Factors determining electrostatic fields in molecular dynamics simulations of the Ras/effector interface

Using molecular dynamics simulations, we explore geometric and physical factors contributing to calculated electrostatic fields at the binding surface of the GTPase Ras with a spectroscopically labeled variant of a downstream effector, the Ras-binding domain of Ral guanine nucleotide dissociation stimulator (RalGDS). A related system (differing by mutation of one amino acid) has been studied in our group using vibrational Stark effect spectroscopy, a technique sensitive to electrostatic fields. Electrostatic fields were computed using the AMBER 2003 force field and averaged over snapshots from molecular dynamics simulation. We investigate geometric factors by exploring how the orientation of the spectroscopic probe changes on Ras-effector binding. In addition, we explore the physical origin of electrostatic fields at our spectroscopic probe by comparing contributions to the field from discrete components of the system, such as explicit solvent, residues on the Ras surface, and residues on the RalGDS surface. These models support our experimental hypothesis that vibrational Stark shifts are caused by Ras binding to its effector and not the structural rearrangements of the effector surface or probe reorientation on Ras-effector binding, for at least some of our experimental probes. These calculations provide physical insight into the origin, magnitude, and importance of electrostatic fields in protein-protein interactions and suggest new experiments to probe the field's role in protein docking.     

Preorientation of protein and RNA just before contacting

Protein and RNA molecules interact and form complexes in many biological processes. However, it is still unclear how they can find the correct docking direction before forming complex. In this paper, we study preorientation of RNA and protein separated at a distance of 5–7?Å just before they form contacts and interact with each other only through pure electrostatic interaction when neglecting the influence of other molecules and complicated environment. Since geometric complementary has no meaning at such a distance, this is not a docking problem and so the conventional docking methods, like FTDock, are inapplicable. However, like the usual docking problem, we need to sample all the positions and orientations of RNA surrounding the protein to find the lowest energy orientations between RNA and protein. Therefore, we propose a long-range electrostatic docking-like method using Fast Fourier Transform-based sampling, LEDock, to study this problem. Our results show that the electrostatically induced orientations between RNA and protein at a distance of 5–7?Å are very different from the random ones and are much closer to those in their native complexes. Meanwhile, electrostatic funnels are found around the RNA-binding sites of the proteins in 62 out of 78 bound protein–RNA complexes. We also tried to use LEDock to find RNA-binding residues and it seems to perform slightly better than BindN Server for 23 unbound protein–RNA complexes.     

Protein molecular dynamics with electrostatic force entirely determined by a single Poisson-Boltzmann calculation

The electrostatic force including the intramolecular Coulombic interactions and the electrostatic contribution of solvation effect were entirely calculated by using the finite difference Poisson-Boltzmann method (FDPB), which was incorporated into the GROMOS96 force field to complete a new finite difference stochastic dynamics procedure (FDSD). Simulations were performed on an insulin dimer. Different relative dielectric constants were successively assigned to the protein interior; a value of 17 was selected as optimal for our system. The simulation data were analyzed and compared with those obtained from 500-ps molecular dynamics (MD) simulation with explicit water and a 500-ps conventional stochastic dynamics (SD) simulation without the mean solvent force. The results indicate that the FDSD method with GROMOS96 force field is suitable to study the dynamics and structure of proteins in solution if used with the optimal protein dielectric constant.     

Comparing bound and unbound protein structures using energy calculation and rotamer statistics

Protein data in the PDB covers only a snapshot of a protein structure. For flexible docking conformational changes need to be considered. Rotamer statistics provide the likelihood for side chain conformations, and further comparison of bound and unbound state yields differences in preferred positions. Furthermore, we do a full sampling of selected chi angles and apply the AMBER force field. Conformation of energy minima complies with the rotamer statistics. Both types of information target the reduction of search space for enumerative docking algorithms and provide parameters for elastic docking.     

