Prion-like Aggregation of the Heptapeptide GNNQQNY into Amyloid Nanofiber Is Governed by Configuration Entropy.

A major cause of prion infectivity is the early formation of small, fibril-like aggregates consisting of the heptapeptide GNNQQNY. The prion aggregates exhibit a unique stacking mode in which the hydrophobic tyrosine (Y) is exposed outward, forming a bilayer ß-sheet-stacking zipper structure. This stacking mode of the prion peptides, termed "Y-outward" structure for convenience, goes against the common understanding that, for other amyloid-forming peptides, the hydrophobic residues should be hidden within the peptide fibril, referred to as "Y-inward" structure. To explore the extraordinary stacking behaviors of the prion GNNQQNY peptides, two fibril models are constructed in a fashion of "Y-outward" and "Y-inward" stackings and then studied in silico to examine their thermodynamic stabilities and disaggregation pathways. The "Y-inward" structure indeed exhibits stronger thermodynamic stability than the "Y-outward" structure, according to potential energy and stacking energy calculations. To show how the peptide fibrils dissociate, we illustrated two disaggregation pathways. A dihedral-based free energy landscape was then calculated to examine the conformational degrees of freedom of the GNNQQNY chains in the "Y-outward" and "Y-inward" structures. Peptide chains lose more configurational entropy in the "Y-inward" structure than in the "Y-outward" structure, indicating that the prion peptides are prone to aggregate in a fashion of "Y-outward" stacking pattern due to its low conformational constraints. The prion-like aggregation of the GNNQQNY peptides into amyloid fibrils is primarily governed by the configuration entropy.

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.

Design of 8-mer peptides that block Clostridioides difficile toxin A in intestinal cells.

Infections by Clostridioides difficile, a bacterium that targets the large intestine (colon), impact a large number of people worldwide. Bacterial colonization is mediated by two exotoxins: toxins A and B. Short peptides that can be delivered to the gut and inhibit the biocatalytic activity of these toxins represent a promising therapeutic strategy to prevent and treat C. diff. infection. We describe an approach that combines a Peptide Binding Design (PepBD) algorithm, molecular-level simulations, a rapid screening assay to evaluate peptide:toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block Toxin A in colon epithelial cells. One peptide, SA1, is found to block TcdA toxicity in primary-derived human colon (large intestinal) epithelial cells. SA1 binds TcdA with a KD of 56.1 ± 29.8 nM as measured by surface plasmon resonance (SPR).

Design of 8-mer Peptides that Block Clostridioides difficile Toxin A in Intestinal Cells.

Clostridioides difficile ( C. diff .) is a bacterium that causes severe diarrhea and inflammation of the colon. The pathogenicity of C. diff . infection is derived from two major toxins, toxins A (TcdA) and B (TcdB). Peptide inhibitors that can be delivered to the gut to inactivate these toxins are an attractive therapeutic strategy. In this work, we present a new approach that combines a pep tide b inding d esign algorithm (PepBD), molecular-level simulations, rapid screening of candidate peptides for toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block the glucosyltransferase activity of TcdA by targeting its glucosyltransferase domain (GTD). Using PepBD and explicit-solvent molecular dynamics simulations, we identified seven candidate peptides, SA1-SA7. These peptides were selected for specific TcdA GTD binding through a custom solid-phase peptide screening system, which eliminated the weaker inhibitors SA5-SA7. The efficacies of SA1-SA4 were then tested using a trans-epithelial electrical resistance (TEER) assay on monolayers of the human gut epithelial culture model. One peptide, SA1, was found to block TcdA toxicity in primary-derived human jejunum (small intestinal) and colon (large intestinal) epithelial cells. SA1 bound TcdA with a K D of 56.1 ± 29.8 nM as measured by surface plasmon resonance (SPR). Significance Statement: Infections by Clostridioides difficile , a bacterium that targets the large intestine (colon), impact a significant number of people worldwide. Bacterial colonization is mediated by two exotoxins: toxins A and B. Short peptides that can inhibit the biocatalytic activity of these toxins represent a promising strategy to prevent and treat C. diff . infection. We describe an approach that combines a Peptide B inding D esign (PepBD) algorithm, molecular-level simulations, a rapid screening assay to evaluate peptide:toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block Toxin A in small intestinal and colon epithelial cells. Importantly, our designed peptide, SA1, bound toxin A with nanomolar affinity and blocked toxicity in colon cells.

