StoneHinge: hinge prediction by network analysis of individual protein structures

Exploring Bacillus species xylanases for industrial applications: screening via thermostability and reaction modelling

CONTEXT

Xylanases derived from Bacillus species hold significant importance in various large-scale production sectors, with increasing demand driven by biofuel production. However, despite their potential, the extreme environmental conditions often encountered in production settings have led to their underutilisation. To address this issue and enhance their efficacy under adverse conditions, we conducted a theoretical investigation on a group of five Bacillus species xylanases belonging to the glycoside hydrolase GH11 family. Bacillus sp. NCL 87-6-10 (sp_NCL 87-6-10) emerged as a potent candidate among the selected biocatalysts; this Bacillus strain exhibited high thermal stability and achieved a transition state with minimal energy requirements, thereby accelerating the biocatalytic reaction process. Our approach aims to provide support for experimentalists in the industrial sector, encouraging them to employ structural-based reaction modelling scrutinisation to predict the ability of targeted xylanases.

METHODS

Utilising crystal structure data available in the Carbohydrate-Active enzymes database, we aimed to analyse their structural capabilities in terms of thermal-stability and activity. Our investigation into identifying the most prominent Bacillus species xylanases unfolds with the help of the semi-empirical quantum mechanics MOPAC method integrated with the DRIVER program is used in calculations of reaction pathways to understand the activation energy. Additionally, we scrutinised the selected xylanases using various analyses, including constrained network analyses, intermolecular interactions of the enzyme-substrate complex and molecular orbital assessments calculated using the AM1 method with the MO-G model (MO-G AM1) to validate their reactivity.

Insight on the mechanism of hexameric Pseudin-4 against bacterial membrane-mimetic environment

As an alternative to antibiotics, Antimicrobial Peptides (AMPs) possess unique properties including cationic, amphipathic and their abundance in nature, but the exact characteristics of AMPs against bacterial membranes are still undetermined. To estimate the structural stability and functional activity of AMPs, the Pseudin AMPs (Pse-1, Pse-2, Pse-3, and Pse-4) from Hylid frog species, Pseudis paradoxa, an abundantly discovered source for AMPs were examined. We studied the intra-peptide interactions and thermal denaturation stability of peptides, as well as the geometrical parameters and secondary structure profiles of their conformational trajectories. On this basis, the peptides were screened out and the highly stable peptide, Pse-4 was subjected to membrane simulation in order to observe the changes in membrane curvature formed by Pse-4 insertion. Monomeric Pse-4 was found to initiate the membrane disruption; however, a stable multimeric form of Pse-4 might be competent to counterbalance the helix-coil transition and to resist the hydrophobic membrane environment. Eventually, hexameric Pse-4 on membrane simulation exhibited the hydrogen bond formation with E. coli bacterial membrane and thereby, leading to the formation of membrane spanning pore that allowed the entry of excess water molecules into the membrane shell, thus causing membrane deformation. Our report points out the mechanism of Pse-4 peptide against the bacterial membrane for the first time. Relatively, Pse-4 works on the barrel stave model against E. coli bacterial membrane; hence it might act as a good therapeutic scaffold in the treatment of multi-drug resistant bacterial strains.

Characterizing and Predicting Protein Hinges for Mechanistic Insight

The functioning of proteins requires highly specific dynamics, which depend critically on the details of how amino acids are packed. Hinge motions are the most common type of large motion, typified by the opening and closing of enzymes around their substrates. The packing and geometries of residues are characterized here by graph theory. This characterization is sufficient to enable reliable hinge predictions from a single static structure, and notably, this can be from either the open or the closed form of a structure. This new method to identify hinges within protein structures is called PACKMAN. The predicted hinges are validated by using permutation tests on B-factors. Hinge prediction results are compared against lists of manually curated hinge residues, and the results suggest that PACKMAN is robust enough to reproduce the known conformational changes and is able to predict hinge regions equally well from either the open or the closed forms of a protein. A group of 167 protein pairs with open and closed structures has been investigated Examples are shown for several additional proteins, including Zika virus nonstructured (NS) proteins where there are 6 hinge regions in the NS5 protein, 5 hinge regions in the NS2B bound in the NS3 protease complex and 5 hinges in the NS3- helicase protein. Results obtained from this method can be important for generating conformational ensembles of protein targets for drug design. PACKMAN is freely accessible at (https://PACKMAN.bb.iastate.edu/).

