Protein–macromolecule Interaction And Bioinformatics Analysis Of The Molecular Disease Mechanism

Proteins play a key role in the process of cell life by interacting with proteins,RNA,and DNA.Disrupting the binding between these molecules may disturb the balance of the cells and cause diseases.Identifying the interaction between these molecules through experimental techniques and computational methods will help us understand the life process and provide ideas for the diagnosis and treatment of diseases.In this article,we mainly study protein–macromolecule interactions and analyze their molecular disease mechanisms.Protein–macromolecules are protein–protein,protein–RNA,protein–DNA,and protein–RNA–DNA interactions.Among them,this article has done a more detailed study of protein–RNA and protein–DNA interactions,and has made preliminary progress.We identified CLIP1 and DMD are indeed RNA-binding proteins(RBPs)through CLIP–seq and i RIP–seq high–throughput sequencing experiments,CLIP1 and DMD are predicted as RBPs by RBPPred at first.We analyzed the binding site data obtained from the experiment and developed a tool phd RBP that can analyze high-throughput sequencing data.We found that the SNPs between DMD and its RNA partners may associate with Becker muscular dystrophy,Duchenne muscular dystrophy,Dilated cardiomyopathy 3B and Cardiovascular phenotype.Among the thirteen cancers data,CLIP1 and another 300 oncogenes always co-occur,and 123 of these 300 genes interact with CLIP1.These cancers may be related to the mutations in both CLIP1 and the genes it interacts with.Although the use of high-throughput sequencing can obtain thousands of RNA binding sites for a specific protein,this process is costly and time-consuming,and there are few protein–RNA three-dimensional data.Therefore,we make full use of the protein–RNA binding site data and RNA secondary structure generated by high-throughput sequencing,and developed a template-based method PRIME-3D2 D to predict the binding site of protein–RNA interaction.Testing on PDB and yeast transcription-wide data show that PRIME-3D2 D performs better than other binding sites predictor.Studies have reported that mutations at the binding site will affect the stability of the protein and thus affect the binding of the protein to other molecules,and then leading to diseases.For protein–DNA,we extended PRIME2.0 to PDIME to predict the structure of the protein–DNA complex.We found that structure-based methods can find more templates than sequence-based methods and DNA structure plays an important role in prediction of protein–DNA complex structure.By exploring the relationship of sequence and structure,we found that in protein–DNA interaction,numerous structures with dissimilar sequences have similar 3D structures and perform the same function.These data and findings can expand the analysis of the relationship between mutations in the complex structure and diseases,and lay the foundation for the construction of useful molecular models of diseases in the future.It is critical to understand and treat diseases by revealing that different genes cause the same diseases.In order to explore the relationship between protein–macromolecular interactions and molecular disease,we developed 3D2 GENDIS for mapping human protein–protein/RNA/DNA complex structures to the genome and disease databases.We found that 440807 mutations occur at the interface of the complex,and most of these mutations are pathogenic.We applied 3D2 GENDIS to analyze the complex structure of the COVID-19 and found that most of the mutations(86.9%)at the interaction interface will decrease the stability of the protein structure.By analyzing three examples which are composed of multi-genes but cause the same disease,we found that these mutations changed the free energy,which in turn changes the structure stability of the protein.