Protein feature engineering framework for AMPylation site prediction.

AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model's capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations.

A network pharmacological approach for the identification of potential therapeutic targets of Brahmi Nei - a complex traditional Siddha formulation.

Brahmi Nei (BN), a traditional Indian polyherbal formulation has been described in classical texts for the treatment of anxiety and depression, as well as to fortify the immune system. The individual herbs of BN have been used for treatment of wide range of disorders including cognition, inflammation, skin ailments and cancer etc., This diverse basket of therapeutic activity suggests that BN may possess therapeutic benefits to other disorders. So, the present study aims to identify the potential therapeutic targets of BN using a network pharmacological approach to comprehend the multi target action of its multiple phytoconstituents. We have employed Randic Index for the first time to calculate the contribution score of module segregated targets towards diseases. Our results suggests that BN targets could also be effective in other diseases such as lysosomal storage disorders, respiratory disorders etc., apart from neurological disorders. The key targets with highest topological measures of Targets-(Pathway)-Targets network were identified as potential therapeutic targets of BN. And the top hit target PTGS2, a gene encoding for cyclooxygenase-2 was further evaluated using molecular docking, molecular dynamic simulation and in vitro studies. Our findings open up new therapeutic facets for BN that can be explored systematically in future.Communicated by Ramaswamy H. Sarma.

Effect of osmolytes on the EcoRI endonuclease: Insights into hydration and protein dynamics from molecular dynamics simulations.

Osmolytes play an important role in cellular physiology by modulating the properties of proteins, including their molecular specificity. EcoRI is a model restriction enzyme whose specificity to DNA is altered in the presence of osmolytes. Here, we investigate the effect of two different osmolytes, glycerol and DMSO, on the dynamics and hydration of the EcoRI enzyme using molecular dynamics simulations. Our results show that the osmolytes, alter the essential dynamics of EcoRI. Particularly, we observe that the dynamics of the arm region of EcoRI which is involved in DNA binding is significantly altered. In addition, conformational free energy analyses reveals that the osmolytes bring about a change in the landscape similar to that of EcoRI bound to cognate DNA. We further observe that the hydration of the enzyme for each of the osmolyte is different, indicating that the mechanism of action of each of these osmolytes could be different. Further analyses of interfacial water dynamics using rotational autocorrelation function reveals that while the protein surface contributes to a slower tumbling motion of water, osmolytes, additionally contribute to the slowing of the angular motion of the water molecules. Entropy analysis also corroborates with this finding. We also find that the slowed rotational motion of interfacial waters in the presence of osmolytes contributes to a slowed relaxation of the hydrogen bonds between the interfacial waters and the functionally important residues in the protein. Taken together, our results show that osmolytes alter the dynamics of the protein by altering the dynamics of water. This altered dynamics, mediated by the changes in the water dynamics and hydrogen bonds with functionally important residues, may contribute to the altered specificity of EcoRI in the presence of osmolytes.

Comprehensive screening of marine metabolites against class B1 metallo-ß-lactamases of Klebsiella pneumoniae using two-pronged in silico approach.

The emergence of antibiotic resistance is one of the major global threats in healthcare. Metallo-ß-Lactamases (MBL) are a class of enzymes in bacteria that cleave ß-lactam antibiotics and confer resistance. MBLs are further divided into subclasses B1, B2 and B3. Of these, subclasses B1-MBLs (including NDM-1, VIM-2 and IMP-1) constitute the clinically prevalent lactamases conferring resistance. To date, no effective drugs are available clinically against MBLs. In this work, we aim to identify potent inhibitors for the B1 subclass of MBL from available marine metabolites in Comprehensive Marine Natural Product database through integrated in silico approaches. We have used two methods, namely, the high-throughput strategy and the pharmacophore-based strategy to identify potential inhibitors from marine metabolites. High-throughput virtual screening identified N-methyl mycosporine-Ser, which had the highest binding affinity to NDM-1. The pharmacophore-based approach based on co-crystallized ligands identified makaluvic acid and didymellamide with higher binding affinity across B1-MBLs. Taking into account of the advantage of a pharmacophore model-based approach with higher binding affinity, we conclude that both makaluvic acid and didymellamide show potential broad-spectrum effects by binding to all three B1-MBL receptors. The study also indicates the need to take multiple in silico approaches to screen and identify novel inhibitors. Together, our study reveals promising inhibitors that can be identified from marine systems.Communicated by Ramaswamy H. Sarma.

