Four distances between pairs of amino acids provide a precise description of their interaction

Insights into in vitro and in silico studies of α-glucosidase inhibitors isolated from the leaves of Grewia optiva (Malvaceae)

α-Glucosidase plays a critical role in glucose metabolism by breaking down complex carbohydrates into simpler sugars for intestinal absorption. Due to the side effects of current α-glucosidase inhibitors, there is increasing interest in exploring alternative therapeutic options. Inspired by the traditional uses of Grewia optiva J.R.Drumm. ex Burret (Malvaceae family) as an anti-diabetic herb, we isolated gnaphaffine A (1), a cyclic glycosylated homolignan, together with kaempferol derivatives (trans-tiliroside 2, cis-tiliroside 3, and astragalin 4) from the ethyl acetate fraction. In vitro antioxidant assays revealed that 1 exhibited anti-DPPH• and anti-ABTS+• activity (IC50 of 39.42 and 52.84 μg/mL, respectively), comparable to ascorbic acid (IC50 of 43.34 and 47.56 μg/mL, respectively). Moreover, 1 demonstrated a seven-fold stronger inhibition of α-glucosidase activity than acarbose (IC50 of 8.2 and 57.8 μg/mL, respectively). Importantly, 1 was non-toxic to AC16 normal cardiomyocyte cell lines. Computational analyses identified two key factors contributing to the α-glucosidase inhibitory activity of 1: (a) hydrogen bonding interactions with catalytic residues (E277 and D352) and (b) a calculated ∆Gbind of -51.20 kcal/mol. Furthermore, 3 showed the most favorable in silico binding profile, with the highest ∆Gbind (-55.89 kcal/mol) and higher hydrogen bond occupancy compared to 1 and 2. These findings suggest that 1 and 3 may serve as promising lead compounds for the development of new α-glucosidase drugs.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

Customized chitooligosaccharide production-controlling their length via engineering of rhizobial chitin synthases and the choice of expression system

Chitooligosaccharides (COS) have attracted attention from industry and academia in various fields due to their diverse bioactivities. However, their conventional chemical production is environmentally unfriendly and in addition, defined and pure molecules are both scarce and expensive. A promising alternative is the in vivo synthesis of desired COS in microbial platforms with specific chitin synthases enabling a more sustainable production. Hence, we examined the whole cell factory approach with two well-established microorganisms-Escherichia coli and Corynebacterium glutamicum-to produce defined COS with the chitin synthase NodC from Rhizobium sp. GRH2. Moreover, based on an in silico model of the synthase, two amino acids potentially relevant for COS length were identified and mutated to direct the production. Experimental validation showed the influence of the expression system, the mutations, and their combination on COS length, steering the production from originally pentamers towards tetramers or hexamers, the latter virtually pure. Possible explanations are given by molecular dynamics simulations. These findings pave the way for a better understanding of chitin synthases, thus allowing a more targeted production of defined COS. This will, in turn, at first allow better research of COS' bioactivities, and subsequently enable sustainable large-scale production of oligomers.

An efficient method to predict protein thermostability in alanine mutation

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔG_exp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors

Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target.

PremPS: Predicting the impact of missense mutations on protein stability

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

In silico prediction of the functional and structural consequences of the non-synonymous single nucleotide polymorphism A122V in bovine CXC chemokine receptor type 1

Abstract The current study aimed to assess whether the A122V causal polymorphism promotes alterations in the functional and structural proprieties of the CXC chemokine receptor type 1 protein (CXCR1) of cattle Bos taurus by in silico analyses. Two amino acid sequences of bovine CXCR1 was selected from database UniProtKB/Swiss-Prot: a) non-polymorphic sequence (A7KWG0) with alanine (A) at position 122, and b) polymorphic sequence harboring the A122V polymorphism, substituting alanine by valine (V) at same position. CXCR1 sequences were submitted as input to different Bioinformatics’ tools to examine the effects of this polymorphism on functional and structural stabilities, to predict eventual alterations in the 3-D structural modeling, and to estimate the quality and accuracy of the predictive models. The A122V polymorphism exerted tolerable and non-deleterious effects on the polymorphic CXCR1, and the predictive structural model for polymorphic CXCR1 revealed an alpha helix spatial structure typical of a receptor transmembrane polypeptide. Although higher variations in the distances between pairs of amino acid residues at target-positions are detected in the polymorphic CXCR1 protein, more than 97% of the amino acid residues in both models were located in favored and allowed conformational regions in Ramachandran plots. Evidences has supported that the A122V polymorphism in the CXCR1 protein is associated with increased clinical mastitis incidence in dairy cows. Thus, the findings described herein prove that the replacement of the alanine by valine amino acids provokes local conformational changes in the A122V-harboring CXCR1 protein, which could directly affect its post-translational folding mechanisms and biological functionality.

