BITS Faculty Publications

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Start date: 2020Subject: search.filters.subject.Biology

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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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    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.
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    Real-time observation of macroscopic helical morphologies under optical microscope: a curious case of π–π stacking driven molecular self-assembly of an organic gelator devoid of hydrogen bonding
    (Wiley, 2022-12) Basu, Sushmita
    Supramolecular assemblies such as tubules/helix/double helix/helical tape etc. are usually submicron objects preventing direct observation under optical microscope. Chiral-pure form of these assemblies is important for potential applications. Herein, we report a rare phenomenon wherein a DMSO gel of a simple terpyridine derivative [(4-CNPhe)4PyTerp] produced macroscopic helical morphologies (μm length scale) which could be observed under optical microscope, formation of which could be monitored by optical videography, stable enough to withstand acidic vapour, robust enough to display reversible gel↔sol in response to acidic and ammonia vapour and sturdy enough to be maneuvered with a needle. These properties appeared to be unique to the title compound as the other related derivatives failed to display such assembly structures. SXRD and MD simulation studies suggested that weak interactions (π-π stacking) played a crucial role in the self-assembly process.
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    DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction
    (OUP, 2023-05) Basu, Sushmita
    Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions
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    CoMemMoRFPred: sequence-based prediction of MemMoRFs by combining predictors of intrinsic disorder, MoRFs and disordered lipid-binding regions
    (Elsevier, 2023-11) Basu, Sushmita
    Molecular recognition features (MoRFs) are a commonly occurring type of intrinsically disordered regions (IDRs) that undergo disorder-to-order transition upon binding to partner molecules. We focus on recently characterized and functionally important membrane-binding MoRFs (MemMoRFs). Motivated by the lack of computational tools that predict MemMoRFs, we use a dataset of experimentally annotated MemMoRFs to conceptualize, design, evaluate and release an accurate sequence-based predictor. We rely on state-of-the-art tools that predict residues that possess key characteristics of MemMoRFs, such as intrinsic disorder, disorder-to-order transition and lipid-binding. We identify and combine results from three tools that include flDPnn for the disorder prediction, DisoLipPred for the prediction of disordered lipid-binding regions, and MoRFCHiBiLight for the prediction of disorder-to-order transitioning protein binding regions. Our empirical analysis demonstrates that combining results produced by these three methods generates accurate predictions of MemMoRFs. We also show that use of a smoothing operator produces predictions that closely mimic the number and sizes of the native MemMoRF regions.
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    DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options
    (OUP, 2023-11) Basu, Sushmita
    The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics