DOT2: Macromolecular docking with improved biophysical models

Fine tuning rigid body docking results using the Dreiding force field: A computational study of 36 known nanobody-protein complexes

This paper aims to understand the binding strategies of a nanobody-protein pair by studying known complexes. Rigid body protein-ligand docking programs produce several complexes, called decoys, which are good candidates with high scores of shape complementarity, electrostatic interactions, desolvation, buried surface area, and Lennard-Jones potentials. However, the decoy that corresponds to the native structure is not known. We studied 36 nanobody-protein complexes from the single domain antibody database, sd-Ab DB, http://www.sdab-db.ca/. For each structure, a large number of decoys are generated using the Fast Fourier Transform algorithm of the software ZDOCK. The decoys were ranked according to their target protein-nanobody interaction energies, calculated by using the Dreiding Force Field, with rank 1 having the lowest interaction energy. Out of 36 protein data bank (PDB) structures, 25 true structures were predicted as rank 1. Eleven of the remaining structures required Ångstrom size rigid body translations of the nanobody relative to the protein to match the given PDB structure. After the translation, the Dreiding interaction (DI) energies of all complexes decreased and became rank 1. In one case, rigid body rotations as well as translations of the nanobody were required for matching the crystal structure. We used a Monte Carlo algorithm that randomly translates and rotates the nanobody of a decoy and calculates the DI energy. Results show that rigid body translations and the DI energy are sufficient for determining the correct binding location and pose of ZDOCK created decoys. A survey of the sd-Ab DB showed that each nanobody makes at least one salt bridge with its partner protein, indicating that salt bridge formation is an essential strategy in nanobody-protein recognition. Based on the analysis of the 36 crystal structures and evidence from existing literature, we propose a set of principles that could be used in the design of nanobodies.

Design and Prediction of Aptamers Assisted by In Silico Methods

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Considering both small and large scale motions of vascular endothelial growth factor (VEGF) is crucial for reliably predicting its binding affinities to DNA aptamers

Considering both small and large scale motions of VEGF is crucial to predict its relative binding affinities to DNA aptamer variants with docking.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

John PC, Bomble YJ. Advances in integrative structural biology: Towards understanding protein complexes in their cellular context

Microorganisms rely on protein interactions to transmit signals, react to stimuli, and grow. One of the best ways to understand these protein interactions is through structural characterization. However, in the past, structural knowledge was limited to stable, high-affinity complexes that could be crystallized. Recent developments in structural biology have revolutionized how protein interactions are characterized. The combination of multiple techniques, known as integrative structural biology, has provided insight into how large protein complexes interact in their native environment. In this mini-review, we describe the past, present, and potential future of integrative structural biology as a tool for characterizing protein interactions in their cellular context.

Methods and Applications of In Silico Aptamer Design and Modeling

Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.

In silico design and validation of high-affinity RNA aptamers targeting epithelial cellular adhesion molecule dimers

Nucleic acid aptamers hold great promise for therapeutic applications due to their favorable intrinsic properties, as well as high-throughput experimental selection techniques. Despite the utility of the systematic evolution of ligands by the exponential enrichment (SELEX) method for aptamer determination, complementary in silico aptamer design is highly sought after to facilitate virtual screening and increased understanding of important nucleic acid-protein interactions. Here, with a combined experimental and theoretical approach, we have developed two optimal epithelial cellular adhesion molecule (EpCAM) aptamers. Our structure-based in silico method first predicts their binding modes and then optimizes them for EpCAM with molecular dynamics simulations, docking, and free energy calculations. Our isothermal titration calorimetry experiments further confirm that the EpCAM aptamers indeed exhibit enhanced affinity over a previously patented nanomolar aptamer, EP23. Moreover, our study suggests that EP23 and the de novo designed aptamers primarily bind to EpCAM dimers (and not monomers, as hypothesized in previous published works), suggesting a paradigm for developing EpCAM-targeted therapies.

Computational Approaches for Elucidating Protein-Protein Interactions in Cation Channel Signaling

BACKGROUND

The lipid bilayer of the plasma membrane is impermeable to ions, yet changes in the flux of ions across the cell membrane are critical regulatory events in cells. Because of their regulatory roles in a range of physiological processes, such as electrical signaling in muscles and neurons, to name a few, these proteins are one of the most important drug targets.

OBJECTIVE

This review mainly focused on the computational approaches for elucidating proteinprotein interactions in cation channel signaling.

