Biology subjects

Madani, M.

Publications and source records attributed to Madani, M..

5 recordsLinked to original sources

bioRxiv2024

Modeling nearshore-offshore water exchange in Lake Ontario

The water quality and resources of Lake Ontarios nearshore ecosystem undergo heightened stress, particularly along the northwest shoreline. Hydrodynamic processes linking the distinct nearshore and offshore trophic structures play a crucial role in transporting nutrient-loaded water along and across the shore. Despite the pivotal connection between algae growth and the development of nuisance proportions, the scales over which these processes operate remain poorly understood. This study delves into the exchange dynamics between nearshore and offshore areas of Lake Ontario throughout 2018, employing a validated three-dimensional numerical model. A virtual passive age tracer is utilized to discern horizontal mixing time scales between nearshore regions of the lake (water depth < 30 m) and offshore locations. The dispersal pattern, as revealed by a passive tracer released from eight points around the model lakes perimeter, indicates more extensive diffusion in late summer when lake-wide stratification is established, compared to the mixed period. Coastal upwelling events, leading to intrusions of hypolimnetic waters, significantly contribute to net cross-shore transport, with the most pronounced effects observed in May and June when the offshore thermocline is shallow. In the northern part of the lake, dispersal predominantly occurs alongshore, mirroring the prevailing cyclonic (counterclockwise) coastal circulation during the stratified season. This pattern is a consequence of a 45% increase in upwelling events compared to three decades ago. In the northwestern and southern sectors of the lake, elevated cross-shore mixing is attributed to geomorphology-induced cross-basin currents.

ecology↗

bioRxiv2022

CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks

Protein contact maps represent spatial pairwise inter-residue interactions, providing a proteins translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure prediction, one of computational biologys most challenging problems in the last half-century. While many computational tools have been developed to this end, most fail to predict accurate contact maps for proteins with insufficient homologous protein sequences, and exhibit low accuracy for long-range contacts. To address these limitations, we develop a novel hybrid model, CGAN-Cmap, that uses a generative adversarial neural network embedded with a series of modified squeeze and excitation residual networks. To exploit features of different dimensions, we build the generator of CGAN-Cmap via two parallel modules: sequential and pairwise modules to capture and interpret distance profiles from 1D sequential and 2D pairwise feature maps, respectively, and combine them during the training process to generate the contact map. This novel architecture helps to improve the contact map prediction by surpassing redundant features and encouraging more meaningful ones from 1D and 2D inputs simultaneously. We also introduce a new custom dynamic binary cross-entropy (BCE) as the loss function to extract essential details from feature maps, and thereby address the input imbalance problem for highly sparse long-range contacts in proteins with insufficient numbers of homologous sequences. We evaluate the performance of CGAN-Cmap on the 11th, 12th, 13th, and 14th Critical Assessment of protein Structure Prediction (CASP 11, 12, 13, and 14) and CAMEO test sets. CGAN-Cmap significantly outperforms state-of-the-art models, and in particular, it improves the precision of medium and long-range contact by at least 3.5%. Furthermore, our model has a low dependency on the number of homologous sequences obtained via multiple sequence alignment, suggesting that it can predict protein contact maps with good accuracy for those proteins that lack homologous templates. These results demonstrate an efficient approach for fast and highly accurate contact map prediction toward construction of protein 3D structure from protein sequence. Data availabilityAll datasets and source codes are provided in: https://github.com/mahan-fcb/CGAN-Cmap-A-protein-contact-map-predictor

bioinformatics↗

bioRxiv2021

Modeling Coronavirus Spike Protein Dynamics: Implications for Immunogenicity and Immune Escape

