- Software
- Open access
- Published:
Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data
Journal of Cheminformatics volume 16, Article number: 28 (2024)
Abstract
Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.
Introduction
To understand and model 3D conformations and interactions crucial for the molecular recognition process and biological activity [1,2,3], different computational methods such as Molecular Dynamics (MD) simulations have been developed [4, 5]. These simulations produce long trajectories, which result in massive amount of time-dependent data and consists of individual atoms and their coordinates at specific time points. Currently, there are several bottlenecks such as computational speed or data analysis. When considering data analysis, as the size and length of the trajectories increase due to the increase in computing power, frame-by-frame analysis becomes more difficult and tedious. [6,7,8].
This is a particular bottleneck when trying to compare multiple simulations and highlight differences in i.e., interactions between simulations [9]. To identify interesting points in the trajectory, where e.g., changes occur, established measures that are commonly analysed and visualised include root-mean-square deviation, root-mean-square fluctuation (RMSF), radius of gyration and energy-based approaches [2, 10]. The identified time points or frames of interest are then often visually inspected by looking at the 3D conformations and interactions.
Systematically analysing and visualising the interactions derived from MD simulations is difficult. Different methods and tools have been proposed to aid in this process and, for example, to investigate interactions between a protein and ligand. Most of those methods are based on visual inspection (e.g., VMD [11]), visualisation of contact maps [12] (e.g., as a contact frequency map (MDContactCom [13]), or as dynamic matrix (CONAN [14])), or a list of interaction partners or distances (e.g., GROMACS [15] or MDAnalysis [16]). Even though different solutions have been proposed, they have certain disadvantages, such as difficulties in perceiving differences in multiple matrix visualisations, lists are complicated to analyse and lack 3D representation, and dynamic visualisation is difficult to remember as trajectories usually have many frames or time steps.
Recently, the concept of interaction fingerprints (IFPs) has also been proposed for MD simulations [17, 18]. They were originally designed to convert static 3D coordinates, such as those obtained from molecular modelling techniques or experimental studies, into a 1D bit vector [4, 5]. Several methods have been developed to derive IFPs of protein-ligand interactions, and most have been used as post-processing methods for virtual screening approaches (i.e., large-scale docking approaches) and conformational space analysis [4, 19,20,21,22,23,24,25,26,27,28] and have also been used for machine learning approaches [3, 29,30,31]. Unlike static structures, IFPs derived from MD simulations are more challenging to analyse because MD simulations allow studying the temporal motion and dynamics (e.g., conformation, interaction) of a system [1]. Therefore, many IFPs are derived from a single MD simulation [17, 18].
One of the first approaches to analyse IFPs of MD simulations was introduced by Kokh et al. [17]. A workflow was proposed to investigate ligand-protein interactions and calculate and analyse MD-IFPs for large systems of several hundred compounds. MD-IFPs developed in this approach were introduced to study unbinding (i.e., dissociation routes) and residence times in a trajectory and conformations of a series of compounds. MDAnalysis [16] and RDKit [32] were used and combined to read and iterate over MD simulation frames and to identify and compute interactions which were mapped to a bit vector. The resulting MD-IFPs were then mapped based on the ligand centre of mass on a 3D grid in either a physical or IFP space, and subsequently clustered with k-means or Gaussian methods. Transitions between the identified clusters were visualised and used to study intermediate states (meta-stable structures) relevant to dissociation. In addition, different matrix visualisations were proposed, which include either Euclidean distance between clusters, or a comparison of interactions between clusters [17].
In 2021 a python library called ProLIF (Protein-Ligand Interaction Fingerprint) was proposed by Bouysset and Fiorucci [18]. ProLIF calculates IFPs from experimental data, docking poses or MD simulation data for a variety of molecules. It supports many different interaction types and additional ones can be added or edited by the user. Similar to the approach by Kokh et al. [17] MDAnalysis is combined with RDKit to analyse interactions either on residue or atomic level and results are provided as data frame for further processing and options for visualisation. Individual interactions are represented as a timeline, while for the analysis of all interactions, a so-called aggregated frame, is calculated. For the calculation of the aggregated frame, all interactions identified in the IFPs are summed up over time and interactions that occur more than 30% of the time are considered as present in the aggregated frame (see Fig. 1). The aggregated frame, or any specific frame at a specific time, can be interactively visualised at the atomic level for the ligand and at the residue level for the protein. The atom group highlighted on the ligand is the atom group most frequently interacting with the protein residues. In addition, a residue interaction network is provided, as well as a Tanimoto similarity matrix, which indicates the similarity of each MD simulation frame (or time point) to assess whether interactions (or IFPs), and therefore protein-ligand binding, change over time [18].
To systematically study and explore interactions that occur in large MD simulations, IFPs are a valuable tool as they are easy to handle as 1D bit vectors. Nevertheless, IFPs proposed for MD simulation data in previous work have the disadvantage of massively aggregating data by considering only frequently occurring interactions (i.e., more than 30% of the time) resulting in one representative IFP [18], or by loosing information after aggregation to clusters [17]. In addition to the one representative IFP, the ProLIF library, for example, also provides access to the individual IFPs corresponding to each frame in the MD simulation trajectory, which can be accessed and visualised as a network [18]. The advantage is that the user can select interesting IFPs from a particular frame of the simulation. The disadvantage is that it produces as many IFPs as the number of MD simulation frames analysed, and gives no indication to the user as to which IFPs might be of interest. For these reasons, the aim of this work was to develop a new method for the analysis and visualisation of IFPs derived from MD simulation data in order to systematically aggregate interactions and thereby reduce the number of IFPs, that is, the number of time frames. Furthermore, the developed methods facilitate the comparison of multiple simulations of the same system, which has been neglected so far.
Application
Data set
As a case study, we use the previously published MD simulations of PPP1 in complex with Microcystin congeners [33,34,35,36]. Microcystin (MC) congeners are a class of potent toxins released during cyanobacterial blooms worldwide [37]. They share a common overall cyclic structure [38] and can cause serious intoxications [39] and in extreme cases death [40,41,42]. The toxicodynamics inside the cell involves reversible and irreversible binding to PPP1, PPP2A, PPP5 and PPP6 [43,44,45]. Therefore, binding of MC congeners to PPP1 has been studied and analysed by Jaeger-Honz et al. [33]. Two simulations of MC-congeners, namely MC-LR and MC-LF, independently in complex with PPP1 were selected to analyse interactions. Since coordination via water molecules and manganese ions (Mn\(^{2+}\)) is crucial for binding, these molecules have been included in the simulation [46]. Three replicates are available for each MC congener with a total length of 280 ns. After discarding the initial, non-equilibrated portion of the simulation, approximately 75,000 frames remain for analysis. [33].
Interaction fingerprint calculation
To calculate IFPs from the MD simulation, ProLIF (v1.1.0) [18] was used with RDKit (v2021.03.5) [32] and MDAnalysis(v2.4.0) [16, 47] as described in the ProLIF tutorials.
Different interaction types are available for IFP calculation in ProLIF: Anionic, CationPi, Cationic, EdgeToFace, FaceToFace, HBAcceptor, HBDonor, Hydrophobic, Interaction, MetalAcceptor, MetalDonor, PiCation, PiStacking, XBAcceptor, XBDonor and VdWContact. X denotes halogen atoms. VdWContact was removed from the IFP calculation, because test runs showed that all interactions were changed to van der Waals contact rather than more specific ones. For Mn\(^{2+}\) VdWContact was analysed separately, as this interaction might be more unspecific. Mn\(^{2+}\) which are crucial for binding do not have a van der Waals radius assigned in MDAnalysis which is necessary for interaction calculation. Therefore, the parameters of magnesium ions were assigned as they have a similar size and coordination preference compared to Mn\(^{2+}\) [48] and were also used for the simulations used here (see Jaeger-Honz et al. [33]). The IFPs were calculated for all frames in our MD simulation data, and replicates were treated as a single entity. Therefore, approximately 75,000 IFPs could be obtained for each MC congener.
Implementation
We here present IFPAggVis, which is a Python library to aggregate, visualise and compare IFPs of MD simulation data. There are two major steps:
-
1.
Pre-processing: IFPs are modified to summarise relevant interactions, and aggregated based on interaction or time (see orange boxes in Fig. 2),
-
2.
