Effective energy function for proteins in solution

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

DIProT: A deep learning based interactive toolkit for efficient and effective Protein design

The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design.

Antioxidant and antibacterial activities of 5-mercapto(substitutedthio)-4-substituted-1,2,4-triazol based on nalidixic acid: A comprehensive study on its synthesis, characterization, and In silico evaluation

This study introduces a series of novel Alkyl thio-1,2,4-triazole (4a-p) and mercapto-1,2,4-triazole (3a-d) compounds derived from nalidixic acid. The synthesis was streamlined, involving interactions between nalidixic acid hydrazide and various isothiocyanates to yield cyclic and alkyl(aryl) sulfide compounds, characterized using 1H NMR, 13C NMR, IR, and elemental analysis. Antioxidant capabilities were quantified through DPPH and ABTS assays, highlighting significant potential, especially for compound 3d, which demonstrated an ABTS IC50 value of 0.397 μM, on par with ascorbic acid (IC50 = 0.87 μM). Antibacterial efficacy was established through MIC assessments against a broad spectrum of Gram-positive and Gram-negative bacteria, including Candida albicans. Compounds 3b, 4e, 4h, 4j, 4i, 4m, and 4o showed broad-spectrum activity, with 4k and 4m exhibiting pronounced potency against E. coli. Molecular docking studies validated the antibacterial potential, with compounds 4f and 4h showing high binding affinities (docking scores of -9.8 and -9.6 kcal/mol, respectively), indicating robust interactions with the bacterial enzyme targets. These scores underscore the compounds' mechanistic basis for their antibacterial action and support their therapeutic promise. Furthermore, compounds 3b, 4i, and 4m, identified through drug-likeness and toxicity predictions, were highlighted for their favorable profiles, suggesting their suitability for oral antibiotic therapies. This comprehensive study, blending synthetic, in vitro, and in silico approaches, emphasizes the triazole derivatives' potential as future candidates for antibiotic and antioxidant applications, particularly spotlighting compounds 3b, 4i, and 4m due to their promising efficacy and safety profiles.

DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

Recent advances in de novo computational design and redesign of intrinsically disordered proteins and intrinsically disordered protein regions

In the early 2000s, the concept of "unstructured biology" has emerged to be an important field in protein science by generating various new research directions. Many novel strategies and methods have been developed that are focused on effectively identifying/predicting intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs), identifying their potential functions, disorder based drug design etc. Due to the range of functions of IDPs/IDPRs and their involvement in various debilitating diseases they are of contemporary interest to the scientific community. Recent researches are focused on designing/redesigning specific IDPs/IDPRs de novo. These de novo design/redesigns of IDPs/IDPRs are carried out by altering compositional biases and specific sequence patterning parameters. The main focus of these researches is to influence specific molecular functions, phase behavior, cellular phenotypes etc. In this review, we first provide the differences of natively folded and natively unfolded or IDPs with respect to their potential energy landscapes. Here, we provide current understandings on the different computational design strategies and methods that have been utilized in de novo design and redesigns of IDPs and IDPRs. Finally, we conclude the review by discussing the challenges that have been faced during the computational design/design attempts of IDPs/IDPRs.

Computational insights into the cross-talk between medin and Aβ: implications for age-related vascular risk factors in Alzheimer's disease

The aggregation of medin forming aortic medial amyloid is linked to arterial wall degeneration and cerebrovascular dysfunction. Elevated levels of arteriolar medin are correlated with an increased presence of vascular amyloid-β (Aβ) aggregates, a hallmark of Alzheimer's disease (AD) and vascular dementia. The cross-interaction between medin and Aβ results in the formation of heterologous fibrils through co-aggregation and cross-seeding processes both in vitro and in vivo. However, a comprehensive molecular understanding of the cross-interaction between medin and Aβ-two intrinsically disordered proteins-is critically lacking. Here, we employed atomistic discrete molecular dynamics simulations to systematically investigate the self-association, co-aggregation and also the phenomenon of cross-seeding between these two proteins. Our results demonstrated that both Aβ and medin were aggregation prone and their mixture tended to form β-sheet-rich hetero-aggregates. The formation of Aβ-medin hetero-aggregates did not hinder Aβ and medin from recruiting additional Aβ and medin peptides to grow into larger β-sheet-rich aggregates. The β-barrel oligomer intermediates observed in the self-aggregations of Aβ and medin were also present during their co-aggregation. In cross-seeding simulations, preformed Aβ fibrils could recruit isolated medin monomers to form elongated β-sheets. Overall, our comprehensive simulations suggested that the cross-interaction between Aβ and medin may contribute to their pathological aggregation, given the inherent amyloidogenic tendencies of both medin and Aβ. Targeting medin, therefore, could offer a novel therapeutic approach to preserving brain function during aging and AD by improving vascular health.

Endothelial leakiness elicited by amyloid protein aggregation

Alzheimer's disease (AD) is a major cause of dementia debilitating the global ageing population. Current understanding of the AD pathophysiology implicates the aggregation of amyloid beta (Aβ) as causative to neurodegeneration, with tauopathies, apolipoprotein E and neuroinflammation considered as other major culprits. Curiously, vascular endothelial barrier dysfunction is strongly associated with Aβ deposition and 80-90% AD subjects also experience cerebral amyloid angiopathy. Here we show amyloid protein-induced endothelial leakiness (APEL) in human microvascular endothelial monolayers as well as in mouse cerebral vasculature. Using signaling pathway assays and discrete molecular dynamics, we revealed that the angiopathy first arose from a disruption to vascular endothelial (VE)-cadherin junctions exposed to the nanoparticulates of Aβ oligomers and seeds, preceding the earlier implicated proinflammatory and pro-oxidative stressors to endothelial leakiness. These findings were analogous to nanomaterials-induced endothelial leakiness (NanoEL), a major phenomenon in nanomedicine depicting the paracellular transport of anionic inorganic nanoparticles in the vasculature. As APEL also occurred in vitro with the oligomers and seeds of alpha synuclein, this study proposes a paradigm for elucidating the vascular permeation, systemic spread, and cross-seeding of amyloid proteins that underlie the pathogeneses of AD and Parkinson's disease.

