DREAMweb: An online tool for graph-based modeling of NMR protein structure.

The value of accurate protein structural models closely conforming to the experimental data is indisputable. DREAMweb deploys an improved DREAM algorithm, DREAMv2, that incorporates a tighter bound in the constraint set of the underlying optimization approach. This reduces the artifacts while modeling the protein structure by solving the distance-geometry problem. DREAMv2 follows a bottom-up strategy of building smaller substructures for regions with a larger concentration of experimental bounds and consolidating them before modeling the rest of the protein structure. This improves secondary structure conformance in the final models consistent with experimental data. The proposed method efficiently models regions with sparse coverage of experimental data by reducing the possibility of artifacts compared to DREAM. To balance performance and accuracy, smaller substructures ( ∼ 200 $\sim 200$ atoms) are solved in this regime, allowing faster builds for the other parts under relaxed conditions. DREAMweb is accessible as an internet resource. The improvements in results are showcased through benchmarks on 10 structures. DREAMv2 can be used in tandem with any NMR-based protein structure determination workflow, including an iterative framework where the NMR assignment for the NOESY spectra is incomplete or ambiguous. DREAMweb is freely available for public use at http://pallab.cds.iisc.ac.in/DREAM/ and downloadable at https://github.com/niladriranjandas/DREAMv2.git.

Improved NMR-data-compliant protein structure modeling captures context-dependent variations and expands the scope of functional inference.

Nuclear magnetic resonance (NMR) spectroscopy can reveal conformational states of a protein in physiological conditions. However, sparsely available NMR data for a protein with large degrees of freedom can introduce structural artifacts in the built models. Currently used state-of-the-art methods deriving protein structure and conformation from NMR deploy molecular dynamics (MD) coupled with simulated annealing for building models. We provide an alternate graph-based modeling approach, where we first build substructures from NMR-derived distance-geometry constraints combined in one shot to form the core structure. The remaining molecule with inadequate data is modeled using a hybrid approach respecting the observed distance-geometry constraints. One-shot structure building is rarely undertaken for large and sparse data systems, but our data-driven bottom-up approach makes this uniquely feasible by suitable partitioning of the problem. A detailed comparison of select models with state-of-art methods reveals differences in the secondary structure regions wherein the correctness of our models is confirmed by NMR data. Benchmarking of 106 protein-folds covering 38-282 length structures shows minimal experimental-constraint violations while conforming to other structure quality parameters such as the proper folding, steric clash, and torsion angle violation based on Ramachandran plot criteria. Comparative MD studies using select protein models from a state-of-art method and ours under identical experimental parameters reveal distinct conformational dynamics that could be attributed to protein structure-function. Our work is thus useful in building enhanced NMR-evidence-based models that encapsulate the contextual secondary and tertiary structure variations present during the experimentation and expand the scope of functional inference.

MD DaVis: interactive data visualization of protein molecular dynamics.

SUMMARY: Molecular dynamics (MD) simulations have become an integral part of biomolecular study. Most MD software suites do not include analysis tools and those which do create very basic visualizations. Molecular Dynamics Data Visualizer (MD DaVis) is a python package developed to facilitate quick comparative analysis of MD trajectories of similar proteins or the same protein under different conditions. MD DaVis can quickly generate interactive visualization from molecular dynamics trajectories with a few simple steps. Interactive plots eliminate the need to make multiple plots for comparison, improving productivity and saving time. AVAILABILITY AND IMPLEMENTATION: MD DaVis is an open-source Python 3 package (https://pypi.org/project/md-davis/) distributed under MIT license. The source code is available at https://github.com/djmaity/md-davis or https://doi.org/10.5281/zenodo.6227047. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Somatic mutation analyses of stem-like cells in gingivobuccal oral squamous cell carcinoma reveals DNA damage response genes.