Turncoat Polypeptides: We Adapt to Our Environment

A common interpretation of Anfinsen's hypothesis states that one amino acid sequence should fold into a single, native, ordered state, or a highly similar set thereof, coinciding with the global minimum in the folding-energy landscape, which, in turn, is responsible for the function of the protein. However, this classical view is challenged by many proteins and peptide sequences, which can adopt exchangeable, significantly dissimilar conformations that even fulfill different biological roles. The similarities and differences of concepts related to these proteins, mainly chameleon sequences, metamorphic proteins, and switch peptides, which are all denoted herein "turncoat" polypeptides, are reviewed. As well as adding a twist to the conventional view of protein folding, the lack of structural definition adds clear versatility to the activity of proteins and can be used as a tool for protein design and further application in biotechnology and biomedicine.

ART-RRT: As-Rigid-As-Possible search for protein conformational transition paths

The possible functions of a protein are strongly related to its structural rearrangements in the presence of other molecules or environmental changes. Hence, the evaluation of transition paths of proteins, which encodes conformational changes between stable states, is important since it may reveal the underlying mechanisms of the biochemical processes related to these motions. During the last few decades, different geometry-based methods have been proposed to predict such transition paths. However, in the cases where the solution requires complex motions, these methods, which typically constrain only locally the molecular structures, could produce physically irrelevant solutions involving self-intersection. Recently, we have proposed ART-RRT, an efficient method for finding ligand-unbinding pathways. It relies on the exploration of energy valleys in low-dimensional spaces, taking advantage of some mechanisms inspired from computer graphics to ensure the consistency of molecular structures. This article extends ART-RRT to the problem of finding probable conformational transition between two stable states for proteins. It relies on a bidirectional exploration rooted on the two end states and introduces an original strategy to attempt connections between the explored regions. The resulting method is able to produce at low computational cost biologically realistic paths free from self-intersection. These paths can serve as valuable input to other advanced methods for the study of proteins. A better understanding of conformational changes of proteins is important since it may reveal the underlying mechanisms of the biochemical processes related to such motions. Recently, the ART-RRT method has been introduced for finding ligand-unbinding pathways. This article presents an adaptation of the method for finding probable conformational transition between two stable states of a protein. The method is not only computationally cost-effective but also able to produce biologically realistic paths which are free from self-intersection.

Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM)

Gaussian network model (GNM) and anisotropic network model (ANM) are some of the most popular methods for the study of protein flexibility and related functions. In this work, we propose generalized GNM (gGNM) and ANM methods and show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter, which is a special case of a wide class of correlation functions underpinning the linear scaling flexibility-rigidity index (FRI) method. Based on the mathematical structure of correlation functions, we propose a unified framework to construct generalized Kirchhoff matrices whose matrix inverse leads to gGNMs, whereas, the direct inverse of its diagonal elements gives rise to FRI method. With this connection, we further introduce two multiscale elastic network models, namely, multiscale GNM (mGNM) and multiscale ANM (mANM), which are able to incorporate different scales into the generalized Kirchhoff matrices or generalized Hessian matrices. We validate our new multiscale methods with extensive numerical experiments. We illustrate that gGNMs outperform the original GNM method in the B-factor prediction of a set of 364 proteins. We demonstrate that for a given correlation function, FRI and gGNM methods provide essentially identical B-factor predictions when the scale value in the correlation function is sufficiently large. More importantly, we reveal intrinsic multiscale behavior in protein structures. The proposed mGNM and mANM are able to capture this multiscale behavior and thus give rise to a significant improvement of more than 11% in B-factor predictions over the original GNM and ANM methods. We further demonstrate the benefits of our mGNM through the B-factor predictions of many proteins that fail the original GNM method. We show that the proposed mGNM can also be used to analyze protein domain separations. Finally, we showcase the ability of our mANM for the analysis of protein collective motions.

