Shape complementarity of protein-protein complexes at multiple resolutions

Computational designing of the ligands of Protein L affinity chromatography based on molecular docking and molecular dynamics simulations

Protein L is a multidomain protein from Peptostreptococcus magnus with binding affinity to kappa light chain of human immunoglobulin (Ig) which is used for the purification of antibody fragments by affinity chromatography. The advances in protein engineering and computational biology approaches lead to the development of engineered affinity ligands with improved properties including binding affinity. In this study, molecular dynamics simulations (MDs) and Osprey software were used to design single B domains of the Protein L with higher affinity to antibody fragments. The modified B domains were then polymerized to ligand with six B domains by homology modeling methods. The results showed that single B domain mutants of MB1 (Thr865Trp) and MB2 (Thr847Met-Thr865Trp) had higher binding affinity to Fab compared to the wild single B domain. Also, MDs and molecular docking results showed that the polymerized Proteins L including the wild and mutated six B domains (6B0, 6B1, and 6B2) were stable during MDs and the two mutants of 6B1 and 6B2 showed higher binding affinity to Fab relative to the wild type.Communicated by Ramaswamy H. Sarma.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Size of the protein-protein energy funnel in crowded environment

Association of proteins to a significant extent is determined by their geometric complementarity. Large-scale recognition factors, which directly relate to the funnel-like intermolecular energy landscape, provide important insights into the basic rules of protein recognition. Previously, we showed that simple energy functions and coarse-grained models reveal major characteristics of the energy landscape. As new computational approaches increasingly address structural modeling of a whole cell at the molecular level, it becomes important to account for the crowded environment inside the cell. The crowded environment drastically changes protein recognition properties, and thus significantly alters the underlying energy landscape. In this study, we addressed the effect of crowding on the protein binding funnel, focusing on the size of the funnel. As crowders occupy the funnel volume, they make it less accessible to the ligands. Thus, the funnel size, which can be defined by ligand occupancy, is generally reduced with the increase of the crowders concentration. This study quantifies this reduction for different concentration of crowders and correlates this dependence with the structural details of the interacting proteins. The results provide a better understanding of the rules of protein association in the crowded environment.

A bioinformatic approach of targeting SARS-CoV-2 replication by silencing a conserved alternative reserve of the orf8 gene using host miRNAs

The causative agent of the COVID-19 pandemic, the SARS-CoV-2 virus has yielded multiple relevant mutations, many of which have branched into major variants. The Omicron variant has a huge similarity with the original viral strain (first COVID-19 strain from Wuhan). Among different genes, the highly variable orf8 gene is responsible for crucial host interactions and has undergone multiple mutations and indels. The sequence of the orf8 gene of the Omicron variant is, however, identical with the gene sequence of the wild type. orf8 modulates the host immunity making it easier for the virus to conceal itself and remain undetected. Variants seem to be deleting this gene without affecting the viral replication. While analyzing, we came across the conserved orf7a gene in the viral genome which exhibits a partial sequence homology as well as functional similarity with the SARS-CoV-2 orf8. Hence, we have proposed here in our hypothesis that, orf7a might be an alternative reserve of orf8 present in the virus which was compensating for the lost gene. A computational approach was adopted where we screened various miRNAs targeted against the orf8 gene. These miRNAs were then docked onto the orf8 mRNA sequences. The same set of miRNAs was then used to check for their binding affinity with the orf7a reference mRNA. Results showed that miRNAs targeting the orf8 had favorable shape complementarity and successfully docked with the orf7a gene as well. These findings provide a basis for developing new therapeutic approaches where both orf8 and orf7a can be targeted simultaneously.

2D Zernike polynomial expansion: Finding the protein-protein binding regions

We present a method for efficiently and effectively assessing whether and where two proteins can interact with each other to form a complex. This is still largely an open problem, even for those relatively few cases where the 3D structure of both proteins is known. In fact, even if much of the information about the interaction is encoded in the chemical and geometric features of the structures, the set of possible contact patches and of their relative orientations are too large to be computationally affordable in a reasonable time, thus preventing the compilation of reliable interactome. Our method is able to rapidly and quantitatively measure the geometrical shape complementarity between interacting proteins, comparing their molecular iso-electron density surfaces expanding the surface patches in term of 2D Zernike polynomials. We first test the method against the real binding region of a large dataset of known protein complexes, reaching a success rate of 0.72. We then apply the method for the blind recognition of binding sites, identifying the real region of interaction in about 60% of the analyzed cases. Finally, we investigate how the efficiency in finding the right binding region depends on the surface roughness as a function of the expansion order.

Structural and free energy landscape of novel mutations in ribosomal protein S1 (rpsA) associated with pyrazinamide resistance

Resistance to key first-line drugs is a major hurdle to achieve the global end tuberculosis (TB) targets. A prodrug, pyrazinamide (PZA) is the only drug, effective in latent TB, recommended in drug resistance and susceptible Mycobacterium tuberculosis (MTB) isolates. The prodrug conversion into active form, pyrazinoic acid (POA), required the activity of pncA gene encoded pyrazinamidase (PZase). Although pncA mutations have been commonly associated with PZA resistance but a small number of resistance cases have been associated with mutationss in RpsA protein. Here in this study a total of 69 PZA resistance isolates have been sequenced for pncA mutations. However, samples that were found PZA resistant but pncA wild type (pncAWT), have been sequenced for rpsA and panD genes mutation. We repeated a drug susceptibility testing according to the WHO guidelines on 18 pncAWT MTB isolates. The rpsA and panD genes were sequenced. Out of total 69 PZA resistant isolates, 51 harbored 36 mutations in pncA gene (GeneBank Accession No. MH46111) while, fifteen different mutations including seven novel, were detected in the fourth S1 domain of RpsA known as C-terminal (MtRpsACTD) end. We did not detect any mutations in panD gene. Among the rpsA mutations, we investigated the molecular mechanism of resistance behind mutations, D342N, D343N, A344P, and I351F, present in the MtRpsACTD through molecular dynamic simulations (MD). WT showed a good drug binding affinity as compared to mutants (MTs), D342N, D343N, A344P, and I351F. Binding pocket volume, stability, and fluctuations have been altered whereas the total energy, protein folding, and geometric shape analysis further explored a significant variation between WT and MTs. In conclusion, mutations in MtRpsACTD might be involved to alter the RpsA activity, resulting in drug resistance. Such molecular mechanism behind resistance may provide a better insight into the resistance mechanism to achieve the global TB control targets.

Insight into novel clinical mutants of RpsA-S324F, E325K, and G341R of Mycobacterium tuberculosis associated with pyrazinamide resistance

Pyrazinamide (PZA) is an important component of first-line anti-tuberculosis drugs which is converted into active form, pyrazinoic acid (POA), by Mycobacterium tuberculosis (MTB) pncA gene encoded, pyrazinamidase (PZase). Mutations in pncA are detected in >70% of PZA resistant isolates but, noticeably, not in all. In this study, we selected 18 PZA-resistant but wild type pncA (pncAWT) MTB isolates. Drug susceptibility testing (DST) of all the isolates were repeated at the critical concentration of PZA drug. All these PZA-resistance but pncAWT isolates were subjected to RpsA sequencing. Fifteen different mutations were identified in eleven isolates, where seven were present in a conserved region including, Ser324Phe, Glu325Lys, Gly341Arg. As the molecular mechanism of resistance behind these variants has not been reported earlier, we have performed multiple analysis to unveil the mechanisms of resistance behind mutations S324F, E325K, and G341R. The mutant and wild type RpsA structures were subjected to comprehensive computational molecular dynamic simulations at 50 ns. Root mean square deviation (RMSD), Root mean square fluctuation (RMSF), and Gibbs free energy of mutants were analyzed in comparison with wild type. Docking score of wild type-RpsA has been found to be maximum, showing a strong binding affinity in comparison with mutants. Pocket volume, RMSD and RMSF have also been found to be altered, whereas total energy, folding effect (radius of gyration) and shape complimentarily analysis showed that variants S324F, E325K, and G341R have been playing a significant role behind PZA-resistance. The study offers valuable information for better management of drug resistance tuberculosis.

Structural Basis of Epitope Recognition by Heavy-Chain Camelid Antibodies

Truncated versions of heavy-chain antibodies (HCAbs) from camelids, also termed nanobodies, comprise only one-tenth the mass of conventional antibodies, yet retain similar, high binding affinities for the antigens. Here we analyze a large data set of nanobody-antigen crystal structures and investigate how nanobody-antigen recognition compares to the one by conventional antibodies. We find that nanobody paratopes are enriched in aromatic residues just like conventional antibodies, but additionally, they also bear a more hydrophobic character. Most striking differences were observed in the characteristics of the antigen's epitope. Unlike conventional antibodies, nanobodies bind to more rigid, concave, conserved and structured epitopes enriched with aromatic residues. Nanobodies establish fewer interactions with the antigens compared to conventional antibodies, and we speculate that high binding affinities are achieved due to less unfavorable conformational and more favorable solvation entropy contributions. We observed that interactions with antigen are mediated not only by three CDR loops but also by numerous residues from the nanobody framework. These residues are not distributed uniformly; rather, they are concentrated into four structurally distinct regions and mediate mostly charged interactions. Our findings suggest that in some respects nanobody-antigen interactions are more similar to the general protein-protein interactions rather than antibody-antigen interactions.

Engineered protein scaffolds as leads for synthetic inhibitors of protein-protein interactions

Rationally designed protein-protein interaction inhibitors mimic interfacial binding epitopes, specifically residues that contribute significantly to binding. However, direct mimicry often does not lead to high affinity ligands because the natural complexes themselves are functionally transient and of low affinity. The mimics typically need to be optimized for potency. Engineered proteins displaying conformationally-defined epitopes may serve as attractive alternatives to natural protein partners as they can be strictly screened for tight binding. The advantage of focused screens with conformationally-defined protein scaffolds is that conservation of the geometry of the natural binding epitopes may preserve binding site specificity while allowing direct mimicry by various synthetic secondary structure scaffolds. Here we review different classes of engineered proteins for their binding epitope geometry and as leads for synthetic secondary and tertiary structure mimics.

Rotation of artificial rotor axles in rotary molecular motors

F₁- and V₁-ATPase are rotary molecular motors that convert chemical energy released upon ATP hydrolysis into torque to rotate a central rotor axle against the surrounding catalytic stator cylinder with high efficiency. How conformational change occurring in the stator is coupled to the rotary motion of the axle is the key unknown in the mechanism of rotary motors. Here, we generated chimeric motor proteins by inserting an exogenous rod protein, FliJ, into the stator ring of F₁ or of V₁ and tested the rotation properties of these chimeric motors. Both motors showed unidirectional and continuous rotation, despite no obvious homology in amino acid sequence between FliJ and the intrinsic rotor subunit of F₁ or V₁ These results showed that any residue-specific interactions between the stator and rotor are not a prerequisite for unidirectional rotation of both F₁ and V₁ The torque of chimeric motors estimated from viscous friction of the rotation probe against medium revealed that whereas the F₁-FliJ chimera generates only 10% of WT F₁, the V₁-FliJ chimera generates torque comparable to that of V₁ with the native axle protein that is structurally more similar to FliJ than the native rotor of F₁ This suggests that the gross structural mismatch hinders smooth rotation of FliJ accompanied with the stator ring of F₁.

Protein-protein docking: from interaction to interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.

Structure of the Sgt2 dimerization domain complexed with the Get5 UBL domain involved in the targeting of tail-anchored membrane proteins to the endoplasmic reticulum

The insertion of tail-anchored membrane (TA) proteins into the appropriate membrane is a post-translational event that requires stabilization of the transmembrane domain and targeting to the proper destination. Sgt2, a small glutamine-rich tetratricopeptide-repeat protein, is a heat-shock protein cognate (HSC) co-chaperone that preferentially binds endoplasmic reticulum-destined TA proteins and directs them to the GET pathway via Get4 and Get5. The N-terminal domain of Sgt2 seems to exert dual functions. It mediates Get5 interaction and allows substrate delivery to Get3. Following the N-terminus of Get5 is a ubiquitin-like (Ubl) domain that interacts with the N-terminus of Sgt2. Here, the crystal structure of the Sgt2 dimerization domain complexed with the Get5 Ubl domain (Sgt2N-Get5Ubl) is reported. This complex reveals an intimate interaction between one Sgt2 dimer and one Get5 monomer. This research further demonstrates that hydrophobic residues from both Sgt2 and Get5 play an important role in cell survival under heat stress. This study provides detailed molecular insights into the specific binding of this GET-pathway complex.

Protein-protein alternative binding modes do not overlap

Proteins often bind other proteins in more than one way. Thus alternative binding modes is an essential feature of protein interactions. Such binding modes may be detected by X-ray crystallography and thus reflected in Protein Data Bank. The alternative binding is often observed not for the protein itself but for its structural homolog. The results of this study based on the analysis of a comprehensive set of co-crystallized protein-protein complexes show that the alternative binding modes generally do not overlap, but are spatially separated. This effect is based on molecular recognition characteristics of the protein structures. The results are also in excellent agreement with the intermolecular energy funnel size estimates obtained previously by an independent methodology. The results provide an important insight into the principles of protein association, as well as potential guidelines for modeling of protein complexes and the design of protein interfaces.

Molecular modeling of protein-protein interaction to decipher the structural mechanism of nonhost resistance in rice

Nonhost resistance (NHR) is the most common and durable form of plant resistance to disease-causing organisms. A successful example of NHR is the cloning of a maize R gene Rxo1 in rice and validating its function in conferring bacterial streak resistance in transgenic rice lines. In order to understand the structural mechanism of NHR in rice, we built the model of the protein-protein interaction between the encoded Rxo1 (RXO1) and AvrRXO1 (avirulence protein of rice pathogen, Xanthomonas oryzae pv. oryzicola). Interestingly, although a RXO1 homolog in rice (RHR) is present, it does not interact with AvrRXO1 in nature. We have confirmed that the specificity of RXO1-AvrRXO1 interaction originates from the structured leucine rich repeat (LRR) domain of RXO1, facilitating the recognition process, while the absence of such ordered LRR region makes RHR unfavorable to recognize AvrRXO1. We postulate that the RXO1-AvrRXO1 complex formation is a three step process where electrostatic interactions, shape complementarity and short-range interactions play an important role. The presence of the structural and physicochemical properties essential for the protein-protein recognition process empowers RXO1 to mediate NHR, which the host protein RHR lacks and consequently loses its specificity to bind with AvrRXO1. To the best of our knowledge, this is the first report on the understanding of NHR in rice from the structural perspective of protein-protein interaction.

Low-resolution structural modeling of protein interactome

Structural characterization of protein-protein interactions across the broad spectrum of scales is key to our understanding of life at the molecular level. Low-resolution approach to protein interactions is needed for modeling large interaction networks, given the significant level of uncertainties in large biomolecular systems and the high-throughput nature of the task. Since only a fraction of protein structures in interactome are determined experimentally, protein docking approaches are increasingly focusing on modeled proteins. Current rapid advancement of template-based modeling of protein-protein complexes is following a long standing trend in structure prediction of individual proteins. Protein-protein templates are already available for almost all interactions of structurally characterized proteins, and about one third of such templates are likely correct.

Optimized grid-based protein-protein docking as a global search tool followed by incorporating experimentally derivable restraints

Unbound protein docking, or the computational prediction of the structure of a protein complex from the structures of its separated components, is of importance but still challenging. A practical approach toward reliable results for unbound docking is to incorporate experimentally derived information with computation. To this end, truly systematic search of the global docking space is desirable. The fast Fourier transform (FFT) docking is a systematic search method with high computational efficiency. However, by using FFT to perform unbound docking, possible conformational changes upon binding must be treated implicitly. To better accommodate the implicit treatment of conformational flexibility, we develop a rational approach to optimize "softened" parameters for FFT docking. In connection with the increased "softness" of the parameters in this global search step, we use a revised rule to select candidate models from the search results. For complexes designated as of low and medium difficulty for unbound docking, these adaptations of the original FTDOCK program lead to substantial improvements of the global search results. Finally, we show that models resulted from FFT-based global search can be further filtered with restraints derivable from nuclear magnetic resonance (NMR) chemical shift perturbation or mutagenesis experiments, leading to a small set of models that can be feasibly refined and evaluated using computationally more expensive methods and that still include high-ranking near-native conformations.

Protein-protein docking using region-based 3D Zernike descriptors

Background

Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur.

Results

We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-αRMSD ≤ 2.5 Å) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases.

Conclusion

We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods.