Multiscale simulation-guided design of enzyme bioconjugates with enhanced catalysis.

Biopolymer-scaffold modification is widely used to enhance enzyme catalysis. A central challenge is predicting the effects of scaffold position on enzyme properties. Here, we use a computational-experimental approach to develop a model for the effects of DNA scaffold position on enzyme kinetics. Using phosphotriesterase modified with a 20bp dsDNA, we demonstrate that conjugation position is as important as the scaffold's chemistry and structure. Multiscale simulations predict the effective substrate concentration increases close to the scaffold, which has µM-strength binding to the substrate. Kinetic analysis shows that the effective concentration that the scaffold provides is best utilized when positioned next to, but not blocking, the active site. At ~5Å distance between scaffold and active site a 7-fold increase in k cat /K M was achieved. A model that accounts for the substrate concentration as well PTE-DNA geometry accurately captures the kinetic enhancements, enabling prediction of the effect across a range of DNA positions.

GeomBD3: Brownian Dynamics Simulation Software for Biological and Engineered Systems.

GeomBD3 is a robust Brownian dynamics simulation package designed to easily handle natural or engineered systems in diverse environments and arrangements. The software package described herein allows users to design, execute, and analyze BD simulations. The simulations use all-atom, rigid molecular models that diffuse according to overdamped Langevin dynamics and interact through electrostatic, Lennard-Jones, and ligand desolvation potentials. The program automatically calculates molecular association rates, surface residence times, and association statistics for any number of user-defined association criteria. Users can also extract molecular association pathways, diffusion coefficients, intermolecular interaction energies, intermolecular contact probability maps, and more using the provided supplementary analysis scripts. We detail the use of the package from start to finish and apply it to a protein-ligand system and a large nucleic acid biosensor. GeomBD3 provides a versatile tool for researchers from various disciplines that can aid in rational design of engineered systems or play an explanatory role as a complement to experiments. GeomBD version 3 is available on our website at http://chemcha-gpu0.ucr.edu/geombd3/ and KBbox at https://kbbox.h-its.org/toolbox/methods/molecular-simulation/geombd/.

Structure and noncanonical Cdk8 activation mechanism within an Argonaute-containing Mediator kinase module.

The Cdk8 kinase module (CKM) in Mediator, comprising Med13, Med12, CycC, and Cdk8, regulates RNA polymerase II transcription through kinase-dependent and -independent functions. Numerous pathogenic mutations causative for neurodevelopmental disorders and cancer congregate in CKM subunits. However, the structure of the intact CKM and the mechanism by which Cdk8 is non-canonically activated and functionally affected by oncogenic CKM alterations are poorly understood. Here, we report a cryo-electron microscopy structure of Saccharomyces cerevisiae CKM that redefines prior CKM structural models and explains the mechanism of Med12-dependent Cdk8 activation. Med12 interacts extensively with CycC and activates Cdk8 by stabilizing its activation (T-)loop through conserved Med12 residues recurrently mutated in human tumors. Unexpectedly, Med13 has a characteristic Argonaute-like bi-lobal architecture. These findings not only provide a structural basis for understanding CKM function and pathological dysfunction, but also further impute a previously unknown regulatory mechanism of Mediator in transcriptional modulation through its Med13 Argonaute-like features.

Modeling Effects of Surface Properties and Probe Density for Nanoscale Biosensor Design: A Case Study of DNA Hybridization near Surfaces.

Electrochemical biosensors have extremely robust applications while offering ease of preparation, miniaturization, and tunability. By adjusting the arrangement and properties of immobilized probes on the sensor surface to optimize target-probe association, one can design highly sensitive and efficient sensors. In electrochemical nucleic acid biosensors, a self-assembled monolayer (SAM) is widely used as a tunable surface with inserted DNA or RNA probes to detect target sequences. The effects of inhomogeneous probe distribution across surfaces are difficult to study experimentally due to inadequate resolution. Regions of high probe density may inhibit hybridization with targets, and the magnitude of the effect may vary depending on the hybridization mechanism on a given surface. Another fundamental question concerns diffusion and hybridization of DNA taking place on surfaces and whether it speeds up or hinders molecular recognition. We used all-atom Brownian dynamics simulations to help answer these questions by simulating the hybridization process of single-stranded DNA (ssDNA) targets with a ssDNA probe on polar, nonpolar, and anionic SAMs at three different probe surface densities. Moreover, we simulated three tightly packed probe clusters by modeling clusters with different interprobe spacing on two different surfaces. Our results indicate that hybridization efficiency depends strongly on finding a balance that allows attractive forces to steer target DNA toward probes without anchoring it to the surface. Furthermore, we found that the hybridization rate becomes severely hindered when interprobe spacing is less than or equal to the target DNA length, proving the need for a careful design to both enhance target-probe association and avoid steric hindrance. We developed a general kinetic model to predict hybridization times and found that it works accurately for typical probe densities. These findings elucidate basic features of nanoscale biosensors, which can aid in rational design efforts and help explain trends in experimental hybridization rates at different probe densities.

Insights Into Dynamics of Inhibitor and Ubiquitin-Like Protein Binding in SARS-CoV-2 Papain-Like Protease.

