Rosetta Software Research Articles

Insights into the binding recognition and computational design of IL-2 muteins with enhanced predicted binding affinity to the IL-2 receptor α

Interleukin-2 (IL-2) is an immune system regulator that has received approval for cancer treatment. However, high-dose IL-2 therapy has seen restricted use due to its low efficacy and on-target toxicity. To enhance the effectiveness of IL-2 therapy, it is essential to engineer IL-2 molecules to enhance their specificity toward target cell populations. In this study, molecular dynamics (MD) simulations and Rosetta software were utilized to design novel high-affinity IL-2Rα-binding IL-2 muteins. MD simulations were used to identify the target residues of IL-2 for design, and Rosetta software were then employed to predict potential IL-2 muteins with higher binding affinity toward IL-2Rα. Rosetta generated two potential designed IL-2 muteins. The results of the MD validation and MM/GBSA analysis indicated that both designed IL-2 muteins exhibited greater predicted binding affinities toward IL-2Rα than that of the native proteins. RMSF analysis demonstrated that the structural fluctuations of free IL-2 and designed muteins were similar, indicating that the mutations did not alter the intramolecular force responsible for IL-2's stability and folding. These designed IL-2 muteins may have potential benefits for cancer immunotherapy.

Development of the Rough Set Method to Determine Lecturer Scholarship Opportunities

Currently, all groups can experience the development of artificial intelligence, this happens because artificial intelligence has experienced very significant changes. Artificial Intelligence (AI) consists of several branches, one of which is machine learning. Machine Learning (ML) technology is a branch of AI that is very interesting because it is a machine that can learn like humans. The method used here is the rough set method. In this research, a case will be raised to determine scholarship opportunities for lecturers based on predetermined criteria. To solve the problem above, machine learning was used using the Rough Set method, using Rosetta software. By the regulations determined by the scholarship provider, in this case, the institution concerned where the lecturer is registered as teaching staff to obtain a scholarship, criteria are needed to determine who will be selected to receive the scholarship. The distribution of scholarships is carried out to improve lecturer performance, as an achievement as well as an appreciation for the lecturer concerned for his long service to the institution.

The chemoenzymatic synthesis of glycan-terminated oligo(Leu)x

Introduction: Glycopeptides contain carbohydrate moieties (glycans) covalently attached to the side chain and/or terminal peptide units. Since glycans are present on cell surfaces, these constructs can potentially address a wide array of therapeutic functions. To overcome the deficiencies associated with current synthetic routes to glycopeptides, such as costly processes and toxic reagents, this work aimed to develop versatile environmentally friendly protease-catalyzed peptide synthesis routes to peptides decorated with a glycan at their N-terminus.Methods: “Grafters” were first synthesized that consist of a glycan conjugated directly, or through a spacer, to the amine group of L-Phe-ethyl ester (Phe-OEt). The role of Phe-OEt is to increase the conjugate’s recognition by the protease (papain) catalytic active site. A series of grafters were synthesized with variation of the glycan structure, linkage-chemistry, and presence of an oligo (ethylene glycol) “spacer” of varied length between the glycan and Phe-OEt moiety. High grafter efficiency will result by the successful acceptance of the grafter at the enzymes S1/S2 subsites, formation of an acyl enzyme complex and subsequent conversion to glycan-terminated oligo(Leu)x (x ≥ 1), as opposed to construction of non-glycan N-terminated oligo(Leu)x.Results and discussion: While glycan-Phe-OEt grafters without a spacer between the glycan and Phe-OEt resulted in low grafter efficiency (8.3% ± 2.0%), insertion of a short oligo (ethylene glycol) spacer between the glycan and Phe-OEt moieties (glycan-PEGn-Phe-OEt, n ≥ 3) increased the grafter efficiency by 3-fold–24.5% ± 1.8%. In addition, computational modeling was performed using Rosetta software provided insights on a molecular level of how grafter efficiency is influenced by the PEG spacer length.

Bioinformatics analysis and validation of the interaction between PML protein and TAB1 protein

To analyze the interaction between PML protein and TAB1 protein using bioinformatic approaches and experimentally verify the results. Using Rosetta software, a 3D model of TAB1 protein was constructed through a comparative modeling approach; the secondary structure of PML protein was retrieved in the PDB database and its crystal structure and 3D structure were resolved. Zdock 3.0.2 software was used to perform protein-protein docking of PML and TAB1, and the best conformation was extracted for molecular structure analysis of the docking model. The interaction between the two proteins was detected using immunoprecipitation in α-MMC-treated M1 inflammatory macrophages. When 6IMQ of PML was used as the docking site, PML protein formed 3 salt bridges, 6 hydrogen bonds and 6 hydrophobic interactions with TAB1 proteins; when 5YUF of PML was used as the docking site, PML protein formed 1 hydrogen bond, 3 electrostatic interactions and 9 hydrophobic interactions with TAB1 proteins, and both of the docking modes formed good molecular docking and interactions. In the M1 inflammatory macrophages treated with α-MMC for 4 h, positive protein bands of PML and TAB1 were detected in the cell lysates in PML-IP group. PML protein can interact strongly with TAB1 protein.

