Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

Mammalian cell display with automated oligo design and library assembly allows for rapid residue level conformational epitope mapping

Precise epitope determination of therapeutic antibodies is of great value as it allows for further comprehension of mechanism of action, therapeutic responsiveness prediction, avoidance of unwanted cross reactivity, and vaccine design. The golden standard for discontinuous epitope determination is the laborious X-ray crystallography method. Here, we present a combinatorial method for rapid mapping of discontinuous epitopes by mammalian antigen display, eliminating the need for protein expression and purification. The method is facilitated by automated workflows and tailored software for antigen analysis and oligonucleotide design. These oligos are used in automated mutagenesis to generate an antigen receptor library displayed on mammalian cells for direct binding analysis by flow cytometry. Through automated analysis of 33930 primers an optimized single condition cloning reaction was defined allowing for mutation of all surface-exposed residues of the receptor binding domain of SARS-CoV-2. All variants were functionally expressed, and two reference binders validated the method. Furthermore, epitopes of three novel therapeutic antibodies were successfully determined followed by evaluation of binding also towards SARS-CoV-2 Omicron BA.2. We find the method to be highly relevant for rapid construction of antigen libraries and determination of antibody epitopes, especially for the development of therapeutic interventions against novel pathogens.

Micro-second time-resolved X-ray single-molecule internal motions of SARS-CoV-2 spike variants

Single-molecule intramolecular dynamics were successfully measured for three variants of SARS-CoV-2 spike protein, alpha: B.1.1.7, delta: B.1.617, and omicron: B.1.1.529, with a time resolution of 100 μs using X-rays. The results were then compared with respect to the magnitude and directions of motions for the three variants. The largest 3-D intramolecular movement was observed for the omicron variant irrespective of ACE2 receptor binding. A more detailed analysis of the intramolecular motions revealed that the distribution state of intramolecular motion for the three variants was completely different with and without ACE2 receptor binding. The molecular dynamics for the trimeric spike protein of the omicron variant increased when ACE2 binding occurred. At that time, the diffusion constant increased from 71.0 [mrad2/ms] to 91.1 [mrad2/ms].

PRUNE1 and NME/NDPK family proteins influence energy metabolism and signaling in cancer metastases

We describe here the molecular basis of the complex formation of PRUNE1 with the tumor metastasis suppressors NME1 and NME2, two isoforms appertaining to the nucleoside diphosphate kinase (NDPK) enzyme family, and how this complex regulates signaling the immune system and energy metabolism, thereby shaping the tumor microenvironment (TME). Disrupting the interaction between NME1/2 and PRUNE1, as suggested, holds the potential to be an excellent therapeutic target for the treatment of cancer and the inhibition of metastasis dissemination. Furthermore, we postulate an interaction and regulation of the other Class I NME proteins, NME3 and NME4 proteins, with PRUNE1 and discuss potential functions. Class I NME1-4 proteins are NTP/NDP transphosphorylases required for balancing the intracellular pools of nucleotide diphosphates and triphosphates. They regulate different cellular functions by interacting with a large variety of other proteins, and in cancer and metastasis processes, they can exert pro- and anti-oncogenic properties depending on the cellular context. In this review, we therefore additionally discuss general aspects of class1 NME and PRUNE1 molecular structures as well as their posttranslational modifications and subcellular localization. The current knowledge on the contributions of PRUNE1 as well as NME proteins to signaling cascades is summarized with a special regard to cancer and metastasis.

Use of in silico approaches, synthesis and profiling of Pan-filovirus GP-1,2 preprotein specific antibodies

Intermolecular interactions of protein-protein complexes play a principal role in the process of discovering new substances used in the diagnosis and treatment of many diseases. Among such complexes of proteins, we have to mention antibodies; they interact with specific antigens of two genera of single-stranded RNA viruses belonging to the family Filoviridae-Ebolavirus and Marburgvirus; both cause rare but fatal viral hemorrhagic fever in Africa, with pandemic potential. In this research, we conduct studies aimed at the design and evaluation of antibodies targeting the filovirus glycoprotein precursor GP-1,2 to develop potential targets for the pan-filovirus easy-to-use rapid diagnostic tests. The in silico research using the available 3D structure of the natural antibody-antigen complex was carried out to determine the stability of individual protein segments in the process of its formation and maintenance. The computed free binding energy of the complex and its decomposition for all amino acids allowed us to define the residues that play an essential role in the structure and indicated the spots where potential antibodies can be improved. Following that, the study involved targeting six epitopes of the filovirus GP1,2 with two polyclonal antibodies (pABs) and 14 monoclonal antibodies (mABs). The evaluation conducted using Enzyme Immunoassays tested 62 different sandwich combinations of monoclonal antibodies (mAbs), identifying 10 combinations that successfully captured the recombinant GP1,2 (rGP). Among these combinations, the sandwich option (3G2G12* - (rGP) - 2D8F11) exhibited the highest propensity for capturing the rGP antigen.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Mechanistic Insights into S-Depalmitolyse Activity of Cln5 Protein Linked to Neurodegeneration and Batten Disease: A QM/MM Study