Visualisation and comparison: Visualisation of similarity within IFPs of the same simulation and in between simulations are compared, evaluated, and visually assessed (see Fig. 3).
IFPAggVis is designed to work with pre-processed data frames of IFPs (e.g., computed with ProLIF). The following libraries were used for implementation: MDAnalysis (v2.4.0) [16, 47], RDKit (v2021.03.5) [32], ProLIF (v1.0.0 [18]), NumPy (v1.21.2) [49], tqdm (v4.62.3) [50], pandas (v1.3.3) [51], scikit-learn (v1.0) [52], Matplotlib (v3.4.3) [53], imageio (v2.28.0) [54], Networkx (v2.6.3) [55] and DyNetx (v0.3.1) [56].
In the following, the individual steps of pre-processing and visualisation, as well as comparison of IFPs are summarised.
Pre-processing of interaction fingerprints
The individual steps of the pre-processing pipeline are shown in Fig. 2 (see orange boxes) and summarised as follows:
-
1.
Load data frame of IFPs which were pre-calculated with ProLIF (see “Interaction fingerprint calculation” section and green box in Fig. 2).
-
2.
Restructure the data frame to resolve the multi-index generated by ProLIF and map the Boolean values (True/False) to a bit vector (1/0) to indicate presence or absence of interactions
-
3.
Processing of the sliding window. To aggregate interactions, a sliding window is calculated over each interaction (i.e., columns) with pandas. To determine the size of the sliding window, x\(_1\) is calculated which is a percentage value based on the trajectory length (i.e., number of IFPs). The sliding window is centred around the currently calculated data points to consider interactions close in time together. The value assigned to the current data point is the mean value across the window.
-
4.
Filtering of the calculated mean based on x\(_2\) (x\(_2\) sets the interaction to present (1) or absent (0) if a mean value is greater than x\(_2\)). The variable x\(_2\) is based on the percentage of occurrence within a sliding window.
-
5.
Aggregation of processed IFPs. The derived IFPs are aggregated based on two different approaches: 1) interaction-based where any identical IFPs independent of the temporal dimension is summarised, and 2) time-based where identical IFPs which occur immediately after each other are summarised. If IFPs are summarised, the number of IFPs summarised are saved.
The filtering based on x\(_1\) and x\(_2\) smooths the data of the retrieved IFP. While the x\(_1\) filter evaluates the occurrence of interactions within a time window, x\(_2\) considers only frequently occurring interactions which occur more often than a threshold within the sliding window. Since numerical simulation data has a limited accuracy and calculation errors occur, and interactions can occur very rarely within a very small-time window, they are filtered out using the x\(_1\) and x\(_2\) filters. As the x\(_1\) and x\(_2\) filter are dependent on the MD simulation and probably also the data set studied, both filters of x\(_1\) and x\(_2\) can be adjusted by the user. To investigate effects of different parameters of x\(_1\) and x\(_2\), different thresholds were studied. For x\(_1\) we evaluated: 0.5 %, 1 %, 1.5 %, 2 %, 2.5 %, 5 %, 7.5 % and 10 %; for x\(_2\) the calculated mean values were filtered based on 0.00, 0.01, 0.02, 0.025, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, and 0.40. Because the mean values range between 0 and 1, the values correspond as well to percentage of occurrence of an interaction within the window. For this reason, the x\(_2\) filter is also referred to as a percentage in this paper for ease of reading. The percentage values evaluated were chosen to cover a wide range of different values to appropriately evaluate, as they are likely to depend on the data set and simulation settings. Both filtering values have been limited at the upper end, as data smoothing is already high at these percentage values and unlikely to provide meaningful results. In addition, for the smaller percentage values the steps chosen were smaller, since smaller values are considered to be more sensitive to changes than larger ones. The aggregation based on interactions and time has different advantages and disadvantages. The interaction-based filtering results in a collection of unique IFPs through the simulation and therefore the lowest number of IFPs without further aggregation, but the temporal evolution of interactions is lost. The time-based filtering on the other hand preserves the temporal component but may lead to duplicates of IFPs as states could be revisited and therefore result in a higher number of IFPs. For these reasons, both aggregation methods have been considered in the workflow with IFPAggVis so that the user can change the aggregation type and different thresholds. The processed, filtered and aggregated IFPs are provided to the user as Pandas DataFrame, which can be accessed computationally and saved to files or used for further downstream processing.