In silico approach to explore the disruption in the molecular mechanism of human hyaluronidase 1 by mutant E268K that directs Natowicz syndrome

Natowicz syndrome (mucopolysaccharidoses type 9) is a lysosomal storage disorder caused by deficient or defective human hyaluronidase 1. The disorder is not well studied at the molecular level. Therefore, a new in silico approach was proposed to study the molecular basis on which one clinically observed mutation, Glu268Lys, results in a defective enzyme. The native and mutant structures were subjected to comparative analyses using a conformational sampling approach for geometrical variables viz, RMSF, RMSD, and Ramachandran plot. In addition, the strength of a Cys207-Cys221 disulfide bond and electrostatic interaction between Arg265 and Asp206 were studied, as they are known to be involved in the catalytic activity of the enzyme. Native and mutant E268K showed statistically significant variations with p < 0.05 in RMSD, Ramachandran plot, strengths of disulfide bond, and electrostatic interactions. Further, single model analysis showed variations between native and mutant structures in terms of intra-protein interactions, hydrogen bond dilution, secondary structure, and dihedral angles. Docking analysis predicted the mutant to have a less favorable substrate binding energy compared to the native protein. Additionally, steered MD analysis indicated that the substrate should have more affinity to the native than mutant enzymes. The observed changes theoretically explain the less favorable binding energy of substrate towards mutant E268K, thereby providing a structural basis for its reduced catalytic activity. Hence, our study provides a basis for understanding the disruption in the molecular mechanism of human hyaluronidase 1 by mutation E268K, which may prove useful for the development of synthetic chaperones as a treatment option for Natowicz syndrome.

Flexibility-rigidity index for protein-nucleic acid flexibility and fluctuation analysis

Protein-nucleic acid complexes are important for many cellular processes including the most essential functions such as transcription and translation. For many protein-nucleic acid complexes, flexibility of both macromolecules has been shown to be critical for specificity and/or function. The flexibility-rigidity index (FRI) has been proposed as an accurate and efficient approach for protein flexibility analysis. In this article, we introduce FRI for the flexibility analysis of protein-nucleic acid complexes. We demonstrate that a multiscale strategy, which incorporates multiple kernels to capture various length scales in biomolecular collective motions, is able to significantly improve the state of art in the flexibility analysis of protein-nucleic acid complexes. We take the advantage of the high accuracy and O(N) computational complexity of our multiscale FRI method to investigate the flexibility of ribosomal subunits, which are difficult to analyze by alternative approaches. An anisotropic FRI approach, which involves localized Hessian matrices, is utilized to study the translocation dynamics in an RNA polymerase.

Exploration of Structural and Functional Variations Owing to Point Mutations in α-NAGA

Schindler disease is a lysosomal storage disorder caused due to deficiency or defective activity of alpha-N-acetylgalactosaminidase (α-NAGA). Mutations in gene encoding α-NAGA cause wide range of diseases, characterized with mild to severe clinical features. Molecular effects of these mutations are yet to be explored in detail. Therefore, this study was focused on four missense mutations of α-NAGA namely, S160C, E325K, R329Q and R329W. Native and mutant structures of α-NAGA were analysed to determine geometrical deviations such as the contours of root mean square deviation, root mean square fluctuation, percentage of residues in allowed regions of Ramachandran plot and solvent accessible surface area, using conformational sampling technique. Additionally, global energy-minimized structures of native and mutants were further analysed to compute their intra-molecular interactions, hydrogen bond dilution and distribution of secondary structure. In addition, docking studies were also performed to determine variations in binding energies between native and mutants. The deleterious effects of mutants were evident due to variations in their active site residues pertaining to spatial conformation and flexibility, comparatively. Hence, variations exhibited by mutants, namely S160C, E325K, R329Q and R329W to that of native, consequently, lead to the detrimental effects causing Schindler disease. This study computationally explains the underlying reasons for the pathogenesis of the disease, thereby aiding future researchers in drug development and disease management.

