Department of Biological Sciences

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    The multifaceted role of lncRNA MEG3 in kidney disease: a focus on mechanisms, therapeutic and diagnostic potential
    (Elsevier, 2025-09) Majumder, Syamantak; Gaikwad, Anil Bhanudas
    Kidney disease represents a global health challenge, affecting millions of people and contributing to significant morbidity and mortality. Long noncoding RNAs are potentially emerging as regulators in cellular processes and are involved in pathophysiological alterations in kidney disease. Among these, MEG3 has gained attention for its diverse regulatory roles in fibrosis, apoptosis and inflammation. MEG3 dysregulation has been implicated in conditions like chronic kidney disease, acute kidney injury, diabetic kidney disease and renal cell carcinoma. However, its involvement in endoplasmic reticulum (ER) stress and autophagy, crosstalk in kidney disease, is poorly understood. Hence, this review aims to highlight the role of MEG3 as a therapeutic and diagnostic viewpoint in kidney disease and its regulatory mechanism in ER stress and autophagy.
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    Molecular mechanism of the enhanced viral fitness contributed by secondary mutations in the hemagglutinin protein of oseltamivir resistant H1N1 influenza viruses: Modeling studies of antibody and receptor binding
    (Elsevier, 2015-02) Basu, Sushmita
    The envelope protein hemagglutinin (HA) of influenza viruses is primarily associated with host antibody and receptor interactions. The HA protein is known to maintain a functional balance with neuraminidase (NA), the other major envelope protein. Prior to 2007–2008, human seasonal H1N1 viruses possessing the NA H274Y mutation, which confers oseltamivir resistance, generally had low growth capability. Subsequently, secondary mutations that compensate for the deleterious effect of the NA H274Y mutation have been identified. The molecular mechanism of how the defect could be counteracted by these secondary mutations is not fully understood. We studied here the effect of three such mutations (T86K, K144E and R192K) in the HA protein, which are located at either the HA receptor binding site or in the H1N1 antigenic sites. Molecular docking and dynamics studies showed that, of the three mutations, the R192K mutation could have mediated neutralizing antibody escape and decreased receptor binding affinity, either or both of which may have contributed to increased viral fitness. The study suggests the molecular basis of enhanced viral fitness induced by secondary mutations in the evolution of oseltamivir-resistant influenza strains.
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    A structural perspective of RNA recognition by intrinsically disordered proteins
    (Springer, 2016-05) Basu, Sushmita
    Protein-RNA recognition is essential for gene expression and its regulation, which is indispensable for the survival of the living organism at one hand, on the other hand, misregulation of this recognition may lead to their extinction. Polymorphic conformation of both the interacting partners is a characteristic feature of such molecular recognition that promotes the assembly. Many RNA binding proteins (RBP) or regions in them are found to be intrinsically disordered, and this property helps them to play a central role in the regulatory processes. Sequence composition and the length of the flexible linkers between RNA binding domains in RBPs are crucial in making significant contacts with its partner RNA. Polymorphic conformations of RBPs can provide thermodynamic advantage to its binding partner while acting as a chaperone. Prolonged extensions of the disordered regions in RBPs also contribute to the stability of the large cellular machines including ribosome and viral assemblies. The involvement of these disordered regions in most of the significant cellular processes makes RBPs highly associated with various human diseases that arise due to their misregulation.