DISCUSSION

Due to continuously advanced facilities and technologies in computer sciences, the physical contacts of macromolecules of channel structures have been virtually visualized. Indeed, techniques like protein-protein docking, homology modeling, and molecular dynamics simulation are valuable tools for predicting the protein complex and refining channels with unreleased structures. Undoubtedly, these approaches will greatly expand the cation channel signaling research, thereby speeding up structure-based drug design and discovery.

CONCLUSION

We introduced a series of valuable computational tools for elucidating protein-protein interactions in cation channel signaling, including molecular graphics, protein-protein docking, homology modeling, and molecular dynamics simulation.

Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data

Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.

EROS-DOCK: protein–protein docking using exhaustive branch-and-bound rotational search

Motivation

Protein–protein docking algorithms aim to predict the 3D structure of a binary complex using the structures of the individual proteins. This typically involves searching and scoring in a 6D space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, FFT docking results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision.

Results

Here, we present EROS-DOCK, an algorithm to rigidly dock two proteins using a series of exhaustive 3D rotational searches in which non-clashing orientations are scored using the ATTRACT coarse-grained force field model. The rotational space is represented as a quaternion ‘π-ball’, which is systematically sub-divided in a ‘branch-and-bound’ manner, allowing efficient pruning of rotations that will give steric clashes. The algorithm was tested on 173 Docking Benchmark complexes, and results were compared with those of ATTRACT and ZDOCK. According to the CAPRI quality criteria, EROS-DOCK typically gives more acceptable or medium quality solutions than ATTRACT and ZDOCK.

Availability and implementation

The EROS-DOCK program is available for download at http://erosdock.loria.fr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Rad54 Phosphorylation Promotes Homologous Recombination by Balancing Rad54 Mobility and DNA Binding

The repair of DNA double-strand breaks by homologous recombination is of crucial importance for maintaining genomic stability. Two major players during this repair pathway are Rad51 and Rad54. Previously, it was shown that Rad54 exists as a monomer or oligomer when bound to DNA and drives the displacement of Rad51 by translocating along the DNA. Moreover, phosphorylation of Rad54 was reported to stimulate this clearance of Rad51 from DNA. However, it is currently unclear how phosphorylation of Rad54 modulates its molecular-structural function and how it affects the activity of monomeric or oligomeric Rad54 during the removal of Rad51. To examine the impact of Rad54 phosphorylation on a molecular-structural level, we applied molecular dynamics simulations of Rad54 monomers and hexamers in the absence or presence of DNA. Our results suggest that 1) phosphorylation of Rad54 stabilizes the monomeric form by reducing the interlobe movement of Rad54 monomers and might therefore facilitate multimer formation around DNA and 2) phosphorylation of Rad54 in a higher-order hexamer reduces its binding strength to DNA, which is a requirement for efficient mobility on DNA. To further address the relationship between the mobility of Rad54 and its phosphorylation state, we performed fluorescence recovery after photobleaching experiments in living cells, which expressed different versions of the Rad54 protein. Here, we could measure that the phosphomimetic version of Rad54 was highly mobile on DNA, whereas a nonphosphorylatable mutant displayed a mobility defect. Taken together, these data show that the phosphorylation of Rad54 is a critical event in balancing the DNA binding strength and mobility of Rad54 and might therefore provide optimal conditions for DNA translocation and subsequent removal of Rad51 during homologous recombination repair.

Protein-protein interactions of human glyoxalase II: findings of a reliable docking protocol

Glyoxalase II (GlxII) is an antioxidant glutathione-dependent enzyme, which catalyzes the hydrolysis of S-d-lactoylglutathione to form d-lactic acid and glutathione (GSH). The last product is the most important thiol reducing agent present in all eukaryotic cells that have mitochondria and chloroplasts. It is generally known that GSH plays a crucial role not only in the cellular redox state but also in various cellular processes. One of them is protein S-glutathionylation, a process that can occur through an oxidation reaction of proteins' thiol groups by GSH. Changes in protein S-glutathionylation have been associated with a range of human diseases such as diabetes, cardiovascular and pulmonary diseases, neurodegenerative diseases and cancer. Within a major project aimed at elucidating the role of GlxII in the mechanism of S-glutathionylation, a reliable computational protocol consisting of a protein-protein docking approach followed by atomistic Molecular Dynamics (MD) simulations was developed and it was applied to the prediction of molecular associations between human GlxII (in the presence and absence of GSH) and some proteins that are known to be S-glutathionylated in vitro, such as actin, malate dehydrogenase (MDH) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The computational results show a high propensity of GlxII to interact with actin and MDH through its active site and a high stability of the GlxII-protein systems when GSH is present. Moreover, close proximities of GSH with actin and MDH cysteine residues have been found, suggesting that GlxII could be able to perform protein S-glutathionylation by using the GSH molecule present in its catalytic site.

Protein-Protein Docking in Drug Design and Discovery

Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

High-Throughput Reconstruction of Ancestral Protein Sequence, Structure, and Molecular Function

Ancestral protein sequence reconstruction is a powerful technique for explicitly testing hypotheses about the evolution of molecular function, allowing researchers to meticulously dissect how historical changes in protein sequence impacted functional repertoire by altering the protein's 3D structure. These techniques have provided concrete, experimentally validated insights into ancient evolutionary processes and help illuminate the complex relationship between protein sequence, structure, and function. Inferring the protein family phylogenies on which ancestral sequence reconstruction depends and reconstructing the sequences, themselves, are amenable to high-throughput computational analysis. However, determining the structures of ancestral-reconstructed proteins and characterizing their functions typically rely on time-consuming and expensive laboratory analyses, limiting most current studies to examining a relatively small number of specific hypotheses. For this reason, we have little detailed, unbiased information about how molecular function evolves across large protein family phylogenies. Here we describe a generalized protocol that integrates ancestral sequence reconstruction with structural homology modeling and structure-based molecular affinity prediction to characterize historical changes in protein function across families with thousands of individual sequences. We highlight key steps in the analysis protocol requiring particularly careful attention to avoid introducing potential errors as well as steps for which computationally efficient subroutines can be substituted for more intensive approaches, allowing researchers to scale the analysis up or down, depending on available resources and requirements for reproducibility and scientific rigor. In our view, this approach provides a compelling compliment to more laboratory-intensive procedures, generating important contextual information that can help guide detailed experiments.

Molecular Modeling Applied to Nucleic Acid-Based Molecule Development

Molecular modeling by means of docking and molecular dynamics (MD) has become an integral part of early drug discovery projects, enabling the screening and enrichment of large libraries of small molecules. In the past decades, special emphasis was drawn to nucleic acid (NA)-based molecules in the fields of therapy, diagnosis, and drug delivery. Research has increased dramatically with the advent of the SELEX (systematic evolution of ligands by exponential enrichment) technique, which results in single-stranded DNA or RNA sequences that bind with high affinity and specificity to their targets. Herein, we discuss the role and contribution of docking and MD to the development and optimization of new nucleic acid-based molecules. This review focuses on the different approaches currently available for molecular modeling applied to NA interaction with proteins. We discuss topics ranging from structure prediction to docking and MD, highlighting their main advantages and limitations and the influence of flexibility on their calculations.

Structure of tRNA-Modifying Enzyme TiaS and Motions of Its Substrate Binding Zinc Ribbon

The accurate modification of the tRNA^Ile anticodon wobble cytosine 34 is critical for AUA decoding in protein synthesis. Archaeal tRNA^Ile2 cytosine 34 is modified with agmatine in the presence of ATP by TiaS (tRNA^Ile2 agmatidine synthetase). However, no structure of apo-form full-length TiaS is available currently. Here, the crystal structures of apo TiaS and a complex of TiaS-agmatine-AMPPCP-Mg are presented, with properly folded zinc ribbon and Cys4-zinc coordination identified. Compared with tRNA^Ile2-bound form, the architecture of apo TiaS shows a totally different conformation of zinc ribbon. Molecular dynamics simulations of the docking complex between free-state TiaS and tRNA^Ile2 suggest that zinc ribbon domain is capable of performing large-scale motions to sample substrate binding-competent conformation. Principle component analysis and normal mode analysis show consistent results about the relative directionality of functionally correlated zinc ribbon motions. Apo TiaS and TiaS-agmatine-AMPPCP-Mg/TiaS-AMPCPP-Mg complex structures capture two snapshots of the flexible ATP-Mg binding p2loop step-by-step stabilization. Research from this study provides new insight into TiaS functional mechanism and the dynamic feature of zinc ribbons.

