Predicting virus Fitness: Towards a structure-based computational model.

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogens. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein (∼380,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

Computational Insight into Substrate-Induced Conformational Changes in Methionyl-tRNA Synthetase of Mycobacterium Tuberculosis.

Tuberculosis caused by Mycobacterium tuberculosis (M.tb) has killed millions worldwide. Antibiotic resistance leads to the ineffectiveness of the current therapies. Aminoacyl tRNA synthetase (aaRS) class of proteins involved in protein synthesis are promising bacterial targets for developing new therapies. Here, we carried out a systematic comparative study on the aaRS sequences from M.tb and human. We listed important M.tb aaRS that could be explored as potential M.tb targets alongside the detailed conformational space analysis of methionyl-tRNA synthetase (MetRS) in apo- and substrate-bound form, which is among the proposed targets. Understanding the conformational dynamics is central to the mechanistic understanding of MetRS, as the substrate binding leads to the conformational changes causing the reaction to proceed. We performed the most complete simulation study of M.tb MetRS for 6 microseconds (2 systems × 3 runs × 1 microsecond) in the apo and substrate-bound states. Interestingly, we observed differential features, showing comparatively large dynamics for the holo simulations, whereas the apo structures became slightly compact with reduced solvent exposed area. In contrast, the ligand size decreased significantly in holo structures possibly to relax ligand conformation. Our findings correlate with experimental studies, thus validating our protocol. Adenosine monophosphate moiety of the substrate exhibited quite higher fluctuations than the methionine. His21 and Lys54 were found to be the important residues forming prominent hydrogen bond and salt-bridge interactions with the ligand. The ligand-protein affinity decreased during simulations as computed by MMGBSA analysis over the last 500 ns trajectories, which indicates the conformational changes upon ligand binding. These differential features could be further explored for designing new M.tb inhibitors.

Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding.

The binding affinity of the SARS-CoV-2 spike (S)-protein to the human membrane protein ACE2 is critical for virus function. Computational structure-based screening of new S-protein mutations for ACE2 binding lends promise to rationalize virus function directly from protein structure and ideally aid early detection of potentially concerning variants. We used a computational protocol based on cryo-electron microscopy structures of the S-protein to estimate the change in ACE2-affinity due to S-protein mutation (ΔΔGbind) in good trend agreement with experimental ACE2 affinities. We then expanded predictions to all possible S-protein mutations in 21 different S-protein-ACE2 complexes (400,000 ΔΔGbind data points in total), using mutation group comparisons to reduce systematic errors. The results suggest that mutations that have arisen in major variants as a group maintain ACE2 affinity significantly more than random mutations in the total protein, at the interface, and at evolvable sites. Omicron mutations as a group had a modest change in binding affinity compared to mutations in other major variants. The single-mutation effects seem consistent with ACE2 binding being optimized and maintained in omicron, despite increased importance of other selection pressures (antigenic drift), however, epistasis, glycosylation and in vivo conditions will modulate these effects. Computational prediction of SARS-CoV-2 evolution remains far from achieved, but the feasibility of large-scale computation is substantially aided by using many structures and mutation groups rather than single mutation effects, which are very uncertain. Our results demonstrate substantial challenges but indicate ways forward to improve the quality of computer models for assessing SARS-CoV-2 mutation effects.

Stability and expression of SARS-CoV-2 spike-protein mutations.

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen between stability and ACE2 binding effects. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.

Structural heterogeneity and precision of implications drawn from cryo-electron microscopy structures: SARS-CoV-2 spike-protein mutations as a test case.

Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged "ensemble" estimates for groups of residues when multiple structures are available.

Structure and Mutations of SARS-CoV-2 Spike Protein: A Focused Overview.