Geometric Simulation Approach for Grading and Assessing the Thermostability of CALBs

Candida antarctica lipase B (CALB) is a known stable and highly active enzyme used widely in biodiesel synthesis. In this work, the stability of native (4K6G) and mutant (4K5Q) CALB was studied through various structural parameters using conformational sampling approach. The contours of polar surface area and surface area of mutant CALB were 11357.67 Å(2) and 30007.4 Å(2), respectively, showing an enhanced stability compared to native CALB with a statistically significant P value of < 0.0001. Moreover, simulated thermal denaturation of CALB, a process involving dilution of hydrogen bond, significantly shielded against different intervals of energy application in mutant CALB revealing its augmentation of structural rigidity against native CALB. Finally, computational docking analysis showed an increase in the binding affinity of CALB and its substrate (triglyceride) in mutant CALB with Atomic Contact Energy (ACE) of -91.23 kcal/mol compared to native CALB (ACE of -70.3 kcal/mol). The computational observations proposed that the use of mutant CALB (4K5Q) could serve as a best template for production of biodiesel in the future. Additionally, it can also be used as a template to identify efficient thermostable lipases through further mutations.

Unraveling the Deleterious Effects of Cancer-Driven STK11 Mutants Through Conformational Sampling Approach

Tumor suppressor gene, STK11, encodes for serine-threonine kinase, which has a critical role in regulating cell growth and apoptosis. Mutations of the same lead to the inactivation of STK11, which eventually causes different types of cancer. In this study, we focused on identifying those driver mutations through analyzing structural variations of mutants, viz., D194N, E199K, L160P, and Y49D. Native and the mutants were analyzed to determine their geometrical deviations such as root-mean-square deviation, root-mean-square fluctuation, radius of gyration, potential energy, and solvent-accessible surface area using conformational sampling technique. Additionally, the global minimized structure of native and mutants was further analyzed to compute their intramolecular interactions and distribution of secondary structure. Subsequently, simulated thermal denaturation and docking studies were performed to determine their structural variations, which in turn alter the formation of active complex that comprises STK11, STRAD, and MO25. The deleterious effect of the mutants would result in a comparative loss of enzyme function due to variations in their binding energy pertaining to spatial conformation and flexibility. Hence, the structural variations in binding energy exhibited by the mutants, viz., D194N, E199K, L160P, and Y49D, to that of the native, consequently lead to pathogenesis.

Innovative computer-aided methods for the discovery of new kinase ligands

Recent evidence points to significant roles played by protein kinases in cell signaling and cellular proliferation. Faulty protein kinases are involved in cancer, diabetes and chronic inflammation. Efforts are continuously carried out to discover new inhibitors for selected protein kinases. In this review, we discuss two new computer-aided methodologies we developed to mine virtual databases for new bioactive compounds. One method is ligand-based exploration of the pharmacophoric space of inhibitors of any particular biotarget followed by quantitative structure-activity relationship-based selection of the best pharmacophore(s). The second approach is structure-based assuming that potent ligands come into contact with binding site spots distinct from those contacted by weakly potent ligands. Both approaches yield pharmacophores useful as 3D search queries for the discovery of new bioactive (kinase) inhibitors.

