Molecular determinants for ATP-binding in proteins: a data mining and quantum chemical analysis

SARS-CoV-2 Is an Electricity-Driven Virus

One of the most important and challenging biological events of recent times has been the pandemic caused by SARS-CoV-2. Since the underpinning argument behind this book is the ubiquity of electrical forces driving multiple disparate biological events, consideration of key aspects of the SARS-CoV-2 structural proteins is included. Electrical regulation of spike protein, nucleocapsid protein, membrane protein, and envelope protein is included, with several of their activities regulated by LLPS and the multivalent and π-cation and π-π electrical forces that drive phase separation.

Functional Roles of the Charged Residues of the C- and M-Gates in the Yeast Mitochondrial NAD+ Transporter Ndt1p

Mitochondrial carriers transport organic acids, amino acids, nucleotides and cofactors across the mitochondrial inner membrane. These transporters consist of a three-fold symmetric bundle of six transmembrane α-helices that encircle a pore with a central substrate binding site, whose alternating access is controlled by a cytoplasmic and a matrix gate (C- and M-gates). The C- and M-gates close by forming two different salt-bridge networks involving the conserved motifs [YF][DE]XX[KR] on the even-numbered and PX[DE]XX[KR] on the odd-numbered transmembrane α-helices, respectively. We have investigated the effects on transport of mutating the C-gate charged residues of the yeast NAD+ transporter Ndt1p and performed molecular docking with NAD+ and other substrates into structural models of Ndt1p. Double-cysteine substitutions and swapping the positions of the C-gate charged-pair residues showed that all of them contribute to the high transport rate of wild-type Ndt1p, although no single salt bridge is essential for activity. The in silico docking results strongly suggest that both the C-gate motif mutations and our previously reported M-gate mutations affect gate closing, whereas those of the M-gate also affect substrate binding, which is further supported by molecular dynamics. In particular, NAD+ most likely interferes with the cation-π interaction between R303-W198, which has been proposed to exist in the Ndt1p M-gate in the place of one of the salt bridges. These findings contribute to understanding the roles of the charged C- and M-gate residues in the transport mechanism of Ndt1p.

Molecular Determinants for Guanine Binding in GTP-Binding Proteins: A Data Mining and Quantum Chemical Study

GTP-binding proteins are essential molecular switches that regulate a wide range of cellular processes. Their function relies on the specific recognition and binding of guanine within their binding pockets. This study aims to elucidate the molecular determinants underlying this recognition. A large-scale data mining of the Protein Data Bank yielded 298 GTP-binding protein complexes, which provided a structural foundation for a systematic analysis of the intermolecular interactions that are responsible for the molecular recognition of guanine in proteins. It was found that multiple modes of non-bonded interactions including hydrogen bonding, cation-π interactions, and π-π stacking interactions are employed by GTP-binding proteins for binding. Subsequently, the strengths of non-bonded interaction energies between guanine and its surrounding protein residues were quantified by means of the double-hybrid DFT method B2PLYP-D3/cc-pVDZ. Hydrogen bonds, particularly those involving the N2 and O6 atoms of guanine, confer specificity to guanine recognition. Cation-π interactions between the guanine ring and basic residues (Lys and Arg) provide significant electrostatic stabilization. π-π stacking interactions with aromatic residues (Phe, Tyr, and Trp) further contribute to the overall binding affinity. This synergistic interplay of multiple interaction modes enables GTP-binding proteins to achieve high specificity and stability in guanine recognition, ultimately underpinning their crucial roles in cellular signaling and regulation. Notably, the NKXD motif, while historically considered crucial for guanine binding in GTP-binding proteins, is not universally required. Our study revealed significant variability in hydrogen bonding patterns, with many proteins lacking the NKXD motif but still effectively binding guanine through alternative arrangements of interacting residues.

Triazoles in Medicinal Chemistry: Physicochemical Properties, Bioisosterism, and Application

Triazole demonstrates distinctive physicochemical properties, characterized by weak basicity, various dipole moments, and significant dual hydrogen bond acceptor and donor capabilities. These features are poised to play a pivotal role in drug-target interactions. The inherent polarity of triazole contributes to its lower logP, suggesting the potential improvement in water solubility. The metabolic stability of triazole adds additional value to drug discovery. Moreover, the metal-binding capacity of the nitrogen atom lone pair electrons of triazole has broad applications in the development of metal chelators and antifungal agents. This Perspective aims to underscore the unique physicochemical attributes of triazole and its application. A comparative analysis involving triazole isomers and other heterocycles provides guiding insights for the subsequent design of triazoles, with the hope of offering valuable considerations for designing other heterocycles in medicinal chemistry.

An Assessment of Dispersion-Corrected DFT Methods for Modeling Nonbonded Interactions in Protein Kinase Inhibitor Complexes

Accurate modeling of nonbonded interactions between protein kinases and their small molecule inhibitors is essential for structure-based drug design. Quantum chemical methods such as density functional theory (DFT) hold significant promise for quantifying the strengths of these key protein-ligand interactions. However, the accuracy of DFT methods can vary substantially depending on the choice of exchange-correlation functionals and associated basis sets. In this study, a comprehensive benchmarking of nine widely used DFT methods was carried out to identify an optimal approach for quantitative modeling of nonbonded interactions, balancing both accuracy and computational efficiency. From a database of 2139 kinase-inhibitor crystal structures, a diverse library of 49 nonbonded interaction motifs was extracted, encompassing CH-π, π-π stacking, cation-π, hydrogen bonding, and salt bridge interactions. The strengths of nonbonded interaction energies for all 49 motifs were calculated at the advanced CCSD(T)/CBS level of theory, which serve as references for a systematic benchmarking of BLYP, TPSS, B97, ωB97X, B3LYP, M062X, PW6B95, B2PLYP, and PWPB95 functionals with D3BJ dispersion correction alongside def2-SVP, def2-TZVP, and def2-QZVP basis sets. The RI, RIJK, and RIJCOSX approximations were used for selected functionals. It was found that the B3LYP/def2-TZVP and RIJK RI-B2PLYP/def2-QZVP methods delivered the best combination of accuracy and computational efficiency, making them well-suited for efficient modeling of nonbonded interactions responsible for molecular recognition of protein kinase inhibitors in their targets.

Interaction Patterns of Pyrazolopyrimidines with Receptor Proteins

Heterocyclic compounds have a prominent role in medicinal chemistry and drug design. They are not only useful as medicinally active compounds but also as a modular structural scaffold for drug design. Therefore, heterocycles are present in many ligands that exhibit a broad spectrum of biological activities. Pyazolopyrimidines are nitrogen heterocycles and are part of many biologically active compounds and marketed drugs. This study examines the non-covalent interactions between the pyrazolopyrimidine rings and receptor proteins through data mining and analysis of high-resolution crystal structures deposited in the Protein Data Bank. The Protein Data Bank contains 471 crystal structures with pyrazolopyrimidine derivatives as ligands, among which 50% contains 1H-pyrazolo[3,4-d]pyrimidines (Pyp1), while 38% contains pyrazolo[1,5-a] pyrimidines (Pyp2). 1H-Pyrazolo[4,3-d]pyrimidines (Pyp3) are found in 11% of the structures, and no structural data is available for pyrazolo[1,5-c]pyrimidine isomers (Pyp4). Among receptor proteins, transferases are found in most examples (67.5%), followed by hydrolases (13.4%) and oxidoreductases (8.9%). Detailed analysis of structures to identify the most prevalent interactions of pyrazolopyrimidines with proteins shows that aromatic π···π interactions are present in ∼91% of the structures and hydrogen bonds/other polar contacts are present in ∼73% of the structures. The centroid-centroid distances (d_cent) between the pyrazolopyrimidine rings and aromatic side chains of the proteins have been retrieved from crystal structures recorded at a high resolution (data resolution <2.0 Å). The average value of d_cent in pyrazolopyrimidine-protein complexes is 5.32 Å. The information on the geometric parameters of aromatic interactions between the core pyrazolopyrimidine ring and the protein would be helpful in future in silico modeling studies on pyrazolopyrimidine-receptor complexes.

Molecular Recognition of FDA-Approved Small Molecule Protein Kinase Drugs in Protein Kinases

Protein kinases are key enzymes that catalyze the covalent phosphorylation of substrates via the transfer of the γ-phosphate of ATP, playing a crucial role in cellular proliferation, differentiation, and various cell regulatory processes. Due to their pivotal cellular role, the aberrant function of kinases has been associated with cancers and many other diseases. Consequently, competitive inhibition of the ATP binding site of protein kinases has emerged as an effective means of curing these diseases. Decades of intense development of protein kinase inhibitors (PKIs) resulted in 71 FDA-approved PKI drugs that target dozens of protein kinases for the treatment of various diseases. How do FDA-approved protein kinase inhibitor PKI drugs compete with ATP in their own binding pocket? This is the central question we attempt to address in this work. Based on modes of non-bonded interactions and their calculated interaction strengths by means of the advanced double hybrid DFT method B2PLYP, the molecular recognition of PKI drugs in the ATP-binding pockets was systematically analyzed. It was found that (1) all the FDA-approved PKI drugs studied here form one or more hydrogen bond(s) with the backbone amide N, O atoms in the hinge region of the ATP binding site, mimicking the adenine base; (2) all the FDA-approved PKI drugs feature two or more aromatic rings. The latter reach far and deep into the hydrophobic regions I and II, forming multiple CH-π interactions with aliphatic residues L(3), V(11), A(15), V(36), G(51), L(77) and π-π stacking interactions with aromatic residues F(47) and F(82), but ATP itself does not utilize these regions extensively; (3) all FDA-approved PKI drugs studied here have one thing in common, i.e., they frequently formed non-bonded interactions with a total of 12 residues L(3),V(11), A(15), K(17), E(24),V(36),T(45), F(47), G(51), L(77), D(81) and F(82) in the ATP binding. Many of those 12 commonly involved residues are highly conserved residues with important structural and catalytic functional roles. K(17) and E(24) are the two highly conserved residues crucial for the catalytic function of kinases. D(81) and F(82) belong to the DFG motif; T(45) was dubbed the gate keeper residue. F(47) is located on the hinge region and G(51) sits on the linker that connects the hinge to the αD-helix. It is this targeting of highly conserved residues in protein kinases that led to promiscuous PKI drugs that lack selectivity. Although the formation of hydrogen bond(s) with the backbone of the hinge gives PKI drugs the added binding affinity and the much-needed directionality, selectivity is sacrificed. That is why so many FDA-approved PKI drugs are known to have multiple targets. Moreover, off-target-mediated toxicity caused by a lack of selectivity was one of the major challenges facing the PKI drug discovery community. This work suggests a road map for future PKI drug design, i.e., targeting non-conserved residues in the ATP binding pocket to gain better selectivity so as to avoid off-target-mediated toxicity.

Quantum biology. π-π entanglement signatures in Protein-DNA interactions

DNA biological functions are carried out by individual proteins that interact with specific sequences along DNA to prime molecular processes required by cellular metabolism. Protein-DNA interactions include DNA replication, gene expression and its regulation, DNA repair, DNA restriction and modification by endonucleases, generally classified as enzymatic functions, or transcription factors functions. To find specific binding target sequences and finalize their activities, proteins must operate in symbiosis with cellular crowded environment identifying extremely small cognate sequences along the DNA chain, ranging from 15-20 bps for repressors to 4-6 bps for restriction enzymes in less than one second.

In-depth analysis of amino acid and nucleotide sequences of Hsp60: how conserved is this protein?

Chaperonin Hsp60, as a protein found in all organisms, is of great interest in medicine, since it is present in many tissues and can be used both as a drug and as an object of targeted therapy. Hence, Hsp60 deserves a fundamental comparative analysis to assess its evolutionary characteristics. It was found that the percent identity of Hsp60 amino acid sequences both within and between phyla was not high enough to identify Hsp60s as highly conserved proteins. However, their ATP binding sites are largely conserved. The amino acid composition of Hsp60s remained relatively constant. At the same time, the analysis of the nucleotide sequences showed that GC content in the Hsp60 genes was comparable to or greater than the genomic values, which may indicate a high resistance to mutations due to tight control of the nucleotide composition by DNA repair systems. Natural selection plays a dominant role in the evolution of Hsp60 genes. The degree of mutational pressure affecting the Hsp60 genes is quite low, and its direction does not depend on taxonomy. Interestingly, for the Hsp60 genes from Chordata, Arthropoda, and Proteobacteria the exact direction of mutational pressure could not be determined. However, upon further division into classes, it was found that the direction of the mutational pressure for Hsp60 genes from Fish differs from that for other chordates. The direction of the mutational pressure affects the synonymous codon usage bias. The number of high and low represented codons increases with increasing GC content, which can improve codon usage. Special server has been created for bioinformatics analysis of Hsp60: http://oka.protres.ru:4202/.

Purine adducts as a presumable missing link for aristolochic acid nephropathy-related cellular energy crisis, potential anti-fibrotic prevention and treatment

Aristolochic acid nephropathy is a progressive exposome-induced disease characterized by tubular atrophy and fibrosis culminating in end-stage renal disease and malignancies. The molecular mechanisms of the energy crisis as a putative cause of fibrosis have not yet been elucidated. In light of the fact that aristolochic acid forms DNA and RNA adducts by covalent binding of aristolochic acid metabolites to exocyclic amino groups of (deoxy)adenosine and (deoxy)guanosine, we hypothesize here that similar aristolochic acid adducts may exist with other purine-containing molecules. We also provide new insights into the aristolochic acid-induced energy crisis and presumably a link between already known mechanisms. In addition, an overview of potential targets in fibrosis treatment is provided, which is followed by recommendations on possible preventive measures that could be taken to at least postpone or partially alleviate aristolochic acid nephropathy.

Aromatic Rings as Molecular Determinants for the Molecular Recognition of Protein Kinase Inhibitors

Protein kinases are key enzymes in many signal transduction pathways, and play a crucial role in cellular proliferation, differentiation, and various cell regulatory processes. However, aberrant function of kinases has been associated with cancers and many other diseases. Consequently, competitive inhibition of the ATP binding site of protein kinases has emerged as an effective means of curing these diseases. Over the past three decades, thousands of protein kinase inhibitors (PKIs) with varying molecular frames have been developed. Large-scale data mining of the Protein Data Bank resulted in a database of 2139 non-redundant high-resolution X-ray crystal structures of PKIs bound to protein kinases. This provided us with a unique opportunity to study molecular determinants for the molecular recognition of PKIs. A chemoinformatic analysis of 2139 PKIs resulted in findings that PKIs are "flat" molecules with high aromatic ring counts and low fractions of sp3 carbon. All but one PKI possessed one or more aromatic rings. More importantly, it was found that the average weighted hydrogen bond count is inversely proportional to the number of aromatic rings. Based on this linear relationship, we put forward the exchange rule of hydrogen bonding interactions and non-bonded π-interactions. Specifically, a loss of binding affinity caused by a decrease in hydrogen bonding interactions is compensated by a gain in binding affinity acquired by an increase in aromatic ring-originated non-bonded interactions (i.e., π-π stacking interactions, CH-π interactions, cation-π interactions, etc.), and vice versa. The very existence of this inverse relationship strongly suggests that both hydrogen bonding and aromatic ring-originated non-bonded interactions are responsible for the molecular recognition of PKIs. As an illustration, two representative PKI-kinase complexes were employed to examine the relative importance of different modes of non-bonded interactions for the molecular recognition of PKIs. For this purpose, two FDA-approved PKI drugs, ibrutinib and lenvatinib, were chosen. The binding pockets of both PKIs were thoroughly examined to identify all non-bonded intermolecular interactions. Subsequently, the strengths of interaction energies between ibrutinib and its interacting residues in tyrosine kinase BTK were quantified by means of the double hybrid DFT method B2PLYP. The resulting energetics for the binding of ibrutinib in tyrosine kinase BTK showed that CH-π interactions and π-π stacking interactions between aromatic rings of the drug and hydrophobic residues in its binding pocket dominate the binding interactions. Thus, this work establishes that, in addition to hydrogen bonding, aromatic rings function as important molecular determinants for the molecular recognition of PKIs. In conclusion, our findings support the following pharmacophore model for ATP-competitive kinase inhibitors: a small molecule features a scaffold of one or more aromatic rings which is linked with one or more hydrophilic functional groups. The former has the structural role of acting as a scaffold and the functional role of participating in aromatic ring-originated non-bonded interactions with multiple hydrophobic regions in the ATP binding pocket of kinases. The latter ensure water solubility and form hydrogen bonds with the hinge region and other hydrophilic residues of the ATP binding pocket.

Anatomy of noncovalent interactions between the nucleobases or ribose and π-containing amino acids in RNA-protein complexes

A set of >300 nonredundant high-resolution RNA–protein complexes were rigorously searched for π-contacts between an amino acid side chain (W, H, F, Y, R, E and D) and an RNA nucleobase (denoted π–π interaction) or ribose moiety (denoted sugar–π). The resulting dataset of >1500 RNA–protein π-contacts were visually inspected and classified based on the interaction type, and amino acids and RNA components involved. More than 80% of structures searched contained at least one RNA–protein π-interaction, with π–π contacts making up 59% of the identified interactions. RNA–protein π–π and sugar–π contacts exhibit a range in the RNA and protein components involved, relative monomer orientations and quantum mechanically predicted binding energies. Interestingly, π–π and sugar–π interactions occur more frequently with RNA (4.8 contacts/structure) than DNA (2.6). Moreover, the maximum stability is greater for RNA–protein contacts than DNA–protein interactions. In addition to highlighting distinct differences between RNA and DNA–protein binding, this work has generated the largest dataset of RNA–protein π-interactions to date, thereby underscoring that RNA–protein π-contacts are ubiquitous in nature, and key to the stability and function of RNA–protein complexes.