Ceroid lipofuscinosis neuronal protein 5 (Cln5) is encoded by the CLN5 gene. The genetic variants of this gene are associated with the CLN5 form of Batten disease. Recently, the first crystal structure of Cln5 was reported. Cln5 shows cysteine palmitoyl thioesterase S-depalmitoylation activity, which was explored via fluorescent emission spectroscopy utilizing the fluorescent probe DDP-5. In this work, the mechanism of the reaction between Cln5 and DDP-5 was studied computationally by applying a QM/MM methodology at the ωB97X-D/6-31G(d,p):AMBER level. The results of our study clearly demonstrate the critical role of the catalytic triad Cys280-His166-Glu183 in S-depalmitoylation activity. This is evidenced through a comparison of the pathways catalyzed by the Cys280-His166-Glu183 triad and those with only Cys280 involved. The computed reaction barriers are in agreement with the catalytic efficiency. The calculated Gibb's free-energy profile suggests that S-depalmitoylation is a rate-limiting step compared to the preceding S-palmitoylation, with barriers of 26.1 and 25.3 kcal/mol, respectively. The energetics were complemented by monitoring the fluctuations in the electron density distribution through NBO charges and bond strength alterations via local mode stretching force constants during the catalytic pathways. This comprehensive protocol led to a more holistic picture of the reaction mechanism at the atomic level. It forms the foundation for future studies on the effects of gene mutations on both the S-palmitoylation and S-depalmitoylation steps, providing valuable data for the further development of enzyme replacement therapy, which is currently the only FDA-approved therapy for childhood neurodegenerative diseases, including Batten disease.

Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding.

Construction of Histone-Protein Complex Structures by Peptide Growing

The structures of histone complexes are master keys to epigenetics. Linear histone peptide tails often bind to shallow pockets of reader proteins via weak interactions, rendering their structure determination challenging. In the present study, a new protocol, PepGrow, is introduced. PepGrow uses docked histone fragments as seeds and grows the full peptide tails in the reader-binding pocket, producing atomic-resolution structures of histone-reader complexes. PepGrow is able to handle the flexibility of histone peptides, and it is demonstrated to be more efficient than linking pre-docked peptide fragments. The new protocol combines the advantages of popular program packages and allows fast generation of solution structures. AutoDock, a force-field-based program, is used to supply the docked peptide fragments used as structural seeds, and the building algorithm of Modeller is adopted and tested as a peptide growing engine. The performance of PepGrow is compared to ten other docking methods, and it is concluded that in situ growing of a ligand from a seed is a viable strategy for the production of complex structures of histone peptides at atomic resolution.

Understanding the Stabilization Mechanism of a Thermostable Mutant of Hygromycin B Phosphotransferase by Protein Sector-Guided Dynamic Analysis

Point mutations can exert beneficial effects on proteins, including stabilization. The stabilizing effects of mutations are typically attributed to changes in free energy and residue interactions. However, these explanations lack detail and physical insights, which hinder the mechanistic study of protein stabilization and prevent accurate computational prediction of stabilizing mutations. Here, we investigate the physical mechanism underlying the enhanced thermostability of a Hygromycin B phosphotransferase mutant, Hph5. We find that the unpredictable mutation A118V induces rotation of F199, allowing it to establish an aromatic-aromatic interaction with W235. In contrast, the predictable mutation T246A acts through static hydrophobic interactions within the protein core. These discoveries were accelerated by a residue-coevolution-based theory, which links mutational effects to stability-associated local structures, providing valuable guidance for mechanistic exploration. The established workflow will benefit the development of accurate stability prediction programs and can be used to mine a protein stability database for undiscovered physical mechanisms.

Rapid and efficient ambient temperature X-ray crystal structure determination at Turkish Light Source

High-resolution biomacromolecular structure determination is essential to better understand protein function and dynamics. Serial crystallography is an emerging structural biology technique which has fundamental limitations due to either sample volume requirements or immediate access to the competitive X-ray beamtime. Obtaining a high volume of well-diffracting, sufficient-size crystals while mitigating radiation damage remains a critical bottleneck of serial crystallography. As an alternative, we introduce the plate-reader module adapted for using a 72-well Terasaki plate for biomacromolecule structure determination at a convenience of a home X-ray source. We also present the first ambient temperature lysozyme structure determined at the Turkish light source (Turkish DeLight). The complete dataset was collected in 18.5 min with resolution extending to 2.39 Å and 100% completeness. Combined with our previous cryogenic structure (PDB ID: 7Y6A), the ambient temperature structure provides invaluable information about the structural dynamics of the lysozyme. Turkish DeLight provides robust and rapid ambient temperature biomacromolecular structure determination with limited radiation damage.

In Silico Analyses of a Promising Drug Candidate for the Treatment of Amyotrophic Lateral Sclerosis Targeting Superoxide Dismutase I Protein

Amyotrophic lateral sclerosis (ALS) is the most prevalent motor neuron disorder in adults, which is associated with a highly disabling condition. To date, ALS remains incurable, and the only drugs approved by the FDA for its treatment confer a limited survival benefit. Recently, SOD1 binding ligand 1 (SBL-1) was shown to inhibit in vitro the oxidation of a critical residue for SOD1 aggregation, which is a central event in ALS-related neurodegeneration. In this work, we investigated the interactions between SOD1 wild-type and its most frequent variants, i.e., A4V (NP_000445.1:p.Ala5Val) and D90A (NP_000445.1:p.Asp91Val), with SBL-1 using molecular dynamics (MD) simulations. The pharmacokinetics and toxicological profile of SBL-1 were also characterized in silico. The MD results suggest that the complex SOD1-SBL-1 remains relatively stable and interacts within a close distance during the simulations. This analysis also suggests that the mechanism of action proposed by SBL-1 and its binding affinity to SOD1 may be preserved upon mutations A4V and D90A. The pharmacokinetics and toxicological assessments suggest that SBL-1 has drug-likeness characteristics with low toxicity. Our findings, therefore, suggested that SBL-1 may be a promising strategy to treat ALS based on an unprecedented mechanism, including for patients with these frequent mutations.