Protein-RNA Docking Using ICM

Protein-RNA interactions play an important role in many biological processes. Computational methods such as docking have been developed to complement existing biophysical and structural biology techniques. Computational prediction of protein-RNA complex structures includes two steps: generating candidate structures from the individual protein and RNA parts and scoring the generated poses to pick out the correct one. In this work, we considered three recently developed data sets of protein-RNA complexes to evaluate and improve the performance of the FFT-based rigid-body docking algorithm implemented in the ICM package. An electrostatic term describing interactions between negatively charged phosphate groups and positively charged protein residues was added to the energy function used during the docking step to take into account the greater role that electrostatic interactions play in protein-RNA complexes. Next, the docking results were used to optimize a scoring function including van der Waals, electrostatic, and solvation terms. This optimization yielded a much smaller weight for the solvation term indicating that solvation energy may be less important for the scoring of protein-RNA structures. Rescoring of the generated poses with the new scoring function led to much higher success rates, while pose clustering by contact fingerprints produced further improvements, achieving a success rate of 0.66 for the top 100 structures.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Combining H/D Exchange Mass Spectrometry and Computational Docking To Derive the Structure of Protein-Protein Complexes

Protein-protein interactions are essential for biological function, but structures of protein-protein complexes are difficult to obtain experimentally. To derive the protein complex of the DNA-repair enzyme human uracil-DNA-glycosylase (hUNG) with its protein inhibitor (UGI), we combined rigid-body computational docking with hydrogen/deuterium exchange mass spectrometry (DXMS). Computational docking of the unbound protein structures provides a list of possible three-dimensional models of the complex; DXMS identifies solvent-protected protein residues. DXMS showed that unbound hUNG is compactly folded, but unbound UGI is loosely packed. An increased level of solvent protection of hUNG in the complex was localized to four regions on the same face. The decrease in the number of incorporated deuterons was quantitatively interpreted as the minimum number of main-chain hUNG amides buried in the protein-protein interface. The level of deuteration of complexed UGI decreased throughout the protein chain, indicating both tighter packing and direct solvent protection by hUNG. Three UGI regions showing the greatest decreases were best interpreted leniently, requiring just one main-chain amide from each in the interface. Applying the DXMS constraints as filters to a list of docked complexes gave the correct complex as the largest favorable energy cluster. Thus, identification of approximate protein interfaces was sufficient to distinguish the protein complex. Surprisingly, incorporating the DXMS data as added favorable potentials in the docking calculation was less effective in finding the correct complex. The filtering method has greater flexibility, with the capability to test each constraint and enforce simultaneous contact by multiple regions, but with the caveat that the list from the unbiased docking must include correct complexes.

Improvements to the APBS biomolecular solvation software suite

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that have provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses the three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this article, we discuss the models and capabilities that have recently been implemented within the APBS software package including a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory-based algorithm for determining pK_a values, and an improved web-based visualization tool for viewing electrostatics.

Modeling of annexin A2-Membrane interactions by molecular dynamics simulations

The annexins are a family of Ca2+-regulated phospholipid binding proteins that are involved in membrane domain organization and membrane trafficking. Although they are widely studied and crystal structures are available for several soluble annexins their mode of membrane association has never been studied at the molecular level. Here we obtained molecular information on the annexin-membrane interaction that could serve as paradigm for the peripheral membrane association of cytosolic proteins by Molecular Dynamics simulations. We analyzed systems containing the monomeric annexin A2 (AnxA2), a membrane with negatively charged phosphatidylserine (POPS) lipids as well as Ca2+ ions. On the atomic level we identify the AnxA2 orientations and the respective residues which display the strongest interaction with Ca2+ ions and the membrane. The simulation results fully agree with earlier experimental findings concerning the positioning of bound Ca2+ ions. Furthermore, we identify for the first time a significant interaction between lysine residues of the protein and POPS lipids that occurs independently of Ca2+ suggesting that AnxA2-membrane interactions can also occur in a low Ca2+ environment. Finally, by varying Ca2+ concentrations and lipid composition in our simulations we observe a calcium-induced negative curvature of the membrane as well as an AnxA2-induced lipid ordering.

