ezCADD: A Rapid 2D/3D Visualization-Enabled Web Modeling Environment for Democratizing Computer-Aided Drug Design

Abstract

As abundant and user-friendly as computer-aided drug design (CADD) software may seem, there is still a large underserved population of biomedical researchers around the world, particularly those with no computational training and limited research funding. To address this important need and help scientists overcome barriers that impede them from leveraging CADD in their drug discovery work, we have developed ezCADD, a web-based CADD modeling environment that manifests four simple design concepts: easy, quick, user-friendly, and 2D/3D visualization-enabled. In this paper, we describe the features of three fundamental applications that have been implemented in ezCADD: small-molecule docking, protein–protein docking, and binding pocket detection, and their applications in drug design against a pathogenic microbial enzyme as an example. To assess user experience and the effectiveness of our implementation, we introduced ezCADD to first-year pharmacy students as an active learning exercise in the Principles of Drug Action course. The web service robustly handled 95 simultaneous molecular docking jobs. Our survey data showed that among the 95 participating students, 97% completed the molecular docking experiment on their own at least partially without extensive training; 88% considered ezCADD easy and user-friendly; 99–100% agreed that ezCADD enhanced the understanding of drug–receptor structures and recognition; and the student experience in molecular modeling and visualization was significantly improved from zero to a higher level. The student feedback represents the baseline data of user experience from noncomputational researchers. It is demonstrated that in addition to supporting drug discovery research, ezCADD is also an effective tool for promoting science, technology, engineering, and mathematics (STEM) education. More advanced CADD applications are being developed and added to ezCADD, available at .

Targeting Beta-Blocker Drug–Drug Interactions with Fibrinogen Blood Plasma Protein: A Computational and Experimental Study

“…Following this idea, to verify the presence or absence of DDI for beta-blockers A and P at the same biophysical environment in the best-ranked fibrinogen binding sites, the corresponding 3D-ligand plot diagrams [ 44 , 45 ] were determined and are shown in Figure 5 .…”

“…Following this idea, to verify the presence or absence of DDI for beta-blockers A and P at the same biophysical environment in the best-ranked fibrinogen binding sites, the corresponding 3D-ligand plot diagrams [44,45] were determined and are shown in Figure 5. blockers associated to a loss of P affinity, may be due to the presence of A interacting simultaneously with the same target-residues (vide infra), clearly affecting the thermodynamic stabilization of P interactions in such sites.…”

“…This result is of great theoretical relevance because it serves as a reference to identify the total absence of DDI patterns when compared to the interactions in sites 1, 2, and 3. Following this idea, to verify the presence or absence of DDI for beta-blockers A and P at the same biophysical environment in the best-ranked fibrinogen binding sites, the corresponding 3Dligand plot diagrams [44,45] were determined and are shown in Figure 5. Representation of 3D-lig-plots diagrams for the best beta-blocker drug-drug binding-poses.…”

“…Additional details such as control simulation experiments for the "worst fibrinogen binding site" are provided in Figure S4 of the Supplementary Material. Let us now assess the perturbations in the depth profiles of target residues making relevant tunnels (THR22:P, SER50: Q/site1, ASP78:O, ASN 30:S/site2 and HIS 74:O, CYS39: Q/site3) that interacted with the beta-blocker ligand atoms previously identified, with the maximum per atom energy contribution to the individual binding affinities [39,40,44] (see Figure 8). Figure 8.…”

“…According to the results shown in Figure 8 (panels: D-F), the relationship between the pharmacodynamic drug-drug interactions and the perturbations on the depth values for the revealed target-residues of A-A and P-P could be modeled by incorporating the latter into Equation 12 which depends on the frequency of the target residues in a certain position for a given chain (c). As referred to in that section, this descriptor might be used as an adjusted residue-binding probability to establish the quasi-sequence-position-order descriptor J i [39,40,44], which depends of the position (i and i + d) and the frequency-based occurrence of the target residues in the chain sequence. In this context, for each target residue type, the depth descriptor q c i is defined by means of the following Equation:…”