Identification of Phase Separating Proteins With Distributed Reduced Alphabet Representations of Sequences.

Phase separation of proteins play key roles in cellular physiology including bacterial division, tumorigenesis etc. Consequently, understanding the molecular forces that drive phase separation has gained considerable attention and several factors including hydrophobicity, protein dynamics, etc., have been implicated in phase separation. Data-driven identification of new phase separating proteins can enable in-depth understanding of cellular physiology and may pave way towards developing novel methods of tackling disease progression. In this work, we exploit the existing wealth of data on phase separating proteins to develop sequence-based machine learning method for prediction of phase separating proteins. We use reduced alphabet schemes based on hydrophobicity and conformational similarity along with distributed representation of protein sequences and biochemical properties as input features to Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. We used both curated and balanced dataset for building the models. RF trained on balanced dataset with hydropathy, conformational similarity embeddings and biochemical properties achieved accuracy of 97%. Our work highlights the use of conformational similarity, a feature that reflects amino acid flexibility, and hydrophobicity for predicting phase separating proteins. Use of such "interpretable" features obtained from the ever-growing knowledgebase of phase separation is likely to improve prediction performances further.

Effect of glycerol on free DNA: A molecular dynamics simulation study.

Osmolytes are a class of organic solutes that are produced in a variety of organisms in response to stress. They exert diverse effects on macromolecules and their functions. In this work, we investigate the effect of glycerol, one such osmolyte, on the hydration and conformation of four DNA sequences that differ by a single base pair and a random DNA sequence. Molecular dynamics simulations reveal DNA sequence-dependent and glycerol concentration-dependent hydration and DNA conformation. Interestingly, we find that the sequence-dependent changes in the hydration reflects the order of preference of these sequences for star activity of the EcoRI enzyme. However, the changes in DNA conformation do not reflect this order of preference. Interaction energy analyses reveal that the per-glycerol interaction energy with DNA is stronger than the per-water interaction energy with DNA. However, the total interaction energy of glycerol with DNA is lower than that of total water-DNA interaction energy indicating that it might be easier for an approaching DNA-binding protein to displace glycerol than water and thus contributing positively to protein-DNA binding. In a larger context, our study brings attention to the need to investigate the effect of osmolytes on free DNA in order to delineate the role of osmolyte in protein-DNA interactions.

In vitro and in silico screening of Klebsiella pneumoniae new Delhi metallo-ß-lactamase-1 inhibitors from endophytic Streptomyces spp.

The increase in drug resistance over the last two decades is a big threat in health care settings. More importantly, the dissemination of carbapenem-resistant Enterobacteriaceae is the major threat to public health with an increase in morbidity and mortality. ß-lactamase is known to confer enteric bacteria with nearly complete resistance to all ß-lactam antibiotics including the late-generation carbapenems. The commercially available ß-lactamase inhibitors, clavulanic acid, sulbactam, and tazobactam are being met with an increasing number of resistant phenotypes and are ineffective against pathogens harbouring New Delhi metallo-ß-lactamase (NDM-1). Inhibition of New Delhi metallo-ß-lactamase-1 activity is one potential way to treat metallo ß-lactamase (MBL) producing multi drug resistant (MDR) pathogen. The present study focused on screening of Klebsiella pneumoniae New Delhi metallo-ß-lactamase-1 (BLIs) from endophytic Streptomyces spp. using in vitro and in silico methods. The study identified three potential inhibitors of New Delhi metallo-ß-lactamase-1, namely dodecanoic acid, dl-alanyl-l-leucine and phenyl propanedioic acid. These molecules were found to bind to other MBLs namely, IMP-1 and VIM-2. To the best of our knowledge, this is the first kind of study reporting the binding mode of these molecules with New Delhi metallo-ß-lactamase-1.Communicated by Ramaswamy H. Sarma.