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    Effect of neighbouring residues in conformational plasticity of intrinsically disordered proteins
    (Elsevier, 2018-02) Basu, Sushmita
    Effect of neighbouring residues in conformational plasticity of intrinsically disordered regions. The concept of unstructured proteins has opened new avenues in the field of structural biology. Intrinsically disordered proteins (IDPs) are the new class of proteins which have been found to be a major player in many significant cellular functions. IDPs have been characterised by its physicochemical properties as well as its molecular interaction behaviour. Detailed study of IDPs can lead to a better understanding of protein folding and its functioning. To understand the source of disorderedness in the disordered regions (IDRs) in IDPs, we studied how the sequence environment of a disordered region correlates to its randomness. Here, we analysed the physicochemical and structural features like amino acid propensities, net charge, hydropathy index, secondary structure propensity, relative surface accessibility, interaction density and H-bonds to characterise the neighbours of the IDRs. Five residues, each towards N and C terminal of the disordered region are considered as the neighbours of IDRs. These neighbouring residues are found to be enriched in disorder promoting amino acids and have higher propensity to form loops than other secondary structures. Solvent accessibility of neighbouring residues also showed increasing trend as we move towards the IDRs. The variation of other parameters along with the above observation indicates that the neighbouring residues of IDRs induce a degree of flexibility to the adjoining IDRs. Based on our findings, we are designing an algorithm using random forest, which shall predict the disordered region based on its neighbouring sequences. The information on IDRs and its neighbours can be useful for proteins to be expressed or characterised for the first time. It can also provide a lead in understanding the molecular mechanism behind the polymorphic interactions that are involved with IDPs.
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    Do sequence neighbours of intrinsically disordered regions promote structural flexibility in intrinsically disordered proteins?
    (Elsevier, 2020-02) Basu, Sushmita
    Intrinsically disordered proteins (IDPs) are crucial players in various cellular activities. Several experimental and computational analyses have been conducted to study structural pliability and functional potential of IDPs. In spite of active research in past few decades, what induces structural disorder in IDPs and how is still elusive. Many studies testify that sequential and spatial neighbours often play important roles in determining structural and functional behaviour of proteins. Considering this fact, we assessed sequence neighbours of intrinsically disordered regions (IDRs) to understand if they have any role to play in inducing structural flexibility in IDPs. Our analysis includes 97% eukaryotic IDPs and 3% from bacteria and viruses. Physicochemical and structural parameters including amino acid propensity, hydrophobicity, secondary structure propensity, relative solvent accessibility, B-factor and atomic packing density are used to characterise the neighbouring residues of IDRs (NRIs). We show that NRIs exhibit a unique nature, which makes them stand out from both ordered and disordered residues. They show correlative occurrences of residue pairs like Ser-Thr and Gln-Asn, indicating their tendency to avoid strong biases of order or disorder promoting amino acids. We also find differential preferences of amino acids between N- and C-terminal neighbours, which might indicate a plausible directional effect on the dynamics of adjacent IDRs. We designed an efficient prediction tool using Random Forest to distinguish the NRIs from the ordered residues. Our findings will contribute to understand the behaviour of IDPs, and may provide potential lead in deciphering the role of IDRs in protein folding and assembly
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    Unusual RNA binding of FUS RRM studied by molecular dynamics simulation and enhanced sampling method
    (Elsevier, 2021-05) Basu, Sushmita
    Amyotrophic lateral sclerosis (ALS) and frontotemporal lobe degeneration (FTLD) are two inter-related intractable diseases of motor neuron degeneration. Fused in sarcoma (FUS) is found in cytoplasmic accumulation of ALS and FTLD patients, which readily link the protein with the diseases. The RNA recognition motif (RRM) of FUS has the canonical a-b folds along with an unusual lysine-rich loop (KK-loop) between a1 and b2. This KK-loop is highly conserved among FET family proteins. Another contrasting feature of FUS RRM is the absence of critical binding residues, which are otherwise highly conserved in canonical RRMs. These residues in FUS RRM are Thr286, Glu336, Thr338, and Ser367, which are substitutions of lysine, phenylalanine, phenylalanine, and lysine, respectively, in other RRMs. Considering the importance of FUS in RNA regulation and metabolism, and its implication in ALS and FTLD, it is important to elucidate the underlying molecular mechanism of RNA recognition. In this study, we have performed molecular dynamics simulation with enhanced sampling to understand the conformational dynamics of noncanonical FUS RRM and its binding with RNA. We studied two sets of mutations: one with alanine mutation of KK-loop and another with KK-loop mutations along with critical binding residues mutated back to their canonical form. We find that concerted movement of KK-loop and loop between b2 and b3 facilitates the folding of the partner RNA, indicating an induced-fit mechanism of RNA binding. Flexibility of the RRM is highly restricted upon mutating the lysine residues of the KK-loop, resulting in weaker binding with the RNA. Our results also suggest that absence of the canonical residues in FUS RRM along with the KK-loop is equally important in regulating its binding dynamics. This study provides a significant structural insight into the binding of FUS RRM with its cognate RNA, which may further help in designing potential drugs targeting noncanonical RNA recognition.
