Designing peptide sequences in flexible chain conformations to bind RNA: a search algorithm combining Monte Carlo, self-consistent mean field and concerted rotation techniques

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.

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.

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 β-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 β-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?".

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.

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.

De novo design of peptides that coassemble into β 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 β-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 β-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.

A ChemE Grows in Brooklyn

I profile my personal and professional journey from being a girl of the 1950s, with expectations typical for the times, to a chemical engineering professor and still-enthusiastic researcher. I describe my family, my early education, my college and graduate school training in physics, my postdoc years in chemistry, and my subsequent transformation into a chemical engineering faculty member-one of the first women to be appointed to a chemical engineering faculty in the United States. I focus on the events that shaped me, the people who noticed and supported me, and the environment for women scientists and engineers in what some would call the "early days." My initial research activities centered on applications of statistical mechanics to predict phase equilibria in simple systems. Over time, my interests evolved to focus on applying molecule-level computer simulations to systems of interest to chemical engineers, e.g., hydrocarbons and polymers. Eventually, spurred on by my personal interest in amyloid diseases and my wish to make a contribution to human health, I turned to more biologically oriented problems having to do with protein aggregation and protein design. I give a candid assessment of my strengths and weaknesses, successes and failures. Finally, I share the most valuable lessons that I have learned over a lifetime of professional and personal experience.

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.

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 tRNA^Lys3 (mcm⁵ s² U₃₄ ,ms² t⁶ A₃₇ ), a reverse transcription primer of HIV replication. Five other ASLs, the singly modified ASL^Lys3 (ms² t⁶ A₃₇ ), ASL^Lys3 (s² U₃₄ ), ASL^Lys3 (Ψ₃₉ ), ASL^Lys1,2 (t⁶ A₃₇ ), and ASL^Glu (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 ASL^Lys3 (mcm⁵ s² U₃₄ ,ms² t⁶ A₃₇ ) and the decoy ASLs. Simulation results demonstrated that Pept10 is capable of recognizing the target ASL^Lys3 (mcm⁵ s² U₃₄ ,ms² t⁶ A₃₇ ) as well as one of the decoys, ASL^Lys3 (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 ASL^Lys3 (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 ASL^Lys3 (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.

Introducing folding stability into the score function for computational design of RNA-binding peptides boosts the probability of success

A computational strategy that integrates our peptide search algorithm with atomistic molecular dynamics simulation was used to design rational peptide drugs that recognize and bind to the anticodon stem and loop domain (ASL(Lys3)) of human tRNAUUULys3 for the purpose of interrupting HIV replication. The score function of the search algorithm was improved by adding a peptide stability term weighted by an adjustable factor λ to the peptide binding free energy. The five best peptide sequences associated with five different values of λ were determined using the search algorithm and then input in atomistic simulations to examine the stability of the peptides' folded conformations and their ability to bind to ASL(Lys3). Simulation results demonstrated that setting an intermediate value of λ achieves a good balance between optimizing the peptide's binding ability and stabilizing its folded conformation during the sequence evolution process, and hence leads to optimal binding to the target ASL(Lys3). Thus, addition of a peptide stability term significantly improves the success rate for our peptide design search.