Computational Design and Experimental Validation of ACE2-Derived Peptides as SARS-CoV-2 Receptor Binding Domain Inhibitors.

The COVID-19 pandemic has caused significant social and economic disruption across the globe. Cellular entry of SARS-CoV-2 into the human body is mediated via binding of the Receptor Binding Domain (RBD) on the viral Spike protein (SARS-CoV-2 RBD) to Angiotensin-Converting Enzyme 2 (ACE2) expressed on host cells. Molecules that can disrupt ACE2:RBD interactions are attractive therapeutic candidates to prevent virus entry into human cells. A computational strategy that combines our Peptide Binding Design (PepBD) algorithm with atomistic molecular dynamics simulations was used to design new inhibitory peptide candidates via sequence iteration starting with a 23-mer peptide, referred to as SBP1. SBP1 is derived from a region of the ACE2 Peptidase Domain α1 helix that binds to the SARS-CoV-2 RBD of the initial Wuhan-Hu-1 strain. Three peptides demonstrated a solution-phase RBD-binding dissociation constant in the micromolar range during tryptophan fluorescence quenching experiments, one peptide did not bind, and one was insoluble at micromolar concentrations. However, in competitive ELISA assays, none of these peptides could outcompete ACE2 binding to SARS-CoV-2-RBD up to concentrations of 50 µM, similar to the parent SBP1 peptide which also failed to outcompete ACE2:RBD binding. Molecular dynamics simulations suggest that P4 would have a good binding affinity for the RBD domain of Beta-B.1.351, Gamma-P.1, Kappa-B.1.617.1, Delta-B.1.617.2, and Omicron-B.1.1.529 variants, but not the Alpha variant. Consistent with this, P4 bound Kappa-B.1.617.1 and Delta-B.1.617.2 RBD with micromolar affinity in tryptophan fluorescence quenching experiments. Collectively, these data show that while relatively short unstructured peptides can bind to SARS-CoV-2 RBD with moderate affinity, they are incapable of outcompeting the strong interactions between RBD and ACE2.

De novo discovery of peptide-based affinity ligands for the fab fragment of human immunoglobulin G.

Antibody fragments and their engineered variants show true potential as next-generation therapeutics as they combine excellent targeting with superior biodistribution and blood clearance. Unlike full antibodies, however, antibody fragments do not yet have a standard platform purification process for large-scale production. Short peptide ligands are viable alternatives to protein ligands in affinity chromatography. In this work, an integrated computational and experimental scheme is described to de novo design 9-mer peptides that bind to Fab fragments. The first cohort of designed sequences was tested experimentally using human polyclonal Fab, and the top performing sequence was selected as a prototype for a subsequent round of ligand refinement in silico. The resulting peptides were conjugated to chromatographic resins and evaluated via equilibrium and dynamic binding studies using human Fab-κ and Fab-λ. The equilibrium studies returned values of binding capacities up to 32 mg of Fab per mL of resin with mild affinity (KD ∼ 10-5 M) that are conducive to high product capture and recovery. Dynamic studies returned values of product yield up to ∼90%. Preliminary purification studies provided purities of 83-93% and yields of 11-89%. These results lay the groundwork for future development of these ligands towards biomanufacturing translation.

In Silico Identification and Experimental Validation of Peptide-Based Inhibitors Targeting Clostridium difficile Toxin A.