Gingivobuccal oral squamous cell carcinoma (OSCC-GB) occurs among persons who excessively chew smokeless tobacco in India. To understand the role of cancer stem cells (CSCs) in the disease, we have performed transcriptomics analysis on RNA-seq data from OSCC-GB primary tumors. The mutational signature analysis of the identified novel and Catalogue of Somatic Mutations in Cancer (COSMIC) variants reveals DNA damage associated etiology based on identified COSMIC signatures showing a higher prevalence of C > T mutations and 1 bp T/(A) nucleotide insertions, pointing to the role of smokeless tobacco carcinogens. The differential somatic mutational, functional impact predictions, and survival analysis reveals the role of DNA damage response-related genes, with the CREBBP gene as a major player. The new CSC somatic variants identified in the study may play a crucial role in cancer metastasis, local-regional recurrence, chemo- and/or radioresistance that contributes to high mortality of the Indian OSCC-GB patients.

Spike protein fusion loop controls SARS-CoV-2 fusogenicity and infectivity.

The high SARS-CoV-2 reproductive number driving the COVID-19 pandemic has been a mystery. Our recent in vitro, and in vivo coronaviral pathogenesis studies involving Mouse Hepatitis Virus (MHV-A59) suggest a crucial role for a small host membrane-virus contact initiator region of the Spike protein, called the fusion peptide that enhances the virus fusogenicity and infectivity. Here I study the Spike from five human ß-coronaviruses (HCoV) including the SARS-CoV-2, and MHV-A59 for comparison. The structural and dynamics analyses of the Spike show that its fusion loop spatially organizes three fusion peptides contiguous to each other to synergistically trigger the virus-host membrane fusion process. I propose a Contact Initiation Model based on the architecture of the Spike quaternary structure that explains the obligatory participation of the fusion loop in the initiation of the host membrane contact for the virus fusion process. Among all the HCoV Spikes in this study, SARS-CoV-2 has the most hydrophobic surface and the extent of hydrophobicity correlates with the reproductive number and infectivity of the other HCoV. Comparison between results from standard and replica exchange molecular dynamics reveal the unique physicochemical properties of the SARS-CoV-2 fusion peptides, accrued in part from the presence of consecutive prolines that impart backbone rigidity which aids the virus fusogenicity. The priming of the Spike by its cleavage and subsequent fusogenic conformational transition steered by the fusion loop may be critical for the SARS-CoV-2 spread. The importance of the fusion loop makes it an apt target for anti-virals and vaccine candidates.

Inferring metal binding sites in flexible regions of proteins.

Metal ions are central to the molecular function of many proteins. Thus their knowledge in experimentally determined structure is important; however, such structures often lose bound metal ions during sample preparation. Identification of these metal-binding site(s) becomes difficult when the receptor is novel and/or their conformations differ in the bound/unbound states. Locating such sites in theoretical models also poses a challenge due to the uncertainties with side-chain modeling. We address the problem by employing the Geometric Hashing algorithm to create a template library of functionally important binding sites and match query structures with the available templates. The matching is done on the structure ensemble obtained from coarse-grained molecular dynamics simulation, where metal-specific amino acids are screened to infer the true site. Test on 1347 non-redundant monomer protein structures show that Ca2+ , Zn2+ , Mg2+ , Cu2+ , and Fe3+ binding site residues can be classified at 0.92, 0.95, 0.80, 0.90, and 0.92 aggregate performance (out of 1) across all possible thresholds. The performance for Ca2+ and Zn2+ is notably superior in comparison to state-of-the-art methods like IonCom and MIB. Specific case studies show that additionally predicted metal-binding site residues in proteins have features necessary for ion binding. These include new sites not predicted by other methods. The use of coarse-grained dynamics thus provides a generalized approach to improve metal-binding site prediction. The work is expected to contribute to improving our ability to correctly predict protein molecular function where knowledge of metal binding is a key requirement.

A computational framework for modeling functional protein-protein interactions.

Protein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three-dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein-complex structures utilizing GO-based semantic similarity technique and co-evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid-body docking and template-based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re-ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template-based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein-protein interactions.

A proline insertion-deletion in the spike glycoprotein fusion peptide of mouse hepatitis virus strongly alters neuropathology.

