Functional annotation of the mesophilic-like character of mutants in a cold-adapted enzyme by self-organising map analysis of their molecular dynamics

Multiple comparison of the Molecular Dynamics (MD) trajectories of mutants in a cold-adapted α-amylase (AHA) could be used to elucidate functional features required to restore mesophilic-like activity. Unfortunately it is challenging to identify the different dynamic behaviors and correctly relate them to functional activity by routine analysis. We here employed a previously developed and robust two-stage approach that combines Self-Organising Maps (SOMs) and hierarchical clustering to compare conformational ensembles of proteins. Moreover, we designed a novel strategy to identify the specific mutations that more efficiently convert the dynamic signature of the psychrophilic enzyme (AHA) to that of the mesophilic counterpart (PPA). The SOM trained on AHA and its variants was used to classify a PPA MD ensemble and successfully highlighted the relationships between the flexibilities of the target enzyme and of the different mutants. Moreover the local features of the mutants that mostly influence their global flexibility in a mesophilic-like direction were detected. It turns out that mutations of the cold-adapted enzyme to hydrophobic and aromatic residues are the most effective in restoring the PPA dynamic features and could guide the design of more mesophilic-like mutants. In conclusion, our strategy can efficiently extract specific dynamic signatures related to function from multiple comparisons of MD conformational ensembles. Therefore, it can be a promising tool for protein engineering.     

Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.     

CRANKITE: A fast polypeptide backbone conformation sampler

Background

CRANKITE is a suite of programs for simulating backbone conformations of polypeptides and proteins. The core of the suite is an efficient Metropolis Monte Carlo sampler of backbone conformations in continuous three-dimensional space in atomic details.

Methods

In contrast to other programs relying on local Metropolis moves in the space of dihedral angles, our sampler utilizes local crankshaft rotations of rigid peptide bonds in Cartesian space.

Results

The sampler allows fast simulation and analysis of secondary structure formation and conformational changes for proteins of average length.     

Trustworthiness and metrics in visualizing similarity of gene expression

Background

Conventionally, the first step in analyzing the large and high-dimensional data sets measured by microarrays is visual exploration. Dendrograms of hierarchical clustering, self-organizing maps (SOMs), and multidimensional scaling have been used to visualize similarity relationships of data samples. We address two central properties of the methods: (i) Are the visualizations trustworthy, i.e., if two samples are visualized to be similar, are they really similar? (ii) The metric. The measure of similarity determines the result; we propose using a new learning metrics principle to derive a metric from interrelationships among data sets.

Results

The trustworthiness of hierarchical clustering, multidimensional scaling, and the self-organizing map were compared in visualizing similarity relationships among gene expression profiles. The self-organizing map was the best except that hierarchical clustering was the most trustworthy for the most similar profiles. Trustworthiness can be further increased by treating separately those genes for which the visualization is least trustworthy. We then proceed to improve the metric. The distance measure between the expression profiles is adjusted to measure differences relevant to functional classes of the genes. The genes for which the new metric is the most different from the usual correlation metric are listed and visualized with one of the visualization methods, the self-organizing map, computed in the new metric.

Conclusions

The conjecture from the methodological results is that the self-organizing map can be recommended to complement the usual hierarchical clustering for visualizing and exploring gene expression data. Discarding the least trustworthy samples and improving the metric still improves it.

     

Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study

Background  

A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples.     

A robust approach based on Weibull distribution for clustering gene expression data

Background

Clustering is a widely used technique for analysis of gene expression data. Most clustering methods group genes based on the distances, while few methods group genes according to the similarities of the distributions of the gene expression levels. Furthermore, as the biological annotation resources accumulated, an increasing number of genes have been annotated into functional categories. As a result, evaluating the performance of clustering methods in terms of the functional consistency of the resulting clusters is of great interest.

Results

In this paper, we proposed the WDCM (Weibull Distribution-based Clustering Method), a robust approach for clustering gene expression data, in which the gene expressions of individual genes are considered as the random variables following unique Weibull distributions. Our WDCM is based on the concept that the genes with similar expression profiles have similar distribution parameters, and thus the genes are clustered via the Weibull distribution parameters. We used the WDCM to cluster three cancer gene expression data sets from the lung cancer, B-cell follicular lymphoma and bladder carcinoma and obtained well-clustered results. We compared the performance of WDCM with k-means and Self Organizing Map (SOM) using functional annotation information given by the Gene Ontology (GO). The results showed that the functional annotation ratios of WDCM are higher than those of the other methods. We also utilized the external measure Adjusted Rand Index to validate the performance of the WDCM. The comparative results demonstrate that the WDCM provides the better clustering performance compared to k-means and SOM algorithms. The merit of the proposed WDCM is that it can be applied to cluster incomplete gene expression data without imputing the missing values. Moreover, the robustness of WDCM is also evaluated on the incomplete data sets.

Conclusions

The results demonstrate that our WDCM produces clusters with more consistent functional annotations than the other methods. The WDCM is also verified to be robust and is capable of clustering gene expression data containing a small quantity of missing values.     

Organic matter properties in soils afforested with Pinus radiata

Aims

Afforestation causes important alterations in SOM content and composition that affect the soil functions and C balance. The aim of this study was to identify the mechanisms that determine the changes in SOM composition following afforestation of grasslands.

Methods

The study included 4 chronosequences and 5 paired plots comprising pastures and land afforested with Pinus radiata. The SOM was characterized by ¹³C CP-MAS NMR spectroscopy and differential scanning calorimetry.

Results

During the first 10–20 year after afforestation, the changes in SOM content varied from slight gains to large losses (>40 %). The analyses revealed that even SOM compounds considered resistant to decomposition were degraded during this time. The SOM gains, observed 20 year after stand establishment, were favoured by the higher recalcitrance of pine litter and possibly by soil acidification. The concentrations of most SOM compounds, particularly the stable compounds, were higher at the end of the rotation. The low degree of protection, along with the favourable climatic conditions, may also explain the rapid decomposition of SOM, including resistant compounds, in these soils. DSC analysis complemented the information about SOM composition provided by other techniques.

Conclusions

The accumulation of stable SOM compounds at the end of the rotation suggests a longer soil C turnover in these afforested soils, which may alleviate the gradual loss of SOC in intensively managed forest soils.     

Structure and functional dynamics characterization of the ion channel of the human respiratory syncytial virus (hRSV) small hydrophobic protein (SH) transmembrane domain by combining molecular dynamics with excited normal modes

The human respiratory syncytial virus (hRSV) is the major cause of lower respiratory tract infection in children and elderly people worldwide. Its genome encodes 11 proteins including SH protein, whose functions are not well known. Studies show that SH protein increases RSV virulence degree and permeability to small compounds, suggesting it is involved in the formation of ion channels. The knowledge of SH structure and function is fundamental for a better understanding of its infection mechanism. The aim of this study was to model, characterize, and analyze the structural behavior of SH protein in the phospholipids bilayer environment. Molecular modeling of SH pentameric structure was performed, followed by traditional molecular dynamics (MD) simulations of the protein immersed in the lipid bilayer. Molecular dynamics with excited normal modes (MDeNM) was applied in the resulting system in order to investigate long time scale pore dynamics. MD simulations support that SH protein is stable in its pentameric form. Simulations also showed the presence of water molecules within the bilayer by density distribution, thus confirming that SH protein is a viroporin. This water transport was also observed in MDeNM studies with histidine residues of five chains (His22 and His51), playing a key role in pore permeability. The combination of traditional MD and MDeNM was a very efficient protocol to investigate functional conformational changes of transmembrane proteins that act as molecular channels. This protocol can support future investigations of drug candidates by acting on SH protein to inhibit viral infection.

Open image in new window

Graphical Abstract The ion channel of the human respiratory syncytial virus (hRSV) small hydrophobic protein (SH) transmembrane domain?

     

Retrospective molecular docking study of WY-25105 ligand to β-secretase and bias of the three-dimensional structure flexibility

β-Secretase (BACE) is a very promising target in the search for a treatment for Alzheimer’s disease using a protein–ligand inhibition approach. Given the many published X-ray structures of BACE protein, structure-based drug design has been used extensively to support new inhibitor discovery programs. Due to the high flexibility and large catalytic site of this protein, sampling of the huge conformational space of the binding site is the big challenge to overcome and is the main limitation of the most widely used docking programs. Incorrect treatment of these pitfalls can introduce bias into ligand docking and could affect the results. This is especially the case with the WY-25105 compound reported by the Wyeth Corporation as a BACE ligand that did not fit into any of the known crystal structures. In the present retrospective study, a set of available X-ray enzyme structures was selected and molecular dynamics simulations were conducted to generate more diverse representative BACE protein conformations. These conformations were then used for a docking study of the WY-25105 compound. The results confirmed the need to use an ensemble of structures in protein–ligand docking for identification of new binding modes in structure-based drug design of BACE inhibitors.

Components of forest soil CO2 efflux estimated from Δ14C values of soil organic matter

Aims

The partitioning of the total soil CO₂ efflux into its two main components: respiration from roots (and root-associated organisms) and microbial respiration (by means of soil organic matter (SOM) and litter decomposition), is a major need in soil carbon dynamics studies in order to understand if a soil is a net sink or source of carbon.

Methods

The heterotrophic component of the CO₂ efflux was estimated for 11 forest sites as the ratio between the carbon stocks of different SOM pools and previously published (Δ¹⁴C derived) turnover times. The autotrophic component, including root and root-associated respiration, was calculated by subtracting the heterotrophic component from total soil chamber measured CO₂ efflux.

Results

Results suggested that, on average, 50.4 % of total soil CO₂ efflux was derived from the respiration of the living roots, 42.4 % from decomposition of the litter layers and less than 10 % from decomposition of belowground SOM.

Conclusions

The Δ¹⁴C method proved to be an efficient tool by which to partition soil CO₂ efflux and quantify the contribution of the different components of soil respiration. However the average calculated heterotrophic respiration was statistically lower compared with two previous studies dealing with soil CO₂ efflux partitioning (one performed in the same study area; the other a meta-analysis of soil respiration partitioning). These differences were probably due to the heterogeneity of the SOM fraction and to a sub-optimal choice of the litter sampling period.     

Soil organic matter dynamics in density and particle-size fractions following destruction of tropical rainforest and the subsequent establishment of Imperata grassland in Indonesian Borneo using stable carbon isotopes

Background and aims

Large portions of the deforested areas in Southeast Asia have been ultimately replaced by the invasive grass Imperata cylindrica, but the dynamics of soil organic matter (SOM) during such land transitions are poorly understood. This study presents SOM dynamics in density and particle-size fractions following rainforest destruction and the subsequent establishment and persistence of Imperata grassland.

Methods

We examined soil C stock and natural ¹³C abundance in these fractions to depths of 100 cm. We predicted future soil C storage and evaluated C turnover rates in these fractions using a simple exponential model. Because soil texture strongly affects soil C storage, two chronosequences of soils differing in soil texture were compared (n?=?1 in each chronosequence).

Results

The clay-associated SOM increased in all soil layers (0–100 cm) along the forest-to-grassland chronosequence, whereas light-fraction SOM in the surface soil layer (0–5 cm) decreased.

Conclusions

In the surface layer, all SOM fractions exhibited rapid replacement of forest-derived C to grassland-derived C, indicating fast turnover. Meanwhile, δ¹³C values of the light fraction in the surface layer indicated that forest-derived charcoal and/or occluded low-density organic matter constituted unexpectedly large proportions of the light fraction. Mathematical modelling (0–50 cm) showed that grassland-derived C in the clay and silt fractions in all soil layers increased almost linearly for at least 50 years after grassland establishment. In the meantime, the forest-derived C stock in the clay fraction constituted 82 % of the total stable C pool at 0–50-cm depths even under steady-state conditions (t = ∞), indicating that residue of forest-derived SOM associated with clay largely contributed to preserving the soil C pool. Comparing soils with different soil textures, clay and silt particles in coarse-textured soil exhibited a substantially higher degree of organo-mineral interactions per unit volume of clay or silt compared to fine-textured soils.     

TILLING in the two-rowed barley cultivar 'Barke' reveals preferred sites of functional diversity in the gene HvHox1

Background

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.

Findings

We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.

Conclusion

Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/     

Soil organic matter accounting in the carbon footprint analysis of the wine chain

Purpose

Concerns about global warming led to the calculation of the carbon footprint (CF) left by human activities. The agricultural sector is a significant source of greenhouse gas (GHG) emissions, though cropland soils can also act as sinks. So far, most LCA studies on agricultural products have not considered changes in soil organic matter (SOM). This paper aimed to: (1) integrate the Hénin–Dupuis SOM model into the CF study and (2) outline the impacts of different vineyard soil management scenarios on the overall CF.