The ongoing COVID-19 pandemic is a global public health emergency requiring urgent development of efficacious vaccines. While concentrated research efforts are underway to develop antibody-based vaccines that would neutralize SARS-CoV-2, and several first-generation vaccine candidates are currently in Phase III clinical trials or have received emergency use authorization, it is forecasted that COVID-19 will become an endemic disease requiring second-generation vaccines. The SARS-CoV-2 surface Spike (S) glycoprotein represents a prime target for vaccine development because antibodies that block viral attachment and entry, i.e. neutralizing antibodies, bind almost exclusively to the receptor binding domain (RBD). Here, we develop computational models for a large subset of S proteins associated with SARS-CoV-2, implemented through coarse-grained elastic network models and normal mode analysis. We then analyze local protein domain dynamics of the S protein systems and their thermal stability to characterize structural and dynamical variability among them. These results are compared against existing experimental data, and used to elucidate the impact and mechanisms of SARS-CoV-2 S protein mutations and their associated antibody binding behavior. We construct a SARS-CoV-2 antigenic map and offer predictions about the neutralization capabilities of antibody and S mutant combinations based on protein dynamic signatures. We then compare SARS-CoV-2 S protein dynamics to SARS-CoV and MERS-CoV S proteins to investigate differing antibody binding and cellular fusion mechanisms that may explain the high transmissibility of SARS-CoV-2. The outbreaks associated with SARS-CoV, MERS-CoV, and SARS-CoV-2 over the last two decades suggest that the threat presented by coronaviruses is ever-changing and long-term. Our results provide insights into the dynamics-driven mechanisms of immunogenicity associated with coronavirus S proteins, and present a new approach to characterize and screen potential mutant candidates for immunogen design, as well as to characterize emerging natural variants that may escape vaccine-induced antibody responses. STATEMENT OF SIGNIFICANCEWe present novel dynamic mechanisms of coronavirus S proteins that encode antibody binding and cellular fusion properties. These mechanisms may offer an explanation for the widespread nature of SARS-CoV-2 and more limited spread of SARS-CoV and MERS-CoV. A comprehensive computational characterization of SARS-CoV-2 S protein structures and dynamics provides insights into structural and thermal stability associated with a variety of S protein mutants. These findings allow us to make recommendations about the future mutant design of SARS-CoV-2 S protein variants that are optimized to elicit neutralizing antibodies, resist structural rearrangements that aid cellular fusion, and are thermally stabilized. The integrated computational approach can be applied to optimize vaccine immunogen design and predict escape of vaccine-induced antibody responses by SARS-CoV-2 variants.

biophysics↗

bioRxiv2021

DSResSol: A sequence-based solubility predictor created with Dilated Squeeze Excitation Residual Networks

Protein solubility is an important thermodynamic parameter critical for the characterization of a proteins function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g. pharmaceutical) applications. Thus, a highly accurate in silico bioinformatics tool for predicting protein solubility from protein sequence is sought. In this study, we developed a deep learning sequence-based solubility predictor, DSResSol, that takes advantage of the integration of squeeze excitation residual networks with dilated convolutional neural networks. The model captures the frequently occurring amino acid k-mers and their local and global interactions, and highlights the importance of identifying long-range interaction information between amino acid k-mers to achieve higher performance in comparison to existing deep learning-based models. DSResSol uses protein sequence as input, outperforming all available sequence-based solubility predictors by at least 5% in accuracy when the performance is evaluated by two different independent test sets. Compared to existing predictors, DSResSol not only reduces prediction bias for insoluble proteins, but also predicts soluble proteins within the test sets with an accuracy that is at least 13% higher. We derive the key amino acids, dipeptides, and tripeptides contributing to protein solubility, identifying glutamic acid and serine as critical amino acids for protein solubility prediction. Overall, DSResSol can be used for fast, reliable, and inexpensive prediction of a proteins solubility to guide experimental design. AvailabilityThe source code, datasets, and web server for this model are available at https://github.com/mahan-fcb/DSResSol

bioinformatics↗

bioRxiv2020

Preventing post-surgical cardiac adhesions with a catechol-functionalized oxime hydrogel

Post-surgical cardiac adhesions represent a significant problem during routine cardiothoracic procedures. This fibrous tissue can impair heart function and inhibit surgical access in reoperation procedures. Here, we propose a novel hydrogel barrier composed of oxime crosslinked poly(ethylene glycol) (PEG) with the inclusion of a catechol (Cat) group to improve retention on the heart for pericardial adhesion prevention. This three component system is comprised of aldehyde (Ald), aminooxy (AO), and Cat functionalized PEG mixed to form the final gel (Ald-AO-Cat). Ald-AO-Cat has favorable mechanical properties, degradation kinetics, and minimal swelling, as well as superior tissue retention compared to an initial Ald-AO gel formulation. We show that the material is cytocompatible, resists cell adhesion, and led to a reduction in the severity of adhesion in an in vivo rat model and a pilot porcine study. The Ald-AO-Cat hydrogel barrier may therefore serve as a promising solution for preventing post-surgical cardiac adhesions.

bioengineering↗