Domain-mediated interactions for protein subfamily identification

Within a protein family, proteins with the same domain often exhibit different cellular functions, despite the shared evolutionary history and molecular function of the domain. We hypothesized that domain-mediated interactions (DMIs) may categorize a protein family into subfamilies because the diversified functions of a single domain often depend on interacting partners of domains. Here we systematically identified DMI subfamilies, in which proteins share domains with DMI partners, as well as with various functional and physical interaction networks in individual species. In humans, DMI subfamily members are associated with similar diseases, including cancers, and are frequently co-associated with the same diseases. DMI information relates to the functional and evolutionary subdivisions of human kinases. In yeast, DMI subfamilies contain proteins with similar phenotypic outcomes from specific chemical treatments. Therefore, the systematic investigation here provides insights into the diverse functions of subfamilies derived from a protein family with a link-centric approach and suggests a useful resource for annotating the functions and phenotypic outcomes of proteins.

DistAA: Database of amino acid distances in proteins and web application for statistical review of distances

Three-dimensional structure of a protein chain is determined by its amino acid interactions. One approach to the analysis of amino acid interactions refers to geometric distances of amino acid pairs in polypeptide chains. For a detailed analysis of the amino acid distances, the database with three types of amino acid distances in a set of chains was created. Web application Distances of Amino Acids has also been developed to enable scientists to explore interactions of amino acids with different properties based on distances stored in the database. Web application calculates and displays descriptive statistics and graphs of amino acid pair distances with selected properties, such as geometric distance threshold, corresponding SCOP class of proteins and secondary structure types. In addition to the analysis of pre-calculated distances stored in the database, the amino acid distances of a single protein with the specified PDB identifier can also be analyzed. The web application is available at http://andromeda.matf.bg.ac.rs/aadis_dynamic/.

Functional specialization of UDP-glycosyltransferase 73P12 in licorice to produce a sweet triterpenoid saponin, glycyrrhizin

Glycyrrhizin, a sweet triterpenoid saponin found in the roots and stolons of Glycyrrhiza species (licorice), is an important active ingredient in traditional herbal medicine. We previously identified two cytochrome P450 monooxygenases, CYP88D6 and CYP72A154, that produce an aglycone of glycyrrhizin, glycyrrhetinic acid, in Glycyrrhiza uralensis. The sugar moiety of glycyrrhizin, which is composed of two glucuronic acids, makes it sweet and reduces its side-effects. Here, we report that UDP-glycosyltransferase (UGT) 73P12 catalyzes the second glucuronosylation as the final step of glycyrrhizin biosynthesis in G. uralensis; the UGT73P12 produced glycyrrhizin by transferring a glucuronosyl moiety of UDP-glucuronic acid to glycyrrhetinic acid 3-O-monoglucuronide. We also obtained a natural variant of UGT73P12 from a glycyrrhizin-deficient (83-555) strain of G. uralensis. The natural variant showed loss of specificity for UDP-glucuronic acid and resulted in the production of an alternative saponin, glucoglycyrrhizin. These results are consistent with the chemical phenotype of the 83-555 strain, and suggest the contribution of UGT73P12 to glycyrrhizin biosynthesis in planta. Furthermore, we identified Arg32 as the essential residue of UGT73P12 that provides high specificity for UDP-glucuronic acid. These results strongly suggest the existence of an electrostatic interaction between the positively charged Arg32 and the negatively charged carboxy group of UDP-glucuronic acid. The functional arginine residue and resultant specificity for UDP-glucuronic acid are unique to UGT73P12 in the UGT73P subfamily. Our findings demonstrate the functional specialization of UGT73P12 for glycyrrhizin biosynthesis during divergent evolution, and provide mechanistic insights into UDP-sugar selectivity for the rational engineering of sweet triterpenoid saponins.

