Using Big Data Analytics to "Back Engineer" Protein Conformational Selection Mechanisms.

In the living cells, proteins bind small molecules (or "ligands") through a "conformational selection" mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of "binding protein conformations" in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such "binding conformations" for proteins and provides a big data approach to the conformational selection mechanism.

Novel Big Data-Driven Machine Learning Models for Drug Discovery Application.

Most contemporary drug discovery projects start with a 'hit discovery' phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, 'virtual docking calculations' are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel 'ensemble docking' calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be 'selected' by chemicals is known as 'conformational selection' process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process.

Structure-based group A streptococcal vaccine design: Helical wheel homology predicts antibody cross-reactivity among streptococcal M protein-derived peptides.

Group A streptococcus (Strep A) surface M protein, an α-helical coiled-coil dimer, is a vaccine target and a major determinant of streptococcal virulence. The sequence-variable N-terminal region of the M protein defines the M type and also contains epitopes that promote opsonophagocytic killing of streptococci. Recent reports have reported considerable cross-reactivity among different M types, suggesting the prospect of identifying cross-protective epitopes that would constitute a broadly protective multivalent vaccine against Strep A isolates. Here, we have used a combination of immunological assays, structural biology, and cheminformatics to construct a recombinant M protein-based vaccine that included six Strep A M peptides that were predicted to elicit antisera that would cross-react with an additional 15 nonvaccine M types of Strep A. Rabbit antisera against this recombinant vaccine cross-reacted with 10 of the 15 nonvaccine M peptides. Two of the five nonvaccine M peptides that did not cross-react shared high sequence identity (≥50%) with the vaccine peptides, implying that high sequence identity alone was insufficient for cross-reactivity among the M peptides. Additional structural analyses revealed that the sequence identity at corresponding polar helical-wheel heptad sites between vaccine and nonvaccine peptides accurately distinguishes cross-reactive from non-cross-reactive peptides. On the basis of these observations, we developed a scoring algorithm based on the sequence identity at polar heptad sites. When applied to all epidemiologically important M types, this algorithm should enable the selection of a minimal number of M peptide-based vaccine candidates that elicit broadly protective immunity against Strep A.

Structure based virtual screening identifies small molecule effectors for the sialoglycan binding protein Hsa.

Infective endocarditis (IE) is a cardiovascular disease often caused by bacteria of the viridans group of streptococci, which includes Streptococcus gordonii and Streptococcus sanguinis. Previous research has found that serine-rich repeat (SRR) proteins on the S. gordonii bacterial surface play a critical role in pathogenesis by facilitating bacterial attachment to sialylated glycans displayed on human platelets. Despite their important role in disease progression, there are currently no anti-adhesive drugs available on the market. Here, we performed structure-based virtual screening using an ensemble docking approach followed by consensus scoring to identify novel small molecule effectors against the sialoglycan binding domain of the SRR adhesin protein Hsa from the S. gordonii strain DL1. The screening successfully predicted nine compounds which were able to displace the native ligand (sialyl-T antigen) in an in vitro assay and bind competitively to Hsa. Furthermore, hierarchical clustering based on the MACCS fingerprints showed that eight of these small molecules do not share a common scaffold with the native ligand. This study indicates that SRR family of adhesin proteins can be inhibited by diverse small molecules and thus prevent the interaction of the protein with the sialoglycans. This opens new avenues for discovering potential drugs against IE.

A Politics of Intellectual Property: Creating a Patent System in Revolutionary France.

Economic interpretations, in particular the lens of the Industrial Revolution, have strongly influenced our understanding of the rise of intellectual property. This article examines the political origins of the 1791 patent law in France, which is usually seen as the birth of the modern patent system in that country. Although calls to reform the Old Regime's privileges of invention were increasingly frequent as the eighteenth century wore on, only the French Revolution provided the ideological resources necessary for such a transformation. The revolutionaries did more than just adopt the procedures of English legislation, such as replacing prior examination with a registration system. I argue that the new patents (brevets d'invention) reflected the Revolution's image of the ideal society-a society built on natural rights, property, and the social contract, and made of rational inventors and an enlightened public. In France, more so than in other countries, intellectual property was the child of a political revolution rather than industrial capitalism.

Ensemble Docking in Drug Discovery.

Ensemble docking corresponds to the generation of an "ensemble" of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.

Novel Small Molecule JP-153 Targets the Src-FAK-Paxillin Signaling Complex to Inhibit VEGF-Induced Retinal Angiogenesis.

