Biology subjects

Madhusudhan, M. S.

Publications and source records attributed to Madhusudhan, M. S..

14 recordsLinked to original sources

bioRxiv2025

Overhang Optimizer for Golden Gate Assembly (OOGGA)

Golden Gate cloning is a popular method for achieving ordered assembly of DNA fragments using complementary four-base pair overhangs. The success of this method relies on the ability of DNA ligases to ligate the different fragments. This ability is affected by the relative efficiency and accuracy with which the ligase recognizes and joins overhanging regions. In this study, we report a dynamic programming approach, called Overhang Optimizer for Golden Gate Assembly (OOGGA), that optimizes these overhangs for their accuracy and efficiency. OOGGA provides the theoretically optimal fragments for Golden Gate assembly, provided a DNA sequence, a length range of the fragments and/or the number of required fragments. As expected, OOGGA outperforms NEBridge SplitSet(R) in both fidelity and efficiency metrics in a set of 3 example DNA sequences. OOGGA also provides support of degenerate nucleotide codes, options to exclude/include motifs for predictions and weights to bias the program for efficiency and fidelity.

bioinformatics↗

bioRxiv2025

Bioinformatics analysis identifies sequence determinants of enzymatic activity for the PHARC associated lipase ABHD12

In humans, PHARC (polyneuropathy, hearing loss, ataxia, retinitis pigmentosa, and cataract) is an early onset autosomal recessive neurological disorder caused by deleterious mutations to ABHD12 (/{beta}-hydrolase domain protein # 12). Biochemically, ABHD12 functions as a lipase, and catalyzes the hydrolysis of lysophosphatidylserine (lyso-PS) (lyso-PS lipase). By doing so, it controls the concentrations and signaling pathways regulated by this potent signaling lysophospholipid in the mammalian brain. While genetic mapping efforts have identified over 30 mutations in ABHD12 from human PHARC subjects, the biochemical activity of these pathogenic mutants remains unknown. To understand this, here, we performed an exhaustive bioinformatics survey, and collated ABHD12 protein sequences from various organisms across evolution. Next, based on sequence alignments and structural modeling, we identified functionally relevant conserved residues in the ABHD12 protein sequence that are potentially important for its enzymatic activity. To validate these in silico findings, we generated numerous mutants of murine ABHD12, including those associated with human PHARC subjects, and assayed them for their enzymatic activity. Taken together, these complementary in silico and biochemical studies provide the first thorough sequence-function relationship for mammalian ABHD12, especially relevant in the context of PHARC. Finally, our evolutionary analysis identified CG15111 as an ABHD12 ortholog in the fruit fly (Drosophila melanogaster), and enzymatic assays indeed confirmed that recombinant CG15111 has robust lyso-PS lipase activity. Flies serve as an excellent animal system to model various human neurological diseases, and the identification of CG15111 as a Drosophila melanogaster ABHD12 ortholog opens new avenues to study PHARC in fly models.

biochemistry↗

bioRxiv2024

Chemical Proteomics Identifies Protein Ligands for Monoacylglycerol Lipids

Signaling lipids are hormone-like small biomolecules that regulate many critical facets of physiology in mammals, including humans. Given their biomedical importance, the past few decades have seen a tremendous increase in our mechanistic understanding of the physiological processes regulated by a handful of such signaling lipids (e.g.: endocannabinoids, lysophospholipids, prostaglandins). However, a significant number of signaling lipid classes still remain poorly characterized, despite their direct associations to human pathophysiology and disease. Over the past decade, the advent of chemical proteomics technologies coupled with the development of multifunctional lipid probes has rapidly expanded our knowledge in terms of the protein ligands and biological pathways that the different signaling lipids interact with and modulate respectively. While the signaling pathways regulated by the endocannabinoid 2-arachidonoyl-glycerol in mammals are extensively characterized, the same cannot be said for the other members of the monoacylglycerol (MAG) family of signaling lipids. To understand this, here, we report the synthesis of a bifunctional MAG probe, containing a photoreactive group and a biorthogonal handle. Using established chemical proteomics approaches, we profile this bifunctional MAG probe in mouse brain and mammalian cell lysates, and leveraging probe competition experiments identify hitherto unknown protein ligands for MAG lipids. Finally, we biochemically validate the neuronal calcium sensor Hippocalcin as a putative MAG protein ligand, and show for the first time, that MAG may have a role to play in calcium sensing and downstream signaling in the mammalian brain.