The spike protein (S-protein) of SARS-CoV-2, the protein that enables the virus to infect human cells, is the basis for many vaccines and a hotspot of concerning virus evolution. Here, we discuss the outstanding progress in structural characterization of the S-protein and how these structures facilitate analysis of virus function and evolution. We emphasize the differences in reported structures and that analysis of structure-function relationships is sensitive to the structure used. We show that the average residue solvent exposure in nearly complete structures is a good descriptor of open vs closed conformation states. Because of structural heterogeneity of functionally important surface-exposed residues, we recommend using averages of a group of high-quality protein structures rather than a single structure before reaching conclusions on specific structure-function relationships. To illustrate these points, we analyze some significant chemical tendencies of prominent S-protein mutations in the context of the available structures. In the discussion of new variants, we emphasize the selectivity of binding to ACE2 vs prominent antibodies rather than simply the antibody escape or ACE2 affinity separately. We note that larger chemical changes, in particular increased electrostatic charge or side-chain volume of exposed surface residues, are recurring in mutations of concern, plausibly related to adaptation to the negative surface potential of human ACE2. We also find indications that the fixated mutations of the S-protein in the main variants are less destabilizing than would be expected on average, possibly pointing toward a selection pressure on the S-protein. The richness of available structures for all of these situations provides an enormously valuable basis for future research into these structure-function relationships.

Computational prediction and molecular mechanism of γ-secretase modulators.

Selective control over Aß production via γ-secretase modulators (GSM) is a promising strategy for treating Alzheimer's disease, yet the specific binding sites and mechanism of action of GSMs remain unknown. Using the recent cryo-electron microscopy structures of substrate-bound γ-secretase we used two distinct methods to identify four potential binding sites for pyridopyrazine-1,6-dione GSMs. We demonstrate binding to site 4 formed between PS1-TM2, PS1-TM5 and the APP-C83-TM, with experimental activity data correlating significantly (95% confidence) with our computed binding-affinities for this site. Charged protonated GSMs may display higher affinities because of π-cation interaction with the polar residue Tyr115 of PS1-NTF. Surprisingly, the pIC50 of these compounds is largely described (R2 > 0.4 for all of these) by the molecular size, hydrophobicity, and polarizability. We thus believe that we have identified the primary modulator binding site in γ-secretase for these compounds, as well as strong descriptors of GSM potency. Our results are consistent with the FIST model of γ-secretase action and suggest that GSMs work in two ways: The binding affinity itself contributes stability to the ternary enzyme-modulator-substrate complex (tight grabbing), thus preventing early release of the substrate and increasing trimming to shorter, innocent Aß peptides. At the same time, drug size, hydrophobicity, and polarizability stabilize the more compact semi-open state over the open PS1 state, to make cleavage more precise and complete.

Systematic Investigation of the Data Set Dependency of Protein Stability Predictors.

Prediction of protein stability changes caused by mutation is of major importance to protein engineering and for understanding protein misfolding diseases and protein evolution. The major limitation to these applications is the fact that different prediction methods vary substantially in terms of performance for specific proteins; i.e., performance is not transferable from one type of mutation or protein to another. In this study, we investigated the performance and transferability of eight widely used methods. We first constructed a new data set composed of 2647 mutations using strict selection criteria for the experimental data and then defined a variety of subdata sets that are unbiased with respect to various aspects such as mutation type, stabilization extent, structure type, and solvent exposure. Benchmarking the methods against these subdata sets enabled us to systematically investigate how data set biases affect predictor performance. In particular, we use a reduced amino acid alphabet to quantify the bias toward mutation type, which we identify as the major bias in current approaches. Our results show that all prediction methods exhibit large biases, stemming not from failures of the models applied but mostly from the selection biases of experimental data used for training or parametrization. Our identification of these biases and the construction of new mutation-type-balanced data should lead to the development of more balanced and transferable prediction methods in the future.

Common mechanism of thermostability in small α- and ß-proteins studied by molecular dynamics.

