The role of shape complementarity in the protein-protein interactions

The electrostatic landscape of MHC-peptide binding revealed using inception networks

Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.

Autonomous macroscopic signal deciphering the geometric self-sorting of pillar[n]arenes

In this communication, we have deciphered the geometric self-sorting of pillar[n]arenes by analyzing the fluid flow pattern obtained during the self-assembly of complementary pillar[n]arenes on the surface. The concept was further extended to demonstrate flow manipulation inside a microchannel where multiple sites were available for self-sorting, and the resultant flow velocity was tuned by the feeding ratio of the complementary pairs.

High affinity protein surface binding through co-engineering of nanoparticles and proteins

Control over supramolecular recognition between proteins and nanoparticles (NPs) is of fundamental importance in therapeutic applications and sensor development. Most NP-protein binding approaches use 'tags' such as biotin or His-tags to provide high affinity; protein surface recognition provides a versatile alternative strategy. Generating high affinity NP-protein interactions is challenging however, due to dielectric screening at physiological ionic strengths. We report here the co-engineering of nanoparticles and protein to provide high affinity binding. In this strategy, 'supercharged' proteins provide enhanced interfacial electrostatic interactions with complementarily charged nanoparticles, generating high affinity complexes. Significantly, the co-engineered protein-nanoparticle assemblies feature high binding affinity even at physiologically relevant ionic strength conditions. Computational studies identify both hydrophobic and electrostatic interactions as drivers for these high affinity NP-protein complexes.

The eptinezumab:CGRP complex structure - the role of conformational changes in binding stabilization

To further elucidate the mechanism of action and binding properties of eptinezumab to calcitonin gene-related peptide (CGRP), X-ray crystallography, computational alanine scanning, and molecular dynamics were used. X-ray diffraction data were collected to determine the three-dimensional structures of the unbound eptinezumab antigen-binding fragment (Fab) and the Fab:CGRP complex. Molecular dynamics simulations were performed to analyze the transition between uncomplexed and complex states. The amidated C-terminus of CGRP was shown to bind in a pocket formed by the Fab heavy and light chains. There was extensive contact between all six complementarity-determining regions (CDRs; composed of light-chain [L1, L2, and L3] and heavy-chain [H1, H2, H3]) of eptinezumab and CGRP. The complex demonstrated a high ligand-binding surface area dominated by aromatic residues. CDR L3 contains a disulfide bond that stabilizes the loop, contributes surface area to the binding pocket, and provides van der Waals contacts. Comparison of the uncomplexed and complex structures revealed motion near the binding cleft. The CDR loops H2 and H3 were displaced ~1.4–2.0 Å and residue H-Tyr33 changed conformation, creating a ‘latch-and-lock’ mechanism for binding CGRP and preventing dissociation. Computational alanine scanning of CGRP identified energetic ‘hot spots’ that contribute to binding energy; mutating these positions to residues in homologous neuropeptides resulted in unfavorable binding energies. The attributes of the Fab region and the conformational changes that occur in eptinezumab during binding to CGRP contribute to the specificity, durability, and strength of the interaction, and likely underlie the rapid and sustained migraine preventive effect observed in clinical studies.

Understanding crown shyness from a 3-D perspective

BACKGROUND AND AIMS

Crown shyness describes the phenomenon whereby tree crowns avoid growing into each other, producing a puzzle-like pattern of complementary tree crowns in the canopy. Previous studies found that tree slenderness plays a role in the development of crown shyness. Attempts to quantify crown shyness have largely been confined to 2-D approaches. This study aimed to expand the current set of metrics for crown shyness by quantifying the characteristic of 3-D surface complementarity between trees displaying crown shyness, using LiDAR-derived tree point clouds. Subsequently, the relationship between crown surface complementarity and slenderness of trees was assessed.

METHODS

Fourteen trees were scanned using a laser scanning device. Individual tree points clouds were extracted semi-automatically and manually corrected where needed. A metric that quantifies the surface complementarity (Sc) of a pair of protein molecules is applied to point clouds of pairs of adjacent trees. Then 3-D tree crown surfaces were generated from point clouds by computing their α shapes.