Polydopamine-assisted one-step modification of nanofiber surfaces with adenosine to tune the osteogenic differentiation of mesenchymal stem cells and the maturation of osteoclasts

Adenosine and its receptors have emerged as alternative targets to control cellular functions for bone healing. However, the soluble delivery of adenosine has not proven effective because of its fast degradation in vivo. We therefore designed a stable coating of adenosine for biomaterial surfaces through polydopamine chemistry to control osteogenesis and osteoclastogenesis via A2bR signaling. First, we prepared electrospun poly (ι-lactic acid) (PLLA) nanofiber sheets, which were modified through a one-step adenosine polydopamine coating process. Scanning electron microscopy (SEM) revealed deposition of particles on the adenosine polydopamine-coated PLLA (AP-PL) sheets compared to the polydopamine-only sheets. Moreover, X-ray photoelectron spectroscopy analysis confirmed an increase in nitrogen signals due to adenosine. Furthermore, adenosine loading efficiency and retention were significantly enhanced in AP-PL sheets compared to polydopamine-only sheets. Human adipose-derived stem cells (hADSCs) cultured on AP-PL expressed A2bR (1.30 ± 0.19 fold) at significantly higher levels than those cultured on polydopamine-only sheets. This in turn significantly elevated the expression of Runx2 (16.94 ± 1.68 and 51.69 ± 0.07 fold), OPN (1.63 ± 0.16 and 30.56 ± 0.25 fold), OCN (1.16 ± 0.13 and 5.23 ± 0.16 fold), and OSX (10.01 ± 0.81 and 62.48 ± 0.25 fold) in cells grown in growth media on days 14 and 21, respectively. Similarly, mineral deposition was enhanced to a greater extent in the AP-PL group than the polydopamine group, while blocking of A2bR significantly downregulated osteogenesis. Finally, osteoclast differentiation of RAW 264.7 cells was significantly inhibited by growth on AP-PL sheets. However, osteoclast differentiation was significantly stimulated after A2bR was blocked. Taken together, we propose that polydopamine-assisted one-step coating of adenosine is a viable method for surface modification of biomaterials to control osteogenic differentiation of stem cells and bone healing.

BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

MOTIVATION

Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.

RESULTS

We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures.

AVAILABILITY AND IMPLEMENTATION

BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Impact of sunflower (Helianthus annuus L.) plastidial lipoyl synthases genes expression in glycerolipids composition of transgenic Arabidopsis plants

Lipoyl synthases are key enzymes in lipoic acid biosynthesis, a co-factor of several enzyme complexes involved in central metabolism. Plant pyruvate dehydrogenase complex (PDH), located in mitochondria and plastids, catalyses the first step of fatty acid biosynthesis in these organelles. Among their different components, the E2 subunit requires the lipoic acid prosthetic group to be active. De novo lipoic acid biosynthesis is achieved by the successive action of two enzymes on octanoyl-ACP: octanoyltransferase (LIP2) and lipoyl synthase (LIP1). In this study, two plastidial lipoyl synthase genes from sunflower (Helianthus annuus L.) were identified (HaLIP1p1 and HaLIP1p2), sequenced and cloned in a heterologous production system (Escherichia coli). Gene expression studies revealed similar expression patterns for both isoforms, with a slight predominance of HaLIP1p1 in vegetative tissues and mature seeds. Tertiary structural models for these enzymes indicate they both have the same theoretical catalytic sites, using lipoyl-lys and 5-deoxyadenosine as docking substrates. The fatty acid profile of E. coli cells overexpressing HaLIP1p1 and HaLIP1p2 did not present major differences, and the in vivo activity of both proteins was confirmed by complementation of an E. coli JW0623 mutant in which lipoyl synthase is defective. Although no significant differences were detected in the total fatty acid composition of transgenic Arabidopsis thaliana seeds overexpressing any of both proteins, a lipidomic analysis revealed a redistribution of the glycerolipid species, accompanied with increased phosphatidylethanolamine (PE) content and a decrease in diacyglycerols (DAG) and phosphatidylcholine (PC). Depletion of the SAM co-factor caused by HaLIP1p1 and HaLIP1p2 overexpression in transgenic plants could explain this remodelling through its effects on PC synthesis.

Crystal structures of the c-di-AMP-synthesizing enzyme CdaA

Cyclic di-AMP (c-di-AMP) is the only second messenger known to be essential for bacterial growth. It has been found mainly in Gram-positive bacteria, including pathogenic bacteria like Listeria monocytogenes CdaA is the sole diadenylate cyclase in L. monocytogenes, making this enzyme an attractive target for the development of novel antibiotic compounds. Here we report crystal structures of CdaA from L. monocytogenes in the apo state, in the post-catalytic state with bound c-di-AMP and catalytic Co²⁺ ions, as well as in a complex with AMP. These structures reveal the flexibility of a tyrosine side chain involved in locking the adenine ring after ATP binding. The essential role of this tyrosine was confirmed by mutation to Ala, leading to drastic loss of enzymatic activity.

Coordination environment changes of the vanadium in vanadium-dependent haloperoxidase enzymes

Vanadium-dependent haloperoxidases are a class of enzymes that catalyze oxidation reactions with halides to form halogenated organic products and water. These enzymes include chloroperoxidase and bromoperoxidase, which have very different protein sequences and sizes, but regardless the coordination environment of the active sites is surprisingly constant. In this manuscript, the comparison of the coordination chemistry of V-containing-haloperoxidases of the trigonal bipyramidal geometry was done by data mining. The catalytic cycle imposes changes in the coordination geometry of the vanadium to accommodate the peroxidovanadium(V) intermediate in an environment we describe as a distorted square pyramidal geometry. During the catalytic cycle, this intermediate converts to a trigonal bipyramidal intermediate before losing the halogen and forming a tetrahedral vanadium-protein intermediate. Importantly, the catalysis is facilitated by a proton-relay system supplied by the second sphere coordination environment and the changes in the coordination environment of the vanadium(V) making this process unique among protein catalyzed processes. The analysis of the coordination chemistry shows that the active site is very tightly regulated with only minor changes in the coordination geometry. The coordination geometry in the protein structures deviates from that found for both small molecules crystalized in the absence of protein and the reported functional small molecule model compounds. At this time there are no examples reported of a structurally similar small molecule with the geometry observed for the peroxidovanadium(V) in the active site of the vanadium-containing haloperoxidases.

Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions

The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.