Protein thermostability is important to evolution, diseases, and industrial applications. Proteins use diverse molecular strategies to achieve stability at high temperature, yet reducing the entropy of unfolding seems required. We investigated five small α-proteins and five ß-proteins with known, distinct structures and thermostability (Tm ) using multi-seed molecular dynamics simulations at 300, 350, and 400 K. The proteins displayed diverse changes in hydrogen bonding, solvent exposure, and secondary structure with no simple relationship to Tm . Our dynamics were in good agreement with experimental B-factors at 300 K and insensitive to force-field choice. Despite the very distinct structures, the native-state (300 + 350 K) free-energy landscapes (FELs) were significantly broader for the two most thermostable proteins and smallest for the three least stable proteins in both the α- and ß-group and with both force fields studied independently (tailed t-test, 95% confidence level). Our results suggest that entropic ensembles stabilize proteins at high temperature due to reduced entropy of unfolding, viz., ΔG = ΔH - TΔS. Supporting this mechanism, the most thermostable proteins were also the least kinetically stable, consistent with broader FELs, typified by villin headpiece and confirmed by specific comparison to a mesophilic ortholog of Thermus thermophilus apo-pyrophosphate phosphohydrolase. We propose that molecular strategies of protein thermostabilization, although diverse, tend to converge toward highest possible entropy in the native state consistent with the functional requirements. We speculate that this tendency may explain why many proteins are not optimally structured and why molten-globule states resemble native proteins so much.

Identification of Structural Calcium Binding Sites in Membrane-Bound Presenilin 1 and 2.

Variants of presenilin (PS1 and PS2) are the main genetic risk factors of familial Alzheimer's disease and thus central to the disease etiology. Although mostly studied as catalytic units of γ-secretase controlling Aß production, presenilins also affect calcium levels, which are disturbed in Alzheimer's disease. We investigated the interaction of calcium with both PS1 and PS2 using all-atom molecular dynamics (MD) simulations in realistic membrane models, with the specific aim to identify any Ca2+ sites. We did not observe any complete Ca2+ leak event, but we identified four persistent Ca2+ sites in membrane-bound PS1 and PS2: One in HL2 near the C-terminal of TM6, one in HL2 toward the N-terminal of TM7, a site at the catalytic aspartate on TM7, and a site at the PALP motif on TM9. The sites feature negatively charged glutamates and aspartates typical of calcium binding. Structural homology to diaspartate calcium transport proteins and mutation studies of calcium efflux support our identified calcium sites. Calcium consistently dampens HL2 motions in all comparisons (PS1, protonated PS1, PS2, protonated PS2). Due to their location in HL2 and the active site, we propose that the calcium sites control autoproteolytic maturation of presenilin by a pH-dependent conformational restriction of the HL2 recognition loop, which also regulates calcium transport proteins such as inositol 1,4,5-triphosphate receptor and ryanodine receptor. Our structural dynamics could provide a possible molecular basis for the need of both calcium and presenilin for lysosome proteolytic function, perhaps relevant also to other protein misfolding diseases.

Cryo-temperature effects on membrane protein structure and dynamics.

Innovations in cryogenic electron microscopy (Cryo-EM) have led to high-quality structures of important proteins such as the ribosome and γ-secretase, the membrane protease that produces Aß involved in Alzheimer's disease. However, freezing may change protein structure and dynamics relative to the physiologically relevant "hot" state. To explore this, we studied substrate-bound γ-secretase (6IYC) by molecular dynamics as a hot, cold, and quickly cooled state in both membrane and water systems. We show that the experimental structure resembles the simulated cooled state, structurally between the hot and cold states and membrane and water systems, but with cold dynamics. We observe "cryo-contraction" in the membrane from 303 to 85 K, reducing radius of gyration (Rg) by 1% from 4.01 to 3.97 nm (6IYC = 3.95 nm). The hot state features an unwound C83-substrate with 10-14 α-helix residues (6IYC: 11) in equilibrium with an intact state with 16 helix residues not previously reported. The ß-sheet is weakened with temperature. Multiple hot conformations probably control the Aß42/Aß40 ratio. We thus propose that MD simulation protocols of hot, cold, and cooled states as applied here can correct cryo-EM coordinates. However, important frozen-out fast modes require specific supplementary hot simulations or experiments.

Side-by-side comparison of Notch- and C83 binding to γ-secretase in a complete membrane model at physiological temperature.

