Protein Structure-function Relationships Research Articles

Overlooked Hydrogen Bond in a Blue Copper Protein Uncovered by Neutron and Sub-Ångström Resolution X-ray Crystallography.

Metalloproteins play fundamental roles in organisms and are utilized as starting points for the directed evolution of artificial enzymes. Knowing the strategies of metalloproteins, by which they exquisitely tune their activities, will not only lead to an understanding of biochemical phenomena but also contribute to various applications. The blue copper protein (BCP) has been a renowned model system to understand the biology, chemistry, and physics of metalloproteins. Pseudoazurin (Paz), a blue copper protein, mediates electron transfer in the bacterial anaerobic respiratory chain. Its redox potential is finely tuned by hydrogen (H) bond networks; however, difficulty in visualizing H atom positions in the protein hinders the detailed understanding of the protein's structure-function relationship. We here used neutron and sub-ångström resolution X-ray crystallography to directly observe H atoms in Paz. The 0.86-Å-resolution X-ray structure shows that the peptide bond between Pro80 and the His81 Cu ligand deviates from the ideal planar structure. The 1.9-Å-resolution neutron structure confirms a long-overlooked H bond formed by the amide of His81 and the S atom of another Cu ligand Cys78. Quantum mechanics/molecular mechanics calculations show that this H bond increases the redox potential of the Cu site and explains the experimental results well. Our study demonstrates the potential of neutron and sub-ångström resolution X-ray crystallography to understand the chemistry of metalloproteins at atomic and quantum levels.

Predicting the functional state of protein kinases using interpretable graph neural networks from sequence and structural data.

Protein kinases are central to cellular activities and are actively pursued as drug targets for several conditions including cancer and autoimmune diseases. Despite the availability of a large structural database for kinases, methodologies to elucidate the structure-function relationship of these proteins (without manual intervention) are lacking. Such techniques are essential in structural biology and to accelerate drug discovery efforts. Here, we implement an interpretable graph neural network (GNN) framework for classifying the functionally active and inactive states of a large set of protein kinases by only using their tertiary structure and amino acid sequence. We show that the GNN models can classify kinase structures with high accuracy (>97%). We implement the Gradient-weighted Class Activation Mapping for graphs (Graph Grad-CAM) to automatically identify structurally important residues and residue-residue contacts of the kinases without any a priori input. We show that the motifs identified through the Graph Grad-CAM methodology are functionally critical, consistent with the existing kinase literature. Notably, the highly conserved DFG and HRD motifs of the well-known hydrophobic spine are identified by the interpretable framework in addition to some of the lesser known motifs. Further, using Grad-CAM maps as the vector embedding of the protein structures, we identify the subtle differences in the crystal structures among different sub-classes of kinases in the Protein Data Bank (PDB). Frameworks such as the one implemented here, for high-throughput identification of protein structure-function relationships are essential in designing targeted small molecules therapies as well as in engineering new proteins for novel applications.

Random Mutagenesis to Enhance the Toxicity of Bacillus thuringiensis Cry Proteins Against Earias Vittella (F.)

Insecticidal proteins derived from Bacillus thuringiensis (Bt) are widely utilized in a variety of insect control applications,including sprays and transgenic crops.The development of resistance in pests, on the other hand, can lessen the effectiveness of Bttoxins. In this study, we made efforts to enhance the toxicity of two cry proteins Cry1Ac and Cry2Aa through random mutagenesis against cotton bollworm (Earias vitella), one of the most destructive cotton pests in Pakistan. Random mutagenesis is an important tool for elucidating protein structure-function relationships and for modifying proteins to enhance or change their characteristics. We focused on whole cry proteins for random mutagenesis througherror-prone PCR and constructed a recombinant library of cry proteins. Sequence analysis of eight mutants showed the mutations of 34 different nucleotides in Cry1Ac and Cry2Aa genes. All mutants were spared for toxicity bioassays against 2nd instar larvae of spotted bollworm. Cry1Ac mutant RM1AcM4 (D242E) and Cry2Aa mutants RM2AaM2 (T354A, T492R, F511L, G585E, D606Y) showed enhanced toxicity as compared to proteins without mutation. These two mutants comprise the mutations in domain-II of cry proteins important in specificity determining regions on midgut receptors in insect pests.

Impact of transition to a hybrid model of biochemistry course-based undergraduate research experience during the COVID-19 pandemic on student science self-efficacy and conceptual knowledge

The COVID-19 outbreak has had a significant impact on higher education worldwide. In-person courses had to be quickly transited to online, including lab courses embedded with Course-based Undergraduate Research Experiences (CUREs). In response to this challenge, we successfully converted a fully in-person biochemistry lab that integrated with a 6-week modular CURE (mCURE) into a hybrid CURE (hCURE) in Fall 2020, with support from the Malate dehydrogenase CUREs Community. The hCURE was structured to have in-person labs and online activities arranged on an alternating weekly basis, so that only half of the regular class size of students attended the hands-on labs at any given time to maintain proper social distancing. To evaluate the efficacy of the hCURE, student science self-efficacy and conceptual understanding of protein structure–function relationships were measured using pre-course and post-course surveys and tests, respectively. Our data showed a significant increase in student science self-efficacy and conceptual knowledge test scores. Furthermore, we compared the pre-lab quiz scores that assessed various biochemical concepts and skills across three different semesters, Fall 2019 with a fully in-person mCURE before the pandemic, Fall 2020 with the hCURE implemented during the pandemic, and Fall 2021 when the lab returned to the fully in-person mCURE following the pandemic. A significant decline in quiz scores from Fall 2019 to Fall 2020, and an even further decline from Fall 2019 to Fall 2021 were observed, suggesting that apart from the impact of course modality, the pandemic may have exerted a lasting adverse effect on student learning.

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted by the exponential growth of experimental structures, which has been greatly expanded by recent breakthroughs in protein structure prediction, most notably RosettaFold, and AlphaFold2. These advances have prompted the development of several computational approaches that leverage these data sources to explore potential biological interactions. However, most methods are generally limited to analysis of single types of interactions, such as protein-protein or protein-ligand interactions, and their complexity limits the usability to expert users. Here we report CSM-Potential2, a deep learning platform for the analysis of binding interfaces on protein structures. In addition to prediction of protein-protein interactions binding sites and classification of biological ligands, our new platform incorporates prediction of interactions with nucleic acids at the residue level and allows for ligand transplantation based on sequence and structure similarity to experimentally determined structures. We anticipate our platform to be a valuable resource that provides easy access to a range of state-of-the-art methods to expert and non-expert users for the study of biological interactions. Our tool is freely available as an easy-to-use web server and API available at https://biosig.lab.uq.edu.au/csm_potential.

Dimer Interface Destabilization of Photodissociative Dronpa Driven by Asymmetric Monomer Dynamics.

Photoswitchable Dronpa (psDronpa) is a unique member of the fluorescent protein family that can undergo reversible photoinduced switching between fluorescent and dark states and has recently been engineered into a dimer (pdDronpaV) that can dissociate and reassociate as part of its photoswitchable pathway. However, the specific details of the protein structure-function relationship of the dimer interface along with how the dimer proteins interact with each other upon chromophore isomerization are not yet clear. Classical molecular dynamics simulations were performed on psDronpa as monomers and dimers as well as the pdDronpaV dimer and with cis/trans chromophore structures. Analysis of the cis and trans isomers of the chromophore illustrated key differences between their interactions with residues in the protein in both the monomer and dimer forms of psDronpa. Examination of the psDronpa dimer showed nonidentical chromophore interactions between the domains, indicating domain directional favoring. Examination of the trans form of pdDronpaV illuminated the importance of hydrogen bonding between the monomeric domains in maintaining their association, as well as illustrating the motion of dissociation of the domains. This discovery offers important information for possible future mutations of pdDronpaV that might be made to accelerate dissociation.

