Nano-Bio Interactions between DNA Nanocages and Human Serum Albumin

DNA nanostructures have emerged as promising nanomedical tools due to their biocompatibility and tunable behavior. Recent work has shown that DNA nanocages decorated with organic dendrimers strongly bind human serum albumin (HSA), yet the dynamic structures of these complexes remain uncharacterized. This theoretical and computational investigation elucidates the fuzzy interactions between dendritically functionalized cubic DNA nanocages and HSA. The dendrimer-HSA interactions occur via nonspecific binding with the protein thermodynamically and kinetically free to cross the open faces of the cubic scaffold. However, the rigidity of the DNA scaffold prevents the binding energetics from scaling with the number of dendrimers. These discoveries not only provide a useful framework by which to model general interactions of DNA nanostructures complexed with serum proteins but also give valuable molecular insight into the design of next-generation DNA nanomedicines.

Two-Dimensional Supramolecular Polymerization of DNA Amphiphiles is Driven by Sequence-Dependent DNA-Chromophore Interactions

Two-dimensional (2D) assemblies of water-soluble block copolymers have been limited by a dearth of systematic studies that relate polymer structure to pathway mechanism and supramolecular morphology. Here, we employ sequence-defined triblock DNA amphiphiles for the supramolecular polymerization of free-standing DNA nanosheets in water. Our systematic modulation of amphiphile sequence shows the alkyl chain core forming a cell membrane-like structure and the distal π-stacking chromophore block folding back to interact with the hydrophilic DNA block on the nanosheet surface. This interaction is crucial to sheet formation, marked by a chiral 'signature', and sensitive to DNA sequence, where nanosheets form with a mixed sequence, but not with a homogenous poly(thymine) sequence. This work opens the possibility of forming well-ordered, bilayer-like assemblies using a single DNA amphiphile for applications in cell sensing, nucleic acid therapeutic delivery and enzyme arrays.

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.

A Photoresponsive Intramolecular Triplex Motif That Enables Rapid and Reversible Control of Aptamer Binding Activity

DNA switches that can change conformation in response to certain wavelengths of light could enable rapid and noninvasive control of chemical processes for a wide range of applications. However, most current photoresponsive DNA switches are limited by either irreversible switching or reversible switching with impractically slow kinetics. Here, we report the design of an intramolecular triplex photoswitch (TPS) design based on single-stranded DNA that undergoes rapid and reversible photoswitching between folded and unfolded states through isomerization of internal azobenzene modifications. After optimizing the performance of our photoswitch design, we used molecular dynamics simulations to reveal how individual azobenzenes contribute to the stabilization or destabilization of the triplex depending on their photoisomerization state. By coupling our TPS to an existing aptamer, we can reversibly modulate its binding affinity with less than 15 s of UV light exposure. We further demonstrate reproducible shifting in affinity over multiple cycles of UV and blue light irradiation without substantial photobleaching.

Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery

The lack of biologically relevant protein structures can hinder rational design of small molecules to target G protein-coupled receptors (GPCRs). While ensemble docking using multiple models of the protein target is a promising technique for structure-based drug discovery, model clustering and selection still need further investigations to achieve both high accuracy and efficiency. In this work, we have developed an original ensemble docking approach, which identifies the most relevant conformations based on the essential dynamics of the protein pocket. This approach is applied to the study of small-molecule antagonists for the PAC1 receptor, a class B GPCR and a regulator of stress. As few as four representative PAC1 models are selected from simulations of a homology model and then used to screen three million compounds from the ZINC database and 23 experimentally validated compounds for PAC1 targeting. Our essential dynamics ensemble docking (EDED) approach can effectively reduce the number of false negatives in virtual screening and improve the accuracy to seek potent compounds. Given the cost and difficulties to determine membrane protein structures for all the relevant states, our methodology can be useful for future discovery of small molecules to target more other GPCRs, either with or without experimental structures.

GPCR Intracellular Loop Regulation of Beta-Arrestin-Mediated Endosomal Signaling Dynamics

G protein-coupled receptors (GPCRs) are currently appreciated to be routed to diverse cellular platforms to generate both G protein-dependent and -independent signals. The latter has been best studied with respect to β-arrestin-associated receptor internalization and trafficking to signaling endosomes for extracellular signal-regulated kinase (ERK) activation. However, how GPCR structural and conformational variants regulate endosomal ERK signaling dynamics, which can be central in neural development, plasticity, and disease processes, is not well understood. Among class B GPCRs, the PACAP-selective PAC1 receptor is unique in the expression of variants that can contain intracellular loop 3 (ICL3) cassette inserts. The nervous system expresses preferentially the PAC1Null (no insert) and PAC1Hop (28-amino acid Hop insert) receptor variants. Our molecular modeling and signaling studies revealed that the PAC1Null and PAC1Hop receptor variants can associate with β-arrestin differentially, resulting in enhanced receptor internalization and ERK activation for the PAC1Hop variant. The study amplifies our understandings of GPCR intracellular loop structure/function relationships with the first example of how the duration of endosomal ERK activation can be guided by ICL3. The results provide a framework for how changes in GPCR variant expression can impact developmental and homeostatic processes and may be contributory to maladaptive neuroplasticity underlying chronic pain and stress-related disorders.

Concerted Rolling and Penetration of Peptides during Membrane Binding

Peptide binding to membranes is common and fundamental in biochemistry and biophysics and critical for applications ranging from drug delivery to the treatment of bacterial infections. However, it is largely unclear, from a theoretical point of view, what peptides of different sequences and structures share in the membrane-binding and insertion process. In this work, we analyze three prototypical membrane-binding peptides (α-helical magainin, PGLa, and β-hairpin tachyplesin) during membrane binding, using molecular details provided by Markov state modeling and microsecond-long molecular dynamics simulations. By leveraging both geometric and data-driven collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions, we reveal how the slowest kinetic process of membrane binding is the dynamic rolling of the peptide from an attached to a fully bound state. These results not only add fundamental knowledge of the theory of how peptides bind to biological membranes but also open new avenues to study general peptides in more complex environments for further applications.

Molecular Basis for Environment Sensing by a Nucleoid-Structuring Bacterial Protein Filament

The histone-like nucleoid structuring (H-NS) protein controls the expression of hundreds of genes in Gram-positive bacteria through its capability to coat and condense DNA. This mechanism requires the formation of superhelical H-NS protein filaments that are sensitive to temperature and salinity, allowing H-NS to act as an environment sensor. We use multiscale modeling and simulations to obtain detailed insights into the mechanism of H-NS filament's sensitivity to environmental changes. Through the simulations of the superhelical H-NS filament, we reveal how different environments induce heterogeneity of H-NS monomers. Further, we observe that transient self-association within the H-NS filament creates temperature-inducible strain and might mildly oppose DNA binding. We also probe different H-NS-DNA complex architectures and show that complexation enhances the stability of both DNA and H-NS superhelices. Overall, our results provide unprecedented molecular insights into the environmental sensing and DNA interactions of a prototypical nucleoid-structuring bacterial protein filament.

A computational study of cooperative binding to multiple SARS-CoV-2 proteins

Structure-based drug design targeting the SARS-CoV-2 virus has been greatly facilitated by available virus-related protein structures. However, there is an urgent need for effective, safe small-molecule drugs to control the spread of the virus and variants. While many efforts are devoted to searching for compounds that selectively target individual proteins, we investigated the potential interactions between eight proteins related to SARS-CoV-2 and more than 600 compounds from a traditional Chinese medicine which has proven effective at treating the viral infection. Our original ensemble docking and cooperative docking approaches, followed by a total of over 16-micorsecond molecular simulations, have identified at least 9 compounds that may generally bind to key SARS-CoV-2 proteins. Further, we found evidence that some of these compounds can simultaneously bind to the same target, potentially leading to cooperative inhibition to SARS-CoV-2 proteins like the Spike protein and the RNA-dependent RNA polymerase. These results not only present a useful computational methodology to systematically assess the anti-viral potential of small molecules, but also point out a new avenue to seek cooperative compounds toward cocktail therapeutics to target more SARS-CoV-2-related proteins.

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.

AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides

Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments, it is necessary to create design approaches with higher precision and selectivity toward resistant targets. Previously, we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2, a bidirectional conditional generative adversarial network (BiCGAN)-based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data-driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate candidates that are novel, diverse, and tailored for specific applications, making it an efficient AMP design tool.

Molecular Basis of Class B GPCR Selectivity for the Neuropeptides PACAP and VIP

The related neuropeptides PACAP and VIP, and their shared PAC1, VPAC1 and VPAC2 receptors, regulate a large array of physiological activities in the central and peripheral nervous systems. However, the lack of comparative and molecular mechanistic investigations hinder further understanding of their preferred binding selectivity and function. PACAP and VIP have comparable affinity at the VPAC1 and VPAC2 receptor, but PACAP is 400-1,000 fold more potent than VIP at the PAC1 receptor. A molecular understanding of the differing neuropeptide-receptor interactions and the details underlying the receptor transitions leading to receptor activation are much needed for the rational design of selective ligands. To these ends, we have combined structural information and advanced simulation techniques to study PACAP/VIP binding selectivity, full-length receptor conformation ensembles and transitions of the PACAP/VIP receptor variants and subtypes, and a few key interactions in the orthosteric-binding pocket. Our results reveal differential peptide-receptor interactions (at the atomistic detail) important for PAC1, VPAC1 and VPAC2 receptor ligand selectivity. Using microsecond-long molecular dynamics simulations and the Markov State Models, we have also identified diverse receptor conformational ensembles and microstate transition paths for each receptor, the potential mechanisms underlying receptor open and closed states, and the interactions and dynamics at the transmembrane orthosteric pocket for receptor activation. These analyses reveal important features in class B GPCR structure-dynamics-function relationships, which provide novel insights for structure-based drug discovery.

Molecular basis for the adaptive evolution of environment-sensing by H-NS proteins

The DNA-binding protein H-NS is a pleiotropic gene regulator in gram-negative bacteria. Through its capacity to sense temperature and other environmental factors, H-NS allows pathogens like Salmonella to adapt their gene expression to their presence inside or outside warm-blooded hosts. To investigate how this sensing mechanism may have evolved to fit different bacterial lifestyles, we compared H-NS orthologs from bacteria that infect humans, plants, and insects, and from bacteria that live on a deep-sea hypothermal vent. The combination of biophysical characterization, high-resolution proton-less nuclear magnetic resonance spectroscopy, and molecular simulations revealed, at an atomistic level, how the same general mechanism was adapted to specific habitats and lifestyles. In particular, we demonstrate how environment-sensing characteristics arise from specifically positioned intra- or intermolecular electrostatic interactions. Our integrative approach clarified the exact modus operandi for H-NS-mediated environmental sensing and suggested that this sensing mechanism resulted from the exaptation of an ancestral protein feature.

Aggregation State of Synergistic Antimicrobial Peptides

By integrating various simulation and experimental techniques, we discovered that antimicrobial peptides (AMPs) may achieve synergy at an optimal concentration and ratio, which can be caused by aggregation of the synergistic peptides. On multiple time and length scales, our studies obtain novel evidence of how peptide coaggregation in solution can affect the disruption of membranes by synergistic AMPs. Our findings provide crucial details about the complex molecular origins of AMP synergy, which will help guide the future development of synergistic AMPs as well as applications of anti-infective peptide cocktail therapies.

Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students

Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply machine learning algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.

Molecular Dynamics Simulations of 2-Aminopurine-Labeled Dinucleoside Monophosphates Reveal Multiscale Stacking Kinetics

Molecular dynamics (MD) simulations of 2-aminopurine (2Ap)-labeled DNA dinucleoside monophosphates (DNMPs) were performed to investigate the hypothesis that base stacking dynamics occur on timescales sufficiently rapid to influence the emission signals measured in time-resolved fluorescence experiments. Analysis of multiple microsecond-length trajectories shows that the DNMPs sample all four coplanar stacking motifs. In addition, three metastable unstacked conformations are detected. A hidden Markov-state model (HMSM) was applied to the simulations to estimate transition rates between the stacked and unstacked states. Transitions between different stacked states generally occur at higher rates when the number of nucleobase faces requiring desolvation is minimized. Time constants for structural relaxation range between 1.6 and 25 ns, suggesting that emission from photoexcited 2Ap, which has an excited-state lifetime of 10 ns, is sensitive to base stacking kinetics. A master equation model for the excited-state population of 2Ap predicts multiexponential emission decays that reproduce the sub-10 ns emission decay lifetimes and amplitudes seen in experiments. Combining MD simulations with HMSM analysis is a powerful way to understand the dynamics that influence 2Ap excited-state relaxation and represents an important step toward using observed emission signals to validate MD simulations.

On the origin of multiexponential fluorescence decays from 2-aminopurine-labeled dinucleotides

The fluorescent probe 2-aminopurine (2Ap) has been used for decades to study local conformational fluctuations in DNA. Steady-state and time-resolved measurements of 2Ap fluorescence have been used to predict specific conformational states through suitable modeling of the quenching of the fluorescence of a 2Ap residue incorporated site-specifically into a DNA strand. The success of this approach has been limited by a lack of understanding of the precise factors responsible for the complex, multiexponential decays observed experimentally. In this study, dinucleotides composed of 2Ap and adenine were studied by the time-correlated single-photon counting technique to investigate the causes of heterogeneous emission kinetics. Contrary to previous reports, we argue that emission from 2Ap that is stacked with a neighboring base contributes negligibly to the emission signals recorded more than 50 ps after excitation, which are instead dominated by emission from unstacked 2Ap. We find that the decay kinetics can be modeled using a continuous lifetime distribution, which arises from the inherent distance dependence of electron transfer rates without the need to postulate a small number of discrete states with decay times derived from multiexponential fits. These results offer a new perspective on the quenching of 2Ap fluorescence and expand the information that can be obtained from experiments.

Subnanosecond Emission Dynamics of AT DNA Oligonucleotides

UV radiation creates excited electronic states in DNA that can decay to mutagenic photoproducts. When excited states return to the electronic ground state, photochemical injury is avoided. Understanding of the available relaxation pathways has advanced rapidly during the past decade, but there has been persistent uncertainty, and even controversy, over how to compare results from transient absorption and time-resolved emission experiments. Here, emission from single- and double-stranded AT DNA compounds excited at 265 nm was studied in aqueous solution using the time-correlated single photon counting technique. There is quantitative agreement between the emission lifetimes ranging from 50 to 200 ps and ones measured in transient absorption experiments, demonstrating that both techniques probe the same excited states. The results indicate that excitations with lifetimes of more than a few picoseconds are weakly emissive excimer and charge transfer states. Only a minute fraction of excitations persist beyond 1 ns in AT DNA strands at room temperature.