Multiscale Simulations to Discover Self-Assembled Oligopeptides: A Benchmarking Study.

Peptide self-assembly is critical for biomedical and material discovery and production. While it is costly to experimentally test every possible peptide design, computational assessment provides an affordable solution to evaluate many designs and prioritize synthesis and characterization. Following a theoretical investigation, we present a systematic analysis of all-atom and coarse-grained simulations to predict peptide self-assembly. Benchmarking studies of two model dipeptides allow us to assess the impacts of intrinsic properties (such as amino acids and terminal modifications) and external environment (such as salinity) on the simulated aggregation. Further examination of 20 oligopeptides containing two to five amino acids shows good agreement among our theory, simulations, and prior experimental observations. The success rate of our prediction is 90%. Therefore, our theory, simulation, and analysis can be useful to identify peptide designs that can self-assemble and predict the potential nanostructures. These findings lay the ground for future virtual screening of peptide-assembled nanostructures and computer-aided biologics design.

Structural and functional fine mapping of cysteines in mammalian glutaredoxin reveal their differential oxidation susceptibility.

Protein-S-glutathionylation is a post-translational modification involving the conjugation of glutathione to protein thiols, which can modulate the activity and structure of key cellular proteins. Glutaredoxins (GLRX) are oxidoreductases that regulate this process by performing deglutathionylation. However, GLRX has five cysteines that are potentially vulnerable to oxidative modification, which is associated with GLRX aggregation and loss of activity. To date, GLRX cysteines that are oxidatively modified and their relative susceptibilities remain unknown. We utilized molecular modeling approaches, activity assays using recombinant GLRX, coupled with site-directed mutagenesis of each cysteine both individually and in combination to address the oxidizibility of GLRX cysteines. These approaches reveal that C8 and C83 are targets for S-glutathionylation and oxidation by hydrogen peroxide in vitro. In silico modeling and experimental validation confirm a prominent role of C8 for dimer formation and aggregation. Lastly, combinatorial mutation of C8, C26, and C83 results in increased activity of GLRX and resistance to oxidative inactivation and aggregation. Results from these integrated computational and experimental studies provide insights into the relative oxidizability of GLRX's cysteines and have implications for the use of GLRX as a therapeutic in settings of dysregulated protein glutathionylation.

GPCRLigNet: rapid screening for GPCR active ligands using machine learning.

Molecules with bioactivity towards G protein-coupled receptors represent a subset of the vast space of small drug-like molecules. Here, we compare machine learning models, including dilated graph convolutional networks, that conduct binary classification to quickly identify molecules with activity towards G protein-coupled receptors. The models are trained and validated using a large set of over 600,000 active, inactive, and decoy compounds. The best performing machine learning model, dubbed GPCRLigNet, was a surprisingly simple feedforward dense neural network mapping from Morgan fingerprints to activity. Incorporation of GPCRLigNet into a high-throughput virtual screening workflow is demonstrated with molecular docking towards a particular G protein-coupled receptor, the pituitary adenylate cyclase-activating polypeptide receptor type 1. Through rigorous comparison of docking scores for molecules selected with and without using GPCRLigNet, we demonstrate an enrichment of potentially potent molecules using GPCRLigNet. This work provides a proof of principle that GPCRLigNet can effectively hone the chemical search space towards ligands with G protein-coupled receptor activity.

Enhanced sampling protocol to elucidate fusion peptide opening of SARS-CoV-2 spike protein.

Large-scale conformational transitions in the spike protein S2 domain are required during host-cell infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although conventional molecular dynamics simulations have been extensively used to study therapeutic targets of SARS-CoV-2, it is still challenging to gain molecular insight into the key conformational changes because of the size of the spike protein and the long timescale required to capture these transitions. In this work, we have developed an efficient simulation protocol that leverages many short simulations, a dynamic selection algorithm, and Markov state models to interrogate the structural changes of the S2 domain. We discovered that the conformational flexibility of the dynamic region upstream of the fusion peptide in S2 is coupled to the proteolytic cleavage state of the spike protein. These results suggest that opening of the fusion peptide likely occurs on a submicrosecond timescale after cleavage at the S2' site. Building on the structural and dynamical information gained to date about S2 domain dynamics, we provide proof of principle that a small molecule bound to a seam neighboring the fusion peptide can slow the opening of the fusion peptide, leading to a new inhibition strategy for experiments to confirm. In aggregate, these results will aid the development of drug cocktails to inhibit infections caused by SARS-CoV-2 and other coronaviruses.

Targeting the PAC1 Receptor for Neurological and Metabolic Disorders.