γ-Secretase cleaves the C99 fragment of the amyloid precursor protein, leading to formation of aggregated ß-amyloid peptide central to Alzheimer's disease, and Notch, essential for cell regulation. Recent cryogenic electron microscopy (cryo-EM) structures indicate major changes upon substrate binding, a ß-sheet recognition motif, and a possible helix unwinding to expose peptide bonds towards nucleophilic attack. Here we report side-by-side comparison of the 303 K dynamics of the two proteins in realistic membranes using molecular dynamics simulations. Our ensembles agree with the cryo-EM data (full-protein Cα-RMSD = 1.62-2.19 Å) but reveal distinct presenilin helix conformation states and thermal ß-strand to coil transitions of C83 and Notch100. We identify distinct 303 K hydrogen bond dynamics and water accessibility of the catalytic sites. The RKRR motif (1758-1761) contributes significantly to Notch binding and serves as a "membrane anchor" that prevents Notch displacement. Water that transiently hydrogen bonds to G1753 and V1754 probably represents the catalytic nucleophile. At 303 K, Notch and C83 binding induce different conformation states, with Notch mostly present in a closed state with shorter Asp-Asp distance. This may explain the different outcome of Notch and C99 cleavage, as the latter is more imprecise with many products. Our identified conformation states may aid efforts to develop conformation-selective drugs that target C99 and Notch cleavage differently, e.g. Notch-sparing γ-secretase modulators.

Transcriptome analysis of Snow Mountain Garlic for unraveling the organosulfur metabolic pathway.

Snow Mountain Garlic grows in the high altitudes of the Himalayas under low temperature conditions. It contains various bioactive compounds whose metabolic pathways have not been worked out at genomic level. The present work is the first report on the transcriptome sequencing of this plant. >43 million paired-end reads (301 × 2) were generated using Illumina Miseq sequencing technology. Assembling of the sequencing data resulted in 326,785 transcripts. Differentially expressed genes between the clove and leaf tissues were identified and characterized. Besides, greater emphasis was laid on the genes, which were highly expressed in clove since the latter is assumed to contain high content of the bioactive compounds. Further analysis led to the identification of the genes plausibly involved in the organosulfur metabolism. We also identified several simple sequence repeats and single nucleotide polymorphism. These constitute valuable genetic resource for research and further genetic improvement of the plant.

Computational analysis of Alzheimer-causing mutations in amyloid precursor protein and presenilin 1.

Single-point mutations in the genes coding for amyloid precursor protein (APP) and presenilin 1 (PS1), the active subunit of γ-secretase that cleaves APP to produce Aß, are the main causes of rare but severe familial Alzheimer's disease (fAD). Recent structures of the transmembrane parts of APP and γ-secretase with a fragment of APP bound enable us to study the origins of the pathogenicity of the single amino acid changes in the context of the actual enzyme-substrate complex, which has not previously been possible. We used the new structures as input for several state-of-the-art computational methods that predict the folding stability effect of mutations. We find that pathogenic mutations almost exclusively reduce the stability of the proteins. Since most "random" mutations of an evolutionarily optimized protein tend to destabilize, we also show that the APP mutations destabilize the complex-bound substrate more than the free substrate, indicating reduced affinity of APP to γ-secretase. We confirmed this using two other methods, BEATMUSIC and mCSM PPI, specifically developed for calculating binding affinities of mutants. Although pathogenic PS1 mutations destabilize the complex and substrate-free form to the same extent, they significantly destabilize the protein more than the control set of random mutations. We conclude that fAD mutations most likely reduce the stability of the protein-substrate complex and thus retention time of APP-C99, leading to premature release of longer toxic Aß42 in accordance with the FIST model of Aß production, whereas the observed general destabilization of the protein may reduce activity towards other substrates.

Cell size effects in the molecular dynamics of the intrinsically disordered Aß peptide.

