Structure-based activity prediction for an enzyme of unknown function

Structure, function and substrate preferences of archaeal S-adenosyl-L-homocysteine hydrolases

S-Adenosyl-L-homocysteine hydrolase (SAHH) reversibly cleaves S-adenosyl-L-homocysteine, the product of S-adenosyl-L-methionine-dependent methylation reactions. The conversion of S-adenosyl-L-homocysteine into adenosine and L-homocysteine plays an important role in the regulation of the methyl cycle. An alternative metabolic route for S-adenosyl-L-methionine regeneration in the extremophiles Methanocaldococcus jannaschii and Thermotoga maritima has been identified, featuring the deamination of S-adenosyl-L-homocysteine to S-inosyl-L-homocysteine. Herein, we report the structural characterisation of different archaeal SAHHs together with a biochemical analysis of various SAHHs from all three domains of life. Homologues deriving from the Euryarchaeota phylum show a higher conversion rate with S-inosyl-L-homocysteine compared to S-adenosyl-L-homocysteine. Crystal structures of SAHH originating from Pyrococcus furiosus in complex with SLH and inosine as ligands, show architectural flexibility in the active site and offer deeper insights into the binding mode of hypoxanthine-containing substrates. Altogether, the findings of our study support the understanding of an alternative metabolic route for S-adenosyl-L-methionine and offer insights into the evolutionary progression and diversification of SAHHs involved in methyl and purine salvage pathways.

DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model

MOTIVATION

Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information.

RESULTS

In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite.

AVAILABILITY AND IMPLEMENTATION

The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.

Iterative Knowledge-Based Scoring Function for Protein-Ligand Interactions by Considering Binding Affinity Information

Scoring functions for protein-ligand interactions play a critical role in structure-based drug design. Owing to the good balance between general applicability and computational efficiency, knowledge-based scoring functions have obtained significant advancements and achieved many successes. Nevertheless, knowledge-based scoring functions face a challenge in utilizing the experimental affinity data and thus may not perform well in binding affinity prediction. Addressing the challenge, we have proposed an improved version of the iterative knowledge-based scoring function ITScore by considering binding affinity information, which is referred to as ITScoreAff, based on a large training set of 6216 protein-ligand complexes with both structures and affinity data. ITScoreAff was extensively evaluated and compared with ITScore, 33 traditional, and 6 machine learning scoring functions in terms of docking power, ranking power, and screening power on the independent CASF-2016 benchmark. It was shown that ITScoreAff obtained an overall better performance than the other 40 scoring functions and gave an average success rate of 85.3% in docking power, a correlation coefficient of 0.723 in scoring power, and an average rank correlation coefficient of 0.668 in ranking power. In addition, ITScoreAff also achieved the overall best screening power when the top 10% of the ranked database were considered. These results demonstrated the robustness of ITScoreAff and its improvement over existing scoring functions.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual screening

Resource- and time-consuming biological experiments are unavoidable in traditional drug discovery, which have directly driven the evolution of various computational algorithms and tools for drug-target interaction (DTI) prediction. For improving the prediction reliability, a comprehensive platform is highly expected as some previously reported webservers are small in scale, single-method, or even out of service. In this study, we integrated the multiple-conformation based docking, 2D/3D ligand similarity search and deep learning approaches to construct a comprehensive webserver, namely D3CARP, for target prediction and virtual screening. Specifically, 9352 conformations with positive control of 1970 targets were used for molecular docking, and approximately 2 million target-ligand pairs were used for 2D/3D ligand similarity search and deep learning. Besides, the positive compounds were added as references, and related diseases of therapeutic targets were annotated for further disease-based DTI study. The accuracies of the molecular docking and deep learning approaches were 0.44 and 0.89, respectively. And the average accuracy of five ligand similarity searches was 0.94. The strengths of D3CARP encompass the support for multiple computational methods, ensemble docking, utilization of positive controls as references, cross-validation of predicted outcomes, diverse disease types, and broad applicability in drug discovery. The D3CARP is freely accessible at https://www.d3pharma.com/D3CARP/index.php.

Cell-Effective Transition-State Analogue of Phenylethanolamine N-Methyltransferase

Phenylethanolamine N-methyltransferase (PNMT) catalyzes the S-adenosyl-l-methionine (SAM)-dependent methylation of norepinephrine to form epinephrine. Epinephrine is implicated in the regulation of blood pressure, respiration, Alzheimer's disease, and post-traumatic stress disorder (PTSD). Transition-state (TS) analogues bind their target enzymes orders of magnitude more tightly than their substrates. A synthetic strategy for first-generation TS analogues of human PNMT (hPNMT) permitted structural analysis of hPNMT and revealed potential for second-generation inhibitors [Mahmoodi, N.; J. Am. Chem. Soc. 2020, 142, 14222-14233]. A second-generation TS analogue inhibitor of PNMT was designed, synthesized, and characterized to yield a Ki value of 1.2 nM. PNMT isothermal titration calorimetry (ITC) measurements of inhibitor 4 indicated a negative cooperative binding mechanism driven by large favorable entropic contributions and smaller enthalpic contributions. Cell-based assays with HEK293T cells expressing PNMT revealed a cell permeable, intracellular PNMT inhibitor with an IC50 value of 81 nM. Structural analysis demonstrated inhibitor 4 filling catalytic site regions to recapitulate both norepinephrine and SAM interactions. Conformation of the second-generation inhibitor in the catalytic site of PNMT improves contacts relative to those from the first-generation inhibitors. Inhibitor 4 demonstrates up to 51,000-fold specificity for PNMT relative to DNA and protein methyltransferases. Inhibitor 4 also exhibits a 12,000-fold specificity for PNMT over the α2-adrenoceptor.

Total enzymatic synthesis of cis-α-irone from a simple carbon source

Metabolic engineering has become an attractive method for the efficient production of natural products. However, one important pre-requisite is to establish the biosynthetic pathways. Many commercially interesting molecules cannot be biosynthesized as their native biochemical pathways are not fully elucidated. Cis-α-irone, a top-end perfumery molecule, is an example. Retrobiosynthetic pathway design by employing promiscuous enzymes provides an alternative solution to this challenge. In this work, we design a synthetic pathway to produce cis-α-irone with a promiscuous methyltransferase (pMT). Using structure-guided enzyme engineering strategies, we improve pMT activity and specificity towards cis-α-irone by >10,000-fold and >1000-fold, respectively. By incorporating the optimized methyltransferase into our engineered microbial cells, ~86 mg l^-1 cis-α-irone is produced from glucose in a 5 l bioreactor. Our work illustrates that integrated retrobiosynthetic pathway design and enzyme engineering can offer opportunities to expand the scope of natural molecules that can be biosynthesized.

Activity-based annotation: the emergence of systems biochemistry

Current tools to annotate protein function have failed to keep pace with the speed of DNA sequencing and exponentially growing number of proteins of unknown function (PUFs). A major contributing factor to this mismatch is the historical lack of high-throughput methods to experimentally determine biochemical activity. Activity-based methods, such as activity-based metabolite and protein profiling, are emerging as new approaches for unbiased, global, biochemical annotation of protein function. In this review, we highlight recent experimental, activity-based approaches that offer new opportunities to determine protein function in a biologically agnostic and systems-level manner.

Location-specific signatures of Crohn's disease at a multi-omics scale

BACKGROUND

Crohn's disease (CD), an inflammatory bowel disease (IBD) subtype, results from pathologic interactions between host cells and its resident gut microbes. CD manifests in both isolated disease locations (ileum or colon) or a combination of locations (ileocolonic). To date, a comprehensive understanding of how isolated CD subtypes influence molecular profiles remains outstanding. To address this, we sought to define CD location signatures by leveraging a large cross-sectional feature set captured from the stool of over 200 IBD patients and healthy controls using metaproteomics, shotgun metagenomics, 16S rRNA sequencing, metabolomic profiling, and host genetics paired with clinical endoscopic assessments.

RESULTS

Neither metagenomic nor host genetics alone distinguished CD location subtypes. In contrast, ileal and colonic CD were distinguished using mass spectrometry-based methods (metabolomics or metaproteomics) or a combined multi-omic feature set. This multi-omic feature set revealed colonic CD was strongly associated with neutrophil-related proteins. Additionally, colonic CD displayed a disease-severity-related association with Bacteroides vulgatus. Colonic CD and ulcerative colitis profiles harbored strikingly similar feature enrichments compared to ileal CD, including neutrophil-related protein enrichments. Compared to colonic CD, ileal CD profiles displayed increased primary and secondary bile acid levels and concomitant shifts in taxa with noted sensitivities such as Faecalibacterium prausnitzii or affinities for bile acid-rich environments, including Gammaproteobacteria and Blautia sp. Having shown robust molecular and microbial distinctions tied to CD locations, we leveraged these profiles to generate location-specific disease severity biomarkers that surpass the performance of Calprotectin.

