Protein Docking Using a Single Representation for Protein Surface, Electrostatics, and Local Dynamics

ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326 175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches. Database URL:  https://proj.cse.cuhk.edu.hk/aihlab/pronet/.

Cleaning of Wastewater Using Crosslinked Poly(Acrylamide-co-Acrylic Acid) Hydrogels: Analysis of Rotatable Bonds, Binding Energy and Hydrogen Bonding

The discharge of untreated wastewater, often contaminated by harmful substances, such as industrially used dyes, can provoke environmental and health risks. Among various techniques, the adsorption of dyes, using three-dimensional (3D) networks consisting of hydrophilic polymers (hydrogels), represents a low-cost, clean, and efficient remediation method. Three industrially used dyes, Methylene Blue, Eosin, and Rose Bengal, were selected as models of pollutants. Poly(acrylamide) (poly(AM)) and poly(acrylamide-co-acrylic acid) (poly(AM-co-AA)) networks were chosen as adsorbent materials (hydrogels). These polymers were synthesized by crosslinking the photopolymerization of their respective monomer(s) in an aqueous medium under exposure to UV light. Experimental adsorption measurements revealed substantially higher dye uptakes for poly(AM-co-AA) compared to poly(AM) hydrogels. In this report, a theoretical model based on docking simulations was applied to analyze the conformation of polymers and pollutants in order to investigate some aspects of the adsorption process. In particular, hydrogen and halogen interactions were studied. The presence of strong hydrogen bonding plays a crucial role in the retention of dyes, whereas halogen bonding has a small or negligible effect on adsorption. An evaluation of binding energies allowed us to obtain information about the degree of affinity between polymers and dyes. The number of rotatable bonds in the copolymer exceeds those of poly(AM),meaning that poly(AM-co-AA) is revealed to be more suitable for obtaining a high retention rate for pollutants.

Four-Dimensional Scaling of Dipole Polarizability in Quantum Systems

Polarizability is a key response property of physical and chemical systems, which has an impact on intermolecular interactions, spectroscopic observables, and vacuum polarization. The calculation of polarizability for quantum systems involves an infinite sum over all excited (bound and continuum) states, concealing the physical interpretation of polarization mechanisms and complicating the derivation of efficient response models. Approximate expressions for the dipole polarizability, α, rely on different scaling laws α∝R^{3}, R^{4}, or R^{7}, for various definitions of the system radius R. Here, we consider a range of single-particle quantum systems of varying spatial dimensionality and having qualitatively different spectra, demonstrating that their polarizability follows a universal four-dimensional scaling law α=C(4μq^{2}/ℏ^{2})L^{4}, where μ and q are the (effective) particle mass and charge, C is a dimensionless excitation-energy ratio, and the characteristic length L is defined via the L^{2} norm of the position operator. This unified formula is also applicable to many-particle systems, as shown by accurately predicting the dipole polarizability of 36 atoms, 1641 small organic molecules, and Bloch electrons in periodic systems.

Protein-Protein Docking: Past, Present, and Future

The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.

Biobox: a toolbox for biomolecular modelling

MOTIVATION

The implementation of biomolecular modelling methods and analyses can be cumbersome, often carried out with in-house software reimplementing common tasks, and requiring the integration of diverse software libraries.

RESULTS

We present Biobox, a Python-based toolbox facilitating the implementation of biomolecular modelling methods.

AVAILABILITY AND IMPLEMENTATION

Biobox is freely available on https://github.com/degiacom/biobox, along with its API and interactive Jupyter notebook tutorials.

Functional antibody characterization via direct structural analysis and information-driven protein-protein docking

Detailed description of the mechanism of action of the therapeutic antibodies is essential for the functional characterization and future optimization of potential clinical agents. We recently developed KD035, a fully human antibody targeting vascular endothelial growth factor receptor 2 (VEGFR2). KD035 blocked VEGF‐A, and VEGF‐C‐mediated VEGFR2 activation, as demonstrated by the in vitro binding and competition assays and functional cellular assays. Here, we report a computational model of the complex between the variable fragment of KD035 (KD035(Fv)) and the domains 2 and 3 of the extracellular portion of VEGFR2 (VEGFR2(D2‐3)). Our modeling was guided by a priori experimental information including the X‐ray structures of KD035 and related antibodies, binding assays, target domain mapping and comparison of KD035 affinity for VEGFR2 from different species. The accuracy of the model was assessed by molecular dynamics simulations, and subsequently validated by mutagenesis and binding analysis. Importantly, the steps followed during the generation of this model can set a precedent for future in silico efforts aimed at the accurate description of the antibody–antigen and more broadly protein–protein complexes.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Transmembrane Protein Docking with JabberDock