Periodic molecular dynamics simulations of proteins may suffer from image interactions. Similarly, the hydrophobic effect required to keep a protein folded may not be enforced by small simulation cells. Accordingly, errors may arise both from the water concentration per se and the image interactions. Intrinsically disordered proteins are particularly sensitive, providing a worst-case estimate of the errors. Following this reasoning, we studied Aß40 (Aß), a disordered peptide central to Alzheimer's disease, by 100 different simulations with variable cell size from very large (20 Å) to very small (3 Å). Even for this very disordered peptide, most properties are not cell-size dependent, justifying the common use of modest-sized (10 Å) cells for simulating proteins. The radius of gyration, secondary structure, intrapeptide, and peptide-water hydrogen bonds are similar relative to standard deviations at any cell size. However, hydrophobic surface area increases significantly in small cells (confidence 95%, two-tailed t-test), as does the standard deviation in exposure and backbone conformations (>40% and >27%). Similar results were obtained for the force fields OPLS3e, Ambersb99-ILDN, and Charmm22*. The similar prevalence of structures and α-ß transitions in long and short simulations indicate small diffusion barriers, which we suggest is a defining hallmark of intrinsically disordered proteins. Whereas hydrophilic exposure dominates in large cells, hydrophobic exposure dominates in small cells, suggesting a weakening of the hydrophobic effect by image interactions and the few water layers available to keep the protein compact, with a critical limit of 2-3 water layers required to enforce the hydrophobic effect.

Contribution of substrate reorganization energies of electron transfer to laccase activity.

Electron transfer is the most fundamental reaction in chemistry, yet its exact mechanistic details are often complex. Laccases are important electron-transfer enzymes of substantial utility in bleaching, bioremediation, catalytic synthesis, and enzymatic fuel cells. These multi-copper oxidases catalyze the one-electron oxidation of substrates by outer-sphere electron transfer to a copper T1 site, and subsequent intramolecular electron transfer to a tri-nuclear copper site where O2 is reduced to water. Understanding the molecular mechanism of the first, supposedly rate-determining pure electron transfer step is of major fundamental and technological interest. It is widely thought that the difference in the half potentials of the substrate and the T1 copper enables the powerful electron abstraction from nearby substrates. However, the reorganization energy during electron transfer could also contribute to catalytic turnover. To explore this, we computed the self-exchange reorganization energies of 54 substrates with experimentally known activity or kcat data using density functional theory. We show that the energy costs of changing the substrate geometries during electron removal correlate significantly with experimental activity data with a physically meaningful direction of correlation. This means that substrate electronic reorganization, rather than only potential differences, plays a role in the activity of electron transfer proteins such as laccases. This finding is consistent with the Marcus theory and suggests that the first electron transfer step from substrate to T1 is rate-determining in the real enzymes; the electronic reorganization energies can rationalize "good" vs. "bad" laccase substrates, which has not previously been possible.

A structural-chemical explanation of fungal laccase activity.

Fungal laccases (EC 1.10.3.2) are multi-copper oxidases that oxidize a wide variety of substrates. Despite extensive studies, the molecular basis for their diverse activity is unclear. Notably, there is no current way to rationally predict the activity of a laccase toward a given substrate. Such knowledge would greatly facilitate the rational design of new laccases for technological purposes. We report a study of three datasets of experimental Km values and activities for Trametes versicolor and Cerrena unicolor laccase, using a range of protein modeling techniques. We identify diverse binding modes of the various substrates and confirm an important role of Asp-206 and His-458 (T. versicolor laccase numbering) in guiding substrate recognition. Importantly, we demonstrate that experimental Km values correlate with binding affinities computed by MMGBSA. This confirms the common assumption that the protein-substrate affinity is a major contributor to observed Km. From quantitative structure-activity relations (QSAR) we identify physicochemical properties that correlate with observed Km and activities. In particular, the ionization potential, shape, and binding affinity of the substrate largely determine the enzyme's Km for the particular substrate. Our results suggest that Km is not just a binding constant but also contains features of the enzymatic activity. In addition, we identify QSAR models with only a few descriptors showing that phenolic substrates employ optimal hydrophobic packing to reach the T1 site, but then require additional electronic properties to engage in the subsequent electron transfer. Our results advance our ability to model laccase activity and lend promise to future rational optimization of laccases toward phenolic substrates.

Screening of antitubercular compound library identifies novel ATP synthase inhibitors of Mycobacterium tuberculosis.