CONCLUSIONS

When compared using multi-omics features, colonic- and ileal-isolated CD subtypes display striking differences that suggest separate location-specific pathologies. Colonic CD's strong similarity to ulcerative colitis, including neutrophil and Bacteroides vulgatus involvement, is also evidence of a shared pathology for colonic-isolated IBD subtypes, while ileal CD maintains a unique, bile acid-driven profile. More broadly, this study demonstrates the power of multi-omics approaches for IBD biomarker discovery and elucidating the underlying biology. Video Abstract.

RadicalSAM.org: A Resource to Interpret Sequence-Function Space and Discover New Radical SAM Enzyme Chemistry

The radical SAM superfamily (RSS), arguably the most functionally diverse enzyme superfamily, is also one of the largest with ~700K members currently in the UniProt database. The vast majority of the members have uncharacterized enzymatic activities and metabolic functions. In this Perspective, we describe RadicalSAM.org, a new web-based resource that enables a user-friendly genomic enzymology strategy to explore sequence-function space in the RSS. The resource attempts to enable identification of isofunctional groups of radical SAM enzymes using sequence similarity networks (SSNs) and the genome context of the bacterial, archaeal, and fungal members provided by genome neighborhood diagrams (GNDs). Enzymatic activities and in vivo functions frequently can be inferred from genome context given the tendency for genes of related function to be clustered. We invite the scientific community to use RadicalSAM.org to (i) guide their experimental studies to discover new enzymatic activities and metabolic functions, (ii) contribute experimentally verified annotations to RadicalSAM.org to enhance the ability to predict novel activities and functions, and (iii) provide suggestions for improving this resource.

FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening

To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITE^comb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITE^comb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITE^comb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITE^comb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITE^comb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.

2D Zernike polynomial expansion: Finding the protein-protein binding regions

We present a method for efficiently and effectively assessing whether and where two proteins can interact with each other to form a complex. This is still largely an open problem, even for those relatively few cases where the 3D structure of both proteins is known. In fact, even if much of the information about the interaction is encoded in the chemical and geometric features of the structures, the set of possible contact patches and of their relative orientations are too large to be computationally affordable in a reasonable time, thus preventing the compilation of reliable interactome. Our method is able to rapidly and quantitatively measure the geometrical shape complementarity between interacting proteins, comparing their molecular iso-electron density surfaces expanding the surface patches in term of 2D Zernike polynomials. We first test the method against the real binding region of a large dataset of known protein complexes, reaching a success rate of 0.72. We then apply the method for the blind recognition of binding sites, identifying the real region of interaction in about 60% of the analyzed cases. Finally, we investigate how the efficiency in finding the right binding region depends on the surface roughness as a function of the expansion order.

Transition-State Analogues of Phenylethanolamine N-Methyltransferase

Phenylethanolamine N-methyltransferase (PNMT) is a critical enzyme in catecholamine synthesis. It transfers the methyl group of S-adenosylmethionine (SAM) to catalyze the synthesis of epinephrine from norepinephrine. Epinephrine has been associated with diverse human processes, including the regulation of blood pressure and respiration, as well as neurodegeneration found in Alzheimer's disease. Human PNMT (hPNMT) proceeds through an S_N2 transition state (TS) in which the transfer of the methyl group is rate limiting. TS analogue enzyme inhibitors are specific for their target and bind orders of magnitude more tightly than their substrates. Molecules resembling the TS of hPNMT were designed, synthesized, and kinetically characterized. This new inhibitory scaffold was designed to mimic the geometry and electronic properties of the hPNMT TS. Synthetic efforts resulted in a tight-binding inhibitor with a K_i value of 12.0 nM. This is among the first of the TS analogue inhibitors of methyltransferase enzymes to show an affinity in the nanomolar range. Isothermal titration calorimetry (ITC) measurements indicated negative cooperative binding of inhibitor to the dimeric protein, driven by favorable entropic contributions. Structural analysis revealed that inhibitor 3 binds to hPNMT by filling the catalytic binding pockets for the cofactor (SAM) and the substrate (norepinephrine) binding sites.