Deciphering the influence of Y12L and N17H substitutions on the conformation and oligomerization of human calcitonin

The abnormal aggregation of human calcitonin (hCT) hormone peptides impairs their physiological function, leading to harmful immune responses and cytotoxicity, which limits their clinical utility. Interestingly, a representative hCT analog incorporating Y12L and N17H substitutions (DM-hCT) has shown reduced aggregation tendencies while maintaining bioactivity. But the molecular mechanism of Y12L and N17H substitutions on the conformational dynamics of hCT remains unclear. Here, we systematically investigated the folding and self-assembly dynamics of hCT and DM-hCT using atomistic discrete molecular dynamics (DMD) simulations. Our findings revealed that hCT monomers predominantly adopted unstructured conformations with dynamic helices. Oligomerization of hCT resulted in the formation of β-sheet-rich aggregates and β-barrel intermediates. The Y12L and N17H substitutions enhanced helical conformations and suppressed β-sheet formation in both monomers and oligomers. These substitutions stabilized the dynamic helices and disrupted aromatic interactions responsible for β-sheet formation at residue 12. Notably, DM-hCT assemblies still exhibited β-sheets in phenylalanine-rich and C-terminal hydrophobic regions, suggesting that future optimizations should focus on these areas. Our simulations provide insights into the molecular mechanisms underlying hCT aggregation and the amyloid-resistant effects of Y12L and N17H substitutions. These findings have valuable implications for the development of clinical hCT analogs.

Computer aided therapeutic tripeptide design, in alleviating the pathogenic proclivities of nocuous α-synuclein fibrils

Parkinson's disorder (PD) exacerbates neuronal degeneration of motor nerves, thereby effectuating uncoordinated movements and tremors. Aberrant alpha-synuclein (α-syn) is culpable of triggering PD, wherein cytotoxic amyloid aggregates of α-syn get deposited in motor neurons to instigate neuro-degeneration. Amyloid aggregates, typically rich in beta sheets are cardinal targets to mitigate their neurotoxic effects. In this analysis, owing to their interaction specificity, we formulated an efficacious tripeptide out of the aggregation-prone region of α-syn protein. With the help of a proficient computational pipeline, systematic peptide shortening and an adept molecular simulation platform, we formulated a tripeptide, VAV from α-syn structure based hexapeptide KISVRV. Indeed, the VAV tripeptide was able to effectively mitigate the α-syn amyloid fibrils' dynamic rate of beta-sheet formation. Additional trajectory analyses of the VAV- α-syn complex indicated that, upon its dynamic interaction, VAV efficiently altered the distinct pathogenic structural dynamics of α-syn, further advocating its potential in alleviating aberrant α-syn's amyloidogenic proclivities. Consistent findings from various computational analyses have led us to surmise that VAV could potentially re-alter the pathogenic conformational orientation of α-syn, essential to mitigate its cytotoxicity. Hence, VAV tripeptide could be an efficacious therapeutic candidate to efficiently ameliorate aberrant α-syn amyloid mediated neurotoxicity, eventually attenuating the nocuous effects of PD.Communicated by Ramaswamy H. Sarma.

Implicit model to capture electrostatic features of membrane environment

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Transmembrane β-Barrel Models of α-Synuclein Oligomers

The aggregation of α-synuclein is implicated in a number of neurodegenerative diseases, such as Parkinson's and Multiple System Atrophy, but the role of these aggregates in disease development is not clear. One possible mechanism of cytotoxicity is the disturbance or permeabilization of cell membranes by certain types of oligomers. However, no high-resolution structure of such membrane-embedded complexes has ever been determined. Here we construct and evaluate putative transmembrane β-barrels formed by this protein. Examination of the α-synuclein sequence reveals two regions that could form membrane-embedded β-hairpins: 64-92 (the NAC), and 35-56, which harbors many familial Parkinson's mutations. The stability of β-barrels formed by these hairpins is examined first in implicit membrane pores and then by multimicrosecond all-atom simulations. We find that a NAC region barrel remains stably inserted and hydrated for at least 10 μs. A 35-56 barrel remains stably inserted in the membrane but dehydrates and collapses if all His50 are neutral or if His50 is replaced by Q. If half of the His50 are doubly protonated, the barrel takes an oval shape but remains hydrated for at least 10 μs. Possible implications of these findings for α-synuclein pathology are discussed.

Simulations suggest a scaffolding mechanism of membrane deformation by the caveolin 8S complex

Caveolins form complexes of various sizes that deform membranes into polyhedral shapes. However, the recent structure of the 8S complex was disk-like with a flat membrane-binding surface. How can a flat complex deform membranes into nonplanar structures? Molecular dynamics simulations revealed that the 8S complex rapidly takes the form of a suction cup. Simulations on implicit membrane vesicles determined that binding is stronger when E140 gets protonated. In that case, the complex binds much more strongly to 5- and 10-nm-radius vesicles. A concave membrane-binding surface readily explains the membrane-deforming ability of caveolins by direct scaffolding. We propose that the 8S complex sits at the vertices of the caveolar polyhedra, rather than at the center of the polyhedral faces.

Light-Sensitive Open Channel Block of Ionotropic Glutamate Receptors by Quaternary Ammonium Azobenzene Derivatives

Glutamate ionotropic receptors mediate fast excitation processes in the central nervous system of vertebrates and play an important role in synaptic plasticity, learning, and memory. Here, we describe the action of two azobenene-containing compounds, AAQ (acrylamide-azobenzene-quaternary ammonium) and QAQ (quaternary ammonium-azobenzene-quaternary ammonium), which produced rapid and fully reversible light-dependent inhibition of glutamate ionotropic receptors. The compounds demonstrated voltage-dependent inhibition with only minor voltage-independent allosteric action. Calcium-impermeable AMPA receptors had weaker sensitivity compared to NMDA and calcium-permeable AMPA receptors. We further revealed that the compounds bound to NMDA and calcium-permeable AMPA receptors in different modes. They were able to enter the wide selectivity filter of AMPA receptors, and strong negative voltages caused permeation into the cytoplasm. The narrow selectivity filter of the NMDA receptors did not allow the molecules to bypass them; therefore, QAQ and AAQ bound to the shallow channel site and prevented channel closure by a foot-in-the-door mechanism. Computer simulations employing available AMPA and NMDA receptor structures readily reproduced the experimental findings, allowing for the structure-based design of more potent and selective drugs in the future. Thus, our work creates a framework for the development of light-sensitive blockers of calcium-permeable AMPA receptors, which are desirable tools for neuroscience.