A limited number of anti-tuberculosis drug candidates with novel mode of action have entered clinical trials in recent years. ATP synthase is one such validated drug target which has yielded a drug recently. The aim of this study was to identify the novel chemical scaffolds targeting the Mycobacterium tuberculosis (M. tuberculosis) ATP synthase. In this study, inverted membrane vesicles of Mycobacterium smegmatis were prepared to establish luciferin based ATP estimation assay. This assay was used to screen 700 compounds which were earlier found to be active on the whole cell of M. tuberculosis. Antibacterial activity of hits against various susceptible and drug-resistant strains of M. tuberculosis was evaluated using the microplate alamar blue assay and their cytotoxicity was also determined to select the safe compounds for further study. Screening of 700 compounds resulted in the identification of two compounds (5228485 and 5220632) exhibiting an IC50 of 0.32 and 4.0 µg/ml respectively. Both compounds showed excellent anti-TB activity (MIC of 0.5-2.0 µg/ml against Mtb H37Rv) and low cytotoxicity in human cell line and sub-mitochondrial particles. The three-dimensional structure of M. tuberculosis ATPase was predicted using in-silico approach and docking studies were performed with the active compounds. The interaction between compounds and bacterial ATP synthase was confirmed by molecular docking analysis. In conclusion screening of compound library has resulted in the identification of two novel chemical scaffolds targeting mycobacterial ATP synthase.

Molecular dynamics derived life times of active substrate binding poses explain K M of laccase mutants.

Fungal laccases (EC 1.10.3.2) are important multi-copper oxidases with broad substrate specificity. Laccases from Trametes versicolor (TvL) are among the best-characterized of these enzymes. Mutations in the substrate-binding site of TvL substantially affect K M, but a molecular understanding of this effect is missing. We explored the effect of TvL mutations on K M for the standard laccase substrate 2,6-dimethoxyphenol using 4500 ns of molecular dynamics, docking, and MMGBSA free energy computations. We show that changes in K M due to mutation consistently correlate with the dynamics of the substrates within the substrate-binding site. We find that K M depends on the lifetime ("dynamic stability") of the enzyme-substrate complex as commonly assumed. We then further show that MMGBSA-derived free energies of substrate binding in the active pose consistently reproduce large vs. small experimental K M values. Our results indicate that hydrophobic packing of the substrate near the T1 binding site of the laccase is instrumental for high turnover via K M. We also address the more general question of how enzymes such as laccases gain advantage of lower K M despite the Sabatier principle, which disfavors a stable enzyme-substrate complex. Our data suggest that the observed K M relates directly to the lifetime of the active substrate pose within a protein. In contrast, the thermochemical stability of the enzyme-substrate complex reflects an ensemble average of all enzyme-substrate binding poses. This distinction may explain how enzymes work by favoring longer residence time in the active pose without too favorable general enzyme-substrate interactions, a principle that may aid the rational design of enzymes.

Anti-tubercular drug discovery: in silico implications and challenges.

Tuberculosis (TB) has been reported as a major public health concern, especially in the developing countries. WHO report on tuberculosis 2016 shows a high mortality rate caused by TB leading to 1.8 million deaths worldwide (including deaths due to TB in HIV positive individuals), which is one of the top 10 causes of mortality in 2015. However, the main therapy used for the treatment of TB is still the Direct Observed Therapy Short-course (DOTS) that consists of four main first-line drugs. Due to the prolonged and unorganized use of these drugs, Mycobacterium tuberculosis (Mtb) has developed drug-resistance against them. To overcome this drug-resistance, efforts are continuously being made to develop new therapeutics. New drug-targets of Mtb are pursued by the researchers to develop their inhibitors. For this, new methodologies that comprise of the computational drug designing techniques are vigorously applied. A major limitation that is found with these techniques is the inability of the newly identified target-based inhibitors to inhibit the whole cell bacteria. A foremost factor for this limitation is the inability of these inhibitors to penetrate the bacterial cell wall. In this regard, various strategies to overcome this limitation have been discussed in detail in this review, along with new targets and new methodologies. A bunch of in silico tools available for the prediction of physicochemical properties that need to be explored to deal with the permeability issue of the Mtb inhibitors has also been discussed.