Chemometric Models of Differential Amino Acids at the Navα and Navβ Interface of Mammalian Sodium Channel Isoforms

(1) Background: voltage-gated sodium channels (Na_vs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Na_vα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Na_vβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Na_v1.1α to Na_v1.9α), the Na_vα and Na_vβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Na_vα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Na_vβ1 or Na_vβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Na_vβ1 or Na_vβ3 subunits on the modulation of Na_vα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing.

High-Efficiency Secretion and Directed Evolution of Chitinase BcChiA1 in Bacillus subtilis for the Conversion of Chitinaceous Wastes Into Chitooligosaccharides

Limitations of enzyme production and activity pose a challenge for efficient degradation of chitinaceous wastes. To solve this problem, we engineered a system for high-yielding extracellular secretion of chitinase A1 from Bacillus circulans (BcChiA1) in B. subtilis. Furthermore, an innovative chitinase high-throughput screening method based on colloidal chitin stained with Remazol Brilliant Blue R (CC-RBB) was established and used to identify three mutants with improved chitinase activity: Y10A/R301A/E327A (Mu1), Y10A/D81A/E327A (Mu2), and F38A/K88A/R301A (Mu3). Their highest specific activity reached 1004.83 ± 0.87 U/mg, representing a 16.89-fold increase in activity compared to native BcChiA1. Additionally, we found that there is a synergistic effect between BcChiA1 and a lytic polysaccharide monooxygenase from Bacillus atrophaeus (BatLPMO10), which increased the chitin processing efficiency by 50% after combining the two enzymes. The yield of chitooligosaccharide (COS) production using the mutant Mu1 and BatLPMO10 reached 2885.25 ± 2.22 mg/L. Taken together, the results indicated that the CC-RBB high-throughput screening method is a useful tool for chitinase screening, and evolution of BcChiA1 in collaboration with BatLPMO10 has tremendous application potential in the biological treatment of chitinaceous wastes for COS production.

Molecular Architect: A User-Friendly Workflow for Virtual Screening

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Computationally Guided Discovery and Experimental Validation of Indole-3-acetic Acid Synthesis Pathways

Elucidating the interaction networks associated with secondary metabolite production in microorganisms is an ongoing challenge made all the more daunting by the rate at which DNA sequencing technology reveals new genes and potential pathways. Developing the culturing methods, expression conditions, and genetic systems needed for validating pathways in newly discovered microorganisms is often not possible. Therefore, new tools and techniques are needed for defining complex metabolic pathways. Here, we describe an in vitro computationally assisted pathway description approach that employs bioinformatic searches of genome databases, protein structural modeling, and protein-ligand-docking simulations to predict the gene products most likely to be involved in a particular secondary metabolite production pathway. This information is then used to direct in vitro reconstructions of the pathway and subsequent confirmation of pathway activity using crude enzyme preparations. As a test system, we elucidated the pathway for biosynthesis of indole-3-acetic acid (IAA) in the plant-associated microbe Pantoea sp. YR343. This organism is capable of metabolizing tryptophan into the plant phytohormone IAA. BLAST analyses identified a likely three-step pathway involving an amino transferase, an indole pyruvate decarboxylase, and a dehydrogenase. However, multiple candidate enzymes were identified at each step, resulting in a large number of potential pathway reconstructions (32 different enzyme combinations). Our approach shows the effectiveness of crude extracts to rapidly elucidate enzymes leading to functional pathways. Results are compared to affinity purified enzymes for select combinations and found to yield similar relative activities. Further, in vitro testing of the pathway reconstructions revealed the "underground" nature of IAA metabolism in Pantoea sp. YR343 and the various mechanisms used to produce IAA. Importantly, our experiments illustrate the scalable integration of computational tools and cell-free enzymatic reactions to identify and validate metabolic pathways in a broadly applicable manner.