Supramolecular nanoparticles of calcitonin and dipeptide for long-term controlled release

Salmon calcitonin (sCT) is a therapeutic polypeptide drug widely used to treat bone diseases such as osteoporosis (more than 200 million patients all over the world). The half-life of sCT is very short (~1h), thus various delivery systems have been developed for sCT in order to avoid frequent injections. However, most delivery systems use polymeric materials, which may limit their applications in clinic formulations due to the biocompatibility issue. We observed that a very simple dipeptide (Asp-Phe, DF) was co-assembled with sCT into supramolecular nanoparticles. These nanoparticles can significantly prolong the acting time of sCT to beyond one month after just a single subcutaneous injection. The assembling and releasing mechanisms were thoroughly investigated by both in vitro and in vivo methods, as well as by molecular dynamics simulations. This work provides an alternative strategy of designing protein/peptide drug delivery systems with long-lasting therapeutic effects.

Mass spectrometry-based cross-linking study shows that the Psb28 protein binds to cytochrome b559 in Photosystem II

Photosystem II (PSII), a large pigment protein complex, undergoes rapid turnover under natural conditions. During assembly of PSII, oxidative damage to vulnerable assembly intermediate complexes must be prevented. Psb28, the only cytoplasmic extrinsic protein in PSII, protects the RC47 assembly intermediate of PSII and assists its efficient conversion into functional PSII. Its role is particularly important under stress conditions when PSII damage occurs frequently. Psb28 is not found, however, in any PSII crystal structure, and its structural location has remained unknown. In this study, we used chemical cross-linking combined with mass spectrometry to capture the transient interaction of Psb28 with PSII. We detected three cross-links between Psb28 and the α- and β-subunits of cytochrome b₅₅₉, an essential component of the PSII reaction-center complex. These distance restraints enable us to position Psb28 on the cytosolic surface of PSII directly above cytochrome b₅₅₉, in close proximity to the Q_B site. Protein-protein docking results also support Psb28 binding in this region. Determination of the Psb28 binding site and other biochemical evidence allow us to propose a mechanism by which Psb28 exerts its protective effect on the RC47 intermediate. This study also shows that isotope-encoded cross-linking with the "mass tags" selection criteria allows confident identification of more cross-linked peptides in PSII than has been previously reported. This approach thus holds promise to identify other transient protein-protein interactions in membrane protein complexes.

Software for molecular docking: a review

Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.

Binding Site Recognition and Docking Dynamics of a Single Electron Transport Protein: Cytochrome c2

Small diffusible redox proteins facilitate electron transfer in respiration and photosynthesis by alternately binding to their redox partners and integral membrane proteins and exchanging electrons. Diffusive search, recognition, binding, and unbinding of these proteins often amount to kinetic bottlenecks in cellular energy conversion, but despite the availability of structures and intense study, the physical mechanisms controlling redox partner interactions remain largely unknown. The present molecular dynamics study provides an all-atom description of the cytochrome c2-docked bc1 complex in Rhodobacter sphaeroides in terms of an ensemble of favorable docking conformations and reveals an intricate series of conformational changes that allow cytochrome c2 to recognize the bc1 complex and bind or unbind in a redox state-dependent manner. In particular, the role of electron transfer in triggering a molecular switch and in altering water-mediated interface mobility, thereby strengthening and weakening complex formation, is described. The results resolve long-standing discrepancies between structural and functional data.

Overall energy conversion efficiency of a photosynthetic vesicle

The chromatophore of purple bacteria is an intracellular spherical vesicle that exists in numerous copies in the cell and that efficiently converts sunlight into ATP synthesis, operating typically under low light conditions. Building on an atomic-level structural model of a low-light-adapted chromatophore vesicle from Rhodobacter sphaeroides, we investigate the cooperation between more than a hundred protein complexes in the vesicle. The steady-state ATP production rate as a function of incident light intensity is determined after identifying quinol turnover at the cytochrome bc1 complex (cytb⁢c1) as rate limiting and assuming that the quinone/quinol pool of about 900 molecules acts in a quasi-stationary state. For an illumination condition equivalent to 1% of full sunlight, the vesicle exhibits an ATP production rate of 82 ATP molecules/s. The energy conversion efficiency of ATP synthesis at illuminations corresponding to 1%–5% of full sunlight is calculated to be 0.12–0.04, respectively. The vesicle stoichiometry, evolutionarily adapted to the low light intensities in the habitat of purple bacteria, is suboptimal for steady-state ATP turnover for the benefit of protection against over-illumination.

DOI:http://dx.doi.org/10.7554/eLife.09541.001

Photosynthesis, or the conversion of light energy into chemical energy, is a process that powers almost all life on Earth. Plants and certain bacteria share similar processes to perform photosynthesis, though the purple bacterium Rhodobacter sphaeroides uses a photosynthetic system that is much less complex than that in plants. Light harvesting inside the bacterium takes place in up to hundreds of compartments called chromatophores. Each chromatophore in turn contains hundreds of cooperating proteins that together absorb the energy of sunlight and convert and store it in molecules of ATP, the universal energy currency of all cells.

The chromatophore of primitive purple bacteria provides a model for more complex photosynthetic systems in plants. Though researchers had characterized its individual components over the years, less was known about the overall architecture of the chromatophore and how its many components work together to harvest light energy efficiently and robustly. This knowledge would provide insight into the evolutionary pressures that shaped the chromatophore and its ability to work efficiently at different light intensities.

Sener et al. now present a highly detailed structural model of the chromatophore of purple bacteria based on the findings of earlier studies. The model features the position of every atom of the constituent proteins and is used to examine how energy is transferred and converted. Sener et al. describe the sequence of energy conversion steps and calculate the overall energy conversion efficiency, namely how much of the light energy arriving at the microorganism is stored as ATP.

These calculations show that the chromatophore is optimized to produce chemical energy at low light levels typical of purple bacterial habitats, and dissipate excess energy to avoid being damaged under brighter light. The chromatophore’s architecture also displays robustness against perturbations of its components. In the future, the approach used by Sener et al. to describe light harvesting in this bacterial compartment can be applied to more complex systems, such as those in plants.

DOI:http://dx.doi.org/10.7554/eLife.09541.002

Structural elucidation of a novel mechanism for the bacteriophage-based inhibition of the RNA degradosome

In all domains of life, the catalysed degradation of RNA facilitates rapid adaptation to changing environmental conditions, while destruction of foreign RNA is an important mechanism to prevent host infection. We have identified a virus-encoded protein termed gp37/Dip, which directly binds and inhibits the RNA degradation machinery of its bacterial host. Encoded by giant phage фKZ, this protein associates with two RNA binding sites of the RNase E component of the Pseudomonas aeruginosa RNA degradosome, occluding them from substrates and resulting in effective inhibition of RNA degradation and processing. The 2.2 Å crystal structure reveals that this novel homo-dimeric protein has no identifiable structural homologues. Our biochemical data indicate that acidic patches on the convex outer surface bind RNase E. Through the activity of Dip, фKZ has evolved a unique mechanism to down regulate a key metabolic process of its host to allow accumulation of viral RNA in infected cells.

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

Background

Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs.

Results

To address this problem, we developed eRank^PPI, an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank^PPI employs multiple features including interface probability estimates calculated by eFindSite^PPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank^PPI consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %.

Conclusions

eRank^PPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi.

Evolution of a Novel Antiviral Immune-Signaling Interaction by Partial-Gene Duplication

The RIG-like receptors (RLRs) are related proteins that identify viral RNA in the cytoplasm and activate cellular immune responses, primarily through direct protein-protein interactions with the signal transducer, IPS1. Although it has been well established that the RLRs, RIG-I and MDA5, activate IPS1 through binding between the twin caspase activation and recruitment domains (CARDs) on the RLR and a homologous CARD on IPS1, it is less clear which specific RLR CARD(s) are required for this interaction, and almost nothing is known about how the RLR-IPS1 interaction evolved. In contrast to what has been observed in the presence of immune-modulating K63-linked polyubiquitin, here we show that-in the absence of ubiquitin-it is the first CARD domain of human RIG-I and MDA5 (CARD1) that binds directly to IPS1 CARD, and not the second (CARD2). Although the RLRs originated in the earliest animals, both the IPS1 gene and the twin-CARD domain architecture of RIG-I and MDA5 arose much later in the deuterostome lineage, probably through a series of tandem partial-gene duplication events facilitated by tight clustering of RLRs and IPS1 in the ancestral deuterostome genome. Functional differentiation of RIG-I CARD1 and CARD2 appears to have occurred early during this proliferation of RLR and related CARDs, potentially driven by adaptive coevolution between RIG-I CARD domains and IPS1 CARD. However, functional differentiation of MDA5 CARD1 and CARD2 occurred later. These results fit a general model in which duplications of protein-protein interaction domains into novel gene contexts could facilitate the expansion of signaling networks and suggest a potentially important role for functionally-linked gene clusters in generating novel immune-signaling pathways.

C-Terminal Domain of Integrase Binds between the Two Active Sites

HIV integrase (HIV-IN), one of three HIV enzymes, is a target for the treatment of AIDS, but the full biological assembly has been difficult to characterize, hampering inhibitor design. The recent crystallographic structures of integrase from prototype foamy virus (PFV-IN) with bound DNA were a breakthrough, revealing how viral DNA organizes two integrase dimers into a tetramer that has the two active sites appropriately spaced for insertion of the viral DNA into host DNA. The organization of domains within each PFV-IN protein chain, however, varies significantly from that found in HIV-IN structures. With the goal of identifying shared structural characteristics, the interactions among components of the PFV-IN and HIV-IN assemblies were investigated with the macromolecular docking program DOT. DOT performs an exhaustive, rigid-body search between two macromolecules. Computational docking reproduced the crystallographic interactions of the PFV-IN catalytic and N-terminal domains with viral DNA and found similar viral DNA interactions for HIV-IN. Computational docking did not reproduce the crystallographic interactions of the PFV-IN C-terminal domain (CTD). Instead, two symmetry-related positions were found for the PFV-IN CTD that indicate formation of a CTD dimer between the two active sites. Our predicted CTD dimer is consistent with cross-linking studies showing interactions of the CTD with viral DNA that appear to be blocked in the PFV-IN structures. The CTD dimer can insert two arginine-rich loops between the two bound vDNA molecules and the host DNA, a region that is unoccupied in the PFV-IN crystallographic structures. The positive potential from these two loops would alleviate the large negative potential created by the close proximity of two viral vDNA ends, helping to bring together the two active sites and assisting host DNA binding. This study demonstrates the ability of computational docking to evaluate complex crystallographic assemblies, identify interactions that are influenced by the crystal environment, and provide plausible alternatives.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

A STIM2 splice variant negatively regulates store-operated calcium entry

Cellular homeostasis relies upon precise regulation of Ca(2+) concentration. Stromal interaction molecule (STIM) proteins regulate store-operated calcium entry (SOCE) by sensing Ca(2+) concentration in the ER and forming oligomers to trigger Ca(2+) entry through plasma membrane-localized Orai1 channels. Here we characterize a STIM2 splice variant, STIM2.1, which retains an additional exon within the region encoding the channel-activating domain. Expression of STIM2.1 is ubiquitous but its abundance relative to the more common STIM2.2 variant is dependent upon cell type and highest in naive T cells. STIM2.1 knockdown increases SOCE in naive CD4(+) T cells, whereas knockdown of STIM2.2 decreases SOCE. Conversely, overexpression of STIM2.1, but not STIM2.2, decreases SOCE, indicating its inhibitory role. STIM2.1 interaction with Orai1 is impaired and prevents Orai1 activation, but STIM2.1 shows increased affinity towards calmodulin. Our results imply STIM2.1 as an additional player tuning Orai1 activation in vivo.

A molecular analysis provides novel insights into androgen receptor signalling in breast cancer

BACKGROUND

Androgen Receptor (AR) is an essential transcription factor for the development of secondary sex characteristics, spermatogenesis and carcinogenesis. Recently AR has been implicated in the development and progression of breast and prostate cancers. Although some of the functions of the AR are known but the mechanistic details of these divergent processes are still not clear. Therefore understanding the regulatory mechanisms of the functioning of the AR in ER-/AR+ breast cancer will provide many novel targets for the purpose of therapeutic intervention.

METHODS/RESULTS

Using bioinformatics tools, we have identified 75 AR targets having prominent roles in cell cycle, apoptosis and metabolism. Herein, we validated 10 genes as AR targets by studying the regulation of these genes in MDA-MB-453 cell line on stimulation by androgens like 5α-dihydrotestosterone (DHT), using RT-qPCR and ChIP assay. It was observed that all the identified genes involved in cell cycle except MAD1L1 were found to be up regulated whereas expression of apoptosis related genes was decreased in response to DHT treatment. We performed an exhaustive, rigid-body docking between individual ARE and DNA binding domain (DBD) of the AR protein and it was found that novel residues K567, K588, K591 and R592 are involved in the process of DNA binding. To verify these specific DNA-protein interactions electrostatic energy term calculations for each residue was determined using the linearized Poisson-Boltzmann equation. Our experimental data showed that treatment of breast cancer cells with DHT promotes cell proliferation and decreases apoptosis. It was observed that bicalutamide treatment was able to reverse the effect of DHT.