Recent Advances in NMR Protein Structure Prediction with ROSETTA.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Rational peptide design for inhibition of the KIX–MLL interaction

The kinase-inducible domain interacting (KIX) domain is an integral part of the general transcriptional coactivator CREB-binding protein, and has been associated with leukemia, cancer, and various viral diseases. Hence, the KIX domain has attracted considerable attention in drug discovery and development. Here, we rationally designed a KIX inhibitor using a peptide fragment corresponding to the transactivation domain (TAD) of the transcriptional activator, mixed-lineage leukemia protein (MLL). We performed theoretical saturation mutagenesis using the Rosetta software to search for mutants expected to bind KIX more tightly than the wild-type MLL TAD. Mutant peptides with higher helical propensities were selected for experimental characterization. We found that the T2857W mutant of the MLL TAD peptide had the highest binding affinity for KIX compared to the other 12 peptides designed in this study. Moreover, the peptide had a high inhibitory effect on the KIX–MLL interaction with a half-maximal inhibitory concentration close to the dissociation constant for this interaction. To our knowledge, this peptide has the highest affinity for KIX among all previously reported inhibitors that target the MLL site of KIX. Thus, our approach may be useful for rationally developing helical peptides that inhibit protein–protein interactions implicated in the progression of various diseases.

Computational protein structure elucidation from covalent labeling mass spectrometry data.

Mass spectrometry (MS)-based methods such as covalent labeling MS are increasingly used to obtain information about protein structure. However, in contrast to other high-resolution structure determination methods, this information is not sufficient to deduce all atom coordinates and can only inform on certain elements of structure, such as solvent exposure of individual residues or location of protein-protein interfaces. However, easy and reliable translation of this information into accurate structural protein and protein complex models remains particularly challenging, due to a lack of standardized and automated methods. Computational methods are needed to predict high-resolution protein structures from the mass spectrometry (MS) data. My group develops algorithms within the Rosetta software package that use mass spectrometry data to guide protein structure prediction. We have successfully implemented the use of MS covalent labeling data into the Rosetta software to predict protein and protein complex structure guided by the experimental data. We developed algorithms to predict protein structure from hydroxyl radical and diethylpyrocarbonate (DEPC) labeling data. To evaluate the effectiveness of our covalent-labeling guided structure prediction algorithms, we benchmarked their performance using proteins of known structure for which experimental covalent labeling data exists. For all benchmark proteins, better quality models were built when MS data was used in the scoring. For modeling with hydroxyl radical labeling data, we measured exposure of labeled residues, while in the case of DEPC labeling we further assessed the local hydrophobic microenvironment of serine, threonine, and tyrosine residues. We are now extending this work to use MS data in deep learning-based predictions of protein structure.

How to Design Peptides.

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

Analisis Algoritma Roughset Pada Penerimaan Beasiswa

The BAZNAS Scholarship Institution or LBB is a program and utilization tasked with providing education funds to ensure the continuity of educational programs for underprivileged or poor students as intergenerational accountability. This study aims to find out (1) how is the procedure for distributing zakat funds in the form of scholarships, (2) the obstacles faced in distributing zakat funds in the form of scholarships, (3) the efforts made in overcoming obstacles in distributing zakat funds in the form of scholarships. Collecting data in this study using the method of documentation and interviews. The results of this study indicate that (1) the procedures carried out in providing scholarships are very good, (2) the obstacles experienced in distributing zakat funds in the form of scholarships, namely BAZNAS RantauPrapat City, lack of coordination between BAZNAS and the school, and the amount of scholarship funding have not met educational needs, (3) efforts to overcome these obstacles can be done by good coordination with schools, increasing coordination between BAZNAS and schools, and providing services and facilities for zakat givers.This study aims to analyze the Labuhanbatu Baznas Program in providing scholarships in Labuhanbatu using the Rough Set algorithm. The analysis was carried out based on facts and data obtained at the BAZNAS Rantauprapat office using Rosetta Software for testing. The results of this study produce a rule that can be used to make future decision-making policies during data processing.