Communication: Capturing protein multiscale thermal fluctuations

Existing elastic network models are typically parametrized at a given cutoff distance and often fail to properly predict the thermal fluctuation of many macromolecules that involve multiple characteristic length scales. We introduce a multiscale flexibility-rigidity index (mFRI) method to resolve this problem. The proposed mFRI utilizes two or three correlation kernels parametrized at different length scales to capture protein interactions at corresponding scales. It is about 20% more accurate than the Gaussian network model (GNM) in the B-factor prediction of a set of 364 proteins. Additionally, the present method is able to deliver accurate predictions for some large macromolecules on which GNM fails to produce accurate predictions. Finally, for a protein of N residues, mFRI is of linear scaling (O(N)) in computational complexity, in contrast to the order of O(N(3)) for GNM.

iGNM 2.0: the Gaussian network model database for biomolecular structural dynamics

Gaussian network model (GNM) is a simple yet powerful model for investigating the dynamics of proteins and their complexes. GNM analysis became a broadly used method for assessing the conformational dynamics of biomolecular structures with the development of a user-friendly interface and database, iGNM, in 2005. We present here an updated version, iGNM 2.0 http://gnmdb.csb.pitt.edu/, which covers more than 95% of the structures currently available in the Protein Data Bank (PDB). Advanced search and visualization capabilities, both 2D and 3D, permit users to retrieve information on inter-residue and inter-domain cross-correlations, cooperative modes of motion, the location of hinge sites and energy localization spots. The ability of iGNM 2.0 to provide structural dynamics data on the large majority of PDB structures and, in particular, on their biological assemblies makes it a useful resource for establishing the bridge between structure, dynamics and function.

Evolution of plant δ(1)-pyrroline-5-carboxylate reductases from phylogenetic and structural perspectives

Proline plays a crucial role in cell growth and stress responses, and its accumulation is essential for the tolerance of adverse environmental conditions in plants. Two routes are used to biosynthesize proline in plants. The main route uses glutamate as a precursor, while in the other route proline is derived from ornithine. The terminal step of both pathways, the conversion of δ(1)-pyrroline-5-carboxylate (P5C) to L-proline, is catalyzed by P5C reductase (P5CR) using NADH or NADPH as a cofactor. Since P5CRs are important housekeeping enzymes, they are conserved across all domains of life and appear to be relatively unaffected throughout evolution. However, global analysis of these enzymes unveiled significant functional diversity in the preference for cofactors (NADPH vs. NADH), variation in metal dependence and the differences in the oligomeric state. In our study we investigated evolutionary patterns through phylogenetic and structural analysis of P5CR representatives from all kingdoms of life, with emphasis on the plant species. We also attempted to correlate local sequence/structure variation among the functionally and structurally characterized members of the family.

Dissipative dynamics of enzymes

We explore enzyme conformational dynamics at sub-Å resolution, specifically, temperature effects. The ensemble-averaged mechanical response of the folded enzyme is viscoelastic in the whole temperature range between the warm and cold denaturation transitions. The dissipation parameter γ of the viscoelastic description decreases by a factor of 2 as the temperature is raised from 10 to 45 °C; the elastic parameter K shows a similar decrease. Thus, when probed dynamically, the enzyme softens for increasing temperature. Equilibrium mechanical experiments with the DNA spring (and a different enzyme) also show, qualitatively, a small softening for increasing temperature.

Computational prediction of hinge axes in proteins

BACKGROUND

A protein's function is determined by the wide range of motions exhibited by its 3D structure. However, current experimental techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion essential for effective drug screening and design. Computational tools are instrumental in the study of the underlying structure-function relationship. We focus on a special type of proteins called "hinge proteins" which exhibit a motion that can be interpreted as a rotation of one domain relative to another.

RESULTS

This work proposes a computational approach that uses the geometric structure of a single conformation to predict the feasible motions of the protein and is founded in recent work from rigidity theory, an area of mathematics that studies flexibility properties of general structures. Given a single conformational state, our analysis predicts a relative axis of motion between two specified domains. We analyze a dataset of 19 structures known to exhibit this hinge-like behavior. For 15, the predicted axis is consistent with a motion to a second, known conformation. We present a detailed case study for three proteins whose dynamics have been well-studied in the literature: calmodulin, the LAO binding protein and the Bence-Jones protein.