Towards understanding the mechanism of action of a polyherbal formulation using a multi-pronged strategy.

Herein, we investigate the cognitive effects of a traditional polyherbal formulation, Brahmi Nei (BN) for its effect on cognitive health. Network pharmacological analysis of the bioactives reported in the phytoconstituents of BN was performed by retrieving information from various databases. The in-silico predictions were experimentally validated using in vitro and in vivo models through a combination of biochemical, behavioural and molecular studies. The network pharmacological analysis of the key molecules in BN revealed their ability to modulate molecular targets implicated in memory, cognition, neuronal survival, proliferation, regulation of cellular bioenergetics and oxidative stress. Behavioral studies performed on normal adult rats administered with BN showed a significant improvement in their cognitive performance. Microarray analysis of their brain tissues exhibited an up-regulation of genes involved in oxidative phosphorylation, learning, neuronal differentiation, extension, regeneration and survival while pro-inflammatory and pro-degenerative genes were down-regulated. The oxygen consumption rate in BN-treated hippocampal cells showed a significant improvement in the bioenergetic health index when compared to untreated cells due to the mitochondrial membrane fortifying effect and anti-inflammatory property of the BN constituents. The neuroregenerative potential of BN was manifested in increase in axonal length and neurite outgrowth. Western blots and 2D gel electrophoresis revealed a reduction in pro-apoptotic proteins while increasing Akt and cyclophilin proteins. Taken together, our data reveal that BN, although traditionally used to treat anxiolytic disorders can be explored as a nutraceutical to improve neuronal health as well as a therapeutic option to treat cognitive disorders.

Support vector machine-based prediction of pore-forming toxins (PFT) using distributed representation of reduced alphabets.

Bacterial virulence can be attributed to a wide variety of factors including toxins that harm the host. Pore-forming toxins are one class of toxins that confer virulence to the bacteria and are one of the promising targets for therapeutic intervention. In this work, we develop a sequence-based machine learning framework for the prediction of pore-forming toxins. For this, we have used distributed representation of the protein sequence encoded by reduced alphabet schemes based on conformational similarity and hydropathy index as input features to Support Vector Machines (SVMs). The choice of conformational similarity and hydropathy indices is based on the functional mechanism of pore-forming toxins. Our methodology achieves about 81% accuracy indicating that conformational similarity, an indicator of the flexibility of amino acids, along with hydrophobic index can capture the intrinsic features of pore-forming toxins that distinguish it from other types of transporter proteins. Increased understanding of the mechanisms of pore-forming toxins can further contribute to the use of such "mechanism-informed" features that may increase the prediction accuracy further.

Investigations on a polyherbal formulation for treatment of cognitive impairment in a cholinergic dysfunctional rodent model.

Alzheimer's disease is a multifactorial neurodegenerative condition manifested through acute cognitive decline, amyloid plaque deposits and neurofibrillary tangles. Complete cure for this disease remains elusive as the conventional drugs address only a single molecular target while Alzheimer's disease involves a complex interplay of different sets of molecular targets and signaling networks. In this context, the possibility of employing multi-drug combinations to rescue neurons from the dysregulated metabolic changes is being actively investigated. The present work investigates a poly-herbal formulation, Brahmi Nei that has been traditionally used for anxiolytic disorders and immunomodulatory effects, for its efficiency in ameliorating cognitive decline through a combination of behavioral, biochemical, histopathological, gene and protein expression analyses. Our results reveal that the formulation shows excellent neuroregenerative properties, rescues neurons from inflammatory damage, reduces neuritic plaque deposits and improves working memory in rodent models with scopolamine-induced dementia. The microarray analysis shows that the formulation induces the expression of pro-survival pathways and positively modulates genes involved in memory consolidation, axonal growth and proliferation in a concentration-dependent manner with therapeutic concentrations restoring the normal conditions in the brain of the diseased animals. The neuritic spine morphology confirms the long-term memory potentiation through improved mushroom spine density, increased dendritic length and connectivity. Taken together, our study provides mechanistic evidence to prove that the traditional formulation can be a superior therapeutic strategy to treat cognitive decline when compared to the conventional mono-drug treatment.