Fusion peptides (FPs) in spike proteins are key players mediating early events in cell-to-cell fusion, vital for intercellular viral spread. A proline residue located at the central FP region has often been suggested to have a distinctive role in this fusion event. The spike glycoprotein from strain RSA59 (PP) of mouse hepatitis virus (MHV) contains two central, consecutive prolines in the FP. Here, we report that deletion of one of these proline residues, resulting in RSA59 (P), significantly affected neural cell syncytia formation and viral titers postinfection in vitro Transcranial inoculation of C57Bl/6 mice with RSA59 (PP) or RSA59 (P) yielded similar degrees of necrotizing hepatitis and meningitis, but only RSA59 (PP) produced widespread encephalitis that extended deeply into the brain parenchyma. By day 6 postinfection, both virus variants were mostly cleared from the brain. Interestingly, inoculation with the RSA59 (P)-carrying MHV significantly reduced demyelination at the chronic stage. We also found that the presence of two consecutive prolines in FP promotes a more ordered, compact, and rigid structure in the spike protein. These effects on FP structure were due to proline's unique stereochemical properties intrinsic to its secondary amino acid structure, revealed by molecular dynamics and NMR experiments. We therefore propose that the differences in the severity of encephalitis and demyelination between RSA59 (PP) and RSA59 (P) arise from the presence or absence, respectively, of the two consecutive prolines in FP. Our studies define a structural determinant of MHV entry in the brain parenchyma important for altered neuropathogenesis.

New facets of larger Nest motifs in proteins.

The Nest is a concave-shaped structural motif in proteins formed by consecutive enantiomeric left-handed (L) and right-handed (R) helical conformation of the backbone. This important motif subsumes many turn and helix capping structures and binds electron-rich ligands. Simple Nests are either RL or LR. Larger Nests (>2 residues long) may be RLR, LRL, RLRL, and so forth, being considered as composed of overlapping simple Nests. The larger Nests remain under-explored despite their widely known contributions to protein function. In our study, we address whether the recurrence of enantiomeric geometry in the larger Nests constrains the peptide backbone such that distinct compositional and conformational preferences are seen compared to simple Nests. Our analysis reveals the critical role of the L helical torsion angle in the formation of larger Nests. This can be observed through the higher propensity of residue or secondary structure combinations in LR and LRL backbone conformation in comparison to RL or RLR, although LR/LRL is considerably lower by occurrence. We also find that the most abundant doublets and triplets in Nests have a propensity for particular secondary structures, suggesting a strong sequence-structure relationship in the larger Nest. Overall, our analysis corroborates distinct features of simple and the larger Nests. Such insights would be helpful towards in-vitro design of peptides and peptidomimetic studies.

Interaction of arsenic with gap junction protein connexin 43 alters gap junctional intercellular communication.

Chronic exposure to Arsenic pollution in ground water is one of the largest environmental health disasters in the world. The toxicity of trivalent Arsenicals primarily happens due to its interaction with sulfhydryl groups in proteins. Arsenic binding to the protein can change the conformation of the protein and alter its interactions with other proteins leading to tissue damage. Therefore, much importance has been given to the studies of Arsenic bound proteins, for the purpose of understanding the origins of toxicity and to explore therapeutics. Here we study the dynamic effect of Arsenic on Connexin 43 (Cx43), a protein that forms the gap junctions, whose alteration deeply perturbs the cell-to-cell communication vital for maintaining tissue homeostasis. In silico molecular modelling and in vitro studies comparing Arsenic treated and untreated conditions show distinct results. Gap junction communication is severely disrupted by Arsenic due to reduced availability of unaltered Cx43 in the membrane bound form. In silico and Inductively Coupled Plasma Mass Spectrometry studies revealed the interaction of Arsenic to the Cx43 preferably occurs through surface exposed cysteines, thereby capping the thiol groups that form disulfide bonds in the tertiary structure. This leads to disruption of Cx43 oligomerization, and altered Cx43 is incompetent for transportation to the membrane surface, often forming aggregates primarily localizing in the endoplasmic reticulum. Loss of functional Cx43 on the cell surface have a deleterious effect on cellular homeostasis leading to selective vulnerability to cell death and tissue damage.

Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations.

