Exploring Protein Flexibility and Allosteric Signalling Mechanism with Rigidity Theory

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Our ability to rationally optimize allosteric regulation is limited by incomplete knowledge of the mutations that tune allostery. Are these mutations few or abundant, structurally localized or distributed? To examine this, we conducted saturation mutagenesis of a synthetic allosteric switch in which Dihydrofolate reductase (DHFR) is regulated by a blue-light sensitive LOV2 domain. Using a high-throughput assay wherein DHFR catalytic activity is coupled to E. coli growth, we assessed the impact of 1548 viable DHFR single mutations on allostery. Despite most mutations being deleterious to activity, fewer than 5% of mutations had a statistically significant influence on allostery. Most allostery disrupting mutations were proximal to the LOV2 insertion site. In contrast, allostery enhancing mutations were structurally distributed and enriched on the protein surface. Combining several allostery enhancing mutations yielded near-additive improvements to dynamic range. Our results indicate a path toward optimizing allosteric function through variation at surface sites. eLife digest Many proteins exhibit a property called ‘allostery’. In allostery, an input signal at a specific site of a protein – such as a molecule binding, or the protein absorbing a photon of light – leads to a change in output at another site far away. For example, the protein might catalyze a chemical reaction faster or bind to another molecule more tightly in the presence of the input signal. This protein ‘remote control’ allows cells to sense and respond to changes in their environment. An ability to rapidly engineer new allosteric mechanisms into proteins is much sought after because this would provide an approach for building biosensors and other useful tools. One common approach to engineering new allosteric regulation is to combine a ‘sensor’ or input region from one protein with an ‘output’ region or domain from another. When researchers engineer allostery using this approach of combining input and output domains from different proteins, the difference in the output when the input is ‘on’ versus ‘off’ is often small, a situation called ‘modest allostery’. McCormick et al. wanted to know how to optimize this domain combination approach to increase the difference in output between the ‘on’ and ‘off’ states. More specifically, McCormick et al. wanted to find out whether swapping out or mutating specific amino acids (each of the individual building blocks that make up a protein) enhances or disrupts allostery. They also wanted to know if there are many possible mutations that change the effectiveness of allostery, or if this property is controlled by just a few amino acids. Finally, McCormick et al. questioned where in a protein most of these allostery-tuning mutations were located. To answer these questions, McCormick et al. engineered a new allosteric protein by inserting a light-sensing domain (input) into a protein involved in metabolism (a metabolic enzyme that produces a biomolecule called a tetrahydrofolate) to yield a light-controlled enzyme. Next, they introduced mutations into both the ‘input’ and ‘output’ domains to see where they had a greater effect on allostery. After filtering out mutations that destroyed the function of the output domain, McCormick et al. found that only about 5% of mutations to the ‘output’ domain altered the allosteric response of their engineered enzyme. In fact, most mutations that disrupted allostery were found near the site where the ‘input’ domain was inserted, while mutations that enhanced allostery were sprinkled throughout the enzyme, often on its protein surface. This was surprising in light of the commonly-held assumption that mutations on protein surfaces have little impact on the activity of the ‘output’ domain. Overall, the effect of individual mutations on allostery was small, but McCormick et al. found that these mutations can sometimes be combined to yield larger effects. McCormick et al.’s results suggest a new approach for optimizing engineered allosteric proteins: by introducing mutations on the protein surface. It also opens up new questions: mechanically, how do surface sites affect allostery? In the future, it will be important to characterize how combinations of mutations can optimize allosteric regulation, and to determine what evolutionary trajectories to high performance allosteric ‘switches’ look like. Introduction In allosteric regulation, protein activity is modulated by an input effector signal spatially removed from the active