CNN-Pred: Prediction of single-stranded and double-stranded DNA-binding protein using convolutional neural networks

DNA-binding proteins play a vital role in biological activity including DNA replication, DNA packing, and DNA reparation. DNA-binding proteins can be classified into single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs). Determining whether a protein is DSB or SSB helps determine the protein's function. Therefore, many studies have been conducted to accurately identify DSB and SSB in recent years. Despite all the efforts have been made so far, the DSB and SSB prediction performance remains limited. In this study, we propose a new method called CNN-Pred to accurately predict DSB and SSB. To build CNN-Pred, we first extract evolutionary-based features in the form of mono-gram and bi-gram profiles using position specific scoring matrix (PSSM). We then, use 1D-convolutional neural network (CNN) as the classifier to our extracted features. Our results demonstrate that CNN-Pred can enhance the DSB and SSB prediction accuracies by more than 4%, on the independent test compared to previous studies found in the literature. CNN-pred as a standalone tool and all its source codes are publicly available at: https://github.com/MLBC-lab/CNN-Pred.

Marmesin and Marmelosin Interact with the Heparan Sulfatase-2 Active Site: Potential Mechanism for Phytochemicals from Bael Fruit Extract as Antitumor Therapeutics

Human heparan sulfatase-2 (HSULF-2) is an oncoprotein overexpressed in the surface of all types of tumor cells and its activity plays a critical role in cancer survival and progression. Our previous studies have shown that bael fruit extract, containing marmesin and marmelosin, inhibits the HSULF-2 activity and kills breast tumor cells, but the mechanism of these processes remains fairly known mainly because the HSULF-2's 3D structure is partially known. Herein, we aimed at providing an in silico molecular mechanism of the inhibition of human HSULF-2 by phytochemicals from bael fruit extract. Pharmacokinetic parameters of the main phytochemicals contained in the bael fruit extract, sequence-based 3D structure of human HSULF-2, and the interaction of bael fruit's phytochemicals with the enzyme active site was modeled, evaluated, and verified. Docking studies revealed marmesin and marmelosin as potential inhibitors with binding score -8.5 and -7.7 Kcal/mol; these results were validated using molecular dynamics simulations, which exhibited higher stability of the protein-ligand complexes. Taking together, with our earlier in vitro data, our computational analyses suggest that marmesin and marmelosin interact at the active site of HSULF-2 providing a potential mechanism for its inhibition and consequent antitumor activity by phytochemicals contained in the bael fruit extract.

Atomic-level characterization of the conformational transition pathways in SARS-CoV-1 and SARS-CoV-2 spike proteins

Severe acute respiratory syndrome (SARS) coronaviruses 1 and 2 (SARS-CoV-1 and SARS-CoV-2) derive transmissibility from spike protein activation in the receptor binding domain (RBD) and binding to the host cell angiotensin converting enzyme 2 (ACE2). However, the mechanistic details that describe the large-scale conformational changes associated with spike protein activation or deactivation are still somewhat unknown. Here, we have employed an extensive set of nonequilibrium all-atom molecular dynamics (MD) simulations, utilizing a novel protocol, for the SARS-CoV-1 (CoV-1) and SARS-CoV-2 (CoV-2) prefusion spike proteins in order to characterize the conformational pathways associated with the active-to-inactive transition. Our results indicate that both CoV-1 and CoV-2 spike proteins undergo conformational transitions along pathways unique to each protein. We have identified a number of key residues that form various inter-domain saltbridges, suggesting a multi-stage conformational change along the pathways. We have also constructed the free energy profiles along the transition pathways for both CoV-1 and CoV-2 spike proteins. The CoV-2 spike protein must overcome larger free energy barriers to undergo conformational changes towards protein activation or deactivation, when compared to CoV-1.

Vibrational dynamics of ethosuximide polymorphs. Infrared absorption and inelastic neutron scattering spectroscopy and model calculations

Commercially available and administered to the patients ethosuximide is a racemic mixture of two enantiomers, each of them exist in different conformations. The presence of the species mentioned are proven by the title experimental methods aided by DFT model calculations. Results of the latter are matched against spectroscopic data by the clustering window analysis. One type of hydrogen bonds exist in the solid forms of ethosuximide NH⋯O, leading to the polymorphic variety of the substance studied.

Hydration-mediated G-protein-coupled receptor activation

Although G-protein–coupled receptors (GPCRs) control vast physiological pathways, their activation remains chemically and physically enigmatic. Our osmotic stress studies of the visual receptor rhodopsin have redefined the standard model of GPCR signaling by revealing the essential role of bulk water. We show results consistent with a large number of water molecules flooding the rhodopsin interior during activation to stabilize the effector binding conformation. These results suggest a model of GPCR activation in which the receptor becomes solvent-swollen upon formation of the active state. We thus demonstrate the mechanism whereby water acts as a powerful allosteric modulator of a pharmacologically important membrane protein family.

