Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively unexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

DNA methylation of the promoter region at the CREB1 binding site is a mechanism for the epigenetic regulation of brain-specific PKMζ.

Protein kinase M zeta, PKMζ, is a brain enriched kinase with a well characterized role in Long-Term Potentiation (LTP), the activity-dependent strengthening of synapses involved in long-term memory formation. However, little is known about the molecular mechanisms that maintain the tissue specificity of this kinase. Here, we characterized the epigenetic factors, mainly DNA methylation, regulating PKMζ expression in the human brain. The PRKCZ gene has an upstream promoter regulating Protein kinase C ζ (PKCζ), and an internal promoter driving PKMζ expression. A demethylated region, including a canonical CREB binding site, situated at the internal promoter was only observed in human CNS tissues. The induction of site-specific hypermethylation of this region resulted in decreased CREB1 binding and downregulation of PKMζ expression. Noteworthy, CREB binding sites were absent in the upstream promoter of PRKCZ locus, suggesting a specific mechanism for regulating PKMζ expression. These observations were validated using a system of human neuronal differentiation from induced pluripotent stem cells (iPSCs). CREB1 binding at the internal promoter was detected only in differentiated neurons, where PKMζ is expressed. The same epigenetic mechanism in the context of CREB binding site was identified in other genes involved in neuronal differentiation and LTP. Additionally, aberrant DNA hypermethylation at the internal promoter was observed in cases of Alzheimer's disease, correlating with decreased expression of PKMζ in patient brains. Altogether, we present a conserved epigenetic mechanism regulating PKMζ expression and other genes enhanced in the CNS with possible implications in neuronal differentiation and Alzheimer's disease.

Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding.

The nucleocapsid (N) protein of the SARS-CoV-2 virus, the causal agent of COVID-19, is a multifunction phosphoprotein that plays critical roles in the virus life cycle, including transcription and packaging of the viral RNA. To play such diverse roles, the N protein has two globular RNA-binding modules, the N- (NTD) and C-terminal (CTD) domains, which are connected by an intrinsically disordered region. Despite the wealth of structural data available for the isolated NTD and CTD, how these domains are arranged in the full-length protein and how the oligomerization of N influences its RNA-binding activity remains largely unclear. Herein, using experimental data from electron microscopy and biochemical/biophysical techniques combined with molecular modeling and molecular dynamics simulations, we show that, in the absence of RNA, the N protein formed structurally dynamic dimers, with the NTD and CTD arranged in extended conformations. However, in the presence of RNA, the N protein assumed a more compact conformation where the NTD and CTD are packed together. We also provided an octameric model for the full-length N bound to RNA that is consistent with electron microscopy images of the N protein in the presence of RNA. Together, our results shed new light on the dynamics and higher-order oligomeric structure of this versatile protein.

pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science.

BACKGROUND: Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. RESULTS: pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. CONCLUSIONS: We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

The Effects of Calcium on Lipid-Protein Interactions and Ion Flux in the Cx26 Connexon Embedded into a POPC Bilayer.

Gap junctions provide a communication pathway between adjacent cells. They are formed by paired connexons that reside in the plasma membrane of their respective cell and their activity can be modulated by the bilayer composition. In this work, we study the dynamic behavior of a Cx26 connexon embedded in a POPC lipid bilayer, studying: the membrane protein interactions and the ion flux though the connexon pore. We analyzed extensive atomistic molecular dynamics simulations for different conditions, with and without calcium ions. We found that lipid-protein interactions were mainly mediated by hydrogen bonds. Specific amino acids were identified forming hydrogen bonds with the POPC lipids (ARG98, ARG127, ARG165, ARG216, LYS22, LYS221, LYS223, LYS224, SER19, SER131, SER162, SER219, SER222, THR18 and TYR97, TYR155, TYR212, and TYR217). In the presence of calcium ions, we found subtle differences on the HB lifetimes. Finally, these MD simulations are able to identify and explain differential chlorine flux through the pore depending on the presence or absence of the calcium ions and its distribution within the pore.

Molecular mechanism of activation of Burkholderia cepacia lipase at aqueous-organic interfaces.

Lipases are water-soluble enzymes that catalyze the hydrolysis of lipids. Since lipids are mostly hydrophobic, lipase activity occurs preferentially at interfaces of aqueous and organic phases. In this work, we study the molecular mechanisms by which the Burkholderia cepacia lipase (BCL) is activated at interfaces of water with octane and with methyl caprylate (CAME). We show that BCL assumes very rapidly a preferential orientation at the interfaces, in which the active site is exposed to the organic phase. With BCL oriented to the interface, we compute the free energy of the aperture of the catalytic pocket using Adaptive Biasing Force MD simulations. The exposure to the organic phase promotes a clear stabilization of the open form of the catalytic pocket relative to the enzyme in water. This stabilization stems from the hydrophobicity of domains U1 and U2, which allows the penetration of organic solvents into the catalytic cleft impeding the closure of the pocket. Our results suggest that the structure and hydrophobicity of BCL are optimized for its activation in biphasic systems through the regulation of the accessibility of the catalytic pocket by, and for, hydrophobic substrates. The understanding of this mechanism may be useful for the design of proteins with targeted activation.

Anthrax Edema Factor: An Ion-Adaptive Mechanism of Catalysis with Increased Transition-State Conformational Flexibility.

Edema Factor (EF) is one of three major toxins of anthrax. EF is an adenylyl cyclase that disrupts cell signaling by accelerating the conversion of ATP into cyclic-AMP. EF has a much higher catalytic rate than that of mammalian adenylyl cyclases (mACs). Crystal structures were obtained for mACs and EF, but the molecular basis for different catalytic activities remained poorly understood. In particular, the arrangement of the active site in EF is unclear in what concerns the number of ions present and the conformation of the substrate. Here, we use quantum mechanics-molecular mechanics simulations to estimate the free-energy profiles for the reaction catalyzed by EF and a mAC. We found that EF catalysis is possible, and faster than that of mACs, in both one and two Mg(2+)-ion-binding modes, providing adaptive plasticity to host-cell environments. In both enzymes, the reaction mechanisms are highly associative. However, mechanistic differences exist. In the mAC, the nucleophile oxygen (ATP-O3') is consistently coordinated to one of the Mg(2+) ions, increasing its acidity. In EF, on the other hand, this coordination is eventual and not essential for the reaction to proceed. The persistent coordination of O3' to the ion is favored in mACs by a greater ion partial charge. In EF, the reduced acidity of the O3' oxygen is compensated by the presence of the His351 residue for proton abstraction. As proton transfer in EF does not require persistent attachment of the substrate to an ion, the substrate (ATP) and transition state display greater conformational flexibilities. These greater flexibilities allow the sampling of lower-energy conformations and might represent an entropic advantage for catalytic efficiency.

Mechanism of the Reaction of Human Manganese Superoxide Dismutase with Peroxynitrite: Nitration of Critical Tyrosine 34.

Human Mn-containing superoxide dismutase (hMnSOD) is a mitochondrial enzyme that metabolizes superoxide radical (O2(•-)). O2(•-) reacts at diffusional rates with nitric oxide to yield a potent nitrating species, peroxynitrite anion (ONOO(-)). MnSOD is nitrated and inactivated in vivo, with active site Tyr34 as the key oxidatively modified residue. We previously reported a k of ∼1.0 × 10(5) M(-1) s(-1) for the reaction of hMnSOD with ONOO(-) by direct stopped-flow spectroscopy and the critical role of Mn in the nitration process. In this study, we further established the mechanism of the reaction of hMnSOD with ONOO(-), including the necessary re-examination of the second-order rate constant by an independent method and the delineation of the microscopic steps that lead to the regio-specific nitration of Tyr34. The redetermination of k was performed by competition kinetics utilizing coumarin boronic acid, which reacts with ONOO(-) at a rate of ∼1 × 10(6) M(-1) s(-1) to yield the fluorescence product, 7-hydroxycoumarin. Time-resolved fluorescence studies in the presence of increasing concentrations of hMnSOD provided a k of ∼1.0 × 10(5) M(-1) s(-1), fully consistent with the direct method. Proteomic analysis indicated that ONOO(-), but not other nitrating agents, mediates the selective modification of active site Tyr34. Hybrid quantum-classical (quantum mechanics/molecular mechanics) simulations supported a series of steps that involve the initial reaction of ONOO(-) with Mn(III) to yield Mn(IV) and intermediates that ultimately culminate in 3-nitroTyr34. The data reported herein provide a kinetic and mechanistic basis for rationalizing how MnSOD constitutes an intramitochondrial target for ONOO(-) and the microscopic events, with atomic level resolution, that lead to selective and efficient nitration of critical Tyr34.

Improving Efficiency in SMD Simulations Through a Hybrid Differential Relaxation Algorithm.

The fundamental object for studying a (bio)chemical reaction obtained from simulations is the free energy profile, which can be directly related to experimentally determined properties. Although quite accurate hybrid quantum (DFT based)-classical methods are available, achieving statistically accurate and well converged results at a moderate computational cost is still an open challenge. Here, we present and thoroughly test a hybrid differential relaxation algorithm (HyDRA), which allows faster equilibration of the classical environment during the nonequilibrium steering of a (bio)chemical reaction. We show and discuss why (in the context of Jarzynski's Relationship) this method allows obtaining accurate free energy profiles with smaller number of independent trajectories and/or faster pulling speeds, thus reducing the overall computational cost. Moreover, due to the availability and straightforward implementation of the method, we expect that it will foster theoretical studies of key enzymatic processes.

Binding of modulators to mouse and human multidrug resistance P-glycoprotein. A computational study.

The human multidrug resistance (MDR) P-glycoprotein (P-gp) mediates the extrusion of chemotherapeutic drugs from cancer cells. Modulators are relevant pharmaceutical targets since they are intended to control or to inhibit its pumping activity. In the present work, a common binding site for Rhodamine 123 and modulators with different modulation activity was found by molecular docking over the crystal structure of the mouse P-gp. The modulators involved a family of compounds, including derivatives of propafenone (3-phenylpropiophenone nucleus) and XR9576 (tariquidar). Our results showed that the relative binding energies estimated by molecular docking were in good correlation with the experimental activities. Preliminary classical molecular dynamics results on selected P-gp/modulator complexes were also performed in order to understand the nature of the prevalent molecular interactions and the possible main molecular features that characterize a modulator. Besides, the results obtained with a human P-gp homology model from the mouse structure are also presented and analyzed. Our observations suggest that the hydrophobicity and molecular flexibility are the main features related to the inhibitory activity. The latter factor would increase the modulator ability to fit the aromatic rings inside the transmembrane domain.

Detailed analysis of water structure in a solvent mediated electron tunneling mechanism.

This work aims at describing the water structure characteristics that influence the electron transfer superexchange mechanism by explicitly calculating the solvent mediated conductance between the donor and acceptor in a generic pair. The method employed here is based on the non-equilibrium Green function formalism for calculating the conductance over solvent trajectories previously determined by molecular dynamics methods. A non-exponential dependence of the conductance is observed with respect to the distance between the donor and the acceptor. Local fluctuations of the solvent structure are responsible for the non-monotonic dependence, mainly due to the formation of solvent bridges that act as a molecular wire connecting the sites. This shortcutting phenomenon is observed for certain ranges of distances between the donor and acceptor in the pair. Charge on the sites strongly affects the local solvent structure and causes qualitative changes in the distance dependence of the tunneling probability.