Interior and surface of monomeric proteins

Structural Descriptors for Subunit Interface Regions in Homodimers: Effect of Lipid Membrane and Secondary Structure Type

A total of 1311 homodimers were collected and analyzed in three different categories to highlight the impact of lipid environment and secondary structure type: 422 cytoplasmic α-helix, 411 cytoplasmic β-strand, and 478 membrane complexes. Structural features of the interface connecting two monomers were investigated and compared to those of the non-interface surface. Every residue on the surface of each monomer was explored based on four attributes: solvent-accessible surface area (SASA), protrusion index (Cx), surface planarity, and surface roughness. SASA and Cx distribution profiles clearly distinguished the interface from the surface in all categories, where the rim of the interface displayed higher SASA and Cx values than the rest of the surface. Surface residues in membrane complexes protruded less than cytoplasmic ones due to the hydrophobic environment, and consequently, the difference between surface and interface residues became less noticeable in that category. Cytoplasmic β-strand complexes displayed markedly lower SASA at the interface core than at the surface. The major distinction between the surface and interface was achieved through surface roughness, which displayed significantly higher values for the interface than the surface, especially in cytoplasmic complexes. Clearly, a surface which is relatively rugged favors the association of two monomers through multiple van der Waals interactions and hydrogen-bond formations. Another structural descriptor with strong distinguishing ability was surface planarity, which was higher at the interface than at the non-interface surface. Surface flatness would eventually facilitate the interconnectedness of an interface with a network of residue pairs bridging two complementary surfaces. Analysis of contact pairs revealed that hydrophobic pairs have the highest frequency of occurrence in the lipid environment of membrane complexes. However, despite the scarcity of polar residues at the interface, the likelihood of observing a contact between polar residues was markedly higher than that of hydrophobic ones.

Antioxidant Effect of Naringin Demonstrated Through a Bayes' Theorem Driven Multidisciplinary Approach Reveals its Prophylactic Potential as a Dietary Supplement for Ischemic Stroke

Naringin (NAR), a flavanone glycoside, occurs widely in citrus fruits, vegetables, and alcoholic beverages. Despite evidence of the neuroprotective effects of NAR on animal models of ischemic stroke, brain cell-type-specific data about the antioxidant efficacy of NAR and possible protein targets of such beneficial effects are limited. Here, we demonstrate the brain cell type-specific prophylactic role of NAR, an FDA-listed food additive, in an in vitro oxygen-glucose deprivation (OGD) model of cerebral ischemia using MTT and DCFDA assays. Using Bayes' theorem-based predictive model, we first ranked the top-10 protein targets (ALDH2, ACAT1, CTSB, FASN, LDHA, PTGS1, CTSD, LGALS1, TARDBP, and CDK1) from a curated list of 289 NAR-interacting proteins in neurons that might be mediating its antioxidant effect in the OGD model. When preincubated with NAR for 2 days, N2a and CTX-TNA2 cells could withstand up to 8 h of OGD without a noticeable decrease in cell viability. This cerebroprotective effect is partly mediated by reducing intracellular ROS production in the above two brain cell types. The antioxidant effect of NAR was comparable with the equimolar (50 µM) concentration of clinically used ROS-scavenger and neuroprotective edaravone. Molecular docking of NAR with the top-10 protein targets from Bayes' analysis showed the lowest binding energy for CDK1 (- 8.8 kcal/M). Molecular dynamics simulation analysis showed that NAR acts by inhibiting CDK1 by stably occupying its ATP-binding cavity. Considering diet has been listed as a risk factor for stroke, NAR may be explored as a component of functional food for stroke or related neurological disorders.

VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance

Various computational methods have been developed to predict the pathogenicity of missense variants, which is crucial for diagnosing rare diseases. Recently, we introduced VarMeter, a diagnostic tool for predicting variant pathogenicity based on normalized solvent-accessible surface area (nSASA) and mutation energy calculated from AlphaFold 3D models, and validated it on arylsulfatase L. To evaluate the broader applicability of VarMeter and enhance its predictive accuracy, here we analyzed 296 pathogenic and 240 benign variants extracted from the ClinVar database. By comparing structural features including nSASA, mutation energy, and predicted local distance difference test (pLDDT) score, we identified distinct characteristics between pathogenic and benign variants. These features were used to develop VarMeter2, which classifies variants based on Mahalanobis distance. VarMeter2 achieved a prediction accuracy of 82 % for the ClinVar dataset, a marked improvement over the original VarMeter (74 %), and 84 % for published missense variants of N-sulphoglucosamine sulphohydrolase (SGSH), an enzyme associated with Sanfillippo syndrome A. Application of VarMeter 2 to SGSH variants in our clinical database identified a novel SGSH variant, Q365P, as pathogenic. The recombinant Q365P protein lacked enzymatic activity as compared with wild-type SGSH. Furthermore, it was largely retained in the endoplasmic reticulum and failed to reach the Golgi, probably due to misfolding. Protein stability assays confirmed reduced stability of the variant, further explaining its loss of function. Consistently, the patient homozygous for this variant was diagnosed with Sanfilippo syndrome A. These results underscore the predictive power and versatility of VarMeter2 in assessing the pathogenicity of missense variants.

AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations

Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences and relatively limited number of structures. Leveraging the highly accurate protein structures predicted by AlphaFold2 (AF2), we introduce AFFIPred, an ensemble machine learning classifier that combines sequence and AF2-based structural characteristics to predict missense variant pathogenicity. Based on the assessments on unseen datasets, AFFIPred reached a comparable level of performance with the state-of-the-art predictors such as AlphaMissense. We also showed that the recruitment of AF2 structures that are full-length and represent the unbound states ensures more precise SASA calculations compared to the recruitment of experimental structures. In line with the completeness of the AF2 structures, their use provide a more comprehensive view of the structural characteristics of the missense variation datasets by capturing all variants. AFFIPred maintains high-level accuracy without the limitations of PDB-based classifiers. AFFIPred has predicted over 210 million variations of the human proteome, which are accessible at https://affipred.timucinlab.com/.

