tools. Subcellular localization of CaSUN1 in nuclear envelop had earlier been verified [ 1 ]. The conserved patterns and their respective position in CaSUN1 represent the characteristic features of its structure.
CaSUN1 displayed high sequence homology with its animal counterparts.
The CaSUN3 predicted the conservation of motifs 7, 10, 11, 13 and 14 only. The amino acid sequence comparison of CaSUN1 with SUN proteins from other photoautotrophs indicates an important role of these proteins across species. The multiple sequence alignments of SUN-domain proteins show the conservation of residues across species.
Blue colour represents the conserved residues and red colour indicates the identical residues with their respective positions. Schematic representation of the conserved motifs in chickpea SUN proteins A. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment.
Analysis of motif enrichment among the co-expressed genes of top candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations. Despite their functional diversity, however, they share remarkably similar attributes such as biases in the overall and binding site-local amino acid compositions.
This feature allows a relatively accurate identification of DBPs from sequence or structural information alone without necessitating further characterization 15 In general, the DNA-binding site residues DBS of DBPs are enriched in positively charged Arg residues, a signal which is further fine-tuned by their sequence and structural environments These compositional biases can be accurately captured by statistical and machine learning models trained over carefully prepared non-redundant and accurately characterized datasets of DNA-binding proteins 18 — These datasets are almost always derived from the known three-dimensional structures of protein—DNA complexes and do not include any non-DBPs 21 Thus, these trained models represent an internal discrimination of the DBS from the rest of the amino acid sequence and it is unclear whether they can also distinguish DBPs from other proteins.
DBP prediction models, on the other hand, exploit the compositional biases in the DBPs compared to other proteins and these biases are not exactly the same as the DBS biases 16 While a number of methods have been proposed for predicting DBPs and DBS separately 1516202123 — 38to the best of our knowledge, no study has been conducted to develop a prediction system that employs DBS as an engine for the DBP prediction, combined with the amino acid compositional biases of the full length proteins, and to evaluate it comprehensively on an entire genome.
In this study, we first investigated the various levels of DBP annotations, ranging from the existence of protein—DNA complexes in the crystal structures to protein domain assignments 39 and gene ontology GO term associations. Benchmarking and Quality Tests Several studies report the use of the previously discussed algorithms in practical approaches to either predict aptamer binding to target proteins or to evaluate the quality of the docking calculations.
In the following paragraphs, recent applications are presented and benchmark studies are emphasized.
RBP4 is a biomarker used for type two diabetes pre-diagnosis, and the binding mode prediction with subsequent MD simulations was intended to elucidate the main aptamer—target interaction mechanics to support further aptamer design [ 72 ]. The aptamers were previously ab initio and in silico, designed using EREs as a template see above, [ 29 ]. The group could not only show that all docking algorithms concluded similar results but could also prove that the in silico predicted specificity and affinity in vitro [ 29 ].
How Are Protein & Nucleic Acids Related?
Additional studies were designed especially to test existing docking programs, including the community-wide Critical Assessment of Prediction of Interactions CAPRI initiative [ 7374 ] and a wide variety of benchmark databases. They evaluated the prediction accuracy without any experimental knowledge compared to the structural data gathered from experimental procedures [ 50 ].
Even more extensive efforts were made to benchmark the performance of the HDOCK algorithm on several known benchmark databases [ 65 ].
Additionally, many databases were created to offer opportunities for easy benchmarking of established docking programs and algorithms [ 49757677 ].
Evaluating the Quality of Docking When evaluating the quality of the docking, some main ideas should be taken into consideration. The driving forces for NA—protein interaction are a milieu of van der Waals forces, hydrophobic interactions, hydrogen bonds, base stacking forces, and ionic interactions between amino acid side chains and either the phosphate groups or bases of NA [ 1024 ].
Additionally, the importance of the shape complementarity provided by secondary and tertiary structures of NA and proteins, which contributes to the binding mode, should be stressed [ 24 ].
How are nucleic acids related to proteins? | Socratic
Ionic interactions commonly formed between positive amino acid side chains and the negatively charged DNA have been repeatedly proven important for NA—protein interaction [ 2478798081 ]. Base stacking forces contribute to the stability of dsDNA but in particular support the binding of ssDNA to proteins involving stacking of bases and aromatic protein side chains [ 82 ].
At the same time, they are highly influenced by electrostatic interactions and van der Waals forces [ 83 ]. Although their energy depends on pressure, angle, the distance between donor and acceptor of at least 2. The number of hydrogen bonds can be counted for each docking complex to measure the quality of the docking pose and the interaction itself, as already mentioned by Jones and colleagues [ 67 ] and discussed by Ahirwar et al. Formation of hydrogen bonds has been demonstrated to be essential for biomolecular function and, hence, structure represents a key parameter of complex stability [ 8788 ].
As an example, Rabal employed clustering of docking results followed by evaluation of electrostatic and polar interactions between protein and RNA aptamers, similarly to ligand interaction fingerprints already employed as docking postprocessing of protein—ligand complexes [ 89 ].
- Comparative Analysis of Sequence-Structure Function Relationship of the SUN-Domain Protein CaSUN1
- Molecular Modeling Applied to Nucleic Acid-Based Molecule Development
- How are nucleic acids related to proteins?
Alternatives, such as consensus scoring, have been proposed to reduce the bias of single scoring functions [ 90 ], and the evaluation of true-positives retrieval rate from different programs can help. However, the notion that the highest dock score directly correlates with real ligand binding—and therefore with a biological effect—can be erroneous.
Especially when applied to small molecules, docking analyses alone can create an inaccurate picture of ligand binding an extensive discussion on the docking limitations was addressed by Chen [ 91 ]. The knowledge of critical residues and, in this sense, the presence of respective interactions can bridge the virtual inferences and experimental results. In this sense, molecular modeling can give the structural perspective of the mutation effects while also benefitting from the experimental information.