site. Allostery is a desirable engineering target because it can yield sensitive, reversible, and rapid control of protein activity in response to diverse inputs (Dagliyan et al., 2019; Pincus et al., 2017; Raman et al., 2014). One common approach for achieving allosteric regulation in both engineered and evolved systems is through domain insertion: the transposition, recombination, or otherwise fusion of an ‘input’ domain into an ‘output’ domain of interest (Aroul-Selvam et al., 2004; Dagliyan et al., 2016; Ostermeier and Benkovic, 2000; Nadler et al., 2016). In natural proteins, domain insertions and rearrangements play a key role in generating regulatory diversity, with kinases serving as a prototypical example (Fan et al., 2018; Huse and Kuriyan, 2002; Peisajovich et al., 2010; Shah et al., 2018). In engineered proteins, domain insertions have been used to generate fluorescent metabolite biosensors (Nadler et al., 2016), sugar-regulated TEM-1 β-lactamase variants (Guntas et al., 2005), and a myriad of light-controlled proteins including kinases, ion channels, guanosine triphosphatases, guanine exchange factors, and Cas9 variants (Dagliyan et al., 2016; Wang et al., 2016; Karginov et al., 2011; Toettcher et al., 2013; Shaaya et al., 2020; Coyote-Maestas et al., 2019; Richter et al., 2016). In all cases, domain insertion provides a powerful means to confer new regulation in a modular fashion. However, naively created domain insertion chimeras sometimes exhibit relatively modest allosteric dynamic range, with small observed differences in activity between the constitutive and activated states (Lee et al., 2008). These fusions then require further optimization by either evolution or empirical mutagenesis, but general principles to guide this process are largely absent. Which mutations tune or improve an allosteric system? Because we lack comprehensive studies of allosteric mutational effects in either engineered or natural systems, it remains unclear whether such mutations are common or rare, and what magnitude of allosteric effect we might typically expect for single mutations. Additionally, it is not obvious if such mutations are structurally distributed or localized (for example, to the insertion site). Answers to these questions would inform practical strategies for optimizing engineered systems and provide insight into the evolution of natural multi-domain regulation in proteins. To address these questions, we performed a deep mutational scan of a synthetic allosteric switch: a fusion between the E. coli metabolic enzyme Dihydrofolate Reductase (DHFR) and the blue-light sensing LOV2 domain from A. sativa (Lee et al., 2008; Reynolds et al., 2011). This modestly allosteric chimera shows a 30% increase in DHFR velocity in response to light. Focusing on mutations to the DHFR residues, we found that only a small fraction (4.4%) of the mutations that retained DHFR activity had a statistically significant impact on allostery. Individual mutations exhibited generally modest effect sizes; the most allosteric single mutant characterized (H124Q) yielded a twofold increase in velocity in response to light relative to the starting construct. Structurally, allostery disrupting mutations tended to cluster near the LOV2 insertion site and were modestly enriched at both conserved and co-evolving amino acid positions. In contrast, allostery enhancing mutations were distributed across the protein, and strongly associated with the protein surface. We observed that combining a few of these mutations yielded near-additive enhancements to allosteric dynamic range. Collectively, our data elucidates practical strategies for optimizing engineered systems, and shows that weakly conserved, structurally distributed surface sites can contribute to allosteric tuning. Results Characterization of an unoptimized allosteric fusion of DHFR-LOV2 To begin our study of allostery tuning mutations, we selected a previously characterized synthetic allosteric fusion between DHFR and LOV2 generated in earlier work (Lee et al., 2008; Reynolds et al., 2011). In this fusion, the LOV2 domain of A. sativa is inserted between residues 120 and 121 of the E. coli DHFR βF-βG loop; we refer to this construct as DL121 (Figure 1A,B). The choice of LOV2 insertion site was guided by Statistical Coupling Analysis (SCA), an approach for analyzing coevolution between pairs of amino acids across a homologous protein family (Rivoire et al., 2016; Lockless and Ranganathan, 1999; Halabi et al., 2009). A central finding of SCA is that co-evolving groups of amino acids, termed sectors, often form physically contiguous networks in the tertiary structure that link