Insight into the binding of a synthetic nitro-flavone derivative with human poly (ADP-ribose) polymerase 1

Flavones are important bioactive compounds, many of which are effective in cancer therapy for their ability to target enzymes related to DNA repair and cell proliferation. In this report, the interaction of a synthetic nitroflavone, 2,4-nitrophenylchromen-4-one (4NCO) with human poly (ADP-ribose) polymerase 1 (hPARP1) was investigated to explore its inhibitory action. Its interaction with hPARP1 was compared with that of other inhibitors through molecular docking studies. Further insight into the 4NCO-hPARP1 interaction was obtained from competitive docking and molecular dynamic simulation studies. In silico mutagenesis studies and per-residue interaction energy calculations were carried out. Quantitative Structure Activity Relationship analysis was also performed to calculate its predictive percent inhibitory activity. Our results indicated that 4NCO exhibited competitive mode of binding to hPARP1. It formed a stable interaction with the protein thereby hindering any further molecular interaction to render it inactive with a predictive inhibition of 96%. It also had good ADMET properties and showed best Autodock binding free energy values compared to other known inhibitors. 4NCO showed good hPARP1 inhibitory properties with higher bioavailability and lower probability of getting effluxed. Development of inhibitors against hPARP1 is important for cell proliferative disorders, where 4NCO can be predicted as a potential new drug.

Predicting receptor functionality of signaling lymphocyte activation molecule for measles virus hemagglutinin by docking simulation

Predicting susceptibility of various species to a virus assists assessment of risk of interspecies transmission. Evaluation of receptor functionality may be useful in screening for susceptibility. In this study, docking simulation was conducted for measles virus hemagglutinin (MV‐H) and immunoglobulin‐like variable domain of signaling lymphocyte activation molecule (SLAM‐V). It was observed that the docking scores for MV‐H and SLAM‐V correlated with the activity of SLAM as an MV receptor. These results suggest that the receptor functionality may be predicted from the docking scores of virion surface proteins and cellular receptor molecules.

Computational tools help improve protein stability but with a solubility tradeoff

Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.

Protein-structure-guided discovery of functional mutations across 19 cancer types

Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation-drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Identification of Amino Acid Residues in Fibroblast Growth Factor 14 (FGF14) Required for Structure-Function Interactions with Voltage-gated Sodium Channel Nav1.6

The voltage-gated Na(+) (Nav) channel provides the basis for electrical excitability in the brain. This channel is regulated by a number of accessory proteins including fibroblast growth factor 14 (FGF14), a member of the intracellular FGF family. In addition to forming homodimers, FGF14 binds directly to the Nav1.6 channel C-tail, regulating channel gating and expression, properties that are required for intrinsic excitability in neurons. Seeking amino acid residues with unique roles at the protein-protein interaction interface (PPI) of FGF14·Nav1.6, we engineered model-guided mutations of FGF14 and validated their impact on the FGF14·Nav1.6 complex and the FGF14:FGF14 dimer formation using a luciferase assay. Divergence was found in the β-9 sheet of FGF14 where an alanine (Ala) mutation of Val-160 impaired binding to Nav1.6 but had no effect on FGF14:FGF14 dimer formation. Additional analysis revealed also a key role of residues Lys-74/Ile-76 at the N-terminal of FGF14 in the FGF14·Nav1.6 complex and FGF14:FGF14 dimer formation. Using whole-cell patch clamp electrophysiology, we demonstrated that either the FGF14(V160A) or the FGF14(K74A/I76A) mutation was sufficient to abolish the FGF14-dependent regulation of peak transient Na(+) currents and the voltage-dependent activation and steady-state inactivation of Nav1.6; but only V160A with a concomitant alanine mutation at Tyr-158 could impede FGF14-dependent modulation of the channel fast inactivation. Intrinsic fluorescence spectroscopy of purified proteins confirmed a stronger binding reduction of FGF14(V160A) to the Nav1.6 C-tail compared with FGF14(K74A/I76A) Altogether these studies indicate that the β-9 sheet and the N terminus of FGF14 are well positioned targets for drug development of PPI-based allosteric modulators of Nav channels.

