Inter-residue interactions in protein folding and stability

Research progress on non covalent interaction dissolution characterization of insoluble wheat protein based on swelling

The non covalent interactions of proteins are usually characterized by solubility, which is based on the principle that specific solvents can disrupt non covalent interactions and promote protein dissolution. However, this method is generally applicable to highly soluble protein materials. The solubility of wheat protein is poor. When using this method to characterize non covalent interactions, there is always a portion of protein aggregates that can only reach a swollen state and cannot be completely dissolved. At present, there are no research reports on the role of non covalent interactions in swelling. In view of this, this article first reviews the swelling and dissolution processes of insoluble proteins such as wheat protein in solvents, focusing on the characterization mechanisms and influencing factors of three non covalent interactions using solubility characterization. At the same time, this article also explores the potential of swelling in characterizing non covalent interactions, aiming to improve the characterization methods of non covalent interactions between wheat proteins and provide methodological support for analyzing processing differences from the hierarchical analysis of wheat protein interactions in the future.

Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2-7 Binding Affinity

Seven treatments are approved for Alzheimer's disease, but five of them only relieve symptoms and do not alter the course of the disease. Aducanumab (Adu) and lecanemab are novel disease-modifying antiamyloid-β (Aβ) human monoclonal antibodies that specifically target the pathophysiology of Alzheimer's disease (AD) and were recently approved for its treatment. However, their administration is associated with serious side effects, and their use is limited to early stages of the disease. Therefore, drug discovery remains of great importance in AD research. To gain new insights into the development of novel drugs for Alzheimer's disease, a combination of techniques was employed, including mutation screening, molecular dynamics, and quantum biochemistry. These were used to outline the interfacial interactions of the Aducanumab::Aβ2-7 complex. Our analysis identified critical stabilizing contacts, revealing up to 40% variation in the affinity of the Adu chains for Aβ2-7 depending on the conformation outlined. Remarkably, two complementarity determining regions (CDRs) of the Adu heavy chain (HCDR3 and HCDR2) and one CDR of the Adu light chain (LCDR3) accounted for approximately 77% of the affinity of Adu for Aβ2-7, confirming their critical role in epitope recognition. A single mutation, originally reported to have the potential to increase the affinity of Adu for Aβ2-7, was shown to decrease its structural stability without increasing the overall binding affinity. Mimetic peptides that have the potential to inhibit Aβ aggregation were designed by using computational outcomes. Our results support the use of these peptides as promising drugs with great potential as inhibitors of Aβ aggregation.

Disorder-mediated interactions target proteins to specific condensates

Selective compartmentalization of cellular contents is fundamental to the regulation of biochemistry. Although membrane-bound organelles control composition by using a semi-permeable barrier, biomolecular condensates rely on interactions among constituents to determine composition. Condensates are formed by dynamic multivalent interactions, often involving intrinsically disordered regions (IDRs) of proteins, yet whether distinct compositions can arise from these dynamic interactions is not known. Here, by comparative analysis of proteins differentially partitioned by two different condensates, we find that distinct compositions arise through specific IDR-mediated interactions. The IDRs of differentially partitioned proteins are necessary and sufficient for selective partitioning. Distinct sequence features are required for IDRs to partition, and swapping these sequence features changes the specificity of partitioning. Swapping whole IDRs retargets proteins and their biochemical activity to different condensates. Our results demonstrate that IDR-mediated interactions can target proteins to specific condensates, enabling the spatial regulation of biochemistry within the cell.

Dominant Chemical Interactions Governing the Folding Mechanism of Oligopeptides

The hydrophobic effect is the main factor that drives the folding of polypeptide chains. In this study, we have examined the influence of the hydrophobic effect in the context of the main mechanical forces approach, mainly in relation to the establishment of specific interplays, such as hydrophobic and CH-π cloud interactions. By adopting three oligopeptides as model systems to assess folding features, we demonstrate herein that these finely tuned interactions dominate over electrostatic interactions, including H-bonds and electrostatic attractions/repulsions. The folding mechanism analysed here demonstrates cooperation at the single-residue level, for which we propose the terminology of "single residues cooperative folding". Overall, hydrophobic and CH-π cloud interactions produce the main output of the hydrophobic effect and govern the folding mechanism, as demonstrated in this study with small polypeptide chains, which in turn represent the main secondary structures in proteins.

