STAT3, an important transcription factor constitutively activated in cancers, is bound specifically by GRIM-19 and this interaction inhibits STAT3-dependent gene expression. GRIM-19 is therefore, considered as an inhibitor of STAT3 and may be an effective anti-cancer therapeutic target. While STAT3 exists in a dimeric form in the cytoplasm and nucleus, it is mostly present in a monomeric form in the mitochondria. Although GRIM-19-binding domains of STAT3 have been identified in independent experiments, yet the identified domains are not the same, and hence, discrepancies exist. Human STAT3-GRIM-19 complex has not been crystallised yet. Dictated by fundamental biophysical principles, the binding region, interactions and effects of hotspot mutations can provide us a clue to the negative regulatory mechanisms of GRIM-19. Prompted by the very nature of STAT3 being a challenging molecule, and to understand the structural basis of binding and interactions in STAT3α-GRIM-19 complex, we performed homology modelling and ab-initio modelling with evolutionary information using I-TASSER and avant-garde AlphaFold2, respectively, to generate monomeric, and subsequently, dimeric STAT3α structures. The dimeric form of STAT3α structure was observed to potentially exist in an anti-parallel orientation of monomers. We demonstrate that during the interactions with both unphosphorylated and phosphorylated STAT3α, the NTD of GRIM-19 binds most strongly to the NTD of STAT3α, in direct contrast to the earlier works. Key arginine residues at positions 57, 58 and 68 of GRIM-19 are mainly involved in the hydrogen-bonded interactions. An intriguing feature of these arginine residues is that these display a consistent interaction pattern across unphosphorylated and phosphorylated monomers as well as unphosphorylated dimers in STAT3α-GRIM-19 complexes. MD studies verified the stability of these complexes. Analysing the binding affinity and stability through free energy changes upon mutation, we found GRIM-19 mutations Y33P and Q61L and among GRIM-19 arginines, R68P and R57M, to be one of the top-most major and minor disruptors of binding, respectively. The proportionate increase in average change in binding affinity upon mutation was inclined more towards GRIM-19 mutants, leading to the surmise that GRIM-19 may play a greater role in the complex formation. These studies propound a novel structural perspective of STAT3α-GRIM-19 binding and inhibitory mechanisms in both the monomeric and dimeric forms of STAT3α as compared to that observed from the earlier experiments, these experimental observations being inconsistent among each other.

Long noncoding RNAs (lncRNAs) are an emerging and a promising class of RNAs, and the lncRNA field is an intense research area. Once trashed as the junk regions of the genome, lncRNAs have now proved to be one of the crucial elements of a functional genome. These comprise a major chunk of the transcriptome, and similar to proteins, the sequence–structure–function paradigm holds true for lncRNAs as well. While some of the earliest lncRNAs like Xist and H19 have been well‐characterized, many of the emerging lncRNAs remain in oblivion. The low sequence conservation of lncRNAs has prompted researchers to decipher its conserved structure in order to gain an insight into the functional mechanisms. Here, we explore the concept of the sequence–structure–function relationship of lncRNAs, and the biophysical and biochemical laws governing a lncRNA structure which are just beginning to be understood. Proceeding from specific structures, much of the functions of lncRNAs revolve around their regulatory roles, through myriad modes of action. Throughout this review, we discuss the powerful computational as well as some experimental approaches that are applied in a synergistic fashion and highlight promising studies that have proved crucial towards an understanding of lncRNA structure and functional mechanisms. We also discuss at length, the existing challenges and the possible strategies to circumvent it. Given the unknown realm, the patterns and insights generated from these studies will be extremely useful in deciphering the way nature selects and uses a specific lncRNA to regulate a specific gene or gene sets in health and disease. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods Structure and Mechanism > Molecular Structures