Substrate-imprinted docking of Agrobacterium tumefaciens uronate dehydrogenase for increased substrate selectivity

Agrobacterium tumefaciens uronate dehydrogenase (AtuUdh) belongs to the short-chain dehydrogenase superfamily, specifically those acting on the CH-OH group of donor with NAD⁺ or NADP⁺ as acceptor. It is apparently required for the production of D-glucaric acid. AtuUdh-catalyzed reaction is reversible with dual substrate-specific activity (D-galacturonic acid and D-glucuronic acid) in nature. In our study, 34 mutants were pre-screened from 155 mutants generated from AtuUdh (wild-type) and selected 10 structurally stable mutants with increased substrate selectivity. The specificity, efficiency, and selectivity of these mutants for different substrates and cofactors were predicted from 121 docked models using a substrate-imprinted docking approach. Q14F, S36L, and S75T mutants have shown a high binding affinity to D-glucuronic acid and its substrate intermediates such as D-glucaro-1,4-lactone and D-glucaro-1,5-lactone. These mutants exhibited a low binding affinity to the substrate and cofactor required for D-galactaric acid. D34S, N112E and S165E mutants found to show a high selectivity of D-galacturonic acid and its substrate intermediates for D-galactaric acid production. Ser75, Ser165, and Arg174 are active residues playing an imperative role in the substrate selectivity and also contributed in the conjecture the mechanism of transition state stabilization catalyzed by AtuUdh mutants. The present approach was used to reveal the substrate binding mechanism of AtuUdh mutants for a better understanding of the structural basis for selectivity and function.

Structure-guided function discovery of an NRPS-like glycine betaine reductase for choline biosynthesis in fungi

Nonribosomal peptide synthetases (NRPSs) and NRPS-like enzymes have diverse functions in primary and secondary metabolisms. By using a structure-guided approach, we uncovered the function of a NRPS-like enzyme with unusual domain architecture, catalyzing two sequential two-electron reductions of glycine betaine to choline. Structural analysis based on the homology model suggests cation-π interactions as the major substrate specificity determinant, which was verified using substrate analogs and inhibitors. Bioinformatic analysis indicates this NRPS-like glycine betaine reductase is highly conserved and widespread in kingdom fungi. Genetic knockout experiments confirmed its role in choline biosynthesis and maintaining glycine betaine homeostasis in fungi. Our findings demonstrate that the oxidative choline-glycine betaine degradation pathway can operate in a fully reversible fashion and provide insight in understanding fungal choline metabolism. The use of an NRPS-like enzyme for reductive choline formation is energetically efficient compared with known pathways. Our discovery also underscores the capabilities of the structure-guided approach in assigning functions of uncharacterized multidomain proteins, which can potentially aid functional discovery of new enzymes by genome mining.

An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking

Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.

Substrate Profile of the Phosphotriesterase Homology Protein from Escherichia coli

The phosphotriesterase homology protein (PHP) from Escherichia coli is a member of a family of proteins that is related to phosphotriestrase (PTE), a bacterial enzyme from cog1735 with unusual substrate specificity toward the hydrolysis of synthetic organic phosphates and phosphonates. PHP was cloned, purified to homogeneity, and functionally characterized. The three-dimensional structure of PHP was determined at a resolution of 1.84 Å with zinc and phosphate in the active site. The protein folds as a distorted (β/α)8-barrel and possesses a binuclear metal center in the active site. The catalytic function and substrate profile of PHP were investigated using a structure-guided approach that combined bioinformatics, computational docking, organic synthesis, and steady-state enzyme kinetics. PHP was found to catalyze the hydrolysis of phosphorylated glyceryl acetates. The best substrate was 1,2-diacetyl glycerol-3-phosphate with a kcat/ Km of 4.9 × 103 M-1 s-1. The presence of a phosphate group in the substrate was essential for enzymatic hydrolysis by the enzyme. It was surprising, however, to find that PHP was unable to hydrolyze any of the lactones tested as potential substrates, unlike most of the other enzymes from cog1735.

Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a "Plug and Play" Domain

The radical SAM superfamily contains over 100,000 homologous enzymes that catalyze a remarkably broad range of reactions required for life, including metabolism, nucleic acid modification, and biogenesis of cofactors. While the highly conserved SAM-binding motif responsible for formation of the key 5'-deoxyadenosyl radical intermediate is a key structural feature that simplifies identification of superfamily members, our understanding of their structure-function relationships is complicated by the modular nature of their structures, which exhibit varied and complex domain architectures. To gain new insight about these relationships, we classified the entire set of sequences into similarity-based subgroups that could be visualized using sequence similarity networks. This superfamily-wide analysis reveals important features that had not previously been appreciated from studies focused on one or a few members. Functional information mapped to the networks indicates which members have been experimentally or structurally characterized, their known reaction types, and their phylogenetic distribution. Despite the biological importance of radical SAM chemistry, the vast majority of superfamily members have never been experimentally characterized in any way, suggesting that many new reactions remain to be discovered. In addition to 20 subgroups with at least one known function, we identified additional subgroups made up entirely of sequences of unknown function. Importantly, our results indicate that even general reaction types fail to track well with our sequence similarity-based subgroupings, raising major challenges for function prediction for currently identified and new members that continue to be discovered. Interactive similarity networks and other data from this analysis are available from the Structure-Function Linkage Database.

Purine and pyrimidine salvage pathway in thermophiles: a valuable source of biocatalysts for the industrial production of nucleic acid derivatives

Due to their similarity to natural counterparts, nucleic acid derivatives (nucleobases, nucleosides, and nucleotides, among others) are interesting molecules for pharmaceutical, biomedical, or food industries. For this reason, there is increasing worldwide demand for the development of efficient synthetic processes for these compounds. Chemical synthetic methodologies require numerous protection-deprotection steps and often lead to the presence of undesirable by-products or enantiomeric mixtures. These methods also require harsh operating conditions, such as the use of organic solvents and hazard reagents. Conversely, enzymatic production by whole cells or enzymes improves regio-, stereo-, and enantioselectivity and provides an eco-friendly alternative. Because of their essential role in purine and pyrimidine scavenging, enzymes from purine and pyrimidine salvage pathways are valuable candidates for the synthesis of many different nucleic acid components. In recent years, many different enzymes from these routes, such as nucleoside phosphorylases, nucleoside kinases, 2'-deoxyribosyltransferases, phosphoribosyl transferases, or deaminases, have been successfully employed as biocatalysts in the production of nucleobase, nucleoside, or nucleotide analogs. Due to their great activity and stability at extremely high temperatures, the use of enzymes from thermophiles in industrial biocatalysis is gaining momentum. Thermophilic enzymes not only display unique characteristics such as temperature, chemical, and pH stability but also provide many different advantages from an industrial perspective. This mini-review aims to cover the most representative enzymatic approaches for the synthesis of nucleic acid derivatives. In this regard, we provide detailed comments about enzymes involved in crucial steps of purine and pyrimidine salvage pathways in thermophiles, as well as their biological role, biochemical characterization, active site mechanism, and substrate specificity. In addition, the most interesting synthetic examples reported in the literature are also included.

Prediction of enzymatic pathways by integrative pathway mapping

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.

Enzyme function and its evolution

With rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.

Discovery of new GPCR ligands to illuminate new biology

Although a plurality of drugs target G-protein-coupled receptors (GPCRs), most have emerged from classical medicinal chemistry and pharmacology programs and resemble one another structurally and functionally. Though effective, these drugs are often promiscuous. With the realization that GPCRs signal via multiple pathways, and with the emergence of crystal structures for this family of proteins, there is an opportunity to target GPCRs with new chemotypes and confer new signaling modalities. We consider structure-based and physical screening methods that have led to the discovery of new reagents, focusing particularly on the former. We illustrate their use against previously untargeted or orphan GPCRs, against allosteric sites, and against classical orthosteric sites that selectively activate one downstream pathway over others. The ligands that emerge are often chemically novel, which can lead to new biological effects.

A simplified characterization of S-adenosyl-l-methionine-consuming enzymes with 1-Step EZ-MTase: a universal and straightforward coupled-assay for in vitro and in vivo setting

Methyltransferases use S-adenosyl-l-methionine (SAM) to deposit methyl marks. Many of these epigenetic 'writers' are associated with gene regulation. As cancer etiology is highly correlated with misregulated methylation patterns, methyltransferases are emerging therapeutic targets. Successful assignment of methyltransferases' roles within intricate biological networks relies on (1) the access to enzyme mechanistic insights and (2) the efficient screening of chemical probes against these targets. To characterize methyltransferases in vitro and in vivo, we report a highly-sensitive one-step deaminase-linked continuous assay where the S-adenosyl-l-homocysteine (SAH) enzyme-product is rapidly and quantitatively catabolized to S-inosyl-l-homocysteine (SIH). To highlight the broad capabilities of this assay, we established enzymatic characteristics of two protein arginine methyltransferases (PRMT5 and PRMT7), a histone-lysine N-methyltransferase (DIM-5) and a sarcosine/dimethylglycine N-methyltransferase (SDMT). Since the coupling deaminase TM0936 displays robust activity over a broad pH-range we determined the pH dependence of SDMT reaction rates. TM0936 reactions are monitored at 263 nm, so a drawback may arise when methyl acceptor substrates absorb within this UV-range. To overcome this limitation, we used an isosteric fluorescent SAM-analog: S-8-aza-adenosyl-l-methionine. Most enzymes tolerated this probe and sustained methyltransfers were efficiently monitored through loss of fluorescence at 360 nm. Unlike discontinuous radioactive- and antibody-based assays, our assay provides a simple, versatile and affordable approach towards the characterization of methyltransferases. Supported by three logs of linear dynamic range, the 1-Step EZ-MTase can detect methylation rates as low as 2 μM h^-1, thus making it possible to quantify low nanomolar concentrations of glycine N-methyltransferase within crude biological samples. With Z'-factors above 0.75, this assay is well suited to high-throughput screening and may promote the identification of novel therapeutics.