Mutations in the WG and GW motifs of the three RNA silencing suppressors of grapevine fanleaf virus alter their systemic suppression ability and affect virus infectivity

Viral suppressors of RNA silencing (VSRs) encoded by grapevine fanleaf virus (GFLV), one of the most economically consequential viruses of grapevine (Vitis spp.), were recently identified. GFLV VSRs include the RNA1-encoded protein 1A and the putative helicase protein 1BHel, as well as their fused form (1ABHel). Key characteristics underlying the suppression function of the GFLV VSRs are unknown. In this study, we explored the role of the conserved tryptophan-glycine (WG) motif in protein 1A and glycine-tryptophan (GW) motif in protein 1BHel in their systemic RNA silencing suppression ability by co-infiltrating Nicotiana benthamiana 16c line plants with a GFP silencing construct and a wildtype or a mutant GFLV VSR. We analyzed and compared wildtype and mutant GFLV VSRs for their (i) efficiency at suppressing RNA silencing, (ii) ability to limit siRNA accumulation, (iii) modulation of the expression of six host genes involved in RNA silencing, (iv) impact on virus infectivity in planta, and (v) variations in predicted protein structures using molecular and biochemical assays, as well as bioinformatics tools such as AlphaFold2. Mutating W to alanine (A) in WG of proteins 1A and 1ABHel abolished their ability to induce systemic RNA silencing suppression, limit siRNA accumulation, and downregulate NbAGO2 expression by 1ABHel. This mutation in the GFLV genome resulted in a non-infectious virus. Mutating W to A in GW of proteins 1BHel and 1ABHel reduced their ability to suppress systemic RNA silencing and abolished the downregulation of NbDCL2, NbDCL4,, and NbRDR6 expression by 1BHel. This mutation in the GFLV genome delayed infection at the local level and inhibited systemic infection in planta. Double mutations of W to A in WG and GW of protein 1ABHel abolished its ability to induce RNA silencing suppression, limit siRNA accumulation, and downregulate NbDCL2 and NbRDR6 expression. Finally, in silico protein structure prediction indicated that a W to A substitution potentially modifies the structure and physicochemical properties of the three GFLV VSRs. Together, this study provided insights into the specific roles of WG/GW not only in GFLV VSR functions but also in GFLV biology.

Process-Induced Molecular-Level Protein-Carbohydrate-Polyphenol Interactions in Milk-Tea Blends: A Review

The rapid increase in the production of powdered milk-tea blends is driven by a growing awareness of the presence of highly nutritious bioactive compounds and consumer demand for convenient beverages. However, the lack of literature on the impact of heat-induced component interactions during processing hinders the production of high-quality milk-tea powders. The production process of milk-tea powder blends includes the key steps of pasteurization, evaporation, and spray drying. Controlling heat-induced interactions, such as protein-protein, protein-carbohydrate, protein-polyphenol, carbohydrate-polyphenol, and carbohydrate-polyphenol, during pasteurization, concentration, and evaporation is essential for producing a high-quality milk-tea powder with favorable physical, structural, rheological, sensory, and nutritional qualities. Adjusting production parameters, such as the type and the composition of ingredients, processing methods, and processing conditions, is a great way to modify these interactions between components in the formulation, and thereby, provide improved properties and storage stability for the final product. Therefore, this review comprehensively discusses how molecular-level interactions among proteins, carbohydrates, and polyphenols are affected by various unit operations during the production of milk-tea powders.

Reviewing the Structure-Function Paradigm in Polyglutamine Disorders: A Synergistic Perspective on Theoretical and Experimental Approaches

Polyglutamine (polyQ) disorders are a group of neurodegenerative diseases characterized by the excessive expansion of CAG (cytosine, adenine, guanine) repeats within host proteins. The quest to unravel the complex diseases mechanism has led researchers to adopt both theoretical and experimental methods, each offering unique insights into the underlying pathogenesis. This review emphasizes the significance of combining multiple approaches in the study of polyQ disorders, focusing on the structure-function correlations and the relevance of polyQ-related protein dynamics in neurodegeneration. By integrating computational/theoretical predictions with experimental observations, one can establish robust structure-function correlations, aiding in the identification of key molecular targets for therapeutic interventions. PolyQ proteins' dynamics, influenced by their length and interactions with other molecular partners, play a pivotal role in the polyQ-related pathogenic cascade. Moreover, conformational dynamics of polyQ proteins can trigger aggregation, leading to toxic assembles that hinder proper cellular homeostasis. Understanding these intricacies offers new avenues for therapeutic strategies by fine-tuning polyQ kinetics, in order to prevent and control disease progression. Last but not least, this review highlights the importance of integrating multidisciplinary efforts to advancing research in this field, bringing us closer to the ultimate goal of finding effective treatments against polyQ disorders.

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA)

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, more than 10 million people were infected worldwide, and 1.3 million were children. The current study considered the in-silico and machine learning (ML) approaches to explore the potential anti-TB molecules from the SelleckChem database against Enoyl-Acyl Carrier Protein Reductase (InhA). Initially, the entire database of ∼ 119000 molecules was sorted out through drug-likeness. Further, the molecular docking study was conducted to reduce the chemical space. The standard TB drug molecule's binding energy was considered a threshold, and molecules found with lower affinity were removed for further analyses. Finally, the molecules were checked for the pharmacokinetic and toxicity studies, and compounds found to have acceptable pharmacokinetic parameters and were non-toxic were considered as final promising molecules for InhA. The above approach further evaluated five molecules for ML-based toxicity and synthetic accessibility assessment. Not a single molecule was found toxic and each of them was revealed as easy to synthesise. The complex between InhA and proposed and standard molecules was considered for molecular dynamics simulation. Several statistical parameters showed the stability between InhA and the proposed molecule. The high binding affinity was also found for each of the molecules towards InhA using the MM-GBSA approach. Hence, the above approaches and findings exposed the potentiality of the proposed molecules against InhA.