The Rhodopsin family of G-protein–coupled receptors (GPCRs) comprises the targets of nearly a third of all pharmaceuticals. Despite structural water present in GPCR X-ray structures, the physiological relevance of these solvent molecules to rhodopsin signaling remains unknown. Here, we show experimental results consistent with the idea that rhodopsin activation in lipid membranes is coupled to bulk water movements into the protein. To quantify hydration changes, we measured reversible shifting of the metarhodopsin equilibrium due to osmotic stress using an extensive series of polyethylene glycol (PEG) osmolytes. We discovered clear evidence that light activation entails a large influx of bulk water (∼80–100 molecules) into the protein, giving insight into GPCR activation mechanisms. Various size polymer osmolytes directly control rhodopsin activation, in which large solutes are excluded from rhodopsin and dehydrate the protein, favoring the inactive state. In contrast, small osmolytes initially forward shift the activation equilibrium until a quantifiable saturation point is reached, similar to gain-of-function protein mutations. For the limit of increasing osmolyte size, a universal response of rhodopsin to osmotic stress is observed, suggesting it adopts a dynamic, hydrated sponge-like state upon photoactivation. Our results demand a rethinking of the role of water dynamics in modulating various intermediates in the GPCR energy landscape. We propose that besides bound water, an influx of bulk water plays a necessary role in establishing the active GPCR conformation that mediates signaling.

Revealing the Organization of Catalytic Sequence-Defined Oligomers via Combined Molecular Dynamics Simulations and Network Analysis

Similar to biological macromolecules such as DNA and proteins, the precise control over the monomer position in sequence-defined polymers is of paramount importance for tuning their structures and properties toward achieving specific functions. Here, we apply molecular network analysis on three-dimensional structures issued from molecular dynamics simulations to decipher how the chain organization of trifunctional catalytic oligomers is influenced by the oligomer sequence and the length of oligo(ethylene oxide) spacers. Our findings demonstrate that the tuning of their primary structures is crucial for favoring cooperative interactions between the catalytic units and thus higher catalytic activities. This combined approach can assist in establishing structure-property relationships, leading to a more rational design of sequence-defined catalytic oligomers via computational chemistry.

Current progress and open challenges for applying deep learning across the biosciences

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

Structure and chemistry of enzymatic active sites that play a role in the switch and conformation mechanism

Enzymes, which are biological molecules, are constructed from polypeptide chains, and these molecules are activated through reaction mechanisms. It is the role of enzymes to speed up chemical reactions that are used to build or break down cell structures. Activation energy is reduced by the enzymes' selective binding of substrates in a protected environment. In enzyme tertiary structures, the active sites are commonly situated in a "cleft," which necessitates the diffusion of substrates and products. The amino acid residues of the active site may be far apart in the primary structure owing to the folding required for tertiary structure. Due to their critical role in substrate binding and attraction, changes in amino acid structure at or near the enzyme's active site usually alter enzyme activity. At the enzyme's active site, or where the chemical reactions occur, the substrate is bound. Enzyme substrates are the primary targets of the enzyme's active site, which is designed to assist in the chemical reaction. This chapter elucidates the summary of structure and chemistry of enzymes, their active site features, charges and role of water in the structures to clarify the biochemistry of the enzymes in the depth of atomic features.

Prefusion spike protein conformational changes are slower in SARS-CoV-2 than in SARS-CoV-1

Within the last 2 decades, severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and SARS-CoV-2) have caused two major outbreaks; yet, for reasons not fully understood, the coronavirus disease 2019 pandemic caused by SARS-CoV-2 has been significantly more widespread than the 2003 SARS epidemic caused by SARS-CoV-1, despite striking similarities between these two viruses. The SARS-CoV-1 and SARS-CoV-2 spike proteins, both of which bind to host cell angiotensin-converting enzyme 2, have been implied to be a potential source of their differential transmissibility. However, the mechanistic details of prefusion spike protein binding to angiotensin-converting enzyme 2 remain elusive at the molecular level. Here, we performed an extensive set of equilibrium and nonequilibrium microsecond-level all-atom molecular dynamics simulations of SARS-CoV-1 and SARS-CoV-2 prefusion spike proteins to determine their differential dynamic behavior. Our results indicate that the active form of the SARS-CoV-2 spike protein is more stable than that of SARS-CoV-1 and the energy barrier associated with the activation is higher in SARS-CoV-2. These results suggest that not only the receptor-binding domain but also other domains such as the N-terminal domain could play a crucial role in the differential binding behavior of SARS-CoV-1 and SARS-CoV-2 spike proteins.

Development of Fluorescence Polarization Immunoassay With scFv to Detect Fumonisin Bs in Maize and Simultaneous Study of Their Molecular Recognition Mechanism

