M-ZDOCK: a grid-based approach for Cn symmetric multimer docking

Exploring the structural and functional dynamics of trimeric and tetrameric states of influenza encoded PB1-F2 viroporin through molecular dynamics simulations

Influenza Viruses have always been a major health concern due to their highly contagious nature. The PB1-F2 viroporin encoded by the influenza A virus is known to be a pro-apoptotic protein involved in cell death induction of the host immune cells. The structural arrangement and the mode of action of PB1-F2 viroporin have not been fully understood yet. Nonetheless, there is limited information on the oligomeric state of PB1-F2 and its possible role in the pore formation which could act as a channel for ion transport. The probable oligomeric structural existences of the viroporin and their channel-like behavior need to be explored in light of experimental reports cited in the literature. In our study, we report on the structural and dynamical properties of the trimeric and tetrameric state of PB1-F2, investigated by molecular dynamics simulations with improved sampling of conformational states as the initial focus of the study is to establish a rationale for their existence in a lipid environment. The simulation study provides detailed information on the mitochondrial membrane permeation pathway which causes the leakage of mitochondrial contents like cytochrome C and induces apoptosis. By focusing on low-order oligomers, trimer, and tetramer, we have identified key pore-forming characteristics that serve as a foundation for understanding the pro-apoptotic activity of PB1-F2. The structural and dynamical properties of these states were evaluated in the light of experimental reports, which reveal the tetrameric form to be the preferable state in the lipid environment, demonstrating superior structural stability, effective channel symmetry, and ion permeation compared to the higher-order oligomers besides trimer including pentameric and hexameric assemblies. The simulation results also explore the typical ion transportation criteria based on finding a less energetic barrier for ions/water molecules crossing the membrane.

Synergistic interaction of amphotericin B and betulinic acid against clinically important fungi: evidence from in vitro and in silico techniques

Betulinic acid (BA), in combined application with amphotericin B, shows a synergistic effect against Candida, Aspergillus, Scedosporium, Fusarium, and Mucorales fungi at a concentration as low as 0.125 µg/mL. Amphotericin B showed slightly higher affinity towards BA than toward ergosterol, according to our in silico molecular docking results, explaining the observed Eagle effect. Moreover, it can bind both molecules simultaneously, suggesting the possibility of the formation of mixed pores, thus increasing the membrane-disrupting activity.IMPORTANCEThe rising incidence of invasive fungal infections, coupled with the emergence of antifungal resistance, presents a significant challenge in clinical settings. The inherent resistance of certain fungi to conventional antifungal agents, alongside the limitations posed by side effects and drug interactions, necessitates the exploration of alternative therapeutic strategies. This study highlights the potential of combining amphotericin B (AmB) with betulinic acid (BA) to enhance antifungal efficacy against clinically relevant pathogens, including Candida albicans and Aspergillus fumigatus, as well as mucormycosis-causing fungi. The results demonstrate the synergistic interactions between AmB and BA, which effectively inhibited fungal growth at lower concentrations and are within reported serum levels. In silico molecular docking studies further support the hypothesis that BA may facilitate AmB's mechanism of action, potentially leading to increased pore formation in fungal membranes.

Characterization of a novel GRHL2 mutation reveals molecular mechanisms underlying autosomal dominant hearing loss (DFNA28): insights from structural and functional studies

The GRHL2 gene, encoding the Grainyhead-like 2 transcription factor, is essential for various biological processes. While GRHL2 has a complex role in cancer biology, its genetic variants have been also implicated in different forms of hearing loss (HL), including autosomal dominant non-syndromic hearing loss (DFNA28). Here, we report a novel c.1061C>T, p.(Ala354Val) mutation within the DNA binding domain (DBD) of GRHL2 that was identified in a three-generation HL family using a targeted multi-gene panel covering 237 HL-related genes. Unlike the previously reported DFNA28-causing variants that result in protein truncation, the impact of the p.(Ala354Val) missense change cannot be attributed to GRHL2 transcript level or composition, but to an alteration in protein function. Molecular dynamics simulations revealed destabilization of the p.(Ala354Val) mutant GRHL2 dimer interface and an altered DNA binding dynamics, leading to chaotic interaction patterns despite increased binding affinity to DNA. Functional assays demonstrated that the p.(Ala354Val) mutation and other DFNA28-related mutations in the DBD lead to loss of GRHL2 transcriptional transactivation activity, while the p.(Arg537Profs*11) mutation in the dimerization domain results in a gain-of-function effect. The findings indicate that both GRHL2 haploinsufficiency and gain-of-function contribute to HL and underscore the complex regulatory role of GRHL2 in maintaining proper function of the auditory system. Our study emphasizes the need to consider structural and functional aspects of gene variants to better understand their pathogenic potential. As GRHL2 is involved in a multitude of cellular processes, the data gathered here can be also applicable to other conditions.