Prediction of the antigenic regions in eight RhD variants identified by computational biology

BACKGROUND AND OBJECTIVES

Changes in RHD generate variations in protein structure that lead to antigenic variants. The classical model divides them into quantitative (weak and Del) and qualitative (partial D). There are two types of protein antigens: linear and conformational. Computational biology analyses the theoretical assembly of tertiary protein structures and allows us to identify the 'topological' differences between isoforms. Our aim was to determine the theoretical antigenic differences between weak RhD variants compared with normal RhD based on structural analysis using bioinformatic techniques.

MATERIALS AND METHODS

We analysed the variations in secondary structures and hydrophobicity of RHD*01, RHD*01W.1, W2, W3, RHD*09.03.01, RHD*09.04, RHD*11, RHD*15 and RHD*21. We then modelled the tertiary structure and calculated their probable antigenic regions, intra-protein interactions, displacement and membrane width and compared them with Rhce.

RESULTS

The 10 proteins are similar in their secondary structure and hydrophobicity, with the main differences observed in the exofacial coils. We identified six potential antigenic regions: one that is unique to RhD (R3), one that is common to all D (R6), three that are highly variable among RhD isoforms (R1, R2 and R4), one that they share with Rhce (R5) and two that are unique to Rhce (Ra and Rbc).

CONCLUSION

The alloimmunization capacity of these subjects could be explained by the variability of the antigen pattern, which is not necessarily recognized or recognized with lower intensity by the commercially available antibodies, and not because they have a lower protein concentration in the membrane.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Revisiting structural organization of proteins at high temperature from a network perspective

Interactions between distantly placed amino acids in the primary chain (long-range) play a very crucial role in the formation and stabilization of the tertiary structure of a protein, while interactions between closely placed amino acids in the primary chain (short-range) mostly stabilize the secondary structures. Every protein needs to maintain marginal stability in order to perform its physiological functions in its native environment. The requirements for this stability in mesophilic and thermophilic proteins are different. Thermophilic proteins need to form more interactions as well as more stable interactions to survive in the extreme environment, they live in. Here, we aim to find out how the interacting amino acids in three-dimensional space are positioned in the primary chains in thermophilic and mesophilic. How does this arrangement help thermophiles to maintain their structural integrity at high temperatures? Working on a dataset of 1560 orthologous pairs we perceive that thermophiles are not only enriched with long-range interactions, they feature bigger connected clusters and higher network densities compared to their mesophilic orthologs, at higher interaction strengths between the amino acids. Moreover, we have observed the enrichment of different types of interactions at different secondary structural regions.

THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model

MOTIVATION

Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level structural information, it can help to understand how single-point variations cause functional changes.

RESULTS

Here, we proposed THPLM, a sequence-based deep learning model for stability change prediction using Meta's ESM-2. With ESM-2 and a simple convolutional neural network, THPLM achieved comparable or even better performance than most methods, including sequence-based and structure-based methods. Furthermore, the experimental results indicate that the PLM's ability to generate representations of sequence can effectively improve the ability of protein function prediction.

AVAILABILITY AND IMPLEMENTATION

The source code of THPLM and the testing data can be accessible through the following links: https://github.com/FPPGroup/THPLM.

TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features

The folding and stability of transmembrane proteins (TMPs) are governed by the insertion of secondary structural elements into the cell membrane followed by their assembly. Understanding the important features that dictate the stability of TMPs is important for elucidating their functions. In this work, we related sequence and structure-based parameters with free energy (ΔG0) of α-helical membrane proteins. Our results showed that the free energy transfer of hydrophobic peptides, relative contact order, total interaction energy, number of hydrogen bonds and lipid accessibility of transmembrane regions are important for stability. Further, we have developed multiple-regression models to predict the stability of α-helical membrane proteins using these features and our method can predict the stability with a correlation and mean absolute error (MAE) of 0.89 and 1.21 kcal/mol, respectively, on jack-knife test. The method was validated with a blind test set of three recently reported experimental ΔG0, which could predict the stability within an average MAE of 0.51 kcal/mol. Further, we developed a webserver for predicting the stability and it is freely available at (https://web.iitm.ac.in/bioinfo2/TMHS/). The importance of selected parameters and limitations are discussed.

Artificial Proteins Designed from G3LEA Contribute to Enhancement of Oxidation Tolerance in E. coli in a Chaperone-like Manner

G3LEA is a family of proteins that exhibit chaperone-like activity when under distinct stress. In previous research, DosH was identified as a G3LEA protein from model extremophile-Deinococcus radiodurans R1 with a crucial core HD domain consisting of eight 11-mer motifs. However, the roles of motifs participating in the process of resistance to stress and their underlying mechanisms remain unclear. Here, eight different proteins with tandem repeats of the same motif were synthesized, named Motif1-8, respectively, whose function and structure were discussed. In this way, the role of each motif in the HD domain can be comprehensively analyzed, which can help in finding possibly crucial amino acid sites. Circular dichroism results showed that all proteins were intrinsically ordered in phosphate buffer, and changed into more α-helical ordered structures with the addition of trifluoroethanol and glycerol. Transformants expressing artificial proteins had significantly higher stress resistance to oxidation, desiccation, salinity and freezing compared with the control group; E. coli with Motif1 and Motif8 had more outstanding performance in particular. Moreover, enzymes and membrane protein protection viability suggested that Motif1 and Motif8 had more positive influences on various molecules, demonstrating a protective role in a chaperone-like manner. Based on these results, the artificial proteins synthesized according to the rule of 11-mer motifs have a similar function to wildtype protein. Regarding the sequence in all motifs, there are more amino acids to produce H bonds and α-helices, and more amino acids to promote interaction between proteins in Motif1 and Motif8; in addition, considering linkers, there are possibly more amino acids forming α-helix and binding substrates in these two proteins, which potentially provides some ideas for us to design potential ideal stress-response elements for synthetic biology. Therefore, the amino acid composition of the 11-mer motif and linker is likely responsible for its biological function.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol^-1 on an independent test set with an R² value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

Revisiting the Sweet Taste Receptor T1R2-T1R3 through Molecular Dynamics Simulations Coupled with a Noncovalent Interactions Analysis

It is nowadays widely accepted that sweet taste perception is elicited by the activation of the heterodimeric complex T1R2-T1R3, customarily known as sweet taste receptor (STR). However, the interplay between STR and sweeteners has not yet been fully clarified. Here through a methodology coupling molecular dynamics and the independent gradient model (igm) approach we determine the main interacting signatures of the closed (active) conformation of the T1R2 Venus flytrap domain (VFD) toward aspartame. The igm methodology provides a rapid and reliable quantification of noncovalent interactions through a score (Δg^inter) based on the attenuation of the electronic density gradient when two molecular fragments approach each other. Herein, this approach is coupled to a 100 ns molecular dynamics simulation (MD-igm) to explore the ligand-cavity contacts on a per-residue basis as well as a series of key inter-residue interactions that stabilize the closed form of VFD. We also apply an atomic decomposition scheme of noncovalent interactions to quantify the contribution of the ligand segments to the noncovalent interplay. Finally, a series of structural modification on aspartame are conducted in order to obtain guidelines for the rational design of novel sweeteners. Given that innovative methodologies to reliably quantify the extent of ligand-protein coupling are strongly demanded, this approach combining a noncovalent analysis and MD simulations represents a valuable contribution, that can be easily applied to other relevant biomolecular systems.

Structural evolution of Delta lineage of SARS-CoV-2

One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.

Characterization of feruloyl esterases from Pecoramyces sp. F1 and the synergistic effect in biomass degradation

Feruloyl esterase (FAE; EC 3.1.1.73) cleaves the ester bond between ferulic acid (FA) and sugar, to assist the release of FAs and degradation of plant cell walls. In this study, two FAEs (Fae13961 and Fae16537) from the anaerobic fungus Pecoramyces sp. F1 were heterologously expressed in Pichia pastoris (P. pastoris). Compared with Fae16537, Fae13961 had higher catalytic efficiency. The optimum temperature and pH of both the FAEs were 45 ℃ and 7.0, respectively. They showed good stability-Fae16537 retained up to 80% activity after incubation at 37 ℃ for 24 h. The FAEs activity was enhanced by Ca²⁺ and reduced by Zn²⁺, Mn²⁺, Fe²⁺ and Fe³⁺. Additionally, the effect of FAEs on the hydrolytic efficiency of xylanase and cellulase was also determined. The FAE Fae13961 had synergistic effect with xylanase and it promoted the degradation of xylan substrates by xylanase, but it did not affect the degradation of cellulose substrates by cellulase. When Fae13961 was added in a mixture of xylanase and cellulase to degrade complex agricultural biomass, it significantly enhanced the mixture's ability to disintegrate complex substrates. These FAEs could serve as superior auxiliary enzymes for other lignocellulosic enzymes in the process of degradation of agricultural residues for industrial applications.

Panoramic Perspective on Human Phosphosites

Protein phosphorylation is the most common reversible post-translational modification of proteins and is key in the regulation of many cellular processes. Due to this importance, phosphorylation is extensively studied, resulting in the availability of a large amount of mass spectrometry-based phospho-proteomics data. Here, we leverage the information in these large-scale phospho-proteomics data sets, as contained in Scop3P, to analyze and characterize proteome-wide protein phosphorylation sites (P-sites). First, we set out to differentiate correctly observed P-sites from false-positive sites using five complementary site properties. We then describe the context of these P-sites in terms of the protein structure, solvent accessibility, structural transitions and disorder, and biophysical properties. We also investigate the relative prevalence of disease-linked mutations on and around P-sites. Moreover, we assess the structural dynamics of P-sites in their phosphorylated and unphosphorylated states. As a result, we show how large-scale reprocessing of available proteomics experiments can enable a more reliable view on proteome-wide P-sites. Furthermore, adding the structural context of proteins around P-sites helps uncover possible conformational switches upon phosphorylation. Moreover, by placing sites in different biophysical contexts, we show the differential preference in protein dynamics at phosphorylated sites when compared to the nonphosphorylated counterparts.

Nanomechanics combined with HDX reveals allosteric drug binding sites of CFTR NBD1

Cystic fibrosis (CF) is a frequent genetic disease in Caucasians that is caused by the deletion of F508 (ΔF508) in the nucleotide binding domain 1 (NBD1) of the CF transmembrane conductance regulator (CFTR). The ΔF508 compromises the folding energetics of the NBD1, as well as the folding of three other CFTR domains. Combination of FDA approved corrector molecules can efficiently but incompletely rescue the ΔF508-CFTR folding and stability defect. Thus, new pharmacophores that would reinstate the wild-type-like conformational stability of the ΔF508-NBD1 would be highly beneficial. The most prominent molecule, 5-bromoindole-3-acetic acid (BIA) that can thermally stabilize the NBD1 has low potency and efficacy. To gain insights into the NBD1 (un)folding dynamics and BIA binding site localization, we combined molecular dynamics (MD) simulations, atomic force spectroscopy (AFM) and hydrogen-deuterium exchange (HDX) experiments. We found that the NBD1 α-subdomain with three adjacent strands from the β-subdomain plays an important role in early folding steps, when crucial non-native interactions are formed via residue F508. Our AFM and HDX experiments showed that BIA associates with this α-core region and increases the resistance of the ΔF508-NBD1 against mechanical unfolding, a phenomenon that could be exploited in future developments of folding correctors.

Understanding structural variability in proteins using protein structural networks

Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.

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Monomeric, single domain protein structures can exhibit non-rigid behaviour and be highly variable.

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The comparison of protein structural networks can better discriminate conformations with similar backbones.

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Specific interactions between solvent accessible and inaccessible residues are poorly preserved.

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Network edge-variation offers insights on which interacting residues are likely to influence their dynamics and function.

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These side-chain network-based studies provide a framework to correlate protein structure-function relationships.

When Dynamic Diselenide Bonds meet Dynamic Imine Bonds in Polymeric Materials

In both natural and artificial functional systems, the cooperation between different dynamic interactions is of vital importance for realizing complicated functions. Dynamic covalent bonds are one kind of relatively stable dynamic interactions, and have shown synergistic effect in natural systems such as functional proteins. However, synergistic interactions between different dynamic covalent bonds in polymeric materials are still unclear. Herein, polymeric materials containing diselenide and imine bonds are prepared, and then the synergistic effect between the two dynamic covalent bonds is quantitatively evaluated in typical processes of dynamic materials. The results reveal that dynamic covalent bonds show weak synergistic effect in the degradation process, and have strong synergistic effect in stress relaxation process. Therefore, introducing multiple dynamic covalent bonds in polymeric materials could extensively enhance their dynamic properties. This article is protected by copyright. All rights reserved.

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.

The SARS-CoV-2 spike protein is vulnerable to moderate electric fields

Most of the ongoing projects aimed at the development of specific therapies and vaccines against COVID-19 use the SARS-CoV-2 spike (S) protein as the main target. The binding of the spike protein with the ACE2 receptor (ACE2) of the host cell constitutes the first and key step for virus entry. During this process, the receptor binding domain (RBD) of the S protein plays an essential role, since it contains the receptor binding motif (RBM), responsible for the docking to the receptor. So far, mostly biochemical methods are being tested in order to prevent binding of the virus to ACE2. Here we show, with the help of atomistic simulations, that external electric fields of easily achievable and moderate strengths can dramatically destabilise the S protein, inducing long-lasting structural damage. One striking field-induced conformational change occurs at the level of the recognition loop L3 of the RBD where two parallel beta sheets, believed to be responsible for a high affinity to ACE2, undergo a change into an unstructured coil, which exhibits almost no binding possibilities to the ACE2 receptor. We also show that these severe structural changes upon electric-field application also occur in the mutant RBDs corresponding to the variants of concern (VOC) B.1.1.7 (UK), B.1.351 (South Africa) and P.1 (Brazil). Remarkably, while the structural flexibility of S allows the virus to improve its probability of entering the cell, it is also the origin of the surprising vulnerability of S upon application of electric fields of strengths at least two orders of magnitude smaller than those required for damaging most proteins. Our findings suggest the existence of a clean physical method to weaken the SARS-CoV-2 virus without further biochemical processing. Moreover, the effect could be used for infection prevention purposes and also to develop technologies for in-vitro structural manipulation of S. Since the method is largely unspecific, it can be suitable for application to other mutations in S, to other proteins of SARS-CoV-2 and in general to membrane proteins of other virus types.

The SARS-CoV-2 Spike protein is essential for viral infectivity and binds to the host receptor ACE2. Here, the authors present MD simulations of the Spike protein and its variants of concern and observe that the Spike protein is destabilised by moderate static electric fields, and undergoes field-induced conformational changes that hinder binding to ACE2.

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

Insights into anti-D formation in carriers of RhD variants through studies of 3D intraprotein interactions

BACKGROUND

Many RhD variants associated with anti-D formation (partial D) in carriers exposed to the conventional D antigen carry mutations affecting extracellular loop residues. Surprisingly, some carry mutations affecting transmembrane or intracellular domains, positions not thought likely to have a major impact on D epitopes.

STUDY DESIGN AND METHODS

A wild-type Rh trimer (RhD₁ RhAG₂ ) was modeled by comparative modeling with the human RhCG structure. Taking trimer conformation, residue accessibility, and position relative to the lipid bilayer into account, we redefine the domains of the RhD protein. We generated models for RhD variants carrying one or two amino acid substitutions associated with anti-D formation in published articles (25 variants) or abstracts (12 variants) and for RHD*weak D type 38. We determined the extracellular substitutions and compared the interactions of the variants with those of the standard RhD.

RESULTS

The findings of the three-dimensional (3D) analysis were correlated with anti-D formation for 76% of RhD variants: 15 substitutions associated with anti-D formation concerned extracellular residues, and structural differences in intraprotein interactions relative to standard RhD were observed in the others. We discuss the mechanisms by which D epitopes may be modified in variants in which the extracellular residues are identical to those of standard RhD and provide arguments for the benignity of p.T379M (RHD*DAU0) and p.G278D (RHD*weak D type 38) in transfusion medicine.

CONCLUSION

The study of RhD intraprotein interactions and the precise redefinition of residue accessibility provide insight into the mechanisms through which RhD point mutations may lead to anti-D formation in carriers.

nSite-Selective, Chemical Modification of Protein at Aromatic Side Chain and Their Emergent Applications

Site-selective chemical modification of protein side chain has probed enormous opportunities in the fundamental understanding of cellular biology and therapeutic applications. Primarily, in the field of biopharmaceutical where formulation of bioconjugates is found to be potential medicine than an individual constituent. In this regard, Lysine and Cysteine are the most widely used endogenous amino acid for these purposes. Recently, the aromatic side chain residues (Trp, Tyr, and His) that are low abundant in protein have gained more attention in therapeutic applications due to their advantages of chemical reactivity and specificity. This review discusses the site-selective bioconjugation methods for aromatic side chains (Trp, Tyr and His) and highlights the developed strategies in the last three years, along with their applications. Also, the review highlights the prevalent methods published earlier. We have examined that metal-catalyzed and photocatalytic reactions are gaining more attention for bioconjugation, though their practical operation is under development. The review has been summarized with the future perspective of protein and peptide conjugations contemplating therapeutic applications and challenges.

MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔT_m) were experimentally determined, we found that the average ΔT_m due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔT_m values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔT_m due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔT_m for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔT_m values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm

As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition. The source code and data of ProtFold-DFG can be downloaded from http://bliulab.net/ProtFold-DFG/download.

Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications

Proteins adopt different higher-order structures (HOS) to enable their unique biological functions. Understanding the complexities of protein higher-order structures and dynamics requires integrated approaches, where mass spectrometry (MS) is now positioned to play a key role. One of those approaches is protein footprinting. Although the initial demonstration of footprinting was for the HOS determination of protein/nucleic acid binding, the concept was later adapted to MS-based protein HOS analysis, through which different covalent labeling approaches "mark" the solvent accessible surface area (SASA) of proteins to reflect protein HOS. Hydrogen-deuterium exchange (HDX), where deuterium in D2O replaces hydrogen of the backbone amides, is the most common example of footprinting. Its advantage is that the footprint reflects SASA and hydrogen bonding, whereas one drawback is the labeling is reversible. Another example of footprinting is slow irreversible labeling of functional groups on amino acid side chains by targeted reagents with high specificity, probing structural changes at selected sites. A third footprinting approach is by reactions with fast, irreversible labeling species that are highly reactive and footprint broadly several amino acid residue side chains on the time scale of submilliseconds. All of these covalent labeling approaches combine to constitute a problem-solving toolbox that enables mass spectrometry as a valuable tool for HOS elucidation. As there has been a growing need for MS-based protein footprinting in both academia and industry owing to its high throughput capability, prompt availability, and high spatial resolution, we present a summary of the history, descriptions, principles, mechanisms, and applications of these covalent labeling approaches. Moreover, their applications are highlighted according to the biological questions they can answer. This review is intended as a tutorial for MS-based protein HOS elucidation and as a reference for investigators seeking a MS-based tool to address structural questions in protein science.

Determining the unbinding events and conserved motions associated with the pyrazinamide release due to resistance mutations of Mycobacterium tuberculosis pyrazinamidase

Pyrazinamide (PZA) is the only first-line antitubercular drug active against latent Mycobacterium tuberculosis (Mtb). It is activated to pyrazinoic acid by the pncA-encoded pyrazinamidase enzyme (PZase). Despite the emergence of PZA drug resistance, the underlying mechanisms of resistance remain unclear. This study investigated part of these mechanisms by modelling a PZA-bound wild type and 82 mutant PZase structures before applying molecular dynamics (MD) with an accurate Fe2+ cofactor coordination geometry. After observing nanosecond-scale PZA unbinding from several PZase mutants, an algorithm was developed to systematically detect ligand release via centre of mass distances (COM) and ligand average speed calculations, before applying the statistically guided network analysis (SGNA) method to investigate conserved protein motions associated with ligand unbinding. Ligand and cofactor perspectives were also investigated. A conserved pair of lid-destabilising motions was found. These consisted of (1) antiparallel lid and side flap motions; (2) the contractions of a flanking region within the same flap and residue 74 towards the core. Mutations affecting the hinge residues (H51 and H71), nearby residues or L19 were found to destabilise the lid. Additionally, other metal binding site (MBS) mutations delocalised the Fe2+ cofactor, also facilitating lid opening. In the early stages of unbinding, a wider variety of PZA poses were observed, suggesting multiple exit pathways. These findings provide insights into the late events preceding PZA unbinding, which we found to occur in some resistant PZase mutants. Further, the algorithm developed here to identify unbinding events coupled with SGNA can be applicable to other similar problems.

A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning

Identifying residue-residue contacts in protein-protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield accurate prediction. Inspired by the success of our deep-learning method for intraprotein contact prediction, we have developed RaptorX-ComplexContact, a web server for interprotein residue-residue contact prediction. Given a pair of interacting protein sequences, RaptorX-ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA) based on genomic distance and phylogeny information, respectively. Then, RaptorX-ComplexContact uses two deep convolutional residual neural networks (ResNet) to predict interprotein contacts from sequential features and coevolution information of paired MSAs. RaptorX-ComplexContact shall be useful for protein docking, protein-protein interaction prediction, and protein interaction network construction.

Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

BACKGROUND

Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge.

RESULTS

In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite-likelihood, i.e., the product of conditional probability of all residue pairs. Composite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, including PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction accuracy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present a successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset.

CONCLUSIONS

Composite likelihood maximization algorithm can efficiently estimate the parameters of Markov Random Fields and can improve the prediction accuracy of protein inter-residue contacts.

Inter-residue interactions in alpha-helical transmembrane proteins

MOTIVATION

The number of available membrane protein structures has markedly increased in the last years and, in parallel, the reliability of the methods to detect transmembrane (TM) segments. In the present report, we characterized inter-residue interactions in α-helical membrane proteins using a dataset of 3462 TM helices from 430 proteins. This is by far the largest analysis published to date.

RESULTS

Our analysis of residue-residue interactions in TM segments of membrane proteins shows that almost all interactions involve aliphatic residues and Phe. There is lack of polar-polar, polar-charged and charged-charged interactions except for those between Thr or Ser sidechains and the backbone carbonyl of aliphatic and Phe residues. The results are discussed in the context of the preferences of amino acids to be in the protein core or exposed to the lipid bilayer and to occupy specific positions along the TM segment. Comparison to datasets of β-barrel membrane proteins and of α-helical globular proteins unveils the specific patterns of interactions and residue composition characteristic of α-helical membrane proteins that are the clue to understanding their structure.

AVAILABILITY AND IMPLEMENTATION

Results data and datasets used are available at http://lmc.uab.cat/TMalphaDB/interactions.php.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting protein residue-residue contacts using random forests and deep networks

BACKGROUND

The ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the computational complexity of predicting the structure of proteins and even to help identify functionally important regions of proteins. These predictions are becoming especially important given the relatively low number of experimentally determined protein structures compared to the amount of available protein sequence data.

RESULTS

Here we have developed and benchmarked a set of machine learning methods for performing residue-residue contact prediction, including random forests, direct-coupling analysis, support vector machines, and deep networks (stacked denoising autoencoders). These methods are able to predict contacting residue pairs given only the amino acid sequence of a protein. According to our own evaluations performed at a resolution of +/- two residues, the predictors we trained with the random forest algorithm were our top performing methods with average top 10 prediction accuracy scores of 85.13% (short range), 74.49% (medium range), and 54.49% (long range). Our ensemble models (stacked denoising autoencoders combined with support vector machines) were our best performing deep network predictors and achieved top 10 prediction accuracy scores of 75.51% (short range), 60.26% (medium range), and 43.85% (long range) using the same evaluation. These tests were blindly performed on targets from the CASP11 dataset; and the results suggested that our models achieved comparable performance to contact predictors developed by groups that participated in CASP11.

CONCLUSIONS

Due to the challenging nature of contact prediction, it is beneficial to develop and benchmark a variety of different prediction methods. Our work has produced useful tools with a simple interface that can provide contact predictions to users without requiring a lengthy installation process. In addition to this, we have released our C++ implementation of the direct-coupling analysis method as a standalone software package. Both this tool and our RFcon web server are freely available to the public at http://dna.cs.miami.edu/RFcon /.

A large-scale comparative assessment of methods for residue-residue contact prediction

Sequence-based prediction of residue-residue contact in proteins becomes increasingly more important for improving protein structure prediction in the big data era. In this study, we performed a large-scale comparative assessment of 15 locally installed contact predictors. To assess these methods, we collected a big data set consisting of 680 nonredundant proteins covering different structural classes and target difficulties. We investigated a wide range of factors that may influence the precision of contact prediction, including target difficulty, structural class, the alignment depth and distribution of contact pairs in a protein structure. We found that: (1) the machine learning-based methods outperform the direct-coupling-based methods for short-range contact prediction, while the latter are significantly better for long-range contact prediction. The consensus-based methods, which combine machine learning and direct-coupling methods, perform the best. (2) The target difficulty does not have clear influence on the machine learning-based methods, while it does affect the direct-coupling and consensus-based methods significantly. (3) The alignment depth has relatively weak effect on the machine learning-based methods. However, for the direct-coupling-based methods and consensus-based methods, the predicted contacts for targets with deeper alignment tend to be more accurate. (4) All methods perform relatively better on β and α + β proteins than on α proteins. (5) Residues buried in the core of protein structure are more prone to be in contact than residues on the surface (22 versus 6%). We believe these are useful results for guiding future development of new approach to contact prediction.

Single-site labeling of histidine in proteins, on-demand reversibility, and traceless metal-free protein purification

A precision methodology distinguishes one His from all the nucleophilic residues and its multiple copies. An easy-to-operate C-N bond formation labels diverse proteins without adversely affecting their structure and function. The late-stage transformation allows installation of distinct probes. The chemically triggered reversibility enables traceless metal-free purification of proteins with a His-tag.

Understanding contact patterns of protein structures from protein contact map and investigation of unique patterns in the globin-like folded domains

Proteins are biochemical compounds made up of one or more polypeptides in a specific order, typically folded into a functionally active form. Proteins are categorized into four different structural classes according to the topology of α-helices and β-strands. In this study, we modeled these four structural classes as an undirected network depicting amino acids as nodes and interaction between them as edges. Results infer that basic protein classes can be easily recognized as well as distinguished by utilizing protein contact maps (PCM). Toward studying the globin-like fold, the helix-loop-helix region contacts were seen to be of a unique pattern, and these remained in all the folds. Further, the averaged diagonal contacts were analyzed and identified those contacts in α/β proteins were higher in comparison with the other class. Interesting, we noticed that anti-parallel beta sheets were dominant in all-β and α + β classes that lead to similar diagonal patterns. Network properties of all four basic classes were analyzed and found to possess small-world property. Findings infer that PCM may assist classify protein structure classes and it also helps in evaluating the predicted protein structures.

Structural and dynamic characterization of human Wnt2-Fzd7 complex using computational approaches

Wnt and Frizzled (Fzd) family members play crucial roles in the self-renewal of tumor-initiating cells. Until now, only a few studies have addressed the distinct mechanism of Wnt-Fzd interactions. In this study, we suggest a possible interaction mode of Wnt2 with the Fzd7 cysteine-rich domain (CRD)-both of which are up-regulated in some types of cancer. A combination of homology modeling, molecular docking and molecular dynamics (MD) simulations was carried out to study this ligand-receptor complex in great detail. The results demonstrated the unique dynamic behavior of Wnt2 upon binding to Fzd7. Interestingly, the β-strand content of the C-terminal binding site of Wnt2 was obviously reduced when bound to Fzd7 CRD. Moreover, the N-terminal and C-terminal binding sites of Wnt2 appeared to interact with the C-terminal and N-terminal binding sites of Fzd7, respectively. Calculation of the binding energies uncovered the pivotal role of electrostatic and hydrophobic interactions in the binding of Wnt2 to Fzd7 CRD. In conclusion, this study provides valuable insights into the mechanism of the Wnt2-Fzd7 CRD interaction for application in colorectal cancer prevention programs. Graphical abstract Flowchart representation of different steps used in this study.

Finding the needle in the haystack: towards solving the protein-folding problem computationally

Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.