CONCLUSIONS

Our results show that incorporating rigidity-theoretic analyses can lead to effective computational methods for understanding hinge motions in macromolecules. This initial investigation is the first step towards a new tool for probing the structure-dynamics relationship in proteins.

A theoretical view of protein dynamics

Proteins are fascinating supramolecular structures, which are able to recognize ligands transforming binding information into chemical signals. They can transfer information across the cell, can catalyse complex chemical reactions, and are able to transform energy into work with much more efficiency than any human engine. The unique abilities of proteins are tightly coupled with their dynamic properties, which are coded in a complex way in the sequence and carefully refined by evolution. Despite its importance, our experimental knowledge of protein dynamics is still rather limited, and mostly derived from theoretical calculations. I will review here, in a systematic way, the current state-of-the-art theoretical approaches to the study of protein dynamics, emphasizing the most recent advances, examples of use and the expected lines of development in the near future.

Towards accurate modeling of noncovalent interactions for protein rigidity analysis

Background

Protein rigidity analysis is an efficient computational method for extracting flexibility information from static, X-ray crystallography protein data. Atoms and bonds are modeled as a mechanical structure and analyzed with a fast graph-based algorithm, producing a decomposition of the flexible molecule into interconnected rigid clusters. The result depends critically on noncovalent atomic interactions, primarily on how hydrogen bonds and hydrophobic interactions are computed and modeled. Ongoing research points to the stringent need for benchmarking rigidity analysis software systems, towards the goal of increasing their accuracy and validating their results, either against each other and against biologically relevant (functional) parameters. We propose two new methods for modeling hydrogen bonds and hydrophobic interactions that more accurately reflect a mechanical model, without being computationally more intensive. We evaluate them using a novel scoring method, based on the B-cubed score from the information retrieval literature, which measures how well two cluster decompositions match.

Results

To evaluate the modeling accuracy of KINARI, our pebble-game rigidity analysis system, we use a benchmark data set of 20 proteins, each with multiple distinct conformations deposited in the Protein Data Bank. Cluster decompositions for them were previously determined with the RigidFinder method from Gerstein's lab and validated against experimental data. When KINARI's default tuning parameters are used, an improvement of the B-cubed score over a crude baseline is observed in 30% of this data. With our new modeling options, improvements were observed in over 70% of the proteins in this data set. We investigate the sensitivity of the cluster decomposition score with case studies on pyruvate phosphate dikinase and calmodulin.

Conclusion

To substantially improve the accuracy of protein rigidity analysis systems, thorough benchmarking must be performed on all current systems and future extensions. We have measured the gain in performance by comparing different modeling methods for noncovalent interactions. We showed that new criteria for modeling hydrogen bonds and hydrophobic interactions can significantly improve the results. The two new methods proposed here have been implemented and made publicly available in the current version of KINARI (v1.3), together with the benchmarking tools, which can be downloaded from our software's website, http://kinari.cs.umass.edu.

SCEDS: protein fragments for molecular replacement in Phaser

A method is described for generating protein fragments suitable for use as molecular-replacement (MR) template models. The template model for a protein suspected to undergo a conformational change is perturbed along combinations of low-frequency normal modes of the elastic network model. The unperturbed structure is then compared with each perturbed structure in turn and the structurally invariant regions are identified by analysing the difference distance matrix. These fragments are scored with SCEDS, which is a combined measure of the sphericity of the fragments, the continuity of the fragments with respect to the polypeptide chain, the equality in number of atoms in the fragments and the density of C(α) atoms in the triaxial ellipsoid of the fragment extents. The fragment divisions with the highest SCEDS are then used as separate template models for MR. Test cases show that where the protein contains fragments that undergo a change in juxtaposition between template model and target, SCEDS can identify fragments that lead to a lower R factor after ten cycles of all-atom refinement with REFMAC5 than the original template structure. The method has been implemented in the software Phaser.