Effect of NaeI-L43K mutation on protein dynamics and DNA conformation: Insights from molecular dynamics simulations.

Protein-DNA interactions are an important class of biomolecular interactions inside the cell. Delineating the mechanisms of protein-DNA interactions and more specifically, how proteins search and bind to their specific cognate sequences has been the quest of many in the scientific community. Restriction enzymes have served as useful model systems to this end. In this work, we have investigated using molecular dynamics simulations the effect of L43K mutation on NaeI, a type IIE restriction enzyme. NaeI has two domains, the Topo and the Endo domains, each binding to identical strands of DNA sequences (GCCGGC)2. The binding of the DNA to the Topo domain is thought to enhance the binding and cleavage of DNA at the Endo domain. Interestingly, it has been found that the mutation of an amino acid that is distantly-located from the DNA cleavage site (L43K) converts the restriction endonuclease to a topoisomerase. Our investigations reveal that the L43K mutation not only induces local structural changes (as evidenced by changes in hydrogen bond propensities and differences in the percentage of secondary structure assignments of the residues in the ligase-like domain) but also alters the overall protein dynamics and DNA conformation which probably leads to the loss of specific cleavage of the recognition site. In a larger context, our study underscores the importance of considering the role of distantly-located amino acids in understanding protein-DNA interactions.

Investigating the effect of charged amino acids on DNA conformation in EcoRI-DNA complex: a molecular dynamics simulation study.

Sequence-specific binding of proteins to DNA is essential for almost all the cellular processes like transcription, translation, replication, etc. One among the various mechanisms that has been identified so far that contributes to the specificity in protein-DNA interaction is the DNA conformational change. Electrostatic neutralization of the phosphate groups by the positively charged amino acid residues in proteins is thought to bring about such conformational changes in DNA. Here, we employ molecular dynamics simulations to examine the effect of charge on amino acids Lys113, Arg145, and Asp91 which are attached to the scissile phosphate on the conformation of DNA in EcoRI-DNA complex. The results indicate that the charge of these amino acids is essential for maintaining the local conformation of DNA in the EcoRI-bound form. Interestingly, we observe that the positively charged amino acids Lys113 and Arg145 have a long-range influence on the DNA conformation, whereas the negatively charged amino acid Asp91 has only a localized effect on the DNA conformation. The charge on the amino acids also alters the collective dynamics of EcoRI. Collectively, the results shed light on the diversity of the effect of charges on DNA conformation as well as on protein dynamics.

Interaction of Tenebrio Molitor Antifreeze Protein with Ice Crystal: Insights from Molecular Dynamics Simulations.

Antifreeze proteins (AFP) observed in cold-adapting organisms bind to ice crystals and prevent further ice growth. However, the molecular mechanism of AFP-ice binding and AFP-inhibited ice growth remains unclear. Here we report the interaction of the insect antifreeze protein (Tenebrio molitor, TmAFP) with ice crystal by molecular dynamics simulation studies. Two sets of simulations were carried out at 263 K by placing the protein near the primary prism plane (PP) and basal plane (BL) of the ice crystal. To delineate the effect of temperatures, both the PP and BL simulations were carried out at 253 K as well. The analyses revealed that the protein interacts strongly with the ice crystal in BL simulation than in PP simulation both at 263 K and 253 K. Further, it was observed that the interactions are primarily mediated through the interface waters. We also observed that as the temperature decreases, the interaction between the protein and the ice increases which can be attributed to the decreased flexibility and the increased structuring of the protein at low temperature. In essence, our study has shed light on the interaction mechanism between the TmAFP antifreeze protein and the ice crystal.

Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations.