NIMS: a database on nucleobase compounds and their interactions in macromolecular structures

The intense exploration of nucleotide-binding protein structures has created a whirlwind in the field of structural biology and bioinformatics. This has led to the conception and birth of NIMS. This database is a collection of detailed data on the nucleobases, nucleosides and nucleotides, along with their analogues as well as the protein structures to which they bind. Interaction details such as the interacting residues and all associated values have been made available. As a pioneering step, the diffraction precision index for protein structures, the atomic uncertainty for each atom, and the computed errors on the interatomic distances and angles are available in the database. Apart from the above, provision has been made to visualize the three-dimensional structures of both ligands and protein–ligand structures and their interactions inJmolas well asJSmol. One of the salient features of NIMS is that it has been interfaced with a user-friendly and query-based efficient search engine. It was conceived and developed with the aim of serving a significant section of researchers working in the area of protein and nucleobase complexes. NIMS is freely available online at http://iris.physics.iisc.ernet.in/nims and it is hoped that it will prove to be an invaluable asset.

Topology of RNA-protein nucleobase-amino acid π-π interactions and comparison to analogous DNA-protein π-π contacts

The present work analyzed 120 high-resolution X-ray crystal structures and identified 335 RNA-protein π-interactions (154 nonredundant) between a nucleobase and aromatic (W, H, F, or Y) or acyclic (R, E, or D) π-containing amino acid. Each contact was critically analyzed (including using a visual inspection protocol) to determine the most prevalent composition, structure, and strength of π-interactions at RNA-protein interfaces. These contacts most commonly involve F and U, with U:F interactions comprising one-fifth of the total number of contacts found. Furthermore, the RNA and protein π-systems adopt many different relative orientations, although there is a preference for more parallel (stacked) arrangements. Due to the variation in structure, the strength of the intermolecular forces between the RNA and protein components (as determined from accurate quantum chemical calculations) exhibits a significant range, with most of the contacts providing significant stability to the associated RNA-protein complex (up to -65 kJ mol(-1)). Comparison to the analogous DNA-protein π-interactions emphasizes differences in RNA- and DNA-protein π-interactions at the molecular level, including the greater abundance of RNA contacts and the involvement of different nucleobase/amino acid residues. Overall, our results provide a clearer picture of the molecular basis of nucleic acid-protein binding and underscore the important role of these contacts in biology, including the significant contribution of π-π interactions to the stability of nucleic acid-protein complexes. Nevertheless, more work is still needed in this area in order to further appreciate the properties and roles of RNA nucleobase-amino acid π-interactions in nature.

On the ATP binding site of the ε subunit from bacterial F-type ATP synthases

F-type ATP synthases are reversible machinery that not only synthesize adenosine triphosphate (ATP) using an electrochemical gradient across the membrane, but also can hydrolyze ATP to pump ions under certain conditions. To prevent wasteful ATP hydrolysis, subunit ε in bacterial ATP synthases changes its conformation from the non-inhibitory down- to the inhibitory up-state at a low cellular ATP concentration. Recently, a crystal structure of the ε subunit in complex with ATP was solved in a non-biologically relevant dimeric form. Here, to derive the functional ATP binding site motif, we carried out molecular dynamics simulations and free energy calculations. Our results suggest that the ATP binding site markedly differs from the experimental resolved one; we observe a reorientation of several residues, which bind to ATP in the crystal structure. In addition we find that an Mg(2+) ion is coordinated by ATP, replacing interactions of the second chain in the crystal structure. Thus we demonstrate more generally the influence of crystallization effects on ligand binding sites and their respective binding modes. Furthermore, we propose a role for two highly conserved residues to control the ATP binding/unbinding event, which have not been considered before. Additionally our results provide the basis for the rational development of new biosensors based on subunit ε, as shown previously for novel sensors measuring the ATP concentration in cells.

Landscape of π-π and sugar-π contacts in DNA-protein interactions

There were 1765 contacts identified between DNA nucleobases or deoxyribose and cyclic (W, H, F, Y) or acyclic (R, E, D) amino acids in 672 X-ray structures of DNA-protein complexes. In this first study to compare π-interactions between the cyclic and acyclic amino acids, visual inspection was used to categorize amino acid interactions as nucleobase π-π (according to biological edge) or deoxyribose sugar-π (according to sugar edge). Overall, 54% of contacts are nucleobase π-π interactions, which involve all amino acids, but are more common for Y, F, and R, and involve all DNA nucleobases with similar frequencies. Among binding arrangements, cyclic amino acids prefer more planar (stacked) π-systems than the acyclic counterparts. Although sugar-π interactions were only previously identified with the cyclic amino acids and were found to be less common (38%) than nucleobase-cyclic amino acid contacts, sugar-π interactions are more common than nucleobase π-π contacts for the acyclic series (61% of contacts). Similar to DNA-protein π-π interactions, sugar-π contacts most frequently involve Y and R, although all amino acids adopt many binding orientations relative to deoxyribose. These DNA-protein π-interactions stabilize biological systems, by up to approximately -40 kJ mol(-1) for neutral nucleobase or sugar-amino acid interactions, but up to approximately -95 kJ mol(-1) for positively or negatively charged contacts. The high frequency and strength, despite variation in structure and composition, of these π-interactions point to an important function in biological systems.

Structures of lipoyl synthase reveal a compact active site for controlling sequential sulfur insertion reactions

Lipoyl cofactors are essential for living organisms and are produced by the insertion of two sulfur atoms into the relatively unreactive C-H bonds of an octanoyl substrate. This reaction requires lipoyl synthase, a member of the radical S-adenosylmethionine (SAM) enzyme superfamily. In the present study, we solved crystal structures of lipoyl synthase with two [4Fe-4S] clusters bound at opposite ends of the TIM barrel, the usual fold of the radical SAM superfamily. The cluster required for reductive SAM cleavage conserves the features of the radical SAM superfamily, but the auxiliary cluster is bound by a CX4CX5C motif unique to lipoyl synthase. The fourth ligand to the auxiliary cluster is an extremely unusual serine residue. Site-directed mutants show this conserved serine ligand is essential for the sulfur insertion steps. One crystallized lipoyl synthase (LipA) complex contains 5'-methylthioadenosine (MTA), a breakdown product of SAM, bound in the likely SAM-binding site. Modelling has identified an 18 Å (1 Å=0.1 nm) deep channel, well-proportioned to accommodate an octanoyl substrate. These results suggest that the auxiliary cluster is the likely sulfur donor, but access to a sulfide ion for the second sulfur insertion reaction requires the loss of an iron atom from the auxiliary cluster, which the serine ligand may enable.