With the advent of rapid sequencing technologies, making sense of all the genomic variations that we see among us has been a major challenge. A plethora of algorithms and methods exist that try to address genome interpretation through genotype-phenotype linkage analysis or evaluating the loss of function/stability mutations in protein. Critical Assessment of Genome Interpretation (CAGI) offers an exceptional platform to blind-test all such algorithms and methods to assess their true ability. We take advantage of this opportunity to explore the use of molecular dynamics simulation as a tool to assess alteration of phenotype, loss of protein function, interaction, and stability. The results show that coarse-grained dynamics based protein flexibility analysis on 34 CHEK2 and 1719 CALM1 single mutants perform reasonably well for class-based predictions for phenotype alteration and two-thirds of the predicted scores return a correlation coefficient of 0.6 or more. When all-atom dynamics is used to predict altered stability due to mutations for Frataxin protein (8 cases), the predictions are comparable to the state-of-the-art methods. The competitive performance of our straightforward approach to phenotype interpretation contrasts with heavily trained machine learning approaches, and open new avenues to rationally improve genome interpretation.

Assessing predictions on fitness effects of missense variants in calmodulin.

This paper reports the evaluation of predictions for the "CALM1" challenge in the fifth round of the Critical Assessment of Genome Interpretation held in 2018. In the challenge, the participants were asked to predict effects on yeast growth caused by missense variants of human calmodulin, a highly conserved protein in eukaryotic cells sensing calcium concentration. The performance of predictors implementing different algorithms and methods is similar. Most predictors are able to identify the deleterious or tolerated variants with modest accuracy, with a baseline predictor based purely on sequence conservation slightly outperforming the submitted predictions. Nevertheless, we think that the accuracy of predictions remains far from satisfactory, and the field awaits substantial improvements. The most poorly predicted variants in this round surround functional CALM1 sites that bind calcium or peptide, which suggests that better incorporation of structural analysis may help improve predictions.

Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge.

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant (ΔΔGH2O) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the ΔΔGH2O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

Structural analysis of glutathionyl hemoglobin using native mass spectrometry.

Glutathionylation is an example of reversible post-translation modification of proteins where free and accessible cysteine residues of proteins undergo thiol-disulfide exchange with oxidized glutathione (GSSG). In general, glutathionylation occurs under the condition of elevated oxidative stress in vivo. In human hemoglobin, Cys93 residue of ß globin chain was found to undergo this oxidative modification. Glutathionyl hemoglobin (GSHb) was reported to act as a biomarker of oxidative stress under several clinical conditions such as chronic renal failure, iron deficiency anemia, hyperlipidemia, diabetes mellitus, Friedreich's ataxia, atherosclerosis. Previously we showed that the functional abnormality associated with six-fold tighter oxygen binding of GSHb supposedly attributed to the conformational transition of the deoxy state of GSHb towards oxy hemoglobin like conformation. In the present study, we investigated the structural integrity and overall architecture of the quaternary structure of GSHb using native mass spectrometry and ion mobility mass spectrometry platforms. The dissociation equilibrium constants of both tetramer/dimer (Kd1) and dimer/monomer equilibrium (Kd2) was observed to increase by 1.91 folds and 3.64 folds respectively. However, the collision cross-section area of the tetrameric hemoglobin molecule remained unchanged upon glutathionylation. The molecular dynamics simulation data of normal human hemoglobin and GSHb was employed to support our experimental findings.

Molecular insights of inhibition in sickle hemoglobin polymerization upon glutathionylation: hydrogen/deuterium exchange mass spectrometry and molecular dynamics simulation-based approach.