Constructing and interpreting a large-scale variant effect map for an ultrarare disease gene: Comprehensive prediction of the functional impact of PSAT1 genotypes.

Reduced activity of the enzymes encoded by PHGDH, PSAT1, and PSPH causes a set of ultrarare, autosomal recessive diseases known as serine biosynthesis defects. These diseases present in a broad phenotypic spectrum: at the severe end is Neu-Laxova syndrome, in the intermediate range are infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end is childhood disease with intellectual disability. However, L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms. Therefore, knowledge of pathogenic variants can improve clinical outcomes. Here, we use a yeast-based assay to individually measure the functional impact of 1,914 SNV-accessible amino acid substitutions in PSAT. Results of our assay agree well with clinical interpretations and protein structure-function relationships, supporting the inclusion of our data as functional evidence as part of the ACMG variant interpretation guidelines. We use existing ClinVar variants, disease alleles reported in the literature and variants present as homozygotes in the primAD database to define assay ranges that could aid clinical variant interpretation for up to 98% of the tested variants. In addition to measuring the functional impact of individual variants in yeast haploid cells, we also assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. Results from our diploid assay successfully distinguish the genotypes of affected individuals from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements to predict the biallelic function of ~1.8 million allele combinations corresponding to potential human genotypes. Taken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of ultrarare diseases.

A project-oriented biochemistry laboratory for protein engineering and structure-function using small laccase enzyme from Streptomyces coelicolor.

An understanding of structure-function relationships in proteins is essential for modern biochemical studies. The integration of common freely accessible bioinformatics tools available online with the knowledge of protein-engineering tools provide a fundamental understanding of the application of protein structure-function for biochemical research. In order for students to apply their prior knowledge of recombinant protein technology into the understanding of protein structure-function relationships, we developed a semester-long project-oriented biochemistry laboratory experience that is the second laboratory course of a series. For easier integration of knowledge and application, we organized this course into four sequential modules: protein structure visualization/modification, mutagenesis target identification, site-directed mutagenesis, and mutant protein expression, purification, and characterization. These tasks were performed on the protein small laccase (SLAC) that was cloned and characterized by students in the previous semester during the first biochemistry laboratory course of the series. This goal-oriented project-based approach helped students apply their prior knowledge to newly introduced techniques to understand protein structure-function relationships in this research-like laboratory setting. A student assessment before and after the course demonstrated an overall increase in learning and enthusiasm for this topic.

Impact of pH and Sucralose on the Non-Covalent Interaction of Ovalbumin: FT-IR Analysis

Analysis of pH and cosolvent effects on protein structure is a popular study in food biophysics research since the function of protein is primarily dependent on its structure. The structure-function relationship of protein could be well reflected in changes in non-covalent interactions of protein. In this aspect, the present work deals with the Fourier transform infrared (FT-IR) spectroscopy analysis of ovalbumin (OVA) in different pH conditions with and without cosolvent sucralose (SUC) inclusion. The FT-IR spectrum of proteins provides an absorption spectrum in the frequency region of 4000-400 cm-1. These absorption bands consist of amide A, amide B, and amide I to amide VII. The results are interpreted in terms of noncovalent interactions, such as van der Waals interactions, hydrogen bonds, and hydrophobic and electrostatic interactions. The obtained results indicate that OVA is denatured from its native state against pH and SUC inclusion.

CaviDB: a database of cavities and their features in the structural and conformational space of proteins.