Clostridium difficile infection is mediated by two major exotoxins: toxins A (TcdA) and B (TcdB). Inhibiting the biocatalytic activities of these toxins with targeted peptide-based drugs can reduce the risk of C. difficile infection. In this work, we used a computational strategy that integrates a peptide binding design (PepBD) algorithm and explicit-solvent atomistic molecular dynamics simulation to determine promising toxin A-targeting peptides that can recognize and bind to the catalytic site of the TcdA glucosyltransferase domain (GTD). Our simulation results revealed that two out of three in silico discovered peptides, viz. the neutralizing peptides A (NPA) and B (NPB), exhibit lower binding free energies when bound to the TcdA GTD than the phage-display discovered peptide, viz. the reference peptide (RP). These peptides may serve as potential inhibitors against C. difficile infection. The efficacy of the peptides RP, NPA, and NPB to neutralize the cytopathic effects of TcdA was tested in vitro in human jejunum cells. Both phage-display peptide RP and in silico peptide NPA were found to exhibit strong toxin-neutralizing properties, thereby preventing the TcdA toxicity. However, the in silico peptide NPB demonstrates a relatively low efficacy against TcdA.

Sequence patterns and signatures: Computational and experimental discovery of amyloid-forming peptides.

Screening amino acid sequence space via experiments to discover peptides that self-assemble into amyloid fibrils is challenging. We have developed a computational peptide assembly design (PepAD) algorithm that enables the discovery of amyloid-forming peptides. Discontinuous molecular dynamics (DMD) simulation with the PRIME20 force field combined with the FoldAmyloid tool is used to examine the fibrilization kinetics of PepAD-generated peptides. PepAD screening of ∼10,000 7-mer peptides resulted in twelve top-scoring peptides with two distinct hydration properties. Our studies revealed that eight of the twelve in silico discovered peptides spontaneously form amyloid fibrils in the DMD simulations and that all eight have at least five residues that the FoldAmyloid tool classifies as being aggregation-prone. Based on these observations, we re-examined the PepAD-generated peptides in the sequence pool returned by PepAD and extracted five sequence patterns as well as associated sequence signatures for the 7-mer amyloid-forming peptides. Experimental results from Fourier transform infrared spectroscopy (FTIR), thioflavin T (ThT) fluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) indicate that all the peptides predicted to assemble in silico assemble into antiparallel ß-sheet nanofibers in a concentration-dependent manner. This is the first attempt to use a computational approach to search for amyloid-forming peptides based on customized settings. Our efforts facilitate the identification of ß-sheet-based self-assembling peptides, and contribute insights towards answering a fundamental scientific question: "What does it take, sequence-wise, for a peptide to self-assemble?".

Engineering ß-Sheet Peptide Coassemblies for Biomaterial Applications.

Peptide coassembly, wherein at least two different peptides interact to form multicomponent nanostructures, is an attractive approach for generating functional biomaterials. Current efforts seek to design pairs of peptides, A and B, that form nanostructures (e.g., ß-sheets with ABABA-type ß-strand patterning) while resisting self-assembly (e.g., AAAAA-type or BBBBB-type ß-sheets). To confer coassembly behavior, most existing designs have been based on highly charged variants of known self-assembling peptides; like-charge repulsion limits self-assembly while opposite-charge attraction promotes coassembly. Recent analyses using solid-state NMR and coarse-grained simulations reveal that preconceived notions of structure and molecular organization are not always correct. This perspective highlights recent advances and key challenges to understanding and controlling peptide coassembly.

De novo design of peptides that coassemble into ß sheet-based nanofibrils.