biochemistry↗

bioRxiv2024

Motif distribution in genomes gives insights into gene clustering and co-regulation

We read the genome as proteins in the cell would - by studying the distributions of 5-6 base motifs of DNA in the whole genome or smaller stretches such as parts of, or whole chromosomes. This led us to some interesting findings about motif clustering and chromosome organisation. It is quite clear that the motif distribution in genomes is not random at the length scales we examined: 1kbps to entire chromosomes. The observed to expected (OE) ratios of motif distributions show strong correlations in pairs of chromosomes that are susceptible to translocations. With the aid of examples, we suggest that similarity in motif distributions in promoter regions of genes could imply co-regulation. A simple extension of this idea empowers us with the ability to construct gene regulatory networks. Further, we could make inferences about the spatial proximity of genomic fragments using these motif distributions. Spatially proximal regions, as deduced by Hi-C or pcHi-C, were [~]3.5 times more likely to have their motif distributions correlated than non-proximal regions. These correlations had strong contributions from the CTCF protein recognizing motifs which are known markers of TADs. In general, correlating genomic regions by motif distribution comparisons alone is rife with functional information. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/613605v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1cf4193org.highwire.dtl.DTLVardef@1da47f5org.highwire.dtl.DTLVardef@1aa4172org.highwire.dtl.DTLVardef@aeaa60_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

COCONUT: An analysis of coiled-coil regions in proteins

MotivationThe molecular rules determine the strength and orientation (parallel or antiparallel) of interacting coiled-coil helices in protein-protein interactions. Interpreting these rules is crucial for identifying novel protein-protein interactions, designing competitive binders, and constructing large assemblies containing coiled-coil domains. This study establishes the molecular principles that dictate the strength and orientation of coiled-coil interactions, providing insights relevant to these applications. ResultsWe examined how hydrophobic contacts determine structural specificity within coiled-coil dimers. Our analysis revealed that the hydrophobic core densities differ between parallel and antiparallel dimer confirmations, highlighting their importance in stabilizing different structural arrangements. We developed COiled-COil aNalysis UTility (COCONUT), a computational platform with machine learning models, validated for predictive capabilities in various applications. Using COCONUTs pipeline for coiled-coil analysis and modeling, we predicted the orientation of substitution-sensitive coiled-coil dimer, identified residue pairings in non-canonical coiled-coil heterodimer, and constructed n-stranded coiled-coil model. These results demonstrate COCONUTs utility as a computational framework for interpreting and modeling coiled-coil structures. Availability and implementationCOCONUT is an open-source and free Python package available here https://github.com/neeleshsoni21/COCONUT. The documentation is available in the source code and here: https://neeleshsoni21.github.io/COCONUT/

bioinformatics↗

bioRxiv2023

Identification of Sequence Determinants for the ABHD14 Enzymes

Over the course of evolution, enzymes have developed remarkable functional diversity in catalyzing important chemical reactions across various organisms, and understanding how new enzyme functions might have evolved remains an important question in modern enzymology. To systematically annotate functions, based on protein sequences and available biochemical studies, enzymes with similar catalytic mechanisms and/or aspects of catalysis have been clustered together into an enzyme superfamily. Typically, enzymes within a superfamily have similar overall three-dimensional structures, conserved key catalytic residues, but large variations in substrate recognition sites and residues to accommodate the diverse biochemical reactions that are catalyzed within the superfamily. The serine hydrolases are an excellent example of such an enzyme superfamily, that based on known enzymatic activities and protein sequences, is split almost equally into the serine proteases and metabolic serine hydrolases. Within the metabolic serine hydrolases, are two outlying members, ABHD14A and ABHD14B, that have high sequence similarity, but their functions remained cryptic till recently. While ABHD14A still lacks any functional annotation to date, we recently showed that ABHD14B functions as a lysine deacetylase in mammals. Given their high sequence similarity, automated databases wrongly assign ABHD14A and ABHD14B as the same enzyme, and therefore, annotating functions to them in various organisms maybe problematic. In this paper, we present a bioinformatics study coupled to biochemical experiments, that identifies key sequence determinants for both ABHD14A and ABHD14B, and enables better classification for them. Additionally, we map these enzymes on an evolutionary timescale, and provide a resource in studying these interesting enzymes in different organisms.