Understanding the interactions between bone mineral crystals and their binding peptides derived from filamentous phage

Hydroxyapatite (HAP) is the main inorganic component of natural bone and assembled along collagen fibers. HAP is formed under the regulation of bone proteins (BPs) including collagen and non-collagenous proteins (NCPs, such as osteocalcin, osteopontin, osteonectin, and bone sialoprotein) during biomineralization. However, the mechanism of the BPs regulating the HAP biomineralization is still unclear and some HAP-interacting domains on BPs have not yet been identified. To understand the interactions between HAP and the peptide motifs, similar to functional motifs on BPs, we employed a sidewall-displayed phage library, instead of a traditional tip-displayed phage library, to discover novel HAP-binding peptides. HAP-binding peptides were expressed as pVIII fusion proteins into the capsid of the filamentous fd phages. By a few rounds of biopanning of the landscape phage library against HAP, we identified multiple HAP-binding peptides. The affinity-selected peptide sequences were aligned to the sequences of BPs using a receptor ligand contacts (RELIC) program, RELIC/MATCH, which is a bioinformatics software that identifies protein-ligand interaction sites and herein discovers the putative HAP-binding domains on these BPs. By both confirming some known HAP-binding domains and identifying new HAP-binding domains, we clarified some mechanisms of HAP-BP interactions. Through a series of binding assays, we discovered that DSSTPSST peptide is the best HAP binder. Using a protein structure prediction model within the Rosetta software suite, we found that the best binding peptides had the lowest peptide-HAP interfacial energy, consistent with the binding assay studies. Simulations suggested that the stable turn-like structure owing to a proline residue in the DSSTPSST peptide enabled favorable electrostatic interactions between aspartate residues in the N-terminal domain and calcium ions in HAP. The phages that displayed DSSTPSST on the sidewall bound and aligned the HAP nanorods along its sidewall, forming HAP-decorated nanofibers. Through a freeze-casting approach, the HAP-decorated nanofibers were assembled into a macroscale scaffold that supported the proliferation of mesenchymal stem cells. Therefore, our work demonstrates a new understanding of HAP-peptide interactions that can be exploited to produce HAP-based biomaterials with potential applications in bone tissue engineering.

Computational identification of HCV neutralizing antibodies with a common HCDR3 disulfide bond motif in the antibody repertoires of infected individuals.

Despite recent success in hepatitis C virus (HCV) treatment using antivirals, an HCV vaccine is still needed to prevent reinfections in treated patients, to avert the emergence of drug-resistant strains, and to provide protection for people with no access to the antiviral therapeutics. The early production of broadly neutralizing antibodies (bNAbs) associates with HCV clearance. Several potent bNAbs bind a conserved HCV glycoprotein E2 epitope using an unusual heavy chain complementarity determining region 3 (HCDR3) containing an intra-loop disulfide bond. Isolation of additional structurally-homologous bNAbs would facilitate the recognition of key determinants of such bNAbs and guide rational vaccine design. Here we report the identification of new antibodies containing an HCDR3 disulfide bond motif using computational screening with the Rosetta software. Using the newly-discovered and already-known members of this antibody family, we review the required HCDR3 amino acid composition and propose determinants for the bent versus straight HCDR3 loop conformation observed in these antibodies.

Improved Repeat Protein Stability by Combined Consensus and Computational Protein Design.

High protein stability is an important feature for proteins used as therapeutics, as diagnostics, and in basic research. We have previously employed consensus design to engineer optimized Armadillo repeat proteins (ArmRPs) for sequence-specific recognition of linear epitopes with a modular binding mode. These designed ArmRPs (dArmRPs) feature high stability and are composed of M-type internal repeats that are flanked by N- and C-terminal capping repeats that protect the hydrophobic core from solvent exposure. While the overall stability of the designed ArmRPs is remarkably high, subsequent biochemical and biophysical experiments revealed that the N-capping repeat assumes a partially unfolded, solvent-accessible conformation for a small fraction of time that renders it vulnerable to proteolysis and aggregation. To overcome this problem, we have designed new N-caps starting from an M-type internal repeat using the Rosetta software. The superior stability of the computationally refined models was experimentally verified by circular dichroism and nuclear magnetic resonance spectroscopy. A crystal structure of a dArmRP containing the novel N-cap revealed that the enhanced stability correlates with an improved packing of this N-cap onto the hydrophobic core of the dArmRP. Hydrogen exchange experiments further show that the level of local unfolding of the N-cap is reduced by several orders of magnitude, resulting in increased resistance to proteolysis and weakened aggregation. As a first application of the novel N-cap, we determined the solution structure of a dArmRP with four internal repeats, which was previously impeded by the instability of the original N-cap.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology.