Visualisation and comparison of interaction fingerprints
To compare and assess the similarity of IFPs within a MD simulation, the number of absolute differences was calculated (see Eq. 1).
To compare and evaluate similarity of IFPs of different MD simulations, the Rogers-Tanimoto dissimilarity metric was computed as implemented in scikit-learn (v1.0) [52] and SciPy distance functions (v1.7.1) [57] as they are optimised for efficient calculation on large amount of data.
The Rogers-Tanimoto dissimilarity is defined in Eq. 2, where \(c_{ij}\) is the number of occurrences in two 1-D vectors at position i and j, \(c_{TT}\) is the number of bits set on (1, interaction present) in both vectors, \(c_{FF}\) is the number of bits set off (0, interaction absent) in both vectors, and \(c_{TF}\) and \(c_{FT}\) is the number of bits set on in the first vector and off in the second vector and vice versa. For IFP comparison, the dissimilarity is a value between 0 (similar) and 1 (dissimilar).
The similarity of two IFPs is calculated as shown in Eq. 3. Up to now, the Tanimoto coefficient has mostly been used to evaluate the similarity of molecules or IFPs. However, a study by Racz at al. [58] has shown that there are other coefficients that produce consistent results on different benchmark datasets and are viable alternatives to the Tanimoto coefficient, i.e., the Rogers Tanimoto. Therefore, this metric was selected in this work because of the possibility for fast computation but can be exchanged with other similarity or dissimilarity metrics as offered by the Python libraries scikit-learn [52] or SciPy [57] for pairwise distance calculation.
The number of differences as well as the similarity calculations are available to the user as a NumPy array and can be used for further downstream processing.
Different visualisations were proposed to support analysis and comparisons of IFPs (see Fig. 3), because it is not possible to cover all aspects of IFPs relevant for analysis and comparison with a single visualisation. IFPAggVis partly provides the visualisation as summarised visualisations. The proposed combinations show different aspects, which together should aid in analysing and understanding the aspects of IFPs derived from simulation.
For visualisation and comparison, two different approaches are available: 1) within the same MD simulation, and 2) between two different MD simulations. For both approaches, the pre-processed and aggregated IFPs are used as input (see green box, Fig. 3). To compare IFPs within the same simulation, circular charts, line plots, histograms, a similarity matrix and a network visualisation were developed (see orange box, Fig. 3).
The visualisation of interactions as circular chart summarise each residue individually with all interaction types occurring. The circular chart gives an overview of the interaction length and makes it easier to compare which interactions appear and disappear over time, or are constantly present or absent over periods of time.
The number of differences between IFPs are visualised as a histogram and as a matrix visualisation. The histogram shows the distribution of the number of differences, and therefore how similar all IFPs are to each other. In the matrix visualisation, this is colour-coded using the viridis colour map, which is perceptually uniform and robust to colour blindness. The matrix gives an impression on the development of differences between the IFPs over the course of the simulation, and can keep the temporal information or individual frames.
Two different line plots are available. One line plot shows the number of occurrence of an individual aggregated frame, that is, how many IFPs have been aggregated structure-based or time-based to the respective IFP. The second line plot links identical IFPs (i.e., the number of difference is zero) within an MD simulation with a vertical line. The frame numbers are shown by two horizontal lines.
The interactions between a ligand and protein are shown as star graphs, where the ligand is in the centre and residues are arranged around it. Initially calculated x, y coordinates can be saved to a file for reuse in further visualisations. The different interaction types that are analysed with ProLIF are encoded with different glyphs and are summarised in Table 1. In addition to the network visualisation, a line plot is provided showing the number of occurrence of an individual IFP as well as its index to give the user an estimate of the occurrence of individual networks in the data set.
For comparison of IFPs of two different simulations (see lower orange box, Fig. 3), further processing of the aggregated IFP sets is necessary, as some interactions may be unique to one of the simulations. Therefore, a so-called merged IFP set is built out of the two original aggregated IFP sets. All detected interactions are added as columns and if not previously present, the interaction is considered absent. To quantify and compare differences between IFPs of two simulations, similarity or dissimilarity of IFPs has to be evaluated. The comparison of two different IFP sets leads to a higher difference between the individual IFPs, therefore comparing the number of differences was not considered appropriate any more. For this reason, the Rogers-Tanimoto dissimilarity was calculated and converted into similarity (as shown in Eqs. 2 and 3).