Targeting vascular endothelial growth factor (VEGF) is a common treatment strategy for neovascular eye disease, a major cause of vision loss in diabetic retinopathy and age-related macular degeneration. However, the decline in clinical efficacy over time in many patients suggests that monotherapy of anti-VEGF protein therapeutics may benefit from adjunctive treatments. Our previous work has shown that through decreased activation of the cytoskeletal protein paxillin, growth factor-induced ischemic retinopathy in the murine oxygen-induced retinopathy model could be inhibited. In this study, we demonstrated that VEGF-dependent activation of the Src/FAK/paxillin signalsome is required for human retinal endothelial cell migration and proliferation. Specifically, the disruption of focal adhesion kinase (FAK) and paxillin interactions using the small molecule JP-153 inhibited Src-dependent phosphorylation of paxillin (Y118) and downstream activation of Akt (S473), resulting in reduced migration and proliferation of retinal endothelial cells stimulated with VEGF. However, this effect did not prevent the initial activation of either Src or FAK. Furthermore, topical application of a JP-153-loaded microemulsion affected the hallmark features of pathologic retinal angiogenesis, reducing neovascular tuft formation and increased avascular area, in a dose-dependent manner. In conclusion, our results suggest that using small molecules to modulate the focal adhesion protein paxillin is an effective strategy for treating pathologic retinal neovascularization. To our knowledge, this is the first paradigm validating modulation of paxillin to inhibit angiogenesis. As such, we have identified and developed a novel class of small molecules aimed at targeting focal adhesion protein interactions that are essential for pathologic neovascularization in the eye.

Highly Dynamic Anion-Quadrupole Networks in Proteins.

The dynamics of anion-quadrupole (or anion-π) interactions formed between negatively charged (Asp/Glu) and aromatic (Phe) side chains are for the first time computationally characterized in RmlC (Protein Data Bank entry 1EP0 ), a homodimeric epimerase. Empirical force field-based molecular dynamics simulations predict anion-quadrupole pairs and triplets (anion-anion-π and anion-π-π) are formed by the protein during the simulated trajectory, which suggests that the anion-quadrupole interactions may provide a significant contribution to the overall stability of the protein, with an average of -1.6 kcal/mol per pair. Some anion-π interactions are predicted to form during the trajectory, extending the number of anion-quadrupole interactions beyond those predicted from crystal structure analysis. At the same time, some anion-π pairs observed in the crystal structure exhibit marginal stability. Overall, most anion-π interactions alternate between an "on" state, with significantly stabilizing energies, and an "off" state, with marginal or null stabilizing energies. The way proteins possibly compensate for transient loss of anion-quadrupole interactions is characterized in the RmlC aspartate 84-phenylalanine 112 anion-quadrupole pair observed in the crystal structure. A double-mutant cycle analysis of the thermal stability suggests a possible loss of anion-π interactions compensated by variations of hydration of the residues and formation of compensating electrostatic interactions. These results suggest that near-planar anion-quadrupole pairs can exist, sometimes transiently, which may play a role in maintaining the structural stability and function of the protein, in an otherwise very dynamic interplay of a nonbonded interaction network as well as solvent effects.

General trends of dihedral conformational transitions in a globular protein.

Dihedral conformational transitions are analyzed systematically in a model globular protein, cytochrome P450cam, to examine their structural and chemical dependences through combined conventional molecular dynamics (cMD), accelerated molecular dynamics (aMD) and adaptive biasing force (ABF) simulations. The aMD simulations are performed at two acceleration levels, using dihedral and dual boost, respectively. In comparison with cMD, aMD samples protein dihedral transitions approximately two times faster on average using dihedral boost, and ∼ 3.5 times faster using dual boost. In the protein backbone, significantly higher dihedral transition rates are observed in the bend, coil, and turn flexible regions, followed by the ß bridge and ß sheet, and then the helices. Moreover, protein side chains of greater length exhibit higher transition rates on average in the aMD-enhanced sampling. Side chains of the same length (particularly Nχ = 2) exhibit decreasing transition rates with residues when going from hydrophobic to polar, then charged and aromatic chemical types. The reduction of dihedral transition rates is found to be correlated with increasing energy barriers as identified through ABF free energy calculations. These general trends of dihedral conformational transitions provide important insights into the hierarchical dynamics and complex free energy landscapes of functional proteins.

Discovery of Novel Nonactive Site Inhibitors of the Prothrombinase Enzyme Complex.

The risk of serious bleeding is a major liability of anticoagulant drugs that are active-site competitive inhibitors targeting the Factor Xa (FXa) prothrombin (PT) binding site. The present work identifies several new classes of small molecule anticoagulants that can act as nonactive site inhibitors of the prothrombinase (PTase) complex composed of FXa and Factor Va (FVa). These new classes of anticoagulants were identified, using a novel agnostic computational approach to identify previously unrecognized binding pockets at the FXa-FVa interface. From about three million docking calculations of 281,128 compounds in a conformational ensemble of FXa heavy chains identified by molecular dynamics (MD) simulations, 97 compounds and their structural analogues were selected for experimental validation, through a series of inhibition assays. The compound selection was based on their predicted binding affinities to FXa and their ability to successfully bind to multiple protein conformations while showing selectivity for particular binding sites at the FXa/FVa interface. From these, thirty-one (31) compounds were experimentally identified as nonactive site inhibitors. Concentration-based assays further identified 10 compounds represented by four small-molecule families of inhibitors that achieve dose-independent partial inhibition of PTase activity in a nonactive site-dependent and self-limiting mechanism. Several compounds were identified for their ability to bind to protein conformations only seen during MD, highlighting the importance of accounting for protein flexibility in structure-based drug discovery approaches.

Ensemble-based docking: From hit discovery to metabolism and toxicity predictions.

This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.

Molecular interactions between photosystem I and ferredoxin: an integrated energy frustration and experimental model.

The stromal domain (PsaC, PsaD, and PsaE) of photosystem I (PSI) reduces transiently bound ferredoxin (Fd) or flavodoxin. Experimental structures exist for all of these protein partners individually, but no experimental structure of the PSI/Fd or PSI/flavodoxin complexes is presently available. Molecular models of Fd docked onto the stromal domain of the cyanobacterial PSI site are constructed here utilizing X-ray and NMR structures of PSI and Fd, respectively. Predictions of potential protein-protein interaction regions are based on experimental site-directed mutagenesis and cross-linking studies to guide rigid body docking calculations of Fd into PSI, complemented by energy landscape theory to bring together regions of high energetic frustration on each of the interacting proteins. The results identify two regions of high localized frustration on the surface of Fd that contain negatively charged Asp and Glu residues. This study predicts that these regions interact predominantly with regions of high localized frustration on the PsaC, PsaD, and PsaE chains of PSI, which include several residues predicted by previous experimental studies.

Conformational coupling between receptor and kinase binding sites through a conserved salt bridge in a signaling complex scaffold protein.

Bacterial chemotaxis is one of the best studied signal transduction pathways. CheW is a scaffold protein that mediates the association of the chemoreceptors and the CheA kinase in a ternary signaling complex. The effects of replacing conserved Arg62 of CheW with other residues suggested that the scaffold protein plays a more complex role than simply binding its partner proteins. Although R62A CheW had essentially the same affinity for chemoreceptors and CheA, cells expressing the mutant protein are impaired in chemotaxis. Using a combination of molecular dynamics simulations (MD), NMR spectroscopy, and circular dichroism (CD), we addressed the role of Arg62. Here we show that Arg62 forms a salt bridge with another highly conserved residue, Glu38. Although this interaction is unimportant for overall protein stability, it is essential to maintain the correct alignment of the chemoreceptor and kinase binding sites of CheW. Computational and experimental data suggest that the role of the salt bridge in maintaining the alignment of the two partner binding sites is fundamental to the function of the signaling complex but not to its assembly. We conclude that a key feature of CheW is to maintain the specific geometry between the two interaction sites required for its function as a scaffold.

Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors.

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.

VinaMPI: facilitating multiple receptor high-throughput virtual docking on high-performance computers.

The program VinaMPI has been developed to enable massively large virtual drug screens on leadership-class computing resources, using a large number of cores to decrease the time-to-completion of the screen. VinaMPI is a massively parallel Message Passing Interface (MPI) program based on the multithreaded virtual docking program AutodockVina, and is used to distribute tasks while multithreading is used to speed-up individual docking tasks. VinaMPI uses a distribution scheme in which tasks are evenly distributed to the workers based on the complexity of each task, as defined by the number of rotatable bonds in each chemical compound investigated. VinaMPI efficiently handles multiple proteins in a ligand screen, allowing for high-throughput inverse docking that presents new opportunities for improving the efficiency of the drug discovery pipeline. VinaMPI successfully ran on 84,672 cores with a continual decrease in job completion time with increasing core count. The ratio of the number of tasks in a screening to the number of workers should be at least around 100 in order to have a good load balance and an optimal job completion time. The code is freely available and downloadable. Instructions for downloading and using the code are provided in the Supporting Information.

STAAR: statistical analysis of aromatic rings.

The statistical analysis of aromatic rings program allows for an automated search for anion-π interactions between phenylalanine residues and carboxylic acid moieties of neighboring aspartic acid or glutamic acid residues in protein data bank (PDB) structures. The program is written in C++ and is available both as a standalone code and through a web implementation that allows users to upload and analyze biomolecular structures in PDB format. The program outputs lists of Phe/Glu or Phe/Asp pairs involved in potential anion-π interactions, together with geometrical (distance and angle between the Phe's center of mass and Glu or Asp's center of charge) and energetic (quantum mechanical Kitaura-Morokuma interaction energy between the residues) descriptions of each anion-π interaction. Application of the program on the latest content of the PDB shows that anion-π interactions are present in thousands of protein structures and can possess strong energies, as low as -8.72 kcal/mol.