Peptides' hierarchical coassembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a computational and experimental approach to design pairs of charge-complementary peptides that selectively coassemble into ß-sheet nanofibers when mixed together but remain unassembled when isolated separately. The key advance is a peptide coassembly design (PepCAD) algorithm that searches for pairs of coassembling peptides. Six peptide pairs are identified from a pool of ~106 candidates via the PepCAD algorithm and then subjected to DMD/PRIME20 simulations to examine their co-/self-association kinetics. The five pairs that spontaneously aggregate in kinetic simulations selectively coassemble in biophysical experiments, with four forming ß-sheet nanofibers and one forming a stable nonfibrillar aggregate. Solid-state NMR, which is applied to characterize the coassembling pairs, suggests that the in silico peptides exhibit a higher degree of structural order than the previously reported CATCH(+/−) peptides.

Novel peptide ligands for antibody purification provide superior clearance of host cell protein impurities.

The quest for ligands alternative to Protein A for the purification of monoclonal antibodies (mAbs) has been pursued for almost three decades. Yet, the IgG-binding peptides known to date still fall short of the host cell protein (HCP) logarithmic removal value (LRV) set by Protein A media (2.5-3.1). In this study, we present an integrated computational-experimental approach leading to the discovery of peptide ligands that provide HCP LRVs on par with Protein A. First, the screening of 60,000 peptide variants was performed using a high-throughput search algorithm to identify sequences that ensure IgG affinity binding. Select sequences WQRHGI, MWRGWQ, RHLGWF, and GWLHQR were then negatively screened in silico against a panel of model HCPs to ensure the selection of peptides with high binding selectivity. Candidate ligands WQRHGI and MWRGWQ were conjugated to chromatographic resins and characterized by isothermal binding and breakthrough assays to quantify static and dynamic binding capacity (Qmax and DBC10%), respectively. The resulting Qmax were 52.6 mg of IgG per mL of adsorbent for WQRHGI and 57.48 mg/mL for MWRGWQ, while the DBC10% (2 minutes residence time) were 30.1 mg/mL for WQRHGI and 36.4 mg/mL for MWRGWQ. Evaluation of the peptides by isothermal titration calorimetry (ITC) confirmed the binding energy predicted in silico, and an amino acid scanning study corroborated the affinity-like binding activity of the peptides. WQRHGI-WorkBeads resin was finally characterized by purification of a monoclonal antibody from a Chinese Hamster Ovary (CHO) cell culture harvest, affording a remarkable HCP LRV of 2.7, and consistent product yield and purity over 100 chromatographic cycles. These results demonstrate the potential of WQRHGI as an effective alternative to Protein A for antibody purification.

In Silico Discovery and Validation of Neuropeptide-Y-Binding Peptides for Sensors.

Wearable sensors for human health, performance, and state monitoring, which have a linear response to the binding of biomarkers found in sweat, saliva, or urine, are of current interest for many applications. A critical part of any device is a biological recognition element (BRE) that is able to bind a biomarker at the surface of a sensor with a high affinity and selectivity to produce a measurable signal response. In this study, we discover and compare 12-mer peptides that bind to neuropeptide Y (NPY), a stress and human health biomarker, using independent and complimentary experimental and computational approaches. The affinities of the NPY-binding peptides discovered by both methods are equivalent and below the micromolar level, which makes them suitable for application in sensors. The in silico design protocol for peptide-based BREs is low cost, highly efficient, and simple, suggesting its utility for discovering peptide binders to a variety of biomarker targets.

Insights into the solvation and dynamic behaviors of a lithium salt in organic- and ionic liquid-based electrolytes.

New-generation lithium-ion batteries use ionic liquids (ILs) as electrolyte solutions, greatly enhancing the safety and energy storage capacity of the battery. Fundamental molecular insights are useful for understanding the advantages of high conductivity of IL solvent electrolytes over organic solvent ones. In this work, we computationally studied two organic solvents (DMC and DEC) and four IL solvents ([Cnmim][BF4] and [Cnmim][TFSI] (n = 2, 4)) to examine the physicochemical properties of high concentration electrolytes. As expected, the IL solvent electrolytes exhibit higher density and viscosity, and larger self-diffusion coefficients and conductivity than the organic solvent electrolytes. Further, the microstructures of the lithium salt LiTFSI in various solvent electrolytes were investigated to explore the effect of the organic and IL solvents on the ionic association of the ions Li+ and TFSI-. The structural analysis of LiTFSI revealed that the organic solvents restrict the free motion of the ions, reducing the conductivity of the electrolytes. The [BF4]-type IL electrolytes have higher conductivity than the [TFSI]-type IL electrolytes, especially [C4mim][BF4] with the highest conductivity among the IL-based electrolytes. More importantly, it was proved that the dissolution of LiTFSI in the IL solvents is an anion-driven process.