Cellular location shapes quaternary structure of enzymes

The main forces driving protein complex evolution are currently not well understood, especially in homomers, where quaternary structure might frequently evolve neutrally. Here we examine the factors determining oligomerisation by analysing the evolution of enzymes in circumstances where homomers rarely evolve. We show that 1) In extracellular environments, most enzymes with known structure are monomers, while in the cytoplasm homomers, indicating that the evolution of oligomers is cellular environment dependent; 2) The evolution of quaternary structure within protein orthogroups is more consistent with the predictions of constructive neutral evolution than an adaptive process: quaternary structure is gained easier than it is lost, and most extracellular monomers evolved from proteins that were monomers also in their ancestral state, without the loss of interfaces. Our results indicate that oligomerisation is context-dependent, and even when adaptive, in many cases it is probably not driven by the intrinsic properties of enzymes, like their biochemical function, but rather the properties of the environment where the enzyme is active. These factors might be macromolecular crowding and excluded volume effects facilitating the evolution of interfaces, and the maintenance of cellular homeostasis through shaping cytoplasm fluidity, protein degradation, or diffusion rates.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited. Thus, we conduct a family wide analysis of the B1 metallo-β-lactamases, combining experiments (deep mutational scanning, DMS) on two distant homologs (NDM-1 and VIM-2) and computational analyses (in silico DMS based on Direct Coupling Analysis, DCA) of 100 homologs. The methods jointly reveal and quantify prevalent epistasis, as ~1/3rd of equivalent mutations are epistatic in DMS. From DCA, half of the positions have a >6.5 fold difference in effective number of tolerated mutations across the entire family. Notably, both methods locate residues with the strongest epistasis in regions of intermediate residue burial, suggesting a balance of residue packing and mutational freedom in forming epistatic networks. We identify entrenched WT residues between NDM-1 and VIM-2 in DMS, which display statistically distinct behaviors in DCA from non-entrenched residues. Entrenched residues are not easily compensated by changes in single nearby interactions, reinforcing existing findings where a complex epistatic network compounds smaller effects from many interacting residues.

Correlation of Solvent Interaction Analysis Signatures with Thermodynamic Properties and In Silico Calculations of the Structural Effects of Point Mutations in Two Proteins

The partition behavior of single and double-point mutants of bacteriophage T4 lysozyme (T4 lysozyme) and staphylococcal nuclease A was examined in different aqueous two-phase systems (ATPSs) and studied by Solvent Interaction Analysis (SIA). Additionally, the solvent accessible surface area (SASA) of modeled mutants of both proteins was calculated. The in silico calculations and the in vitro analyses of the staphylococcal nuclease and T4 lysozyme mutants correlate, indicating that the partition analysis in ATPSs provides a valid descriptor (SIA signature) covering various protein features, such as structure, structural dynamics, and conformational stability.

On the Stabilizing Effect of Aspartate and Glutamate and Its Counteraction by Common Denaturants

By performing differential scanning calorimetry(DSC) measurements on RNase A, we studied the stabilization provided by the addition of potassium aspartate(KAsp) or potassium glutamate (KGlu) and found that it leads to a significant increase in the denaturation temperature of the protein. The stabilization proves to be mainly entropic in origin. A counteraction of the stabilization provided by KAsp or KGlu is obtained by adding common denaturants such as urea, guanidinium chloride, or guanidinium thiocyanate. A rationalization of the experimental data is devised on the basis of a theoretical approach developed by one of the authors. The main contribution to the conformational stability of globular proteins comes from the gain in translational entropy of water and co-solute ions and/or molecules for the decrease in solvent-excluded volume associated with polypeptide folding (i.e., there is a large decrease in solvent-accessible surface area). The magnitude of this entropic contribution increases with the number density and volume packing density of the solution. The two destabilizing contributions come from the conformational entropy of the chain, which should not depend significantly on the presence of co-solutes, and from the direct energetic interactions between co-solutes and the protein surface in both the native and denatured states. It is the magnitude of the latter that discriminates between stabilizing and destabilizing agents.

Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases

Many dominant genetic disorders result from protein-altering mutations, acting primarily through dominant-negative (DN), gain-of-function (GOF), and loss-of-function (LOF) mechanisms. Deciphering the mechanisms by which dominant diseases exert their effects is often experimentally challenging and resource intensive, but is essential for developing appropriate therapeutic approaches. Diseases that arise via a LOF mechanism are more amenable to be treated by conventional gene therapy, whereas DN and GOF mechanisms may require gene editing or targeting by small molecules. Moreover, pathogenic missense mutations that act via DN and GOF mechanisms are more difficult to identify than those that act via LOF using nearly all currently available variant effect predictors. Here, we introduce a tripartite statistical model made up of support vector machine binary classifiers trained to predict whether human protein coding genes are likely to be associated with DN, GOF, or LOF molecular disease mechanisms. We test the utility of the predictions by examining biologically and clinically meaningful properties known to be associated with the mechanisms. Our results strongly support that the models are able to generalise on unseen data and offer insight into the functional attributes of proteins associated with different mechanisms. We hope that our predictions will serve as a springboard for researchers studying novel variants and those of uncertain clinical significance, guiding variant interpretation strategies and experimental characterisation. Predictions for the human UniProt reference proteome are available at https://osf.io/z4dcp/.

Transglutaminase-mediated proximity labeling of a specific Lys residue in a native IgG antibody

The fusion protein of an engineered zymogen of microbial transglutaminase (EzMTG) with a protein G variant, EzMTG-pG, enabled the proximity-based, tag-free labeling of Lys65 in the heavy chain of a native IgG antibody (trastuzumab) with a Gln-donor peptidyl substrate functionalized with a fluorescent molecule.

Effects of stand-alone polar residue on membrane protein stability and structure

Helical membrane proteins generally have a hydrophobic nature, with apolar side chains comprising the majority of the transmembrane (TM) helices. However, whenever polar side chains are present in the TM domain, they often exert a crucial role in structural interactions with other polar residues, such as TM helix associations and oligomerization. Moreover, polar residues in the TM region also often participate in protein functions, such as the Schiff base bonding between Lys residues and retinal in rhodopsin-like membrane proteins. Although many studies have focused on these functional polar residues, our understanding of stand-alone polar residues that are energetically unfavored in TM helixes is limited. Here, we adopted bacteriorhodopsin (bR) as a model system and systematically mutated 17 of its apolar Leu or Phe residues to polar Asn. Stability measurements of the resulting mutants revealed that all of these polar substitutions reduced bR stability to various extents, and the extent of destabilization of each mutant bR is also correlated to different structural factors, such as the relative accessible surface area and membrane depth of the mutation site. Structural analyses of these Asn residues revealed that they form sidechain-to-backbone hydrogen bonds that alleviate the unfavorable energetics in hydrophobic and apolar surroundings. Our results indicate that membrane proteins are able to accommodate certain stand-alone polar residues in the TM region without disrupting overall structures.

Computational identification of antibody-binding epitopes from mimotope datasets

Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods.

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability

Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.

Predicting the pathogenicity of missense variants based on protein instability to support diagnosis of patients with novel variants of ARSL

Rare diseases are estimated to affect 3.5%-5.9% of the population worldwide and are difficult to diagnose. Genome analysis is useful for diagnosis. However, since some variants, especially missense variants, are also difficult to interpret, tools to accurately predict the effect of missense variants are very important and needed. Here we developed a method, "VarMeter", to predict whether a missense variant is damaging based on Gibbs free energy and solvent-accessible surface area calculated from the AlphaFold 3D protein model. We applied this method to the whole-exome sequencing data of 900 individuals with rare or undiagnosed disease in our in-house database, and identified four who were hemizygous for missense variants of arylsulfatase L (ARSL; known as the genetic cause of chondrodysplasia punctata 1, CPDX1). Two individuals had a novel Ser89 to Asn (Ser89Asn) or Arg469 to Trp (Arg469Trp) substitution, respectively predicted as "damaging" or "benign"; the other two had an Arg111 to His (Arg111His) or Gly117 to Arg (Gly117Arg) substitution, respectively predicted as "damaging" or "possibly damaging" and previously reported in patients showing clinical manifestations of CDPX1. Expression and analysis of the missense variant proteins showed that the predicted pathogenic variants (Ser89Asn, Arg111His, and Gly117Arg) had complete loss of sulfatase activity and reduced protease resistance due to destabilization of protein structure, while the predicted benign variant (Arg469Trp) had activity and protease resistance comparable to those of wild-type ARSL. The individual with the novel pathogenic Ser89Asn variant exhibited characteristics of CDPX1, while the individual with the benign Arg469Trp variant exhibited no such characteristics. These findings demonstrate that VarMeter may be used to predict the deleteriousness of variants found in genome sequencing data and thereby support disease diagnosis.

HCV E1 influences the fitness landscape of E2 and may enhance escape from E2-specific antibodies

The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a non-covalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remain largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modeling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson's and Alzheimer's. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases.

Human γS-Crystallin Mutation F10_Y11delinsLN in the First Greek Key Pair Destabilizes and Impairs Tight Packing Causing Cortical Lamellar Cataract

Aromatic residues forming tyrosine corners within Greek key motifs are critical for the folding, stability, and order of βγ-crystallins and thus lens transparency. To delineate how a double amino acid substitution in an N-terminal-domain tyrosine corner of the CRYGS mutant p.F10_Y11delinsLN causes juvenile autosomal dominant cortical lamellar cataracts, human γS-crystallin c-DNA was cloned into pET-20b (+) and a p.F10_Y11delinsLN mutant was generated via site-directed mutagenesis, overexpressed, and purified using ion-exchange and size-exclusion chromatography. Structure, stability, and aggregation properties in solution under thermal and chemical stress were determined using spectrofluorimetry and circular dichroism. In benign conditions, the p.F10_Y11delinsLN mutation does not affect the protein backbone but alters its tryptophan microenvironment slightly. The mutant is less stable to thermal and GuHCl-induced stress, undergoing a two-state transition with a midpoint of 60.4 °C (wild type 73.1 °C) under thermal stress and exhibiting a three-state transition with midpoints of 1.25 and 2.59 M GuHCl (wild type: two-state transition with Cm = 2.72 M GuHCl). The mutant self-aggregates upon heating at 60 °C, which is inhibited by α-crystallin and reducing agents. Thus, the F10_Y11delinsLN mutation in human γS-crystallin impairs the protein's tryptophan microenvironment, weakening its stability under thermal and chemical stress, resulting in self-aggregation, lens opacification, and cataract.

A small non-interface surface epitope in human IL18 mediates the dynamics and self-assembly of IL18-IL18BP heterodimers

Interleukin 18 (IL18) is a pro-inflammatory cytokine that modulates innate and adaptive immune responses. IL18 activity is tightly controlled by the constitutively secreted IL18 binding protein (IL18BP). PDB structures of human IL18 showed that a short stretch of amino acids between 68 and 81 adopted a disordered conformation in all IL18-IL18BP complexes while adopting a 3₁₀ helical structure in other IL18 structures, including the receptor complexes. The C74 of human IL18, which was reported to form a novel intermolecular disulfide bond in the human tetrameric assembly, is also located in this short epitope. These observations reflected the importance of this short surface epitope for the structure and dynamics of the IL18-IL18BP heterodimers. We have analyzed all known IL18-IL18BP complexes in the PDB by all-atom MD simulations. The analysis also included two computed complex models adopting a helical structure for the surface epitope. Heterodimer simulations showed a stabilizing impact of the small surface region at the helical form by reducing flexibility of the complex backbone. Analysis of the symmetry-related human IL18-IL18BP tetramer showed that the unfolding of this small surface region also contributed to the IL18-IL18BP stability through a completely exposed C74 sidechain to form an intermolecular disulfide bond in the self-assembled human IL18-IL18BP dimer. Our findings showed how the conformation of the short IL18 epitope between amino acids 68 and 81 would affect IL18 activity by mediating the intermolecular interactions of IL18.

The Y46D Mutation Destabilizes Dense Packing of the Second Greek Key Pair of Human γC-Crystallin Causing Congenital Nuclear Cataracts

The γ-crystallins are highly expressed structural lens proteins comprising four Greek key motifs arranged in two domains. Their globular structure and short-range spatial ordering are essential for lens transparency. Aromatic residues play a vital role in stabilizing Greek key folds by forming Greek key or non-Greek key pairs or tyrosine corners. We investigated the effects of the cataractogenic Y46D mutation in the second Greek key pair (Y46-Y51) of human γC-crystallin on its stability and aggregation. Wild-type and Y46D mutant human γC-crystallin were overexpressed in E. coli BL-21(DE3) PLysS cells, purified using ion-exchange and size-exclusion chromatography, and analyzed by fluorescence spectroscopy and circular dichroism spectroscopy. The Y46D mutation does not affect the γC-crystallin backbone conformation under benign conditions but alters the tryptophan microenvironment, exposing hydrophobic residues to the surface. The Y46D mutant undergoes a three-state transition under thermal stress with midpoints of 54.6 and 67.7 °C while the wild type shows a two-state transition with a midpoint of 77.6 °C. The Y46D mutant also shows a three-state transition under GuHCl stress with Cm values of 0.9 and 2.1 M while the wild type shows a two-state transition with a Cm of 2.4 M GuHCl. Mutant but not wild-type γC-crystallin forms light scattering particles upon heating at 65 °C. Overall, the Y46D CRYGS mutation leaves the protein fold intact under benign conditions but destabilizes the molecule by altering the tryptophan microenvironment and exposing hydrophobic residues to its surface, thus increasing its susceptibility to thermal and chemical stress with resultant self-aggregation, light scattering, and cataract.

Globular Proteins and Where to Find Them within a Polymer Brush-A Case Study

Protein adsorption by polymerized surfaces is an interdisciplinary topic that has been approached in many ways, leading to a plethora of theoretical, numerical and experimental insight. There is a wide variety of models trying to accurately capture the essence of adsorption and its effect on the conformations of proteins and polymers. However, atomistic simulations are case-specific and computationally demanding. Here, we explore universal aspects of the dynamics of protein adsorption through a coarse-grained (CG) model, that allows us to explore the effects of various design parameters. To this end, we adopt the hydrophobic-polar (HP) model for proteins, place them uniformly at the upper bound of a CG polymer brush whose multibead-spring chains are tethered to a solid implicit wall. We find that the most crucial factor affecting the adsorption efficiency appears to be the polymer grafting density, while the size of the protein and its hydrophobicity ratio come also into play. We discuss the roles of ligands and attractive tethering surfaces to the primary adsorption as well as secondary and ternary adsorption in the presence of attractive (towards the hydrophilic part of the protein) beads along varying spots of the backbone of the polymer chains. The percentage and rate of adsorption, density profiles and the shapes of the proteins, alongside with the respective potential of mean force are recorded to compare the various scenarios during protein adsorption.

Developing Enzyme Immobilization with Fibrous Membranes: Longevity and Characterization Considerations

Fibrous membranes offer broad opportunities to deploy immobilized enzymes in new reactor and application designs, including multiphase continuous flow-through reactions. Enzyme immobilization is a technology strategy that simplifies the separation of otherwise soluble catalytic proteins from liquid reaction media and imparts stabilization and performance enhancement. Flexible immobilization matrices made from fibers have versatile physical attributes, such as high surface area, light weight, and controllable porosity, which give them membrane-like characteristics, while simultaneously providing good mechanical properties for creating functional filters, sensors, scaffolds, and other interface-active biocatalytic materials. This review examines immobilization strategies for enzymes on fibrous membrane-like polymeric supports involving all three fundamental mechanisms of post-immobilization, incorporation, and coating. Post-immobilization offers an infinite selection of matrix materials, but may encounter loading and durability issues, while incorporation offers longevity but has more limited material options and may present mass transfer obstacles. Coating techniques on fibrous materials at different geometric scales are a growing trend in making membranes that integrate biocatalytic functionality with versatile physical supports. Biocatalytic performance parameters and characterization techniques for immobilized enzymes are described, including several emerging techniques of special relevance for fibrous immobilized enzymes. Diverse application examples from the literature, focusing on fibrous matrices, are summarized, and biocatalyst longevity is emphasized as a critical performance parameter that needs increased attention to advance concepts from lab scale to broader utilization. This consolidation of fabrication, performance measurement, and characterization techniques, with guiding examples highlighted, is intended to inspire future innovations in enzyme immobilization with fibrous membranes and expand their uses in novel reactors and processes.

Molecular Dynamics Simulation of Antimicrobial Peptide CM15 in Staphylococcus Aureus and Escherichia coli Model Bilayer Lipid

Background

In animals and plants, antimicrobial peptides (AMPs) are crucial components of defense mechanisms, as they play a crucial role in innate immunity, which protects hosts from pathogenic bacteria. The CM15 has attracted considerable interest as a novel antibiotic against gram-negative and positive pathogens.

Objective

The aim of this study was to investigate the permeation potential of the CM15 with membrane bilayers of Staphylococcus aureus and Escherichia coli.

Material and Methods

The bilayer membranes of Escherichia coli and Staphylococcus aureus were modelled with the resemblance in lipid composition to its biological sample. This study followed Protein-Membrane Interaction (PMI) through successive applications of molecular dynamics simulation by GROMACS and CHARMM36 force field for two sets of 120-ns simulations.

Results

Significant results were obtained from analyzing the trajectory of the unsuccessful insertion of CM15 during simulation. Our data suggested that Lysine residues in CM15 and Cardiolipins in membrane leaflets play a crucial role in stability and interaction terms.

Conclusion

The obtained results strengthen the insertion possibility through the toroidal model, which should consider for further studies on AMPs interaction.

Charge reversal mutations in mesophilic-thermophilic orthologous protein pairs and their role in enhancing coulombic interaction energy

Proteins from thermophilic organisms are a matter of immense interest for decades because of its application in fields like de-novo protein design, thermostable variants of biocatalysts etc. Previous studies have found several sequence and structural adaptations related to thermal stability, while charge reversal study remains ignored. Here we address whether charge reversal mutations naturally occur in mesophilic-thermophilic/hyperthermophilic orthologous proteins. Do they contribute to thermal stability? Our systematic study on 1550 mesophilic-thermophilic/hyperthermophilic orthologous protein pairs with remarkable structural and topological similarity, shows gain in coulombic interaction energy in thermophilic/hyperthermophilic proteins at short range associated with partially exposed and buried charge reversal mutations, which may enhance thermostability. Our findings call forth its application in future protein engineering studies. Communicated by Ramaswamy H. Sarma.

Epistasis between promoter activity and coding mutations shapes gene evolvability

The evolution of protein-coding genes proceeds as mutations act on two main dimensions: regulation of transcription level and the coding sequence. The extent and impact of the connection between these two dimensions are largely unknown because they have generally been studied independently. By measuring the fitness effects of all possible mutations on a protein complex at various levels of promoter activity, we show that promoter activity at the optimal level for the wild-type protein masks the effects of both deleterious and beneficial coding mutations. Mutations that are deleterious at low activity but masked at optimal activity are slightly destabilizing for individual subunits and binding interfaces. Coding mutations that increase protein abundance are beneficial at low expression but could potentially incur a cost at high promoter activity. We thereby demonstrate that promoter activity in interaction with protein properties can dictate which coding mutations are beneficial, neutral, or deleterious.

Using AlphaFold to predict the impact of single mutations on protein stability and function

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding.

Modelling and Simulations of Extracellular Glycoproteins

While the knowledge of protein structure and function has seen vast advances in previous decades, the understanding of how their posttranslational modifications, such as glycosylations, influence their structure and function remains poor. However, advances in in silico methodologies to study glycosylations in recent past have enabled us to study this and understand the role of glycosylations in protein structure and function in ways that would not be possible by conventional experimental methods. In this chapter, we will demonstrate how to leverage these methodologies to study glycoproteins and their structural and dynamic properties using molecular modelling techniques.

Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.

MUG: A mutation overview of GPCR subfamily A17 receptors

G protein-coupled receptors (GPCRs) mediate several signaling pathways through a general mechanism that involves their activation, upholding a chain of events that lead to the release of molecules responsible for cytoplasmic action and further regulation. These physiological functions can be severely altered by mutations in GPCR genes. GPCRs subfamily A17 (dopamine, serotonin, adrenergic and trace amine receptors) are directly related with neurodegenerative diseases, and as such it is crucial to explore known mutations on these systems and their impact in structure and function. A comprehensive and detailed computational framework - MUG (Mutations Understanding GPCRs) - was constructed, illustrating key reported mutations and their effect on receptors of the subfamily A17 of GPCRs. We explored the type of mutations occurring overall and in the different families of subfamily A17, as well their localization within the receptor and potential effects on receptor functionality. The mutated residues were further analyzed considering their pathogenicity. The results reveal a high diversity of mutations in the GPCR subfamily A17 structures, drawing attention to the considerable number of mutations in conserved residues and domains. Mutated residues were typically hydrophobic residues enriched at the ligand binding pocket and known activating microdomains, which may lead to disruption of receptor function. MUG as an interactive web application is available for the management and visualization of this dataset. We expect that this interactive database helps the exploration of GPCR mutations, their influence, and their familywise and receptor-specific effects, constituting the first step in elucidating their structures and molecules at the atomic level.

A Structure-Based Mechanism for the Denaturing Action of Urea, Guanidinium Ion and Thiocyanate Ion

An exhaustive analysis of all the protein structures deposited in the Protein Data Bank, here performed, has allowed the identification of hundredths of protein-bound urea molecules and the structural characterization of such binding sites. It emerged that, even though urea molecules are largely involved in hydrogen bonds with both backbone and side chains, they are also able to make van der Waals contacts with nonpolar moieties. As similar findings have also been previously reported for guanidinium and thiocyanate, this observation suggests that promiscuity is a general property of protein denaturants. Present data provide strong support for a mechanism based on the protein-denaturant direct interactions with a denaturant binding model to equal and independent sites. In this general framework, our investigations also highlight some interesting insights into the different denaturing power of urea compared to guanidinium/thiocyanate.

Confinement dependence of protein-associated solvent dynamics around different classes of proteins, from the EPR spin probe perspective

Protein function is modulated by coupled solvent fluctuations, subject to the degree of confinement from the surroundings. To identify universal features of the external confinement effect, the temperature dependence of the dynamics of protein-associated solvent over 200-265 K for proteins representative of different classes and sizes is characterized by using the rotational correlation time (detection bandwidth, 10-10-10-7 s) of the electron paramagnetic resonance (EPR, X-band) spin probe, TEMPOL, which is restricted to regions vicinal to protein in frozen aqueous solution. Weak (protein surrounded by aqueous-dimethylsulfoxide cryosolvent mesodomain) and strong (no added crysolvent) conditions of ice boundary confinement are imposed. The panel of soluble proteins represents large and small oligomeric (ethanolamine ammonia-lyase, 488 kDa; streptavidin, 52.8 kDa) and monomeric (myoglobin, 16.7 kDa) globular proteins, an intrinsically disordered protein (IDP, β-casein, 24.0 kDa), an unstructured peptide (protamine, 4.38 kDa) and a small peptide with partial backbone order (amyloid-β residues 1-16, 1.96 kDa). Expanded and condensate structures of β-casein and protamine are resolved by the spin probe under weak and strong confinement, respectively. At each confinement condition, the soluble globular proteins display common T-dependences of rotational correlation times and normalized weights, for two mobility components, protein-associated domain, PAD, and surrounding mesodomain. Strong confinement induces a detectable PAD component and emulation of globular protein T-dependence by the amyloid-β peptide. Confinement uniformly impacts soluble globular protein PAD dynamics, and is therefore a generic control parameter for modulation of soluble globular protein function.

Thermodynamics for the Self-Assembly of Alkylated Peptides

Self-assembling peptides form aggregates with various nanostructures such as spheres, sheets, and fibers and have potential applications in nanomedicine and drug delivery. The alkylation of peptides is a promising strategy for controlling the self-assembly of peptides. In this study, we investigated the thermodynamic properties associated with the aggregation of alkyl-chain-modified self-assembling peptides. The tripeptide sequence, KYF, which has been reported to form fibrous aggregates via self-assembly, was modified with various fatty acids at the N-terminus. The fibrous morphology of the aggregates was observed by transmission electron microscopy and atomic force microscopy. Thioflavin T fluorescence and circular dichroism spectroscopy revealed the formation of β-sheet structures. The critical micelle concentration and its temperature dependence were determined to obtain the thermodynamic parameters for aggregation. The results showed that the aggregation was an entropy-driven process at low temperatures, whereas it was enthalpy-driven at high temperatures. The negative heat capacity changes for aggregation suggested that hydrophobic interactions were the major driving force for self-assembly. Other entropic and enthalpic interactions were also contributed in part to the self-assembly. We individually identified the contributions of the peptide and alkyl chain moiety to the self-assembly. These contributions can be explained by the theoretical values for the self-assembly of each component. The results of this study provide fundamental insights into the design of self-associating peptides.

Best templates outperform homology models in predicting the impact of mutations on protein stability

MOTIVATION

Prediction of protein stability change upon mutation (ΔΔG) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. In case, protein 3D structure is not available, one can predict the structure from protein sequence; however, the perspectives of ΔΔG predictions for predicted protein structures are unknown. The accuracy of using 3D structures of the best templates for the ΔΔG prediction is also unclear.

RESULTS

To investigate these questions, we used a representative set of seven diverse and accurate publicly available tools (FoldX, Eris, Rosetta, DDGun, ACDC-NN, ThermoNet and DynaMut) for stability change prediction combined with AlphaFold or I-Tasser for protein 3D structure prediction. We found that best templates perform consistently better than (or similar to) homology models for all ΔΔG predictors. Our findings imply using the best template structure for the prediction of protein stability change upon mutation if the protein 3D structure is not available.

AVAILABILITY AND IMPLEMENTATION

The data are available at https://github.com/ivankovlab/template-vs-model.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assignment of structural domains in proteins using diffusion kernels on graphs

Though proposing algorithmic approaches for protein domain decomposition has been of high interest, the inherent ambiguity to the problem makes it still an active area of research. Besides, accurate automated methods are in high demand as the number of solved structures for complex proteins is on the rise. While majority of the previous efforts for decomposition of 3D structures are centered on the developing clustering algorithms, employing enhanced measures of proximity between the amino acids has remained rather uncharted. If there exists a kernel function that in its reproducing kernel Hilbert space, structural domains of proteins become well separated, then protein structures can be parsed into domains without the need to use a complex clustering algorithm. Inspired by this idea, we developed a protein domain decomposition method based on diffusion kernels on protein graphs. We examined all combinations of four graph node kernels and two clustering algorithms to investigate their capability to decompose protein structures. The proposed method is tested on five of the most commonly used benchmark datasets for protein domain assignment plus a comprehensive non-redundant dataset. The results show a competitive performance of the method utilizing one of the diffusion kernels compared to four of the best automatic methods. Our method is also able to offer alternative partitionings for the same structure which is in line with the subjective definition of protein domain. With a competitive accuracy and balanced performance for the simple and complex structures despite relying on a relatively naive criterion to choose optimal decomposition, the proposed method revealed that diffusion kernels on graphs in particular, and kernel functions in general are promising measures to facilitate parsing proteins into domains and performing different structural analysis on proteins. The size and interconnectedness of the protein graphs make them promising targets for diffusion kernels as measures of affinity between amino acids. The versatility of our method allows the implementation of future kernels with higher performance. The source code of the proposed method is accessible at https://github.com/taherimo/kludo . Also, the proposed method is available as a web application from https://cbph.ir/tools/kludo .

Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices

The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs). As ERGMs with dyadic dependence involve normalizing factors that are extremely costly to compute, practical strategies for ERGMs inference generally employ a variety of approximations or other workarounds. Markov Chain Monte Carlo maximum likelihood (MCMC MLE) provides a powerful tool to approximate the maximum likelihood estimator (MLE) of ERGM parameters, and is generally feasible for typical models on single networks with as many as a few thousand nodes. MCMC-based algorithms for Bayesian analysis are more expensive, and high-quality answers are challenging to obtain on large graphs. For both strategies, extension to the pooled case—in which we observe multiple networks from a common generative process—adds further computational cost, with both time and memory scaling linearly in the number of graphs. This becomes prohibitive for large networks, or cases in which large numbers of graph observations are available. Here, we exploit some basic properties of the discrete exponential families to develop an approach for ERGM inference in the pooled case that (where applicable) allows an arbitrarily large number of graph observations to be fit at no additional computational cost beyond preprocessing the data itself. Moreover, a variant of our approach can also be used to perform Bayesian inference under conjugate priors, again with no additional computational cost in the estimation phase. The latter can be employed either for single graph observations, or for observations from graph sets. As we show, the conjugate prior is easily specified, and is well-suited to applications such as regularization. Simulation studies show that the pooled method leads to estimates with good frequentist properties, and posterior estimates under the conjugate prior are well-behaved. We demonstrate the usefulness of our approach with applications to pooled analysis of brain functional connectivity networks and to replicated x-ray crystal structures of hen egg-white lysozyme.

Stability profile of the neuronal SNARE complex reflects its potency to drive fast membrane fusion

Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) form the SNARE complex to mediate most fusion events of the secretory pathway. The neuronal SNARE complex is featured by its high stability and half-zippered conformation required for driving robust and fast synaptic exocytosis. However, these two features seem to be thermodynamically mutually exclusive. In this study, we have employed temperature-dependent disassociation assays and single-molecule Förster resonance energy transfer (FRET) experiments to analyze the stability and conformation of the neuronal SNARE complex. We reclassified the amino acids of the SNARE motif into four sub-groups (core, core-side I and II, and non-contact). Our data showed that the core residues predominantly contribute to the complex stability to meet a basal requirement for SNARE-mediated membrane fusion, while the core-side residues exert an unbalanced effect on the N- and C-half bundle stability that determines the half-zippered conformation of the neuronal SNARE complex, which would accommodate essential regulations by complexins and synaptotagmins for fast Ca2+-triggered membrane fusion. Furthermore, our data confirmed a strong coupling of folding energy between the N- and C-half assembly of the neuronal SNARE complex, which rationalizes the strong potency of the half-zippered conformation to conduct robust and fast fusion. Overall, these results uncovered that the stability profile of the neuronal SNARE complex reflects its potency to drive fast and robust membrane fusion. Based on these results, we also developed a new parameter, the stability factor (Fs), to characterize the overall stability of the neuronal SNARE complex and resolved a linear correlation between the stability and inter-residue coulombic interactions of the neuronal SNARE complex, which would help rationally design artificial SNARE complexes and remold functional SNARE complexes with desirable stability.

Physicochemical classification of organisms

The hypervariable residues that compose the major part of proteins’ surfaces are generally considered outside evolutionary control. Yet, these “nonconserved” residues determine the outcome of stochastic encounters in crowded cells. It has recently become apparent that these encounters are not as random as one might imagine, but carefully orchestrated by the intracellular electrostatics to optimize protein diffusion, interactivity, and partner search. The most influential factor here is the protein surface-charge density, which takes different optimal values across organisms with different intracellular conditions. In this study, we examine how far the net-charge density and other physicochemical properties of proteomes will take us in terms of distinguishing organisms in general. The results show that these global proteome properties not only follow the established taxonomical hierarchy, but also provide clues to functional adaptation. In many cases, the proteome–property divergence is even resolved at species level. Accordingly, the variable parts of the genes are not as free to drift as they seem in sequence alignment, but present a complementary tool for functional, taxonomic, and evolutionary assignment.

Improved Protein Decoy Selection via Non-Negative Matrix Factorization

A central challenge in protein modeling research and protein structure prediction in particular is known as decoy selection. The problem refers to selecting biologically-active/native tertiary structures among a multitude of physically-realistic structures generated by template-free protein structure prediction methods. Research on decoy selection is active. Clustering-based methods are popular, but they fail to identify good/near-native decoys on datasets where near-native decoys are severely under-sampled by a protein structure prediction method. Reasonable progress is reported by methods that additionally take into account the internal energy of a structure and employ it to identify basins in the energy landscape organizing the multitude of decoys. These methods, however, incur significant time costs for extracting basins from the landscape. In this paper, we propose a novel decoy selection method based on non-negative matrix factorization. We demonstrate that our method outperforms energy landscape-based methods. In particular, the proposed method addresses both the time cost issue and the challenge of identifying good decoys in a sparse dataset, successfully recognizing near-native decoys for both easy and hard protein targets.

Crystal structure of a human MUC16 SEA domain reveals insight into the nature of the CA125 tumor marker

MUC16 is a membrane bound glycoprotein involved in the progression and metastasis of pancreatic and ovarian cancer. The protein is shed into the serum and the resulting cancer antigen 125 (CA125) can be detected by immunoassays. The CA125 epitope is used for monitoring ovarian cancer treatment progression, and has emerged as a potential target for antibody mediated immunotherapy. The extracellular tandem repeat domain of the protein is composed of repeating segments of heavily glycosylated sequence intermixed with homologous SEA (Sperm protein, Enterokinase and Agrin) domains. Here we report the purification and the first X-ray structure of a human MUC16 SEA domain. The structure was solved by molecular replacement using a Rosetta generated structure as a search model. The SEA domain reacted with three different MUC16 therapeutic antibodies, confirming that the CA125 epitope is localized to the SEA domain. The structure revealed a canonical ferredoxin-like fold, and contained a conserved disulfide bond. Analysis of the relative solvent accessibility of side chains within the SEA domain clarified the assignment of N-linked and O-linked glycosylation sites within the domain. A model of the glycosylated SEA domain revealed two major accessible faces, which likely represent the binding sites of CA125 specific antibodies. The results presented here will serve to accelerate future work to understand the functional role of MUC16 SEA domains and antibody recognition of the CA125 epitope.

Characterization of Hydrophilic α-Helical Hot Spots on the Protein-Protein Interaction Interfaces for the Design of α-Helix Mimetics

The cooperativity index, K_c, was developed to examine the binding synergy between hot spots of the ligand-protein. For the first time, the convergence of the side-chain spatial arrangements of hydrophilic α-helical hot spots Thr, Tyr, Asp, Asn, Ser, Cys, and His in protein-protein interaction (PPI) complex structures was disclosed and quantified by developing novel clustering models. In-depth analyses revealed the driving force for the protein-protein binding conformation convergence of hydrophilic α-helical hot spots. This observation allows deriving pharmacophore models to design new mimetics for hydrophilic α-helical hot spots. A computational protocol was developed to search amino acid analogues and small-molecule mimetics for each hydrophilic α-helical hot spot. As a pilot study, diverse building blocks of commercially available nonstandard L-type α-amino acids and the phenyl ring-containing small-molecule fragments were obtained, which serve as a fragment collection to mimic hydrophilic α-helical hot spots for the improvement of binding affinity, selectivity, physicochemical properties, and synthesis accessibility of α-helix mimetics.

Contiguously hydrophobic sequences are functionally significant throughout the human exome

SignificanceProteins rely on the hydrophobic effect to maintain structure and interactions with the environment. Surprisingly, natural selection on amino acid hydrophobicity has not been detected using modern genetic data. Analyses that treat each amino acid separately do not reveal significant results, which we confirm here. However, because the hydrophobic effect becomes more powerful as more hydrophobic molecules are introduced, we tested whether unbroken stretches of hydrophobic amino acids are under selection. Using genetic variant data from across the human genome, we find evidence that selection increases with the length of the unbroken hydrophobic sequence. These results could lead to improvements in a wide range of genomic tools as well as insights into protein-aggregation disease etiology and protein evolutionary history.

Protein Nanoparticles: Uniting the Power of Proteins with Engineering Design Approaches

Protein nanoparticles, PNPs, have played a long-standing role in food and industrial applications. More recently, their potential in nanomedicine has been more widely pursued. This review summarizes recent trends related to the preparation, application, and chemical construction of nanoparticles that use proteins as major building blocks. A particular focus has been given to emerging trends related to applications in nanomedicine, an area of research where PNPs are poised for major breakthroughs as drug delivery carriers, particle-based therapeutics or for non-viral gene therapy.

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

Nearly all proteins interact with other molecules as part of their biological function. For example, proteins can interact with other copies of the same type of protein, with different proteins, with DNA, or with small ligand molecules. Many mutations at protein interfaces, the regions of proteins that interact with other molecules, are known to cause human genetic disease. In this study, we first investigate how different types of protein interfaces have different tendencies to be associated with disease. We also show that the closer a mutation is to an interface, the more likely it is to cause disease. Finally, we study how mutations at different types of interfaces tend to be associated with different changes in amino acid properties, which appears to influence our ability to computationally predict the effects of mutations. Ultimately, we hope that consideration of protein interface properties will eventually improve our ability to identify new disease-causing mutations.

Conformational variation in enzyme catalysis: A structural study on catalytic residues

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We introduce a pipeline to compare and contrast active sites from homologous enzymes in 3D.

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Comprehensive structural study covering enzymes from a large functional space.

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High heterogeneity in magnitude of active site flexibililty between enzyme families.

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Diffferent catalytic residue types and functions relate to different degrees of flexibility.

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Four paradigms classify enzymes according to the structural behaviour during catalysis.

