In Silico Design and Analysis of Plastic-Binding Peptides

Peptides that bind to inorganic materials can be used to functionalize surfaces, control crystallization, or assist in interfacial self-assembly. In the past, inorganic-binding peptides have been found predominantly through peptide library screening. While this method has successfully identified peptides that bind to a variety of materials, an alternative design approach that can intelligently search for peptides and provide physical insight for peptide affinity would be desirable. In this work, we develop a computational, physics-based approach to design inorganic-binding peptides, focusing on peptides that bind to the common plastics polyethylene, polypropylene, polystyrene, and poly(ethylene terephthalate). The PepBD algorithm, a Monte Carlo method that samples peptide sequence and conformational space, was modified to include simulated annealing, relax hydration constraints, and an ensemble of conformations to initiate design. These modifications led to the discovery of peptides with significantly better scores compared to those obtained using the original PepBD. PepBD scores were found to improve with increasing van der Waals interactions, although strengthening the intermolecular van der Waals interactions comes at the cost of introducing unfavorable electrostatic interactions. The best designs are enriched in amino acids with bulky side chains and possess hydrophobic and hydrophilic patches whose location depends on the adsorbed conformation. Future work will evaluate the top peptide designs in molecular dynamics simulations and experiment, enabling their application in microplastic pollution remediation and plastic-based biosensors

Designing Peptide Sequences in Flexible Chain Conformations to Bind RNA: A Search Algorithm Combining Monte Carlo, Self-Consistent Mean Field and Concerted Rotation Techniques

A search algorithm combining Monte Carlo, self-consistent mean field, and concerted rotation techniques was developed to discover peptide sequences that are reasonable HIV drug candidates due to their exceptional binding to human tRNA_UUU^Lys3, the primer of HIV replication. The search algorithm allows for iteration between sequence mutations and conformation changes during sequence evolution. Searches conducted for different classes of peptides identified several potential peptide candidates. Analysis of the energy revealed that the asparagine and cysteine at residues 11 and 12 play important roles in “recognizing” tRNA^Lys3 via van der Waals interactions, contributing to binding specificity. Arginines preferentially attract the phosphate linkage via charge–charge interaction, contributing to binding affinity. Evaluation of the RNA/peptide complex’s structure revealed that adding conformation changes to the search algorithm yields peptides with better binding affinity and specificity to tRNA^Lys3 than a previous mutation-only algorithm

Molecular recognition mechanism of peptide chain bound to the tRNA^Lys3 anticodon loop <i>in silico</i>

<div><p>The mechanism by which proteins recognize and bind the post-transcriptional modifications of RNAs is unknown, yet these interactions play important functions in biology. Atomistic molecular dynamics simulations were performed to examine the folding of the model peptide chain –<i>RVTHHAFLGAHRTVG</i>– and the complex formed by the folded peptide with the native anticodon stem and loop of the human tRNA^Lys3 (hASL^Lys3) in order to explore the binding mechanism. By analyzing and comparing two folded conformations of this peptide obtained from the folding simulation, we found that the van der Waals (VDW) energy is necessary for the thermal stability of the peptide, and the charge–charge (ELE + EGB) energy is crucial for determining the three-dimensional folded structure of the peptide backbone. Subsequently, two conformations of the peptide were employed to investigate their binding behaviors to hASL^Lys3. The metastable folded peptide was found to bind to hASL^Lys3 much easier than the stable folded peptide in the binding simulations. An energetic analysis reveals that the VDW energy favors the binding, whereas the ELE + EGB energies disfavor the binding. Arginines on the peptide preferentially attract the phosphate backbone via the inter-chain ELE + EGB interaction, significantly contributing to the binding affinity. The hydrophobic phenylalanine interacts with the anticodon loop of hASL^Lys3 via the inter-chain VDW interaction, significantly contributing to the binding specificity.</p></div

The design of a peptide sequence to inhibit HIV replication: a search algorithm combining Monte Carlo and self-consistent mean field techniques

<div><p>We developed a search algorithm combining Monte Carlo (MC) and self-consistent mean field techniques to evolve a peptide sequence that has good binding capability to the anticodon stem and loop (ASL) of human lysine tRNA species, tRNA^Lys3, with the ultimate purpose of breaking the replication cycle of human immunodeficiency virus-1. The starting point is the 15-amino-acid sequence, <i>RVTHHAFLGAHRTVG</i>, found experimentally by Agris and co-workers to bind selectively to hypermodified tRNA^Lys3. The peptide backbone conformation is determined via atomistic simulation of the peptide-ASL^Lys3 complex and then held fixed throughout the search. The proportion of amino acids of various types (hydrophobic, polar, charged, etc.) is varied to mimic different peptide hydration properties. Three different sets of hydration properties were examined in the search algorithm to see how this affects evolution to the best-binding peptide sequences. Certain amino acids are commonly found at fixed sites for all three hydration states, some necessary for binding affinity and some necessary for binding specificity. Analysis of the binding structure and the various contributions to the binding energy shows that: 1) two hydrophilic residues (asparagine at site 11 and the cysteine at site 12) “recognize” the ASL^Lys3 due to the VDW energy, and thereby contribute to its binding specificity and 2) the positively charged arginines at sites 4 and 13 preferentially attract the negatively charged sugar rings and the phosphate linkages, and thereby contribute to the binding affinity.</p></div

Phase Separation Behavior of Mixed Lipid Systems at Neutral and Low pH: Coarse-Grained Simulations with DMD/LIME

We extend LIME, an intermediate resolution, implicit solvent model for phospholipids previously used in discontinuous molecular dynamics simulations of 1,2-dipalmitoyl-<i>sn</i>-glycero-3-phosphocholine (DPPC) bilayer formation at 325 K, to the description of the geometry and energetics of 1,2-distearoyl-<i>sn</i>-glycero-3-phospho-l-serine (DSPS) and 1,2-dihenarachidoyl-<i>sn</i>-glycero-3-phosphocholine (21PC) and mixtures thereof at both neutral and low pH at 310 K. A multiscale modeling approach is used to calculate the LIME parameters from atomistic simulation data on a mixed DPPC/DSPS system at different pH values. In the model, 17 coarse-grained sites represent DSPS and 18 coarse-grained sites represent 21PC. Each of these coarse-grained sites is classified as 1 of 9 types. LIME/DMD simulations of equimolar bilayers show the following: (1) 21PC/DSPS bilayers with and without surface area restrictions separate faster at low pH than at neutral pH, (2) 21PC/DSPS systems separate at approximately the same rate regardless of whether they are subjected to surface area restrictions, and (3) bilayers with a molar ratio of 9:1 (21PC:DSPS) phase separate to form heterogeneous domains faster at low pH than at neutral pH. Our results are consistent with experimental findings of Sofou and co-workers (Bandekar et al. <i>Mol. Pharmaceutics</i>, 2013, <i>10</i>, 152–160; Karve et al. <i>Biomaterials</i>, 2010, <i>31</i>, 4409–4416) that more doxorubicin is released from 21PC/DSPS liposomes at low pH than at neutral pH, presumably because greater phase separation is achieved at low pH than at neutral pH. These are the first molecular-level simulations of the phase separation in mixed lipid bilayers induced by a change in pH

Extended Concerted Rotation Technique Enhances the Sampling Efficiency of the Computational Peptide-Design Algorithm

To enhance the sampling efficiency of our computational peptide-design algorithm in conformational space, the concerted rotation (CONROT) technique is extended to enable larger conformational perturbations of peptide chains. This allows us to make relatively large peptide conformation changes during the process of designing peptide sequences to bind with high affinity to a specific target. Searches conducted using the new algorithm identified six potential λ N­(2-22) peptide variants, called B1–B6, which bind to <i>boxB</i> RNA with high affinity. The results of explicit-solvent atomistic molecular dynamics simulations revealed that four of the evolved peptides, viz. B1, B2, B3, and B5, are excellent candidate binders to the target <i>boxB</i> RNA as they have lower binding free energies than the original λ N­(2-22) peptide. Three of the four peptides, B2, B3, and B5, result from searches that contain both sequence and conformation changes, indicating that adding backbone motif changes to the peptide-design algorithm improves its performance considerably

Capture of pure toxic gases through porous materials from molecular simulations

<p>In the last three decades, the air pollution is the main problem to affect human health and the environment in China and its contaminants include SO_2, NH_3, H₂S, NO₂, NO and CO. In this work, we employed grand canonical Monte Carlo simulations to investigate the adsorption capability of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) for these toxic gases. Eighty-nine MOFs and COFs were studied, and top-10 adsorption materials were screened for each toxic gas at room temperature. Dependence of the adsorption performance on the geometry and constructed element of MOFs/COFs was determined and the adsorption conditions were optimised. The open metal sites have mainly influenced the adsorption of NH₃, H₂S, NO₂ and NO. Especially, the X-DOBDC and XMOF-74 (X = Mg, Co, Ni, Zn) series of materials containing open metal sites are all best performance for adsorption of NH₃ to illustrate the importance of electrostatic interaction. Our simulation results also showed that ZnBDC and IRMOF-13 are good candidates to capture the toxic gases NH_3, H₂S, NO₂, NO and CO. This work provides important insights in screening MOF and COF materials with satisfactory performance for toxic gas removal.</p

Advancing Peptide-Based Biorecognition Elements for Biosensors Using <i>in-Silico</i> Evolution

Sensors for human health and performance monitoring require biological recognition elements (BREs) at device interfaces for the detection of key molecular biomarkers that are measurable biological state indicators. BREs, including peptides, antibodies, and nucleic acids, bind to biomarkers in the vicinity of the sensor surface to create a signal proportional to the biomarker concentration. The discovery of BREs with the required sensitivity and selectivity to bind biomarkers at low concentrations remains a fundamental challenge. In this study, we describe an <i>in-silico</i> approach to evolve higher sensitivity peptide-based BREs for the detection of cardiac event marker protein troponin I (cTnI) from a previously identified BRE as the parental affinity peptide. The P2 affinity peptide, evolved using our <i>in-silico</i> method, was found to have ∼16-fold higher affinity compared to the parent BRE and ∼10 fM (0.23 pg/mL) limit of detection. The approach described here can be applied towards designing BREs for other biomarkers for human health monitoring