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 c...
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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
Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stabil...
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Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases;2) the large intrinsic variability of Delta Delta G values due to different experimental conditions;3) biases in the development of predictive methods caused by ignoring the anti-symmetry of Delta Delta G values between mutant and native protein forms;4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of computational and Structural Biotechnology.
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