Conformational variation in catalytic residues can be captured as alternative snapshots in enzyme crystal structures. Addressing the question of whether active site flexibility is an intrinsic and essential property of enzymes for catalysis, we present a comprehensive study on the 3D variation of active sites of 925 enzyme families, using explicit catalytic residue annotations from the Mechanism and Catalytic Site Atlas and structural data from the Protein Data Bank. Through weighted pairwise superposition of the functional atoms of active sites, we captured structural variability at single-residue level and examined the geometrical changes as ligands bind or as mutations occur. We demonstrate that catalytic centres of enzymes can be inherently rigid or flexible to various degrees according to the function they perform, and structural variability most often involves a subset of the catalytic residues, usually those not directly involved in the formation or cleavage of bonds. Moreover, data suggest that 2/3 of active sites are flexible, and in half of those, flexibility is only observed in the side chain. The goal of this work is to characterise our current knowledge of the extent of flexibility at the heart of catalysis and ultimately place our findings in the context of the evolution of catalysis as enzymes evolve new functions and bind different substrates.

Evolutionary modeling reveals enhanced mutational flexibility of HCV subtype 1b compared with 1a

Hepatitis C virus (HCV) is a leading cause of liver-associated disease and liver cancer. Of the major HCV subtypes, patients infected with subtype 1b have been associated with having a higher risk of developing chronic infection and hepatocellular carcinoma. However, underlying reasons for this increased disease severity remain unknown. Here, we provide an evolutionary rationale, based on a comparative study of fitness landscape and in-host evolutionary models of the E2 glycoprotein of HCV subtypes 1a and 1b. Our analysis demonstrates that a higher chronicity rate of 1b may be attributed to lower fitness constraints, enabling 1b viruses to more easily escape antibody responses. More generally, our results suggest that differences in evolutionary constraints between HCV subtypes may be an important factor in mediating distinct disease outcomes. Our analysis also identifies antibodies that appear escape-resistant against both subtypes 1a and 1b, providing directions for designing HCV vaccines having cross-subtype protection.

Luminol anchors improve the electrochemical-tyrosine-click labelling of proteins

New methods for chemo-selective modifications of peptides and native proteins are important in chemical biology and for the development of therapeutic conjugates. Less abundant and uncharged amino-acid residues are interesting targets to form less heterogeneous conjugates and preserve biological functions. Phenylurazole (PhUr), N-methylphenylurazole (NMePhUr) and N-methylluminol (NMeLum) derivatives were described as tyrosine (Y) anchors after chemical or enzymatic oxidations. Recently, we developed the first electrochemical Y-bioconjugation method coined eY-click to activate PhUr in biocompatible media. In this work, we assessed the limitations, benefits and relative efficiencies of eY-click conjugations performed with a set of PhUr, NMePhUr and NMeLum derivatives. Results evidenced a high efficiency of NMeLum that showed a complete Y-chemoselectivity on polypeptides and biologically relevant proteins after soft electrochemical activation. Side reactions on nucleophilic or heteroaromatic amino-acids such as lysine or tryptophan were never observed during mass spectrometry analysis. Myoglobine, bovine serum albumin, a plant mannosidase, glucose oxidase and the therapeutically relevant antibody trastuzumab were efficiently labelled with a fluorescent probe in a two-step approach combining eY-click and strain-promoted azide–alkyne cyclization (SPAAC). The proteins conserved their structural integrity as observed by circular dichroism and the trastuzumab conjugate showed a similar binding affinity for the natural HER2 ligand as shown by bio-layer interferometry. Compared to our previously described protocol with PhUr, eY-click with NMeLum species showed faster reaction kinetics, higher (complete) Y-chemoselectivity and reactivity, and offers the interesting possibility of the double tagging of solvent-exposed Y.

We assessed the relative efficiencies of tyrosine anchors in the electrochemical conjugation of peptides and proteins. Luminol derivatives showed faster reaction kinetics, complete tyrosine-chemoselectivity, and possible double modification.

A rational blueprint for the design of chemically-controlled protein switches

Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. However, the repertoire of small-molecule protein switches is insufficient for many applications, including those in the translational spaces, where properties such as safety, immunogenicity, drug half-life, and drug side-effects are critical. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions as OFF- and ON-switches. The designed binders and drug-receptors form chemically-disruptable heterodimers (CDH) which dissociate in the presence of small molecules. To design ON-switches, we converted the CDHs into a multi-domain architecture which we refer to as activation by inhibitor release switches (AIR) that incorporate a rationally designed drug-insensitive receptor protein. CDHs and AIRs showed excellent performance as drug responsive switches to control combinations of synthetic circuits in mammalian cells. This approach effectively expands the chemical space and logic responses in living cells and provides a blueprint to develop new ON- and OFF-switches.

Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions into OFF- and ON-switches active in cells.

Quantification of Conformational Entropy Unravels Effect of Disordered Flanking Region in Coupled Folding and Binding

Intrinsic disorder (ID) constitutes a new dimension to the protein structure-function relationship. The ability to undergo conformational changes upon binding is a key property of intrinsically disordered proteins and remains challenging to study using conventional methods. A 1994 paper by R. S. Spolar and M. T. Record presented a thermodynamic approach for estimating changes in conformational entropy based on heat capacity changes, allowing quantification of residues folding upon binding. Here, we adapt the method for studies of intrinsically disordered proteins. We integrate additional data to provide a broader experimental foundation for the underlying relations and, based on >500 protein-protein complexes involving disordered proteins, reassess a key relation between polar and nonpolar surface area changes, previously determined using globular protein folding. We demonstrate the improved suitability of the adapted method to studies of the folded αα-hub domain RST from radical-induced cell death 1, whose interactome is characterized by ID. From extensive thermodynamic data, quantifying the conformational entropy changes upon binding, and comparison to the NMR structure, the adapted method improves accuracy for ID-based studies. Furthermore, we apply the method, in conjunction with NMR, to reveal hitherto undetected effects of interaction-motif context. Thus, inclusion of the disordered context of the DREB2A RST-binding motif induces structuring of the binding motif, resulting in major enthalpy-entropy compensation in the interaction interface. This study, also evaluating additional interactions, demonstrates the strength of the ID-adapted Spolar-Record thermodynamic approach for dissection of structural features of ID-based interactions, easily overlooked in traditional studies, and for translation of these into mechanistic knowledge.