Novel statistical-thermodynamic methods to predict protein-ligand binding positions using probability distribution functions

We present two novel methods to predict native protein-ligand binding positions. Both methods identify the native binding position as the most probable position corresponding to a maximum of a probability distribution function (PDF) of possible binding positions in a protein active site. Possible binding positions are the origins of clusters composed, on the basis of root-mean square deviations (RMSD), from the multiple ligand positions determined by a docking algorithm. The difference between the methods lies in the ways the PDF is derived. To validate the suggested methods, we compare the averaged RMSD of the predicted ligand docked positions relative to the experimentally determined positions for a set of 135 PDB protein-ligand complexes. We demonstrate that the suggested methods improve docking accuracy by as much as 21-24% in comparison with a method that simply identifies the binding position as the energy top-scored ligand position.     

Positioning hydrogen atoms by optimizing hydrogen-bond networks in protein structures

A method is presented that positions polar hydrogen atoms in protein structures by optimizing the total hydrogen bond energy. For this goal, an empirical hydrogen bond force field was derived from small molecule crystal structures. Bifurcated hydrogen bonds are taken into account. The procedure also predicts ionization states of His, Asp, and Glu residues. During optimization, sidechain conformations of His, Gln, and Asn residues are allowed to change their last χ angle by 180° to compensate for crystallographic misassignments. Crystal structure symmetry is taken into account where appropriate. The results can have significant implications for molecular dynamics simulations, protein engineering, and docking studies. The largest impact, however, is in protein structure verification: over 85% of protein structures tested can be improved by using our procedure. Proteins 26:363–376 © 1996 Wiley-Liss, Inc.     

Exposure of hydrophobic core in human prion protein pathogenic mutant H187R

Pathogenesis studies have revealed that H187R mutation of human prion protein (huPrP) is related to GSS type of TSE diseases. Its pathogenic mechanism is still unclear. We here studied the globular domain of this mutant protein by molecular dynamics simulations. Compared to the wide-type protein, the mutant has similar dynamics and stability profiles in our simulation. Conformational rearrangements are concentrated around the mutation site, due to the introduction the positively charged side chain of Arg187. The strong electrostatic repulsion between Arg156 and Arg187 drives both side chains away from their original positions, leaving its hydrophobic core to be solvent accessible. Such a unfavorable conformational change may destabilize the mutant protein and make it more susceptible to unfolding.     

Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.     

C2 domain of protein kinase C alpha: elucidation of the membrane docking surface by site-directed fluorescence and spin labeling

The C2 domain is a conserved signaling motif that triggers membrane docking in a Ca(2+)-dependent manner, but the membrane docking surfaces of many C2 domains have not yet been identified. Two extreme models can be proposed for the docking of the protein kinase C alpha (PKC alpha) C2 domain to membranes. In the parallel model, the membrane-docking surface includes the Ca(2+) binding loops and an anion binding site on beta-strands 3-4, such that the beta-strands are oriented parallel to the membrane. In the perpendicular model, the docking surface is localized to the Ca(2+) binding loops and the beta-strands are oriented perpendicular to the membrane surface. The present study utilizes site-directed fluorescence and spin-labeling to map out the membrane docking surface of the PKC alpha C2 domain. Single cysteine residues were engineered into 18 locations scattered over all regions of the protein surface, and were used as attachment sites for spectroscopic probes. The environmentally sensitive fluorescein probe identified positions where Ca(2+) activation or membrane docking trigger measurable fluorescence changes. Ca(2+) binding was found to initiate a global conformational change, while membrane docking triggered the largest fluorescein environmental changes at labeling positions on the three Ca(2+) binding loops (CBL), thereby localizing these loops to the membrane docking surface. Complementary EPR power saturation measurements were carried out using a nitroxide spin probe to determine a membrane depth parameter, Phi, for each spin-labeled mutant. Positive membrane depth parameters indicative of membrane insertion were found for three positions, all located on the Ca(2+) binding loops: N189 on CBL 1, and both R249 and R252 on CBL 3. In addition, EPR power saturation revealed that five positions near the anion binding site are partially protected from collisions with an aqueous paramagnetic probe, indicating that the anion binding site lies at or near the surface of the headgroup layer. Together, the fluorescence and EPR results indicate that the Ca(2+) first and third Ca(2+) binding loops insert directly into the lipid headgroup region of the membrane, and that the anion binding site on beta-strands 3-4 lies near the headgroups. The data support a model in which the beta-strands are tilted toward the parallel orientation relative to the membrane surface.     

A Web Interface for Easy Flexible Protein-Protein Docking with ATTRACT

Protein-protein docking programs can give valuable insights into the structure of protein complexes in the absence of an experimental complex structure. Web interfaces can facilitate the use of docking programs by structural biologists. Here, we present an easy web interface for protein-protein docking with the ATTRACT program. While aimed at nonexpert users, the web interface still covers a considerable range of docking applications. The web interface supports systematic rigid-body protein docking with the ATTRACT coarse-grained force field, as well as various kinds of protein flexibility. The execution of a docking protocol takes up to a few hours on a standard desktop computer.     

Mobile learning applications to improve invertebrate zoology online teaching

The use of new technologies including personal mobile devices has become an indispensable tool in our daily lives, and thus its presence in education is becoming ever more ubiquitous. In the current scenario imposed by the COVID‐19 pandemic, in which in‐person presence in classrooms has been enormously reduced at all educational levels, the use of mobile learning and cutting‐edge methods can greatly improve the way students learn and enhance their online‐learning experience. Mobile applications, combined with extended reality technologies such as virtual reality (VR) and augmented reality (AR), are powerful tools that connect real and virtual environments and allow higher interaction for the user. We have leveraged the advantages of mobile learning and extended reality technologies to develop a series of mobile applications and associated educational activities for university‐level courses involving invertebrate zoology field work. In particular, we have developed (a) a VR SCUBA diving video to explore the diversity of a marine protected area; (b) an AR mobile app to visualize 3D models of marine invertebrates; and (c) a mobile‐based catalogue to explore the terrestrial biodiversity of one of the most diverse regions of Spain. Here we provide detailed information describing the design and creation of these tools, as well as their application in class, to facilitate and encourage their use in higher education. Despite the relatively recent application of these technologies in education, they have an enormous potential: they improve student motivation and learning, can be adapted to different learning styles, reduce social inequalities, and facilitate inclusiveness and diversity practices in the classroom.     

Molecular modelling of protein-carbohydrate interactions. Docking of monosaccharides in the binding site of concanavalin A.

A general procedure is described for addressing the computer simulation of protein-carbohydrate interactions. First, a molecular mechanical force field capable of performing conformational analysis of oligosaccharides has been derived by the addition of new parameters to the Tripos force field; it is also compatible with the simulation of protein. Second, a docking procedure which allows for a systematic exploration of the orientations and positions of a ligand into a protein cavity has been designed. This so-called 'crankshaft' method uses rotations and variations about/of virtual bonds connecting, via dummy atoms, the ligand to the protein binding site. Third, calculation of the relative stability of protein ligand complexes is performed. This strategy has been applied to search for all favourable interactions occurring between a lectin [concanavalin A (ConA)] and methyl alpha-D-mannopyranoside or methyl alpha-D-glucopyranoside. For each monosaccharide, different stable orientations and positions within the binding site can be distinguished. Among them, one corresponds to very favourable interactions, not only in terms of hydrogen bonding, but also in terms of van der Waals interactions. It corresponds precisely to the binding mode of methyl alpha-D-mannopyranoside into ConA as revealed by the 2.9 A resolution of the crystalline complex (Derewenda et al., 1989). Some implications of the present modelling study with respect to the molecular basis of the specificity of the interaction of lectins with various monosaccharides are presented.     

iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement

Protein‐protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure‐based force field for intramolecular contributions. The approach was systematically evaluated on a large protein‐protein docking benchmark, starting from an enriched decoy set of rigidly docked protein–protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%. Proteins 2015; 83:248–258. © 2014 Wiley Periodicals, Inc.     

Progress toward virtual screening for drug side effects

The development and application of a computational protocol for conducting virtual screens of drug side interactions is described. A conventional drug-docking algorithm (AutoDock) is used to conduct two separate studies. First, a series of docking simulations is performed by using guanosine diphosphate and adenosine diphosphate as prototype drugs with the goal of successfully differentiating known receptors from a large number of bait receptors. Using the electrostatic energy of the purine ring as a basis for discrimination allows the correct identification of receptors in blind studies with 100% specificity and 94% sensitivity. In a second study, similar methodology is used to investigate the binding of clinically relevant inhibitors (Gleevec, purvalanol A, and hymenialdisine) to a variety of protein kinase targets. Overall, excellent agreement between experimental and predicted preferences for kinase targets is obtained. An important conclusion from the latter study is that homology-modeled structures of putative receptors may reasonably be used as targets for docking when directly solved crystal structures are not available. The prospects for routine application of the methodology as a means of identifying potential side interactions of candidate drugs are discussed.     

Influence of the protein conformation on the interaction between α-lactalbumin and dimyristoylphosphatidylcholine vesicles

α-Lactalbumin is a globular protein containing helical regions with highly amphiphathic character. In this work, the interaction between bovine α-lactalbumin and sonicated dimyristoylphosphatidylcholine vesicles has been compared in different circumstances which influence the protein conformation i.e., pH, ionic strength, decalcification, guanidine hydrochloride denaturation. Above the isoelectric point the interaction is mainly electrostatic; improved electrostatic interaction results in better contact with the apolar lipid phase. Below the isoelectric point, hydrophobic forces dominate the interaction and the vesicles are solubilized. The mode of interaction is not determined to a great extent by the demetallization of the protein. However, by a more explicit unfolding of the globular structure with guanidine hydrochloride, micellar complexes can be formed with the lipid, even at neutral pH. From this study it is obvious that the presence or capability for formation of helices with high amphipathic character is not a sufficient condition for lipid solubilization by a globular protein. Also, the capability of a globular protein to unfold its tertiary structure seems to be a prerequisite for its capability to lipid solubilization.     

Protein-ligand docking: current status and future challenges

Understanding the ruling principles whereby protein receptors recognize, interact, and associate with molecular substrates and inhibitors is of paramount importance in drug discovery efforts. Protein-ligand docking aims to predict and rank the structure(s) arising from the association between a given ligand and a target protein of known 3D structure. Despite the breathtaking advances in the field over the last decades and the widespread application of docking methods, several downsides still exist. In particular, protein flexibility-a critical aspect for a thorough understanding of the principles that guide ligand binding in proteins-is a major hurdle in current protein-ligand docking efforts that needs to be more efficiently accounted for. In this review the key concepts of protein-ligand docking methods are outlined, with major emphasis being given to the general strengths and weaknesses that presently characterize this methodology. Despite the size of the field, the principal types of search algorithms and scoring functions are reviewed and the most popular docking tools are briefly depicted. Recent advances that aim to address some of the traditional limitations associated with molecular docking are also described. A selection of hand-picked examples is used to illustrate these features.     

Accounting for loop flexibility during protein-protein docking

Although reliable docking can now be achieved for systems that do not undergo important induced conformational change upon association, the presence of flexible surface loops, which must adapt to the steric and electrostatic properties of a partner, generally presents a major obstacle. We report here the first docking method that allows large loop movements during a systematic exploration of the possible arrangements of the two partners in terms of position and rotation. Our strategy consists in taking into account an ensemble of possible loop conformations by a multi-copy representation within a reduced protein model. The docking process starts from regularly distributed positions and orientations of the ligand around the whole receptor. Each starting configuration is submitted to energy minimization during which the best-fitting loop conformation is selected based on the mean-field theory. Trials were carried out on proteins with significant differences in the main-chain conformation of the binding loop between isolated form and complexed form, which were docked to their partner considered in their bound form. The method is able to predict complexes very close to the crystal complex both in terms of relative position of the two partners and of the geometry of the flexible loop. We also show that introducing loop flexibility on the isolated protein form during systematic docking largely improves the predictions of relative position of the partners in comparison with rigid-body docking.