Serine and Cysteine π-Interactions in Nature: A Comparison of the Frequency, Structure, and Stability of Contacts Involving Oxygen and Sulfur

Despite many DNA–protein π-interactions in high-resolution crystal structures, only four X–H···π or X···π interactions were found between serine (Ser) or cysteine (Cys) and DNA nucleobase π-systems in over 100 DNA–protein complexes (where X = O for Ser and X = S for Cys). Nevertheless, 126 non-covalent contacts occur between Ser or Cys and the aromatic amino acids in many binding arrangements within proteins. Furthermore, Ser and Cys protein–protein π-interactions occur with similar frequencies and strengths. Most importantly, due to the great stability that can be provided to biological macromolecules (up to –20 kJ mol–1 for neutral π-systems or –40 kJ mol–1 for cationic π-systems), Ser and Cys π-interactions should be considered when analyzing protein stability and function.

A new supervised over-sampling algorithm with application to protein-nucleotide binding residue prediction

Protein-nucleotide interactions are ubiquitous in a wide variety of biological processes. Accurately identifying interaction residues solely from protein sequences is useful for both protein function annotation and drug design, especially in the post-genomic era, as large volumes of protein data have not been functionally annotated. Protein-nucleotide binding residue prediction is a typical imbalanced learning problem, where binding residues are extremely fewer in number than non-binding residues. Alleviating the severity of class imbalance has been demonstrated to be a promising means of improving the prediction performance of a machine-learning-based predictor for class imbalance problems. However, little attention has been paid to the negative impact of class imbalance on protein-nucleotide binding residue prediction. In this study, we propose a new supervised over-sampling algorithm that synthesizes additional minority class samples to address class imbalance. The experimental results from protein-nucleotide interaction datasets demonstrate that the proposed supervised over-sampling algorithm can relieve the severity of class imbalance and help to improve prediction performance. Based on the proposed over-sampling algorithm, a predictor, called TargetSOS, is implemented for protein-nucleotide binding residue prediction. Cross-validation tests and independent validation tests demonstrate the effectiveness of TargetSOS. The web-server and datasets used in this study are freely available at http://www.csbio.sjtu.edu.cn/bioinf/TargetSOS/.

AFAL: a web service for profiling amino acids surrounding ligands in proteins

With advancements in crystallographic technology and the increasing wealth of information populating structural databases, there is an increasing need for prediction tools based on spatial information that will support the characterization of proteins and protein-ligand interactions. Herein, a new web service is presented termed amino acid frequency around ligand (AFAL) for determining amino acids type and frequencies surrounding ligands within proteins deposited in the Protein Data Bank and for assessing the atoms and atom-ligand distances involved in each interaction (availability: http://structuralbio.utalca.cl/AFAL/index.html ). AFAL allows the user to define a wide variety of filtering criteria (protein family, source organism, resolution, sequence redundancy and distance) in order to uncover trends and evolutionary differences in amino acid preferences that define interactions with particular ligands. Results obtained from AFAL provide valuable statistical information about amino acids that may be responsible for establishing particular ligand-protein interactions. The analysis will enable investigators to compare ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. Such patterns can be used subsequently to predict ligand binding in proteins that currently have no structural information and to refine the interpretation of existing protein models. The application of AFAL is illustrated by the analysis of proteins interacting with adenosine-5'-triphosphate.

QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFR

Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC₅₀) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs.

DNA-protein π-interactions in nature: abundance, structure, composition and strength of contacts between aromatic amino acids and DNA nucleobases or deoxyribose sugar

Four hundred twenty-eight high-resolution DNA-protein complexes were chosen for a bioinformatics study. Although 164 crystal structures (38% of those searched) contained no interactions, 574 discrete π-contacts between the aromatic amino acids and the DNA nucleobases or deoxyribose were identified using strict criteria, including visual inspection. The abundance and structure of the interactions were determined by unequivocally classifying the contacts as either π-π stacking, π-π T-shaped or sugar-π contacts. Three hundred forty-four nucleobase-amino acid π-π contacts (60% of all interactions identified) were identified in 175 of the crystal structures searched. Unprecedented in the literature, 230 DNA-protein sugar-π contacts (40% of all interactions identified) were identified in 137 crystal structures, which involve C-H···π and/or lone-pair···π interactions, contain any amino acid and can be classified according to sugar atoms involved. Both π-π and sugar-π interactions display a range of relative monomer orientations and therefore interaction energies (up to -50 (-70) kJ mol(-1) for neutral (charged) interactions as determined using quantum chemical calculations). In general, DNA-protein π-interactions are more prevalent than perhaps currently accepted and the role of such interactions in many biological processes may yet to be uncovered.

A single amino acid substitution affects the substrate specificity of the seryl-tRNA synthetase homologue

Recently described and characterizedBradyrhizobium japonicumglycine:[carrier protein] ligase 1 (Bj Gly:CP ligase 1), a homologue of methanogenic type seryl-tRNA synthetase (SerRS) is an intriguing enzyme whose physiological role is not yet known.

Examination of tyrosine/adenine stacking interactions in protein complexes

The π-stacking interactions between tyrosine amino acid side chains and adenine-bearing ligands are examined. Crystalline protein structures from the protein data bank (PDB) exhibiting face-to-face tyrosine/adenine arrangements were used to construct 20 unique 4-methylphenol/N9-methyladenine (p-cresol/9MeA) model systems. Full geometry optimization of the 20 crystal structures with the M06-2X density functional theory method identified 11 unique low-energy conformations. CCSD(T) complete basis set (CBS) limit interaction energies were estimated for all of the structures to determine the magnitude of the interaction between the two ring systems. CCSD(T) computations with double-ζ basis sets (e.g., 6-31G*(0.25) and aug-cc-pVDZ) indicate that the MP2 method overbinds by as much as 3.07 kcal mol(-1) for the crystal structures and 3.90 kcal mol(-1) for the optimized structures. In the 20 crystal structures, the estimated CCSD(T) CBS limit interaction energy ranges from -4.00 to -6.83 kcal mol(-1), with an average interaction energy of -5.47 kcal mol(-1), values remarkably similar to the corresponding data for phenylalanine/adenine stacking interactions. Geometry optimization significantly increases the interaction energies of the p-cresol/9MeA model systems. The average estimated CCSD(T) CBS limit interaction energy of the 11 optimized structures is 3.23 kcal mol(-1) larger than that for the 20 crystal structures.