Three different classes have been chosen to categorise IFPs to assess similarity: identical, similar and dissimilar. Similarity of fingerprints is a fuzzy concept and also data set dependent [59,60,61,62,63]. For molecular similarity, different thresholds have been suggested. In some papers, a Tanimoto coefficient (Tc) of \(Tc > 0.85\) is considered as structurally similar [62, 64], others consider a \(Tc > 0.5\) as similar, and \(Tc \le 0.5\) as dissimilar [65]. Thresholds for IFPs for MD simulations have not been systematically evaluated, and exact thresholds are likely to also depend on the data set.
Based on visual inspection of the IFP sets derived from the MD simulation, we decided to set the threshold for similarity of \(T_{c} \ge 0.95\) as identical, \(0.85 \le T_{c} < 0.95\) as similar and \(T_{c} < 0.5\) as dissimilar. The thresholds can be adjusted by the user dependent on the data set studied. The IFPs classified as identical, similar and dissimilar are returned as a dictionary for each class and can be saved with Pickle to file.
To compare the IFPs of two different sets, a line plot was developed to evaluate similarity within and in between simulations. Each IFP set (i.e., MD simulation set) is represented by three lines: 1) as dark blue lines (a, b, c) and 2) as bright blue lines (d, e, f) with IFP number on x-axis. Identical IFPs within the same MD simulation are shown between a and b, and e and f. Identical and similar IFPs between simulations are visualised between c and d, and they are shown in black and red, respectively.
All introduced visualisations are returned as Matplotlib figure, which can be saved to file. The network visualisations are saved as image files or GIFs due to the large number of figures generated.
Results and discussion
In the following, the MD simulations are referred to by MC congener name instead of PPP1-MC congener as in Jaeger-Honz et al. [33] for easier readability. The aggregated frame derived based on the ProLIF paper (with occurrence more than 30%) is referred to as aggregated\(_{occ30}\) IFP to distinguish from interaction- and time-based aggregation. Key findings are briefly summarised: Filtering and aggregation of IFPs 1) massively reduces their number, 2) helps to identify important residues of major representatives, 3) retrieves interactions known from the literature that never occur simultaneously in an IFP, and 4) aids in comparing IFPs across MD simulation sets to assess similarity of binding patterns.
Aggregation and filtering of interaction fingerprints
The influence of pre-processing was investigated by calculating the number of interactions and IFPs retrieved. First, the aggregation by interaction or time is compared to the aggregated\(_{\hbox {occ30}}\) IFP, and second, the effect of applying different filtering options with aggregation by interaction or time was investigated. For an explanation on the filtering and aggregation options, see “Pre-processing of interaction fingerprints” section.
By generating and analysing IFPs of different MC congeners simulation without further processing with IFPAggVis, 86 interactions could be detected for MC-LR, and 55 for MC-LF. The number of detected interactions in the aggregated\(_{\hbox {occ30}}\) frame drops to 14 and 20, for MC-LR and MC-LF, respectively. Therefore, we conclude that the aggregated\(_{\hbox {occ30}}\) frame provides a good overview of the major interactions, but looses a lot of information by aggregating to one IFP.
The number of IFPs after aggregation by interaction or time varies dependent on the simulation and aggregation type. In the original data set approximately 75,000 IFPs could be retrieved. When aggregating the IFPs by interaction, the number of IFPs could be reduced to 36.7% (27529) for MC-LR and 30.7% (23009) for MC-LF, which is still too many to analyse visually. In comparison, the aggregation by time reduces the number of IFPs to 95.7% (71764) for MC-LR and to 95.5% (71613) for MC-LF, which is almost as many as retrieved without aggregation.
