From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction

Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold.Comment: Supporting information can be downloaded from: http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.100317

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Investigation of molecular interactions of prenylflavonoids at GABAA receptor subtypes

Prenylated flavonoids derived from Hops (Humulus lupulus) activate the γ-aminobutyric acid (GABA) type A receptors through positive and negative modulation. Currently, these compounds’ binding site at the different GABAAR subtypes is still unknown. Molecular interactions of several prenylated flavonoids were investigated at different GABAAR binding sites. The focus was on the receptor subtypes containing αβγ and αβδ subunits and the aim was to identify the most likely binding site for the prenylflavonoids by studying the relation between a ligand structure and the residues defining its putative pocket and the ligand’s calculated binding affinity. Available GABAAR crystal structures were obtained from the Protein Data Bank, and a comparative model of the α6β3δ receptor subtype was built using the MODELLER software. The compounds were docked at the putative binding sites of the studied GABAAR subtype structures with the GLIDE tool of the Maestro molecular modeling package. An estimate of the free energy of binding was calculated with the Prime/MMGBSA tool of Maestro for all the docked receptor-ligand complexes. The obtained results suggest that prenylflavonoids may bind to more than one pocket in the extracellular domain of the studied GABAAR subtypes. It was not possible to distinguish high affinity binding sites from low affinity binding sites as the docking results varied for each compound in the studied pockets. Discrepancy in results is likely caused by modeling binding site without knowing the correct conformations of the side chains forming the pockets. Based on the modelled α6β3δ subtype, β3δ interface may be the most likely binding site for the hops compounds. To determine the binding of the prenylated flavonoids most accurately, experimental structure determination by X-ray crystallography could be attempted

Integral membrane pyrophosphatases : A novel drug target for human pathogens?

Membrane-integral pyrophosphatases (mPPases) are found in several human pathogens, including Plasmodium species, the protozoan parasites that cause malaria. These enzymes hydrolyze pyrophosphate and couple this to the pumping of ions (H+ and/or Na+) across a membrane to generate an electrochemical gradient. mPPases play an important role in stress tolerance in plants, protozoan parasites, and bacteria. The solved structures of mPPases from Vigna radiata and Thermotoga maritima open the possibility of using structure-based drug design to generate novel molecules or repurpose known molecules against this enzyme. Here, we review the current state of knowledge regarding mPPases, focusing on their structure, the proposed mechanism of action, and their role in human pathogens. We also summarize different methodologies in structure-based drug design and propose an example region on the mPPase structure that can be exploited by these structure-based methods for drug targeting. Since mPPases are not found in animals and humans, this enzyme is a promising potential drug target against livestock and human pathogens. © 2016, Adrian Goldman, et al.Peer reviewe

Binding mode of novel multimodal serotonin transporter compounds in 5-hydroxytryptamine receptors

Antidepressants are the most common treatment of depression, one of the leading causes of suicide and disability worldwide. Currently marketed antidepressants have certain limitations; they have a delayed response time, only about 1/3 of the patients respond to the first agent prescribed, and many of them produce side effects that reduce the quality of life. The need for more efficacious and faster-acting antidepressants with fewer side effects is thus apparent. Studies have shown that 5-HT receptors (5-HTRs) are involved in many of the adverse effects of antidepressants, and may be responsible for efficacy issues and the delayed onset of therapeutic action. Some novel multimodal (two or more pharmacological actions) antidepressants combine inhibition of the serotonin transporter (SERT) with agonist or antagonist activity at 5-HTRs, to counteract the activity responsible for the aforementioned problems with the present antidepressants. This study continues a previous virtual screening study, where we identified new compounds for SERT. Several of the compounds also showed affinity for one or more 5-HTRs. Although affinities are known, their ligand – 5-HTRs binding modes and their mode of action (agonist or antagonist action) for the target 5-HTRs have not been established. The aim of this study was to predict their mode of action, and to identify binding modes important for high affinity, by the use of computational methods. Homology modeling was used to construct models of 5-HT1AR, 5-HT2AR, 5-HT6R and 5-HT7R. The models were used for molecular docking and calculations of structural interaction fingerprints. Several residues important for affinity to the target receptors were identified, and preferable binding modes were determined. The mode of action of the compounds was predicted based on their preferences for agonist/antagonist-selective models, and on previous studies of agonists and antagonists showing that agonists form strong polar interactions transmembrane helix 5 (TM5). The results indicated that several of the compounds might have potential to be developed into new antidepressant drugs

Structural Refinement of Membrane Proteins by Restrained Molecular Dynamics and Solvent Accessibility Data

AbstractWe present an approach for incorporating solvent accessibility data from electron paramagnetic resonance experiments in the structural refinement of membrane proteins through restrained molecular dynamics simulations. The restraints have been parameterized from oxygen (ΠO2) and nickel-ethylenediaminediacetic acid (ΠNiEdda) collision frequencies, as indicators of lipid or aqueous exposed spin-label sites. These are enforced through interactions between a pseudoatom representation of the covalently attached Nitroxide spin-label and virtual “solvent” particles corresponding to O2 and NiEdda in the surrounding environment. Interactions were computed using an empirical potential function, where the parameters have been optimized to account for the different accessibilities of the spin-label pseudoatoms to the surrounding environment. This approach, “pseudoatom-driven solvent accessibility refinement”, was validated by refolding distorted conformations of the Streptomyces lividans potassium channel (KcsA), corresponding to a range of 2–30Å root mean-square deviations away from the native structure. Molecular dynamics simulations based on up to 58 electron paramagnetic resonance restraints derived from spin-label mutants were able to converge toward the native structure within 1–3Å root mean-square deviations with minimal computational cost. The use of energy-based ranking and structure similarity clustering as selection criteria helped in the convergence and identification of correctly folded structures from a large number of simulations. This approach can be applied to a variety of integral membrane protein systems, regardless of oligomeric state, and should be particularly useful in calculating conformational changes from a known reference crystal structure