Specificity rendering 'hot-spots' for aurora kinase inhibitor design: the role of non-covalent interactions and conformational transitions

The present study examines the conformational transitions occurring among the major structural motifs of Aurora kinase (AK) concomitant with the DFG-flip and deciphers the role of non-covalent interactions in rendering specificity. Multiple sequence alignment, docking and structural analysis of a repertoire of 56 crystal structures of AK from Protein Data Bank (PDB) has been carried out. The crystal structures were systematically categorized based on the conformational disposition of the DFG-loop [in (DI) 42, out (DO) 5 and out-up (DOU) 9], G-loop [extended (GE) 53 and folded (GF) 3] and αC-helix [in (CI) 42 and out (CO) 14]. The overlapping subsets on categorization show the inter-dependency among structural motifs. Therefore, the four distinct possibilities a) 2W1C (DI, CI, GE) b) 3E5A (DI, CI, GF) c) 3DJ6 (DI, CO, GF) d) 3UNZ (DOU, CO, GF) along with their co-crystals and apo-forms were subjected to molecular dynamics simulations of 40 ns each to evaluate the variations of individual residues and their impact on forming interactions. The non-covalent interactions formed by the 157 AK co-crystals with different regions of the binding site were initially studied with the docked complexes and structure interaction fingerprints. The frequency of the most prominent interactions was gauged in the AK inhibitors from PDB and the four representative conformations during 40 ns. Based on this study, seven major non-covalent interactions and their complementary sites in AK capable of rendering specificity have been prioritized for the design of different classes of inhibitors.

Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics

DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules.

PyInteraph: a framework for the analysis of interaction networks in structural ensembles of proteins

In the last years, a growing interest has been gathering around the ability of Molecular Dynamics (MD) to provide insight into the paths of long-range structural communication in biomolecules. The knowledge of the mechanisms related to structural communication helps in the rationalization in atomistic details of the effects induced by mutations, ligand binding, and the intrinsic dynamics of proteins. We here present PyInteraph, a tool for the analysis of structural ensembles inspired by graph theory. PyInteraph is a software suite designed to analyze MD and structural ensembles with attention to binary interactions between residues, such as hydrogen bonds, salt bridges, and hydrophobic interactions. PyInteraph also allows the different classes of intra- and intermolecular interactions to be represented, combined or alone, in the form of interaction graphs, along with performing network analysis on the resulting interaction graphs. The program also integrates the network description with a knowledge-based force field to estimate the interaction energies between side chains in the protein. It can be used alone or together with the recently developed xPyder PyMOL plugin through an xPyder-compatible format. The software capabilities and associated protocols are here illustrated by biologically relevant cases of study. The program is available free of charge as Open Source software via the GPL v3 license at http://linux.btbs.unimib.it/pyinteraph/.

Computational and theoretical methods for protein folding

A computational approach is essential whenever the complexity of the process under study is such that direct theoretical or experimental approaches are not viable. This is the case for protein folding, for which a significant amount of data are being collected. This paper reports on the essential role of in silico methods and the unprecedented interplay of computational and theoretical approaches, which is a defining point of the interdisciplinary investigations of the protein folding process. Besides giving an overview of the available computational methods and tools, we argue that computation plays not merely an ancillary role but has a more constructive function in that computational work may precede theory and experiments. More precisely, computation can provide the primary conceptual clues to inspire subsequent theoretical and experimental work even in a case where no preexisting evidence or theoretical frameworks are available. This is cogently manifested in the application of machine learning methods to come to grips with the folding dynamics. These close relationships suggested complementing the review of computational methods within the appropriate theoretical context to provide a self-contained outlook of the basic concepts that have converged into a unified description of folding and have grown in a synergic relationship with their computational counterpart. Finally, the advantages and limitations of current computational methodologies are discussed to show how the smart analysis of large amounts of data and the development of more effective algorithms can improve our understanding of protein folding.

The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.