Proteins are the structural, functional and evolutionary units of cells. On their surface, proteins are shaped into numerous depressions and protrusions that provide unique microenvironments for ligand binding and catalysis. The dynamics, size and chemical properties of these cavities are essential for a mechanistic understanding of protein function. Here, we present CaviDB, a novel database of cavities and their features in known protein structures. It integrates the results of commonly used cavity detection software with protein features derived from sequence, structural and functional analyses. Each protein in CaviDB is linked to its corresponding conformers, which also facilitates the study of conformational changes in cavities. Our initial release includes ∼927 773 distinct proteins, as well as the characterization of 36 136 869 cavities, of which 1 147 034 were predicted to be drug targets. The structural focus of CaviDB provides the ability to compare cavities and their properties from different conformational states of the protein. CaviDB not only aims to provide a comprehensive database that can be used for various aspects of drug design and discovery but also contributes to a better understanding of the fundamentals of protein structure-function relationships. With its unique approach, CaviDB represents an indispensable resource for the large community of bioinformaticians in particular and biologists in general. Database URL https://www.cavidb.org.

Glycosylation increases active site rigidity leading to improved enzyme stability and turnover.

Glycosylation is the most prevalent protein post-translational modification, with a quarter of glycosylated proteins having enzymatic properties. Yet, the full impact of glycosylation on the protein structure-function relationship, especially in enzymes, is still limited. Here, we show that glycosylation rigidifies the important commercial enzyme horseradish peroxidase (HRP), which in turn increases its turnover and stability. Circular dichroism spectroscopy revealed that glycosylation increased holo-HRP's thermal stability and promoted significant helical structure in the absence of haem (apo-HRP). Glycosylation also resulted in a 10-fold increase in enzymatic turnover towards o-phenylenediamine dihydrochloride when compared to its nonglycosylated form. Utilising a naturally occurring site-specific probe of active site flexibility (Trp117) in combination with red-edge excitation shift fluorescence spectroscopy, we found that glycosylation significantly rigidified the enzyme. In silico simulations confirmed that glycosylation largely decreased protein backbone flexibility, especially in regions close to the active site and the substrate access channel. Thus, our data show that glycosylation does not just have a passive effect on HRP stability but can exert long-range effects that mediate the 'native' enzyme's activity and stability through changes in inherent dynamics.

Computational Design of Peptides for Biomaterials Applications.

Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.

Computational Methods to unveil the Structural Bases for the recognition of SARS-CoV-2 by Human Nicotinic Acetylcholine Receptors

The novel pathogen SARS-CoV-2 has caused the global pandemic of Covid-19. The hypothesis of this study is that Nicotinic Acetylcholine Receptors (nAChRs) are involved in SARS-CoV-2 infection, explaining the hyper-inflammatory characteristics observed in a subset of Covid-19 patients. nAChRs represent specific receptors for a wide variety of toxins and have been proposed to serve as receptors for Rabies Virus (RABV) and Human Immunodeficiency Virus (HIV) as well, based on sequence homology with the so called “toxic loop” of α-bungarotoxin. Sequence similarities between a motif found in SARS-CoV-2 S protein and in snake neurotoxins, as well as RABV neurotoxin-like region, HIV-1 gp120 and α-conotoxin from Conus geographus, highlights the existence of a correlation between these proteins’ functional sites. In this study, in silico procedures were used to determine SARS-CoV-2 S protein structure-function relationships, revealing the presence of features characteristic of the “toxic loop” known to bind nAChRs and their involvement in the S protein-nAChR interaction. Our results suggest that a polybasic sequence-carrying motif found in SARS-CoV-2 S protein could be involved in the binding, in particular underling the role of Arg685 in the interaction with the receptor.

Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation

Muscle contraction is performed by arrays of contractile proteins in the sarcomere. Serious heart diseases, such as cardiomyopathy, can often be results of mutations in myosin and actin. Direct characterization of how small changes in the myosin-actin complex impact its force production remains challenging. Molecular dynamics (MD) simulations, although capable of studying protein structure-function relationships, are limited owing to the slow timescale of the myosin cycle as well as a lack of various intermediate structures for the actomyosin complex. Here, employing comparative modeling and enhanced sampling MD simulations, we show how the human cardiac myosin generates force during the mechanochemical cycle. Initial conformational ensembles for different myosin-actin states are learned from multiple structural templates with Rosetta. This enables us to efficiently sample the energy landscape of the system using Gaussian accelerated MD. Key myosin loop residues, whose substitutions are related to cardiomyopathy, are identified to form stable or metastable interactions with the actin surface. We find that the actin-binding cleft closure is allosterically coupled to the myosin motor core transitions and ATP-hydrolysis product release from the active site. Furthermore, a gate between switch I and switch II is suggested to control phosphate release at the prepowerstroke state. Our approach demonstrates the ability to link sequence and structural information to motor functions.

In silico prediction of molecular mechanisms of toxicity mediated by the leptospiral PF07598 gene family-encoded virulence-modifying proteins.

Mechanisms of leptospirosis pathogenesis remain unclear despite the identification of a number of potential leptospiral virulence factors. We recently demonstrated potential mechanisms by which the virulence-modifying (VM) proteins-defined as containing a Domain of Unknown function (DUF1561), encoded by the PF07598 gene family-found only in group 1 pathogenic Leptospira-might mediate the clinical pathogenesis of leptospirosis. VM proteins belongs to classical AB toxin paradigm though have a unique AB domain architecture, unlike other AB toxins such as diphtheria toxin, pertussis toxin, shiga toxin, or ricin toxin which are typically encoded by two or more genes and self-assembled into a multi-domain holotoxin. Leptospiral VM proteins are secreted R-type lectin domain-containing exotoxins with discrete N-terminal ricin B-like domains involved in host cell surface binding, and a C-terminal DNase/toxin domain. Here we use the artificial intelligence-based AlphaFold algorithm and other computational tools to predict and elaborate on details of the VM protein structure-function relationship. Comparative AlphaFold and CD-spectroscopy defined the consistent secondary structure (Helix and ß-sheet) content, and the stability of the functional domains were further supported by molecular dynamics simulation. VM proteins comprises distinctive lectic family (QxW)3 motifs, the Mycoplasma CARDS toxin (D3 domain, aromatic patches), C-terminal similarity with mammalian DNase I. In-silico study proposed that Gln412, Gln523, His533, Thr59 are the high binding energy or ligand binding residues plausibly anticipates in the functional activities. Divalent cation (Mg+2-Gln412) and phosphate ion (PO4]-3-Arg615) interaction further supports the functional activities driven by C-terminal domain. Computation-driven structure-function studies of VM proteins will guide experimentation towards mechanistic understandings of leptospirosis pathogenesis, which underlie development of new therapeutic and preventive measures for this devastating disease.

Bioinformatic Approaches for Characterizing Molecular Structure and Function of Food Proteins.

Structural bioinformatics analyzes protein structural models with the goal of uncovering molecular drivers of food functionality. This field aims to develop tools that can rapidly extract relevant information from protein databases as well as organize this information for researchers interested in studying protein functionality. Food bioinformaticians take advantage of millions of protein amino acid sequences and structures contained within these databases, extracting features such as surface hydrophobicity that are then used to model functionality, including solubility, thermostability, and emulsification. This work is aided by a protein structure-function relationship framework, in which bioinformatic properties are linked to physicochemical experimentation. Strong bioinformatic correlations exist for protein secondary structure, electrostatic potential, and surface hydrophobicity. Modeling changes in protein structures through molecular mechanics is an increasingly accessible field that will continue to propel food science research.

CroMaSt: a workflow for assessing protein domain classification by cross-mapping of structural instances between domain databases and structural alignment.