allosteric sites to active sites (Halabi et al., 2009; Süel et al., 2003; Pincus et al., 2018). To create the DL121 fusion, Lee et al. followed the guiding principle that sector connected surface sites in DHFR might serve as preferred sites (or ‘hot spots’) for the introduction of allosteric regulation (Lee et al., 2008). The resulting DL121 fusion covalently attaches the N- and C-termini of LOV2 into a sector connected surface on DHFR, and displays a twofold increase in DHFR hydride transfer rate (khyd) in response to blue light (Lee et al., 2008). Under steady-state conditions, we measured a 28% increase in the turnover number (kcat) in response to light and a statistically insignificant change in the Michaelis constant (Km) (Figure 1C). Thus, the DL121 fusion is modestly allosteric in vitro. As DHFR has no known natural allosteric regulation, the LOV2 insertion confers a new, evolutionarily unoptimized regulatory input. Figure 1 Download asset Open asset The DL121 DHFR/LOV2 fusion. (A) Composite structures of the individual DHFR and LOV2 domains (PDB ID: 1R × 2 and 2V0U), indicating the LOV2 insertion site between positions 120 and 121 of DHFR (Sawaya and Kraut, 1997; Halavaty and Moffat, 2007). DHFR is in gray cartoon, NADP co-factor in green sticks, and folate substrate in yellow sticks. In LOV2 signaling, blue light triggers the formation of a covalent adduct between a cysteine residue (C450) and a flavin mononucleotide (FMN, yellow sticks) (Salomon et al., 2001; Crosson and Moffat, 2002; Swartz et al., 2002) and associated unfolding of the C-terminal Jα-helix (red cartoon); this order-to-disorder transition is used for regulation in several synthetic and natural systems (Pudasaini et al., 2015; Glantz et al., 2016). (B) DHFR loop conformational changes near the LOV2 insertion site. While the mechanism of DHFR regulation by LOV2 is currently unknown, inspecting the native DHFR structure provides some insight. The substrate-bound Michaelis complex of native DHFR is in the ‘closed’ conformation (gray cartoon), while the product ternary complex is in the ‘occluded’ state (purple cartoon). The βF-βG loop, where LOV2 is inserted, is highlighted in cyan. In native DHFR, hydrogen bonds between this loop (Asp122) and the Met20 loop (Gly15, Glu17) are thought to stabilize the closed conformation (Sawaya and Kraut, 1997; Schnell et al., 2004). Mutations to positions 121 and 122 reduce activity and cause the enzyme to prefer the occluded conformation (Cameron and Benkovic, 1997; Mhashal et al., 2018; Miller and Benkovic, 1998). (C) Steady state Michaelis Menten kinetics for the DL121 fusion under lit (blue) and dark (gray) conditions. The kcat of DHFR increases 28% in response to light; the difference in Km is statistically insignificant (Supplementary file 1a). Error bars represent standard deviation for three replicates. (D) Quantifying the allosteric effect of mutation. Allostery for the DL121 fusion is reported as the ratio between lit and dark velocity. The effect of a mutation on allostery is then computed as the ratio of mutant allostery to wt-DL121 allostery (bottom blue box). But can this relatively small allosteric effect generate measurable physiological differences that could provide the basis for evolutionary selection? DHFR catalyzes the reduction of 7,8-dihydrofolate (DHF) to 5,6,7,8-tetrahydrofolate (THF) using NADPH as a co-factor. THF then serves as a one-carbon donor and acceptor in the synthesis of thymidine, purine nucleotides, serine, glycine, and methionine. Because of these critical metabolic functions, DHFR activity is strongly linked to growth rate, and under appropriate conditions, E. coli growth rate can be used as a proxy for DHFR activity (Reynolds et al., 2011; Thompson et al., 2020). Prior work found that the modest in vitro allosteric effect of DL121 conferred a selectable growth rate advantage in vivo: when an E. coli DHFR deletion strain (ER2566 ΔfolAΔthyA) was complemented with DL121, the resulting strain grew 17% faster in the light than in the dark (Reynolds et al., 2011). Thus, DL121 is a system where: (1) allosteric control is rapidly and reversibly applied, (2) the allosteric effects on activity can be readily quantified both in vitro and in vivo, and (3) there remains potential for large improvements in regulatory dynamic range through mutation. A high-throughput assay to resolve small changes in DHFR catalytic activity Our goal was to measure the effect of every single amino acid mutation in DHFR on the allosteric regulation of DL121. To do this, we aimed to follow a strategy loosely akin to a double mutant cycle (Figure 1D). The starting DL121 construct shows so-called V-type allostery, in which the effector (light) regulates the catalytic turnover number (kcat) (Carlson and Fenton, 2016). Thus, allostery can be quantified as the ratio of kcat between lit and dark states. More generally, allostery might be considered as a ratio of velocities (v = kcat [S]/(Km + [S])) between the lit and dark states, as the allosteric effector could regulate turnover, substrate affinity, or both. In either case, we defined the allosteric effect of mutation as the fold change in allosteric regulation upon mutation (Figure 1D, blue box). We sought to infer this quantity for every mutation in a saturation mutagenesis library of DHFR by using growth rate as a proxy for catalytic activity. As in prior work, we measured the growth rate of many E. coli strains in parallel by using next generation sequencing (NGS) to monitor the frequency of individual DHFR mutants over time in a mixed culture (Figure 2; Reynolds et al., 2011; Thompson et al., 2020). Allele frequencies (fa) at each time point (t) were normalized as follows: fa=ln⁡NaNWTt-lnNaNWTt=0 where Na and NWT are the number of mutant and wildtype (WT) counts at a given time point. By performing a linear fit of the log normalized allele frequencies vs. time we calculated a slope corresponding to relative growth rate: this value is the difference in growth rate for the mutant relative to a reference ('WT') construct. Figure 2 Download asset Open asset A high-throughput, high-resolution assay for DHFR activity. (A) The turbidostat. The instrument has 15 individual growth chambers (vials), positioned on a stir plate inside an incubator. Illumination was provided by blue LEDs in each vial holder. (B) Log-normalized relative allele frequency over time for 11 DHFR point mutations of known catalytic activity and the DL121 fusion. Allele frequency (colored circles) was determined by next-generation sequencing of mixed-population culture samples at each time point. All frequencies were normalized to t = 0 and WT DHFR (no LOV2 insertion). Error bars reflect standard error across four measurements, they are sometimes obscured by the marker. The slope for each line of best fit provides the growth rate of each mutant allele relative to WT DHFR. (C) Relative growth rate vs. log10(velocity) for the 11 DHFR mutants and DL121 as characterized in panel B. Color coding of mutations is matched to panel B. Error bars reflect standard error of the mean over four replicates. The dashed line was fit by linear regression to all mutants in the linear regime (M42F excluded). As individual mutations tend to exhibit modest effects on allosteric regulation, we optimized the linear regime and resolution of the growth rate assay in two ways (Reynolds et al., 2011). First, we grew the E. coli populations in a turbidostat outfitted with blue LEDs to activate LOV2 (Figure 2A). The turbidostat maintains each culture in exponential growth by dynamically sensing optical density and adjusting media dilution rate accordingly Toprak et al., 2013; this ensures near-constant media conditions and eliminates the need for manual serial dilutions. Second, we selected media conditions – M9 minimal media with 0.4% glucose and 1 µg/ml thymidine supplementation – in which growth rate can resolve subtle differences in catalytic activity near the DL121 fusion. We evaluated the resolution of our assay using a ‘standard curve’ of 11 point mutations of known catalytic activity in non-chimeric DHFR (Figure 2B). Under these conditions, we observed a log-linear relationship between relative growth rate and DHFR velocity over nearly four orders of magnitude; this relationship saturates (plateaus) for the most active mutants (WT and M42F, Figure 2C). Importantly, the relative growth rate and velocity of DL121 were near the center of the linear regime of our assay. In using velocity to describe our data, we have incorporated two assumptions: (1) we presume minimal variation in protein abundance between mutants (enzyme concentration is equal to one) and (2) we fix the substrate concentration at 25 µM, which was previously reported as the endogenous concentration for WT E. coli (Kwon et al., 2008). Individual mutations may cause variation in protein abundance, but because allostery concerns a relative change in activity, light-independent differences in abundance can be removed by appropriate normalization (as discussed further below). As previously observed, the exponential divergence of mutants with different growth rates in a population makes it possible to detect even small biochemical effects (Breslow et al., 2008). More specifically, we can discriminate a change of ±0.02 µM−1 s-1 in catalytic power (kcat/Km) under these conditions. This level of precision is on par with – and in some cases better than – literature-reported errors for in vitro steady state kinetics measurements of DHFR (Reynolds et al., 2011; Wagner et al., 1992; et al., we can resolve small catalytic and allosteric effects of mutations on DL121 through this high-throughput assay. mutations are enriched at conserved, positions in DHFR In to the of individual DHFR positions to we a deep mutational library over all DHFR positions in the DL121 fusion (Figure we measured the growth rate effect of each mutation in under both lit and dark conditions using the assay (Figure Figure Figure data In this all growth rates were calculated relative to the DL121 fusion, which activity growth to WT DHFR. Mutations into four in of growth rate deleterious (Figure (Figure or light Figure We were to measure growth rate for of the possible mutations over were at the of the = for one or more to as and an were from the library the of three time for growth rate refer to these as see also Materials and Figure We these rapidly mutants as deleterious to growth rate and DHFR activity. The relative growth rates for the mutations were with a between pairs (Figure Figure with see all Download asset Open asset The effect of DL121 DHFR mutations on growth relative growth rate trajectories for three mutations. (A) DL121 was deleterious in both lit and dark conditions. (B) DL121 was under both lit and dark conditions. (C) DL121 was deleterious in the and near in the light. were by linear the slope of these provides the difference in growth rate relative to the DL121 construct. Relative growth rates were measured in for each mutant under lit (blue) and dark (gray) conditions. (D) of relative growth rates under dark conditions. The for all mutations with measurable growth rate effects is in gray and the for sector mutations is in The relative growth rate of DL121 a mutation that disrupts catalytic activity, is with a dashed The fraction of DL121 mutations with measurable growth rates that can be DHFR and evolutionarily conserved Materials and methods for The fraction is for both the library (gray and the library after mutations with growth rate DL121 The number of mutations is each A the between mutations in the sector a of and the mutations that yield growth rate DL121 of positions enriched for mutations with growth rates as as or than DL121 (red The DHFR is in gray cartoon, the folate substrate in yellow sticks, and the NADP co-factor in green sticks. of the sector blue to positions enriched for disrupting mutations (red as in Figure data 1 Relative growth rates under lit and dark conditions for DL121 point mutations as determined by next-generation 1 is the mutation are relative growth rates in the light is the lit relative growth rate, and is the standard deviation across lit replicates. are relative growth rates in the dark is the dark relative growth rate, and 11 is the standard deviation across dark replicates. Relative growth rate of indicate mutations with counts to fit a growth rate of indicate mutations from the library at t = 0 Download the allosteric effects of mutations, we considered the effects of mutations on growth rate DHFR in a single growth Prior work has found that deleterious mutations are enriched at evolutionarily conserved positions and the protein sector et al., The DHFR sector was defined by analyzing coevolution in a of native DHFR we to examine if sector positions were critical to function in the DL121 fusion. between the DHFR evolutionary and deleterious mutations in DL121 would provide that the of native DHFR in the The of mutations were at modestly deleterious to growth, with a relative growth rate of in the dark and in the light (Figure A cluster of mutations was observed just the LOV2 insertion site at 121 in both conditions, some potential to for the inserted LOV2 (Figure The of effects shows some differences relative to prior studies of natural proteins including native E. coli DHFR et al., 2020; et al., First, the of effects for mutations in natural proteins is often a of mutational et al., et al., of native DHFR – under conditions to resolve mutational effects near WT – many mutations et al., 2020). are two for the relative of and mutations in the First, the DL121 fusion is because the unoptimized LOV2 insertion an to DHFR the conditions of our assay and from prior work et al., and were selected to resolve mutational effects near mutations with (or activity are in the regime of our assay. To the – and or – mutations, we an empirical growth rate of to the lit and dark growth This to the growth rate for DL121 is an active site mutation that strongly the activity of WT DHFR (Figure The DL121 mutant in the conditions of our assay and is in the of thymidine supplementation (Figure We found that mutations with growth rates at or this the were enriched in both the sector Figure file and at evolutionarily conserved positions Figure file When to the WT DHFR positions enriched for deleterious mutations the active site and co-factor (Figure structurally with the sector (Figure and a number of positions known to play a critical role in WT DHFR and et al., et al., These data are with the that sector positions to play a key role in DHFR catalytic activity in the DL121 fusion. the that DHFR variants are both and associated with the growth rate measurements (Figure we removed the of rate and mutations prior to the of allostery. The retained 1548 mutations – of the growth assay data – between the DL121 and evolutionarily conserved positions (Figure These a and of the data for mutational effects on DL121 allosteric Allostery tuning mutations are To the allosteric effect of we considered the measurements of lit and dark relative growth rate for each mutant (Figure the log-linear relationship between growth rate and DHFR velocity (Figure growth rates of Thus, we the allosteric effect of mutation by the difference in the relative growth rates between lit and dark In the is relative growth rate is measured in our and to growth indicate allostery enhancing mutations and indicate allostery disrupting mutations and the 1548 mutations the allosteric effect is distributed with a mean near = Figure To the of allosteric we computed a for each mutation by under the that the lit and dark measurements have equal These were to a of determined by of Figure and 2011). Under these only mutations of all viable mutations enhanced allostery while disrupted allostery. We not a between the magnitude of growth rate effect and the allosteric effect mutations a range of growth rates and exhibited modest effects on light regulation (Figure Figure with see all Download asset Open asset The effect of DL121 DHFR mutations on

Network theory methods and molecular dynamics (MD) simulations are accepted tools to study allosteric regulation. Indeed, dynamic networks built upon correlation analysis of MD trajectories provide detailed information about communication paths between distant sites. In this context, we aimed to understand whether the efficiency of intramolecular communication could be used to predict the allosteric potential of a given site. To this end, we performed MD simulations and network theory analyses in cathepsin K (catK), whose allosteric sites are well defined. To obtain a quantitative measure of the efficiency of communication, we designed a new protocol that enables the comparison between properties related to ensembles of communication paths obtained from different sites. Further, we applied our strategy to evaluate the allosteric potential of different catK cavities not yet considered for drug design. Our predictions of the allosteric potential based on intramolecular communication correlate well with previous catK experimental and theoretical data. We also discuss the possibility of applying our approach to other proteins from the same family.

Chapter 5 - Homology modeling: Developing 3D structures of target proteins missing in databases

Protein adsorption at oil-water interfaces interfacial structure and function of proteins in emulsions

2 - The Physiological Structure and Function of Proteins

Phosphorylation-Induced Mechanical Regulation of Intrinsically Disordered Neurofilament Proteins

This article has no abstract.

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Protein is one of the giant biomolecules that act as the main component of the organism. Protein is formed from building blocks namely amino acids. Hierarchically, the structure of protein is divided into four levels: primary, secondary, tertiary, and quaternary structure. Protein secondary structure is formed by amino acid sequences that would form three-dimensional structures and have information about the tertiary structure and function of proteins. This study used 277,389 protein residue data from enzyme categories. Position-specific scoring matrix (PSSM) profile and physicochemical are used for features. This study developed support vector machine models to predict the protein secondary structure by recognizing patterns of amino acid sequences. The Q3 results showed that the best scores obtained are 93.16% from the dataset that has 260 features with the radial kernel. Combining PSSM and physicochemical feature additions can be used for prediction.

Louis Sullivan (1856 - 1924) revolutionized architecture by designing the first skyscraper and he became famous by proclaiming that “form follows function”. When x-ray crystallographers visualized the structures of proteins for the first time, the structural biology field embraced the view that “function follows form” as the 3D-architecture of proteins could unveil various aspects of their function. Despite the original “1 gene - 1 protein structure - 1 function” relationship, nowadays a far more complicated picture emerges where the flexibility and dynamics of a protein can play a central role in a multitude of functions. The ultimate form(s) that a protein adopt when interacting with (a) partner molecule(s) are the most biologically relevant and in this context Sullivan’s quote is still appropriate: the conformation that the protein adopts follows from the function of that protein. Despite the fact that many well-characterized proteins have a well-folded structure, there is a growing interest in the conformational flexibility within proteins. This flexibility is also a balanced phenomenon: excess of flexibility can be detrimental for protein behaviour, as well as the lack thereof. Notwithstanding its importance, studying intrinsically disordered protein regions or conformational rearrangements can be a very challenging. Therefore, flexibility can be perceived as a friend or a foe, depending on the context. This e-book showcases the impact of the study of protein flexibility on the structural biology field and presents protein flexibility in the context of disease as well as its benign aspects. As detailed knowledge of the structural aspects of polypeptides remains essential to comprehend protein function, one of the future challenges for structural biology also lies with large macromolecular protein complexes. Also there the dynamics and flexibility are essential for proper functioning and molecular movement, which is an important aspect of living matter. This challenge stimulated the development of advanced techniques to study protein flexibility and the use of those techniques to address fundamental biological and biomedical problems. Those innovations should help us to unravel the intimate link between protein function and flexibility and explore new horizons.

Lipoxygenases, important enzymes in inflammation, can regulate their substrate specificity by allosteric interactions with their own hydroperoxide products. In this work, addition of both 13-(S)-hydroxy-(9Z,11E)-octadecadienoic acid [13-(S)-HODE] and 13-(S)-hydroperoxy-(6Z,9Z,11E)-octadecatrienoic acid to human epithelial 15-lipoxygenase-2 (15-LOX-2) increases the kcat/KM substrate specificity ratio of arachidonic acid (AA) and γ-linolenic acid (GLA) by 4-fold. 13-(S)-HODE achieves this change by activating kcat/KM(AA) but inhibiting kcat/KM(GLA), which indicates that the allosteric structural changes at the active site discriminate between the length and unsaturation differences of AA and GLA to achieve opposite kinetic effects. The substrate specificity ratio is further increased, 11-fold in total, with an increase in pH, suggesting mechanistic differences between the pH and allosteric effects. Interestingly, the loss of the PLAT domain affects substrate specificity but does not eliminate the allosteric properties of 15-LOX-2, indicating that the allosteric site is located in the catalytic domain. However, the removal of the PLAT domain does change the magnitude of the allosteric effect. These data suggest that the PLAT domain moderates the communication pathway between the allosteric and catalytic sites, thus affecting substrate specificity. These results are discussed in the context of protein dimerization and other structural changes.

Intermediate structure of normal human haemoglobin: Methaemoglobin in the deoxy quaternary conformation

Background and Objectives: Changes in the secondary and tertiary structures of protein molecules during thermal denaturation lead to changes in their vibrational spectra. Vibrations corresponding to elements of the primary and secondary structure of the protein are manifested in the fingerprint range, while vibrational modes of large fragments corresponding to higher levels of the protein structural hierarchy can be observed in the low-frequency (40–500 cm−1) spectral range. The purpose of this work is to reveal changes in the vibrational spectra of proteins resulting from denaturation that can be used to characterize changes in the protein structure. Materials and Methods: Two proteins (collagen and chymotrypsin) having different secondary and tertiary structures are studied using Raman spectroscopy. The experimental data are analyzed using bandpass filtering of the Fourier transforms of the spectral curves. Results: Vibrational spectra of two proteins (collagen and chymotrypsin) having different secondary and tertiary structures, as well as the spectra of thermally denatured samples of these proteins have been measured in the fingerprint and low-frequency ranges. Several low-frequency spectral features that can be used to characterize structural changes of protein molecules have been considered. A few bands may correspond to vibrations of tertiary structure elements (both in the low-frequency range and in the “fingerprint” range). Conclusion: A comparison of the vibrational spectra of native and denatured (superhelical) collagen, as well as native and denatured (globular) chymotrypsin shows that changes in the amide I and amide III bands sensitive to the secondary structure are supplemented with spectral changes in bands that are not assigned to elements of the secondary structure and can be related to changes at higher levels of structural hierarchy. Comparison of the low-frequency vibrational spectra indicates a lower sensitivity of presumably tertiary structure of the globular protein to denaturation.