Association of a common TLR-6 polymorphism with coronary artery disease - implications for healthy ageing?

BACKGROUND

The pro-inflammatory status of the elderly triggers most of the age-related diseases such as cancer and atherosclerosis. Atherosclerosis, the leading cause world wide of morbidity and death, is an inflammatory disease influenced by life-style and genetic host factors. Stimuli such as oxLDL or microbial ligands have been proposed to trigger inflammation leading to atherosclerosis. It has recently been shown that oxLDL activates immune cells via the Toll-like receptor (TLR) 4/6 complex. Several common single nucleotide polymorphisms (SNPs) of the TLR system have been associated with atherosclerosis. To investigate the role of TLR-6 we analyzed the association of the TLR-6 SNP Pro249Ser with atherogenesis.

RESULTS

Genotyping of two independent groups with CAD, as well as of healthy controls revealed a significant association of the homozygous genotype with a reduced risk for atherosclerosis (odds ratio: 0.69, 95% CI 0.51-0.95, P = 0.02). In addition, we found a trend towards an association with the risk of restenosis after transluminal coronary angioplasty (odds ratio: 0.53, 95% CI 0.24-1.16, P = 0.12). In addition, first evidence is presented that the frequency of this protective genotype increases in a healthy population with age. Taken together, our results define a role for TLR-6 and its genetic variations in modulating the inflammatory response leading to atherosclerosis.

CONCLUSIONS

These results may lead to a better risk stratification, and potentially to an improved prophylactic treatment of high-risk populations. Furthermore, the protective effect of this polymorphism may lead to an increase of this genotype in the healthy elderly and may therefore be a novel genetic marker for the well-being during aging.

Conformational analysis of Clostridium difficile toxin B and its implications for substrate recognition

Clostridium difficile (C. difficile) is an opportunistic pathogen that can cause potentially lethal hospital-acquired infections. The cellular damage that it causes is the result of two large clostridial cytotoxins: TcdA and TcdB which act by glucosylating cytosolic G-proteins, mis-regulation of which induces apoptosis. TcdB is a large flexible protein that appears to undergo significant structural rearrangement upon accommodation of its substrates: UDP-glucose and a Rho-family GTPase. To characterize the conformational space of TcdB, we applied normal mode and hinge-region analysis, followed by long-timescale unbiased molecular dynamics. In order to examine the TcdB and RhoA interaction, macromolecular docking and simulation of the TcdB/RhoA complex was performed. Generalized Masked Delaunay analysis of the simulations determined the extent of significant motions. This combination of methods elucidated a wide range of motions within TcdB that are reiterated in both the low-cost normal mode analysis and the extensive MD simulation. Of particular interest are the coupled motions between a peripheral 4-helix bundle and a small loop in the active site that must rearrange to allow RhoA entry to the catalytic site. These extensive coupled motions are indicative of TcdB using a conformational capture mechanism for substrate accommodation.

PredyFlexy: flexibility and local structure prediction from sequence

Protein structures are necessary for understanding protein function at a molecular level. Dynamics and flexibility of protein structures are also key elements of protein function. So, we have proposed to look at protein flexibility using novel methods: (i) using a structural alphabet and (ii) combining classical X-ray B-factor data and molecular dynamics simulations. First, we established a library composed of structural prototypes (LSPs) to describe protein structure by a limited set of recurring local structures. We developed a prediction method that proposes structural candidates in terms of LSPs and predict protein flexibility along a given sequence. Second, we examine flexibility according to two different descriptors: X-ray B-factors considered as good indicators of flexibility and the root mean square fluctuations, based on molecular dynamics simulations. We then define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. This method does not resort to sophisticate learning of flexibility but predicts flexibility from average flexibility of predicted local structures. The method is implemented in PredyFlexy web server. Results are similar to those obtained with the most recent, cutting-edge methods based on direct learning of flexibility data conducted with sophisticated algorithms. PredyFlexy can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/predyflexy/.