Putative Pore Structures of Amyloid β 25-35 in Lipid Bilayers

The amyloid β peptide aggregates to form extracellular plaques in the brains of Alzheimer's disease patients. Certain of its fragments have been found to have similar properties to those of the full-length peptide. The best-studied of these is 25-35, which aggregates into fibrils, is toxic to neurons, and forms ion channels in synthetic lipid bilayers. Here, we investigate possible pore-forming structures of oligomers of this peptide in a POPC/POPG membrane. We consider octameric and decameric β-barrels of different topology, strand orientation, and shear, evaluate their stability in an implicit membrane model, and subject the best models to multimicrosecond all-atom molecular dynamics simulations. We find two decameric structures that are kinetically stable in membranes on this time scale: an imperfectly closed antiparallel β-barrel with K28 in the pore lumen and a short parallel β-barrel with K28 toward the membrane interface. Both structures exhibit dehydrated gaps in the pore lumen, which are larger for the antiparallel barrel. Based on these results, the experimental cation selectivity, the dependence of ion channel activity on voltage direction, and certain mutation data, the parallel model seems more compatible with experimental data.

Spike Protein Fragments Promote Alzheimer's Amyloidogenesis

Alzheimer's disease (AD) is a major cause of dementia inducing memory loss, cognitive decline, and mortality among the aging population. While the amyloid aggregation of peptide Aβ has long been implicated in neurodegeneration in AD, primarily through the production of toxic polymorphic aggregates and reactive oxygen species, viral infection has a less explicit role in the etiology of the brain disease. On the other hand, while the COVID-19 pandemic is known to harm human organs and function, its adverse effects on AD pathobiology and other human conditions remain unclear. Here we first identified the amyloidogenic potential of 1058HGVVFLHVTYV1068, a short fragment of the spike protein of SARS-CoV-2 coronavirus. The peptide fragment was found to be toxic and displayed a high binding propensity for the amyloidogenic segments of Aβ, thereby promoting the aggregation and toxicity of the peptide in vitro and in silico, while retarding the hatching and survival of zebrafish embryos upon exposure. Our study implicated SARS-CoV-2 viral infection as a potential contributor to AD pathogenesis, a little explored area in our quest for understanding and overcoming Long Covid.

Theoretical and Data-Driven Approaches for Biomolecular Condensates

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.

Defensin-based therapeutic peptide design in attenuating V30M TTR-induced Familial Amyloid Polyneuropathy

In the present study, we aimed to formulate an effective therapeutic candidate against V30M mutant transthyretin (TTR) protein to hinder its pathogenic misfolding. Nicotiana alata Defensin 1 (NaD1) Antimicrobial Peptide (AMP) was availed due to its tendency to aggregate, which may compete for aggregation-prone regions of pathogenic TTR protein. Based on NaD1's potential to bind to V30M TTR, we proposed NaD1-derived tetra peptides: CKTE and SKIL to be initial therapeutic candidates. Based on their association with mutant TTR protein, CKTE tetra peptide showed considerable interaction and curative potential as compared to SKIL tetra peptide. Further analyses from discrete molecular dynamics simulation corroborate CKTE tetra peptide's effectiveness as a 'beta-sheet breaker' against V30M TTR. Various post-simulation trajectory analyses suggested that CKTE tetra peptide alters the structural dynamics of pathogenic V30M TTR protein, thereby potentially attenuating its beta-sheets and impeding its aggregation. Normal mode analysis simulation corroborated that V30M TTR conformation is altered upon its interaction with CKTE peptide. Moreover, simulated thermal denaturation findings suggested that CKTE-V30M TTR complex is more susceptible to simulated denaturation, relative to pathogenic V30M TTR; further substantiating CKTE peptide's potential to alter V30M TTR's pathogenic conformation. Moreover, the residual frustration analysis augmented CKTE tetra peptide's proclivity in reorienting the conformation of V30M TTR. Therefore, we predicted that the tetra peptide, CKTE could be a promising therapeutic candidate in mitigating the amyloidogenic detrimental effects of V30M TTR-mediated familial amyloid polyneuropathy (FAP).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03646-4.

Implicit model to capture electrostatic features of membrane environment

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Dissecting the Self-assembly Dynamics of Imperfect Repeats in α-Synuclein

The pathological aggregation of α-synuclein (αS) into amyloid fibrils is the hallmark of Parkinson's disease (PD). The self-assembly and membrane interactions of αS are mainly governed by the seven imperfect 11-residue repeats of the XKTKEGVXXXX motif around residues 1-95. However, the particular role of each repeat in αS fibrillization remains unclear. To answer this question, we studied the aggregation dynamics of each repeat with up to 10 peptides in silico by conducting multiple independent micro-second atomistic discrete molecular dynamics simulations. Our simulations revealed that only repeats R3 and R6 readily self-assembled into β-sheet-rich oligomers, while the other repeats remained as unstructured monomers with weak self-assembly and β-sheet propensities. The self-assembly process of R3 featured frequent conformational changes with β-sheet formation mainly in the non-conserved hydrophobic tail, whereas R6 spontaneously self-assembled into extended and stable cross-β structures. These results of seven repeats are consistent with their structures and organization in recently solved αS fibrils. As the primary amyloidogenic core, R6 was buried inside the central cross-β core of all αS fibrils, attracting the hydrophobic tails of adjacent R4, R5, and R7 repeats forming β-sheets around R6 in the core. Further away from R6 in the sequence but with a moderate amyloid aggregation propensity, the R3 tail could serve as a secondary amyloidogenic core and form independent β-sheets in the fibril. Overall, our results demonstrate the critical role of R3 and R6 repeats in αS amyloid aggregation and suggest their potential as targets for the peptide-based and small-molecule amyloid inhibitors.

