Engineering a Rigid Nucleic Acid Structure to Improve the Limit of Detection for Genetic Assays.

Detecting nucleic acids at ultralow concentrations is critical for research and clinical applications. Particle-based assays are commonly used to detect nucleic acids. However, DNA hybridization on particle surfaces is inefficient due to the instability of tethered sequences, which negatively influences the assay's detection sensitivity. Here, we report a method to stabilize sequences on particle surfaces using a double-stranded linker at the 5' end of the tethered sequence. We termed this method Rigid Double Stranded Genomic Linkers for Improved DNA Analysis (RIGID-DNA). Our method led to a 3- and 100-fold improvement of the assays' clinical and analytical sensitivity, respectively. Our approach can enhance the hybridization efficiency of particle-based assays without altering existing assay workflows. This approach can be adapted to other platforms and surfaces to enhance the detection sensitivity.

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem. RESULTS: Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel α-helical structures by matching the exact conformations of selected hotspot residues. The designed α-helical structures can bind specific targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery. AVAILABILITY AND IMPLEMENTATION: https://github.com/xxiexuezhi/helix_gan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design.

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.

Targeting the Receptor-Binding Motif of SARS-CoV-2 with D-Peptides Mimicking the ACE2 Binding Helix: Lessons for Inhibiting Omicron and Future Variants of Concern.

The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein's receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro. Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4-31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 µM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD's surface.

Exploring the conformational dynamics of PD1 in complex with different ligands: What we can learn for designing novel PD1 signaling blockers?

Activation of T cells triggers the expression of regulatory molecules like the programmed cell death 1 (PD1) protein. The association of PD1 with the natural ligands PDL1 and PDL2 induces an inhibitory signal that prevents T cells from proliferating and exerting effector functions. However, little is known about how the binding of the ligands induce the PD1 inhibitory signal over T cells effector functions. Here, we explore the dynamics of PD1 free, and in complex with different PDL1 variants as well as the therapeutic antibodies nivolumab and pembrolizumab in order to assess the conformational changes in PD1 related to the signaling process. Our simulations suggest a pre-conformational selection mechanism for the binding of the different PDL1 variants, while an induced-fit model fits better for the molecular recognition process of the therapeutic antibodies. A deep analysis of the changes on PD1 movement upon the binding to different ligands revealed that as larger is the difference in the conformation adopted by loop C'D with respect to the complex with PDL1 is higher the ligand ability to reduce the PD1 inhibitory signaling. This behavior suggests that targeting specific conformations of this loop can be useful for designing therapies able to recover T cells effector functions.

Differential Effects of IL2Rα and IL15Rα over the Stability of the Common Beta-Gamma Signaling Subunits of the IL2 and IL15 Receptors.

Interleukin (IL) 2 and IL15 are two members of the common gamma chain cytokine family, involved in the regulation of the T cell differentiation process. Both molecules use a specific alpha subunit, IL2Rα and IL15Rα, and share the same beta and gamma chains signaling receptors. The presence of the specific alpha subunit modulates the T cell ability to compete for both soluble cytokines while the beta and gamma subunits are responsible for the signal transduction. Recent experimental results point out that the specific alpha subunits modulate the capacity of IL2 and IL15 to induce the differentiation of stimulated T cells. In other membrane receptors, the outcome of the signal transduction has been associated with the strength of the interaction of the signaling subunits. Here, we investigate how IL2Rα and IL15Rα modulate the stability of their signaling complexes by combining molecular dynamics simulations and free energy calculations. Our simulations predict that IL2Rα binding destabilizes the ß-γc interaction mediated by IL2, while IL15Rα has the opposite effect. These results explain the ability of IL2Rα and IL15Rα to modulate the signaling outcome and suggest new strategies for the development of better CD8+ T cell differentiation protocols for adoptive cell transfer (ACT).

Understanding the disrupting mechanism of the Tau aggregation motif "306 VQIVYK311 " by phenylthiazolyl-hydrazides inhibitors.

Alzheimer's disease is a progressive neurodegenerative disorder characterized by the abnormal processing of the Tau and the amyloid precursor proteins. The unusual aggregation of Tau is based on the formation of intermolecular ß-sheets through two motifs: 275 VQIINK280 and 306 VQIVYK311 . Phenylthiazolyl-hydrazides (PTHs) are capable of inhibiting/disassembling Tau aggregates. However, the disaggregation mechanism of Tau oligomers by PTHs is still unknown. In this work, we studied the disruption of the oligomeric form of the Tau motif 306 VQIVYK311 by PTHs through molecular docking, molecular dynamics, and free energy calculations. We predicted hydrophobic interactions as the major driving forces for the stabilization of Tau oligomer, with V306 and I308 being the major contributors. Nonpolar component of the binding free energy is essential to stabilize Tau-PTH complexes. PTHs disrupted mainly the van der Waals interactions between the monomers, leading to oligomer destabilization. Destabilization of full Tau filament by PTHs and emodin was not observed in the sampled 20 ns; however, in all cases, the nonpolar component of the binding free energy is essential for the formation of Tau filament-PTH and Tau filament-emodin. These results provide useful clues for the design of more effective Tau-aggregation inhibitors.

A Method to Calculate the Relative Binding Free Energy Differences of α-Helical Stapled Peptides.

Hydrocarbon-stapled peptides are a class of bioactive α-helical ligands developed to target protein-protein interactions. Peptide stapling has benefited from the development of several chemical reactions to modulate their membrane permeability and binding affinity. However, in most current programs, choosing the best stapling positions is usually a trial-and-error process. Here, we develop a protocol to obtain optimal stapling positions computationally. Our method is based on molecular dynamics simulations and free energy calculations with nonequilibrium approaches; here, we predict the binding poses, hot-spot residues, and binding affinity differences of a set of perfluoroarene stapled α-helical peptides of the BIM BH3 peptide to the BCLXL receptor. The prediction of the hot-spot residues within the target peptide through computational alanine scanning anticipates not only the key residues for the receptor-peptide complex formation but also which positions should be avoided when applying the stapling groups. The staple moieties introduce local conformational changes not only in the replaced positions but also on their neighbor residues of the template peptide further affecting their binding behavior. Our approach is successful at rank-ordering the binding affinities of these stapled peptides with respect to the BIM BH3 peptide.

Membrane Remodeling by the Lytic Fragment of SticholysinII: Implications for the Toroidal Pore Model.

Sticholysins are pore-forming toxins of biomedical interest and represent a prototype of proteins acting through the formation of protein-lipid or toroidal pores. Peptides spanning the N-terminus of sticholysins can mimic their permeabilizing activity and, together with the full-length toxins, have been used as a tool to understand the mechanism of pore formation in membranes. However, the lytic mechanism of these peptides and the lipid shape modulating their activity are not completely clear. In this article, we combine molecular dynamics simulations and experimental biophysical tools to dissect different aspects of the pore-forming mechanism of StII1-30, a peptide derived from the N-terminus of sticholysin II (StII). With this combined approach, membrane curvature induction and flip-flop movement of the lipids were identified as two important membrane remodeling steps mediated by StII1-30. Pore formation by this peptide was enhanced by the presence of the negatively curved lipid phosphatidylethanolamine in membranes. This lipid emerged not only as a facilitator of membrane interactions but also as a structural element of the StII1-30 pore that is recruited to the ring upon its assembly. Collectively, these, to our knowledge, new findings support a toroidal model for the architecture of the pore formed by StII1-30 and provide new molecular insight into the role of phosphatidylethanolamine as a membrane component that can easily integrate into the ring of toroidal pores, thus probably aiding in their stabilization. This study contributes to a better understanding of the molecular mechanism underlying the permeabilizing activity of StII1-30 and peptides or proteins acting via a toroidal pore mechanism and offers an informative framework for the optimization of the biomedical application of this and similar molecules.

NMR structure of CmPI-II, a non-classical Kazal protease inhibitor: Understanding its conformational dynamics and subtilisin A inhibition.

Subtilisin-like proteases play crucial roles in host-pathogen interactions. Thus, protease inhibitors constitute important tools in the regulation of this interaction. CmPI-II is a Kazal proteinase inhibitor isolated from Cenchritis muricatus that inhibits subtilisin A, trypsin and elastases. Based on sequence analysis it defines a new group of non-classical Kazal inhibitors. Lacking solved 3D structures from this group prevents the straightforward structural comparison with other Kazal inhibitors. The 3D structure of CmPI-II, solved in this work using NMR techniques, shows the typical fold of Kazal inhibitors, but has significant differences in its N-terminal moiety, the disposition of the CysI-CysV disulfide bond and the reactive site loop (RSL) conformation. The high flexibility of its N-terminal region, the RSL, and the α-helix observed in NMR experiments and molecular dynamics simulations, suggest a coupled motion of these regions that could explain CmPI-II broad specificity. The 3D structure of the CmPI-II/subtilisin A complex, obtained by modeling, allows understanding of the energetic basis of the subtilisin A inhibition. The residues at the P2 and P2' positions of the inhibitor RSL were predicted to be major contributors to the binding free energy of the complex, rather than those at the P1 position. Site directed mutagenesis experiments confirmed the Trp14 (P2') contribution to CmPI-II/subtilisin A complex formation. Overall, this work provides the structural determinants for the subtilisin A inhibition by CmPI-II and allows the designing of more specific and potent molecules. In addition, the 3D structure obtained supports the existence of a new group in non-classical Kazal inhibitors.

Protein surface roughness accounts for binding free energy of Plasmepsin II-ligand complexes.

The calculation of absolute binding affinities for protein-inhibitor complexes remains as one of the main challenges in computational structure-based ligand design. The present work explored the calculations of surface fractal dimension (as a measure of surface roughness) and the relationship with experimental binding free energies of Plasmepsin II complexes. Plasmepsin II is an attractive target for novel therapeutic compounds to treat malaria. However, the structural flexibility of this enzyme is a drawback when searching for specific inhibitors. Concerning that, we performed separate explicitly solvated molecular dynamics simulations using the available high-resolution crystal structures of different Plasmepsin II complexes. Molecular dynamics simulations allowed a better approximation to systems dynamics and, therefore, a more reliable estimation of surface roughness. This constitutes a novel approximation in order to obtain more realistic values of fractal dimension, because previous works considered only x-ray structures. Binding site fractal dimension was calculated considering the ensemble of structures generated at different simulation times. A linear relationship between binding site fractal dimension and experimental binding free energies of the complexes was observed within 20 ns. Previous studies of the subject did not uncover this relationship. Regression model, coined FD model, was built to estimate binding free energies from binding site fractal dimension values. Leave-one-out cross-validation showed that our model reproduced accurately the absolute binding free energies for our training set (R2  = 0.76; <|error|> =0.55 kcal/mol; SDerror  = 0.19 kcal/mol). The fact that such a simple model may be applied raises some questions that are addressed in the article.

Predicting binding modes of reversible peptide-based inhibitors of falcipain-2 consistent with structure-activity relationships.

Falcipain-2 (FP-2) is a major hemoglobinase of Plasmodium falciparum, considered an important drug target for the development of antimalarials. A previous study reported a novel series of 20 reversible peptide-based inhibitors of FP-2. However, the lack of tridimensional structures of the complexes hinders further optimization strategies to enhance the inhibitory activity of the compounds. Here we report the prediction of the binding modes of the aforementioned inhibitors to FP-2. A computational approach combining previous knowledge on the determinants of binding to the enzyme, docking, and postdocking refinement steps, is employed. The latter steps comprise molecular dynamics simulations and free energy calculations. Remarkably, this approach leads to the identification of near-native ligand conformations when applied to a validation set of protein-ligand structures. Overall, we proposed substrate-like binding modes of the studied compounds fulfilling the structural requirements for FP-2 binding and yielding free energy values that correlated well with the experimental data. Proteins 2017; 85:1666-1683. © 2017 Wiley Periodicals, Inc.

Structures of a bi-functional Kunitz-type STI family inhibitor of serine and aspartic proteases: Could the aspartic protease inhibition have evolved from a canonical serine protease-binding loop?

Bi-functional inhibitors from the Kunitz-type soybean trypsin inhibitor (STI) family are glycosylated proteins able to inhibit serine and aspartic proteases. Here we report six crystal structures of the wild-type and a non-glycosylated mutant of the bifunctional inhibitor E3Ad obtained at different pH values and space groups. The crystal structures show that E3Ad adopts the typical ß-trefoil fold of the STI family exhibiting some conformational changes due to pH variations and crystal packing. Despite the high sequence identity with a recently reported potato cathepsin D inhibitor (PDI), three-dimensional structures obtained in this work show a significant conformational change in the protease-binding loop proposed for aspartic protease inhibition. The E3Ad binding loop for serine protease inhibition is also proposed, based on structural similarity with a novel non-canonical conformation described for the double-headed inhibitor API-A from the Kunitz-type STI family. In addition, structural and sequence analyses suggest that bifunctional inhibitors of serine and aspartic proteases from the Kunitz-type STI family are more similar to double-headed inhibitor API-A than other inhibitors with a canonical protease-binding loop.

Improving the LIE Method for Binding Free Energy Calculations of Protein-Ligand Complexes.

In this work, we introduced an improved linear interaction energy (LIE) method parameterization for computations of protein­ligand binding free energies. The protocol, coined LIE-D, builds on the linear relationship between the empirical coefficient γ in the standard LIE scheme and the D parameter, introduced in our work. The D-parameter encompasses the balance (difference) between electrostatic (polar) and van der Waals (nonpolar) energies in protein­ligand complexes. Leave-one-out cross-validation showed that LIE-D reproduced accurately the absolute binding free energies for our training set of protein­ligand complexes (<|error|> = 0.92 kcal/mol, SDerror = 0.66 kcal/mol, R(2) = 0.90, QLOO(2) = 0.89, and sPRESS(LOO) = 1.28 kcal/mol). We also demonstrated LIE-D robustness by predicting accurately the binding free energies for three different protein­ligand systems outside the training data set, where the electrostatic and van der Waals interaction energies were calculated with different force fields.

HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures.

Here, we present HelixDiff, a score-based diffusion model for generating all-atom helical structures. We developed a hot spot-specific generation algorithm for the conditional design of α-helices targeting critical hotspot residues in bioactive peptides. HelixDiff generates α-helices with near-native geometries for most test scenarios with root-mean-square deviations (RMSDs) less than 1 Å. Significantly, HelixDiff outperformed our prior GAN-based model with regard to sequence recovery and Rosetta scores for unconditional and conditional generations. As a proof of principle, we employed HelixDiff to design an acetylated GLP-1 D-peptide agonist that activated the glucagon-like peptide-1 receptor (GLP-1R) cAMP accumulation without stimulating the glucagon-like peptide-2 receptor (GLP-2R). We predicted that this D-peptide agonist has a similar orientation to GLP-1 and is substantially more stable in MD simulations than our earlier D-GLP-1 retro-inverse design. This D-peptide analogue is highly resistant to protease degradation and induces similar levels of AKT phosphorylation in HEK293 cells expressing GLP-1R compared to the native GLP-1. We then discovered that matching crucial hotspots for the GLP-1 function is more important than the sequence orientation of the generated D-peptides when constructing D-GLP-1 agonists.

Computational Design of Potent and Selective d-Peptide Agonists of the Glucagon-like Peptide-2 Receptor.

Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower ß-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.

Predicting a Stable Dimeric Form of the PD1-PDL1 Complex: Implications for Understanding the PD1 Activation Mechanism.

The activation of T cells is typically accompanied by inhibitory mechanisms within which the programmed cell death (PD1) receptor stands out. Upon binding the ligands PDL1 and PDL2, PD1 drives T cells to an unresponsive state called exhaustion, characterized by a markedly decreased capacity to exert effector functions. For this reason, PD1 has become one of the most important targets in cancer immunotherapy. Despite the numerous studies about PD1 signaling modulation, how the PD1 signaling is activated upon the ligands' binding remains an open question. Several experimental facts suggest that the activation of the PD1-PLD1 pathway depends on the interaction with an unknown partner at the cellular membrane. In this work, we investigate the possibility that the target of PD1-PDL1 is the same PD1-PDL1 complex. We combined molecular docking with molecular dynamics and umbrella sampling simulations to explore different binding modes and assess the complexes' stability. We predicted a stable dimeric form of the extracellular domains of the PD1-PDL1 complex. This dimeric complex has an affinity comparable to the PD1-PDL1 interaction and resembles the form of a linear lattice. We proposed a new model for PD1 activation where the PD1-PDL1 dimeric form could facilitate the interaction of the intracellular domains of PD1 and the further binding and activation of the SHP2 phosphatase. This model might explain the inhibitory effect of anti-PD1/PDL1 antibodies through the prevention of the formation of the PD1-PDL1 dimers and, subsequently, the abrogation of the SHP2 phosphatase activation.

Assessment of Different Parameters on the Accuracy of Computational Alanine Scanning of Protein-Protein Complexes with the Molecular Mechanics/Generalized Born Surface Area Method.

Computational alanine scanning with the molecular mechanics generalized Born surface area (MM/GBSA) method constitutes a widely used approach for identifying critical residues at protein-protein interfaces. Despite its popularity, the MM/GBSA method still has certain drawbacks due to its dependence on many factors. Here, we performed a systematical study on the impact of four different parameters, namely, the internal dielectric constant, the generalized Born model, the entropic term, and the inclusion of structural waters on the accuracy of computational alanine scanning calculations with the MM/GBSA method. Our results show that the internal dielectric constant is the most critical parameter for getting accurate predictions. The introduction of entropy and interfacial water molecules decreased the quality of the predictions, while the generalized Born model had little to no effect. Considering the significance of the internal dielectric value, we proposed a methodology based on the energetic predominance of a particular set of amino acids at the protein-protein interface for selecting an appropriate value for this variable. We hope that these results serve as a guideline for future studies of protein-protein complexes using the MM/GBSA method.

Three decades targeting falcipains to develop antiplasmodial agents: what have we learned and what can be done next?

Malaria is a devastating infectious disease that affects large swathes of human populations across the planet's tropical regions. It is caused by parasites of the genus Plasmodium, with Plasmodium falciparum being responsible for the most lethal form of the disease. During the intraerythrocytic stage in the human hosts, malaria parasites multiply and degrade hemoglobin (Hb) using a battery of proteases, which include two cysteine proteases, falcipains 2 and 3 (FP-2 and FP-3). Due to their role as major hemoglobinases, FP-2 and FP-3 have been targeted in studies aiming to discover new antimalarials and numerous inhibitors with activity against these enzymes, and parasites in culture have been identified. Nonetheless, cross-inhibition of human cysteine cathepsins remains a serious hurdle to overcome for these compounds to be used clinically. In this article, we have reviewed key functional and structural properties of FP-2/3 and described different compound series reported as inhibitors of these proteases during decades of active research in the field. Special attention is also paid to the wide range of computer-aided drug design (CADD) techniques successfully applied to discover new active compounds. Finally, we provide guidelines that, in our understanding, will help advance the rational discovery of new FP-2/3 inhibitors.

Structural insights into serine protease inhibition by a marine invertebrate BPTI Kunitz-type inhibitor.

Proteins isolated from marine invertebrates are frequently characterized by exceptional structural and functional properties. ShPI-1, a BPTI Kunitz-type inhibitor from the Caribbean Sea anemone Stichodactyla helianthus, displays activity not only against serine-, but also against cysteine-, and aspartate proteases. As an initial step to evaluate the molecular basis of its activities, we describe the crystallographic structure of ShPI-1 in complex with the serine protease bovine pancreatic trypsin at 1.7Å resolution. The overall structure and the important enzyme-inhibitor interactions of this first invertebrate BPTI-like Kunitz-type inhibitor:trypsin complex remained largely conserved compared to mammalian BPTI-Kunitz inhibitor complexes. However, a prominent stabilizing role within the interface was attributed to arginine at position P3. Binding free-energy calculations indicated a 10-fold decrease for the inhibitor affinity against trypsin, if the P3 residue of ShPI-1 is mutated to alanine. Together with the increased role of Arg(11) at P3 position, slightly reduced interactions at the prime side (Pn') of the primary binding loop and at the secondary binding loop of ShPI-1 were detected. In addition, the structure provides important information for site directed mutagenesis to further optimize the activity of rShPI-1A for biotechnological applications.