Transmembrane proteins act as an intermediary for a broad range of biological process. Making up 20% to 30% of the proteome, their ubiquitous nature has resulted in them comprising 50% of all targets in drug design. Despite their importance, they make up only 4% of all structures in the PDB database, primarily owing to difficulties associated with isolating and characterizing them. Membrane protein docking algorithms could help to fill this knowledge gap, yet only few exist. Moreover, these existing methods achieve success rates lower than the current best soluble proteins docking software. We present and test a pipeline using our software, JabberDock, to dock membrane proteins. JabberDock docks shapes representative of membrane protein structure and dynamics in their biphasic environment. We verify JabberDock's ability to yield accurate predictions by applying it to a benchmark of 20 transmembrane dimers, returning a success rate of 75.0%. This makes our software very competitive among available membrane protein-protein docking tools.

Mass Photometry of Membrane Proteins

Integral membrane proteins (IMPs) are biologically highly significant but challenging to study because they require maintaining a cellular lipid-like environment. Here, we explore the application of mass photometry (MP) to IMPs and membrane-mimetic systems at the single-particle level. We apply MP to amphipathic vehicles, such as detergents and amphipols, as well as to lipid and native nanodiscs, characterizing the particle size, sample purity, and heterogeneity. Using methods established for cryogenic electron microscopy, we eliminate detergent background, enabling high-resolution studies of membrane-protein structure and interactions. We find evidence that, when extracted from native membranes using native styrene-maleic acid nanodiscs, the potassium channel KcsA is present as a dimer of tetramers—in contrast to results obtained using detergent purification. Finally, using lipid nanodiscs, we show that MP can help distinguish between functional and non-functional nanodisc assemblies, as well as determine the critical factors for lipid nanodisc formation.

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We introduce a label-free, single molecule approach for membrane-protein characterization

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Mass photometry quantifies membrane proteins in different membrane-mimetic systems

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MP reveals carrier and protein heterogeneity

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It helps distinguish different functional states of membrane proteins

Membrane proteins are some of the most important biological molecules, carrying out vital functions and being frequent drug targets. Yet, preferring lipid environments and so requiring solubilization, they are challenging to study. Here, we show that mass photometry can characterize the heterogeneity of membrane proteins and the carriers in which they are solubilized. It can also distinguish different functional states of membrane proteins. Our approach thus opens the door to more comprehensive studies of function, structure, and interaction of these critical proteins in their native membrane environment at the single-molecule level.

Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking

Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is a lack of rigorous uncertainty quantification (UQ). To fill the gap, we introduce a novel algorithm, Bayesian active learning (BAL), for optimization and UQ of such black-box functions with applications to flexible protein docking. BAL directly models the posterior distribution of the global optimum (i.e., native structures) with active sampling and posterior estimation iteratively feeding each other. Furthermore, it uses complex normal modes to span a homogeneous, Euclidean conformation space suitable for high-dimensional optimization and constructs funnel-like energy models for quality estimation of encounter complexes. Over a protein-docking benchmark set and a CAPRI set including homology docking, we establish that BAL significantly improves against starting points from rigid docking and refinements by particle swarm optimization, providing a top-3 near-native prediction for one third targets. Quality assessment empowered with UQ leads to tight quality intervals with half range around 25% of the actual interface root-mean-square deviation and confidence level at 85%. BAL's estimated probability of a prediction being near-native achieves binary classification AUROC at 0.93 and area under the precision recall curve over 0.60 (compared to 0.50 and 0.14, respectively, by chance), which also improves ranking predictions. This study represents the first UQ solution for protein docking, with rigorous theoretical frameworks and comprehensive empirical assessments.