CONCLUSION

Taken together, our results provide new insights into the mechanism by which AR promotes breast cancer progression. Moreover our work proposes to use bicalutamide along with taxanes as novel therapy for the treatment of TNBCs, which are positive for downstream AR signalling.

Conformational dynamics of individual antibodies using computational docking and AFM

Molecular recognition between a receptor and a ligand requires a certain level of flexibility in macromolecules. In this study, we aimed at analyzing the conformational variability of receptors portrayed by monoclonal antibodies that have been individually imaged using atomic force microscopy (AFM). Individual antibodies were chemically coupled to activated mica surface, and they have been imaged using AFM in ambient conditions. The resulting topographical surface of antibodies was used to assemble the three subunits constituting antibodies: two antigen-binding fragments and one crystallizable fragment using a surface-constrained computational docking approach. Reconstructed structures based on 10 individual topographical surfaces of antibodies are presented for which separation and relative orientation of the subunits were measured. When compared with three X-ray structures of antibodies present in the protein data bank database, results indicate that several arrangements of the reconstructed subunits are comparable with those of known structures. Nevertheless, no reconstructed structure superimposes adequately to any particular X-ray structure consequence of the antibody flexibility. We conclude that high-resolution AFM imaging with appropriate computational reconstruction tools is adapted to study the conformational dynamics of large individual macromolecules deposited on mica.

Structure-based design of small-molecule protein–protein interaction modulators: the story so far

As the pivotal role of protein–protein interactions in cell growth, transcriptional activity, intracellular trafficking, signal transduction and pathological conditions has been assessed, experimental and in silico strategies have been developed to design protein–protein interaction modulators. State-of-the-art structure-based design methods, mainly pharmacophore modeling and docking, which have succeeded in the identification of enzyme inhibitors, receptor agonists and antagonists, and new tools specifically conceived to target protein–protein interfaces (e.g., hot-spot and druggable pocket prediction methods) have been applied in the search for small-molecule protein–protein interaction modulators. Many successful applications of structure-based design approaches that, despite the challenge of targeting protein–protein interfaces with small molecules, have led to the identification of micromolar and submicromolar hits are reviewed here.

On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions

Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.

Predicting protein-DNA interactions by full search computational docking

Protein-DNA interactions are essential for many biological processes. X-ray crystallography can provide high-resolution structures, but protein-DNA complexes are difficult to crystallize and typically contain only small DNA fragments. Thus, there is a need for computational methods that can provide useful predictions to give insights into mechanisms and guide the design of new experiments. We used the program DOT, which performs an exhaustive, rigid-body search between two macromolecules, to investigate four diverse protein-DNA interactions. Here, we compare our computational results with subsequent experimental data on related systems. In all cases, the experimental data strongly supported our structural hypotheses from the docking calculations: a mechanism for weak, nonsequence-specific DNA binding by a transcription factor, a large DNA-binding footprint on the surface of the DNA-repair enzyme uracil-DNA glycosylase (UNG), viral and host DNA-binding sites on the catalytic domain of HIV integrase, and a three-DNA-contact model of the linker histone bound to the nucleosome. In the case of UNG, the experimental design was based on the DNA-binding surface found by docking, rather than the much smaller surface observed in the crystallographic structure. These comparisons demonstrate that the DOT electrostatic energy gives a good representation of the distinctive electrostatic properties of DNA and DNA-binding proteins. The large, favourably ranked clusters resulting from the dockings identify active sites, map out large DNA-binding sites, and reveal multiple DNA contacts with a protein. Thus, computational docking can not only help to identify protein-DNA interactions in the absence of a crystal structure, but also expand structural understanding beyond known crystallographic structures.

DockAFM: benchmarking protein structures by docking under AFM topographs

Proteins can adopt a variety of conformations. We present a simple server for scoring the agreement between 3D atomic structures and experimental envelopes obtained by atomic force microscopy. Three different structures of immunoglobulins (IgG) or blood coagulation factor V activated were tested and their agreement with several topographical surfaces was computed. This approach can be used to test structural variability within a family of proteins.

AVAILABILITY AND IMPLEMENTATION

DockAFM is available at http://biodev.cea.fr/dockafm.