Virtual screening applications in short-chain dehydrogenase/reductase research

Several members of the short-chain dehydrogenase/reductase (SDR) enzyme family play fundamental roles in adrenal and gonadal steroidogenesis as well as in the metabolism of steroids, oxysterols, bile acids, and retinoids in peripheral tissues, thereby controlling the local activation of their cognate receptors. Some of these SDRs are considered as promising therapeutic targets, for example to treat estrogen-/androgen-dependent and corticosteroid-related diseases, whereas others are considered as anti-targets as their inhibition may lead to disturbances of endocrine functions, thereby contributing to the development and progression of diseases. Nevertheless, the physiological functions of about half of all SDR members are still unknown. In this respect, in silico tools are highly valuable in drug discovery for lead molecule identification, in toxicology screenings to facilitate the identification of hazardous chemicals, and in fundamental research for substrate identification and enzyme characterization. Regarding SDRs, computational methods have been employed for a variety of applications including drug discovery, enzyme characterization and substrate identification, as well as identification of potential endocrine disrupting chemicals (EDC). This review provides an overview of the efforts undertaken in the field of virtual screening supported identification of bioactive molecules in SDR research. In addition, it presents an outlook and addresses the opportunities and limitations of computational modeling and in vitro validation methods.

Structure-based prediction and identification of 4-epimerization activity of phosphate sugars in class II aldolases

Sugar 4-epimerization reactions are important for the production of rare sugars and their derivatives, which have various potential industrial applications. For example, the production of tagatose, a functional sweetener, from fructose by sugar 4-epimerization is currently constrained because a fructose 4-epimerase does not exist in nature. We found that class II d-fructose-1,6-bisphosphate aldolase (FbaA) catalyzed the 4-epimerization of d-fructose-6-phosphate (F6P) to d-tagatose-6-phosphate (T6P) based on the prediction via structural comparisons with epimerase and molecular docking and the identification of the condensed products of C3 sugars. In vivo, the 4-epimerization activity of FbaA is normally repressed. This can be explained by our results showing the catalytic efficiency of d-fructose-6-phosphate kinase for F6P phosphorylation was significantly higher than that of FbaA for F6P epimerization. Here, we identified the epimerization reactions and the responsible catalytic residues through observation of the reactions of FbaA and l-rhamnulose-1-phosphate aldolases (RhaD) variants with substituted catalytic residues using different substrates. Moreover, we obtained detailed potential epimerization reaction mechanism of FbaA and a general epimerization mechanism of the class II aldolases l-fuculose-1-phosphate aldolase, RhaD, and FbaA. Thus, class II aldolases can be used as 4-epimerases for the stereo-selective synthesis of valuable carbohydrates.

A correlation between the degradability of poly(butylene succinate)-based copolyesters and catalytic behavior with Candida antarctica lipase B

In the present study, the degradation performance of PBS-based copolyesters by CALB was investigated from a molecular point of view.

Structure to function of an α-glucan metabolic pathway that promotes Listeria monocytogenes pathogenesis

Here we employ a 'systems structural biology' approach to functionally characterize an unconventional α-glucan metabolic pathway from the food-borne pathogen Listeria monocytogenes (Lm). Crystal structure determination coupled with basic biochemical and biophysical assays allowed for the identification of anabolic, transport, catabolic and regulatory portions of the cycloalternan pathway. These findings provide numerous insights into cycloalternan pathway function and reveal the mechanism of repressor, open reading frame, kinase (ROK) transcription regulators. Moreover, by developing a structural overview we were able to anticipate the cycloalternan pathway's role in the metabolism of partially hydrolysed starch derivatives and demonstrate its involvement in Lm pathogenesis. These findings suggest that the cycloalternan pathway plays a role in interspecies resource competition-potentially within the host gastrointestinal tract-and establish the methodological framework for characterizing bacterial systems of unknown function.

What's My Substrate? Computational Function Assignment of Candida parapsilosis ADH5 by Genome Database Search, Virtual Screening, and QM/MM Calculations

Zinc-dependent medium chain reductase from Candida parapsilosis can be used in the reduction of carbonyl compounds to pharmacologically important chiral secondary alcohols. To date, the nomenclature of cpADH5 is differing (CPCR2/RCR/SADH) in the literature, and its natural substrate is not known. In this study, we utilized a substrate docking based virtual screening method combined with KEGG, MetaCyc pathway, and Candida genome databases search for the discovery of natural substrates of cpADH5. The virtual screening of 7834 carbonyl compounds from the ZINC database provided 94 aldehydes or methyl/ethyl ketones as putative carbonyl substrates. Out of which, 52 carbonyl substrates of cpADH5 with catalytically active docking pose were identified by employing mechanism based substrate docking protocol. Comparison of the virtual screening results with KEGG, MetaCyc database search, and Candida genome pathway analysis suggest that cpADH5 might be involved in the Ehrlich pathway (reduction of fusel aldehydes in leucine, isoleucine, and valine degradation). Our QM/MM calculations and experimental activity measurements affirmed that butyraldehyde substrates are the potential natural substrates of cpADH5, suggesting a carbonyl reductase role for this enzyme in butyraldehyde reduction in aliphatic amino acid degradation pathways. Phylogenetic tree analysis of known ADHs from Candida albicans shows that cpADH5 is close to caADH5. We therefore propose, according to the experimental substrate identification and sequence similarity, the common name butyraldehyde dehydrogenase cpADH5 for Candida parapsilosis CPCR2/RCR/SADH.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Docking and Linking of Fragments To Discover Jumonji Histone Demethylase Inhibitors

Development of tool molecules that inhibit Jumonji demethylases allows for the investigation of cancer-associated transcription. While scaffolds such as 2,4-pyridinedicarboxylic acid (2,4-PDCA) are potent inhibitors, they exhibit limited selectivity. To discover new inhibitors for the KDM4 demethylases, enzymes overexpressed in several cancers, we docked a library of 600,000 fragments into the high-resolution structure of KDM4A. Among the most interesting chemotypes were the 5-aminosalicylates, which docked in two distinct but overlapping orientations. Docking poses informed the design of covalently linked fragment compounds, which were further derivatized. This combined approach improved affinity by ∼ 3 log-orders to yield compound 35 (Ki = 43 nM). Several hybrid inhibitors were selective for KDM4C over the related enzymes FIH, KDM2A, and KDM6B while lacking selectivity against the KDM3 and KDM5 subfamilies. Cocrystal structures corroborated the docking predictions. This study extends the use of structure-based docking from fragment discovery to fragment linking optimization, yielding novel KDM4 inhibitors.

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

DOCK 6: Impact of new features and current docking performance

This manuscript presents the latest algorithmic and methodological developments to the structure-based design program DOCK 6.7 focused on an updated internal energy function, new anchor selection control, enhanced minimization options, a footprint similarity scoring function, a symmetry-corrected root-mean-square deviation algorithm, a database filter, and docking forensic tools. An important strategy during development involved use of three orthogonal metrics for assessment and validation: pose reproduction over a large database of 1043 protein-ligand complexes (SB2012 test set), cross-docking to 24 drug-target protein families, and database enrichment using large active and decoy datasets (Directory of Useful Decoys [DUD]-E test set) for five important proteins including HIV protease and IGF-1R. Relative to earlier versions, a key outcome of the work is a significant increase in pose reproduction success in going from DOCK 4.0.2 (51.4%) → 5.4 (65.2%) → 6.7 (73.3%) as a result of significant decreases in failure arising from both sampling 24.1% → 13.6% → 9.1% and scoring 24.4% → 21.1% → 17.5%. Companion cross-docking and enrichment studies with the new version highlight other strengths and remaining areas for improvement, especially for systems containing metal ions. The source code for DOCK 6.7 is available for download and free for academic users at http://dock.compbio.ucsf.edu/.