Methods

A representative wine chain in the Maremma Rural District, Tuscany (Italy), made up of a cooperative winery and nine of its associated farms, was selected to investigate the production of a non-aged, high-quality red wine. The system boundary was established from vineyard planting to waste management after use. The functional unit (FU) chosen for this study was a 0.75-L bottle of wine, and all data refer to the year 2009. The SOM balance, based on Hénin–Dupuis’ equation, was integrated and run using GaBi4 software. A sensitivity analysis was performed, and four scenarios were developed to assess the impact of vineyard soil management types with decreasing levels of organic matter inputs.

Results and discussion

SOM accounting reduced the overall CF of one wine bottle from 0.663 to 0.531 kg CO₂-eq/FU. The vineyard planting sub-phase produced a loss of SOM while, in the pre-production and production sub-phases, the loss/accumulation of SOM was related to the soil management practices. On average, soil management in the production sub-phase led to a net accumulation of SOM, and the overall vineyard phase was a sink of CO₂. Residue incorporation and grassing were identified as the main factors affecting changes in SOM in vineyard soils.

Conclusions

Our results showed that incorporating SOM accounting into the wine chain’s CF analysis changed the vineyard phase from a GHG source to a modest net GHG sink. These results highlighted the need to include soil C dynamics in the CF of the agricultural product. Here, the SOM balance method proposed was sensitive to changes in management practices and was site specific. Moreover, we were also able to define a minimum data set for SOM accounting. The EU recognises soil carbon sequestration as one of the major European strategies for mitigation. However, specific measures have yet to be included in the CAP 2020. It would be desirable to include soil in the new ISO 14067—Carbon Footprint of Products.     

Conformational flexibility of the leucine binding protein examined by protein domain coarse-grained molecular dynamics

Periplasmic binding proteins are the initial receptors for the transport of various substrates over the inner membrane of gram-negative bacteria. The binding proteins are composed of two domains, and the substrate is entrapped between these domains. For several of the binding proteins it has been established that a closed-up conformation exists even without substrate present, suggesting a highly flexible apo-structure which would compete with the ligand-bound protein for the transporter interaction. For the leucine binding protein (LBP), structures of both open and closed conformations are known, but no closed-up structure without substrate has been reported. Here we present molecular dynamics simulations exploring the conformational flexibility of LBP. Coarse grained models based on the MARTINI force field are used to access the microsecond timescale. We show that a standard MARTINI model cannot maintain the structural stability of the protein whereas the ELNEDIN extension to MARTINI enables simulations showing a stable protein structure and nanosecond dynamics comparable to atomistic simulations, but does not allow the simulation of conformational flexibility. A modification to the MARTINI-ELNEDIN setup, referred to as domELNEDIN, is therefore presented. The domELNEDIN setup allows the protein domains to move independently and thus allows for the simulation of conformational changes. Microsecond domELNEDIN simulations starting from either the open or the closed conformations consistently show that also for LBP, the apo-structure is flexible and can exist in a closed form.

Unbiased,scalable sampling of protein loop conformations from probabilistic priors

Background

Protein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges. Discrete search techniques scale poorly to large loops, optimization and molecular dynamics techniques are prone to local minima, and inverse kinematics techniques can only incorporate structural preferences in adhoc fashion. This paper presents Sub-Loop Inverse Kinematics Monte Carlo (SLIKMC), a new Markov chain Monte Carlo algorithm for generating conformations of closed loops according to experimentally available, heterogeneous structural preferences.

Results

Our simulation experiments demonstrate that the method computes high-scoring conformations of large loops (> 10 residues) orders of magnitude faster than standard Monte Carlo and discrete search techniques. Two new developments contribute to the scalability of the new method. First, structural preferences are specified via a probabilistic graphical model (PGM) that links conformation variables, spatial variables (e.g., atom positions), constraints and prior information in a unified framework. The method uses a sparse PGM that exploits locality of interactions between atoms and residues. Second, a novel method for sampling sub-loops is developed to generate statistically unbiased samples of probability densities restricted by loop-closure constraints.

Conclusion

Numerical experiments confirm that SLIKMC generates conformation ensembles that are statistically consistent with specified structural preferences. Protein conformations with 100+ residues are sampled on standard PC hardware in seconds. Application to proteins involved in ion-binding demonstrate its potential as a tool for loop ensemble generation and missing structure completion.

