Leucine and Isoleucine are two amino acids that differ only by the positioning of one methyl group. This small difference has however important consequences in α-helices, as the β-branching of Ile results in helix destabilization. We set out to investigate whether there are general trends for the occurrences of Leu and Ile residues in structures and sequences of class A GPCRs (G protein-coupled receptors). GPCRs are integral membrane proteins in which α-helices span the plasma membrane seven times and which play a crucial role in signal transmission into the cell. We found that Leu side chains are generally present in less densely packed regions and are more protein-surface exposed than Ile side chains. We explored whether this difference might be attributed to different functions of the two amino acids and tested if Leu adjusts the hydrophobicity of the transmembrane domain based on the Wimley-White whole-residue hydrophobicity scales. In class A GPCRs, Leu decreases the variation in hydropathy between receptors and Leu content correlates positively with hydropathy calculated without Leu. Both measures indicate that hydropathy is tuned by Leu. To test this idea further, we generated protein sequences with random amino acid compositions using a simple numerical model, in which hydropathy was tuned by adjusting the number of Leu residues. The model was able to replicate the observations made with class A GPCR sequences. We speculate that Leu tunes the hydropathy of the transmembrane domain of class A GPCRs to facilitate correct insertion into membranes and/or for stability within them.

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and ranked first out of 24 predictors in estimating the global accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analayzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA.

Processing of CASP15 targets into evaluation units (EUs) and assigning them to evolutionary-based prediction classes is presented in this study. The targets were first split into structural domains based on compactness and similarity to other proteins. Models were then evaluated against these domains and their combinations. The domains were joined into larger EUs if predictors’ performance on the combined units was similar to that on individual domains. Alternatively, if most predictors performed better on the individual domains, then they were retained as EUs. As a result, 112 evaluation units were created from 77 tertiary structure prediction targets. The EUs were assigned to four prediction classes roughly corresponding to target difficulty categories in previous CASPs: TBM (template-based modeling, easy or hard), FM (free modeling), and the TBM/FM overlap category. More than a third of CASP15 EUs were attributed to the historically most challenging FM class, where homology or structural analogy to proteins of known fold cannot be detected.

A document by Eduardo Howard. Click on the document to view its contents.

Methyl parathion hydrolase (MPH) is an enzyme of the metallo-β-lactamase superfamily, which hydrolyses a wide range of organophosphates (OP). Recently, MPH has attracted attention as a promising enzymatic bioremediator. The crystal structure of MPH enzyme shows a dimeric form, with each subunit containing a binuclear metal ion center. MPH also demonstrates metal ion-dependent selectivity patterns. The origins of these patterns remain unclear but are linked to open questions about the more general role of metal ions in functional evolution and divergence within enzyme superfamilies. We aimed to investigate and compare the binding of different OP pesticides to MPH with cobalt(II) metal ions. In this study, MPH was modelled from Ochrobactrum sp. with two different classes of OP pesticides bound, including phosphomonoester (methyl paraoxon and dichlorvos) and S-substituted thiophsphotriester (profenofos). The docked structures for each substrate optimized by DFT calculation was selected and subjected to atomistic molecular dynamics simulations for 500 ns. It was found that alpha metal ions did not coordinate with all the pesticides. Rather, the pesticides coordinated with less buried beta metal ions. It was also observed that the coordination of beta metal ions was perturbed to accommodate the bulky pesticides. The binding free energy calculations and structure-based pharmacophore model revealed that all the three substrates could bind well at the active site. However, profenofos exhibits the stronger binding affinity to MPH in comparison to other two substrates. Therefore, the ability of the in silico analysis presented here could be informative for increasing enzyme stability and activity.

The bacterial adhesin FimH is a model for the study of protein allostery because its structure has been resolved in multiple configurations, including the active and the inactive state. FimH consists of a pilin domain (PD) that anchors it to the rest of the fimbria and an allosterically regulated lectin domain (LD) that binds mannose on the surface of infected cells. Under normal conditions, the two domains are docked to each other and LD binds mannose weakly. However, in the presence of tensile force generated by shear the domains separate and conformational changes propagate across LD resulting in a stronger bond to mannose. Recently, the crystallographic structure of a variant of FimH has been resolved, called FimH FocH, where PD contains 10 mutations near the inter-domain interface. Although the X-ray structures of FimH and FimH FocH are almost identical, experimental evidence shows that FimH FocH is activated even in the absence of shear. Here, molecular dynamics simulations combined with the Jarzinski equality were used to investigate the discrepancy between the crystallographic structures and the functional assays. The results indicate that the free energy barrier of the unbinding process between LD and PD is drastically reduced in FimH FocH. Rupture of an inter-domain hydrogen bond involving R166 constitutes a rate limiting step of the domains separation process and occurs more readily in FimH FocH than FimH. In conclusion, the mutations in FimH FocH shift the equilibrium towards an equal occupancy of bound and unbound states for LD and PD by reducing a rate limiting step.

This paper aims to understand the binding strategies of a nanobody-protein pair by studying known complexes. Rigid body protein-ligand docking programs produce several complexes, called decoys, which are good candidates with high scores of shape complementarity, electrostatic interactions, desolvation, buried surface area, and Lennard-Jones potentials. It is not known which decoy represents the true structure. We studied thirty-seven nanobody-protein complexes from the Single Domain Antibody Database, sd-Ab DB, [http://www.sdab-db.ca/](http://www.sdab-db.ca/). For each structure, a large number of decoys are generated using the Fast Fourier Transform algorithm of the software ZDOCK. The decoys were ranked according to their target protein-nanobody interaction energies, calculated by using the Dreiding Force Field, with rank 1 having the lowest interaction energy. Out of thirty-six PDB structures, twenty-five true structures were predicted as rank 1. Eleven of the remaining structures required Ångstrom size rigid body translations of the nanobody relative to the protein to match the given PDB structure. After the translation the Dreiding interaction (DI) energies of all complexes decreased and became rank 1. In one case, rigid body rotations as well as translations of the nanobody were required for matching the crystal structure. We used a Monte Carlo algorithm that randomly translates and rotates the nanobody of a decoy and calculates the DI energy. Results show that rigid body translations and the DI energy are sufficient for determining the correct binding location and pose of ZDOCK created decoys. A survey of the sd-Ab DB showed that each nanobody makes at least one salt bridge with its partner protein, indicating that salt bridge formation is an essential strategy in nanobody-protein recognition. Based on the analysis of the thirty-six crystal structures and evidence from existing literature, we propose a set of principles that could be used in the design of nanobodies.

No report exists on the role of Mastoparan B (MP-B) as an RND efflux pump inhibitor in multi-drug resistant (MDR) Acinetobacter baumannii. Here, we performed a series of in-silico experiments to predict the inhibition of the AdeB efflux pump by MP-B as a drug target agent. For this reason, an MDR strain of A. baumannii was subjected to minimum inhibitory concentration (MIC) against 12 antibiotics as well as MP-B. Expression of the a deB gene in the absence and presence of sub-MIC of MP-B was studied by qRT-PCR. It was found that MP-B had potent antimicrobial activity (MIC=1 µg/ml) associated with a 20-fold decrease in its expression at sub-MIC of MP-B. The stereochemical analysis using several automated servers confirmed that the AdeB is an inner membrane of the RND tripartite complex system with helix-turn-helix conformation and a pore rich in Phe, Ala, and Lys residue. The best model that showed high accuracy (Z=1.2, C=1.41, TM=0.99, and RMSD=4.4) was selected for docking purposes using the Site Map tool, and the correct protein-peptide complexes were simulated in the BioLiP platform. The molecular docking via AutoDock/Vina suggested that MP-B form H-bound with amino acid residues of the AdeB helix-5 and caused a shift in the dihedral angle by distances of 9.0 Å, 9.3 Å, and 9.6 Å, respectively. This shift was detected by the AlphaFold 2 tool and influenced the overall druggability of the protein. From the results, we concluded that, MP-B can be a good candidate for inhibition of bacterial efflux pump.

Probing the structures of amyloid-beta (Aβ) peptides in the early steps of aggregation is extremely difficult experimentally and computationally. Yet, this knowledge is extremely important as small oligomers are the most toxic species. Experiments and simulations on Aβ42 monomer point to random coil conformations with either transient helical or β-strand content. Our current conformational description of small Aβ42 oligomers is funneled toward amorphous aggregates with some β-sheet content and rare excited states with well-ordered assemblies of β-sheets. In this study, we emphasize another view based on metastable α-helix bundle oligomers spanning the C-terminus residues which are predicted by the machine-learning AlphaFold2 method and supported indirectly by low-resolution experimental data on many amyloid polypeptides. This finding has consequences in designing drugs to reduce aggregation and toxicity.

The rapid adaptation of SARS-CoV-2 within the host species and the increased viral transmission triggered the evolution of different SARS-CoV-2 variants. Though numerous monoclonal antibodies (mAbs) have been identified as prophylactic therapy for SARS-CoV-2, the ongoing surge in the number of SARS-CoV-2 infections shows the importance of understanding the mutations in the spike and developing novel vaccine strategies to target all variants. Here, we report the map of experimentally validated 74 SARS-CoV-2 neutralizing mAb binding epitopes of all variants. The majority (87.84%) of the potent neutralizing epitopes are localized to the receptor-binding domain (RBD) and overlap with each other, whereas limited (12.16%) epitopes are found in the N-terminal domain (NTD). Notably, 69 out of 74 mAb targets have at least one mutation at the epitope sites. The potent epitopes found in the RBD show higher mutations (4-10aa) compared to lower or modest neutralizing antibodies, suggesting that these epitopes might co-evolve with the immune pressure. The current study shows the importance of determining the critical mutations at the antibody recognition epitopes, leading to the development of broadly reactive immunogens targeting multiple SARS-CoV-2 variants. Further, vaccines inducing both humoral and cell-mediated immune responses might prevent the escape of SARS-CoV-2 variants from neutralizing antibodies.

The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without the need for time-consuming multiple sequence alignment (MSA) calculations. LSTM-based SPOT-1D-LM and NetSurfP-3.0 are the latest examples of such predictors. We present the ProteinUnetLM model using a convolutional Attention U-Net architecture that provides prediction quality and inference times at least as good as the best LSTM-based models for 8-class SS prediction (SS8). Additionally, we address the issue of the heavily imbalanced nature of the SS8 problem by extending the loss function with the Matthews correlation coefficient (MCC), and by proper assessment using previously introduced adjusted geometric mean metric (AGM). ProteinUnetLM achieved better AGM and sequence overlap score (SOV) than LSTM-based predictors, especially for the rare structures 310-helix (G), beta-bridge (B), and high curvature loop (S). It is also competitive on challenging datasets without homologs, free-modeling targets, and chameleon sequences. Moreover, ProteinUnetLM outperformed its previous MSA-based version ProteinUnet2, and provided better AGM than AlphaFold2 for 1/3 of proteins from the CASP14 dataset, proving its potential for making a significant step forward in the domain. To facilitate the usage of our solution by protein scientists, we provide an easy-to-use web interface under [https://biolib.com/SUT/ProteinUnetLM/](https://biolib.com/SUT/ProteinUnetLM/).

Many proteins must interact with molecular chaperones to achieve their native folded state in the cell. Yet, how chaperone binding and binding-site characteristics affect the folding process is poorly understood. The ubiquitous Hsp70 chaperone system prevents client-protein aggregation by holding unfolded conformations or by unfolding misfolded states. Hsp70 binding sites of client proteins comprise a nonpolar core surrounded by positively charged residues. However, a detailed analysis of Hsp70 binding sites on a proteome-wide scale is still lacking. Further, it is not known whether proteins undergo some degree of folding while chaperone bound. Here, we begin to address the above questions by identifying Hsp70 binding sites in 2,258 E. coli proteins. We find that most proteins bear at least one Hsp70 binding site and that the number of Hsp70 binding sites is directly proportional to protein size. Aggregation propensity upon release from the ribosome correlates with number of Hsp70 binding sites only in the case of large proteins. Interestingly, Hsp70 binding sites are more solvent-exposed than other nonpolar sites, in protein native states. Our findings show that the majority of E. coli proteins are systematically enabled to interact with Hsp70 even if this interaction only takes place during a fraction of the protein lifetime. In addition, our data suggest that some conformational sampling may take place within Hsp70-bound states, due to the solvent exposure of some chaperone binding sites in native proteins. In all, we propose that Hsp70-chaperone-binding traits have evolved to favor Hsp70-assisted protein folding devoid of aggregation.

The human C14orf166 protein, also known as RTRAF, shows positive modulatory activity on the cellular RNA polymerase II enzyme. This protein is a component of the tRNA-splicing ligase complex and is involved in RNA metabolism. It also functions in the nucleo-cytoplasmic transport of RNA molecules. The C14orf166 protein has been reported to be associated with some types of cancer. It has been shown that the C14orf166 protein binds to the influenza A virus RNA polymerase PA subunit and has a stimulating effect on viral replication. In this study, candidate interactor proteins for influenza A virus PA protein were screened with a Y2H assay using HEK293 Matchmaker cDNA. The C14orf166 protein fragments in different sizes were found to interact with the PA. The three-dimensional structures of the viral PA and C14orf166 proteins interacting with the PA were generated using the I-TASSER algorithm. The interaction models between these proteins were predicted with the ClusPro protein docking algorithm and analyzed with PyMol software. The results revealed that the carboxy-terminal end of the C14orf166 protein is involved in this interaction, and it is highly possible that it binds to the carboxy-terminal of the PA protein. Although amino acid residues in the interaction area of the PA protein with the C14orf166 showed distribution from 450th to 700th position, the intense interaction region was revealed to be at amino acid positions 610 to 630.

Lignocellulose is the most abundant natural biopolymer on earth and a potential raw material for the production of fuels and chemicals. However, only some organisms such as bacteria and fungi produce the necessary enzymes to metabolize it. In this work we detected the presence of extracellular cellulases in the genome of five species of Scenedesmus. These microalgae grow in both, freshwater and saltwater regions as well as in soils, displaying highly flexible metabolic properties. The comparison of sequences of the different cellulases with hydrolytic enzymes from other organisms by means of multi-sequence alignments and phylogenetic trees showed that these enzymes belong to the families of glycosyl hydrolases 1, 5, 9 and 10. In addition, most of these presented a greater similarity of sequence with enzymes from invertebrates, fungi, bacteria and other microalgae than with cellulases from plants; and the 3D modeling data obtained showed that both the main structures of the modeled proteins and the main amino acid residues implicated in catalysis and substrate binding are well conserved in Scenedesmus enzymes. We propose that these cellulase-producing phototrophic microorganisms could act as catalysts for the hydrolysis of cellulosic biomass fueled by sunlight.

The recognition of Cannabis as a source of new compounds suitable for medical use has attracted strong interest from the scientific community in its research, and substantial progress has accumulated regarding cannabinoids’ activity; however, a thorough description of their molecular mechanisms of action remains a task to complete. Highlighting their complex pharmacology, the list of cannabinoids’ interactors has vastly expanded beyond the canonical cannabinoid receptors. Among those, we have focused our study on the glycine receptor (GlyR), an ion channel involved in the modulation of nervous system responses, including, to our interest, sensitivity to peripheral pain. Here, we report the use of computational methods to investigate possible binding modes between the GlyR and Δ 9-tetrahydrocannabinol (THC). After obtaining a first pose for the THC binding from a biased molecular docking simulation and subsequently evaluating it by molecular dynamic simulations, we found a dynamic system with an identifiable representative binding mode characterized by the specific interaction with two transmembrane residues (Phe293 and Ser296). Complementarily, we assessed the role of membrane cholesterol in this interaction and positively established its relevance for THC binding to GlyR. Lastly, the use of restrained molecular dynamics simulations allowed us to refine the description of the binding mode and of the cholesterol effect. Altogether, our findings contribute to the current knowledge about the GlyR-THC mode of binding and propose a new starting point for future research on how cannabinoids in general, and THC in particular, modulate pain perception in view of its possible clinical applications.

Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are – and stay – fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the Hydrophobic Cluster Analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe (soluble domains) and OPM (transmembrane domains)). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold2 databases. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by AlphaFold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.

σ factors are essential parts of bacterial RNA polymerase (RNAP) as they allow to recognize promotor sequences and initiate transcription. Domain 1.1 of vegetative σ factors occupies the primary channel of RNAP and also prevents binding of the σ factor to promoter DNA alone. Here, we show that domain 1.1 of Bacillus subtilis σ A exists in two structurally distinct variants in dynamic equilibrium. The major conformation at room temperature is represented by a previously reported well-folded structure solved by nuclear magnetic resonance (NMR), but 4% of the protein molecules are present in a less thermodynamically favorable state. We show that this population increases with temperature and may represent as much as 20% at 43.5 ◦ C. We characterized the minor state of the domain 1.1 using specialized methods of NMR. We found that, in contrast to the major state, the detected minor state is partially unfolded. Its propensity to form secondary structure elements is especially decreased for the first and third α helices, while the second α helix and β strand close to the C-terminus are more stable. In summary, this study reveals conformational dynamics of domain 1.1 and provides a basis for studies of its interaction with RNAP and effects on transcription regulation.

Degradation of solid polyethylene terephthalate (PET) by leaf branch compost cutinase (LCC) produces various PET-derived degradation intermediates (DIs), in addition to terephthalic acid (TPA), which is the recyclable terminal product of all PET degradation. Although DIs can also be converted into TPA, in solution, by LCC, the TPA that is obtained through enzymatic degradation of PET, in practice, is always contaminated by DIs. Here, we demonstrate that the primary reason for non-degradation of DIs into TPA in solution is the efficient binding of LCC onto the surface of solid PET. Although such binding enhances the degradation of solid PET, it depletes the surrounding solution of enzyme that could otherwise have converted DIs into TPA. To retain a sub-population of enzyme in solution that would mainly degrade DIs, we introduced mutations to reduce the hydrophobicity of areas surrounding LCC’s active site, with the express intention of reducing LCC’s binding to solid PET. Despite the consequent reduction in invasion and degradation of solid PET, overall levels of production of TPA were ~3.6-fold higher, due to the partitioning of enzyme between solid PET and the surrounding solution, and the consequent heightened production of TPA from DIs. Further, synergy between such mutated LCC (F125L/F243I LCC) and wild-type LCC resulted in even higher yields, and TPA of nearly ~100% purity.