Biasing reaction pathways with mechanical force.

During the course of chemical reactions, reactant molecules need to surmount an energy barrier to allow their transformation into products. The energy needed for this process is usually provided by heat, light, pressure or electrical potential, which act either by changing the distribution of the reactants on their ground-state potential energy surface or by moving them onto an excited-state potential energy surface and thereby facilitate movement over the energy barrier. A fundamentally different way of initiating or accelerating a reaction is the use of force to deform reacting molecules along a specific direction of the reaction coordinate. Mechanical force has indeed been shown to activate covalent bonds in polymers, but the usual result is chain scission. Here we show that mechanically sensitive chemical groups make it possible to harness the mechanical forces generated when exposing polymer solutions to ultrasound, and that this allows us to accelerate rearrangement reactions and bias reaction pathways to yield products not obtainable from purely thermal or light-induced reactions. We find that when placed within long polymer strands, the trans and cis isomers of a 1,2-disubstituted benzocyclobutene undergo an ultrasound-induced electrocyclic ring opening in a formally conrotatory and formally disrotatory process, respectively, that yield identical products. This contrasts with reaction initiation by light or heat alone, in which case the isomers follow mutually exclusive pathways to different products. Mechanical forces associated with ultrasound can thus clearly alter the shape of potential energy surfaces so that otherwise forbidden or slow processes proceed under mild conditions, with the directionally specific nature of mechanical forces providing a reaction control that is fundamentally different from that achieved by adjusting chemical or physical parameters. Because rearrangement in our system occurs before chain scission, the effect we describe might allow the development of materials that are activated by mechanical stress fields.

Quantifying online citizen science: Dynamics and demographics of public participation in science.

Citizen scientists around the world are collecting data with their smartphones, performing scientific calculations on their home computers, and analyzing images on online platforms. These online citizen science projects are frequently lauded for their potential to revolutionize the scope and scale of data collection and analysis, improve scientific literacy, and democratize science. Yet, despite the attention online citizen science has attracted, it remains unclear how widespread public participation is, how it has changed over time, and how it is geographically distributed. Importantly, the demographic profile of citizen science participants remains uncertain, and thus to what extent their contributions are helping to democratize science. Here, we present the largest quantitative study of participation in citizen science based on online accounts of more than 14 million participants over two decades. We find that the trend of broad rapid growth in online citizen science participation observed in the early 2000s has since diverged by mode of participation, with consistent growth observed in nature sensing, but a decline seen in crowdsourcing and distributed computing. Most citizen science projects, except for nature sensing, are heavily dominated by men, and the vast majority of participants, male and female, have a background in science. The analysis we present here provides, for the first time, a robust 'baseline' to describe global trends in online citizen science participation. These results highlight current challenges and the future potential of citizen science. Beyond presenting our analysis of the collated data, our work identifies multiple metrics for robust examination of public participation in science and, more generally, online crowds. It also points to the limits of quantitative studies in capturing the personal, societal, and historical significance of citizen science.

Coupled flexibility change in cytochrome P450cam substrate binding determined by neutron scattering, NMR, and molecular dynamics simulation.

Neutron scattering and nuclear magnetic resonance relaxation experiments are combined with molecular dynamics (MD) simulations in a novel, to our knowledge, approach to investigate the change in internal dynamics on substrate (camphor) binding to a protein (cytochrome P450cam). The MD simulations agree well with both the neutron scattering, which furnishes information on global flexibility, and the nuclear magnetic resonance data, which provides residue-specific order parameters. Decreased fluctuations are seen in the camphor-bound form using all three techniques, dominated by changes in specific regions of the protein. The combined experimental and simulation results permit a detailed description of the dynamical change, which involves modifications in the coupling between the dominant regions and concomitant substrate access channel closing, via specific salt-bridge, hydrogen-bonding, and hydrophobic interactions. The work demonstrates how the combination of complementary experimental spectroscopies with MD simulation can provide an in-depth description of functional dynamical protein changes.

Temperature-dependent dynamical transitions of different classes of amino acid residue in a globular protein.

The temperature dependences of the nanosecond dynamics of different chemical classes of amino acid residue have been analyzed by combining elastic incoherent neutron scattering experiments with molecular dynamics simulations on cytochrome P450cam. At T = 100-160 K, anharmonic motion in hydrophobic and aromatic residues is activated, whereas hydrophilic residue motions are suppressed because of hydrogen-bonding interactions. In contrast, at T = 180-220 K, water-activated jumps of hydrophilic side chains, which are strongly coupled to the relaxation rates of the hydrogen bonds they form with hydration water, become apparent. Thus, with increasing temperature, first the hydrophobic core awakens, followed by the hydrophilic surface.