In sickle cell anemia, polymerization of hemoglobin in its deoxy state leads to the formation of insoluble fibers that result in sickling of red blood cells. Stereo-specific binding of isopropyl group of ßVal6, the mutated amino-acid residue of a tetrameric sickle hemoglobin molecule (HbS), with hydrophobic groove of another HbS tetramer initiates the polymerization. Glutathionylation of ßCys93 in HbS was reported to inhibit the polymerization. However, the mechanism of inhibition in polymerization is unknown to date. In our study, the molecular insights of inhibition in polymerization were investigated by monitoring the conformational dynamics in solution phase using hydrogen/deuterium exchange-based mass spectrometry. The conformational rigidity imparted due to glutathionylation of HbS results in solvent shielding of ßVal6 and perturbation in the conformation of hydrophobic groove of HbS. Additionally, molecular dynamics simulation trajectory showed that the stereo-specific localization of glutathione moiety in the hydrophobic groove across the globin subunit interface of tetrameric HbS might contribute to inhibition in polymerization. These conformational insights in the inhibition of HbS polymerization upon glutathionylation might be translated in the molecularly targeted therapeutic approaches for sickle cell anemia.

Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins.

cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.

Computational design of model scaffold for anion recognition based on the 'Cα NN' motif.

The 'novel phosphate binding 'Cα NN' motif', consisting of three consecutive amino acid residues, usually occurs in the protein loop regions preceding a helix. Recent computational and complementary biophysical experiments on a series of chimeric peptides containing the naturally occurring 'Cα NN' motif at the N-terminus of a designed helix establishes that the motif segment recognizes the anion (sulfate and phosphate ions) through local interaction along with extension of the helical conformation which is thermodynamically favored even in a context-free, nonproteinaceous isolated system. However, the strength of the interaction depends on the amino acid sequence/conformation of the motif. Such a locally-mediated recognition of anions validates its intrinsic affinity towards anions and confirms that the affinity for recognition of anions is embedded within the 'local sequence' of the motif. Based on the knowledge gathered on the sequence/structural aspects of the naturally occurring 'Cα NN' segment, which provides the guideline for rationally engineering model scaffolds, we have modeled a series of templates and investigated their interactions with anions using computational approach. Two of these designed scaffolds show more efficient anion recognition than those of the naturally occurring 'Cα NN' motif which have been studied. This may provide an avenue in designing better anion receptors suitable for various biochemical applications.

Numerical simulation of a glucose sensitive composite membrane closed-loop insulin delivery system.

Closed-loop insulin delivery system works on pH modulation by gluconic acid production from glucose, which in turn allows regulation of insulin release across membrane. Typically, the concentration variation of gluconic acid can be numerically modeled by a set of non-linear, non-steady state reaction diffusion equations. Here, we report a simpler numerical approach to time and position dependent diffusivity of species using finite difference and differential quadrature (DQ) method. The results are comparable to that obtained by analytical method. The membrane thickness directly determines the concentrations of the glucose and oxygen in the system, and inversely to the gluconic acid. The advantage with the DQ method is that its parameter values need not be altered throughout the analysis to obtain the concentration profiles of the glucose, oxygen and gluconic acid. Our work would be useful for modeling diabetes and other systems governed by such non-linear and non-steady state reaction diffusion equations.

Mass Spectrometry-Based Diagnosis of Hemoglobinopathies: A Potential Tool for the Screening of Genetic Disorder.

Hemoglobinopathies are caused by point mutation in globin gene that results in structural variant of hemoglobin. While 7 % of world populations are carrier of hemoglobinopathies, the prevalence of the disease varies between 3 to 17 % across different population groups in India. In a diagnostic laboratory, alkaline gel electrophoresis and cation exchange-based HPLC (CE-HPLC) are most widely used techniques for characterization of hemoglobin variants. In the above methods, the differential surface charge of hemoglobin molecule in variants is exploited for their characterization. Sometime, co-migration of variants in gel electrophoresis and co-elution or elution with unknown retention time in automated CE-HPLC might lead to ambiguity in the analysis of hemoglobinopathies. Under such circumstances, it is necessary to use other analytical methods that provide unambiguous results. Mass spectrometry-based proteomics approach and DNA sequence analysis are examples of such alternative methods. In the present study, liquid chromatography coupled to mass spectrometry has been used for three commonly observed variants in India, e.g., HbE, HbQ India and HbD Punjab that appeared with inappropriate results in the conventional analysis. A customized hemoglobin variant database has been used in the mass spectrometry-based analysis of those three variants. Mass spectrometry-based proteomics approach was used to analyze above variant sample accurately.