In this study, a fluorescence polarization immunoassay (FPIA) was developed based on the single-chain variable fragments (scFvs) for fumonisin B_s (FB_s). The scFvs were prepared from FB_s-specific monoclonal antibody secreting hybridomas (4F5 and 4B9). The established FPIA could determine the sum of fumonisin B₁ (FB₁) and fumonisin B₂ (FB₂) within a short time. The IC₅₀ of FPIA for the detection of FB₁ and FB₂ were 29.36 ng/ml and 1,477.82 ng/ml with 4F5 scFv, and 125.16 ng/ml and 30.44 ng/ml with 4B9 scFv, so the 4B9 scFv was selected for detection of FB₁ and FB₂ in maize samples with a limit of detection of 441.54 μg/kg and 344.933 μg/kg. The recoveries ranged from 84.7 to 104.1% with a coefficient of variation less than 14.1% in spiked samples, and the result of the FPIA method was in good consistency with that of HPLC-MS/MS. To supply a better understanding of the immunoassay results, the interactions mechanism of scFvs-FB_s was further revealed by the homology modelling, molecular docking, and molecular dynamic simulation. It was indicated that six complementarity-determining regions (CDRs) were involved in 4B9 scFv recognition, forming a narrow binding cavity, and FB₁/FB₂ could be inserted into this binding cavity stably through strong hydrogen bonds and other interactions. While in 4F5 scFv, only the FB₁ stably inserted in the binding pocket formed by four CDRs through strong hydrogen bonds, and FB₂ did not fit the binding cavity due to the lack of hydroxyl at C10, which is the key recognition site of 4F5 scFv. Also, the binding energy of FB₂-4B9 scFv complex is higher than the FB₂-4F5 scFv complex. This study established a FPIA method with scFv for the detection of FB₁ and FB₁ in maize, and systematically predicted recognition mechanism of FB_s and scFvs, which provided a reference for the better understanding of the immunoassay mechanism.

Integrating dynamics into enzyme engineering

Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.

Allosteric binding on nuclear receptors: Insights on screening of non-competitive endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) can compete with endogenous hormones and bind to the orthosteric site of nuclear receptors (NRs), affecting normal endocrine system function and causing severe symptoms. Recently, a series of pharmaceuticals and personal care products (PPCPs) have been discovered to bind to the allosteric sites of NRs and induce similar effects. However, it remains unclear how diverse EDCs work in this new way. Therefore, we have systematically summarized the allosteric sites and underlying mechanisms based on existing studies, mainly regarding drugs belonging to the PPCP class. Advanced methods, classified as structural biology, biochemistry and computational simulation, together with their advantages and hurdles for allosteric site recognition and mechanism insight have also been described. Furthermore, we have highlighted two available strategies for virtual screening of numerous EDCs, relying on the structural features of allosteric sites and lead compounds, respectively. We aim to provide reliable theoretical and technical support for a broader view of various allosteric interactions between EDCs and NRs, and to drive high-throughput and accurate screening of potential EDCs with non-competitive effects.

Mechanistic Picture for Monomeric Human Fibroblast Growth Factor 1 Stabilization by Heparin Binding

Human fibroblast growth factor (FGF) 1 or hFGF1 is a member of the FGF family that is involved in various vital processes such as cell proliferation, cell differentiation, angiogenesis, and wound healing. hFGF1, which is associated with low stability in vivo, is known to be stabilized by binding heparin sulfate, a glycosaminoglycan that aids the protein in the activation of its cell surface receptor. The poor thermal and proteolytic stability of hFGF1 and the stabilizing role of heparin have long been observed experimentally; however, the mechanistic details of these phenomena are not well understood. Here, we have used microsecond-level equilibrium molecular dynamics (MD) simulations to quantitatively characterize the structural dynamics of monomeric hFGF1 in the presence and absence of heparin hexasaccharide. We have observed a conformational change in the heparin-binding pocket of hFGF1 that occurs only in the absence of heparin. Several intramolecular interactions were also identified within the heparin-binding pocket that form only when hFGF1 interacts with heparin. The loss of both intermolecular and intramolecular interactions in the absence of heparin plausibly leads to the observed conformational change. This conformational transition results in increased flexibility of the heparin-binding pocket and provides an explanation for the susceptibility of apo hFGF1 to proteolytic degradation and thermal instability. This study provides a glimpse into mechanistic details of the heparin-mediated stabilization of hFGF1 and encourages the use of microsecond-level MD in studying the effect of binding on protein structure and dynamics. In addition, the observed differential behavior of hFGF1 in the absence and presence of heparin provides an example, where microsecond-level all-atom MD simulations are necessary to see functionally relevant biomolecular phenomena that otherwise will not be observed on sub-microsecond time scales.

Hydration and its Hydrogen Bonding State on a Protein Surface in the Crystalline State as Revealed by Molecular Dynamics Simulation

Protein hydration is crucial for the stability and molecular recognition of a protein. Water molecules form a hydration water network on a protein surface via hydrogen bonds. This study examined the hydration structure and hydrogen bonding state of a protein, staphylococcal nuclease, at various hydration levels in its crystalline state by all-atom molecular dynamics (MD) simulation. Hydrophilic residues were more hydrated than hydrophobic residues. As the water content increases, both types of residues were uniformly more hydrated. The number of hydrogen bonds per single water asymptotically approaches 4, the same as bulk water. The distances and angles of hydrogen bonds in hydration water in the protein crystal were almost the same as those in the tetrahedral structure of bulk water regardless of the hydration level. The hydrogen bond structure of hydration water observed by MD simulations of the protein crystalline state was compared to the Hydrogen and Hydration Database for Biomolecule from experimental protein crystals.

Protein structure prediction using residue-resolved protection factors from hydrogen-deuterium exchange NMR

Hydrogen-deuterium exchange (HDX) measured by nuclear magnetic resonance (NMR) provides structural information for proteins relating to solvent accessibility and flexibility. While this structural information is beneficial, the data cannot be used exclusively to elucidate structures. However, the structural information provided by the HDX-NMR data can be supplemented by computational methods. In previous work, we developed an algorithm in Rosetta to predict structures using qualitative HDX-NMR data (categories of exchange rate). Here we expand on the effort, and utilize quantitative protection factors (PFs) from HDX-NMR for structure prediction. From observed correlations between PFs and solvent accessibility/flexibility measures, we present a scoring function to quantify the agreement with HDX data. Using a benchmark set of 10 proteins, an average improvement of 5.13 Å in root-mean-square deviation (RMSD) is observed for cases of inaccurate Rosetta predictions. Ultimately, seven out of 10 predictions are accurate without including HDX data, and nine out of 10 are accurate when using our PF-based HDX score.

Identification of Novel HBV/HDV Entry Inhibitors by Pharmacophore- and QSAR-Guided Virtual Screening

The hepatic bile acid transporter Na⁺/taurocholate co-transporting polypeptide (NTCP) was identified in 2012 as the high-affinity hepatic receptor for the hepatitis B and D viruses (HBV/HDV). Since then, this carrier has emerged as promising drug target for HBV/HDV virus entry inhibitors, but the synthetic peptide Hepcludex^® of high molecular weight is the only approved HDV entry inhibitor so far. The present study aimed to identify small molecules as novel NTCP inhibitors with anti-viral activity. A ligand-based bioinformatic approach was used to generate and validate appropriate pharmacophore and QSAR (quantitative structure–activity relationship) models. Half-maximal inhibitory concentrations (IC₅₀) for binding inhibition of the HBV/HDV-derived preS1 peptide (as surrogate parameter for virus binding to NTCP) were determined in NTCP-expressing HEK293 cells for 150 compounds of different chemical classes. IC₅₀ values ranged from 2 µM up to >1000 µM. The generated pharmacophore and QSAR models were used for virtual screening of drug-like chemicals from the ZINC¹⁵ database (~11 million compounds). The 20 best-performing compounds were then experimentally tested for preS1-peptide binding inhibition in NTCP-HEK293 cells. Among them, four compounds were active and revealed experimental IC₅₀ values for preS1-peptide binding inhibition of 9, 19, 20, and 35 µM, which were comparable to the QSAR-based predictions. All these compounds also significantly inhibited in vitro HDV infection of NTCP-HepG2 cells, without showing any cytotoxicity. The best-performing compound in all assays was ZINC000253533654. In conclusion, the present study demonstrates that virtual compound screening based on NTCP-specific pharmacophore and QSAR models can predict novel active hit compounds for the development of HBV/HDV entry inhibitors.

Structural analysis of mammalian protein phosphorylation at a proteome level

Phosphorylation is an essential post-translational modification for almost all cellular processes. Several global phosphoproteomics analyses have revealed phosphorylation profiles under different conditions. Beyond identification of phospho-sites, protein structures add another layer of information about their functionality. In this study, we systematically characterize phospho-sites based on their 3D locations in the protein and establish a location map for phospho-sites. More than 250,000 phospho-sites have been analyzed, of which 8,686 sites match at least one structure and are stratified based on their respective 3D positions. Core phospho-sites possess two distinct groups based on their dynamicity. Dynamic core phosphorylations are significantly more functional compared with static ones. The dynamic core and the interface phospho-sites are the most functional among all 3D phosphorylation groups. Our analysis provides global characterization and stratification of phospho-sites from a structural perspective that can be utilized for predicting functional relevance and filtering out false positives in phosphoproteomic studies.

Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

In silico studies reveal structural deviations of mutant profilin-1 and interaction with riluzole and edaravone in amyotrophic lateral sclerosis

This study aimed to investigate four of the eight PFN-1 mutations that are located near the actin-binding domain and determine the structural changes due to each mutant and unravel how these mutations alter protein structural behavior. Swapaa's command in UCSF chimera for generating mutations, FTMAP were employed and the data was analyzed by RMSD, RMSF graphs, Rg, hydrogen bonding analysis, and RRdisMaps utilizing Autodock4 and GROMACS. The functional changes and virtual screening, structural dynamics, and chemical bonding behavior changes, molecular docking simulation with two current FDA-approved drugs for ALS were investigated. The highest reduction and increase in Rg were found to exist in the G117V and M113T mutants, respectively. The RMSF data consistently shows changes nearby to this site. The in silico data described indicate that each of the mutations is capable of altering the structure of PFN-1 in vivo. The potential effect of riluzole and edaravone two FDA approved drugs for ALS, impacting the structural deviations and stabilization of the mutant PFN-1 is evaluated using in silico tools. Overall, the analysis of data collected reveals structural changes of mutant PFN-1 protein that may explain the neurotoxicity and the reason(s) for possible loss and gain of function of PFN-1 in the neurotoxic model of ALS.

