Computational Studies of Molecular Mechanisms Mediating Protein Adsorption on Material Surfaces

Protein adsorption at material surfaces is a fundamental concept in many scientific applications ranging from the biocompatibility of implant materials in bioengineering to cleaning environmental material surfaces from toxic proteins in the area of biodefense. Understanding the molecular-level details of protein-surface interactions is crucial for controlling protein adsorption. While a range of experimental techniques has been developed to study protein adsorption, these techniques cannot produce the fundamental molecular-level information of protein adsorption. All-atom empirical force field molecular dynamics (MD) simulations hold great promise as a valuable tool for elucidating and predicting the mechanisms governing protein adsorption. However, current MD simulation methods have not been validated for this application. This research addresses three limitations of the standard MD when applied to the simulations of the protein-surface interactions: (1) representation of the force field parameters governing the interactions of protein amino acids with the material surface; (2) cluster analysis of ensembles of adsorbed protein states obtained in protein-adsorption simulations, in which in addition to the conformation the orientation of the sampled states is also important; and (3) simulation time to ensure a significant level of conformational sampling to cover the entire rough energy landscape of such a large molecular system as protein adsorption. This study, thus, attempted to further advance protein-adsorption simulation methods using high-density polyethylene as a model materials surface

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Site of Tagging Influences the Ochratoxin Recognition by Peptide NFO4: A Molecular Dynamics Study

Molecular recognition by synthetic peptides is growing in importance in the design of biosensing elements used in the detection and monitoring of a wide variety of hapten bioanlaytes. Conferring specificity via bioimmobilization and subsequent recovery and purification of such sensing elements are aided by the use of affinity tags. However, the tag and its site of placement can potentially compromise the hapten recognition capabilities of the peptide, necessitating a detailed experimental characterization and optimization of the tagged molecular recognition entity. The objective of this study was to assess the impact of site-specific tags on a native peptide’s fold and hapten recognition capabilities using an advanced molecular dynamics (MD) simulation approach involving bias-exchange metadynamics and Markov State Models. The in-solution binding preferences of affinity tagged NFO4 (VYMNRKYYKCCK) to chlorinated (OTA) and non-chlorinated (OTB) analogues of ochratoxin were evaluated by appending hexa-histidine tags (6× His-tag) to the peptide’s N-terminus (NterNFO4) or C-terminus (CterNFO4), respectively. The untagged NFO4 (NFO4), previously shown to bind with high affinity and selectivity to OTA, served as the control. Results indicate that the addition of site-specific 6× His-tags altered the peptide’s native fold and the ochratoxin binding mechanism, with the influence of site-specific affinity tags being most evident on the peptide’s interaction with OTA. The tags at the N-terminus of NFO4 preserved the native fold and actively contributed to the nonbonded interactions with OTA. In contrast, the tags at the C-terminus of NFO4 altered the native fold and were agnostic in its nonbonded interactions with OTA. The tags also increased the penalty associated with solvating the peptide–OTA complex. Interestingly, the tags did not significantly influence the nonbonded interactions or the penalty associated with solvating the peptide–OTB complex. Overall, the combined contributions of nonbonded interaction and solvation penalty were responsible for the retention of the native hapten recognition capabilities in NterNFO4 and compromised native recognition capabilities in CterNFO4. Advanced MD approaches can thus provide structural and energetic insights critical to evaluate the impact of site-specific tags and may aid in the selection and optimization of the binding preferences of a specific biosensing element