     

Rapid sampling of local minima in protein energy surface and effective reduction through a multi-objective filter

Background

Many problems in protein modeling require obtaining a discrete representation of the protein conformational space as an ensemble of conformations. In ab-initio structure prediction, in particular, where the goal is to predict the native structure of a protein chain given its amino-acid sequence, the ensemble needs to satisfy energetic constraints. Given the thermodynamic hypothesis, an effective ensemble contains low-energy conformations which are similar to the native structure. The high-dimensionality of the conformational space and the ruggedness of the underlying energy surface currently make it very difficult to obtain such an ensemble. Recent studies have proposed that Basin Hopping is a promising probabilistic search framework to obtain a discrete representation of the protein energy surface in terms of local minima. Basin Hopping performs a series of structural perturbations followed by energy minimizations with the goal of hopping between nearby energy minima. This approach has been shown to be effective in obtaining conformations near the native structure for small systems. Recent work by us has extended this framework to larger systems through employment of the molecular fragment replacement technique, resulting in rapid sampling of large ensembles.

Methods

This paper investigates the algorithmic components in Basin Hopping to both understand and control their effect on the sampling of near-native minima. Realizing that such an ensemble is reduced before further refinement in full ab-initio protocols, we take an additional step and analyze the quality of the ensemble retained by ensemble reduction techniques. We propose a novel multi-objective technique based on the Pareto front to filter the ensemble of sampled local minima.

Results and conclusions

We show that controlling the magnitude of the perturbation allows directly controlling the distance between consecutively-sampled local minima and, in turn, steering the exploration towards conformations near the native structure. For the minimization step, we show that the addition of Metropolis Monte Carlo-based minimization is no more effective than a simple greedy search. Finally, we show that the size of the ensemble of sampled local minima can be effectively and efficiently reduced by a multi-objective filter to obtain a simpler representation of the probed energy surface.

     

OSIRISv1.2: A named entity recognition system for sequence variants of genes in biomedical literature

Background

The hierarchical clustering tree (HCT) with a dendrogram [1] and the singular value decomposition (SVD) with a dimension-reduced representative map [2] are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures.

Results

This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen [3] as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends.

Conclusion

We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.     

Integrated management systems and N fertilization: effect on soil organic matter in rice-rapeseed rotation

Aims

Understanding the effects of long-term crop management on soil organic matter (SOM) is necessary to improve the soil quality and sustainability of agroecosystems.

Method

The present 7-year long-term field experiment was conducted to evaluate the effect of integrated management systems and N fertilization on SOM fractions and carbon management index (CMI). Two integrated soil-crop system management (ISSM-1 and ISSM-2, combined with improved cultivation pattern, water management and no-tillage) were compared with a traditional farming system at three nitrogen (N) fertilization rates (0, 150 and 225 kg N ha^?1).

Results

Management systems had greater effects on SOM and its fractions than did N fertilization. Compared with traditional farming practice, the integrated management systems increased soil organic carbon (SOC) by 13 % and total nitrogen (TN) by 10 % (averaged over N levels) after 7 years. Integrated management systems were more effective in increasing labile SOM fractions and CMI as compared to traditional farming practice. SOC, TN and dissolved organic matter in nitrogen increased with N fertilization rates. Nonetheless, N addition decreased other labile fractions: particulate organic matter, dissolved organic matter in carbon, microbial biomass nitrogen and potassium permanganate-oxidizable carbon.

Conclusions

We conclude that integrated management systems increased total SOM, labile fractions and CMI, effectively improved soil quality in rice-rapeseed rotations. Appropriate N fertilization (N150) resulted in higher SOC and TN. Though N application increased dissolved organic matter in nitrogen, it was prone to decrease most of the other labile SOM fractions, especially under higher N rate (N250), implying the decline of SOM quality.     

Why tazobactam and sulbactam have different intermediates population with SHV-1 β-lactamase: a molecular dynamics study

The imine intermediates of tazobactam and sulbactam bound to SHV-1 β-lactamase were investigated by molecular dynamics (MD) simulation respectively. Hydrogen bond networks around active site were found different between tazobactam and sulbactam acyl-enzymes. In tazobactam imine intermediate, it was observed that the triazolyl ring formed stable hydrogen bonds with Asn170 and Thr167. The results suggest that conformation of imine determined the population of intermediates. In imine intermediate of tazobactam, the triazolyl ring is trapped in Thr_Asn pocket, and it restricts the rotation of C5-C6 bond so that tazobactam can only generate trans enamine intermediate. Further, conformational cluster analyses are performed to substantiate the results. These findings provide an explanation for the corresponding experimental results, and will be potentially useful in the development of new inhibitors.