Multiscale and multidisciplinary analysis of aging processes in bone.

The world population is increasingly aging, deeply affecting our society by challenging our healthcare systems and presenting an economic burden, thus turning the spotlight on aging-related diseases: exempli gratia, osteoporosis, a silent disease until you suddenly break a bone. The increase in bone fracture risk with age is generally associated with a loss of bone mass and an alteration in the skeletal architecture. However, such changes cannot fully explain increased fragility with age. To successfully tackle age-related bone diseases, it is paramount to comprehensively understand the fundamental mechanisms responsible for tissue degeneration. Aging mechanisms persist at multiple length scales within the complex hierarchical bone structure, raising the need for a multiscale and multidisciplinary approach to resolve them. This paper aims to provide an overarching analysis of aging processes in bone and to review the most prominent outcomes of bone aging. A systematic description of different length scales, highlighting the corresponding techniques adopted at each scale and motivating the need for combining diverse techniques, is provided to get a comprehensive description of the multi-physics phenomena involved.

Changes in elastin structure and extensibility induced by hypercalcemia and hyperglycemia.

Elastin is a key elastomeric protein responsible for the elasticity of many organs, including heart, skin, and blood vessels. Due to its intrinsic long life and low turnover rate, damage in elastin induced by pathophysiological conditions, such as hypercalcemia and hyperglycemia, accumulates during biological aging and in aging-associated diseases, such as diabetes mellitus and atherosclerosis. Prior studies have shown that calcification induced by hypercalcemia deteriorates the function of aortic tissues. Glycation of elastin is triggered by hyperglycemia and associated with elastic tissue damage and loss of mechanical functions via the accumulation of advanced glycation end products. To evaluate the effects on elastin's structural conformations and elasticity by hypercalcemia and hyperglycemia at the molecular scale, we perform classical atomistic and steered molecular dynamics simulations on tropoelastin, the soluble precursor of elastin, under different conditions. We characterize the interaction sites of glucose and calcium and associated structural conformational changes. Additionally, we find that elevated levels of calcium ions and glucose hinder the extensibility of tropoelastin by rearranging structural domains and altering hydrogen bonding patterns, respectively. Overall, our investigation helps to reveal the behavior of tropoelastin and the biomechanics of elastin biomaterials in these physiological environments. STATEMENT OF SIGNIFICANCE: Elastin is a key component of elastic fibers which endow many important tissues and organs, from arteries and veins, to skin and heart, with strength and elasticity. During aging and aging-associated diseases, such as diabetes mellitus and atherosclerosis, physicochemical stressors, including hypercalcemia and hyperglycemia, induce accumulated irreversible damage in elastin, and consequently alter mechanical function. Yet, molecular mechanisms associated with these processes are still poorly understood. Here, we present the first study on how these changes in elastin structure and extensibility are induced by hypercalcemia and hyperglycemia at the molecular scale, revealing the essential roles that calcium and glucose play in triggering structural alterations and mechanical stiffness. Our findings yield critical insights into the first steps of hypercalcemia- and hyperglycemia-mediated aging.

Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.

Protein contact maps represent spatial pairwise inter-residue interactions, providing a protein's translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure determination. Contact maps can also be used as a stand-alone tool for varied applications such as prediction of protein-protein interactions, structure-aware thermal stability or physicochemical properties. We develop a novel hybrid contact map prediction model, CGAN-Cmap, that uses a generative adversarial neural network embedded with a series of modified squeeze and excitation residual networks. To exploit features of different dimensions, we introduce two parallel modules. This architecture improves the prediction by increasing receptive fields, surpassing redundant features and encouraging more meaningful ones from 1D and 2D inputs. We also introduce a new custom dynamic binary cross-entropy loss function to address the input imbalance problem for highly sparse long-range contacts in proteins with insufficient homologs. We evaluate the model's performance on CASP 11, 12, 13, 14, and CAMEO test sets. CGAN-Cmap outperforms state-of-the-art models, improving precision of medium and long-range contacts by at least 3.5%. As a direct assessment between our model and AlphaFold2, the leading available protein structure prediction model, we compare extracted contact maps from AlphaFold2 and predicted contact maps from CGAN-Cmap. The results show that CGAN-Cmap has a mean precision higher by 1% compared to AlphaFold2 for most ranges of contacts. These results demonstrate an efficient approach for highly accurate contact map prediction toward accurate characterization of protein structure, properties and functions from sequence.

DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks.

Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharmaceutical) applications. Experimental approaches to predict protein solubility are costly, time-consuming, and frequently offer only low success rates. To reduce cost and expedite the development of therapeutic and industrially relevant proteins, a highly accurate computational tool for predicting protein solubility from protein sequence is sought. While a number of in silico prediction tools exist, they suffer from relatively low prediction accuracy, bias toward the soluble proteins, and limited applicability for various classes of proteins. In this study, we developed a novel deep learning sequence-based solubility predictor, DSResSol, that takes advantage of the integration of squeeze excitation residual networks with dilated convolutional neural networks and outperforms all existing protein solubility prediction models. This model captures the frequently occurring amino acid k-mers and their local and global interactions and highlights the importance of identifying long-range interaction information between amino acid k-mers to achieve improved accuracy, using only protein sequence as input. DSResSol outperforms all available sequence-based solubility predictors by at least 5% in terms of accuracy when evaluated by two different independent test sets. Compared to existing predictors, DSResSol not only reduces prediction bias for insoluble proteins but also predicts soluble proteins within the test sets with an accuracy that is at least 13% higher than existing models. We derive the key amino acids, dipeptides, and tripeptides contributing to protein solubility, identifying glutamic acid and serine as critical amino acids for protein solubility prediction. Overall, DSResSol can be used for the fast, reliable, and inexpensive prediction of a protein's solubility to guide experimental design.

Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape.

The ongoing COVID-19 pandemic is a global public health emergency requiring urgent development of efficacious vaccines. While concentrated research efforts have focused primarily on antibody-based vaccines that neutralize SARS-CoV-2, and several first-generation vaccines have either been approved or received emergency use authorization, it is forecasted that COVID-19 will become an endemic disease requiring updated second-generation vaccines. The SARS-CoV-2 surface spike (S) glycoprotein represents a prime target for vaccine development because antibodies that block viral attachment and entry, i.e., neutralizing antibodies, bind almost exclusively to the receptor-binding domain. Here, we develop computational models for a large subset of S proteins associated with SARS-CoV-2, implemented through coarse-grained elastic network models and normal mode analysis. We then analyze local protein domain dynamics of the S protein systems and their thermal stability to characterize structural and dynamical variability among them. These results are compared against existing experimental data and used to elucidate the impact and mechanisms of SARS-CoV-2 S protein mutations and their associated antibody binding behavior. We construct a SARS-CoV-2 antigenic map and offer predictions about the neutralization capabilities of antibody and S mutant combinations based on protein dynamic signatures. We then compare SARS-CoV-2 S protein dynamics to SARS-CoV and MERS-CoV S proteins to investigate differing antibody binding and cellular fusion mechanisms that may explain the high transmissibility of SARS-CoV-2. The outbreaks associated with SARS-CoV, MERS-CoV, and SARS-CoV-2 over the last two decades suggest that the threat presented by coronaviruses is ever-changing and long term. Our results provide insights into the dynamics-driven mechanisms of immunogenicity associated with coronavirus S proteins and present a new, to our knowledge, approach to characterize and screen potential mutant candidates for immunogen design, as well as to characterize emerging natural variants that may escape vaccine-induced antibody responses.

Fuzzy binding model of molecular interactions between tropoelastin and integrin alphaVbeta3.

Tropoelastin is the highly flexible monomer subunit of elastin, required for the resilience of the extracellular matrix in elastic tissues. To elicit biological signaling, multiple sites on tropoelastin bind to cell surface integrins in a poorly understood multifactorial process. We constructed a full atomistic molecular model of the interactions between tropoelastin and integrin αvß3 using ensemble-based computational methodologies. Conformational changes of integrin αvß3 associated with outside-in signaling were more frequently facilitated in an ensemble in which tropoelastin bound the integrin's α1 helix rather than the upstream canonical binding site. Our findings support a model of fuzzy binding, whereby many tropoelastin conformations and defined sites cooperatively interact with multiple αvß3 regions. This model explains prior experimental binding to distinct tropoelastin regions, domains 17 and 36, and points to the cooperative participation of domain 20. Our study highlights the utility of ensemble-based approaches in helping to understand the interactive mechanisms of functionally significant flexible proteins.

Tropoelastin and Elastin Assembly.

Elastic fibers are an important component of the extracellular matrix, providing stretch, resilience, and cell interactivity to a broad range of elastic tissues. Elastin makes up the majority of elastic fibers and is formed by the hierarchical assembly of its monomer, tropoelastin. Our understanding of key aspects of the assembly process have been unclear due to the intrinsic properties of elastin and tropoelastin that render them difficult to study. This review focuses on recent developments that have shaped our current knowledge of elastin assembly through understanding the relationship between tropoelastin's structure and function.

Transglutaminase-Mediated Cross-Linking of Tropoelastin to Fibrillin Stabilises the Elastin Precursor Prior to Elastic Fibre Assembly.

Elastic fibres are essential components of all mammalian elastic tissues such as blood vessels, lung and skin, and are critically important for the mechanical properties they endow. The main components of elastic fibres are elastin and fibrillin, where correct formation of elastic fibres requires a fibrillin microfibril scaffold for the deposition of elastin. It has been demonstrated previously that the interaction between fibrillin and tropoelastin, the elastin precursor, increases the rate of assembly of tropoelastin. Furthermore, tropoelastin and fibrillin can be cross-linked by transglutaminase-2, but the function of cross-linking on their elastic properties is yet to be elucidated. Here we show that transglutaminase cross-linking supports formation of a 1:1 stoichiometric fibrillin-tropoelastin complex. SAXS data show that the complex retains features of the individual proteins but is elongated supporting end-to-end assembly. Elastic network models were constructed to compare the dynamics of tropoelastin and fibrillin individually as well as in the cross-linked complex. Normal mode analysis was performed to determine the structures' most energetically favourable, biologically accessible motions which show that within the complex, tropoelastin is less mobile and this molecular stabilisation extends along the length of the tropoelastin molecule to regions remote from the cross-linking site. Together, these data suggest a long-range stabilising effect of cross-linking that occurs due to the covalent linkage of fibrillin to tropoelastin. This work provides insight into the interactions of tropoelastin and fibrillin and how cross-link formation stabilises the elastin precursor so it is primed for elastic fibre assembly.

Cartilage and collagen mechanics under large-strain shear within in vivo and at supraphysiogical temperatures.

Human joints, particularly those of extremities, experience a significant range of temperatures in vivo. Joint temperature influences the mechanics of both joint and cartilage, and the mechanics of cartilage can affect the temperature of both joint and cartilage. Thermal treatments and tissue repairs, such as thermal chondroplasty, and ex vivo tissue engineering may also expose cartilage to supraphysiological temperatures. Furthermore, although cartilage undergoes principally compressive loads in vivo, shear strain plays a significant role at larger compressive strains. Thus, we aimed to determine whether and how the bulk mechanical responses of cartilage undergoing large-strain shear change (1) within the range of temperatures relevant in vivo, and (2) both during and after supraphysiological thermal treatments. We completed large-strain shear tests (10 and 15%) at four thermal conditions: 24∘C and 40∘C to span the in vivo range, and 70∘C and 24∘C repeated after 70∘C to explore mechanics during and after potential treatments. We calculated the bulk mechanical responses (strain-energy dissipation densities, peak-to-peak shear stresses, and peak-effective shear moduli) as of function of temperature and used statistical methods to probe significant differences. To probe the mechanisms underlying differences we assessed specimens, principally the type II collagen, with imaging (second harmonic generation and transmission electron microscopies, and histology) and assessed the temperature-dependent mechanics of type II collagen molecules within cartilage using steered molecular dynamics simulations. Our results suggest that the bulk mechanical responses of cartilage depend significantly on temperature both within the in vivo range and at supraphysiological temperatures, showing significant reductions in all mechanical measures with increasing temperature. Using imaging and simulations we determined that one underlying mechanism explaining our results may be changes in the molecular deformation profiles of collagen molecules versus temperature, likely compounded at larger length scales. These new insights into the mechanics of cartilage and collagen may suggest new treatment targets for damaged or osteoarthritic cartilage.