A novel approach to modeling side chain ensembles of the bifunctional spin label RX

We introduce a novel approach to modeling side chain ensembles of bifunctional spin labels. This approach utilizes rotamer libraries to generate side chain conformational ensembles. Because the bifunctional label is constrained by two attachment sites, the label is split into two monofunctional rotamers which are first attached to their respective sites, then rejoined by a local optimization in dihedral space. We validate this method against a set of previously published experimental data using the bifunctional spin label, RX. This method is relatively fast and can readily be used for both experimental analysis and protein modeling, providing significant advantages over modeling bifunctional labels with molecular dynamics simulations. Use of bifunctional labels for site directed spin labeling (SDSL) electron paramagnetic resonance (EPR) spectroscopy dramatically reduces label mobility, which can significantly improve resolution of small changes in protein backbone structure and dynamics. Coupling the use of bifunctional labels with side chain modeling methods allows for improved quantitative application of experimental SDSL EPR data to protein modeling.

GS-SMD server for steered molecular dynamics of peptide substrates in the active site of the γ-secretase complex

Despite recent advances in research, the mechanism of Alzheimer's disease is not fully understood yet. Understanding the process of cleavage and then trimming of peptide substrates, can help selectively block γ-secretase (GS) to stop overproduction of the amyloidogenic products. Our GS-SMD server (https://gs-smd.biomodellab.eu/) allows cleaving and unfolding of all currently known GS substrates (more than 170 peptide substrates). The substrate structure is obtained by threading of the substrate sequence into the known structure of GS complex. The simulations are performed in an implicit water-membrane environment so they are performed rather quickly, 2-6 h per job, depending on the mode of calculations (part of GS complex or the whole structure). It is also possible to introduce mutations to the substrate and GS and pull any part of the substrate in any direction using the steered molecular dynamics (SMD) simulations with constant velocity. The obtained trajectories are visualized and analyzed in the interactive way. One can also compare multiple simulations using the interaction frequency analysis. GS-SMD server can be useful for revealing mechanisms of substrate unfolding and role of mutations in this process.

Differential Binding and Conformational Dynamics of Tau Microtubule-Binding Repeats with a Preformed Amyloid-β Fibril Seed

Both senile plaques formed by amyloid-β (Aβ) and neurofibrillary tangles (NFTs) comprised of tau are pathological hallmarks of Alzheimer's disease (AD). The accumulation of NFTs better correlates with the loss of cognitive function than senile plaques, but NFTs are rarely observed without the presence of senile plaques. Hence, cross-seeding of tau by preformed Aβ amyloid fibril seeds has been proposed to drive the aggregation of tau and exacerbate AD progression, but the molecular mechanism remains unknown. Here, we first identified cross-interaction hotspots between Aβ and tau using atomistic discrete molecular dynamics simulations (DMD) and confirmed the critical role of the four microtubule-binding repeats of tau (R1-R4) in the cross-interaction with Aβ. We further investigated the binding structure and dynamics of each tau repeat with a preformed Aβ fibril seed. Specifically, R1 and R3 preferred to bind the Aβ fibril lateral surface instead of the elongation end. In contrast, R2 and R4 had higher binding propensities to the fibril elongation end than the lateral surface, enhancing β-sheet content by forming hydrogen bonds with the exposed hydrogen bond donors and acceptors. Together, our results suggest that the four repeats play distinct roles in driving the binding of tau to different surfaces of an Aβ fibril seed. Binding of tau to the lateral surface of Aβ fibril can increase the local concentration, while the binding to the elongation surface promotes β-sheet formation, both of which reduce the free energy barrier for tau aggregation nucleation and subsequent fibrillization.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Zinc-Epigallocatechin-3-gallate Network-Coated Nanocomposites against the Pathogenesis of Amyloid-Beta

The aggregation of amyloid beta (Aβ) is a hallmark of Alzheimer's disease (AD), a major cause of dementia and an unmet challenge in modern medicine. In this study, we constructed a biocompatible metal-phenolic network (MPN) comprising a polyphenol epigallocatechin gallate (EGCG) scaffold coordinated by physiological Zn(II). Upon adsorption onto gold nanoparticles, the MPN@AuNP nanoconstruct elicited a remarkable potency against the amyloid aggregation and toxicity of Aβ in vitro. The superior performance of MPN@AuNP over EGCG@AuNP was attributed to the porosity and hence larger surface area of the MPN in comparison with that of EGCG alone. The atomic detail of Zn(II)-EGCG coordination was unraveled by density functional theory calculations and the structure and dynamics of Aβ aggregation modulated by the MPN were further examined by discrete molecular dynamics simulations. As MPN@AuNP also displayed a robust capacity to cross a blood-brain barrier model through the paracellular pathway, and given the EGCG's function as an anti-amyloidosis and antioxidation agent, this MPN-based strategy may find application in regulating the broad AD pathology beyond protein aggregation inhibition.

Protein structure prediction using the evolutionary algorithm USPEX

Protein structure prediction is one of major problems of modern biophysics: current attempts to predict the tertiary protein structure from amino acid sequence are successful mostly when the use of big data and machine learning allows one to reduce the "prediction problem" to the "problem of recognition". Compared with recent successes of deep learning, classical predictive methods lag behind in their accuracy for the prediction of stable conformations. Therefore, in this work we extended the evolutionary algorithm USPEX to predict protein structure based on global optimization starting with the amino acid sequence. Moreover, we compared frequently used force fields for the task of protein structure prediction. Protein structure relaxation and energy calculations were performed using Tinker (with several different force fields) and Rosetta (with REF2015 force field) codes. To create new protein structure models in the USPEX algorithm, we developed novel variation operators. The test of the new method on seven proteins having (for simplicity) no cis-proline (with ω ≈ 0°) residues, and a length of up to 100 residues, revealed that our algorithm predicts tertiary structures of proteins with high accuracy. The comparison of the final potential energies of the predicted protein structures obtained using the USPEX and the Rosetta Abinitio approach showed that in most cases the developed algorithm found structures with close or even lower energy (Amber/Charmm/Oplsaal) and scoring function (REF2015). While USPEX has clearly demonstrated its ability to find very deep energy minima, our study showed that the existing force fields are not sufficiently accurate for accurate blind prediction of protein structures without further experimental verification.

Cumulative Millisecond-Long Sampling for a Comprehensive Energetic Evaluation of Aqueous Ionic Liquid Effects on Amino Acid Interactions

The interactions of amino acid side-chains confer diverse energetic contributions and physical properties to a protein's stability and function. Various computational tools estimate the effect of changing a given amino acid on the protein's stability based on parametrized (free) energy functions. When parametrized for the prediction of protein stability in water, such energy functions can lead to suboptimal results for other solvents, such as ionic liquids (IL), aqueous ionic liquids (aIL), or salt solutions. However, to our knowledge, no comprehensive data are available describing the energetic effects of aIL on intramolecular protein interactions. Here, we present the most comprehensive set of potential of mean force (PMF) profiles of pairwise protein-residue interactions to date, covering 50 relevant interactions in water, the two biotechnologically relevant aIL [BMIM/Cl] and [BMIM/TfO], and [Na/Cl]. These results are based on a cumulated simulation time of >1 ms. aIL and salt ions can weaken, but also strengthen, specific residue interactions by more than 3 kcal mol^-1, depending on the residue pair, residue-residue configuration, participating ions, and concentration, necessitating considering such interactions specifically. These changes originate from a complex interplay of competitive or cooperative noncovalent ion-residue interactions, changes in solvent structural dynamics, or unspecific charge screening effects and occur at the contact distance but also at larger, solvent-separated distances. This data provide explanations at the atomistic and energetic levels for complex IL effects on protein stability and should help improve the prediction accuracies of computational tools that estimate protein stability based on (free) energy functions.

Putative Structures of Membrane-Embedded Amyloid β Oligomers

Perturbation of cell membranes by amyloid β (Ab) peptide oligomers is one possible mechanism of cytotoxicity in Alzheimer's disease, but the structure of such Ab-membrane complexes is unknown. Here we examine the stability of several putative structures by implicit membrane and all-atom molecular dynamics simulations. The structures include (a) a variety of models proposed by other researchers in the past, (b) a heptameric β barrel determined by grafting the Ab sequence onto α-hemolysin, (c) a similar structure with modified strand orientation and turn location based on an experimental β-hairpin structure, (d) oligomers inserting C-terminal β hairpins into one leaflet of the bilayer, (e) oligomers forming parallel C-terminal β barrels, and (f) a helical hexamer made of C-terminal fragments. The α-hemolysin-grafted structure and its alternately oriented variant are stable in the membrane and form an aqueous pore. In contrast, the C-terminal parallel barrels are not stable, presumably due to excessive hydrophobicity of their inner surface. The helical hexamer also failed to stabilize an aqueous pore for the same reason. The C-terminal hairpin-inserting structures remain stably inserted but, again, do not form an aqueous pore. Our results suggest that only β-barrels inserting a combination of C-terminal and other residues can form stable aqueous pores.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology

Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features

Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ , of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.

Hydration Free Energies of Polypeptides from Popular Implicit Solvent Models versus All-Atom Simulation Results Based on Molecular Quasichemical Theory

Calculating the hydration free energy of a macromolecule in all-atom simulations has long remained a challenge, necessitating the use of models wherein the effect of the solvent is captured without explicit account of solvent degrees of freedom. This situation has changed with developments in the molecular quasi-chemical theory (QCT)─an approach that enables calculation of the hydration free energy of macromolecules within all-atom simulations at the same resolution as is possible for small molecular solutes. The theory also provides a rigorous and physically transparent framework to conceptualize and model interactions in molecular solutions and thus provides a convenient framework to investigate the assumptions in implicit solvent models. In this study, we compare the results using molecular QCT versus predictions from EEF1, ABSINTH, and GB/SA implicit solvent models for polyglycine and polyalanine solutes covering a range of chain lengths and conformations. The hydration free energies or the differences in hydration free energies between conformers obtained from the implicit solvent models do not agree with explicit solvent results, with the deviations being largest for the group additive EEF1 and ABSINTH models. GB/SA does better in capturing the qualitative trends seen in explicit solvent results. Analysis founded on QCT reveals the critical importance of the cooperativity of hydration that is inherent in the hydrophilic and hydrophobic contributions to hydration─physics that is not well captured in additive models but somewhat better accounted for by means of a dielectric in the GB/SA approach.

Mechanisms of acid-sensing ion channels inhibition by nafamostat, sepimostat and diminazene

Acid-sensing ion channels (ASICs) are blocked by many cationic compounds. Mechanisms of action, which may include pore block, modulation of activation and desensitization, need systematic analysis to allow predictable design of new potent and selective drugs. In this work, we studied the action of the serine protease inhibitors nafamostat, sepimostat, gabexate and camostat, on native ASICs in rat giant striatal interneurons and recombinant ASIC1a and ASIC2a channels, and compared it to that of well-known small molecule ASIC blocker diminazene. All these compounds have positively charged amidine and/or guanidine groups in their structure. Nafamostat, sepimostat and diminazene inhibited pH 6.5-induced currents in rat striatal interneurons at -80 mV holding voltage with IC₅₀ values of 0.78 ± 0.12 μM, 2.4 ± 0.3 μM and 0.40 ± 0.09 μM, respectively, whereas camostat and gabexate were practically ineffective. The inhibition by nafamostat, sepimostat and diminazene was voltage-dependent evidencing binding in the channel pore. They were not trapped in the closed channels, suggesting "foot-in-the-door" mechanism of action. The inhibitory activity of nafamostat, sepimostat and diminazene was similar in experiments on native ASICs and recombinant ASIC1a channels, while all of them were drastically less active against ASIC2a channels. According to our molecular modeling, three active compounds bind in the channel pore between Glu 433 and Ala 444 in a similar way. In view of the relative safety of nafamostat for clinical use in humans, it can be considered as a potential candidate for the treatment of pathophysiological conditions linked to ASICs disfunction, including inflammatory pain and ischemic stroke.

Rational Design and In Vivo Characterization of mRNA-Encoded Broadly Neutralizing Antibody Combinations against HIV-1

Monoclonal antibodies have been used successfully as recombinant protein therapy; however, for HIV, multiple broadly neutralizing antibodies may be necessary. We used the mRNA-LNP platform for in vivo co-expression of 3 broadly neutralizing antibodies, PGDM1400, PGT121, and N6, directed against the HIV-1 envelope protein. mRNA-encoded HIV-1 antibodies were engineered as single-chain Fc (scFv-Fc) to overcome heavy- and light-chain mismatch. In vitro neutralization breadth and potency of the constructs were compared to their parental IgG form. We assessed the ability of these scFv-Fcs to be expressed individually and in combination in vivo, and neutralization and pharmacokinetics were compared to the corresponding full-length IgGs. Single-chain PGDM1400 and PGT121 exhibited neutralization potency comparable to parental IgG, achieving peak systemic concentrations ≥ 30.81 μg/mL in mice; full-length N6 IgG achieved a peak concentration of 974 μg/mL, but did not tolerate single-chain conversion. The mRNA combination encoding full-length N6 IgG and single-chain PGDM1400 and PGT121 was efficiently expressed in mice, achieving high systemic concentration and desired neutralization potency. Analysis of mice sera demonstrated each antibody contributed towards neutralization of multiple HIV-1 pseudoviruses. Together, these data show that the mRNA-LNP platform provides a promising approach for antibody-based HIV treatment and is well-suited for development of combination therapeutics.

Computationally-Aided Modeling of Hsp70-Client Interactions: Past, Present, and Future

Hsp70 molecular chaperones play central roles in maintaining a healthy cellular proteome. Hsp70s function by binding to short peptide sequences in incompletely folded client proteins, thus preventing them from misfolding and/or aggregating, and in many cases holding them in a state that is competent for subsequent processes like translocation across membranes. There is considerable interest in predicting the sites where Hsp70s may bind their clients, as the ability to do so sheds light on the cellular functions of the chaperone. In addition, the capacity of the Hsp70 chaperone family to bind to a broad array of clients and to identify accessible sequences that enable discrimination of those that are folded from those that are not fully folded, which is essential to their cellular roles, is a fascinating puzzle in molecular recognition. In this article we discuss efforts to harness computational modeling with input from experimental data to develop a predictive understanding of the promiscuous yet selective binding of Hsp70 molecular chaperones to accessible sequences within their client proteins. We trace how an increasing understanding of the complexities of Hsp70-client interactions has led computational modeling to new underlying assumptions and design features. We describe the trend from purely data-driven analysis toward increased reliance on physics-based modeling that deeply integrates structural information and sequence-based functional data with physics-based binding energies. Notably, new experimental insights are adding to our understanding of the molecular origins of "selective promiscuity" in substrate binding by Hsp70 chaperones and challenging the underlying assumptions and design used in earlier predictive models. Taking the new experimental findings together with exciting progress in computational modeling of protein structures leads us to foresee a bright future for a predictive understanding of selective-yet-promiscuous binding exploited by Hsp70 molecular chaperones; the resulting new insights will also apply to substrate binding by other chaperones and by signaling proteins.

Graph Clustering Analyses of Discontinuous Molecular Dynamics Simulations: Study of Lysozyme Adsorption on a Graphene Surface

Understanding the interfacial behaviors of biomolecules is crucial to applications in biomaterials and nanoparticle-based biosensing technologies. In this work, we utilized autoencoder-based graph clustering to analyze discontinuous molecular dynamics (DMD) simulations of lysozyme adsorption on a graphene surface. Our high-throughput DMD simulations integrated with a Go̅-like protein-surface interaction model makes it possible to explore protein adsorption at a large temporal scale with sufficient accuracy. The graph autoencoder extracts a low-dimensional feature vector from a contact map. The sequence of the extracted feature vectors is then clustered, and thus the evolution of the protein molecule structure in the absorption process is segmented into stages. Our study demonstrated that the residue-surface hydrophobic interactions and the π-π stacking interactions play key roles in the five-stage adsorption. Upon adsorption, the tertiary structure of lysozyme collapsed, and the secondary structure was also affected. The folding stages obtained by autoencoder-based graph clustering were consistent with detailed analyses of the protein structure. The combination of machine learning analysis and efficient DMD simulations developed in this work could be an important tool to study biomolecules' interfacial behaviors.

COGRIMEN: Coarse-Grained Method for Modeling of Membrane Proteins in Implicit Environments

The presented methodology is based on coarse-grained representation of biomolecules in implicit environments and is designed for the molecular dynamics simulations of membrane proteins and their complexes. The membrane proteins are not only found in the cell membrane but also in all membranous compartments of the cell: Golgi apparatus, mitochondria, endosomes and lysosomes, and they usually form large complexes. To investigate such systems the methodology is proposed based on two independent approaches combining the coarse-grained MARTINI model for proteins and the effective energy function to mimic the water/membrane environments. The latter is based on the implicit environment developed for all-atom simulations in the IMM1 method. The force field solvation parameters for COGRIMEN were initially calculated from IMM1 all-atom parameters and then optimized using Genetic Algorithms. The new methodology was tested on membrane proteins, their complexes and oligomers. COGRIMEN method is implemented as a patch for NAMD program and can be useful for fast and brief studies of large membrane protein complexes.

Anionic nanoplastic exposure induces endothelial leakiness

The global-scale production of plastics has been instrumental in advancing modern society, while the rising accumulation of plastics in landfills, oceans, and anything in between has become a major stressor on environmental sustainability, climate, and, potentially, human health. While mechanical and chemical forces of man and nature can eventually break down or recycle plastics, our understanding of the biological fingerprints of plastics, especially of nanoplastics, remains poor. Here we report on a phenomenon associated with the nanoplastic forms of anionic polystyrene and poly(methyl methacrylate), where their introduction disrupted the vascular endothelial cadherin junctions in a dose-dependent manner, as revealed by confocal fluorescence microscopy, signaling pathways, molecular dynamics simulations, as well as ex vivo and in vivo assays with animal model systems. Collectively, our results implicated nanoplastics-induced vasculature permeability as primarily biophysical-biochemical in nature, uncorrelated with cytotoxic events such as reactive oxygen species production, autophagy, and apoptosis. This uncovered route of paracellular transport has opened up vast avenues for investigating the behaviour and biological effects of nanoplastics, which may offer crucial insights for guiding innovations towards a sustainable plastics industry and environmental remediation.

Probing the polyphenolic flavonoid, morin as a highly efficacious inhibitor against amyloid(A4V) mutant SOD1 in fatal amyotrophic lateral sclerosis

Deposition of misfolded protein aggregates in key areas of human brain is the quintessential trait of various pertinent neurodegenerative disorders including amyotrophic lateral sclerosis (ALS). Genetic point mutations in Cu/Zn superoxide dismutase (SOD1) are found to be the most important contributing factor behind familial ALS. Especially, single nucleotide polymorphism (SNP) A4V is the most nocuous since it substantially decreases life expectancy of patients. Besides, the use of naturally occurring polyphenolic flavonoids is profoundly being advocated for palliating amyloidogenic behavior of proteopathic proteins. In the present analysis, through proficient computational tools, we have attempted to ascertain a pharmacodynamically promising flavonoid compound that effectively curbs the pathogenic behavior of A4V SOD1 mutant. Initial screening of flavonoids that exhibit potency against amyloids identified morin, myricetin and epigallocatechin gallate as promising leads. Further, with the help of feasible and yet adept protein-ligand interaction studies and stalwart molecular simulation analyses, we were able to observe that aforementioned flavonoids were able to considerably divert mutant A4V SOD1 from its distinct pathogenic behavior. Among which, morin showed the most curative potential against A4V SOD1. Therefore, morin holds a great therapeutic potential in contriving highly efficacious inhibitors in mitigating fatal and insuperable ALS.

Virtual Quasi-2D Intermediates as Building Blocks for Plausible Structural Models of Amyloid Fibrils from Proteins with Complex Topologies: A Case Study of Insulin

Conformational transitions of globular proteins into amyloid fibrils are complex multistage processes exceedingly challenging to simulate using molecular dynamics (MD). Slow monomer diffusion rates and rugged free energy landscapes disfavor swift self-assembly of orderly amyloid architectures within timescales accessible to all-atom MD. Here, we conduct a multiscale MD study of the amyloidogenic self-assembly of insulin: a small protein with a complex topology defined by two polypeptide chains interlinked by three disulfide bonds. To avoid kinetic traps, unconventional preplanarized insulin conformations are used as amyloid building blocks. These starting conformers generated through uniaxial compression of the native monomer in various spatial directions represent 6 distinct (out of 16 conceivable) two-dimensional (2D) topological classes varying in N-/C-terminal segments of insulin's A- and B-chains being placed inside or outside of the central loop constituted by the middle sections of both chains and Cys7A-Cys7B/Cys19B-Cys20A disulfide bonds. Simulations of the fibrillar self-assembly are initiated through a biased in-register alignment of two, three, or four layers of flat conformers belonging to a single topological class. The various starting topologies are conserved throughout the self-assembly process resulting in polymorphic amyloid fibrils varying in structural features such as helical twist, presence of cavities, and overall stability. Some of the protofilament structures obtained in this work are highly compatible with the earlier biophysical studies on insulin amyloid and high-resolution studies on insulin-derived amyloidogenic peptide models postulating the presence of steric zippers. Our approach provides in silico means to study amyloidogenic tendencies and viable amyloid architectures of larger disulfide-constrained proteins with complex topologies.

Higher Affinity Antibodies Bind With Lower Hydration and Flexibility in Large Scale Simulations

We have carried out a long-timescale simulation study on crystal structures of nine antibody-antigen pairs, in antigen-bound and antibody-only forms, using molecular dynamics with enhanced sampling and an explicit water model to explore interface conformation and hydration. By combining atomic level simulation and replica exchange to enable full protein flexibility, we find significant numbers of bridging water molecules at the antibody-antigen interface. Additionally, a higher proportion of interactions excluding bulk waters and a lower degree of antigen bound CDR conformational sampling are correlated with higher antibody affinity. The CDR sampling supports enthalpically driven antibody binding, as opposed to entropically driven, in that the difference between antigen bound and unbound conformations do not correlate with affinity. We thus propose that interactions with waters and CDR sampling are aspects of the interface that may moderate antibody-antigen binding, and that explicit hydration and CDR flexibility should be considered to improve antibody affinity prediction and computational design workflows.

Exploring the Effect of Mechanical Anisotropy of Protein Structures in the Unfoldase Mechanism of AAA+ Molecular Machines

Essential cellular processes of microtubule disassembly and protein degradation, which span lengths from tens of μm to nm, are mediated by specialized molecular machines with similar hexameric structure and function. Our molecular simulations at atomistic and coarse-grained scales show that both the microtubule-severing protein spastin and the caseinolytic protease ClpY, accomplish spectacular unfolding of their diverse substrates, a microtubule lattice and dihydrofolate reductase (DHFR), by taking advantage of mechanical anisotropy in these proteins. Unfolding of wild-type DHFR requires disruption of mechanically strong β-sheet interfaces near each terminal, which yields branched pathways associated with unzipping along soft directions and shearing along strong directions. By contrast, unfolding of circular permutant DHFR variants involves single pathways due to softer mechanical interfaces near terminals, but translocation hindrance can arise from mechanical resistance of partially unfolded intermediates stabilized by β-sheets. For spastin, optimal severing action initiated by pulling on a tubulin subunit is achieved through specific orientation of the machine versus the substrate (microtubule lattice). Moreover, changes in the strength of the interactions between spastin and a microtubule filament, which can be driven by the tubulin code, lead to drastically different outcomes for the integrity of the hexameric structure of the machine.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

An efficient algorithm calculating common solvent accessible volume

The solvent accessible surface area and the solvent accessible volume are measurements commonly used in implicit solvent models to include the effect of forces exerted by solvents on the protein surfaces (or the atoms on protein surfaces). The two measurements have limitations in describing interactions between proteins (or proteins’ atoms) mediated/bridged by solvents. This is because describing the interactions between proteins should be able to capture the chain of protein-solvent-protein interactions while the solvent accessible surface area or the solvent accessible volume can capture only protein-solvent interactions. If we represent the solvent as a continuous medium, we can consider an atom of a protein can effectively interact with the solvent within a certain distance from its surface (or its own solvent-interacting sphere). In this case, the protein-solvent-protein interactions can be measured by the amount of solvent interacting with two proteins’ atoms at the same time (or the volume shared by the two atoms’ solvent-interacting spheres excluding the volumes occupied by proteins’ atoms). We call the shared volume as the common solvent accessible volume (CSAV); there has been no method developed to determine the CSAV. In this work, we propose a new sweep-line-based method that efficiently calculates the common solvent accessible volume. The performance and accuracy of the proposed sweep-line-based method are compared with those of the naïve voxel-based method. The proposed method takes log-linear time to the number of atoms involved in a CSAV calculation and linear time to the resolution. Our results, tested with 52 protein structures of various sizes, show that the proposed sweep-line-based method is superior to the voxel-based method in both computational efficiency and accuracy.

A TCR mimic monoclonal antibody reactive with the "public" phospho-neoantigen pIRS2/HLA-A*02:01 complex

Phosphopeptides derived from dysregulated protein phosphorylation in cancer cells can be processed and presented by MHC class I and class II molecules and, therefore, represent an untapped class of tumor-specific antigens that could be used as widely expressed “public” cancer neoantigens (NeoAgs). We generated a TCR mimic (TCRm) mAb, 6B1, specific for a phosphopeptide derived from insulin receptor substrate 2 (pIRS2) presented by HLA-A*02:01. The pIRS2 epitope’s presentation by HLA-A*02:01 was confirmed by mass spectrometry. The TCRm 6B1 specifically bound to pIRS2/HLA-A2 complex on tumor cell lines that expressed pIRS2 in the context of HLA-A*02:01. Bispecific mAbs engaging CD3 of T cells were able to kill tumor cell lines in a pIRS2- and HLA-A*02:01–restricted manner. Structure modeling shows a prerequisite for an arginine or lysine at the first position to bind mAb. Therefore, 6B1 could recognize phosphopeptides derived from various phosphorylated proteins with similar amino acid compositions. This raised the possibility that a TCRm specific for the pIRS2/HLA-A2 complex could target a range of phosphopeptides presented by HLA-A*02:01 in various tumor cells. This is the first TCRm mAb to our knowledge targeting a phosphopeptide/MHC class I complex; the potential of this class of agents for clinical applications warrants further investigation.

Computational Design of Peptides with Improved Recognition of the Focal Adhesion Kinase FAT Domain

We describe a two-stage computational protein design (CPD) methodology for the design of peptides binding to the FAT domain of the protein focal adhesion kinase. The first stage involves high-throughput CPD calculations with the Proteus software. The energies of the folded state are described by a physics-based energy function and of the unfolded peptides by a knowledge-based model that reproduces aminoacid compositions consistent with a helicity scale. The obtained sequences are filtered in terms of the affinity and the stability of the complex. In the second stage, design sequences are further evaluated by all-atom molecular dynamics simulations and binding free energy calculations with a molecular mechanics/implicit solvent free energy function.

Mechanism of negative membrane curvature generation by I-BAR domains

The membrane sculpting ability of BAR domains has been attributed to the intrinsic curvature of their banana-shaped dimeric structure. However, there is often a mismatch between this intrinsic curvature and the diameter of the membrane tubules generated. I-BAR domains are especially mysterious since they are almost flat but generate high negative membrane curvature. Here, we use atomistic implicit-solvent computer modeling to show that the membrane bending of the IRSp53 I-BAR domain is dictated by its higher oligomeric structure, whose curvature is completely unrelated to the intrinsic curvature of the dimer. Two other I-BARs give similar results, whereas a flat F-BAR sheet develops a concave membrane-binding interface, consistent with its observed positive membrane curvature generation. Laterally interacting helical spirals of I-BAR dimers on tube interiors are stable and have an enhanced binding energy that is sufficient for membrane bending to experimentally observed tubule diameters at a reasonable surface density.

A Framework of Paracellular Transport via Nanoparticles-Induced Endothelial Leakiness

Nanomaterial-induced endothelial leakiness (NanoEL) is an interfacial phenomenon denoting the paracellular transport of nanoparticles that is pertinent to nanotoxicology, nanomedicine and biomedical engineering. While the NanoEL phenomenon is complementary to the enhanced permeability and retention effect in terms of their common applicability to delineating the permeability and behavior of nanoparticles in tumoral environments, these two effects significantly differ in scope, origin, and manifestation. In the current study, the descriptors are fully examined of the NanoEL phenomenon elicited by generic citrate-coated gold nanoparticles (AuNPs) of changing size and concentration, from microscopic gap formation and actin reorganization down to molecular signaling pathways and nanoscale interactions of AuNPs with VE-cadherin and its intra/extracellular cofactors. Employing synergistic in silico methodologies, for the first time the molecular and statistical mechanics of cadherin pair disruption, especially in response to AuNPs of the smallest size and highest concentration are revealed. This study marks a major advancement toward establishing a comprehensive NanoEL framework for complementing the understanding of the transcytotic pathway and for guiding the design and application of future nanomedicines harnessing the myriad functions of the mammalian vasculature.

When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes

The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information. The discussion focusses on two specific cases, microtubules and homologous recombination nucleoprotein filaments, where a network of intrinsically disordered tails exerts regulatory function in recruiting partner macromolecules, proteins or DNA and tuning the atomic level association. Notably, we show how computational approaches such as molecular dynamics simulations can bring new knowledge to help bridging the gap between experimental analysis, that mostly concerns ensemble properties, and the behavior of individual disordered protein chains that contribute to regulation functions.