FCRH 14th Annual Undergraduate Research Symposium

The association between completing one high risk behavior and then engaging in another has been long established (Benthin et al., 1993). Further, individuals are more likely to engage in risky behavior if they evaluate risks of the behavior to be outweighed by the possible benefits (Benthin et al., 1993; Zimmerman, 2010). In addition to the evaluation of risk/benefit analysis, NSSI versatility, measured as the number of NSSI methods used, may also play a role in the perception of these factors. Past literature has suggested that NSSI versatility may be reflective of acceptance of and familiarity with self-injury (Anestis, 2014) and potentially injury more generally, which may include other risky behaviors. The aim of the current study was to investigate differences in perceptions of risk and benefit for risky behaviors among individuals with and without an NSSI history. Perceptions of risk/benefit were investigated for both self-injurious behaviors and risky behaviors more generally. Further, we aimed to supplement gaps in the existing literature by evaluating the role that NSSI versatility and specific methods of NSSI may play in the risk/benefit analysis in individuals with a history of NSSI. Most notably, the outcomes of this study indicate that those who engage in NSSI have an understanding of the risks associated with NSSI, these findings could influence the way NSSI intervention is done moving forward.

Kathryn Evans <kevans23@fordham.edu>

Fordham University, Department of Psychology

Focusing on the media coverage of Palestinian women’s engagement in the two Intifadas, this paper examines patterns of gendered media framing of participants in political violence. Assuming that gendered narratives are employed strategically, the paper investigates how Arab and Western media exploited gendered representations to position female perpetrators of violence in categories that reinforced certain conceptions of gender. Based upon the analysis of discourse in several Arab, American, and British newspapers, the study found that Arab and Western media employed similar gendered stereotypes in their representation of female Palestinian agents of violence, which served to diminish their political agency. However, there was considerable variation in the interpretation of female violence across media sources. These empirical results contribute to both gender theory and international relations literature by indicating the malleability in representations of gender and the potential consequences of gendering agents of violence on conceptualizations of conflicts.

Kelsie Greene

Fordham University, Department of Political Science

The incidence of amyloid diseases, including Alzheimer’s and Parkinsons disease, in the global population has increased significantly in the past few years. The hallmark of such diseases, is the formation of misfolded amyloid plaques that aggregate extracellularly in the brain and are cytotoxic. There are currently few approved treatment options that specifically address the pathology of these amyloid-based diseases, though there have been studies that employ designed peptides, metal nanoparticles, polyphenols, and certain ionic liquids for amyloid defibrillation. In this work, we conducted computational studies using three newly designed plant-based polyphenol-amino acid conjugates that may be potentially be applicable in mitigating beta-amyloid (Aβ) and alpha-synuclein (ASN). The self-assembly of the conjugates was simulated using Gromacs in a solvent containing a salt concentration similar to plasma to mimic the initial metabolism of the drug in the circulatory system. Autodock Vina, Biotec’s Protein-Ligand Interaction Profiler, and Metapocket were used to study the interactions of the drugs with Aβ(1-42) and ASN. SwissADME was used to examine the pharmacokinetic and cytotoxic properties of the conjugates. We found that the designed conjugates had better affinities for Aβ(1-42) and ASN in comparison to the neat polyphenol and amino acids. The conjugates also had favorable pharmacokinetic properties and low cytotoxicity. Such polyphenol-amino acid drug conjugates may have potential applications in reducing protein misfolding that causes Aβ plaques and ASN and thereby may have potential applications in alleviating neurodegenerative diseases. 

Margaret Whalen

Fordham University, Department of Chemistry

In this work, we focused on designing new glucosyloxy stilbene-peptide conjugates to target overexpressed CD22, neuropilin 1, and integrin alpha 1 receptors. Using ChemDraw and Chem 3D we first designed specifically targeted peptide conjugates of stilbene. The peptides utilized were tumor targeting peptides, and rich in arginine moieties. Then using AutoDockVina, we docked each receptor with the conjugates and examined the various binding affinities. After molecular docking, each combination of receptor and peptide was analyzed using a Protein-Ligand Interaction Profiler. The conjugates were then analyzed using Turbomole and COSMOthermX20 in order to determine the sigma profiles of each. We then used the web server ACP to determine our peptides’ physio-chemical properties, followed by the web server POCASA in order to determine the surface cavities and binding pockets of the three receptors. We then prepared each receptor-conjugate combination to conduct molecular dynamics simulations to explore the stability of the receptor bound conjugates. Lastly, we analyzed the compounds using the web server SwissADME in order to determine pharmacokinetic properties. The results indicate that integrin receptors showed higher binding affinities and stibilities. 

Mia Rico

Fordham University Department of Chemistry

Ligand-based virtual screening is a widely used application of artificial intelligence for drug discovery. A common approach is to encode the chemical motifs within a set of molecular structures as a binary fingerprint vector, and then use machine learning methods to predict the activity or inactivity in receptor binding screening assays. However, such ligand-based methods are hindered when chemical motifs exist simply by chance and play no role in interacting with the receptor. Recently, it has been observed that artificial neural network architectures that more explicitly mimic the architecture of biological networks can have significant advantages for limited training data. One of these—the Drosophila olfactory network—implements a locality and novelty-sensitive Bloom filter algorithm. Bloom filters are an efficient way of “remembering” if an item has been encountered using only a very small amount of storage space. This variant calculates the similarity of a query item to previously stored inputs. This is relevant to the problem of drug design because it provides a method for evaluating the structural similarity between a small set of molecules known to be chemically active with a receptor compared to an unknown molecule. Using an experimental dataset of candidate drugs for the human muscarinic acetylcholine receptor M1, a target for diseases such as Alzheimer’s and schizophrenia, we compared the performance of a simulated Drosophila olfactory network to other traditional approaches for ligand based screening.

Nolan Chiles <nchiles@fordham.edu>

Fordham University, Department of Chemistry

Rapid forward and retrosynthetic organic reaction prediction remains an active area of artificial intelligence research in chemistry. IBM’s freely available predictive AI, RXN for Chemistry (https://rxn.res.ibm.com/), treats reaction prediction as a machine translation problem, converting a sequence of reactant molecules, in the form of SMILES strings, to products. In this way, prediction is done by finding a conversion from the “language” of reactants to the “language” of products, without considering the minutia of chemical transformations or reaction mechanisms. Though this method circumvents the arduous task of encoding all the fundamental “rules” of organic chemistry, it does not necessarily obey conservation laws or other physical principles, and thus is potentially susceptible to “alchemy” and other pathologies. This work aims to illuminate areas where transformer models fall short and to consider how to best remedy these cases where the fundamentals of organic chemistry are violated. By using a data set of 100 organic chemistry reactions sourced from an undergraduate level textbook, we evaluated the performance of the RXN for Chemistry model on classic reaction classes in chemistry and identify areas where this model is generally successful, as in substitution reactions, and unsuccessful, as in Diels-Alder or Robinson annulation reactions, at predicting outcomes. We also compared the change in molecular complexity between predicted and human retrosynthesis strategies using an additive measure of molecular complexity. We found that specific reaction classes, such as elimination, Diels-Alder, or Robinson annulations, have characteristically low predictive accuracies and tend to instead favor other reaction mechanisms, such as substitution, where the model’s accuracy is superior. Moreover, forward predictions replicate a similar distribution of changes in molecular complexity as the literature, whereas retrosynthetic predictions do not.

William Borrelli <wborrelli@fordham.edu>

Fordham University, Department of Chemistry