Human Serum Albumin (HSA) has been suggested to be an alternate biomarker to the existing Hemoglobin-A1c (HbA1c) marker for glycemic monitoring. Development and usage of HSA as an alternate biomarker requires the identification of glycation sites, or equivalently, glucose-binding pockets. In this work, we combine molecular dynamics simulations of HSA and the state-of-art machine learning method Support Vector Machine (SVM) to predict glucose-binding pockets in HSA. SVM uses the three dimensional arrangement of atoms and their chemical properties to predict glucose-binding ability of a pocket. Feature selection reveals that the arrangement of atoms and their chemical properties within the first 4Å from the centroid of the pocket play an important role in the binding of glucose. With a 10-fold cross validation accuracy of 84 percent, our SVM model reveals seven new potential glucose-binding sites in HSA of which two are exposed only during the dynamics of HSA. The predictions are further corroborated using docking studies. These findings can complement studies directed towards the development of HSA as an alternate biomarker for glycemic monitoring.

Dynamics and thermodynamics of water around EcoRI bound to a minimally mutated DNA chain.

Water plays an important role in protein-DNA interactions. Here, we examine using molecular dynamics simulations the differences in the dynamic and thermodynamic properties of water in the interfacial and intercalating regions of EcoRI bound to the cognate and to a minimally mutated noncognate DNA chain. The results show that the noncognate complex is not only more hydrated than the cognate complex, but the interfacial waters in the noncognate complex exhibit a faster dynamics, which in turn reduces the hydrogen-bond lifetimes. Thus, the higher hydration, faster reorientation dynamics and faster hydrogen-bond-relaxation times of water, taken together, indicate that, even with a minimal mutation of the DNA sequence, the interfacial regions of the noncognate complex are more poised to allowing the protein to diffuse away than to promoting the formation of a stable complex. Alternatively, the results imply that the slowed water dynamics in the interfacial regions when the protein chances upon a cognate sequence allow the formation of a stable specific protein-DNA complex leading to catalytic action.

Dynamic and structural changes in the minimally restructuring EcoRI Bound to a minimally mutated DNA chain.

The dynamics of a protein plays an important role in protein functionality. Here, we examine the differences in the dynamics of a minimally restructuring protein, EcoRI, when it is bound to its cognate DNA and to a noncognate sequence which differs by just a single basepair. Molecular dynamics simulations of the complexes and essential dynamics analyses reveal that the overall dynamics of the protein subunits change from a coordinated motion in the cognate complex to a scrambled motion in the noncognate complex. This dynamical difference extends to the protein-DNA interface where EcoRI tries to constrict the DNA in the cognate complex. In the noncognate complex, absence of the constricting motion of interfacial residues, overall change in backbone dynamics and structural relaxation of the arms enfolding the DNA leave the DNA less-kinked relative to the situation in the cognate complex, thus indicating that the protein is poised for linear diffusion along the DNA rather than for catalytic action. In a larger context, the results imply that the DNA sequences dictate protein dynamics and that when a protein chances upon the recognition sequence some of the key domains of the protein undergo dynamical changes that prepare the protein for eventual catalytic action.

Effects of polydisperse crowders on aggregation reactions: a molecular thermodynamic analysis.

Intracellular crowding in biological systems is usually mimicked in in vitro experiments by adding single crowders at high volume fractions, without taking into consideration the polydispersity of the crowders in the cellular environment. Here, we develop a molecular thermodynamic formalism to examine the effects of size-polydispersity of crowders on aggregation reaction equilibria. Although the predominantly common practice so far has been to appeal to the entropic (excluded-volume) effects in describing crowding effects, we show that the internal energy (hence, the enthalpy) of the system could dramatically alter the effects, even qualitatively, particularly in the case of a mixture of crowders, depending on the changes in the covolume of the products relative to that of the reactants and on the preferential binding or exclusion of the crowders by the reactants and products. We also show that in the case of polydisperse crowders the crowders with the largest size difference dominate the overall changes in the yield of the reaction, depending on the individual concentrations of the crowders.