KEY RESULTS

Tree pairs that were visually determined to have overlapping crowns scored significantly lower Sc values than pairs that did not overlap (n = 14, P < 0.01). Furthermore, average slenderness of pairs of trees correlated positively with their Sc score (R2 = 0.484, P < 0.01), showing agreement with previous studies on crown shyness.

CONCLUSIONS

The characteristic of crown surface complementarity present in trees displaying crown shyness was succesfully quantified using a 3-D surface complementarity metric adopted from molecular biology. Crown surface complementarity showed a positive relationship to tree slenderness, similar to other metrics used for measuring crown shyness. The 3-D metric developed in this study revealed how trees adapt the shape of their crowns to those of adjacent trees and how this is linked to the slenderness of the trees.

The role of complementary shape in protein dimerization

Shape guides colloidal nanoparticles to form complex assemblies, but its role in defining interfaces in biomolecular complexes is less clear. In this work, we isolate the role of shape in protein complexes by studying the reversible binding processes of 46 protein dimer pairs, and investigate when entropic effects from shape complementarity alone are sufficient to predict the native protein binding interface. We employ depletants using a generic, implicit depletion model to amplify the magnitude of the entropic forces arising from lock-and-key binding and isolate the effect of shape complementarity in protein dimerization. For 13% of the complexes studied here, protein shape is sufficient to predict native complexes as equilibrium assemblies. We elucidate the results by analyzing the importance of competing binding configurations and how it affects the assembly. A machine learning classifier, with a precision of 89.14% and a recall of 77.11%, is able to identify the cases where shape alone predicts the native protein interface.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

Targeting protein-protein interaction interfaces in COVID-19 drug discovery

To date, the COVID-19 pandemic has claimed over 1 million human lives, infected another 50 million individuals and wreaked havoc on the global economy. The crisis has spurred the ongoing development of drugs targeting its etiological agent, the SARS-CoV-2. Targeting relevant protein-protein interaction interfaces (PPIIs) is a viable paradigm for the design of antiviral drugs and enriches the targetable chemical space by providing alternative targets for drug discovery. In this review, we will provide a comprehensive overview of the theory, methods and applications of PPII-targeted drug development towards COVID-19 based on recent literature. We will also highlight novel developments, such as the successful use of non-native protein-protein interactions as targets for antiviral drug screening. We hope that this review may serve as an entry point for those interested in applying PPIIs towards COVID-19 drug discovery and speed up drug development against the pandemic.

Atomistic Modeling of Peptide Aggregation and β-Sheet Structuring in Corn Zein for Viscoelasticity

The structure-function relationships of plant-based proteins that give rise to desirable texture attributes in order to mimic meat products are generally unknown. In particular, it is not clear how to engineer viscoelasticity to impart cohesiveness and proper mouthfeel; however, it is known that intermolecular β-sheet structures have the potential to enhance the viscoelastic property. Here, we investigated the propensity of selected peptide segments within common corn α-zein variants to maintain stable aggregates and β-sheet structures. Simulations on dimer systems showed that stability was influenced by the initial orientation and the presence of contiguous small hydrophobic residues. Simulations using eight-peptide β-sheet oligomers revealed that peptide sequences without proline had higher levels of β-sheet structuring. Additionally, we identified that sequences with a dimer hydrogen-bonding density of >22% tended to have a larger percent β-sheet conformation. These results contribute to understanding how the viscoelasticity of zein can be increased for use in plant-based meat analogues.

Membrane transporter dimerization driven by differential lipid solvation energetics of dissociated and associated states

Over two-thirds of integral membrane proteins of known structure assemble into oligomers. Yet, the forces that drive the association of these proteins remain to be delineated, as the lipid bilayer is a solvent environment that is both structurally and chemically complex. In this study, we reveal how the lipid solvent defines the dimerization equilibrium of the CLC-ec1 Cl^-/H⁺ antiporter. Integrating experimental and computational approaches, we show that monomers associate to avoid a thinned-membrane defect formed by hydrophobic mismatch at their exposed dimerization interfaces. In this defect, lipids are strongly tilted and less densely packed than in the bulk, with a larger degree of entanglement between opposing leaflets and greater water penetration into the bilayer interior. Dimerization restores the membrane to a near-native state and therefore, appears to be driven by the larger free-energy cost of lipid solvation of the dissociated protomers. Supporting this theory, we demonstrate that addition of short-chain lipids strongly shifts the dimerization equilibrium toward the monomeric state, and show that the cause of this effect is that these lipids preferentially solvate the defect. Importantly, we show that this shift requires only minimal quantities of short-chain lipids, with no measurable impact on either the macroscopic physical state of the membrane or the protein's biological function. Based on these observations, we posit that free-energy differentials for local lipid solvation define membrane-protein association equilibria. With this, we argue that preferential lipid solvation is a plausible cellular mechanism for lipid regulation of oligomerization processes, as it can occur at low concentrations and does not require global changes in membrane properties.

A cell’s outer membrane is made of molecules called lipids, which band together to form a flexible thin film, just two molecules thick. This membrane is dotted with proteins that transport materials in to and out of cells. Most of these membrane proteins join with other proteins to form structures known as oligomers. Except, how membrane-bound proteins assemble into oligomers – the physical forces driving these molecules to take shape – remains unclear.

This is partly because the structural, physical and chemical properties of fat-like lipid membranes are radically different to the cell’s watery interior. Consequently, the conditions under which membrane oligomers form are distinct from those surrounding proteins inside cells. Membrane proteins are also more difficult to study and characterize than water-soluble proteins inside the cell, and yet many therapeutic drugs such as antibiotics specifically target membrane proteins. Overall, our understanding of how the unique properties of lipid membranes affect the formation of protein structures embedded within, is lacking and warrants further investigation.

Now, Chadda, Bernhardt et al. focused on one membrane protein, known as CLC, which tends to exist in pairs – or dimers. To understand why these proteins form dimers (a process called dimerization) Chadda, Bernhardt et al. first used computer simulations, and then validated the findings in experimental tests.

These complementary approaches demonstrated that the main reason CLC proteins ‘dimerize’ lies in their interaction with the lipid membrane, and not the attraction of one protein to its partner. When CLC proteins are on their own, they deform the surrounding membrane and create structural defects that put the membrane under strain. But when two CLC proteins join as a dimer, this membrane strain disappears – making dimerization the more stable and energetically favorable option.

Chadda, Bernhardt et al. also showed that with the addition of a few certain lipids, specifically smaller lipids, cell membranes become more tolerant of protein-induced structural changes. This might explain how cells could use various lipids to fine-tune the activity of membrane proteins by controlling how oligomers form. However, the theory needs to be examined further. Altogether, this work has provided fundamental insights into the physical forces shaping membrane-bound proteins, relevant to researchers studying cell biology and pharmacology alike.

Entropy at Bio-Nano Interfaces

Entropy, one of the central concepts of thermodynamics, can be a predominant contribution to structural formation and transition. Although it is well-known that diverse forces and energies can significantly contribute to the structures and activities at bio-nano interfaces, the potential entropic contribution remains less well understood. Therefore, this review article seeks to provide a conceptual framework demonstrating that entropy can be exploited to shape the physicochemical properties of bio-nano interfaces and thereby regulate the structures, responses, and functions of biological systems. We introduce the typical types of entropy that matter at bio-nano interfaces. Moreover, some key characteristics featuring entropy at bio-nano interfaces, such as the difference between entropic force and energetic interaction and the associated implications for biomimetic research, are discussed. We expect that this review could stimulate further effort in the fundamental research of entropy in biology and in the biological applications of entropic effects in designer biomaterials.

Refinement of pairwise potentials via logistic regression to score protein-protein interactions

Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the accurate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, and then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared with conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. The scripts used are available at https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR.

Ab-initio binding of barnase–barstar with DelPhiForce steered Molecular Dynamics (DFMD) approach

Receptor–ligand interactions are involved in various biological processes, therefore understanding the binding mechanism and ability to predict the binding mode are essential for many biological investigations. While many computational methods exist to predict the 3D structure of the corresponding complex provided the knowledge of the monomers, here we use the newly developed DelPhiForce steered Molecular Dynamics (DFMD) approach to model the binding of barstar to barnase to demonstrate that first-principles methods are also capable of modeling the binding. Essential component of DFMD approach is enhancing the role of long-range electrostatic interactions to provide guiding force of the monomers toward their correct binding orientation and position. Thus, it is demonstrated that the DFMD can successfully dock barstar to barnase even if the initial positions and orientations of both are completely different from the correct ones. Thus, the electrostatics provides orientational guidance along with pulling force to deliver the ligand in close proximity to the receptor.

Annexin B12 Trimer Formation is Governed by a Network of Protein-Protein and Protein-Lipid Interactions

Membrane protein oligomerization mediates a wide range of biological events including signal transduction, viral infection and membrane curvature induction. However, the relative contributions of protein-protein and protein-membrane interactions to protein oligomerization remain poorly understood. Here, we used the Ca²⁺-dependent membrane-binding protein ANXB12 as a model system to determine the relative contributions of protein-protein and protein-membrane interactions toward trimer formation. Using an EPR-based detection method, we find that some protein-protein interactions are essential for trimer formation. Surprisingly, these interactions are largely hydrophobic, and they do not include the previously identified salt bridges, which are less important. Interfering with membrane interaction by mutating selected Ca²⁺-ligands or by introducing Lys residues in the membrane-binding loops had variable, strongly position-dependent effects on trimer formation. The strongest effect was observed for the E226Q/E105Q mutant, which almost fully abolished trimer formation without preventing membrane interaction. These results indicate that lipids engage in specific, trimer-stabilizing interactions that go beyond simply providing a concentration-enhancing surface. The finding that protein-membrane interactions are just as important as protein-protein interactions in ANXB12 trimer formation raises the possibility that the formation of specific lipid contacts could be a more widely used driving force for membrane-mediated oligomerization of proteins in general.

Exploring designability of electrostatic complementarity at an antigen-antibody interface directed by mutagenesis, biophysical analysis, and molecular dynamics simulations

Antibodies protect organisms from a huge variety of foreign antigens. Antibody diversity originates from both genetic and structural levels. Antigen recognition relies on complementarity between antigen-antibody interfaces. Recent methodological advances in structural biology and the accompanying rapid increase of the number of crystal structures of proteins have enabled atomic-level manipulation of protein structures to effect alterations in function. In this study, we explored the designability of electrostatic complementarity at an antigen-antibody interface on the basis of a crystal structure of the complex. We designed several variants with altered charged residues at the interface and characterized the designed variants by surface plasmon resonance, circular dichroism, differential scanning calorimetry, and molecular dynamics simulations. Both successes and failures of the structure-based design are discussed. The variants that compensate electrostatic interactions can restore the interface complementarity, enabling the cognate antigen-antibody binding. Retrospectively, we also show that these mutational effects could be predicted by the simulations. Our study demonstrates the importance of charged residues on the physical properties of this antigen-antibody interaction and suggests that computational approaches can facilitate design of antibodies that recognize a weakly immunogenic antigen.

Extracting lipid vesicles from plasma membranes via self-assembly of clathrin-inspired scaffolding nanoparticles

Single-cell analysis is a new and rapidly expanding field, the goal of which is obtaining fresh information from individual cells to understand the regulatory mechanisms of cell development and diseases. Conventional approaches generally rely on the cell lysis which, however, is destructive to cells and against multiple sampling from the living cell. Here, we propose and design a scaffolding nanoparticle (NP) system that enables us to sample cytoplasmic contents without rupturing the cellular membrane, by mimicking the unusual features of clathrin. Our simulation results reveal the design principles, following which scaffolding NPs can extract lipid vesicles from plasma membranes, with both the pathway and the mechanism resembling the clathrin-mediated endocytosis, i.e. multiple NPs deposit at the membrane, assembling into cage-like structures to deform the membrane into a vesicle shape. As important design parameters, the interaction between different NPs should be properly stronger than that between each NP and the membrane to ensure the cage formation, and optimal NP concentration and the membrane surface tension are also requisite for extracting lipid vesicles. Our results provide useful guidelines for design of bio-inspired scaffolding NPs as an intelligent machine for practical use in but not limited to the single-cell analysis.

Shape induced sorting via rim-to-rim complementarity in the formation of pillar[5, 6]arene-based supramolecular organogels

The first two-component rim-to-rim pillar[6]arene-based supramolecular organogels were prepared. Shape complementarity was found to be an important determinant in the formation of such gels which also show shape-induced sorting in their formation.

Computational Design of a Functionalized Substrate for Capturing Nanoparticles with Specific Size and Shape

The efficient capture of nanoscopic particulates plays a key role in many scientific fields like filtration and fabrication of nanocomposites as well as biosensors. In this work, we design two types of nanosubstrates to capture the nanoparticle with specific property by using Brownian dynamics simulations. It is found that the substrate coated with copolymers (composed of nonspecific block and specific block) can be used to capture the nanoparticle with different sizes but its capture efficiency of nanoparticles with different shapes is very low. To overcome such problem, the other substrate containing shaped holes is also designed. By conducting a serial of control simulations, we find that the nonspecific polymers at the bottom and on the rim of the hole have great impact on the sensitive capture. The present study may provide some physical insights into the experimental design of nanodevices in real applications.

Supramolecular arrays by the self-assembly of terpyridine-based monomers with transition metal ions

Hierarchical construction of a highly ordered supramolecular array has been, in general, a challenge due to the complexation of building blocks and the hard-to-control weak interactions. Herein, we present a type of well-ordered nanoribbon, which was self-assembled via shape complimentary and hydrophobic effects from the bowl-shaped supramolecular components, which were synthesized by combining designer terpyridine-based monomers and two different metal ions (Ru2+, Zn2+). Interestingly, switching counter ions or changing monomer concentrations, a transformation between a uniform nanosphere and nanoribbon occurred. This opens a door to fabricate readily tailorable, large-scale, supramacromolecular materials.

Capturing the multiscale dynamics of membrane protein complexes with all-atom, mixed-resolution, and coarse-grained models

The structures and dynamics of protein complexes are often challenging to model in heterogeneous environments such as biological membranes. Herein, we meet this fundamental challenge at attainable cost with all-atom, mixed-resolution, and coarse-grained models of vital membrane proteins. We systematically simulated five complex models formed by two distinct G protein-coupled receptors (GPCRs) in the lipid-bilayer membrane on the ns-to-μs timescales. These models, which suggest the swinging motion of an intracellular loop, for the first time, provide the molecular details for the regulatory role of such a loop. For the models at different resolutions, we observed consistent structural stability but various levels of speed-ups in protein dynamics. The mixed-resolution and coarse-grained models show two and four times faster protein diffusion than the all-atom models, in addition to a 4- and 400-fold speed-up in the simulation performance. Furthermore, by elucidating the strengths and challenges of combining all-atom models with reduced resolution models, this study can serve as a guide to simulating other complex systems in heterogeneous environments efficiently.

Modulation of interaction of mutant TP53 and wild type BRCA1 by alkaloids: a computational approach towards targeting protein-protein interaction as a futuristic therapeutic intervention strategy for breast cancer impediment

Protein-protein interactions (PPI) are a new emerging class of novel therapeutic targets. In order to probe these interactions, computational tools provide a convenient and quick method towards the development of therapeutics. Keeping this in view the present study was initiated to analyse interaction of tumour suppressor protein p53 (TP53) and breast cancer associated protein (BRCA1) as promising target against breast cancer. Using computational approaches such as protein-protein docking, hot spot analyses, molecular docking and molecular dynamics simulation (MDS), stepwise analyses of the interactions of the wild type and mutant TP53 with that of wild type BRCA1 and their modulation by alkaloids were done. Protein-protein docking method was used to generate both wild type and mutant complexes of TP53-BRCA1. Subsequently, the complexes were docked using sixteen different alkaloids, fulfilling ADMET and Lipinski's rule of five criteria, and were compared with that of a well-known inhibitor of PPI, namely nutlin. The alkaloid dicentrine was found to be the best docked alkaloid among all the docked alklaloids as well as that of nutlin. Furthermore, MDS analyses of both wild type and mutant complexes with the best docked alkaloid i.e. dicentrine, revealed higher stability of mutant complex than that of the wild one, in terms of average RMSD, RMSF and binding free energy, corroborating the results of docking. Results suggested more pronounced interaction of BRCA1 with mutant TP53 leading to increased expression of mutated TP53 thus showing a dominant negative gain of function and hampering wild type TP53 function leading to tumour progression.

Polymorphic Assembly from Beveled Gold Triangular Nanoprisms

The shape anisotropy of nanoparticle building blocks is of critical importance in determining their packing symmetry and assembly directionality. While there has been extensive research on the effect of their overall geometric shapes, the importance of nanometer morphology details is not well-recognized or understood. Here we draw on shape-anisotropic gold triangular nanoprism building blocks synthesized based on a method we recently developed; besides the "large-scale" triangular prism shape (79.8 nm in side length and 22.0 nm in thickness), the prisms are beveled with their sides convexly enclosed by two flat {100} facets. We engineer the balance between electrostatic repulsion and entropically driven depletion attraction in the system to generate self-assemblies without or with the effect of the nanoscale beveling detail. A conventional, planar honeycomb (p-honeycomb) lattice forms with the triangular basal planes packed on the same plane at low depletion attraction, whereas an unexpected interlocking honeycomb (i-honeycomb) lattice and its "supracrystal" forms are assembled with additional close-paralleling of side facets at high depletion attraction. The i-honeycomb lattice renders all the metallic surfaces in close proximity and leads to a surface-enhanced Raman scattering signal nearly 5-fold higher than that in the p-honeycomb lattice and high sensitivity for detecting the model molecule Rhodamine 6G at a concentration as low as 10^-8 M. Our study can guide future work in both nanoparticle synthesis and self-assembly; nanoscale geometrical features in anisotropic nanoparticles can be used as an important handle to control directional interactions for nonconventional ordered assemblies and to enrich diversity in self-assembly structure and function.

Counterintuitive cooperative endocytosis of like-charged nanoparticles in cellular internalization: computer simulation and experiment

The nanoparticles (NPs) functionalized with charged ligands are of particular significance due to their potential drug/gene delivery and biomedical applications. However, the molecular mechanism of endocytosis of the charged NPs by cells, especially the effect of the NP-NP and NP-biomembrane interactions on the internalization pathways is still poorly understood. In this work, we systematically investigate the internalization behaviors of the positively charged NPs by combining experiment technology and dissipative particle dynamics (DPD) simulation. We experimentally find an interesting but highly counterintuitive phenomenon, i.e. the multiple positively charged NPs prefer to enter cells cooperatively although the like-charged NPs have obvious electrostatic repulsion. Furthermore, we adopt the DPD simulation to confirm the experimental findings, and reveal that the mechanism of the cooperative endocytosis between like-charged NPs is definitely caused by the interplay of particle size, the charged ligand density on particle surface and local concentration of NPs. Importantly, we not only observe the normal cooperative endocytosis of like-charged NPs in cell biomembrane like neutral NP case, but also predict the 'bud' cooperative endocytosis of like-charged NPs which is absence in the neutral NP case. The results indicate that electrostatic repulsion between the positively charged nanoparticles plays an important role in the 'bud' cooperative endocytosis of like-charged NPs.

Effects of protein crowding on membrane systems

Cellular membranes are typically decorated with a plethora of embedded and adsorbed macromolecules, e.g. proteins, that participate in numerous vital processes. With typical surface densities of 30,000 proteins per μm(2) cellular membranes are indeed crowded places that leave only few nanometers of private space for individual proteins. Here, we review recent advances in our understanding of protein crowding in membrane systems. We first give a brief overview on state-of-the-art approaches in experiment and simulation that are frequently used to study crowded membranes. After that, we review how crowding can affect diffusive transport of proteins and lipids in membrane systems. Next, we discuss lipid and protein sorting in crowded membrane systems, including effects like protein cluster formation, phase segregation, and lipid droplet formation. Subsequently, we highlight recent progress in uncovering crowding-induced conformational changes of membranes, e.g. membrane budding and vesicle formation. Finally, we give a short outlook on potential future developments in the field of crowded membrane systems. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Theoretical and computational investigations of nanoparticle-biomembrane interactions in cellular delivery

With the rapid development of nanotechnology, nanoparticles have been widely used in many applications such as phototherapy, cell imaging, and drug/gene delivery. A better understanding of how nanoparticles interact with bio-system (especially cells) is of great importance for their potential biomedical applications. In this review, the current status and perspective of theoretical and computational investigations is presented on the nanoparticle-biomembrane interactions in cellular delivery. In particular, the determining parameters (including the properties of nanoparticles, cell membranes and environments) that govern the cellular uptake of nanoparticles (direct penetration and endocytosis) are discussed. Further, some special attention is paid to their interactions beyond the translocation of nanoparticles across membranes (e.g., nanoparticles escaping from endosome and entering into nucleus). Finally, a summary is given, and the challenging problems of this field in the future are identified.

A spontaneous penetration mechanism of patterned nanoparticles across a biomembrane

Recent experimental studies have shown the ability of tailoring the nanoparticle (NP)-cell interaction via the engineering of NP surfaces. Although the considerable progress has been made in design of patterned NPs for drug delivery, the effect of surface pattern on the NP-cell interaction is not fully understood yet. In this work, we used a dissipative particle dynamics method to systematically investigate the effects of NP surface pattern on its penetration across a membrane. For stripy NPs or patchy NPs having a large stripe width or patch size, an "insertion-rotation" penetration mechanism is found. Results indicate that stripy NPs and patchy NPs coated with narrow stripes or small patches can directly penetrate the cell membrane with a less constrained rotation. By considering the spontaneous penetration of many NPs into a vesicle, we found that NP aggregation would lead to the shape change of the vesicle, and therefore cause the leakage of encapsulated solvent or membrane rupture, implying the possible cytotoxicity. In short, this work gives a fundamental understanding for the penetration mechanism of the ligand patterned NPs, which provides useful reference for the design of NPs for controllable cell penetrability and targeted delivery of drugs.

A simulated intermediate state for folding and aggregation provides insights into ΔN6 β2-microglobulin amyloidogenic behavior

A major component of ex vivo amyloid plaques of patients with dialysis-related amyloidosis (DRA) is a cleaved variant of β₂-microglobulin (ΔN6) lacking the first six N-terminal residues. Here we perform a computational study on ΔN6, which provides clues to understand the amyloidogenicity of the full-length β₂-microglobulin. Contrary to the wild-type form, ΔN6 is able to efficiently nucleate fibrillogenesis in vitro at physiological pH. This behavior is enhanced by a mild acidification of the medium such as that occurring in the synovial fluid of DRA patients. Results reported in this work, based on molecular simulations, indicate that deletion of the N-terminal hexapeptide triggers the formation of an intermediate state for folding and aggregation with an unstructured strand A and a native-like core. Strand A plays a pivotal role in aggregation by acting as a sticky hook in dimer assembly. This study further predicts that the detachment of strand A from the core is maximized at pH 6.2 resulting into higher aggregation efficiency. The structural mapping of the dimerization interface suggests that Tyr10, His13, Phe30 and His84 are hot-spot residues in ΔN6 amyloidogenesis.

Dialysis-related amyloidosis (DRA) is a conformational disease that affects individuals undergoing long-term haemodialysis. In DRA the progressive accumulation of protein human β₂-microglobulin (Hβ₂m) in the osteoarticular system, followed by its assembly into amyloid fibrils, eventually leads to tissue erosion and destruction. Disclosing the aggregation mechanism of Hβ₂m under physiologically relevant conditions represents a major challenge due to the inability of the protein to efficiently nucleate fibrillogenesis in vitro at physiological pH. On the other hand, ΔN6, a truncated variant of Hβ₂m, which is also a major component of ex vivo amyloid deposits extracted from DRA patients, is able to efficiently form amyloid fibrils de novo in physiological conditions. This amyloidogenic behavior is dramatically enhanced in a slightly more acidic pH (6.2) compatible with the mild acidification that occurs in the synovial fluid of DRA patients. In this work, an innovative three-stage methodological approach, relying on an array of molecular simulations, spanning different levels of resolution is used to investigate the initial stage of the de novo aggregation mechanism of ΔN6 in a physiologically relevant pH range. We identify an intermediate state for folding and aggregation, whose potential to dimerize is enhanced at pH 6.2. Our results provide rationalizations for previous experimental observations and new insights into the molecular basis of DRA.