Protein-protein docking with dynamic residue protonation states

Fine-tuning pH sensor H98 by remote essential residues in the hydrogen-bond network of mTASK-3

TASK-3 generates a background K+ conductance which when inhibited by acidification depolarizes membrane potential and increases cell excitability. These channels sense pH by protonation of histidine residue H98, but recent evidence revealed that several other amino acid residues also contribute to TASK-3 pH sensitivity, suggesting that the pH sensitivity is determined by an intermolecular network. Here we use electrophysiology and molecular modeling to characterize the nature and requisite role(s) of multiple amino acids in pH sensing by TASK-3. Our results suggest that the pH sensor H98 and consequently pH sensitivity is influenced by remote amino acids that function as a hydrogen-bonding network to modulate ionic conductivity. Among the residues in the network, E30 and K79 are the most important for passing external signals near residue S31 to H98. The hydrogen-bond network plays a key role in selectivity or pH sensing in mTASK-3, and E30 and S31 in the network can modulate the conductive properties (E30) or reverse the pH sensitivity and selectivity of the channel (S31). Molecular dynamics simulations and pK1/2 calculation revealed that double mutants involving H98 + S31 primarily regulate the structure stability of the pore selectivity filter and pore loop regions, further strengthen the stability of the cradle suspension system, and alter the ionization state of E30 and K79, thereby preventing pore conformational change that normally occurs in response to varying extracellular pH. These results demonstrate that crucial residues in the hydrogen-bond network can remotely tune the pH sensing of mTASK-3 and may be a potential allosteric regulatory site for therapeutic molecule development.

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45

Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.

Cysteine Cathepsins Inhibition Affects Their Expression and Human Renal Cancer Cell Phenotype

Renal cancer would greatly benefit from new therapeutic strategies since, in advanced stages, it is refractory to classical chemotherapeutic approaches. In this context, lysosomal protease cysteine cathepsins may represent new pharmacological targets. In renal cancer, they are characterized by a higher expression, and they were shown to play a role in its aggressiveness and spreading. Traditional studies in the field were focused on understanding the therapeutic potentialities of cysteine cathepsin inhibition, while the direct impact of such therapeutics on the expression of these enzymes was often overlooked. In this work, we engineered two fluoromethyl ketone-based peptides with inhibitory activity against cathepsins to evaluate their potential anticancer activity and impact on the lysosomal compartment in human renal cancer. Molecular modeling and biochemical assays confirmed the inhibitory properties of the peptides against cysteine cathepsin B and L. Different cell biology experiments demonstrated that the peptides could affect renal cancer cell migration and organization in colonies and spheroids, while increasing their adhesion to biological substrates. Finally, these peptide inhibitors modulated the expression of LAMP1, enhanced the expression of E-cadherin, and altered cathepsin expression. In conclusion, the inhibition of cysteine cathepsins by the peptides was beneficial in terms of cancer aggressiveness; however, they could affect the overall expression of these proteases.

Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data

Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.

Human Albumin Impairs Amyloid β-peptide Fibrillation Through its C-terminus: From docking Modeling to Protection Against Neurotoxicity in Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of extracellular deposits of amyloid β-peptide (Aβ), which induces neuronal death. Monomeric Aβ is not toxic but tends to aggregate into β-sheets that are neurotoxic. Therefore to prevent or delay AD onset and progression one of the main therapeutic approaches would be to impair Aβ assembly into oligomers and fibrils and to promote disaggregation of the preformed aggregate. Albumin is the most abundant protein in the cerebrospinal fluid and it was reported to bind Aβ impeding its aggregation. In a previous work we identified a 35-residue sequence of clusterin, a well-known protein that binds Aβ, that is highly similar to the C-terminus (CTerm) of albumin. In this work, the docking experiments show that the average binding free energy of the CTerm-Aβ1-42 simulations was significantly lower than that of the clusterin-Aβ1-42 binding, highlighting the possibility that the CTerm retains albumin's binding properties. To validate this observation, we performed in vitro structural analysis of soluble and aggregated 1 μM Aβ1-42 incubated with 5 μM CTerm, equimolar to the albumin concentration in the CSF. Reversed-phase chromatography and electron microscopy analysis demonstrated a reduction of Aβ1-42 aggregates when the CTerm was present. Furthermore, we treated a human neuroblastoma cell line with soluble and aggregated Aβ1-42 incubated with CTerm obtaining a significant protection against Aβ-induced neurotoxicity. These in silico and in vitro data suggest that the albumin CTerm is able to impair Aβ aggregation and to promote disassemble of Aβ aggregates protecting neurons.

Structural and kinetic basis for the selectivity of aducanumab for aggregated forms of amyloid-β

Aducanumab, a human-derived antibody targeting amyloid-β (Aβ), is in Phase 3 clinical trials for the treatment of Alzheimer’s disease. Biochemical and structural analyses show that aducanumab binds a linear epitope formed by amino acids 3–7 of the Aβ peptide. Aducanumab discriminates between monomers and oligomeric or fibrillar aggregates based on weak monovalent affinity, fast binding kinetics and strong avidity for epitope-rich aggregates. Direct comparative studies with analogs of gantenerumab, bapineuzumab and solanezumab demonstrate clear differentiation in the binding properties of these antibodies. The crystal structure of the Fab fragment of aducanumab bound to its epitope peptide reveals that aducanumab binds to the N terminus of Aβ in an extended conformation, distinct from those seen in structures with other antibodies that target this immunodominant epitope. Aducanumab recognizes a compact epitope that sits in a shallow pocket on the antibody surface. In silico analyses suggest that aducanumab interacts weakly with the Aβ monomer and may accommodate a variety of peptide conformations, further supporting its selectivity for Aβ aggregates. Our studies provide a structural rationale for the low affinity of aducanumab for non-pathogenic monomers and its greater selectivity for aggregated forms than is seen for other Aβ-targeting antibodies.

Properties that rank protein:protein docking poses with high accuracy

The development of docking algorithms to predict near-native structures of protein:protein complexes from the structure of the isolated monomers is of paramount importance for molecular biology and drug discovery.

Structure-based cross-docking analysis of antibody-antigen interactions

Antibody–antigen interactions are critical to our immune response, and understanding the structure-based biophysical determinants for their binding specificity and affinity is of fundamental importance. We present a computational structure-based cross-docking study to test the identification of native antibody–antigen interaction pairs among cognate and non-cognate complexes. We picked a dataset of 17 antibody–antigen complexes of which 11 have both bound and unbound structures available, and we generated a representative ensemble of cognate and non-cognate complexes. Using the Rosetta interface score as a classifier, the cognate pair was the top-ranked model in 80% (14/17) of the antigen targets using bound monomer structures in docking, 35% (6/17) when using unbound, and 12% (2/17) when using the homology-modeled backbones to generate the complexes. Increasing rigid-body diversity of the models using RosettaDock’s local dock routine lowers the discrimination accuracy with the cognate antibody–antigen pair ranking in bound and unbound models but recovers additional top-ranked cognate complexes when using homology models. The study is the first structure-based cross-docking attempt aimed at distinguishing antibody–antigen binders from non-binders and demonstrates the challenges to address for the methods to be widely applicable to supplement high-throughput experimental antibody sequencing workflows.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.

An Integrated Framework Advancing Membrane Protein Modeling and Design

Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.