The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation.

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease.

Spider dragline silk as torsional actuator driven by humidity.

Self-powered actuation driven by ambient humidity is of practical interest for applications such as hygroscopic artificial muscles. We demonstrate that spider dragline silk exhibits a humidity-induced torsional deformation of more than 300°/mm. When the relative humidity reaches a threshold of about 70%, the dragline silk starts to generate a large twist deformation independent of spider species. The torsional actuation can be precisely controlled by regulating the relative humidity. The behavior of humidity-induced twist is related to the supercontraction behavior of spider dragline silk. Specifically, molecular simulations of MaSp1 and MaSp2 proteins in dragline silk reveal that the unique torsional property originates from the presence of proline in MaSp2. The large proline rings also contribute to steric exclusion and disruption of hydrogen bonding in the molecule. This property of dragline silk and its structural origin can inspire novel design of torsional actuators or artificial muscles and enable the development of designer biomaterials.

Allysine modifications perturb tropoelastin structure and mobility on a local and global scale.

Elastin provides elastic tissues with resilience through stretch and recoil cycles, and is primarily made of its extensively cross-linked monomer, tropoelastin. Here, we leverage the recently published full atomistic model of tropoelastin to assess how allysine modifications, which are essential to cross-linking, contribute to the dynamics and structural changes that occur in tropoelastin in the context of elastin assembly. We used replica exchange molecular dynamics to generate structural ensembles of allysine containing tropoelastin. We conducted principal component analysis on these ensembles and found that the molecule departs from the canonical structural ensemble. Furthermore, we showed that, while the canonical scissors-twist movement was retained, new movements emerged that deviated from those of the wild type protein, providing evidence for the involvement of a variety of molecular motions in elastin assembly. Additionally, we highlighted secondary structural changes and linked these perturbations to the longevity of specific salt bridges. We propose a model where allysines in tropoelastin contribute to hierarchical elastin assembly through global and local perturbations to molecular structure and dynamics.

Multiscale Modeling of Silk and Silk-Based Biomaterials-A Review.

Silk embodies outstanding material properties and biologically relevant functions achieved through a delicate hierarchical structure. It can be used to create high-performance, multifunctional, and biocompatible materials through mild processes and careful rational material designs. To achieve this goal, computational modeling has proven to be a powerful platform to unravel the causes of the excellent mechanical properties of silk, to predict the properties of the biomaterials derived thereof, and to assist in devising new manufacturing strategies. Fine-scale modeling has been done mainly through all-atom and coarse-grained molecular dynamics simulations, which offer a bottom-up description of silk. In this work, a selection of relevant contributions of computational modeling is reviewed to understand the properties of natural silk, and to the design of silk-based materials, especially combined with experimental methods. Future research directions are also pointed out, including approaches such as 3D printing and machine learning, that may enable a high throughput design and manufacturing of silk-based biomaterials.

Tropoelastin is a Flexible Molecule that Retains its Canonical Shape.

Tropoelastin is the dominant building block of elastic fibers, which form a major component of the extracellular matrix, providing structural support to tissues and imbuing them with elasticity and resilience. Recently, the atomistic structure of human tropoelastin is described, obtained through accelerated sampling via replica exchange molecular dynamics simulations. Here, principal component analysis is used to consider the ensemble of structures accessible to tropoelastin at body temperature (37 °C) at which tropoelastin naturally self-assembles into aggregated coacervates. These coacervates are relevant because they are an essential intermediate assembly stage, where tropoelastin molecules are then cross-linked at lysine residues and integrated into growing elastic fibers. It is found that the ensemble preserves the canonical tropoelastin structure with an extended molecular body flanked by two protruding legs, and identifies variations in specific domain positioning within this global shape. Furthermore, it is found that lysine residues show a large variation in their location on the tropoelastin molecule compared with other residues. It is hypothesized that this perturbation of the lysines increases their accessibility and enhances cross-linking. Finally, the principal component modes are extracted to describe the range of tropoelastin's conformational fluctuation to validate tropoelastin's scissor-twist motion that was predicted earlier.

Fabrication and Characterization of Recombinant Silk-Elastin-Like-Protein (SELP) Fiber.

Silk-elastin-like-protein polymers (SELPs) are genetically engineered recombinant protein sequences consisting of repeating units of silk-like and elastin-like blocks. By combining these entities, it is shown that both the characteristic strength of silk and the temperature-dependent responsiveness of elastin can be leveraged to create an enhanced stimuli-responsive material. It is hypothesized that SELP behavior can be influenced by varying the silk-to-elastin ratio. If the responsiveness of the material at different ratios is significantly different, this would allow for the design of materials with specific temperature-based swelling and mechanical properties. This study demonstrates that SELP fiber properties can be controlled via a temperature transition dependent on the ratio of silk-to-elastin in the material. SELP fibers are experimentally wet spun from polymers with different ratios of silk-to-elastin and conditioned in either a below or above transition temperature (T t ) water bath prior to characterization. The fibers with higher elastin content showed more stimuli-responsive behavior compared to the fibers with lower elastin content in the hot (57-60 °C) versus cold (4-7 °C) environment, both computationally and experimentally. This work builds a foundation for developing SELP materials with well-characterized mechanical properties and responsive features.

Unraveling the Molecular Mechanisms of Thermo-responsive Properties of Silk-Elastin-Like Proteins by Integrating Multiscale Modeling and Experiment.

Adaptive hydrogels tailor-made from silk-elastin-like proteins (SELPs) possess excellent biocompatibility and biodegradability with properties that are tunable and responsive to multiple simultaneous external stimuli. To unravel the molecular mechanisms of their physical response to external stimuli in tandem with experiments, here we predict and measure the variation in structural properties as a function of temperature through coarse-grained (CG) modeling of individual and crosslinked SE8Y and S4E8Y molecules, which have ratios of 1:8 and 4:8 of silk to elastin blocks respectively. Extensive structural reshuffling in single SE8Y molecules led to the increased compactness of the structure, whereas S4E8Y molecules did not experience any significant changes as they already adopted very compact structures at low temperatures. Crosslinking of SE8Y molecules at high concentrations impeded their structural transition at high temperatures that drastically reduced the degree of deswelling through extensive suppression of the structural shuffling and the trapping of the molecules in high potential energy states due to inter-molecular constraints. This integrative experimental and computational understanding of the thermal response in single molecules of SELPs and their crosslinked networks should lead to further improvements in the properties of SELP hydrogels through predictive designs and their wider applications in biomaterials and tissue engineering.

Molecular model of human tropoelastin and implications of associated mutations.

Protein folding poses unique challenges for large, disordered proteins due to the low resolution of structural data accessible in experiment and on the basis of short time scales and limited sampling attainable in computation. Such molecules are uniquely suited to accelerated-sampling molecular dynamics algorithms due to a flat-energy landscape. We apply these methods to report here the folded structure in water from a fully extended chain of tropoelastin, a 698-amino acid molecular precursor to elastic fibers that confer elasticity and recoil to tissues, finding good agreement with experimental data. We then study a series of artificial and disease-related mutations, yielding molecular mechanisms to explain structural differences and variation in hierarchical assembly observed in experiment. The present model builds a framework for studying assembly and disease and yields critical insight into molecular mechanisms behind these processes. These results suggest that proteins with disordered regions are suitable candidates for characterization by this approach.

Multiscale modeling of keratin, collagen, elastin and related human diseases: Perspectives from atomistic to coarse-grained molecular dynamics simulations.

Scleroproteins are an important category of proteins within the human body that adopt filamentous, elongated conformations in contrast with typical globular proteins. These include keratin, collagen, and elastin, which often serve a common mechanical function in structural support of cells and tissues. Genetic mutations alter these proteins, disrupting their functions and causing diseases. Computational characterization of these mutations has proven to be extremely valuable in identifying the intricate structure-function relationships of scleroproteins from the molecular scale up, especially if combined with multiscale experimental analysis and the synthesis of model proteins to test specific structure-function relationships. In this work, we review numerous critical diseases that are related to keratin, collagen, and elastin, and through several case studies, we propose ways of extensively utilizing multiscale modeling, from atomistic to coarse-grained molecular dynamics simulations, to uncover the molecular origins for some of these diseases and to aid in the development of novel cures and therapies. As case studies, we examine the effects of the genetic disease Epidermolytic Hyperkeratosis (EHK) on the structure and aggregation of keratins 1 and 10; we propose models to understand the diseases of Osteogenesis Imperfecta (OI) and Alport syndrome (AS) that affect the mechanical and aggregation properties of collagen; and we develop atomistic molecular dynamics and elastic network models of elastin to determine the role of mutations in diseases such as Cutis Laxa and Supravalvular Aortic Stenosis on elastin's structure and molecular conformational motions and implications for assembly.

Computational smart polymer design based on elastin protein mutability.

Soluble elastin-like peptides (ELPs) can be engineered into a range of physical forms, from hydrogels and scaffolds to fibers and artificial tissues, finding numerous applications in medicine and engineering as "smart polymers". Elastin-like peptides are attractive candidates as a platform for novel biomaterial design because they exhibit a highly tunable response spectrum, with reversible phase transition capabilities. Here, we report the design of the first virtual library of elastin-like protein models using methods for enhanced sampling to study the effect of peptide chemistry, chain length, and salt concentration on the structural transitions of ELPs, exposing associated molecular mechanisms. We describe the behavior of the local molecular structure under increasing temperatures and the effect of peptide interactions with nearest hydration shell water molecules on peptide mobility and propensity to exhibit structural transitions. Shifts in the magnitude of structural transitions at the single-molecule scale are explained from the perspective of peptide-ion-water interactions in a library of four unique elastin-like peptide systems. Predictions of structural transitions are subsequently validated in experiment. This library is a valuable resource for recombinant protein design and synthesis as it elucidates mechanisms at the single-molecule level, paving a feedback path between simulation and experiment for smart material designs, with applications in biomedicine and diagnostic devices.

Synergistic Integration of Experimental and Simulation Approaches for the de Novo Design of Silk-Based Materials.

Tailored biomaterials with tunable functional properties are crucial for a variety of task-specific applications ranging from healthcare to sustainable, novel bio-nanodevices. To generate polymeric materials with predictive functional outcomes, exploiting designs from nature while morphing them toward non-natural systems offers an important strategy. Silks are Nature's building blocks and are produced by arthropods for a variety of uses that are essential for their survival. Due to the genetic control of encoded protein sequence, mechanical properties, biocompatibility, and biodegradability, silk proteins have been selected as prototype models to emulate for the tunable designs of biomaterial systems. The bottom up strategy of material design opens important opportunities to create predictive functional outcomes, following the exquisite polymeric templates inspired by silks. Recombinant DNA technology provides a systematic approach to recapitulate, vary, and evaluate the core structure peptide motifs in silks and then biosynthesize silk-based polymers by design. Post-biosynthesis processing allows for another dimension of material design by controlled or assisted assembly. Multiscale modeling, from the theoretical prospective, provides strategies to explore interactions at different length scales, leading to selective material properties. Synergy among experimental and modeling approaches can provide new and more rapid insights into the most appropriate structure-function relationships to pursue while also furthering our understanding in terms of the range of silk-based systems that can be generated. This approach utilizes nature as a blueprint for initial polymer designs with useful functions (e.g., silk fibers) but also employs modeling-guided experiments to expand the initial polymer designs into new domains of functional materials that do not exist in nature. The overall path to these new functional outcomes is greatly accelerated via the integration of modeling with experiment. In this Account, we summarize recent advances in understanding and functionalization of silk-based protein systems, with a focus on the integration of simulation and experiment for biopolymer design. Spider silk was selected as an exemplary protein to address the fundamental challenges in polymer designs, including specific insights into the role of molecular weight, hydrophobic/hydrophilic partitioning, and shear stress for silk fiber formation. To expand current silk designs toward biointerfaces and stimuli responsive materials, peptide modules from other natural proteins were added to silk designs to introduce new functions, exploiting the modular nature of silk proteins and fibrous proteins in general. The integrated approaches explored suggest that protein folding, silk volume fraction, and protein amino acid sequence changes (e.g., mutations) are critical factors for functional biomaterial designs. In summary, the integrated modeling-experimental approach described in this Account suggests a more rationally directed and more rapid method for the design of polymeric materials. It is expected that this combined use of experimental and computational approaches has a broad applicability not only for silk-based systems, but also for other polymer and composite materials.