CRANKITE: A fast polypeptide backbone conformation sampler

Jankowski W, Várnai C, Irwin BWJ, Payne MC, Chau PL. Efficiently Differentiating Agonists and Competitive Antagonists for Weak Allosteric Protein-Ligand Interactions with Linear Response Theory

What makes an agonist and a competitive antagonist? In this work, we aim to answer this question by performing parallel tempering Monte Carlo simulations on the serotonin type 3A (5-HT3A) receptor. We use linear response theory to predict conformational changes in the 5-HT3A receptor active site after weak perturbations are applied to its allosteric binding sites. A covariance tensor is built from conformational sampling of its apo state, and a harmonic approximation allows us to substitute the calculation of ligand-induced forces with the binding site's displacement vector. Remarkably, our study demonstrates the feasibility of effectively discerning between agonists and competitive antagonists for multiple ligands, requiring computationally expensive calculations only once per protein.

Finding Novel AMPs Secreted from the Human Microbiome as Potent Antibacterial and Antibiofilm Agents and Studying Their Synergistic Activity with Ag NCs

Due to the enhanced resistance of bacteria to antibiotics, researchers always try to find effective alternatives to treat drug-resistant bacterial infections. In this context, we have explored antimicrobial peptides (AMPs), which are a broad class of small peptide molecules, and investigated their efficacy as potent antibacterial and antibiofilm agents. AMPs can cause cell death either through disruption of the cell membrane or by inhibiting vital intracellular functions, by binding to RNA, DNA, or intracellular components upon transversion through the cell membrane. We attempted to find potent intracellular cationic AMPs that can demonstrate antibacterial activity through interaction with DNA. As a source of AMPs, we have utilized those that are secreted from the human microbiome with the anticipation that these will be non-toxic in nature. Out of the total 1087 AMPs, 27 were screened on the basis of amino acid length and efficacy to cross the cell membrane barrier. From the list of 27 peptides, 4 candidates were selected through the docking score of these peptides with the DNA binding domain of H2A proteins. Further, the molecular dynamics simulation analysis demonstrated that 2 AMPs, i.e., peptides 7 and 25, are having considerable membrane permeation and DNA binding ability. Further, the in vitro analysis indicated that both peptides 7 and 25 could exhibit potent antibacterial and antibiofilm activities. In order to further enhance the antibiofilm potency, the above AMPs were used as supplements to silver nanoclusters (Ag NCs) to get synergistic activity. The synergistic activity of Ag NCs was found to be significantly increased with both the above AMPs.

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

MOTIVATION

Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization.

RESULTS

Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/hpepdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antimicrobial Peptides from Human Microbiome Against Multidrug Efflux Pump of Pseudomonas aeruginosa: a Computational Study

The excess use of antibiotics has led to the evolution of multidrug-resistant pathogenic strains causing worldwide havoc. These multidrug-resistant strains require potent inhibitors. Pseudomonas aeruginosa is a lead cause of nosocomial infections and also feature in the critical priority list of the world health organization (WHO) for the development of new antibiotics against their antimicrobial resistance. Antimicrobial peptides (AMPs) found in almost every life form from microorganisms to humans are known to defend their hosts against various pathogens. Owing to the diversity of the human microbiome, in this study, we have identified the cell-penetrating AMPs from the human microbiome and studied their inhibitory activity against the outer membrane protein OprM of the MexAB-OprM, a constitutively expressed multidrug efflux pump of the Ps. aeruginosa. Screening of the AMPs from the human microbiome resulted in the identification of 147 cell-penetrating AMPs (CPAMPs). The virtual screening of these CPAMPs against the OprM protein showed significant inhibitory results with the top docked AMP showing binding affinity exceeding -30 kcal/mol. The molecular dynamic simulation determined the interaction stabilities between the AMPs and the OprM at the binding site. Further, the residue interaction networks (RINs) are analyses to identify the inhibitory patterns. Later, these patterns were confirmed by MM-PBSA analysis suggesting that the AMPs are majorly stabilized by electrostatic interactions at the binding site. Thus, the high binding affinity and insights from the molecular interaction signify that the identified CPAMPs from the human microbiome can be further explored as inhibitory agents against multidrug-resistant Ps. aeruginosa.

How does Sec63 affect the conformation of Sec61 in yeast?

The Sec complex catalyzes the translocation of proteins of the secretory pathway into the endoplasmic reticulum and the integration of membrane proteins into the endoplasmic reticulum membrane. Some substrate peptides require the presence and involvement of accessory proteins such as Sec63. Recently, a structure of the Sec complex from Saccharomyces cerevisiae, consisting of the Sec61 channel and the Sec62, Sec63, Sec71 and Sec72 proteins was determined by cryo-electron microscopy (cryo-EM). Here, we show by co-precipitation that the Sec61 channel subunit Sbh1 is not required for formation of stable Sec63-Sec61 contacts. Molecular dynamics simulations started from the cryo-EM conformation of Sec61 bound to Sec63 and of unbound Sec61 revealed how Sec63 affects the conformation of Sec61 lateral gate, plug, pore region and pore ring diameter via three intermolecular contact regions. Molecular docking of SRP-dependent vs. SRP-independent signal peptide chains into the Sec61 channel showed that the pore regions affected by presence/absence of Sec63 play a crucial role in positioning the signal anchors of SRP-dependent substrates nearby the lateral gate.

The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.

AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes

MOTIVATION

Protein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs.

RESULTS

Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein-peptide complex. We show that it outperforms leading peptide docking methods on two protein-peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein-peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein-protein interactions and interactions with disordered proteins.

AVAILABILITY AND IMPLEMENTATION

ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional movements of the GABA type A receptor

We have performed a Monte Carlo simulation of the GABA type A receptor. We have analysed the configurations and developed a correlation tensor method to predict receptor gating.

Docking Flexible Cyclic Peptides with AutoDock CrankPep

While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.

Systematic Analysis of the Binding Surfaces between tRNAs and Their Respective Aminoacyl tRNA Synthetase Based on Structural and Evolutionary Data

To determine the mechanism underlying the flow of genetic information, it is important to understand the relationship between a tRNA and its binding enzyme, a member of the aminoacyl-tRNA synthetase (aaRS) family. We have developed a novel method to project the interacting regions of tRNA-aaRS complexes, obtained from their three-dimensional structures, onto two-dimensional space. The interacting surface between each tRNA and its aaRS was successfully identified by determining these interactions with an atomic distance threshold of 3.3 Å. We analyzed their interactions, using 60 mainly bacterial and eukaryotic tRNA-aaRS complexes, and showed that the tRNA sequence regions that interacted most strongly with each aaRS are the anticodon loop and the CCA terminal region, followed by the D-stem. A sequence conservation analysis of the canonical tRNAs was conducted in 83 bacterial, 182 archaeal, and 150 eukaryotic species. Our results show that the three tRNA regions that interact with the aaRS and two additional loop regions (D-loop and TΨC-loop) known to be important for formation of the tRNA L-shaped structure are broadly conserved. We also found sequence conservations near the tRNA discriminator in the Bacteria and Archaea, and an enormous number of noncanonical tRNAs in the Eukaryotes. This is the first global view of tRNA evolution based on its structure and an unprecedented number of sequence data.

Impact of Rett Syndrome Mutations on MeCP2 MBD Stability

Rett syndrome causing missense mutations in the methyl-CpG-binding domain (MBD) of methyl CpG-binding protein 2 (MeCP2) were investigated both in silico and in vitro to reveal their effect on protein stability. It is demonstrated that the vast majority of frequently occurring mutations in the human population indeed alter the MBD folding free energy by a fraction of a kcal/mol up to more than 1 kcal/mol. While the absolute magnitude of the change of the free energy is small, the effect on the MBD functionality may be substantial since the folding free energy of MBD is about 2 kcal/mol only. Thus, it is emphasized that the effect of mutations on protein integrity should be evaluated with respect to the wild-type folding free energy but not with the absolute value of the folding free energy change. Furthermore, it was observed that the magnitude of the effect is correlated neither with the burial of the mutation sites nor with the basic amino acid physicochemical property change. Mutations that strongly perturb the immediate structural features were found to have little effect on folding free energy, while very conservative mutations resulted in large changes of the MBD stability. This observation was attributed to the protein's ability to structurally relax and reorganize to reduce the effect of mutation. Comparison between in silico and in vitro results indicated that some Web servers perform relatively well, while the free energy perturbation approach frequently overpredicts the magnitude of the free energy change especially when a charged amino acid is involved.

Efficient Parameter Estimation of Generalizable Coarse-Grained Protein Force Fields Using Contrastive Divergence: A Maximum Likelihood Approach

Maximum Likelihood (ML) optimization schemes are widely used for parameter inference. They maximize the likelihood of some experimentally observed data, with respect to the model parameters iteratively, following the gradient of the logarithm of the likelihood. Here, we employ a ML inference scheme to infer a generalizable, physics-based coarse-grained protein model (which includes Go̅-like biasing terms to stabilize secondary structure elements in room-temperature simulations), using native conformations of a training set of proteins as the observed data. Contrastive divergence, a novel statistical machine learning technique, is used to efficiently approximate the direction of the gradient ascent, which enables the use of a large training set of proteins. Unlike previous work, the generalizability of the protein model allows the folding of peptides and a protein (protein G) which are not part of the training set. We compare the same force field with different van der Waals (vdW) potential forms: a hard cutoff model, and a Lennard-Jones (LJ) potential with vdW parameters inferred or adopted from the CHARMM or AMBER force fields. Simulations of peptides and protein G show that the LJ model with inferred parameters outperforms the hard cutoff potential, which is consistent with previous observations. Simulations using the LJ potential with inferred vdW parameters also outperforms the protein models with adopted vdW parameter values, demonstrating that model parameters generally cannot be used with force fields with different energy functions. The software is available at https://sites.google.com/site/crankite/.

Predicting protein β-sheet contacts using a maximum entropy-based correlated mutation measure

MOTIVATION

The problem of ab initio protein folding is one of the most difficult in modern computational biology. The prediction of residue contacts within a protein provides a more tractable immediate step. Recently introduced maximum entropy-based correlated mutation measures (CMMs), such as direct information, have been successful in predicting residue contacts. However, most correlated mutation studies focus on proteins that have large good-quality multiple sequence alignments (MSA) because the power of correlated mutation analysis falls as the size of the MSA decreases. However, even with small autogenerated MSAs, maximum entropy-based CMMs contain information. To make use of this information, in this article, we focus not on general residue contacts but contacts between residues in β-sheets. The strong constraints and prior knowledge associated with β-contacts are ideally suited for prediction using a method that incorporates an often noisy CMM.

RESULTS

Using contrastive divergence, a statistical machine learning technique, we have calculated a maximum entropy-based CMM. We have integrated this measure with a new probabilistic model for β-contact prediction, which is used to predict both residue- and strand-level contacts. Using our model on a standard non-redundant dataset, we significantly outperform a 2D recurrent neural network architecture, achieving a 5% improvement in true positives at the 5% false-positive rate at the residue level. At the strand level, our approach is competitive with the state-of-the-art single methods achieving precision of 61.0% and recall of 55.4%, while not requiring residue solvent accessibility as an input.

AVAILABILITY

http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/

Exploring the energy landscapes of protein folding simulations with Bayesian computation

Nested sampling is a Bayesian sampling technique developed to explore probability distributions localized in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model. The nested sampling algorithm also provides an efficient way to calculate free energies and the expectation value of thermodynamic observables at any temperature, through a simple post processing of the output. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques, including parallel tempering. In this article, we describe a parallel implementation of the nested sampling algorithm and its application to the problem of protein folding in a Gō-like force field of empirical potentials that were designed to stabilize secondary structure elements in room-temperature simulations. We demonstrate the method by conducting folding simulations on a number of small proteins that are commonly used for testing protein-folding procedures. A topological analysis of the posterior samples is performed to produce energy landscape charts, which give a high-level description of the potential energy surface for the protein folding simulations. These charts provide qualitative insights into both the folding process and the nature of the model and force field used.