biochemistry↗

bioRxiv2023

High-affinity biomolecular interactions are modulated by low-affinity binders

Molecular interactions play a central role in all biological processes. The strength of these interactions is often characterized by their dissociation constants (KD). The high affinity interactions (KD [≤] 10-8 M) are crucial for the proper execution of cellular processes and are thus extensively investigated. Detailed molecular and biochemical analyses of such high affinity interactions have lent considerable support to the concept of binary on/off switches in different biological contexts. However, such studies have typically discounted the presence of low-affinity binders (KD > 10-5 M) in the cellular environment. In this study, we have assessed the potential influence of such low affinity binders on high affinity interactions. By employing Gillespie stochastic simulations as well as continuous methods, we demonstrate that the presence of low-affinity binders can indeed alter the kinetics and the steady state of high-affinity interactions. We refer to this effect as herd regulation and have evaluated its possible impact in two different contexts including sex determination in Drosophila melanogaster and in signalling systems that employ molecular thresholds. Lastly, we suggest an in vitro experimental strategy to validate herd regulation. Based on these analyses, we propose that low-affinity binders are likely prevalent in different biological contexts where the outcomes are contingent upon threshold value determinants and thus potentially impact their homeostatic regulation.

systems biology↗

bioRxiv2022

Prediction of SUMOylation targets in Drosophila melanogaster

SUMOylation is a post translational modification that involves covalent attachment of SUMO C-terminus to side chain amino group of lysine residues in target proteins. Disruption of the modification has been linked to neurodegenerative diseases and cancer. Recent improvements in mass spectrometry-coupled proteomics experiments have enabled high-throughput identification of SUMOylated lysines in mammalian cells. One such study was Hendriks et al, 2018, wherein the authors identified SUMOylated lysines in human and mouse cells. Information from this study was used as an input to a sequence homology based method to annotate putative SUMOylatable lysines from the proteome of fruit fly Drosophila melanogaster. 5283 human and 468 mouse SUMOylated proteins led to the identification of 8539 and 1700 fly homologs and putative SUMOylation sites therein respectively. Clustering analysis was carried out on these annotated sites to obtain three typs of information. First type of information revealed amino acid preferences in the local sequence vicinity of the annotated sites. This exercise confirmed that {psi} - K - x - (E/D) where {psi} = I/V/L, is the most frequently occurring sequence motif involving SUMOylated lysines. Second type of information revealed protein families that contain the annotated sites. Results from this exercise reveal that members of thousands of protein families contain annotated SUMOylation sites. Third type of information revealed preferred biological and cellular functions of proteins containing the annotated lysines. This exercise revealed that nucleus and transcription are preferred cellular localization and biological function respectively.

bioinformatics↗

bioRxiv2022

3-D structure based prediction of SUMOylation sites

SUMOylation is a posttranslational modification that involves lysine residues from eukaryotic proteins. Misregulation of the modification has been linked to neuro-degenrative diseases and cancer. All the presently available tools to predict SUMOylation sites are sequence based. Here, we propose a novel structure based prediction tool to discriminate between SUMOylated and non-SUMOylated lysines. We demonstrate the method by carrying out a proof-of-concept study. The method achieved an accuracy of 81% and a Matthews Correlation Coefficient of 0.4.

bioinformatics↗

bioRxiv2022

JEDII: Juxtaposition Enabled DNA-binding Interface Identifier

The specific recognition of small stretches of the genomic sequence by their cognate binding protein partners is crucial for various biological processes. Traditionally the prediction of DNA-protein interactions has been treated as two separate problems - one where we predict the most probable DNA sequence that a given protein would bind to and another where we determine the amino acids constituting the DNA binding pocket on a protein. In this study, we introduce JEDII, a template-based method that combines these two aspects of DNA-protein interactions and predicts the residues, nucleotides and amino acids, that would mediate the interaction. Our computational method utilises known structures of DNA-protein complexes in a protocol that superimposes amino acid-nucleotide hydrogen-bonding donor and acceptors atoms on one another to identify the protein-DNA interface. The corner stone of the method is that specificity bestowing hydrogen-bonding interactions are structurally conserved. We validated the accuracy of our procedure on a dataset of 285 DNA-protein complexes where JEDII predicted the cognate DNA sequence with a 62% accuracy. It predicted the DNA-binding amino acids on the protein with 94 % accuracy and an MCC of 0.70. JEDII was also separately compared to other popular methods that predict the cognate DNA sequence and to methods that predict the DNA binding residues. The comparisons were done over four different datasets and JEDII outperformed most methods over all these data sets. JEDII is a robust method following a simple replicable algorithm to determine the molecular basis of DNA-protein specificity and could be instrumental in predicting DNA-protein complexes that are central to key biological phenomena.