Significant strength of charged DNA-protein π-π interactions: a preliminary study of cytosine

The present work characterized the preferred gas-phase structure and optimum interaction energy of both parallel stacked and perpendicular T-shaped dimers between cytosine (C), as a representative nucleobase, and aspartic/glutamic acid (DE), aspartate/glutamate (DE(-)) or arginine (R(+)), using detailed M06-2X/6-31+G(d,p) potential energy surface scans as a function of the relative monomer orientation. Through comparison to previous literature on the π-π interactions between the DNA nucleobases and the aromatic amino acid residues, this work will allow for comparisons between DNA-protein interactions involving aromatic and acyclic R-side chains, as well as comparisons of the relative geometric dependence and magnitude of π-π (C:DE), πcation-π (C:R(+)), and πanion-π (C:DE(-)) interactions. Our results show that the preferred relative monomer orientation is highly dependent on the monomer composition and charge, and is dictated by electrostatic-driven interactions. More importantly, for the first time, we report that the π-π interactions between cytosine and (neutral) aspartic/glutamic acid are up to approximately -40 kJ mol(-1), while the πcation-π or πanion-π interactions between cytosine and arginine or aspartate/glutamate are up to approximately -90 and -99 kJ mol(-1), respectively. An extensive investigation of the effects of the computational methodology implemented, including comparisons to detailed CCSD(T)/CBS potential energy surfaces and interaction energies, supports the use of M06-2X, as well as ωB97X-D, to study DNA-protein π-π interactions of varying composition and charge. Most importantly, the CCSD(T)/CBS results verify the strong nature of these DNA-protein π-π interactions, as well as the unique nature of the πcation-π and πanion-π counterparts. Therefore, our results emphasize that a wide variety of different types of noncovalent interactions between both cyclic and acyclic π-containing components can significantly contribute to the stability of DNA-protein complexes and likely play a larger role in biology than currently accepted.

Molecular Imprinting of Tri-O-Acetyladenosine for the Synthetic Imitation of an ATP-Binding Cleft in Protein Kinases

A number of diseases, including cancer, diabetes, and inflammation, are linked to deregulation of cell signaling pathways controlled by protein kinases. Inhibition of the kinases involved can interrupt aberrant signaling and have a specific therapeutic effect. Protein kinases are recognized as validated therapeutic targets for the treatment of a number of diseases and there are considerable efforts to discover new kinase inhibitors suitable for drug development. The main goal of this study was to fabricate the synthetic imitations of the adenosine triphosphate (ATP) binding cleft in protein kinases and thus produce polymers suitable for screening and isolation of new protein kinase ATP-mimetic inhibitors from different sources. Such polymers were created by the imprinting of tri-O-acetyladenosine in acrylic polymer matrix with the use of methacrylic acid (MAA) or 3-vinylbenzoic acid (VBA) as a functional monomer and ethylene glycol dimethacrylate as a cross-linking agent. The imprints prepared with the use of VBA demonstrate substantially better binding efficiency than that with MAA and particularly high affinity to the initial template (K_d as low as 1.2 μM), sufficient concentration of binding sites N (up to 32 μmol g^-1 ), and pronounced specificity (imprinting factor up to 11). Under flow conditions, the fabricated polymers also demonstrate high capacity and template affinity. The produced imprints reproduce spatially noncovalent interactions present in the ATP binding site of protein kinases and can be anticipated as approximate synthetic imitations of the binding cleft.

Conformational dynamics of ATP/Mg:ATP in motor proteins via data mining and molecular simulation

The conformational diversity of ATP/Mg:ATP in motor proteins was investigated using molecular dynamics and data mining. Adenosine triphosphate (ATP) conformations were found to be constrained mostly by inter cavity motifs in the motor proteins. It is demonstrated that ATP favors extended conformations in the tight pockets of motor proteins such as F(1)-ATPase and actin whereas compact structures are favored in motor proteins such as RNA polymerase and DNA helicase. The incorporation of Mg(2+) leads to increased flexibility of ATP molecules. The differences in the conformational dynamics of ATP/Mg:ATP in various motor proteins was quantified by the radius of gyration. The relationship between the simulation results and those obtained by data mining of motor proteins available in the protein data bank is analyzed. The data mining analysis of motor proteins supports the conformational diversity of the phosphate group of ATP obtained computationally.

Identification of nucleotide-binding sites in protein structures: a novel approach based on nucleotide modularity

Nucleotides are involved in several cellular processes, ranging from the transmission of genetic information, to energy transfer and storage. Both sequence and structure based methods have been developed to predict the location of nucleotide-binding sites in proteins. Here we propose a novel methodology that leverages the observation that nucleotide-binding sites have a modular structure. Nucleotides are composed of identifiable fragments, i.e. the phosphate, the nucleobase and the carbohydrate moieties. These fragments are bound by specific structural motifs that recur in proteins of different fold. Moreover these motifs behave as modules and are found in different combinations across fold space. Our method predicts binding sites for each nucleotide fragment by comparing a query protein with a database of templates extracted from proteins of known structure. Whenever a similarity is found the fragment bound by the template is transferred on the query protein, thus identifying a putative binding site. Predictions falling inside the surface of the protein are discarded, and the remaining ones are scored using clustering and conservation. The method is able to rank as first a correct prediction in the 48%, 48% and 68% of the analyzed proteins for the nucleobase, carbohydrate and phosphate respectively, while considering the first five predictions the performances change to 71%, 65% and 86% respectively. Furthermore we attempted to reconstruct the full structure of the binding site, starting from the predicted positions of the fragments. We calculated that in the 59% of the analyzed proteins the method ranks as first a reconstructed binding site or a part of it. Finally we tested the reliability of our method in a real world case in which it has to predict nucleotide-binding sites in unbound proteins. We analyzed proteins whose structure has been solved with and without the nucleotide and observed only little variations in the method performance.

Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors

MOTIVATION

Nucleotides are multifunctional molecules that are essential for numerous biological processes. They serve as sources for chemical energy, participate in the cellular signaling and they are involved in the enzymatic reactions. The knowledge of the nucleotide-protein interactions helps with annotation of protein functions and finds applications in drug design.

RESULTS

We propose a novel ensemble of accurate high-throughput predictors of binding residues from the protein sequence for ATP, ADP, AMP, GTP and GDP. Empirical tests show that our NsitePred method significantly outperforms existing predictors and approaches based on sequence alignment and residue conservation scoring. The NsitePred accurately finds more binding residues and binding sites and it performs particularly well for the sites with residues that are clustered close together in the sequence. The high predictive quality stems from the usage of novel, comprehensive and custom-designed inputs that utilize information extracted from the sequence, evolutionary profiles, several sequence-predicted structural descriptors and sequence alignment. Analysis of the predictive model reveals several sequence-derived hallmarks of nucleotide-binding residues; they are usually conserved and flanked by less conserved residues, and they are associated with certain arrangements of secondary structures and amino acid pairs in the specific neighboring positions in the sequence.

AVAILABILITY

http://biomine.ece.ualberta.ca/nSITEpred/

CONTACT

lkurgan@ece.ualberta.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluating how discrete water molecules affect protein-DNA π-π and π(+)-π stacking and T-shaped interactions: the case of histidine-adenine dimers

Changes in the magnitude of (M06-2X/6-31+G(d,p)) π-π stacking and T-shaped (nucleobase-edge and amino acid-edge) interactions between (neutral or protonated) histidine (His) and adenine (A) dimers upon microsolvation with up to four discrete water molecules were determined. A variety of histidine-water interactions were considered including conventional (N-H···O, N···H-O, C-H···O) hydrogen bonding and nonconventional (X-H···π (neutral His) or lone-pair···π (protonated His)) contacts. Overall, the effects of discrete His-H(2)O interactions on the neutral histidine-adenine π-π contacts are negligible (<3 kJ mol(-1) or 15%) regardless of the type of water binding, the number of water molecules bound, or the His-A dimer (stacked or (amino acid- or nucleobase-edge) T-shaped) configuration. This suggests that previously reported gas-phase binding strengths for a variety of neutral amino acid-nucleobase dimers are likely relevant for a wide variety of (microsolvated) environments. In contrast, the presence of water decreases the histidine-adenine π(+)-π interaction by up to 15 kJ mol(-1) (or 30%) for all water binding modes and orientations, as well as different stacked and T-shaped His(+)-A dimers. Regardless of the larger effect of discrete histidine-water interactions on the magnitude of the π(+)-π compared with π-π interactions, the π(+)-π binding strengths remain substantially larger than the corresponding π-π contacts. These findings emphasize the distinct nature of π(+)-π and π-π interactions and suggest that π(+)-π contacts can provide significant stabilization in biological systems relative to π-π contacts under many different environmental conditions.

Identification and modelling of a PPM protein phosphatase fold in the Legionella pneumophila deAMPylase SidD

The intracellular parasitic bacterium Legionella pneumophila subverts host vesicle transport through reversible AMPylation of Rab1. The effector enzyme for deAMPylation is SidD. Here a complete PPM protein phosphatase fold catalytic domain in SidD is identified and modelled. The SidD model reveals insertions and deletions near the metal ion containing catalytic site which presumably determine its novel activity. It also sheds light on possible substrate binding residues and highlights the lack of an obvious group to act as general acid during reaction. Assignment of a PPM fold to SidD offers an important pointer towards identification of further deAMPylases.

Aromatic rings in chemical and biological recognition: energetics and structures

This review describes a multidimensional treatment of molecular recognition phenomena involving aromatic rings in chemical and biological systems. It summarizes new results reported since the appearance of an earlier review in 2003 in host-guest chemistry, biological affinity assays and biostructural analysis, data base mining in the Cambridge Structural Database (CSD) and the Protein Data Bank (PDB), and advanced computational studies. Topics addressed are arene-arene, perfluoroarene-arene, S⋅⋅⋅aromatic, cation-π, and anion-π interactions, as well as hydrogen bonding to π systems. The generated knowledge benefits, in particular, structure-based hit-to-lead development and lead optimization both in the pharmaceutical and in the crop protection industry. It equally facilitates the development of new advanced materials and supramolecular systems, and should inspire further utilization of interactions with aromatic rings to control the stereochemical outcome of synthetic transformations.

ATP changes the fluorescence lifetime of cyan fluorescent protein via an interaction with His148

Recently, we described that ATP induces changes in YFP/CFP fluorescence intensities of Fluorescence Resonance Energy Transfer (FRET) sensors based on CFP-YFP. To get insight into this phenomenon, we employed fluorescence lifetime spectroscopy to analyze the influence of ATP on these fluorescent proteins in more detail. Using different donor and acceptor pairs we found that ATP only affected the CFP-YFP based versions. Subsequent analysis of purified monomers of the used proteins showed that ATP has a direct effect on the fluorescence lifetime properties of CFP. Since the fluorescence lifetime analysis of CFP is rather complicated by the existence of different lifetimes, we tested a variant of CFP, i.e. Cerulean, as a monomer and in our FRET constructs. Surprisingly, this CFP variant shows no ATP concentration dependent changes in the fluorescence lifetime. The most important difference between CFP and Cerulean is a histidine residue at position 148. Indeed, changing this histidine in CFP into an aspartic acid results in identical fluorescence properties as observed for the Cerulean fluorescent based FRET sensor. We therefore conclude that the changes in fluorescence lifetime of CFP are affected specifically by possible electrostatic interactions of the negative charge of ATP with the positively charged histidine at position 148. Clearly, further physicochemical characterization is needed to explain the sensitivity of CFP fluorescence properties to changes in environmental (i.e. ATP concentrations) conditions.