The interactions of small molecules with proteins (protein–ligand interactions) mediate various biological phenomena including signal transduction and protein transcription and translation. Synthetic compounds such as drugs can also bind to target proteins, leading to the inhibition of protein–ligand interactions. These interactions typically accompany association–dissociation equilibrium according to the free energy difference between free and bound states; therefore, the quantitative biophysical analysis of the interactions, which uncovers the stoichiometry and dissociation constant, is important for understanding biological reactions as well as for rational drug development. Mass spectrometry (MS) has been used to determine the precise molecular masses of molecules. Recent advancements in MS enable us to determine the molecular masses of protein–ligand complexes without disrupting the non-covalent interactions through the gentle desolvation of the complexes by increasing the vacuum pressure of a chamber in a mass spectrometer. This method is called MS under non-denaturing conditions or native MS and allows the unambiguous determination of protein–ligand interactions. Under a few assumptions, MS has also been applied to determine the dissociation constants for protein–ligand interactions. The structural information of a protein–ligand interaction, such as the location of the interaction and conformational change in a protein, can also be analyzed using hydrogen/deuterium exchange MS. In this paper, we briefly describe the history, principle, and recent applications of MS for the study of protein–ligand interactions.

Interactive haptics-assisted docking provides a virtual environment for the study of molecular complex formation. It enables the user to interact with the virtual molecules, experience the interaction forces via their sense of touch, and gain insights about the docking process itself. Here we use a recently developed haptics software tool, Haptimol_RD, for the rigid docking of protein subunits to form complexes. Dimers, both homo and hetero, are loaded into the software with their subunits separated in space for the purpose of assessing whether they can be brought back into the correct docking pose via rigid-body movements. Four dimers were classified into two types: two with an interwinding subunit interface and two with a non-interwinding subunit interface. It was found that the two with an interwinding interface could not be docked whereas the two with the non-interwinding interface could be. For the two that could be docked a “sucking” effect could be felt on the haptic device when the correct binding pose was approached which is associated with a minimum in the interaction energy. It is clear that for those that could not be docked, the conformation of one or both of the subunits must change upon docking. This leads to the steric-based concept of a locked or non-locked interface. Non-locked interfaces have shapes that allow the subunits to come together or come apart without the necessity of intra-subunit conformational change, whereas locked interfaces require a conformational change within one or both subunits for them to be able to come apart.

Analysis of protein-protein interaction networks has revealed the presence of proteins with multiple inter­action ligand proteins, such as hub proteins. For such proteins, multiple ligands would be predicted as interacting partners when predicting all-to-all protein-protein interactions (PPIs). In this work, to obtain a better understanding of PPI mechanisms, we focused on protein interaction surfaces, which differ between protein pairs. We then performed rigid-body docking to obtain information of interfaces of a set of decoy structures, which include many possible interaction surfaces between a certain protein pair. Then, we investigated the specificity of sets of decoy interactions between true binding partners in each case of alpha-chymotrypsin, actin, and cyclin-dependent kinase 2 as test proteins having mul­tiple true binding partners. To observe differences in interaction surfaces of docking decoys, we introduced broad interaction profiles (BIPs), generated by assembling interaction profiles of decoys for each protein pair. After cluster analysis, the specificity of BIPs of true binding partners was observed for each receptor. We used two types of BIPs: those involved in amino acid sequences (BIP-seqs) and those involved in the compositions of interacting amino acid residue pairs (BIP-AAs). The specificity of a BIP was defined as the number of group members including all true binding partners. We found that BIP-AA cases were more specific than BIP-seq cases. These results indicated that the composition of inter­acting amino acid residue pairs was sufficient for determining the properties of protein interaction surfaces.