The pituitary adenylate cyclase-activating polypeptide (PACAP)-selective PAC1 receptor (PAC1R, ADCYAP1R1) is a member of the vasoactive intestinal peptide (VIP)/secretin/glucagon family of G protein-coupled receptors (GPCRs). PAC1R has been shown to play crucial roles in the central and peripheral nervous systems. The activation of PAC1R initiates diverse downstream signal transduction pathways, including adenylyl cyclase, phospholipase C, MEK/ERK, and Akt pathways that regulate a number of physiological systems to maintain functional homeostasis. Accordingly, at times of tissue injury or insult, PACAP/PAC1R activation of these pathways can be trophic to blunt or delay apoptotic events and enhance cell survival. Enhancing PAC1R signaling under these conditions has the potential to mitigate cellular damages associated with cerebrovascular trauma (including stroke), neurodegeneration (such as Parkinson's and Alzheimer's disease), or peripheral organ insults. Conversely, maladaptive PACAP/PAC1R signaling has been implicated in a number of disorders, including stressrelated psychopathologies (i.e., depression, posttraumatic stress disorder, and related abnormalities), chronic pain and migraine, and metabolic diseases; abrogating PAC1R signaling under these pathological conditions represent opportunities for therapeutic intervention. Given the diverse PAC1R-mediated biological activities, the receptor has emerged as a relevant pharmaceutical target. In this review, we first describe the current knowledge regarding the molecular structure, dynamics, and function of PAC1R. Then, we discuss the roles of PACAP and PAC1R in the activation of a variety of signaling cascades related to the physiology and diseases of the nervous system. Lastly, we examine current drug design and development of peptides and small molecules targeting PAC1R based on a number of structure- activity relationship studies and key pharmacophore elements. At present, the rational design of PAC1R-selective peptide or small-molecule therapeutics is largely hindered by the lack of structural information regarding PAC1R activation mechanisms, the PACAP-PAC1R interface, and the core segments involved in receptor activation. Understanding the molecular basis governing the PACAP interactions with its different cognate receptors will undoubtedly provide a basis for the development and/or refinement of receptor-selective therapeutics.

Top-down Multiscale Approach To Simulate Peptide Self-Assembly from Monomers.

Modeling peptide assembly from monomers on large time and length scales is often intractable at the atomistic resolution. To address this challenge, we present a new approach which integrates coarse-grained (CG), mixed-resolution, and all-atom (AA) modeling in a single simulation. We simulate the initial encounter stage with the CG model, while the further assembly and reorganization stages are simulated with the mixed-resolution and AA models. We have implemented this top-down approach with new tools to automate model transformations and to monitor oligomer formations. Further, a theory was developed to estimate the optimal simulation length for each stage using a model peptide, melittin. The assembly level, the oligomer distribution, and the secondary structures of melittin simulated by the optimal protocol show good agreement with prior experiments and AA simulations. Finally, our approach and theory have been successfully validated with three amyloid peptides (ß-amyloid 16-22, GNNQQNY fragment from the yeast prion protein SUP35, and α-synuclein fibril 35-55), which highlight the synergy from modeling at multiple resolutions. This work not only serves as proof of concept for multiresolution simulation studies but also presents practical guidelines for further self-assembly simulations at more physically and chemically relevant scales.

Parameterization of a B3LYP specific correction for non-covalent interactions and basis set superposition error on a gigantic dataset of CCSD(T) quality non-covalent interaction energies.

A vast number of non-covalent interaction energies at the counterpoise corrected CCSD(T) level have been collected from the literature to build a diverse new dataset. The whole dataset, which consists of 2027 CCSD(T) energies, includes most of the published data at this level. A large subset of the data was then used to train a novel, B3LYP specific, empirical correction scheme for non-covalent interactions and basis set superposition error (abbreviated as B3LYP-MM). Results obtained with our new correction scheme were directly compared to benchmark results obtained with B3LYP-D3(1) and M06-2X(2) (two popular density functions designed specifically to accurately model non-covalent interactions). For non-covalent complexes dominated by dispersion or dipole-dipole interactions all three tested methods give accurate results with the medium size aug-cc-pVDZ(3-6) basis set with MUE's of 0.27 (B3LYP-MM), 0.32 (B3LYP-D3) and 0.47 kcal/mol (M06-2X) (with explicit counterpoise corrections). These results validate both B3LYP-D3 and M06-2X for interactions of this type using a much larger data set than was presented in prior work. However, our new dispersion correction scheme shows some clear advantages for dispersion and dipole-dipole dominated complexes with the small LACVP* basis set, which is very popular in use due to its low associated computational cost: The MUE for B3LYP-MM with the LACVP* basis set for this subset of complexes (without explicit counterpoise corrections) is only 0.28 kcal/mol, compared to 0.65 kcal/mol for M06-2X or 1.16 kcal/mol for B3LYP-D3. Additionally, our new correction scheme also shows major improvements in accuracy for hydrogen-bonded systems and for systems involving ionic interactions, for example cation-π interactions. Compared to B3LYP-D3 and M06-2X, we also find that our new B3LYP-MM correction scheme gives results of higher or equal accuracy for a large dataset of conformer energies of di- and tripeptides, sugars, and cysteine.

Single-molecule conductance through multiple π-π-stacked benzene rings determined with direct electrode-to-benzene ring connections.

Understanding electron transport across π-π-stacked systems will help to answer fundamental questions about biochemical redox processes and benefit the design of new materials and molecular devices. Herein we employed the STM break-junction technique to measure the single-molecule conductance of multiple π-π-stacked aromatic rings. We studied electron transport through up to four stacked benzene rings held together in an eclipsed fashion via a paracyclophane scaffold. We found that the strained hydrocarbons studied herein couple directly to gold electrodes during the measurements; hence, we did not require any heteroatom binding groups as electrical contacts. Density functional theory-based calculations suggest that the gold atoms of the electrodes bind to two neighboring carbon atoms of the outermost cyclophane benzene rings in η(2) fashion. Our measurements show an exponential decay of the conductance with an increasing number of stacked benzene rings, indicating a nonresonant tunneling mechanism. Furthermore, STM tip-substrate displacement data provide additional evidence that the electrodes bind to the outermost benzene rings of the π-π-stacked molecular wires.