Modulation of phase transition of thermosensitive liposomes with leucine zipper-structured lipopeptides.

Targeted therapy for cancer requires thermosensitive components in drug carriers for controlled drug release against viral cells. The conformational transition characteristic of leucine zipper-structured lipopeptides is utilized in our lab to modulate the phase transition temperature of liposomes, thus achieving temperature-responsive control. In this study, we computationally examined the conformational transition behaviors of leucine zipper-structured lipopeptides that were modified at the N-terminus by distinct functional groups. The conformational transition temperatures of these lipopeptides were determined by structural analysis of the implicit-solvent replica exchange molecular dynamics simulation trajectories using the dihedral angle principal component analysis and the dictionary of protein secondary structure method. Our calculations revealed that the computed transition temperatures of the lipopeptides are in good agreement with the experimental measurements. The effect of hydrogen bonds on the conformational stability of the lipopeptide dimers was examined in conventional explicit-solvent molecular dynamics simulations. A quantitative correlation of the degree of structural dissociation of the dimers and their binding strength is well described by an exponential fit of the binding free energies to the conformation transition temperatures of the lipopeptides.

Advancing Peptide-Based Biorecognition Elements for Biosensors Using in-Silico 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 in-silico 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 in-silico 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.

Potent aromatase inhibitors and molecular mechanism of inhibitory action.

Estrogen is a significant factor in the maintenance and progression of hormone-dependent breast cancer. As well known, aromatase mediates the production of estrogen. Thus, inhibition of aromatase with chemical molecules has been considered to be an effective treatment for estrogen receptor-positive (ER+) breast cancer. In this work, we designed and synthesized a series of novel non-steroidal molecules containing 2-phenylindole scaffold and moiety of either imidazole or 1,2,4-triazole to enhance their binding capacity with the aromatase. Among these molecules, a compound named as 8o was confirmed experimentally to have the highest inhibitory activity to aromatase. Further cell activity assay proved that compound 8o has low cytotoxicity and is a promising lead for developing novel aromatase inhibitors. Molecular modeling and simulation techniques were performed to identify the binding modes of letrozole and 8o with the aromatase. Analysis of energy of the two compound-aromatase complexes revealed that the 8o has low binding energy (strong binding affinity) to the aromatase as compared to letrozole, which was in accordance with the experimental results. As concluded, a combination of experimental and computational approaches facilitates us to understand the molecular mechanism of inhibitory action and discover more potent non-steroidal AIs against aromatase, thereby opening up a novel therapeutic strategy for hormone-dependent breast cancer.

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 boxB 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 boxB 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.

Computational insights into the destabilization of α-helical conformations formed by leucine zipper peptides in response to temperature.