We examine the dynamic features of non-trivial allosteric binding sites to elucidate potential drug binding sites. These allosteric sites were previously found to be allosteric after determination of the protein-drug co-crystal structure. After comprehensive search in the Protein Data Bank, we identify 10 complex structures with allosteric ligands whose structures are very similar to their functional forms. Then, possible pockets on the protein surface are searched as potential ligand binding sites. To mimic ligand binding to the pocket, complex models are generated to fill out each pocket with pseudo ligand blocks consisting of spheres. Normal mode analysis of the elastic network model is performed for the complex models and unbound structures to assess the change of protein dynamics induced by ligand binding. We examine nine profiles to describe the dynamic and positional characteristics of the pockets, and identify the change of fluctuation around the ligand, ΔMSF_bs, as the best profile for distinguishing the allosteric sites from the other sites in 8 structures. These cases should be considered as examples of dynamics-driven allostery, which accompanies significant changes in protein dynamics. ΔMSF_bs is suggested to be used for the search of potential dynamics-driven allosteric sites in proteins for drug discovery.

We investigated the effect of ATP binding to GroEL and elucidated a role of ATP in the conformational change of GroEL. GroEL is a tetradecamer chaperonin that helps protein folding by undergoing a conformational change from a closed state to an open state. This conformational change requires ATP, but does not require the hydrolysis of the ATP. The following three types of conformations are crystalized and the atomic coordinates are available; closed state without ATP, closed state with ATP and open state with ADP. We conducted simulations of the conformational change using Elastic Network Model from the closed state without ATP targeting at the open state, and from the closed state with ATP targeting at the open state. The simulations emphasizing the lowest normal mode showed that the one started with the closed state with ATP, rather than the one without ATP, reached a conformation closer to the open state. This difference was mainly caused by the changes in the positions of residues in the initial structure rather than the changes in “connectivity” of residues within the subunit. Our results suggest that ATP should behave as an insulator to induce conformation population shift in the closed state to the conformation that has a pathway leading to the open state.

We analyzed the correlation between the conformational strain and the binding kinetics in antigen-antibody interactions. The catalytic antibodies 6D9, 9C10, and 7C8 catalyze the hydrolysis of a nonbioactive chloramphenicol monoester derivative to generate a bioactive chloramphenicol. The crystal structure of 6D9 complexed with a transition-state analog (TSA) suggests that 6D9 binds the substrate to change the conformation of the ester moiety to a thermodynamically unstable twisted conformation, enabling the substrate to reach the transition state during catalysis. The present binding kinetic analysis showed that the association rate for 6D9 binding to the substrate was much lower than that to TSA, whereas those for 9C10 and 7C8 binding were similar to those to TSA. Considering that 7C8 binds to the substrate with little conformational change in the substrate, the slow association rate observed in 6D9 could be attributed to the conformational strain in the substrate.

Comprehensive analysis and comparison of protein ligand-binding pockets are important to predict the ligands which bind to parts of putative ligand binding pockets. Because of the recent increase of protein structure information, such analysis demands a fast and efficient method for comparing ligand binding pockets. Previously we proposed a fast alignment-free method based on a simple representation of a ligand binding pocket with one 11-dimensional vector, which is suitable for such analysis. Based on that method, we conducted this study to expand and revise similarity measures of binding pockets and to investigate the effects of those modifications with two datasets for improving the ability to detect similar binding pockets. The new method exhibits higher detection performance of similar binding pockets than the previous methods and another existing accurate alignment-dependent method: APoc. Results also show that the effects of the modifications depend on the difficulty of the dataset, implying some avenues for methods of improvement.

We discuss methods and ideas of virtual screening (VS) for drug discovery by examining the performance of VS-APPLE, a recently developed VS method, which extensively utilizes the tendency of single binding pockets to bind diversely different ligands, i.e. promiscuity of binding pockets. In VS-APPLE, multiple ligands bound to a pocket are spatially arranged by maximizing structural overlap of the protein while keeping their relative position and orientation with respect to the pocket surface, which are then combined into a multiple-ligand template for screening test compounds. To greatly reduce the computational cost, comparison of test compound structures are made only with limited regions of the multiple-ligand template. Even when we use the narrow regions with most densely populated atoms for the comparison, VS-APPLE outperforms other conventional VS methods in terms of Area Under the Curve (AUC) measure. This region with densely populated atoms corresponds to the consensus region among multiple ligands. It is typically observed that expansion of the sampled region including more atoms improves screening efficiency. However, for some target proteins, considering only a small consensus region is enough for the effective screening of test compounds. These results suggest that the performance test of VS methods sheds light on the mechanisms of protein-ligand interactions, and elucidation of the protein-ligand interactions should further help improvement of VS methods.

Functional sites on proteins play an important role in various molecular interactions and reactions between proteins and other molecules. Thus, mutations in functional sites can severely affect the overall phenotype. Progress of genome sequencing projects has yielded a wealth of information on single nucleotide variants (SNVs), especially those with less than 1% minor allele frequency (rare variants). To understand the functional influence of genetic variants at a protein level, we investigated the relationship between SNVs and protein functional sites in terms of minor allele frequency and the structural position of variants. As a result, we observed that SNVs were less abundant at ligand binding sites, which is consistent with a previous study on SNVs and protein interaction sites. Additionally, we found that non-rare variants tended to be located slightly apart from enzyme active sites. Examination of non-rare variants revealed that most of the mutations resulted in moderate changes of the physico-chemical properties of amino acids, suggesting the existence of functional constraints. In conclusion, this study shows that the mapping of genetic variants on protein structures could be a powerful approach to evaluate the functional impact of rare genetic variations.

In mammals, bitter taste is mediated by TAS2Rs, which belong to the family of seven transmembrane G protein-coupled receptors. Since TAS2Rs are directly involved in the interaction between mammals and their dietary sources, it is likely that these genes evolved to reflect species-specific diets during mammalian evolution. Here, we analyzed the amino acids responsible for the difference in sensitivities of TAS2R16s of various primates using a cultured cell expression system. We found that the sensitivity of TAS2R16 varied due to several amino acid residues. Mutation of amino acid residues at E86T, L247M, and V260F in human and langur TAS2R16 for mimicking the macaque TAS2R16 decreased the sensitivity of the receptor in an additive manner, which suggests its contribution to the potency of salicin, possibly via direct interaction. However, mutation of amino acid residues 125 and 133 in human TAS2R16, which are situated in helix 4, to the macaque sequence increased the sensitivity of the receptor. These results suggest the possibility that bitter taste sensitivities evolved independently by replacing specific amino acid residues of TAS2Rs in different primate species to adapt to species-specific food.

Tumor necrosis factor-alpha (TNF-α) is a cytokine that plays an important role in inflammatory process and tumor development. Recent studies demonstrate that triterpene saponins from Vietnamese ginseng are efficient inhibitors of TNF-α. But the interactions between TNF-α and the saponins are still unclear. In this study, molecular docking and molecular dynamics simulations of TNF-α with three different triterpene saponins (majonoside R2, vina-ginsenoside R1 and vina-ginsenoside R2) were performed to evaluate their binding ability. Our results showed that the triterpene saponins have a good binding affinity with protein TNF-α. The saponins were docked to the pore at the top of the “bell” or “cone” shaped TNF-α trimer and the complexes were structurally stable during 100 ns molecular dynamics simulation. The predicted binding sites would help to subsequently investigate the inhibitory mechanism of triterpene saponins.

The accurate prediction of a ligand–protein complex structure is important for computer-assisted drug development. Although many docking methods have been developed over the last three decades, the success of binding structure prediction remains greatly limited. The purpose of this study was to demonstrate the usefulness of molecular dynamics (MD) simulation in assessing a docking pose predicted using a docking program. If the predicted pose is not unstable in an aqueous environment, MD simulation equilibrates the system and removes the ligand from the predicted position. Here we investigated two proteins that are important potential therapeutic targets: β2 adrenergic receptor (β2AR) and PR-Set7. While β2AR is rigid and its ligands are very similar to the template ligand (carazolol), PR-Set7 is very flexible and its ligands vary greatly from the template ligand (histone H4 tail peptide). On an empirical basis, we usually expect that the docking prediction is accurate when the protein is rigid and its ligands are similar to the template ligand. The MD analyses in this study clearly suggested such a tendency. Furthermore, we discuss the possibility that the MD simulation can predict the binding pose of a ligand.

Metabolic pathway reconstruction presents a challenge for understanding metabolic pathways in organisms of interest. Different strategies, i.e., reference-based vs. de novo, must be used for pathway reconstruction depending on the availability of well-characterized enzymatic reactions. If at least one enzyme is already known to catalyze a reaction, its amino acid sequence can be used as a reference for identifying homologous enzymes in the genome of an organism of interest. Where there is no known enzyme able to catalyze a corresponding reaction, however, the reaction and the corresponding enzyme must be predicted de novo from chemical transformations of the putative substrate-product pair. This review summarizes studies involving reference-based and de novo metabolic pathway reconstruction and discusses the importance of the classification and structure-function relationships of enzymes.

Molecular dynamics (MD) is a promising computational approach to investigate dynamical behavior of molecular systems at the atomic level. Here, we present a new MD simulation engine named “myPresto/omegagene” that is tailored for enhanced conformational sampling methods with a non-Ewald electrostatic potential scheme. Our enhanced conformational sampling methods, e.g., the virtual-system-coupled multi-canonical MD (V-McMD) method, replace a multi-process parallelized run with multiple independent runs to avoid inter-node communication overhead. In addition, adopting the non-Ewald-based zero-multipole summation method (ZMM) makes it possible to eliminate the Fourier space calculations altogether. The combination of these state-of-the-art techniques realizes efficient and accurate calculations of the conformational ensemble at an equilibrium state. By taking these advantages, myPresto/omegagene is specialized for the single process execution with Graphics Processing Unit (GPU). We performed benchmark simulations for the 20-mer peptide, Trp-cage, with explicit solvent. One of the most thermodynamically stable conformations generated by the V-McMD simulation is very similar to an experimentally solved native conformation. Furthermore, the computation speed is four-times faster than that of our previous simulation engine, myPresto/psygene-G. The new simulator, myPresto/omegagene, is freely available at the following URLs: http://www.protein.osaka-u.ac.jp/rcsfp/pi/omegagene/ and http://presto.protein.osaka-u.ac.jp/myPresto4/.

SPOC (spontaneous oscillatory contraction) is a phenomenon observed in striated muscle under intermediate activation conditions. Recently, we constructed a theoretical model of SPOC for a sarcomere, a unit sarcomere model, which explains the behavior of SPOC at each sarcomere level. We also constructed a single myofibril model, which visco-elastically connects the unit model in series, and explains the behaviors of SPOC at the myofibril level. In the present study, to understand the SPOC properties in a bundle of myofibrils, we extended the single myofibril model to a two-dimensional (2D) model and a three-dimensional (3D) model, in which myofibrils were elastically connected side-by-side through cross-linkers between the Z-lines and M-lines. These 2D and 3D myofibril models could reproduce various patterns of SPOC waves experimentally observed in a 2D sheet and a 3D bundle of myofibrils only by choosing different values of elastic constants of the cross-linkers and the external spring. The results of these 2D and 3D myofibril models provide insight into the SPOC properties of the higher-ordered assembly of myofibrils.

Many bacteria move using their flagellar motor, which generates torque through the interaction between the stator and rotor. The most important component of the rotor for torque generation is FliG. FliG consists of three domains: FliG_N, FliG_M, and FliG_C. FliG_C contains a site(s) that interacts with the stator. In this study, we examined the physical properties of three FliG constructs, FliG_Full, FliG_MC, and FliG_C, derived from sodium-driven polar flagella of marine Vibrio. Size exclusion chromatography revealed that FliG changes conformational states under two different pH conditions. Circular dichroism spectroscopy also revealed that the contents of α-helices in FliG slightly changed under these pH conditions. Furthermore, we examined the thermal stability of the FliG constructs using differential scanning calorimetry. Based on the results, we speculate that each domain of FliG denatures independently. This study provides basic information on the biophysical characteristics of FliG, a component of the flagellar motor.

KaiC, a core protein of the cyanobacterial circadian clock, is rhythmically autophosphorylated and autodephosphorylated with a period of approximately 24 h in the presence of two other Kai proteins, KaiA and KaiB. In vitro experiments to investigate the KaiC phosphorylation cycle consume considerable time and effort. To automate the fractionation, quantification, and evaluation steps, we developed a suite consisting of an automated sampling device equipped with an 8-channel temperature controller and accompanying analysis software. Eight sample tables can be controlled independently at different temperatures within a fluctuation of ±0.01°C, enabling investigation of the temperature dependency of clock activities simultaneously in a single experiment. The suite includes an independent software that helps users intuitively conduct a densitometric analysis of gel images in a short time with improved reliability. Multiple lanes on a gel can be detected quasi-automatically through an auto-detection procedure implemented in the software, with or without correction for lane ‘smiling.’ To demonstrate the performance of the suite, robustness of the period against temperature variations was evaluated using 32 datasets of the KaiC phosphorylation cycle. By using the software, the time required for the analysis was reduced by approximately 65% relative to the conventional method, with reasonable reproducibility and quality. The suite is potentially applicable to other clock or clock-related systems in higher organisms, relieving users from having to repeat multiple manual sampling and analytical steps.