bioinformatics↗

bioRxiv2022

A structural database of chain-chain and domain-domain interfaces of proteins

In this study, we have mined the PDB and created a structural library of 178,465 interfaces that mediate protein-protein or domain-domain interactions. Interfaces involving the same CATH fold(s) were clustered together. Our analysis of the entries in the library reveals the similarity between chain-chain and domain-domain interactions. The library also illustrates how a single protein fold can interact with multiple folds using similar interfaces. The library is hence a useful resource to study the types of interactions between protein folds. Analyzing the data in the library reveals various interesting aspects of protein-protein and domain-domain interactions such as how proteins belonging to folds that interact with many other folds also have high EC values. These data could be utilized to seek potential binding partners. It can also be utilized to investigate the different ways in which two or more folds interact with one another structurally. We constructed a statistical potential of pair preferences of amino acids across the interface for chain-chain and domain-domain interactions separately. They are quite similar further lending credence to the notion that domain-domain interfaces could be used to study chain-chain interactions. Lastly and importantly, the library includes predicted small molecule binding sites at the protein-protein interfaces. This has applications as interfaces containing small molecule binding sites can be easily targeted to prevent the interaction and perhaps form a part of a therapeutic strategy.

bioinformatics↗

bioRxiv2021

Packpred: Predicting the functional effect of missense mutations

1.Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth dependent amino acid substitution matrix (FADHM) and positional Shannon Entropy to predict the functional consequences of point mutations in proteins. Parameters were trained over a saturation mutagenesis data set of T4-lysozyme (1966 mutations). The method was tested over another saturation mutagenesis data set (CcdB; 1534 mutations) and the Missense3D data set (4099 mutations). The performance of Packpred was compared against those of six other contemporary methods. With MCC values of 0.42, 0.47 and 0.36 on the training and testing data sets respectively, Packpred outperforms all method in all data sets, with the exception of marginally underperforming to FADHM in the CcdB data set. On analyzing the results, we could build meta servers that chose best performing methods of wild type amino acids and for wild type-mutant amino acid pairs. This lead to an increase of MCC value of 0.40 and 0.51 for the two meta predictors respectively on the Missense3D data set. We conjecture that it is possible to improve accuracy with better meta predictors as among the 7 methods compared, at the least one method or another is able to correctly predict [~]99% of the data.

bioinformatics↗

bioRxiv2020

Combining Information from Crosslinks and Monolinks in the Modelling of Protein Structures

Monolinks are produced in a Chemical Crosslinking Mass Spectrometry experiment and are more abundant than crosslinks. They convey residue exposure information, but so far have not been used in the modelling of protein structures. Here we present the Monolink Depth Score (MoDS), for assessing structural models based on the depth of monolinked residues, corresponding to their distance to the nearest bulk water. Using simulated and reprocessed experimental data from the Proteomic Identification Database, we compare the performance of MoDS to MNXL - our previously-developed score for assessing models based on crosslinking data. Our results show that MoDS can be used to effectively score model structures based on monolinks, and that combining it with MNXL leads to overall higher scoring performance. The work strongly supports the use of monolink data in the context of integrative structure determination. We also present XLM-Tools, a programme to assist in this effort, available at: https://github.com/Topf-Lab/XLM-Tools.

bioinformatics↗

bioRxiv2019

A knowledge-based scoring function to assess the stability of quaternary protein assemblies

MotivationElucidation of protein-protein interactions is a necessary step towards understanding the complete repertoire of cellular biochemistry. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study we have devised a new scheme for scoring protein-protein interactions. ResultsOur method, PIZSA (Protein Interaction Z Score Assessment) is a binary classification scheme for identification of stable protein quaternary assemblies (binders/non-binders) based on statistical potentials. The scoring scheme incorporates residue-residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other recently published scoring functions, demonstrated through testing on a benchmark set and the CAPRI Score_set. Though not explicitly trained for this purpose, PIZSA potentials can identify spurious interactions that are artefacts of the crystallization process. AvailabilityPIZSA is implemented as awebserverat http://cospi.iiserpune.ac.in/pizsa/. Contactmadhusudhan@iiserpune.ac.in

bioinformatics↗