Databases are the computerized platform where information is stored and can be retrieved easily by public users. Biological databases are the repositories of biological data. These biological data libraries contain facts and figures related to various disciplines of research including genomics, proteomics, microarray technology, metabolomics and phylogenetics. By using biological databases, a broad collection of essential biological information can be exploited ranging from function, structure and localization of gene, clinical consequences of mutation to similarity index among biological sequences and structures. Nowadays, different kinds of biological databases are available on the web. The present write up focuses on biological databases and bioinformatics tools for protein structure analysis. This review also aims to elaborate the searching schemes, available in different structural databases. The wide variety of different levels and types of information content related to 3D protein structures are available on web-based databases. Regarding the biological functions and 3D structures of various proteins, these databases provide a huge range of useful links, schematic diagrams as well as strategies for detailed analysis of proteins and other macromolecules structures. 3D structural illustration of proteins stored in structural databases is determined and visualized by X-ray crystallography, electron microscopy and NMR spectroscopy. On regular basis, a large number of protein structures are submitted by structural biologists, updated and curated by subject experts. Most familiar biological databases that store 3D protein and other macromolecules structures include, PDB, 3D Genomics, CATH, & SCOP. These databases contain valuable information of overall protein structures, domains and motif structures, protein-protein complex systems and complex of protein with other biomolecules.

Series Preface. Preface. Rigidity Theory. Generic and Abstract Rigidity B. Servatius, H. Servatius. Rigidity of Molecular Structures: Generic and Geometric Analysis W. Whiteley. Tensegrity Structures: Why Are They Stable? R. Connelly. The Role of Tensegrity in Distance Geometry T.F. Havel. Applications to Networks. Comparison of Connectivity and Rigidity Percolation C.F. Moukarzel, P.M. Duxbury. Rigidity Percolation on Trees P.L. Leath, C. Zeng. Rigidity as an Emergent Property of Random Networks: A Statistical Mechanical View P.M. Goldbart. Granular Matter Instability: A Structural Rigidity Point of View C.F. Moukarzel. Rigidity and Memory in a Simple Glass P. Chandra, L.B. Ioffe. Constraint Theory, Stiffness Percolation and the Rigidity Transition in Network Glasses J.C. Phillips. Topologically Disordered Networks of Rigid Polytopes: Applications to Noncrystalline Solids and Constrained Viscous Sintering P.K. Gupta. Rigidity Constraints in Amorphization of Singly- and Multiply-Polytopic Structures L.W. Hobbs, et al. Floppy Modes in Crystalline and Amorphous Silicates M.T. Dove, et al. Generic Rigidity of Networks Glasses M.F. Thorpe, et al. Rigidity Transition in Chalcogenide Glasses P. Boolchand, et al. Rigidity, Fragility, Bond Models and the `Energy Landscape' for Covalent Glassformers C.A. Angell. Entropic Rigidity B. Joos, et al. Applications to Proteins. Molecular Dynamics and Normal Mode Analysis of Biomolecular Rigidity D.A. Case. Efficient Stochastic Global Optimization for Protein Structure Prediction Y. Zhou, R. Abagyan. Flexible and Rigid Regions in Proteins D.J. Jacobs, et al. Flexibly Screening for Molecules Interacting with Proteins V. Schnecke, L.A. Kuhn. Studying Macromolecular Motions in a Database Framework: From Structure to Sequence M. Gerstein, et al. List of Participants. Index.