The so-called island model of protein structural transition holds that hydrophobic interactions are the key to both the folding and function of proteins. Herein, the genesis and statistical mechanical basis of the island model of transitions are reviewed, by presenting the results of simulations of such transitions. Elucidating the physicochemical mechanism of protein structural formation is the foundation for understanding the hierarchical structure of life at the microscopic level. Based on the results obtained to date using the island model, remaining problems and future work in the field of protein structures are discussed, referencing Professor Saitô’s views on the hierarchic structure of science.

The Φ-value analysis approach provides information about transition-state structures along the folding pathway of a protein by measuring the effects of an amino acid mutation on folding kinetics. Here we compared the theoretically calculated Φ values of 27 proteins with their experimentally observed Φ values; the theoretical values were calculated using a simple statistical-mechanical model of protein folding. The theoretically calculated Φ values reflected the corresponding experimentally observed Φ values with reasonable accuracy for many of the proteins, but not for all. The correlation between the theoretically calculated and experimentally observed Φ values strongly depends on whether the protein-folding mechanism assumed in the model holds true in real proteins. In other words, the correlation coefficient can be expected to illuminate the folding mechanisms of proteins, providing the answer to the question of which model more accurately describes protein folding: the framework model or the nucleation-condensation model. In addition, we tried to characterize protein folding with respect to various properties of each protein apart from the size and fold class, such as the free-energy profile, contact-order profile, and sensitivity to the parameters used in the Φ-value calculation. The results showed that any one of these properties alone was not enough to explain protein folding, although each one played a significant role in it. We have confirmed the importance of characterizing protein folding from various perspectives. Our findings have also highlighted that protein folding is highly variable and unique across different proteins, and this should be considered while pursuing a unified theory of protein folding.

A simple statistical mechanical model proposed by Wako and Saitô has explained the aspects of protein folding surprisingly well. This model was systematically applied to multiple proteins by Muñoz and Eaton and has since been referred to as the Wako-Saitô-Muñoz-Eaton (WSME) model. The success of the WSME model in explaining the folding of many proteins has verified the hypothesis that the folding is dominated by native interactions, which makes the energy landscape globally biased toward native conformation. Using the WSME and other related models, Saitô emphasized the importance of the hierarchical pathway in protein folding; folding starts with the creation of contiguous segments having a native-like configuration and proceeds as growth and coalescence of these segments. The Φ-values calculated for barnase with the WSME model suggested that segments contributing to the folding nucleus are similar to the structural modules defined by the pattern of native atomic contacts. The WSME model was extended to explain folding of multi-domain proteins having a complex topology, which opened the way to comprehensively understanding the folding process of multi-domain proteins. The WSME model was also extended to describe allosteric transitions, indicating that the allosteric structural movement does not occur as a deterministic sequential change between two conformations but as a stochastic diffusive motion over the dynamically changing energy landscape. Statistical mechanical viewpoint on folding, as highlighted by the WSME model, has been renovated in the context of modern methods and ideas, and will continue to provide insights on equilibrium and dynamical features of proteins.

Understanding how proteins fold through a vast number of unfolded states is a major subject in the study of protein folding. Herein, we present itinerary profiling as a simple method to analyze molecular dynamics trajectories, and apply this method to Trp-cage. In itinerary profiling, structural clusters included in a trajectory are represented by a bit sequence, and a number of trajectories, as well as the structural clusters, can be compared and classified. As a consequence, the structural clusters that characterize the foldability of trajectories were able to be identified. The connections between the clusters were then illustrated as a network and the structural features of the clusters were examined. We found that in the true folding funnel, Trp-cage formed a left-handed main-chain topology and the Trp6 side-chain was located at the front of the main-chain ring, even in the initial unfolded states. In contrast, in the false folding funnel of the pseudo-native states, in which the Trp6 side-chain is upside down in the protein core, Trp-cage had a right-handed main-chain topology and the Trp side-chain was at the back. The initial topological partition, as determined by the main-chain handedness and the location of the Trp residue, predetermines Trp-cage foldability and the destination of the trajectory to the native state or the pseudo-native states.

“Life” is a particular state of matter, and matter is composed of various molecules. The state corresponding to “life” is ultimately determined by the genome sequence, and this sequence determines the conditions necessary for survival of the organism. In order to elucidate one parameter characterizing the state of “life”, we analyzed the amino acid sequences encoded in the total genomes of 557 prokaryotes and 40 eukaryotes using a membrane protein prediction online tool called SOSUI. SOSUI uses only the physical parameters of the encoded amino acid sequences to make its predictions. The ratio of membrane proteins in a genome predicted by the SOSUI online tool was around 23% for all genomes, indicating that this parameter is controlled by some mechanism in cells. In order to identify the property of genome DNA sequences that is the possible cause of the constant ratio of membrane proteins, we analyzed the nucleotide compositions at codon positions and observed the existence of systematic biases distinct from those expected based on random distribution. We hypothesize that the constant ratio of membrane proteins is the result of random mutations restricted by the systematic biases inherent to nucleotide codon composition. A new approach to the biological sciences based on the holistic analysis of whole genomes is discussed in order to elucidate the principles underlying “life” at the biological system level.

As for UV-induced DNA damage, which may induce skin cancer in animals and growth inhibition in plants, there are two types of photoproducts, namely cis-sin cyclobutane pyrimidine dimers (CPD) and pyrimidine-pyrimidone (6-4) photoproducts. When they are to be repaired, base-flipping occurs, and they bind to enzymes. However, this process remains relatively unknown at a molecular level. We analyze conformation and interaction energy changes upon base-flipping using classical molecular dynamics (CMD) simulations and ab initio electronic structure calculations. CMD simulations starting with a CPD in the flipped-in and flipped-out states showed that both states were unchanged for 500 ns, indicating the flipped-in and flipped-out processes do not occur spontaneously (without any help of the enzyme) after photo-damage. To deeply understand the reasons, we investigated interaction energy changes among bases upon structure changes during the flipped-in and flipped-out processes using Parallel Cascade Selection-MD (PaCS-MD) simulations at 400 K, followed by a fragment molecular orbital (FMO) method. The total inter-fragment interaction energy (IFIE) between CPD and other bases at the flipped-in state is estimated to be –60.08 kcal/mol. In particular, four bases strongly interact with CPD with interaction energies being –10.96, –13.70, –21.52, and –14.46 kcal/mol each. On the other hand, the total IFIE at the obtained flipped-out state increased to –10.40 kcal/mol by partly losing hydrogen bonds and π-π stacking interactions, respectively. These results clearly indicate that the base-flipping process of DNA lesions occurs with the help of external forces like interactions with appropriate enzymes such as photolyases.

Actin filaments in different parts of a cell interact with specific actin binding proteins (ABPs) and perform different functions in a spatially regulated manner. However, the mechanisms of those spatially-defined interactions have not been fully elucidated. If the structures of actin filaments differ in different parts of a cell, as suggested by previous in vitro structural studies, ABPs may distinguish these structural differences and interact with specific actin filaments in the cell. To test this hypothesis, we followed the translocation of the actin binding domain of filamin (ABD_FLN) fused with photoswitchable fluorescent protein (mKikGR) in polarized Dictyostelium cells. When ABD_FLN-mKikGR was photoswitched in the middle of a polarized cell, photoswitched ABD_FLN-mKikGR rapidly translocated to the rear of the cell, even though actin filaments were abundant in the front. The speed of translocation (>3 µm/s) was much faster than that of the retrograde flow of cortical actin filaments. Rapid translocation of ABD_FLN-mKikGR to the rear occurred normally in cells lacking GAPA, the only protein, other than actin, known to bind ABD_FLN. We suggest that ABD_FLN recognizes a certain feature of actin filaments in the rear of the cell and selectively binds to them, contributing to the posterior localization of filamin.