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    Conservation and coevolution determine evolvability of different classes of disordered residues in human intrinsically disordered proteins
    (Wiley, 2021-10) Basu, Sushmita
    Structure, function, and evolution are interdependent properties of proteins. Diversity of protein functions arising from structural variations is a potential driving force behind protein evolvability. Intrinsically disordered proteins or regions (IDPs or IDRs) lack well-defined structure under normal physiological conditions, yet, they are highly functional. Increased occurrence of IDPs in eukaryotes compared to prokaryotes indicates strong correlation of protein evolution and disorderedness. IDPs generally have higher evolution rate compared to globular proteins. Structural pliability allows IDPs to accommodate multiple mutations without affecting their functional potential. Nevertheless, how evolutionary signals vary between different classes of disordered residues (DRs) in IDPs is poorly understood. This study addresses variation of evolutionary behavior in terms of residue conservation and intra-protein coevolution among structural and functional classes of DRs in IDPs. Analyses are performed on 579 human IDPs, which are classified based on length of IDRs, interacting partners and functional classes. We find short IDRs are less conserved than long IDRs or full IDPs. Functional classes which require flexibility and specificity to perform their activity comparatively evolve slower than others. Disorder promoting amino acids evolve faster than order promoting amino acids. Pro, Gly, Ile, and Phe have unique coevolving nature which further emphasizes on their roles in IDPs. This study sheds light on evolutionary footprints in different classes of DRs from human IDPs and enhances our understanding of the structural and functional potential of IDPs.
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    Impaired nuclear transport induced by juvenile ALS causing P525L mutation in NLS domain of FUS: A molecular mechanistic study
    (Elsevier, 2022-04) Basu, Sushmita
    Amyotrophic lateral sclerosis (ALS) and fronto-temporal lobar degeneration (FTLD) are progressive neurological disorders affecting motor neurons. Cellular aggregates of fused in sarcoma (FUS) protein are found in cytoplasm of ALS and FTLD patients. Nuclear localisation signal (NLS) domain of FUS binds to Karyopherin β2 (Kapβ2), which drives nuclear transport of FUS from cytoplasm. Several pathogenic mutations are reported in FUS NLS, which are associated with its impaired nuclear transport and cytoplasmic mis-localisation. P525L mutation in NLS is most commonly found in cases of juvenile ALS (jALS), which affects individuals below 25 years of age. jALS progresses aggressively causing death within a year of its onset. This study elucidates the molecular mechanism behind jALS-causing P525L mutation hindering nuclear transport of FUS. We perform multiple molecular dynamics simulations in aqueous and hydrophobic solvent to understand the effect of the mutation at molecular level. Dynamics of Kapβ2-FUS complex is better captured in hydrophobic solvent compared to aqueous solvent. P525 and Y526 (PY-motif) of NLS exhibit fine-tuned stereochemical arrangement, which is essential for optimum Kapβ2 binding. P525L causes loss of several native contacts at interface leading to weaker binding, which promotes self-aggregation of FUS in cytoplasm. Native complex samples closed conformation, while mutant complex exhibits open conformation exposing hydrophilic residues of Kapβ2 to hydrophobic solvent. Mutant complex also fails to exhibit spring-like motion essential for its transport through nuclear pore complex. This study provides a mechanistic insight of binding affinity between NLS and Kapβ2 that inhibits self-aggregation of FUS preventing the disease condition.
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    qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids
    (Wiley, 2022-12) Basu, Sushmita
    Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein–NA interactions for large protein families and proteomes.
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    Computational prediction of disordered binding regions
    (Elsevier, 2023) Basu, Sushmita
    One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.