Biology subjects

Violi, A.

Publications and source records attributed to Violi, A..

5 recordsLinked to original sources

bioRxiv2022

From proteins to nanoparticles: domain-agnostic predictions of nanoscale interactions.

The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and engage biological machinery in a purposeful manner requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarsegrained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models and it shows cross-domain validity. We anticipate that our framework will contribute to both basic research and rapid prototyping and design of diverse nanostructures in nanobiotechnology.

bioinformatics↗

bioRxiv2020

Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions

BackgroundDevelopment of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. ResultsIn this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein-protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein-protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy. ConclusionsIn this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

bioinformatics↗

bioRxiv2020

Molecular Dynamics Study of Dehydrated Lipopolysaccharide Membrane

The outer membrane of bacteria is known to play an important role in the rapid response to desiccation, although the causes and the extent of these effects are still mostly unclear. For this reason, in this work we study the desiccation response of the Gram-negative lipopolysaccha-ride (LPS) bacterial outer membranes. By analyzing molecular dynamics simulations of LPS membranes of different composition during desiccation, we identified the formation of a rigid protective layer of polysaccharides that not only reduces the water evaporation but is also able to indirectly preserve several structural features and as such membrane functionality. Overall, we found that the presence of polysaccharide layer is critical in the conservation of a layer of water in proximity of the hydrophobic region as well as the structure of the lipids acyl chain structure.

biophysics↗

bioRxiv2019

Predicting the time of entry of nanoparticles in cellular membranes

The understanding of the molecular interactions between nanoparticles (NPs) and biological systems is crucial for the systematic advance in many high-impact fields, such as biomedicine and nanotechnology. A key aspect to understand and predict the biological effect of NPs, e.g., cytotoxicity, bioavailability, is their interaction with membranes, specifically the mechanisms that regulate passive transport, which controls the permeation of most small molecules. In this paper, we introduce a new streamlined theoretical model that is able to predict the interactions between NPs and biological membranes (average permeation time), by separating the NPs characteristics (i.e., size, shape, solubility) from the membrane properties (density distribution). This factorization allows the inclusion of data obtained from both experimental and computational sources, as well as rapid estimation of large sets of permutation in new membranes. We validated our approach, by comparing our prediction for the interactions between different carbonaceous NPs and lipid bilayers with both experiments of measuring graphene quantum dot leakage encapsulated in lipid vesicles and time of entry from MD simulations.

biophysics↗

bioRxiv2019

Anti-biofilm Activity of Graphene Quantum Dots via Self-Assembly with Bacterial Amyloid Proteins

Bacterial biofilms represent an essential part of Earths ecosystem that can cause multiple ecological, technological and health problems. The environmental resilience and sophisticated organization of biofilms are enabled by the extracellular matrix that creates a protective network of biomolecules around the bacterial community. Current anti-biofilm agents can interfere with extracellular matrix production but, being based on small molecules, are degraded by bacteria and rapidly diffuse away from biofilms. Both factors severely reduce their efficacy, while their toxicity to higher organisms create additional barriers to their practicality. In this paper we report on the ability of graphene quantum dots to effectively disperse mature Staphylococcus aureus biofilms, interfering with the self-assembly of amyloid fibers - a key structural component of the extracellular matrix. Mimicking peptide-binding biomolecules, graphene quantum dots form supramolecular complexes with phenol soluble modulins, the peptide monomers of amyloid fibers. Experimental and computational results show that graphene quantum dots efficiently dock near the N-terminus of the peptide and change the secondary structure of phenol soluble modulins, which disrupts their fibrillation and represents a novel strategy for mitigation of bacterial communities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/550285v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@a94402org.highwire.dtl.DTLVardef@b009b3org.highwire.dtl.DTLVardef@14cfb70org.highwire.dtl.DTLVardef@10f8e52_HPS_FORMAT_FIGEXP M_FIG GQD mediated staphylococcal biofilm dispersal. GQDs interact with PSM peptides and frustrate the fibrillation process. The reduction in amyloid fibers prevents robust stabilization of the biofilm. In addition, there is an increase in free monomeric and oligomeric PSM peptides which trigger dispersal events. C_FIG

bioengineering↗