Protein domains can be viewed as building blocks, essential for understanding structure-function relationships in proteins. However, each domain database classifies protein domains using its own methodology. Thus, in many cases, domain models and boundaries differ from one domain database to the other, raising the question of domain definition and enumeration of true domain instances. We propose an automated iterative workflow to assess protein domain classification by cross-mapping domain structural instances between domain databases and by evaluating structural alignments. CroMaSt (for Cross-Mapper of domain Structural instances) will classify all experimental structural instances of a given domain type into four different categories ('Core', 'True', 'Domain-like' and 'Failed'). CroMast is developed in Common Workflow Language and takes advantage of two well-known domain databases with wide coverage: Pfam and CATH. It uses the Kpax structural alignment tool with expert-adjusted parameters. CroMaSt was tested with the RNA Recognition Motif domain type and identifies 962 'True' and 541 'Domain-like' structural instances for this domain type. This method solves a crucial issue in domain-centric research and can generate essential information that could be used for synthetic biology and machine-learning approaches of protein domain engineering. The workflow and the Results archive for the CroMaSt runs presented in this article are available from WorkflowHub (doi: 10.48546/workflowhub.workflow.390.2). Supplementary data are available at Bioinformatics Advances online.

Hyperthermostable recombinant human heteropolymer ferritin derived from a novel plasmid design.

Mammalian ferritins are predominantly heteropolymeric species consisting of 2 structurally similar, but functionally and genetically distinct subunit types, called H (Heavy) and L (Light). The two subunits co-assemble in different H and L ratios to form 24-mer shell-like protein nanocages where thousands of iron atoms can be mineralized inside a hollow cavity. Here, we use differential scanning calorimetry (DSC) to study ferritin stability and understand how various combinations of H and L subunits confer aspects of protein structure-function relationships. Using a recently engineered plasmid design that enables the synthesis of complex ferritin nanostructures with specific H to L subunit ratios, we show that homopolymer L and heteropolymer L-rich ferritins have a remarkable hyperthermostability (Tm=115 ± 1°C) compared to their H-ferritin homologues (Tm=93 ± 1°C). Our data reveal a significant linear correlation between protein thermal stability and the number of L subunits present on the ferritin shell. A strong and unexpected iron-induced protein thermal destabilization effect (ΔTm up to 20°C) is observed. To our knowledge, this is the first report of recombinant human homo- and hetero-polymer ferritins that exhibit surprisingly high dissociation temperatures, the highest among all known ferritin species, including many known hyperthermophilic proteins and enzymes. This extreme thermostability of our L and L-rich ferritins may have great potential for biotechnological applications.

CRISPR-Suppressor Scanning for Systematic Discovery of Drug-Resistance Mutations.

CRISPR-Cas9 genome editing technologies have enabled complex genetic manipulations in situ, including large-scale, pooled screening approaches to probe and uncover mechanistic insights across various biological processes. The RNA-programmable nature of CRISPR-Cas9 greatly empowers tiling mutagenesis approaches to elucidate molecular details of protein function, in particular the interrogation of mechanisms of resistance to small molecules, an approach termed CRISPR-suppressor scanning. In a typical CRISPR-suppressor scanning experiment, a pooled library of single-guide RNAs is designed to target across the coding sequence(s) of one or more genes, enabling the Cas9 nuclease to systematically mutate the targeted proteins and generate large numbers of diverse protein variants in situ. This cellular pool of protein variants is then challenged with drug treatment to identify mutations conferring a fitness advantage. Drug-resistance mutations identified with this approach can not only elucidate drug mechanism of action but also reveal deeper mechanistic insights into protein structure-function relationships. In this article, we outline the framework for a standard CRISPR-suppressor scanning experiment. Specifically, we provide instructions for the design and construction of a pooled sgRNA library, execution of a CRISPR-suppressor scanning screen, and basic computational analysis of the resulting data. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Design and generation of a pooled sgRNA library Support Protocol 1: sgRNA library design using command-line CRISPOR Support Protocol 2: Production and titering of pooled sgRNA library lentivirus Basic Protocol 2: Execution and analysis of a CRISPR-suppressor scanning experiment.

Effects of External Perturbations on Protein Systems: A Microscopic View.

Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure-function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottom-up approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.