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The low permeability of coal seams is a constraint in the efficient production of coalbed methane. However, the presence of natural fractures in coal enhances the permeability, and prior knowledge of sub‐surface fractures in coal seams is vital to identify the prospective seam. This paper investigates the anisotropy and identifies fractures by processing the advanced sonic and resistivity image logs to mitigate challenges in the reservoir. Anisotropy is estimated from the difference in the travel time between fast and slow shear waves. The application of Alford's rotation technique determines the fast shear wave polarization angle which is consistent with the fracture orientation along the NE–SW or NW–SE direction in coal seams. Moreover, the crossover of fast and slow shear waves in the slowness versus frequency plot indicates stress‐induced anisotropy that originates from fractures. Besides, drilling‐induced fractures observed along NE–SW in the resistivity image log indicate the maximum horizontal stress direction. Results from this study compare coal seams based on fractures in adopting better operational activities for optimized production and future geomechanical studies in the Bokaro coalfield situated in India. [ABSTRACT FROM AUTHOR]

This study applies machine learning techniques to improve petrographic classification in India's Bokaro coalfield's Barakar Formation, using conventional geophysical well logs from three wells. We analysed natural gamma ray, true resistivity, bulk density, neutron porosity, and photoelectric factor data using k-nearest neighbor (kNN), support vector machine (SVM) and random forest (RF) classifiers. A master well provided initial reference log measurement cut-off values for typical lithologies like shale, sandstone, carbonaceous shale, and coal, forming the basis of our training dataset. We assessed model accuracy using precision, recall, and F1-score metrics, finding the random forest model to be the most effective in litho-type discrimination. During the training phase, the computed overall accuracy of the predicted ML modes exceeded 89% and model accuracy hierarchy was RF>SVM>kNN. These classifiers were then applied to other well locations to predict lithological sequences, aiding in lithofacies sequence identification and potential fault extension detection. The study demonstrates the random forest model's superior precision and efficiency in lithological discrimination. Our findings enhance automated processes for identifying missing lithology during well correlation, offering valuable insights for geological interpretation in resource exploration and development. This machine learning-driven approach marks a significant advancement in subsurface geological studies.Graphical abstract: This study applies machine learning techniques to improve petrographic classification in India's Bokaro coalfield's Barakar Formation, using conventional geophysical well logs from three wells. We analysed natural gamma ray, true resistivity, bulk density, neutron porosity, and photoelectric factor data using k-nearest neighbor (kNN), support vector machine (SVM) and random forest (RF) classifiers. A master well provided initial reference log measurement cut-off values for typical lithologies like shale, sandstone, carbonaceous shale, and coal, forming the basis of our training dataset. We assessed model accuracy using precision, recall, and F1-score metrics, finding the random forest model to be the most effective in litho-type discrimination. During the training phase, the computed overall accuracy of the predicted ML modes exceeded 89% and model accuracy hierarchy was RF>SVM>kNN. These classifiers were then applied to other well locations to predict lithological sequences, aiding in lithofacies sequence identification and potential fault extension detection. The study demonstrates the random forest model's superior precision and efficiency in lithological discrimination. Our findings enhance automated processes for identifying missing lithology during well correlation, offering valuable insights for geological interpretation in resource exploration and development. This machine learning-driven approach marks a significant advancement in subsurface geological studies. [ABSTRACT FROM AUTHOR]

Reservoir characterization is necessary to compute reservoir parameters for hydrocarbon potential and production optimization. The limitation of robust data and the presence of cultural noise is a constraint for reservoir characterization in the Raniganj basin located in India. Based on available well logs and two-dimensional post-stack seismic data, a model-based seismic inversion is executed to generate acoustic impedance by converting acoustic reflectivity into rock elastic parameters. Moreover, the seismic attributes obtained from the inversion are implemented in neural network architectures to map shale volume, Young's modulus, and Poisson's ratio. Error analysis between predicted and actual results demonstrate multi-layered feed-forward or probabilistic neural network display a better result in obtaining reservoir parameters. The mapped reservoir section shows the acoustic impedance varying from 5000 to 16,000 (g/cc)*(m/s), shale volume ranging from 15% to 55%, Young's modulus, and Poisson's ratio vary from 0.5–9.5 GPa and 0.23–0.27 respectively. Cross-plot between Young's modulus versus Poisson's ratio classifies lithology from brittleness and it increases with depth. Neural network architectures help to identify the best model in delineating shale barriers for designing hydraulic fracturing treatments. Results from this study have added significant values in engineering application and will help in ongoing coalbed methane exploration and future geomechanical studies. However, limitations exist in resolving thin coal seams as the seismic resolution depends on the wavelength, velocity, and frequency of waves in the formation. [ABSTRACT FROM AUTHOR]

Pore pressure and stress analysis are vital to avoid well complications while drilling. A methodology was used to map pore pressure in the Raniganj basin located in India using sonic log and seismic data. A model-based seismic inversion technique was used to characterize the reservoir. Moreover, seismic attributes generated from inversion were integrated with estimated pore pressure from Bower’s and Eaton’s method using sonic logs to map sub-surface pore pressure. The probabilistic neural network architecture was used as an integration algorithm for mapping the pore pressure. Besides, the magnitude of overburden (Sv), maximum (SH), and minimum (Sh) horizontal stress were estimated, and the present-day SH direction was determined from drilling-induced fractures in the resistivity image log. Pore pressure equals hydrostatic pressure at shallower depth while marginal overpressure was observed at deeper depth as the estimated pore pressure deviates from the normal compaction trend line. Bower’s method slightly overestimates the pore pressure at deeper depth compared to Eaton’s, though the minimum difference between estimated and measured pore pressure supports the result. Stress analysis identifies a normal fault stress regime (Sv > SH > Sh), in which existing normal faults were stable based on frictional faulting theory. The SH is oriented along NE-SW direction and the estimated Sh magnitude corroborates with direct field measurements. The result from this study helps to understand pore pressure distribution and stress profiles, mitigates associated drilling risk, predicts the pressure limit for the reactivation of existing faults during fluid injection in hydraulic fracturing treatments, and also provides vital inputs for future geomechanical studies. [ABSTRACT FROM AUTHOR]

The atomic-resolution crystal structures of human carbonic anhydrases I and II complexed with ‘two-prong’ inhibitors are reported. Each inhibitor contains a benzenesulfonamide prong and a cupric iminodiacetate (IDA-Cu2+) prong separated by linkers of different lengths and compositions. The ionized NH- group of each benzenesulfonamide coordinates to the active site Zn2+ ion; the IDA-Cu2+ prong of the tightest-binding inhibitor, BR30, binds to H64 of CAll and H200 of CAl. This work provides the first evidence verifying the structural basis of nanomolar affinity measured for two-prong inhibitors targeting the carbonic anhydrases. [ABSTRACT FROM AUTHOR]

Sulfonamide derivatives serve as potent inhibitors of carbonic anhydrases (CAs), and a few such inhibitors have been currently used as drugs for the treatment of different pathogenic conditions in humans. In pursuit of designing the isozyme-specific inhibitors of human CAs, we observed that the fluorescence spectral properties and binding profiles of a fluorogenic sulfonamide derivative, 5-(di- methylamino)- I -naphthalenesulfonamide (dansylamide, DNSA), were markedly different between the recombinant forms of human carbonic anhydrase I (hCA I) and II (hCA II). The kinetic evaluation of the overall microscopic pathways for the binding of DNSA to hCA I versus hCA II revealed that the protein isomerization step served as a major determinant of the above discrepancy. Arguments are presented that the detailed structural -functional investigations of enzyme-ligand interactions may provide insights into designing the isozyme-specific inhibitors of CAs. [ABSTRACT FROM AUTHOR]

Benzenesulfonamide and iminodiacetate (IDA)-conjugated Cu2+ independently interact at the active site and a peripheral site of carbonic anhydrases, respectively [Banerjee, A. L., Swanson, M., Roy, B. C., Jia, X., Haldar, M. K., Mallik, S., and Srivastava, D. K. (2004) J. Am. Chem. Soc. 126, 10875- 10883]. By attaching IDA-bound Cu2+ to benzenesulfonamide via different chain length spacers, we synthesized two "two-prong" ligands, L1 and L2, in which the distances between Cu2+ and NH2 group of sulfonamide were 29 and 22 Å, respectively. We compared the binding affinities of L1 and L2, vis- à-vis their parent compound, benzenesulfonamide, for recombinant human carbonic anhydrase I (hCA-I) by performing the fluorescence titration and steady-state kinetic experiments. The experimental data revealed that whereas the binding affinity of L1 for hCA-I was similar to that of benzenesulfonamide, the binding affinity of L2 was ∼2 orders of magnitude higher, making L2 one of the most potent ligands or inhibitors of hCA-I. Since the enhanced binding or inhibitory potency of L2 is diminished (to the level of benzenesulfonamide) either in the presence of EDTA or upon treatment of the enzyme with diethyl pyrocarbonate, it is proposed that Cu2+ of L2 interacts with one of the surface-exposed histidine residues of the enzyme. A cumulative account of the experimental data leads to the suggestion that the differential binding of L1 versus L2 to hCA-I is encoded in the chain length of the spacer moiety. [ABSTRACT FROM AUTHOR]

Due to their involvement in diverse pathological conditions, carbonic anhydrases have been the targets of drug developments for the treatments of glaucoma, epilepsy, high altitude sickness, as well as cancer. Of about 14 isozymes of carbonic anhydrases, carbonic anhydrase-II (hCA-II) has been most extensively investigated from the structural, functional, and inhibitor design point of view. We discovered that hCA-II preferentially binds to the Sepharose–iminodiacetate (IDA)-Zn2+ column, and such binding does not require incorporation of either N- or C-terminal histidine tags in the protein structure. By using the Sepharose–IDA-Zn2+ affinity column, we purified the Escherichia coli expressed hCA-II with an overall recovery of 76%. The purified enzyme showed a single band on the SDS–PAGE. Due to ease in preparing the Sepharose–IDA-Zn2+ column, and purifying hCA-II just in one step, the overall protocol will be ideal for producing bulk quantities of the enzyme for high throughput screening of inhibitors. [Copyright &y& Elsevier]

We elaborate on a novel strategy for enhancing the binding affinity of an active-site directed inhibitor by attaching a tether group, designed to interact with the surface-exposed histidine residue(s) of enzymes. In this approach, we have utilized the recombinant form of human carbonic anhydrase-Il (hCA- II) as the enzyme source and benzenesulfohamide and its derivatives as inhibitors. The steady-state kinetic and the ligand binding data revealed that the attachment of iminodiacetate (lDA)-Cu2+ to benzenesulfonamide (via a triethylene glycol spacer) enhanced its binding affinity for hCA-II by about 40-fold. No energetic contribution of either IDA or triethylene glycol spacer was found (at least in the ground state of the enzyme- inhibitor complex) when Cu2+ was stripped off from the tether group-conjugated sulfonamide derivative. Arguments are presented that the overall strategy of enhancing the binding affinities of known inhibitors by attaching the IDA-Cu2+ groups to interact with the surface-exposed histidine residues will find a general application in designing the isozyme-specific inhibitors as potential drugs. [ABSTRACT FROM AUTHOR]

This article focuses on a study related to two-prong inhibitors for human carbonic anhydrase II. Carbonic anhydrases are ubiquitously distributed Zn-containing metalloenzymes that catalyze the reversible hydration of CO2 to bicarbonate. In humans, there are 14 isozymes of the a-type, some of which have been implicated in pathological conditions. Also, the intrinsic flexibility in the protein allows for the binding of structurally unrelated compounds, and it is difficult to predict the nature and magnitude of such structural flexibility.

Despite the similarity in the active site pockets of carbonic anhydrase (CA) isozymes I and II, the binding affinities of benzenesulfonamide inhibitors are invariably higher with CA II as compared to CA I. To explore the structural basis of this molecular recognition phenomenon, we have designed and synthesized simple benzenesulfonamide inhibitors substituted at the para position with positively charged, negatively charged, and neutral functional groups, and we have determined the affinities and X-ray crystal structures of their enzyme complexes. The para-substituents are designed to bind in the midsection of the 15 Å deep active site cleft, where interactions with enzyme residues and solvent molecules are possible. We find that a para-substituted positively charged amino group is more poorly tolerated in the active site of CA I compared with CA II. In contrast, a para-substituted negatively charged carboxylate substituent is tolerated equally well in the active sites of both CA isozymes. Notably, enzyme—inhibitor affinity increases upon neutralization of inhibitor charged groups by amidation or esterification. These results inform the design of short molecular linkers connecting the benzenesulfonamide group and a para-substituted tail group in ‘two-prong’ CA inhibitors: an optimal linker segment will be electronically neutral, yet capable of engaging in at least some hydrogen bond interactions with protein residues and/or solvent. Microcalorimetric data reveal that inhibitor binding to CA I is enthalpically less favorable and entropically more favorable than inhibitor binding to CA II. This contrasting behavior may arise in part from differences in active site desolvation and the conformational entropy of inhibitor binding to each isozyme active site. [ABSTRACT FROM AUTHOR]

5-Hydroxydecanoate (5-HD) blocks pharmacological and ischaemic preconditioning, and has been postulated to be a specific inhibitor of mitochondrial ATP-sensitive K+ (KATP) channels. However, recent work has shown that 5-HD is activated to 5-hydroxydecanoyl-CoA (5-HD-CoA), which is a substrate for the first step ofβ-oxidation. We have now analysed the completeβ-oxidation of 5-HD-CoA using specially synthesised (and purified) substrates and enzymes, as well as isolated rat liver and heart mitochondria, and compared it with the metabolism of the physiological substrate decanoyl-CoA. At the second step ofβ-oxidation, catalysed by enoyl-CoA hydratase, enzyme kinetics were similar using either decenoyl-CoA or 5-hydroxydecenoyl-CoA as substrate. The last two steps were investigated using l-3-hydroxyacyl-CoA dehydrogenase (HAD) coupled to 3-ketoacyl-CoA thiolase.Vmax for the metabolite of 5-HD (3,5-dihydroxydecanoyl-CoA) was fivefold slower than for the corresponding metabolite of decanoate (l-3-hydroxydecanoyl-CoA). The slower kinetics were not due to accumulation ofd-3-hydroxyoctanoyl-CoA since this enantiomer did not inhibit HAD. Molecular modelling of HAD complexed with 3,5-dihydroxydecanoyl-CoA suggested that the 5-hydroxyl group could decrease HAD turnover rate by interacting with critical side chains. Consistent with the kinetic data, 5-hydroxydecanoyl-CoA alone acted as a weak substrate in isolated mitochondria, whereas addition of 100μm5-HD-CoA inhibited the metabolism of decanoyl-CoA or lauryl-carnitine. In conclusion, 5-HD is activated, transported into mitochondria and metabolised viaβ-oxidation, albeit with rate-limiting kinetics at the penultimate step. This creates a bottleneck forβ-oxidation of fatty acids. The complex metabolic effects of 5-HD invalidate the use of 5-HD as a blocker of mitochondrial KATP channels in studies of preconditioning. [ABSTRACT FROM AUTHOR]