Differential Dynamic Behavior of Prefusion Spike Proteins of SARS Coronaviruses 1 and 2

The coronavirus spike protein, which binds to the same human receptor in both SARS-CoV-1 and 2, has been implied to be a potential source of their differential transmissibility. However, the mechanistic details of spike protein binding to its human receptor remain elusive at the molecular level. Here, we have used an extensive set of unbiased and biased microsecond-level all-atom molecular dynamics (MD) simulations of SARS-CoV-1 and 2 spike proteins to determine the differential dynamic behavior of prefusion spike protein structure in the two viruses. Our results indicate that the active form of the SARS-CoV-2 spike protein is more stable than that of SARS-CoV-1 and the energy barrier associated with the activation is higher in SARS-CoV-2. Our results also suggest that not only the receptor binding domain (RBD) but also other domains such as the N-terminal domain (NTD) could play a role in the differential binding behavior of SARS-CoV-1 and 2 spike proteins.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

Molecular Structure, Binding Affinity, and Biological Activity in the Epigenome

Development of valid structure–activity relationships (SARs) is a key to the elucidation of pathomechanisms of epigenetic diseases and the development of efficient, new drugs. The present review is based on selected methodologies and applications supplying molecular structure, binding affinity and biological activity data for the development of new SARs. An emphasis is placed on emerging trends and permanent challenges of new discoveries of SARs in the context of proteins as epigenetic drug targets. The review gives a brief overview and classification of the molecular background of epigenetic changes, and surveys both experimental and theoretical approaches in the field. Besides the results of sophisticated, cutting edge techniques such as cryo-electron microscopy, protein crystallography, and isothermal titration calorimetry, examples of frequently used assays and fast screening techniques are also selected. The review features how different experimental methods and theoretical approaches complement each other and result in valid SARs of the epigenome.

Mesoscale Modeling of Nucleosome-Binding Antibody PL2-6: Mono- versus Bivalent Chromatin Complexes

Interactions of chromatin with bivalent immunoglobin nucleosome-binding antibodies and their monovalent (papain-derived) antigen-binding fragment analogs are useful probes for examining chromatin conformational states. To help interpret antibody-chromatin interactions and explore how antibodies might compete for interactions with chromatin components, we incorporate coarse-grained PL2-6 antibody modeling into our mesoscale chromatin model. We analyze interactions and fiber structures for the antibody-chromatin complexes in open and condensed chromatin, with and without H1 linker histone (LH). Despite minimal and transient interactions at physiological salt, we capture significant differences in antibody-chromatin complex configurations in open fibers, with more intense interactions between the bivalent antibody and chromatin compared to monovalent antigen-binding fragments. For these open chromatin fiber morphologies, antibody binding to histone tails is increased and compaction is greater for bivalent compared to monovalent and antibody-free systems. Differences between monovalent and bivalent binding result from antibody competition with internal chromatin fiber components (nucleosome core and linker DNA) for histone tail (H3, H4, H2A, H2B) interactions. This antibody competition for tail contacts reduces tail-core and tail-linker interactions and increases tail-antibody interactions. Such internal structural changes in open fibers resemble mechanisms of LH condensation, driven by charge screening and entropy changes. For condensed fibers at physiological salt, the three systems are much more similar overall, but some subtle tail interaction differences can be noted. Adding LH results in less-dramatic changes for all systems, except that the bivalent complex at physiological salt shows cooperative effects between LH and the antibodies in condensing chromatin fibers. Such dynamic interactions that depend on the internal structure and complex-stabilizing interactions within the chromatin fiber have implications for gene regulation and other chromatin complexes such as with LH, remodeling proteins, and small molecular chaperones that bind and modulate chromatin structure.

Small Conformational Changes Underlie Evolution of Resistance to NNRTI in HIV Reverse Transcriptase

Despite achieving considerable success in reducing the number of fatalities due to acquired immunodeficiency syndrome, emergence of resistance against the reverse transcriptase (RT) inhibitor drugs remains one of the biggest challenges of the human immunodeficiency virus antiretroviral therapy (ART). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) form a large class of drugs and a crucial component of ART. In NNRTIs, even a single resistance mutation is known to make the drugs completely ineffective. Additionally, several inhibitor-bound RTs with single resistance mutations do not exhibit any significant variations in their three-dimensional structures compared with the inhibitor-bound RT but completely nullify their inhibitory functions. This makes understanding the structural mechanism of these resistance mutations crucial for drug development. Here, we study several single resistance mutations in the allosteric inhibitor (nevirapine)-bound RT to analyze the mechanism of small structural changes leading to these large functional effects. In this study, we have shown that in absence of significant conformational variations in the inhibitor-bound wild-type RT and RT with single resistance mutations, the protein contact network analysis of their static structures, along with molecular dynamics simulations, can be a useful approach to understand the functional effect of small local conformational variations. The simple network analysis exposes the localized contact changes that lead to global rearrangement in the communication pattern within RT. Furthermore, these conformational changes have implications on the overall dynamics of RT. Using various measures, we show that a single resistance mutation can change the network structure and dynamics of RT to behave more like unbound RT, even in the presence of the inhibitor. This combined coarse-grained contact network and molecular dynamics approach promises to be a useful tool to analyze structure-function studies of proteins that show large functional changes with negligible variations in their overall conformation.

The First 3D Model of the Full-Length KIT Cytoplasmic Domain Reveals a New Look for an Old Receptor

Receptor tyrosine kinases (RTKs) are key regulators of normal cellular processes and have a critical role in the development and progression of many diseases. RTK ligand-induced stimulation leads to activation of the cytoplasmic kinase domain that controls the intracellular signalling. Although the kinase domain of RTKs has been extensively studied using X-ray analysis, the kinase insert domain (KID) and the C-terminal are partially or fully missing in all reported structures. We communicate the first structural model of the full-length RTK KIT cytoplasmic domain, a crucial target for cancer therapy. This model was achieved by integration of ab initio KID and C-terminal probe models into an X-ray structure, and by their further exploration through molecular dynamics (MD) simulation. An extended (2-µs) MD simulation of the proper model provided insight into the structure and conformational dynamics of the full-length cytoplasmic domain of KIT, which can be exploited in the description of the KIT transduction processes.