Covid-19 is caused by a novel form of coronavirus for which there are currently no vaccines or anti-viral drugs. This virus, termed SARS-CoV-2 (CoV2), contains Papain-like protease (PLpro) involved in viral replication and immune response evasion. Drugs targeting this protease therefore have great potential for inhibiting the virus, and have proven successful in older coronaviruses. Here, we introduce two effective inhibitors of SARS-CoV-1 (CoV1) and MERS-CoV to assess their potential for inhibiting CoV2 PLpro. We ran 1 µs molecular dynamics (MD) simulations of CoV2, CoV1, and MERS-CoV ligand-free PLpro to characterize the dynamics of CoV2 PLpro, and made comparisons between the three to elucidate important similarities and differences relevant to drug design and ubiquitin-like protein binding for deubiquitinating and deISGylating activity of CoV2. Next, we simulated the inhibitors bound to CoV1 and CoV2 PLpro in various poses and at different known binding sites to analyze their binding modes. We found that the naphthalene-based ligand shows strong potential as an inhibitor of CoV2 PLpro by binding at the putative naphthalene inhibitor binding site in both computational predictions and experimental assays. Our modeling work suggested strategies to improve naphthalene-based compounds, and our results from molecular docking showed that the newly designed compounds exhibited improved binding affinity. The other ligand, chemotherapy drug 6-mercaptopurine (6MP), showed little to no stable intermolecular interaction with PLpro and quickly dissociated or remained highly mobile. We demonstrate multiple ways to improve the binding affinity of the naphthalene-based inhibitor scaffold by engaging new residues in the unused space of the binding site. Analysis of CoV2 PLpro also brings insights into recognition of ubiquitin-like proteins that may alter innate immune response.

Amyloid-ß (Aß42) Peptide Aggregation Rate and Mechanism on Surfaces with Widely Varied Properties: Insights from Brownian Dynamics Simulations.

Amyloid-ß (Aß) plaques, which form by aggregation of harmless Aß peptide monomers into larger fibrils, are characteristic of neurodegenerative disorders such as Alzheimer's disease. Efforts to treat Alzheimer's disease focus on stopping or reversing the aggregation process that leads to fibril formation. However, effective treatments are elusive due to certain unknown aspects of the process. Many hypotheses point to disruption of cell membranes by adsorbed Aß monomers or oligomers, but how Aß behaves and aggregates on surfaces of widely varying properties, such as those present in a cell, is unclear. Elucidating the effects of various surfaces on the dynamics of Aß and the kinetics of the aggregation process from bulk solution to a surface-adsorbed multimer can help identify what drives aggregation, leading to new methods of intervention by inhibitory drugs or other means. In this work, we used all-atom Brownian dynamics simulations to study the association of two distinct Aß42 monomer conformations with a surface-adsorbed or free-floating Aß42 dimer. We calculated the association time, surface interaction energy, surface diffusion coefficient, surface residence time, and the mechanism of association on four different surfaces and two different bulk solution scenarios. In the presence of a surface, the majority of monomers underwent a two-dimensional surface-mediated association that depended primarily on an Aß42 electrostatic interaction with the self-assembled monolayer (SAM) surfaces. Moreover, aggregation could be inhibited greatly by surfaces with high affinity for Aß42 and heterogeneous charge distribution. Our results can be used to identify new opportunities for disrupting or reversing the Aß42 aggregation process.

Dynamics and molecular interactions of single-stranded DNA in nucleic acid biosensors with varied surface properties.

Electrochemical DNA biosensors utilizing self-assembled monolayers (SAMs) with inserted DNA probes are promising biosensor designs because of their ease of preparation, miniaturization, and tunability. However, much is still unknown about the interactions between biomolecules such as DNA and various surfaces. A fundamental question regarding these sensors concerns the nature of diffusion of target molecules taking place on sensor surfaces and whether it speeds up the molecular recognition process. Lack of understanding of molecular interaction and surface diffusion in addition to questions regarding the behavior of DNA probes immobilized on these surfaces currently limits the rational design of nucleic acid biosensors. Using all-atom unbiased molecular dynamics (MD) simulations we found that single-stranded DNA (ssDNA) behavior on SAMs is drastically altered by different surface chemistries, with ssDNA adopting very different orientations upon adsorption and surface diffusivity varying over an order of magnitude. Probe behavior varies equally broadly as probes are considerably more stable in certain SAMs than others, which affects the accessibility of probes to the target molecules and likely changes DNA hybridization kinetics in multiple ways. We also found that nearby probes can alter each other's orientations substantially, which highlights the importance of surface density control. Our results elucidate nucleic acid biosensor dynamics vital to rational design and offer insights that can aid in the design of surface properties and patterning for specific applications.

A molecular dynamics investigation of CDK8/CycC and ligand binding: conformational flexibility and implication in drug discovery.

Abnormal activity of cyclin-dependent kinase 8 (CDK8) along with its partner protein cyclin C (CycC) is a common feature of many diseases including colorectal cancer. Using molecular dynamics (MD) simulations, this study determined the dynamics of the CDK8-CycC system and we obtained detailed breakdowns of binding energy contributions for four type-I and five type-II CDK8 inhibitors. We revealed system motions and conformational changes that will affect ligand binding, confirmed the essentialness of CycC for inclusion in future computational studies, and provide guidance in development of CDK8 binders. We employed unbiased all-atom MD simulations for 500 ns on twelve CDK8-CycC systems, including apoproteins and protein-ligand complexes, then performed principal component analysis (PCA) and measured the RMSF of key regions to identify protein dynamics. Binding pocket volume analysis identified conformational changes that accompany ligand binding. Next, H-bond analysis, residue-wise interaction calculations, and MM/PBSA were performed to characterize protein-ligand interactions and find the binding energy. We discovered that CycC is vital for maintaining a proper conformation of CDK8 to facilitate ligand binding and that the system exhibits motion that should be carefully considered in future computational work. Surprisingly, we found that motion of the activation loop did not affect ligand binding. Type-I and type-II ligand binding is driven by van der Waals interactions, but electrostatic energy and entropic penalties affect type-II binding as well. Binding of both ligand types affects protein flexibility. Based on this we provide suggestions for development of tighter-binding CDK8 inhibitors and offer insight that can aid future computational studies.