Density-cluster NMA: A new protein decomposition technique for coarse-grained normal mode analysis

Normal mode analysis has emerged as a useful technique for investigating protein motions on long time scales. This is largely due to the advent of coarse-graining techniques, particularly Hooke's Law-based potentials and the rotational-translational blocking (RTB) method for reducing the size of the force-constant matrix, the Hessian. Here we present a new method for domain decomposition for use in RTB that is based on hierarchical clustering of atomic density gradients, which we call Density-Cluster RTB (DCRTB). The method reduces the number of degrees of freedom by 85-90% compared with the standard blocking approaches. We compared the normal modes from DCRTB against standard RTB using 1-4 residues in sequence in a single block, with good agreement between the two methods. We also show that Density-Cluster RTB and standard RTB perform well in capturing the experimentally determined direction of conformational change. Significantly, we report superior correlation of DCRTB with B-factors compared with 1-4 residue per block RTB. Finally, we show significant reduction in computational cost for Density-Cluster RTB that is nearly 100-fold for many examples.

Flexibility analysis of biomacromolecules with application to computer-aided drug design

Flexibility characteristics of biomacromolecules can be efficiently determined down to the atomic level by a graph-theoretical technique as implemented in the FIRST (Floppy Inclusion and Rigid Substructure Topology) and ProFlex software packages. The method has been successfully applied to a series of protein and nucleic acid structures. Here, we describe practical guidelines for setting up and performing a flexibility analysis, discuss current bottlenecks of the approach, and provide sample applications as to how this technique can support computer-aided drug design approaches.

Integrating structure-based and ligand-based approaches for computational drug design

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

Structure of the RuBisCO chaperone RbcX from the thermophilic cyanobacterium Thermosynechococcus elongatus

The crystal structure of TeRbcX, a RuBisCO assembly chaperone from the cyanobacterium Thermosynechococcus elongatus, a thermophilic organism, has been determined at 1.7 Å resolution. TeRbcX has an unusual cysteine residue at position 103 that is not found in RbcX proteins from mesophilic organisms. Unlike wild-type TeRbcX, a mutant protein with Cys103 replaced by Ala (TeRbcX-C103A) could be readily crystallized. The structure revealed that the overall fold of the TeRbcX homodimer is similar to those of previously crystallized RbcX proteins. Normal-mode analysis suggested that TeRbcX might adopt an open or closed conformation through a hinge movement pivoted on a kink in two long α4 helices. This type of conformational transition is presumably connected to RbcL (the large RuBisCO subunit) binding during the chaperone function of the RuBisCO assembly.

Significant enhancement of docking sensitivity using implicit ligand sampling

The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the 'ligand-model' concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to rerank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.

Accounting for large amplitude protein deformation during in silico macromolecular docking

Rapid progress of theoretical methods and computer calculation resources has turned in silico methods into a conceivable tool to predict the 3D structure of macromolecular assemblages, starting from the structure of their separate elements. Still, some classes of complexes represent a real challenge for macromolecular docking methods. In these complexes, protein parts like loops or domains undergo large amplitude deformations upon association, thus remodeling the surface accessible to the partner protein or DNA. We discuss the problems linked with managing such rearrangements in docking methods and we review strategies that are presently being explored, as well as their limitations and success.

Functional aspects of protein flexibility

Proteins are dynamic entities, and they possess an inherent flexibility that allows them to function through molecular interactions within the cell, among cells and even between organisms. Appreciation of the non-static nature of proteins is emerging, but to describe and incorporate this into an intuitive perception of protein function is challenging. Flexibility is of overwhelming importance for protein function, and the changes in protein structure during interactions with binding partners can be dramatic. The present review addresses protein flexibility, focusing on protein-ligand interactions. The thermodynamics involved are reviewed, and examples of structure-function studies involving experimentally determined flexibility descriptions are presented. While much remains to be understood about protein flexibility, it is clear that it is encoded within their amino acid sequence and should be viewed as an integral part of their structure.