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

Development of a Tuned Interfacial Force Field Parameter Set for the Simulation of Protein Adsorption to Silica Glass

Adsorption free energies for eight host–guest peptides (TGTG-X-GTGT, with X = N, D, G, K, F, T, W, and V) on two different silica surfaces [quartz (100) and silica glass] were calculated using umbrella sampling and replica exchange molecular dynamics and compared with experimental values determined by atomic force microscopy. Using the CHARMM force field, adsorption free energies were found to be overestimated (i.e., too strongly adsorbing) by about 5–9 kcal/mol compared to the experimental data for both types of silica surfaces. Peptide adsorption behavior for the silica glass surface was then adjusted using a modified version of the CHARMM program, which we call dual force-field CHARMM, which allows separate sets of nonbonded parameters (i.e., partial charge and Lennard-Jones parameters) to be used to represent intra-phase and inter-phase interactions within a given molecular system. Using this program, interfacial force field (IFF) parameters for the peptide-silica glass systems were corrected to obtain adsorption free energies within about 0.5 kcal/mol of their respective experimental values, while IFF tuning for the quartz (100) surface remains for future work. The tuned IFF parameter set for silica glass will subsequently be used for simulations of protein adsorption behavior on silica glass with greater confidence in the balance between relative adsorption affinities of amino acid residues and the aqueous solution for the silica glass 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

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available

Presentation1_Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.pdf

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.</p