Design of Multimodal Supramolecular Protein Assemblies via Enzyme-Substrate Interactions for Intracellular Antioxidant Regulation

Allosteric modulation of protein function, which involves effector binding triggering distant conformational changes, is crucial for cellular and metabolic control. However, achieving tunable control, structural diversity, and precise intracellular regulation remains challenging. Here, we designed dynamic supramolecular protein assemblies driven by enzyme-substrate interactions for antioxidant regulation in cells. Using a glutathione S-transferase modified with a cysteine mutation (GSTK77C), we engineered an effector molecule (GMP4M) containing a glutathione (GSH) moiety and maleimide group linked by a PEG chain. This system forms hierarchical protein assemblies with diverse morphologies, including nanowires, nanorings, nanobranches, and nanotwists, and switchable "ON/OFF" enzymatic activity modulated by endogenous GSH. The assemblies maintain structural integrity under physiological conditions, show remarkable reversibility, and outperform native GST in stability and environmental adaptability. This approach provides a versatile platform for creating tunable and diverse protein assemblies with broad applications in antioxidant therapies and biomedical interventions.

Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47-55

We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approaches integrated classical pipelines developed in our group as well as more recently developed deep learning pipelines. In the cases of human group prediction, we surveyed the literature to find information to integrate into the modeling, such as assayed interface residues. In addition to any literature information, generated complex models were selected by a rank aggregation of statistical scoring functions, by generative model confidence, or by expert inspection. In these CAPRI rounds, our human group successfully modeled eight interfaces and achieved the top quality level among the submissions for all of them, including two where no other group did. We note that components of our modeling pipelines have become increasingly unified within deep learning approaches. Finally, we discuss several case studies that illustrate successful and unsuccessful modeling using our approaches.

Prediction of the Trimer Protein Interface Residue Pair by CNN-GRU Model Based on Multi-Feature Map

Most life activities of organisms are realized through protein-protein interactions, and these interactions are mainly achieved through residue-residue contact between monomer proteins. Consequently, studying residue-residue contact at the protein interaction interface can contribute to a deeper understanding of the protein-protein interaction mechanism. In this paper, we focus on the research of the trimer protein interface residue pair. Firstly, we utilize the amino acid k-interval product factor descriptor (AAIPF(k)) to integrate the positional information and physicochemical properties of amino acids, combined with the electric properties and geometric shape features of residues, to construct an 8 × 16 multi-feature map. This multi-feature map represents a sample composed of two residues on a trimer protein. Secondly, we construct a CNN-GRU deep learning framework to predict the trimer protein interface residue pair. The results show that when each dimer protein provides 10 prediction results and two protein-protein interaction interfaces of a trimer protein needed to be accurately predicted, the accuracy of our proposed method is 60%. When each dimer protein provides 10 prediction results and one protein-protein interaction interface of a trimer protein needs to be accurately predicted, the accuracy of our proposed method is 93%. Our results can provide experimental researchers with a limited yet precise dataset containing correct trimer protein interface residue pairs, which is of great significance in guiding the experimental resolution of the trimer protein three-dimensional structure. Furthermore, compared to other computational methods, our proposed approach exhibits superior performance in predicting residue-residue contact at the trimer protein interface.

AlphaFold and Docking Approaches for Antibody-Antigen and Other Targets: Insights From CAPRI Rounds 47-55

Accurate modeling of the structures of protein-protein complexes and other biomolecular interactions represents a longstanding and important challenge for computational biology. The Critical Assessment of PRedicted Interactions (CAPRI) experiment has served for over two decades as a key means to assess and compare current approaches and methods through blind predictive scenarios, highlighting useful strategies, and new developments. Here we describe the performance of our laboratory's team in recent CAPRI rounds, which included submissions for 10 modeling targets. Our team utilized a range of docking and modeling approaches, including ZDOCK, Rosetta, and ZRANK, to model, refine, and score protein-protein and protein-DNA complexes. For recent targets we utilized adaptations of AlphaFold to generate models, leading to near-native models for an antibody-peptide target, and a highly accurate (but low ranked) model for an antibody-MHC complex. These results underscore the utility of AlphaFold-based protocols for predictive protein complex modeling, including for immune recognition, and highlight considerations regarding the use of AlphaFold confidence metrics in model selection.

Casein kinase 1α mediates estradiol secretion via CYP19A1 expression in mouse ovarian granulosa cells

BACKGROUND

Casein kinase 1α (CK1α), expressed in both ovarian germ and somatic cells, is involved in the initial meiosis and primordial follicle formation of mouse oocytes. Using in vitro and in vivo experiments in this study, we explored the function and mechanism of CK1α in estrogen synthesis in mice ovarian granulosa cells.

METHODS

A CK1α knockout (cKO) mouse model, targeted specifically to ovarian granulosa cells (GCs), was employed to establish the influence of CK1α on in vivo estrogen synthesis. The influence of CK1α deficiency on GCs was determined in vivo and in vitro by immunofluorescence analysis and Western blot assay. Transcriptome profiling, differentially expressed genes and gene functional enrichment analyses, and computation protein-protein docking, were further employed to assess the CK1α pathway. Furthermore, wild-type female mice were treated with the CK1α antagonist D4476 to elucidate the CK1α's role in estrogen regulation.

RESULTS

Ovarian GCs CK1α deficiency impaired fertility and superovulation of female mice; also, the average litter size and the estradiol (E2) level in the serum of cKO female mice were decreased by 57.3% and 87.4% vs. control mice, respectively. This deficiency disrupted the estrous cycle and enhanced the apoptosis in the GCs. We observed that CK1α mediated the secretion of estradiol in mouse ovarian GCs via the cytochrome P450 subfamily 19 member 1 (CYP19A1).

CONCLUSIONS

These findings improve the existing understanding of the regulation mechanism of female reproduction and estrogen synthesis.

TRIAL REGISTRATION

Not applicable.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

A morpheein equilibrium regulates catalysis in phosphoserine phosphatase SerB2 from Mycobacterium tuberculosis

Mycobacterium tuberculosis phosphoserine phosphatase MtSerB2 is of interest as a new antituberculosis target due to its essential metabolic role in L-serine biosynthesis and effector functions in infected cells. Previous works indicated that MtSerB2 is regulated through an oligomeric transition induced by L-Ser that could serve as a basis for the design of selective allosteric inhibitors. However, the mechanism underlying this transition remains highly elusive due to the lack of experimental structural data. Here we describe a structural, biophysical, and enzymological characterisation of MtSerB2 oligomerisation in the presence and absence of L-Ser. We show that MtSerB2 coexists in dimeric, trimeric, and tetrameric forms of different activity levels interconverting through a conformationally flexible monomeric state, which is not observed in two near-identical mycobacterial orthologs. This morpheein behaviour exhibited by MtSerB2 lays the foundation for future allosteric drug discovery and provides a starting point to the understanding of its peculiar multifunctional moonlighting properties.

New insights on the differential interaction of sulfiredoxin with members of the peroxiredoxin family revealed by protein-protein docking and experimental studies

Sulfiredoxin (Srx) is the enzyme that restores the peroxidase activity of peroxiredoxins (Prxs) through catalyzing the reduction of hyperoxidized Prxs back to their active forms. This process involves protein-protein interaction in an enzyme-substrate binding manner. The integrity of the Srx-Prx axis contributes to the pathogenesis of various oxidative stress related human disorders including cancer, inflammation, cardiovascular and neurological diseases. The purpose of this study is to understand the structural and molecular biology of the Srx-Prx interaction, which may be of significance for prediction of target site for the novel drug-discovery. Homology modeling and protein-protein docking approaches were applied to examine the Srx-Prx interaction using online platforms including ITASSER, Phyre2, Swissmodel, AlphaFold, MZDOCK and ZDOCK. By in-silico studies, A 26-amino acid motif at the C-terminus of Prx1 was predicted to cause a steric hindrance for the kinetics of the Srx-Prx1 interaction. These predictions were tested in-vitro using purified recombinant proteins including Srx, full-length Prxs, and C-terminus deleted Prxs. We confirmed that deletion of the C-terminus of Prxs significantly enhanced its rate of association with Srx (i.e. >1000 fold increase in the ka of the Srx-Prx1 interaction) with minimal effect on the rate of dissociation (kd). Differential interaction of Srx with individual members of the Prx family was further examined in cultured cells. Taken together, these data add novel molecular and structural insights critical for the understanding of the biology of the Srx-Prx interaction that may be of value for the development of targeted therapy for human disorders.

Dimerization of European Robin Cryptochrome 4a

Homo-dimer formation is important for the function of many proteins. Although dimeric forms of cryptochromes (Cry) have been found by crystallography and were recently observed in vitro for European robin Cry4a, little is known about the dimerization of avian Crys and the role it could play in the mechanism of magnetic sensing in migratory birds. Here, we present a combined experimental and computational investigation of the dimerization of robin Cry4a resulting from covalent and non-covalent interactions. Experimental studies using native mass spectrometry, mass spectrometric analysis of disulfide bonds, chemical cross-linking, and photometric measurements show that disulfide-linked dimers are routinely formed, that their formation is promoted by exposure to blue light, and that the most likely cysteines are C317 and C412. Computational modeling and molecular dynamics simulations were used to generate and assess a number of possible dimer structures. The relevance of these findings to the proposed role of Cry4a in avian magnetoreception is discussed.

In silico insights into the dimer structure and deiodinase activity of type III iodothyronine deiodinase from bioinformatics, molecular dynamics simulations, and QM/MM calculations

The homodimeric family of iodothyronine deiodinases (Dios) regioselectively remove iodine from thyroid hormones. Currently, structural data has only been reported for the monomer of the mus type III thioredoxin (Trx) fold catalytic domain (Dio3Trx), but the mode of dimerization has not yet been determined. Various groups have proposed dimer structures that are similar to the A-type and B-type dimerization modes of peroxiredoxins. Computational methods are used to compare the sequence of Dio3Trx to related proteins known to form A-type and B-type dimers. Sequence analysis and in silico protein-protein docking methods suggest that Dio3Trx is more consistent with proteins that adopt B-type dimerization. Molecular dynamics (MD) simulations of the refined Dio3Trx dimer constructed using the SymmDock and GalaxyRefineComplex databases indicate stable dimer formation along the β4α3 interface consistent with other Trx fold B-type dimers. Free energy calculations show that the dimer is stabilized by interdimer interactions between the β-sheets and α-helices. A comparison of MD simulations of the apo and thyroxine-bound dimers suggests that the active site binding pocket is not affected by dimerization. Determination of the transition state for deiodination of thyroxine from the monomer structure using QM/MM methods provides an activation barrier consistent with previous small model DFT studies.Communicated by Ramaswamy H. Sarma.

Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Domain-Based Protein Docking with Extremely Large Conformational Changes

Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes.

RL-MLZerD: Multimeric protein docking using reinforcement learning

Numerous biological processes in a cell are carried out by protein complexes. To understand the molecular mechanisms of such processes, it is crucial to know the quaternary structures of the complexes. Although the structures of protein complexes have been determined by biophysical experiments at a rapid pace, there are still many important complex structures that are yet to be determined. To supplement experimental structure determination of complexes, many computational protein docking methods have been developed; however, most of these docking methods are designed only for docking with two chains. Here, we introduce a novel method, RL-MLZerD, which builds multiple protein complexes using reinforcement learning (RL). In RL-MLZerD a multi-chain assembly process is considered as a series of episodes of selecting and integrating pre-computed pairwise docking models in a RL framework. RL is effective in correctly selecting plausible pairwise models that fit well with other subunits in a complex. When tested on a benchmark dataset of protein complexes with three to five chains, RL-MLZerD showed better modeling performance than other existing multiple docking methods under different evaluation criteria, except against AlphaFold-Multimer in unbound docking. Also, it emerged that the docking order of multi-chain complexes can be naturally predicted by examining preferred paths of episodes in the RL computation.

A mutation in the S6 segment of the KvAP channel changes the secondary structure and alters ion channel activity in a lipid bilayer membrane

The peptide segment S6 is known to form the inner lining of the voltage-gated K⁺ channel KvAP (potassium channel of archaea-bacterium, Aeropyrum pernix). In our previous work, it has been demonstrated that S6 itself can form an ion channel on a bilayer lipid membrane (BLM). In the present work, the role of a specific amino acid sequence 'LIG' in determining the secondary structure of S6 has been investigated. For this purpose, 22-residue synthetic peptides named S6-Wild (S6W) and S6-Mutant (S6M) were used. Sequences of these peptides are similar except that the two amino acids isoleucine and glycine of the wild peptide interchanged in the mutant peptide. Channel forming capabilities of both the peptides were checked electro-physiologically on BLM composed of DPhPC and cholesterol. Bilayer electrophysiological experiments showed that the conductance of S6M is higher than that of S6W. Significant differences in the current versus voltage (I-V) plot, open probability, and gating characteristics were observed. Interestingly, two sub-types of channels, S6M Type 1 and Type 2, were identified in S6M differing in conductances and open probability patterns. Circular dichroism (CD) spectroscopy indicated that the secondary structures of the two peptides are different in phosphatidyl choline/asolectin liposomes and 1% SDS detergent. Reduced helicity of S6M was also noticed in membrane mimetic liposomes and 1% SDS detergent micelles. These results are interpreted in view of the difference in hydrophobicity of the two amino acids, isoleucine and glycine. It is concluded that the 'LIG' stretch regulates the structure and pore-forming ability of the S6 peptide.

Predictive Modeling of Neurotoxic α-Synuclein Polymorphs

Assembly of monomeric α-synuclein (αS) into aggregation-resistant helically folded tetramers and related multimers is a key target for Parkinson's disease (PD). Protein dynamics hampers experimental characterization of the polymorphism of these structures and so computational modeling and simulation is providing a complementary approach to obtain high-resolution structural information on the assembly of αS and interactions with biological surfaces. These computational techniques are particularly valuable for intrinsically disordered proteins (IDPs) and short-lived peptide and protein assemblies with as yet undetermined 3D structures. Experimental observables such as NMR J-coupling constants and chemical shifts can be predicted directly from simulation data, and compared with available experimental data to generate the most physically realistic atomic-resolution structure. For appropriately validated and benchmarked computational models, macroscopic aggregation properties can be related to the calculated thermodynamic properties at an atomic level. In this chapter, we describe a useful protocol for designing helical αS multimers, especially tetramers, and scanning the peptide-membrane interface for cell-bound αS tetramers. These computationally modeled structures are validated by comparison with the range of available known experimental parameters at time of writing in early 2020, and used to generate predictive design rules to motivate and guide experiments.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches

Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.

Modeling Perturbations in Protein Filaments at the Micro and Meso Scale Using NAMD and PTools/Heligeom

Protein filaments are dynamic entities that respond to external stimuli by slightly or substantially modifying the internal binding geometries between successive protomers. This results in overall changes in the filament architecture, which are difficult to model due to the helical character of the system. Here, we describe how distortions in RecA nucleofilaments and their consequences on the filament-DNA and bound DNA-DNA interactions at different stages of the homologous recombination process can be modeled using the PTools/Heligeom software and subsequent molecular dynamics simulation with NAMD. Modeling methods dealing with helical macromolecular objects typically rely on symmetric assemblies and take advantage of known symmetry descriptors. Other methods dealing with single objects, such as MMTK or VMD, do not integrate the specificities of regular assemblies. By basing the model building on binding geometries at the protomer-protomer level, PTools/Heligeom frees the building process from a priori knowledge of the system topology and enables irregular architectures and symmetry disruption to be accounted for. Graphical abstract: Model of ATP hydrolysis-induced distortions in the recombinant nucleoprotein, obtained by combining RecA-DNA and two RecA-RecA binding geometries.

Atomistic basis of force generation, translocation, and coordination in a viral genome packaging motor

Double-stranded DNA viruses package their genomes into pre-assembled capsids using virally-encoded ASCE ATPase ring motors. We present the first atomic-resolution crystal structure of a multimeric ring form of a viral dsDNA packaging motor, the ATPase of the asccφ28 phage, and characterize its atomic-level dynamics via long timescale molecular dynamics simulations. Based on these results, and previous single-molecule data and cryo-EM reconstruction of the homologous φ29 motor, we propose an overall packaging model that is driven by helical-to-planar transitions of the ring motor. These transitions are coordinated by inter-subunit interactions that regulate catalytic and force-generating events. Stepwise ATP binding to individual subunits increase their affinity for the helical DNA phosphate backbone, resulting in distortion away from the planar ring towards a helical configuration, inducing mechanical strain. Subsequent sequential hydrolysis events alleviate the accumulated mechanical strain, allowing a stepwise return of the motor to the planar conformation, translocating DNA in the process. This type of helical-to-planar mechanism could serve as a general framework for ring ATPases.

Peroxiredoxin1 Knockdown Inhibits Oral Carcinogenesis via Inducing Cell Senescence Dependent on Mitophagy

Purpose

Cellular senescence is a physiological phenomenon by which cells irreversibly lose their proliferative potential. It is not clear whether senescent cells are related to malignant transformation in oral precancerous lesions. The role of peroxiredoxin1 (Prx1)-induced cell senescence in OLK malignant transformation has not been reported. The aim of this study is to investigate the role and mechanism of cell senescence in oral carcinogenesis.

Methods

In this study, 4-nitro-quinoline-1-oxide (4NQO) induced tongue carcinogenesis model in Prx1^+/+ and Prx1^+/- mice and dysplastic oral keratinocyte (DOK) were used. Prx1 knockdown DOK cells were harvested with shRNA injection, and cell senescence was detected via the senescence-associated β-galactosidase (SA β-gal) assay. The senescence and mitophagy-related proteins were observed by immunohistochemistry (IHC), Western blot and qRT-PCR. The binding of Prx1 with prohibitin 2 (PHB2) and light chain 3 (LC3) was predicted via ZDOCK and measured in mice by Duolink analysis.

Results

Histologically, 4NQO treatment induced epithelial hyperplasia, dysplasia (mild, moderate and severe), carcinomas in situ and oral squamous cell carcinoma (OSCC) in mouse tongue mucosa. The malignant transformation rate in Prx1^+/- mice (37.5%) was significantly lower compared with Prx1^+/+ mice (57.1%). In Prx1^+/+ mice, a higher number of senescent cells and greater expression of p53 and p21 were observed in hyperplastic and dysplastic tongue tissues when compared with those in OSCC tissues. Prx1 knockdown induced a greater number of senescent cells in hyperplastic tissues, and DOK cells accompanied cell cycle arrest at the G1 phase and PHB2/LC3II downregulation. Prx1 was predicted to dock with PHB2 and LC3 via ZDOCK, and the interactions were confirmed by in situ Duolink analysis.

Conclusion

Prx1 silencing inhibits the oral carcinogenesis by inducing cell senescence dependent on mitophagy.

Computational structure modeling for diverse categories of macromolecular interactions

Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.

Prediction of human tau 3D structure, and interplay between O-β-GlcNAc and phosphorylation modifications in Alzheimer's disease: C. elegans as a suitable model to study these interactions in vivo

Tau protein regulates, maintains and stabilizes microtubule assembly under normal physiological conditions. In certain pathological circumstances, tau is post-translationally modified predominantly via phosphorylation and glycosylation. Hyper-phosphorylation of tau in Alzheimer's disease (AD) resulted in aggregated neurofibrillary tangles (NFTs) formation. Unfortunately, absence of tau 3D structure makes difficult to understand exact mechanism involved in tau pathology. Here by using ab-initio modelling, we predicted a tau 3D structure that not only explains its binding with microtubules but also elucidates NFTs formation. O-linked β-N-acetylglucosaminylation (O-β-GlcNAc) is thought to regulate tau phosphorylation on single or proximal Ser/Thr residues (called as Yin-Yang sites). In this study, we not only validate the previously described three-serine residues (208, 238 and 400) as Yin-Yang sites but also predicted 22 more possible Ser/Thr O-glycosylation sites. Among them seventeen residues were predicted as possible Yin-Yang sites and are proposed to mediate NFT formation in AD. These predicted Yin-Yang sites may act as attractive therapeutic targets for the drug development in AD. Predicted 3D structure of tau₄₄₁ was highly accessible for phosphorylation and hyperphosphorylation, and showed higher surface accessibility for interplay between O-β-GlcNAc and phosphorylation modifications. Kinases and phosphatases involved in tau phosphorylation are conserved in human and other organisms. Homology modelling revealed conserved catalytic domain for both human and C. elegans O-GlcNAc transferase (OGT), suggesting that transgenic C. elegans expressing human tau may be a suitable model system to study these modifications.

Structural analysis of the PATZ1 BTB domain homodimer

PATZ1 is a ubiquitously expressed transcriptional repressor belonging to the ZBTB family that is functionally expressed in T lymphocytes. PATZ1 targets the CD8 gene in lymphocyte development and interacts with the p53 protein to control genes that are important in proliferation and in the DNA-damage response. PATZ1 exerts its activity through an N-terminal BTB domain that mediates dimerization and co-repressor interactions and a C-terminal zinc-finger motif-containing domain that mediates DNA binding. Here, the crystal structures of the murine and zebrafish PATZ1 BTB domains are reported at 2.3 and 1.8 Å resolution, respectively. The structures revealed that the PATZ1 BTB domain forms a stable homodimer with a lateral surface groove, as in other ZBTB structures. Analysis of the lateral groove revealed a large acidic patch in this region, which contrasts with the previously resolved basic co-repressor binding interface of BCL6. A large 30-amino-acid glycine- and alanine-rich central loop, which is unique to mammalian PATZ1 amongst all ZBTB proteins, could not be resolved, probably owing to its flexibility. Molecular-dynamics simulations suggest a contribution of this loop to modulation of the mammalian BTB dimerization interface.

A computational model of ESAT-6 complex in membrane

One quarter of the world's population are infected by Mycobacterium tuberculosis (Mtb), which is a leading death-causing bacterial pathogen. Recent evidence has demonstrated that two virulence factors, ESAT-6 and CFP-10, play crucial roles in Mtb's cytosolic translocation. Many efforts have been made to study the ESAT-6 and CFP-10 proteins. Some studies have shown that ESAT-6 has an essential role in rupturing phagosome. However, the mechanisms of how ESAT-6 interacts with the membrane have not yet been fully understood. Recent studies indicate that the ESAT-6 disassociates with CFP-10 upon their interaction with phagosome membrane, forming a membrane-spanning pore. Based on these observations, as well as the available structure of ESAT-6, ESAT-6 is hypothesized to form an oligomer for membrane insertion as well as rupturing. Such an ESAT-6 oligomer may play a significant role in the tuberculosis infection. Therefore, deeper understanding of the oligomerization of ESAT-6 will establish new directions for tuberculosis treatment. However, the structure of the oligomer of ESAT-6 is not known. Here, we proposed a comprehensive approach to model the complex structures of ESAT-6 oligomer inside a membrane. Several computational tools, including MD simulation, symmetrical docking, MM/PBSA, are used to obtain and characterize such a complex structure. Results from our studies lead to a well-supported hypothesis of the ESAT-6 oligomerization as well as the identification of essential residues in stabilizing the ESAT-6 oligomer which provide useful insights for future drug design targeting tuberculosis. The approach in this research can also be used to model and study other cross-membrane complex structures.

DR-SIP: protocols for higher order structure modeling with distance restraints- and cyclic symmetry-imposed packing

MOTIVATION

Quaternary structure determination for transmembrane/soluble proteins requires a reliable computational protocol that leverages observed distance restraints and/or cyclic symmetry (Cn symmetry) found in most homo-oligomeric transmembrane proteins.

RESULTS

We survey 118 X-ray crystallographically solved structures of homo-oligomeric transmembrane proteins (HoTPs) and find that ∼97% are Cn symmetric. Given the prevalence of Cn symmetric HoTPs and the benefits of incorporating geometry restraints in aiding quaternary structure determination, we introduce two new filters, the distance-restraints (DR) and the Symmetry-Imposed Packing (SIP) filters. SIP relies on a new method that can rebuild the closest ideal Cn symmetric complex from docking poses containing a homo-dimer without prior knowledge of the number (n) of monomers. Using only the geometrical filter, SIP, near-native poses of 7 HoTPs in their monomeric states can be correctly identified in the top-10 for 71% of all cases, or 29% among 31 HoTP structures obtained through homology modeling, while ZDOCK alone returns 14 and 3%, respectively. When the n is given, the optional n-mer filter is applied with SIP and returns the near-native poses for 76% of the test set within the top-10, outperforming M-ZDOCK's 55% and Sam's 47%. While applying only SIP to three HoTPs that comes with distance restraints, we found the near-native poses were ranked 1st, 1st and 10th among 54 000 possible decoys. The results are further improved to 1st, 1st and 3rd when both DR and SIP filters are used. By applying only DR, a soluble system with distance restraints is recovered at the 1st-ranked pose.

AVAILABILITY AND IMPLEMENTATION

https://github.com/capslockwizard/drsip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioengineering oligomerization and monomerization of enzymes: learning from natural evolution to matching the demands for industrial applications

It is generally accepted that oligomeric enzymes evolve from their monomeric ancestors, and the evolution process generates superior structural benefits for functional advantages. Furthermore, adjusting the transition between different oligomeric states is an important mechanism for natural enzymes to regulate their catalytic functions for adapting environmental fluctuations in nature, which inspires researchers to mimic such a strategy to develop artificially oligomerized enzymes through protein engineering for improved performance under specific conditions. On the other hand, transforming oligomeric enzymes into their monomers is needed in fundamental research for deciphering catalytic mechanisms as well as exploring their catalytic capacities for better industrial applications. In this article, strategies for developing artificially oligomerized and monomerized enzymes are reviewed and highlighted by their applications. Furthermore, advances in the computational prediction of oligomeric structures are introduced, which would accelerate the systematic design of oligomeric and monomeric enzymes. Finally, the current challenges and future directions in this field are discussed.

Molecular Modeling of the Interaction Between Stem Cell Peptide and Immune Receptor in Plants

Molecular docking enables comprehensive exploration of interactions between chemical moieties and proteins. Modeling and docking approaches are useful to determine the three-dimensional (3D) structure of experimentally uncrystallized proteins and subsequently their interactions with various inhibitors and activators or peptides. Here, we describe a protocol for carrying out molecular modeling and docking of stem cell peptide CLV3p on plant innate immune receptor FLS2.

Modeling Protein Homo-Oligomer Structures with GalaxyHomomer Web Server

Cellular processes, such as metabolism, signal transduction, or immunity, often depend on the homo-oligomerization of proteins. Detailed structural knowledge of the homo-oligomer structure is therefore crucial for molecular-level understanding of protein functions and their regulation. In this chapter, we introduce the GalaxyHomomer server, which supports easy-to-use web interfaces for general users. It is freely accessible at http://galaxy.seoklab.org/homomer . GalaxyHomomer carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality by performing symmetric loop modeling and overall structure refinement. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state, and locations of unreliable/flexible loops or termini.

Modeling of Multimolecular Complexes

Macromolecular complexes play a key role in cellular function. Predicting the structure and dynamics of these complexes is one of the key challenges in structural biology. Docking applications have traditionally been used to predict pairwise interactions between proteins. However, few methods exist for modeling multi-protein assemblies. Here we present two methods, CombDock and DockStar, that can predict multi-protein assemblies starting from subunit structural models. CombDock can assemble subunits without any assumptions about the pairwise interactions between subunits, while DockStar relies on the interaction graph or, alternatively, a homology model or a cryo-electron microscopy (EM) density map of the entire complex. We demonstrate the two methods using RNA polymerase II with 12 subunits and TRiC/CCT chaperonin with 16 subunits.

Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45

Computational structural prediction of macromolecular interactions is a fundamental tool toward the global understanding of cellular processes. The Critical Assessment of PRediction of Interactions (CAPRI) community-wide experiment provides excellent opportunities for blind testing computational docking methods and includes original targets, thus widening the range of docking applications. Our participation in CAPRI rounds 38 to 45 enabled us to expand the way we include evolutionary information in structural predictions beyond our standard free docking InterEvDock pipeline. InterEvDock integrates a coarse-grained potential that accounts for interface coevolution based on joint multiple sequence alignments of two protein partners (co-alignments). However, even though such co-alignments could be built for none of the CAPRI targets in rounds 38 to 45, including host-pathogen and protein-oligosaccharide complexes and a redesigned interface, we identified multiple strategies that can be used to incorporate evolutionary constraints, which helped us to identify the most likely macromolecular binding modes. These strategies include template-based modeling where only local adjustments should be applied when query-template sequence identity is above 30% and larger perturbations are needed below this threshold; covariation-based structure prediction for individual protein partners; and the identification of evolutionarily conserved and structurally recurrent anchoring interface motifs. Overall, we submitted correct predictions among the top 5 models for 12 out of 19 interface challenges, including four High- and five Medium-quality predictions. Our top 20 models included correct predictions for three out of the five targets we missed in the top 5, including two targets for which misleading biological data led us to downgrade correct free docking models.

Alterations in plasma membrane ion channel structures stimulate NLRP3 inflammasome activation in APOL1 risk milieu

We evaluated alterations in the structural configurations of channels and activation of nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP3) inflammasome formation in apolipoprotein L1 (APOL1) risk and nonrisk milieus. APOL1G1- and APOL1G2-expressing podocytes (PD) displayed enhanced K⁺ efflux, induction of pyroptosis, and escalated transcription of interleukin (IL)-1β and IL-18. APOL1G1- and APOL1G2-expressing PD promoted the transcription as well as translation of proteins involved in the formation of inflammasomes. Since glyburide (a specific inhibitor of K⁺ efflux channels) inhibited the transcription of NLRP3, IL-1β, and IL-18, the role of K⁺ efflux in the activation of inflammasomes in APOL1 risk milieu was implicated. To evaluate the role of structural alterations in K⁺ channels in plasma membranes, bioinformatics studies, including molecular dynamic simulation, were carried out. Superimposition of bioinformatics reconstructions of APOL1G0, G1, and G2 showed several aligned regions. The analysis of pore-lining residues revealed that Ser342 and Tyr389 are involved in APOL1G0 pore formation and the altered conformations resulting from the Ser342Gly and Ile384Met mutation in the case of APOLG1 and deletion of the Tyr389 residue in the case of APOL1G2 are expected to alter pore characteristics, including K⁺ ion selectivity. Analysis of multiple membrane (lipid bilayer) models of interaction with the peripheral protein, integral membrane protein, and multimer protein revealed that for an APOL1 multimer model, APOL1G0 is not energetically favorable while the APOL1G1 and APOL1G2 moieties favor the insertion of multiple ion channels into the lipid bilayer. We conclude that altered pore configurations carry the potential to facilitate K⁺ ion transport in APOL1 risk milieu.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

The Plasma Factor XIII Heterotetrameric Complex Structure: Unexpected Unequal Pairing within a Symmetric Complex

Factor XIII (FXIII) is a predominant determinant of clot stability, strength, and composition. Plasma FXIII circulates as a pro-transglutaminase with two catalytic A subunits and two carrier-protective B subunits in a heterotetramer (FXIII-A₂B₂). FXIII-A₂ and -B₂ subunits are synthesized separately and then assembled in plasma. Following proteolytic activation by thrombin and calcium-mediated dissociation of the B subunits, activated FXIII (FXIIIa) covalently cross links fibrin, promoting clot stability. The zymogen and active states of the FXIII-A subunits have been structurally characterized; however, the structure of FXIII-B subunits and the FXIII-A₂B₂ complex have remained elusive. Using integrative hybrid approaches including atomic force microscopy, cross-linking mass spectrometry, and computational approaches, we have constructed the first all-atom model of the FXIII-A₂B₂ complex. We also used molecular dynamics simulations in combination with isothermal titration calorimetry to characterize FXIII-A₂B₂ assembly, activation, and dissociation. Our data reveal unequal pairing of individual subunit monomers in an otherwise symmetric complex, and suggest this unusual structure is critical for both assembly and activation of this complex. Our findings enhance understanding of mechanisms associating FXIII-A₂B₂ mutations with disease and have important implications for the rational design of molecules to alter FXIII assembly or activity to reduce bleeding and thrombotic complications.

Prediction of protein oligomer structures using GALAXY in CASP13

Many proteins need to form oligomers to be functional, so oligomer structures provide important clues to biological roles of proteins. Prediction of oligomer structures therefore can be a useful tool in the absence of experimentally resolved structures. In this article, we describe the server and human methods that we used to predict oligomer structures in the CASP13 experiment. Performances of the methods on the 42 CASP13 oligomer targets consisting of 30 homo-oligomers and 12 hetero-oligomers are discussed. Our server method, Seok-assembly, generated models with interface contact similarity measure greater than 0.2 as model 1 for 11 homo-oligomer targets when proper templates existed in the database. Model refinement methods such as loop modeling and molecular dynamics (MD)-based overall refinement failed to improve model qualities when target proteins have domains not covered by templates or when chains have very small interfaces. In human predictions, additional experimental data such as low-resolution electron microscopy (EM) map were utilized. EM data could assist oligomer structure prediction by providing a global shape of the complex structure.

Accelerated Molecular Dynamics Applied to the Peptaibol Folding Problem

The use of enhanced sampling molecular dynamics simulations to facilitate the folding of proteins is a relatively new approach which has quickly gained momentum in recent years. Accelerated molecular dynamics (aMD) can elucidate the dynamic path from the unfolded state to the near-native state, “flattened” by introducing a non-negative boost to the potential. Alamethicin F30/3 (Alm F30/3), chosen in this study, belongs to the class of peptaibols that are 7–20 residue long, non-ribosomally synthesized, amphipathic molecules that show interesting membrane perturbing activity. The recent studies undertaken on the Alm molecules and their transmembrane channels have been reviewed. Three consecutive simulations of ~900 ns each were carried out where N-terminal folding could be observed within the first 100 ns, while C-terminal folding could only be achieved almost after 800 ns. It took ~1 μs to attain the near-native conformation with stronger potential boost which may take several μs worth of classical MD to produce the same results. The Alm F30/3 hexamer channel was also simulated in an E. coli mimicking membrane under an external electric field that correlates with previous experiments. It can be concluded that aMD simulation techniques are suited to elucidate peptaibol structures and to understand their folding dynamics.

GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure

Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion.

Flexible Backbone Assembly and Refinement of Symmetrical Homomeric Complexes

Symmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries-starting from homology-modeled monomers-we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 17 out of 31 cyclic complexes and 3 out of 12 dihedral complexes.

Benchmarking of different molecular docking methods for protein-peptide docking

Background

Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD.

Result

Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively.

Conclusion

The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2449-y) contains supplementary material, which is available to authorized users.

On identifying collective displacements in apo-proteins that reveal eventual binding pathways

Binding of small molecules to proteins often involves large conformational changes in the latter, which open up pathways to the binding site. Observing and pinpointing these rare events in large scale, all-atom, computations of specific protein-ligand complexes, is expensive and to a great extent serendipitous. Further, relevant collective variables which characterise specific binding or un-binding scenarios are still difficult to identify despite the large body of work on the subject. Here, we show that possible primary and secondary binding pathways can be discovered from short simulations of the apo-protein without waiting for an actual binding event to occur. We use a projection formalism, introduced earlier to study deformation in solids, to analyse local atomic displacements into two mutually orthogonal subspaces—those which are “affine” i.e. expressible as a homogeneous deformation of the native structure, and those which are not. The susceptibility to non-affine displacements among the various residues in the apo- protein is then shown to correlate with typical binding pathways and sites crucial for allosteric modifications. We validate our observation with all-atom computations of three proteins, T4-Lysozyme, Src kinase and Cytochrome P450.

Designing drugs which target specific proteins involved in diseases consumes a lot of time and effort in the pharmaceutical industry. In recent times, in silico design of drugs using all-atom molecular modelling has started to provide crucial inputs. Even so, discovery of binding pathways of small molecules both at the primary binding site, as well as sites for allosteric control, is time consuming and often fortuitous. We provide here a framework within which critical conformational changes likely to occur during binding are quantified from statistical analysis of configurations of proteins in their apo, or inactive form, greatly simplifying identification of target residues. We illustrate this idea by analysing ligand binding pathways for three proteins T4- Lysozyme, P450 and Src kinase, which are active respectively in the immune system, metabolism and cancer.

On the ubiquity of helical α-synuclein tetramers

The stability of oligomers linearly increases from dimers to octamers, but assembly of oligomers larger than tetramers requires high activation energies.

Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information

Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.