Recent experiments in our lab (Phys. Chem. Chem. Phys., 2016, 18, 10129-10137) suggested using leucine zipper peptides to enhance the thermosensitivity of liposomes. To understand the mechanisms of temperature-responsive control by the leucine zipper peptide in liposomes, we firstly performed quantum mechanics calculations and implicit-solvent replica exchange molecular dynamics simulations to study the thermo-stability of two leucine zipper peptides, CH3(CH2)4-CO-[VAQLEVK-VAQLESK-VSKLESK-VSSLESK] (termed the capped peptide) and A-[VAQLEVK-VAQLESK-VSKLESK-VSSLESK] (termed the ALA peptide). The analysis of dihedral angle principal components and protein secondary structures was conducted to determine the temperature-dependence conformation transition of the two peptides. Simulation results revealed that our computed transition temperature of the capped peptide is 319.1 K that accords with experimental measurement, 321.1 K. Later, explicit-solvent conventional molecular dynamics simulations were carried out to examine the process of folding and unfolding of the ALA and capped peptides complexed with a lipid bilayer and water in the vicinity of their transition temperatures. A further analysis of conformation and energy of the folded peptides showed that the increase of temperature gives rise to a notable decrease in the number of intra-chain hydrogen bonds and a significant increase in the potential energy of the peptides, thereby reducing the folding stability of the two peptides. As compared to the ALA peptide, a lower transition temperature caused by less intra-chain hydrogen bonds was observed in the capped peptide, which is closer to the temperature of tumor cells. This fact suggests that the capped peptide is more suitable to produce highly sensitive liposomes for the delivery of cancer drugs.

Simulation study of the ability of a computationally-designed peptide to recognize target tRNALys3 and other decoy tRNAs.

In this paper, we investigate the ability of our computationally-designed peptide, Pept10 (PNWNGNRWLNNCLRG), to recognize the anticodon stem and loop (ASL) domain of the hypermodified tRNALys3 (mcm5 s2 U34 ,ms2 t6 A37 ), a reverse transcription primer of HIV replication. Five other ASLs, the singly modified ASLLys3 (ms2 t6 A37 ), ASLLys3 (s2 U34 ), ASLLys3 (Ψ39 ), ASLLys1,2 (t6 A37 ), and ASLGlu (s2 U34 ), were used as decoys. Explicit-solvent atomistic molecular dynamics simulations were performed to examine the process of binding of Pept10 with the target ASLLys3 (mcm5 s2 U34 ,ms2 t6 A37 ) and the decoy ASLs. Simulation results demonstrated that Pept10 is capable of recognizing the target ASLLys3 (mcm5 s2 U34 ,ms2 t6 A37 ) as well as one of the decoys, ASLLys3 (Ψ39 ), but screens out the other four decoy ASLs. The interchain van der Waals (VDW) and charge-charge (ELE + EGB) energies for the two best complexes were evaluated to shed light on the molecular recognition mechanism between Pept10 and ASLs. The results indicated that Pept10 recognizes and binds to the target ASLLys3 (mcm5 s2 U34 ,ms2 t6 A37 ) through residues W3 and R7 which interact with the nucleotides mcm5 s2 U34 , U35 , and ms2 t6 A37 via the interchain VDW energy. Pept10 also recognizes the decoy ASLLys3 (Ψ39 ) through residue R14 which contacts the nucleotide U36 via the interchain VDW energy. Regardless of the type of ASL, the positively charged arginines on Pept10 are attracted to the negatively charged phosphate linkages on the ASL via the interchain ELE + EGB energy, thereby enhancing the binding affinity.

Adding energy minimization strategy to peptide-design algorithm enables better search for RNA-binding peptides: Redesigned λ N peptide binds boxB RNA.

Our previously developed peptide-design algorithm was improved by adding an energy minimization strategy which allows the amino acid sidechains to move in a broad configuration space during sequence evolution. In this work, the new algorithm was used to generate a library of 21-mer peptides which could substitute for λ N peptide in binding to boxB RNA. Six potential peptides were obtained from the algorithm, all of which exhibited good binding capability with boxB RNA. Atomistic molecular dynamics simulations were then conducted to examine the ability of the λ N peptide and three best evolved peptides, viz. Pept01, Pept26, and Pept28, to bind to boxB RNA. Simulation results demonstrated that our evolved peptides are better at binding to boxB RNA than the λ N peptide. Sequence searches using the old (without energy minimization strategy) and new (with energy minimization strategy) algorithms confirm that the new algorithm is more effective at finding good RNA-binding peptides than the old algorithm. © 2016 Wiley Periodicals, Inc.