A conserved tryptophan at the membrane-water interface acts as a gatekeeper for Kir6.2/SUR1 channels and causes neonatal diabetes when mutated

We identified a novel heterozygous mutation, W68R, in the Kir6.2 subunit of the ATP-sensitive potassium (KATP) channel, in a patient with transient neonatal diabetes. This tryptophan is absolutely conserved in mammalian Kir channels. The functional effects of mutations at residue 68 of Kir6.2 were studied by heterologous expression in Xenopus oocytes, and by homology modelling. We found the Kir6.2-W68R mutation causes a small reduction in ATP inhibition in the heterozygous state and an increase in the whole-cell KATP current. This can explain the clinical phenotype of the patient. The effect of the mutation was not charge or size dependent, the order of potency for ATP inhibition being W Collapse

Key Words Collapse

MESH Headings

• ATP-Binding Cassette Transporters/genetics
• ATP-Binding Cassette Transporters/metabolism
• Amino Acid Sequence
• Animals
• Cell Membrane/genetics
• Cell Membrane/metabolism
• Child
• Conserved Sequence/genetics
• Diabetes Mellitus/genetics
• Diabetes Mellitus/metabolism
• Female
• Genetic Carrier Screening
• Humans
• Infant, Newborn
• Infant, Newborn, Diseases/genetics
• Infant, Newborn, Diseases/metabolism
• Molecular Sequence Data
• Mutation/genetics
• Oocytes/metabolism
• Phenotype
• Potassium Channels, Inwardly Rectifying/genetics
• Potassium Channels, Inwardly Rectifying/metabolism
• Rats
• Receptors, Drug/genetics
• Receptors, Drug/metabolism
• Sulfonylurea Receptors
• Tryptophan/genetics
• Xenopus laevis

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Grants

• 084655 Wellcome Trust
• 089795 Wellcome Trust
• 076436 Wellcome Trust

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Affiliation(s)

Roope Männikkö Henry Wellcome Centre for Gene Function, Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3PT, UK
Phillip J Stansfeld
Alexandra S Ashcroft
Andrew T Hattersley
Mark S P Sansom
Sian Ellard
Frances M Ashcroft

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Collaborators Collapse

NCACO-score: an effective main-chain dependent scoring function for structure modeling

Background

Development of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.

Results

Based on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.

Conclusions

Although NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.

From isotropic to anisotropic side chain representations: comparison of three models for residue contact estimation

The criterion to determine residue contact is a fundamental problem in deriving knowledge-based mean-force potential energy calculations for protein structures. A frequently used criterion is to require the side chain center-to-center distance or the -to- atom distance to be within a pre-determined cutoff distance. However, the spatially anisotropic nature of the side chain determines that it is challenging to identify the contact pairs. This study compares three side chain contact models: the Atom Distance criteria (ADC) model, the Isotropic Sphere Side chain (ISS) model and the Anisotropic Ellipsoid Side chain (AES) model using 424 high resolution protein structures in the Protein Data Bank. The results indicate that the ADC model is the most accurate and ISS is the worst. The AES model eliminates about 95% of the incorrectly counted contact-pairs in the ISS model. Algorithm analysis shows that AES model is the most computational intensive while ADC model has moderate computational cost. We derived a dataset of the mis-estimated contact pairs by AES model. The most misjudged pairs are Arg-Glu, Arg-Asp and Arg-Tyr. Such a dataset can be useful for developing the improved AES model by incorporating the pair-specific information for the cutoff distance.

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions

Background

Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design.

Results

Here we introduce a method for accurate protein structure modelling and evaluation based on a novel 4-distance description of residue-residue interaction geometry. Statistical 4-distance preferences were extracted from high-resolution protein structures and were used as a basis for a knowledge-based potential, called Hunter. We demonstrate that 4-distance description of side chain interactions can be used reliably to discriminate the native structure from a set of decoys. Hunter ranked the native structure as the top one in 217 out of 220 high-resolution decoy sets, in 25 out of 28 "Decoys 'R' Us" decoy sets and in 24 out of 27 high-resolution CASP7/8 decoy sets. The same concept was applied to side chain modelling in protein structures. On a set of very high-resolution protein structures the average RMSD was 1.47 Å for all residues and 0.73 Å for buried residues, which is in the range of attainable accuracy for a model. Finally, we show that Hunter performs as good or better than other top methods in homology modelling based on results from the CASP7 experiment. The supporting web site http://bioinfo.weizmann.ac.il/hunter/ was developed to enable the use of Hunter and for visualization and interactive exploration of 4-distance distributions.

Conclusions

Our results suggest that Hunter can be used as a tool for evaluation and for accurate modelling of residue-residue interactions in protein structures. The same methodology is applicable to other areas involving high-resolution modelling of biomolecules.