Forecasting the Growth of Structures from NMR and X-Ray Crystallography Experiments Released Per Year

In this paper, we present a forecasting scheme for the growth of molecular structures from NMR and X-ray Crystallography experimental techniques released every year by employing an autoregressive (AR) process. The proposed scheme maximises the forecasting accuracy by utilising the optimal AR process order. The optimal model order was derived as the model with the least prediction error. Therefore, the proposed scheme has been efficiently employed to model and predict the annual growth of structures-based NMR and X-ray Crystallography experimental data for the next decade 2019–2028 using the time series of the past 43 years of both experimental datasets. The experimental results showed that the optimal model order to estimate both datasets was [Formula: see text] which belongs to a forecasting accuracy of [Formula: see text], for both datasets. Indeed, such a high level of accuracy referred to the amount of linearity between the consecutive elements of the original times series. Hence, the forecasting results reveals of an exponential increasing behaviour in the future growth in the annual structures released from both NMR and X-ray Crystallography experiments.

Enhanced sampling and free energy calculations for protein simulations

Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

In Silico Modeling of the α7 Nicotinic Acetylcholine Receptor: New Pharmacological Challenges Associated with Multiple Modes of Signaling

The α7 nicotinic acetylcholine receptor is a homopentameric ion-channel of the Cys-loop superfamily characterized by its low probability of opening, high calcium permeability, and rapid desensitization. The α7 receptor has been targeted for the treatment of the cognitive symptoms of schizophrenia, depression, and Alzheimer's disease, but it is also involved in inflammatory modulation as a part of the cholinergic anti-inflammatory pathway. Despite its functional importance, in silico studies of the α7 receptor cannot produce a general model explaining the structural features of receptor activation, nor predict the mode of action for various ligand classes. Two particular problems in modeling the α7 nAChR are the absence of a high-resolution structure and the presence of five potentially nonequivalent orthosteric ligand binding sites. There is wide variability regarding the templates used for homology modeling, types of ligands investigated, simulation methods, and simulation times. However, a systematic survey focusing on the methodological similarities and differences in modeling α7 has not been done. In this work, we make a critical analysis of the modeling literature of α7 nAChR by comparing the findings of computational studies with each other and with experimental studies under the main topics of structural studies, ligand binding studies, and comparisons with other nAChR. In light of our findings, we also summarize current problems in the field and make suggestions for future studies concerning modeling of the α7 receptor.

Cyclodextrin Inclusion of Medicinal Compounds for Enhancement of their Physicochemical and Biopharmaceutical Properties

Owing to their wide structural diversity and unique complexing properties, cyclodextrins (CDs) find manifold applications in drug discovery and development. The focus of this mini-review is on their uses as 'enabling excipients' both in the context of early drug discovery and in subsequent optimisation of drug performance. Features highlighted here include descriptions of the structures of CDs, synthetic derivatisation to fine-tune their properties, the nature of inclusion complexation of drugs within the CD cavity, methodology for the study of free and complexed hosts in the solid state and in solution, the inherent pharmacological activity of several CDs and its utility, novel CD-based drug delivery systems, and the role of CDs in drug discovery and optimisation. Illustrative examples are generally based on research reported during the last two decades. Application of CDs to the optimisation of the performance of established drugs is commonplace, but there are many opportunities for the intervention of CDs during the early stages of drug discovery, which could guide the selection of suitable candidates for development, thereby contributing to reducing the attrition rate of new molecular entities.

Investigation of Molecular Details of Keap1-Nrf2 Inhibitors Using Molecular Dynamics and Umbrella Sampling Techniques

In this study, we investigate the atomistic details of Keap1-Nrf2 inhibitors by in-depth modeling techniques, including molecular dynamics (MD) simulations, and the path-based free energy method of umbrella sampling (US). The protein–protein interaction (PPI) of Keap1-Nrf2 is implicated in several neurodegenerative diseases like cancer, diabetes, and cardiomyopathy. A better understanding of the five sub-pocket binding sites for Nrf2 (ETGE and DLG motifs) inside the Kelch domain would expedite the inhibitor design process. We selected four protein–ligand complexes with distinct co-crystal ligands and binding occupancies inside the Nrf2 binding site. We performed 100 ns of MD simulation for each complex and analyzed the trajectories. From the results, it is evident that one ligand (1VV) has flipped inside the binding pocket, whereas the remaining three were stable. We found that Coulombic (Arg483, Arg415, Ser363, Ser508, and Ser602) and Lennard–Jones (Tyr525, Tyr334, and Tyr572) interactions played a significant role in complex stability. The obtained binding free energy values from US simulations were consistent with the potencies of simulated ligands. US simulation highlight the importance of basic and aromatic residues in the binding pocket. A detailed description of the dissociation process brings valuable insight into the interaction of the four selected protein–ligand complexes, which could help in the future to design more potent PPI inhibitors.

Reconstruction of low-resolution molecular structures from simulated atomic force microscopy images

BACKGROUND

Atomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial.

METHOD

We aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image.

RESULTS

The algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations.

CONCLUSIONS

The proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail.

GENERAL SIGNIFICANCE

High-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule.