The Damietta Server: a comprehensive protein design toolkit.

The growing importance of protein design to various research disciplines motivates the development of integrative computational platforms that enhance the accessibility and interoperability of different design tools. To this end, we describe a web-based toolkit that builds on the Damietta protein design engine, which deploys a tensorized energy calculation framework. The Damietta Server seamlessly integrates different design tools, in addition to other tools such as message-passing neural networks and molecular dynamics routines, allowing the user to perform multiple operations on structural models and forward them across tools. The toolkit can be used for tasks such as core or interface design, symmetric design, mutagenic scanning, or conformational sampling, through an intuitive user interface. With the envisioned integration of more tools, the Damietta Server will provide a central resource for protein design and analysis, benefiting basic and applied biomedical research communities. The toolkit is available with no login requirement through https://damietta.de/.

The design of functional proteins using tensorized energy calculations.

In protein design, the energy associated with a huge number of sequence-conformer perturbations has to be routinely estimated. Hence, enhancing the throughput and accuracy of these energy calculations can profoundly improve design success rates and enable tackling more complex design problems. In this work, we explore the possibility of tensorizing the energy calculations and apply them in a protein design framework. We use this framework to design enhanced proteins with anti-cancer and radio-tracing functions. Particularly, we designed multispecific binders against ligands of the epidermal growth factor receptor (EGFR), where the tested design could inhibit EGFR activity in vitro and in vivo. We also used this method to design high-affinity Cu2+ binders that were stable in serum and could be readily loaded with copper-64 radionuclide. The resulting molecules show superior functional properties for their respective applications and demonstrate the generalizable potential of the described protein design approach.

A topological refactoring design strategy yields highly stable granulopoietic proteins.

Protein therapeutics frequently face major challenges, including complicated production, instability, poor solubility, and aggregation. De novo protein design can readily address these challenges. Here, we demonstrate the utility of a topological refactoring strategy to design novel granulopoietic proteins starting from the granulocyte-colony stimulating factor (G-CSF) structure. We change a protein fold by rearranging the sequence and optimising it towards the new fold. Testing four designs, we obtain two that possess nanomolar activity, the most active of which is highly thermostable and protease-resistant, and matches its designed structure to atomic accuracy. While the designs possess starkly different sequence and structure from the native G-CSF, they show specific activity in differentiating primary human haematopoietic stem cells into mature neutrophils. The designs also show significant and specific activity in vivo. Our topological refactoring approach is largely independent of sequence or structural context, and is therefore applicable to a wide range of protein targets.

Accelerating therapeutic protein design.

Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.

Expanding the versatility of natural and de novo designed coiled coils and helical bundles.

Natural helical bundles (HBs) constitute a ubiquitous class of protein folds built of two or more longitudinally arranged α-helices. They adopt topologies that include symmetric, highly regular assemblies all the way to asymmetric, loosely packed domains. The diverse functional spectrum of HBs ranges from structural scaffolds to complex and dynamic effectors as molecular motors, signaling and sensing molecules, enzymes, and molecular switches. Symmetric HBs, particularly coiled coils, offer simple model systems providing an ideal entry point for protein folding and design studies. Herein, we review recent progress unveiling new structural features and functional mechanisms in natural HBs and cover staggering advances in the de novo design of HBs, giving rise to exotic structures and the creation of novel functions.

Design of novel granulopoietic proteins by topological rescaffolding.

Computational protein design is rapidly becoming more powerful, and improving the accuracy of computational methods would greatly streamline protein engineering by eliminating the need for empirical optimization in the laboratory. In this work, we set out to design novel granulopoietic agents using a rescaffolding strategy with the goal of achieving simpler and more stable proteins. All of the 4 experimentally tested designs were folded, monomeric, and stable, while the 2 determined structures agreed with the design models within less than 2.5 Å. Despite the lack of significant topological or sequence similarity to their natural granulopoietic counterpart, 2 designs bound to the granulocyte colony-stimulating factor (G-CSF) receptor and exhibited potent, but delayed, in vitro proliferative activity in a G-CSF-dependent cell line. Interestingly, the designs also induced proliferation and differentiation of primary human hematopoietic stem cells into mature granulocytes, highlighting the utility of our approach to develop highly active therapeutic leads purely based on computational design.

Optogenetic investigation of BMP target gene expression diversity.

Signaling molecules activate distinct patterns of gene expression to coordinate embryogenesis, but how spatiotemporal expression diversity is generated is an open question. In zebrafish, a BMP signaling gradient patterns the dorsal-ventral axis. We systematically identified target genes responding to BMP and found that they have diverse spatiotemporal expression patterns. Transcriptional responses to optogenetically delivered high- and low-amplitude BMP signaling pulses indicate that spatiotemporal expression is not fully defined by different BMP signaling activation thresholds. Additionally, we observed negligible correlations between spatiotemporal expression and transcription kinetics for the majority of analyzed genes in response to BMP signaling pulses. In contrast, spatial differences between BMP target genes largely collapsed when FGF and Nodal signaling were inhibited. Our results suggest that, similar to other patterning systems, combinatorial signaling is likely to be a major driver of spatial diversity in BMP-dependent gene expression in zebrafish.

Mapping Local Conformational Landscapes of Proteins in Solution.

The ability of proteins to adopt multiple conformational states is essential to their function, and elucidating the details of such diversity under physiological conditions has been a major challenge. Here we present a generalized method for mapping protein population landscapes by NMR spectroscopy. Experimental NOESY spectra are directly compared with a set of expectation spectra back-calculated across an arbitrary conformational space. Signal decomposition of the experimental spectrum then directly yields the relative populations of local conformational microstates. In this way, averaged descriptions of conformation can be eliminated. As the method quantitatively compares experimental and expectation spectra, it inherently delivers an R factor expressing how well structural models explain the input data. We demonstrate that our method extracts sufficient information from a single 3D NOESY experiment to perform initial model building, refinement, and validation, thus offering a complete de novo structure determination protocol.

Asymmetric protein design from conserved supersecondary structures.

Computational design with supersecondary structures as building blocks has proven effective in the construction of new proteins with controlled geometries. So far, this approach has primarily exploited amplification, effectively harnessing the internal folding propensity of self-compatible fragments to achieve sufficient enthalpy for folding. Here we exploit an interface-driven strategy to depart from the repeat design realm, constructing an asymmetric, globular domain from heterologous supersecondary structures. We report the successful design of a dRP lyase domain fold, which agrees with the experimental NMR structure at atomic accuracy (backbone RMSD of 0.94 Å). Our results show that the residual folding information within conserved fragments, combined with efficient interface-directed sampling, can effectively yield globular proteins with novel sequences and biophysical properties.

Prediction of GABARAP interaction with the GABA type A receptor.

We have performed docking simulations on GABARAP interacting with the GABA type A receptor using SwarmDock. We have also used a novel method to study hydration sites on the surface of these two proteins; this method identifies regions around proteins where desolvation is relatively easy, and these are possible locations where proteins can bind each other. There is a high degree of consistency between the predictions of these two methods. Moreover, we have also identified binding sites on GABARAP for other proteins, and listed possible binding sites for as yet unknown proteins on both GABARAP and the GABA type A receptor intracellular domain.

An Interface-Driven Design Strategy Yields a Novel, Corrugated Protein Architecture.

Designing proteins with novel folds remains a major challenge, as the biophysical properties of the target fold are not known a priori and no sequence profile exists to describe its features. Therefore, most computational design efforts so far have been directed toward creating proteins that recapitulate existing folds. Here we present a strategy centered upon the design of novel intramolecular interfaces that enables the construction of a target fold from a set of starting fragments. This strategy effectively reduces the amount of computational sampling necessary to achieve an optimal sequence, without compromising the level of topological control. The solenoid architecture has been a target of extensive protein design efforts, as it provides a highly modular platform of low topological complexity. However, none of the previous efforts have attempted to depart from the natural form, which is characterized by a uniformly handed superhelical architecture. Here we aimed to design a more complex platform, abolishing the superhelicity by introducing internally alternating handedness, resulting in a novel, corrugated architecture. We employed our interface-driven strategy, designing three proteins and confirming the design by solving the structure of two examples.

A fast topological analysis algorithm for large-scale similarity evaluations of ligands and binding pockets.

MOTIVATION: With the rapid increase of the structural data of biomolecular complexes, novel structural analysis methods have to be devised with high-throughput capacity to handle immense data input and to construct massive networks at the minimal computational cost. Moreover, novel methods should be capable of handling a broad range of molecular structural sizes and chemical natures, cognisant of the conformational and electrostatic bases of molecular recognition, and sufficiently accurate to enable contextually relevant biological inferences. RESULTS: A novel molecular topology comparison method was developed and tested. The method was tested for both ligand and binding pocket similarity analyses and a PDB-wide ligand topological similarity map was computed. CONCLUSION: The unprecedentedly wide scope of ligand definition and large-scale topological similarity mapping can provide very robust tools, of performance unmatched by the present alignment-based methods. The method remarkably shows potential for application for scaffold hopping purposes. It also opens new frontiers in the areas of ligand-mediated protein connectivity, ligand-based molecular phylogeny, target fishing, and off-target predictions. Graphical abstract:A novel molecular topology comparison method based on a combined shape distribution and charge binning scheme is presented.

Multiple virtual screening approaches for finding new hepatitis C virus RNA-dependent RNA polymerase inhibitors: structure-based screens and molecular dynamics for the pursue of new poly pharmacological inhibitors.

The RNA polymerase NS5B of Hepatitis C virus (HCV) is a well-characterised drug target with an active site and four allosteric binding sites. This work presents a workflow for virtual screening and its application to Drug Bank screening targeting the Hepatitis C Virus (HCV) RNA polymerase non-nucleoside binding sites. Potential polypharmacological drugs are sought with predicted active inhibition on viral replication, and with proven positive pharmaco-clinical profiles. The approach adopted was receptor-based. Docking screens, guided with contact pharmacophores and neural-network activity prediction models on all allosteric binding sites and MD simulations, constituted our analysis workflow for identification of potential hits. Steps included: 1) using a two-phase docking screen with Surflex and Glide Xp. 2) Ranking based on scores, and important H interactions. 3) a machine-learning target-trained artificial neural network PIC prediction model used for ranking. This provided a better correlation of IC50 values of the training sets for each site with different docking scores and sub-scores. 4) interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures for the interaction pharmacophore (common interaction modes) of inhibitors for the five non-nucleoside binding sites were constructed. These were used for filtering the hits according to the critical binding feature of formerly reported inhibitors. This filtration process resulted in identification of potential new inhibitors as well as formerly reported ones for the thumb II and Palm I sites (HCV-81) NS5B binding sites. Eventually molecular dynamics simulations were carried out, confirming the binding hypothesis and resulting in 4 hits.

New inhibitors of VEGFR-2 targeting the extracellular domain dimerization process.

We are reporting the discovery of small molecule inhibitors for vascular endothelial growth factor receptor type 2 (VEGFR-2) extracellular domain. The VEGFR-2 extracellular domain is responsible for the homo-dimerization process, which has been recently reported as a main step in VEGFR signal transduction cascade. This cascade is essential for the vascularization and survival of most types of cancers. Two main design strategies were used; Molecular docking-based Virtual Screening and Fragment Based Design (FBD). A virtual library of drug like compounds was screened using a cascade of docking techniques in order to discover an inhibitor that binds to this new binding site. Rapid docking methodology was used first to filter the large number of compounds followed by more accurate and slow ones. Fragment based molecular design was adopted afterwards due to unsatisfactory results of screening process. Screening and design process resulted in a group of inhibitors with superior binding energies exceeding that of the natural substrate. Molecular dynamics simulation was used to test the stability of binding of these inhibitors and finally the drug ability of these compounds was assisted using Lipinski rule of five. By this way the designed compounds have shown to possess high pharmacologic potential as novel anticancer agents.