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

Extraction of rules related to marketing mix on customers’ buying behavior using Rough set theory and fuzzy 2-tuple approach

ABSTRACT Marketing mix elements are sets of controllable marketing variables that an organization combines to achieve marketing goals in the target market and meet customer needs. These are the most essential criteria to increase sales and profits. The purpose of this study is to explore how these factors influence customers’ purchasing behavior at chain stores in Tehran. Based on this, Five of Tehran’s best-selling chain stores are chosen, and 125 samples are gathered from each. To collect information, the 2-tuple fuzzy approach is used to avoid losing the linguistic information obtained from customers. The Rough Theory Set and decision tree algorithm are used to analyze the data and extract the rules Five rules guiding customer behavior are identified as essential aspects influencing buying behavior in shopping based on the data analyzed by Rosetta software. A comparison is made between the extracted rules using the proposed Rough Set Theory and the tree diagram of the data acquired by RapidMiner software to assess the result.

SPATIAL VARIABILITY OF HYDRO-RELATED PHYSICAL PROPERTIES OF AL-RASHEED LOAM

A field study was conducted on 2 hectare area at Al- Rashid district, south of Baghdad to analyze the spatial variability of saturated hydraulic conductivity (Ks), initial infiltration rate (IR), Porosity (F) and bulk density (BD). Based on measured BD values Rosetta software was used in this study to estimate water retention parameters, water content at θ33 and θ1500 kPa and unsaturated hydraulic conductivity at 33 kPa(k33), 100 kPa(k100) and 1500 kPa(k1500) according to Van Genuchten-Mualem model. Measured and predicted data were analyzed both statistically and geostatistically and, the results showed a strong to moderate spatial dependence for the studied characteristics. The spatial correlation values (r2) were obtained with a spherical model for Ks, θ33, θ1500, k33, k100 and k1500, an exponential model for IR, and a Gaussian model for F and BD. Ks increased significantly with increasing of IR (r2 = 0.49**) and decreased with increasing of F and BD, IR increased with decreasing of BD (r2 = -0.326*) and BD increased with increasing of F (r2 = 0.989 **). In general the spatial distribution was moderately skewed (-0.5 to 0.5) for the studied characteristics with pronounced kurtosis (&gt;2.5). The limit distance for the search radius to estimating spatial dependency varied from 29.9 m for BD to 105 m for IR.

Rule of Land Potential for Paddy Use Rough Set Method

Blitar district has become one of the many cities in Java the land situation is largely a good soil of vuikanik to be used as farmland. Agriculture is one of the priority sectors in Blitar district and is supported by culture, geographical conditions and the number of people whose livelihoods are farmers.Hence, it requires a way of knowing where a region might have a potential paddy commodity. It is hoped that the government of blitar will be able to make the best use of the number of paddy commodities produced in blitar district with the many farmers available. A rough set is able to produce information with a rule pattern (rule) which can determine the potential areas for paddy commodities in Blitar district by using factors of harvested area, production amount, and number of farmers per sub-district. This research is not only done analytically but also help from Rosetta's software to test analytic data analysis use rough set. The result of this study is rule as many as 38 rule that can explain the possibility of stake based on the 3 decision attributes: potential, low potential, and not potential. For those areas there is a good chance paddy commodity potential area based on the rules that have been formed is area have a large crop, a large amount of paddy produced, and a small number of farmers.

Normal-mode driven exploration of protein domain motions.

Domain motions involved in the function of proteins can often be well described as a combination of motions along a handfull of low-frequency modes, that is, with the values of a few normal coordinates. This means that, when the functional motion of a protein is unknown, it should prove possible to predict it, since it amounts to guess a few values. However, without the help of additional experimental data, using normal coordinates for generating accurate conformers far away from the initial one is not so straightforward. To do so, a new approach is proposed: instead of building conformers directly with the values of a subset of normal coordinates, they are built in two steps, the conformer built with normal coordinates being just used for defining a set of distance constraints, the final conformer being built so as to match them. Note that this approach amounts to transform the problem of generating accurate protein conformers using normal coordinates into a better known one: the distance-geometry problem, which is herein solved with the help of the ROSETTA software. In the present study, this approach allowed to rebuild accurately six large amplitude conformational changes, using at most six low-frequency normal coordinates. As a consequence of the low-dimensionality of the corresponding subspace, random exploration also proved enough for generating low-energy conformers close to the known end-point of the conformational change of the LAO binding protein, lysozyme T4 and adenylate kinase.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data.

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability.

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.