The x\(_1\) filter (sliding window) massively reduces the number of IFPs (see Fig. 4 a and b). For the interaction-based aggregation, the number of IFPs is lower, as states can be revisited if aggregated by time. Independent of the size of the window chosen, less than 2% of the original number of IFPs remain. Smaller window sizes (0.5% and 1%) result in a higher difference between structural and temporal aggregation, which gradually disappears for larger window sizes (2%, 2.5%, 5%, 7.5% and 10%). For some sliding window filter sizes, a U-shaped curve is obtained (see Fig. 4 a and b). The curve shape is dependent on: 1) the aggregation type, as this effect is smaller with interaction-based aggregation, 2) the size of the sliding window as larger window sizes average out variance that may be present, and 3) the MC congener or the simulation analysed. This result seems to be counterintuitive at first. However, the number of IFPs left is not necessarily reflected by the filtering of occurrences. For some filtering options, the mean value before x\(_2\) filter is close to the selected x\(_2\) filter. Therefore, a change in threshold can lead to generation of several new IFPs by fluctuating absent and present interactions.
The number of interactions (see Fig. 4 c and d) is quickly reduced by using small x\(_2\) filter (percentage of occurrence) values (0 to 2.5%). Those interactions rarely occur within a small-time window and are probably not relevant, as they only exist for a short time span in the simulation and might be an artefact. Increasing the x\(_2\) filter above 20% does not lead to much further reduction of the number of interactions.
The filtering and aggregation settings for further IFP comparison and visualisation on our data set is therefore:
-
Temporal aggregation: Revisitation of IFPs is considered important. Aggregation by interaction does not result in a significantly lower number of IFPs and is therefore not used.
-
x\(_1\) filter (sliding filter) of 1% and x\(_2\) filter (occurrence filter) of 20%: The number of interactions decreases rapidly for a x\(_1\) filter below 1%, probably due to noise in the data. Window sizes of 2% and 2.5% show little difference to 1%. Larger window sizes than 5% have a high level of data smoothing, as interaction- and time based aggregation do almost not differ in the number of interactions. The 1% window on this dataset is approximately 7.5ns. With an x\(_2\) filter of 20 % an interaction is detected as present, if it occurred approximately 1.5 ns in the simulation, which is roughly the timescale where side-chain rotation and fluctuation occurs (\(10^{-9}\)s) [66]. This is considered as biologically appropriate, since shorter time scales are less relevant to interaction. In addition, the number of IFP and interactions retrieved between 10% and 20% are relatively constant.
Analysis of interaction fingerprints of molecular dynamics simulation
IFP comparison within MC congeners simulation
The visualisations of filtered and aggregated IFPs of MC-LR and MC-LF MD simulation with PPP1 (see Fig. 5 and Additional file 1: Fig. S1a) show similar trends. Both matrix visualisations have areas of higher similarity indicated by large blue squares, which are divided into smaller nested squares that indicate regions of high similarity with a low number of differences. The number of differences increase over time (see yellow areas for distant IFPs), therefore changes accumulate over time. Identical IFPs are close together in time, which is visualised as vertical connections in the line plot above the matrix. The histogram visualising the total number of differences has two peaks: 1) small, around 10 differences, and 2) around 20 differences, indicating a group of IFPs that are close to each other but distant from others. The line plot with number of occurrence of individual IFPs show that there are major representatives that occur frequently.
The residues important for interaction between MC congeners and PPP1 have been reviewed and summarised by Fontanillo and Köhn [46]. In brief the following interactions are important: 1) hydrogen bonds form with Arg96, Tyr134 and water to enable indirect coordination to manganese ions, 2) interaction with water is replaced by interaction between MC congener and Asn124, His125, Ile130, Tyr134 and Trp206, 3) hydrophobic interactions with Cys127, Ile130, Ile133, Trp206, Tyr272 and Gly274, and 4) a covalent bond which can be formed with Cys273 which is irrelevant here as bonds are not broken or formed with classical MD simulations as described here. Since different residues can interact in multiple ways with MC congeners, one residue is not restricted to one interaction. The three most common IFPs for MC-LR are 1) IFP 36 (Fig. 6a) located at the beginning of the simulation, 2) IFP 497 (Fig. 6b) and 3) IFP 507 (Fig. 6c) located at the end of the simulation. Some known interactions (Trp206, HOH) described in literature [46] were observed for all IFPs, others only for IFP 36 (His125, Tyr272, Cys273, Gly274) or for IFP 497 and IFP 507 (Asn124, Ile130, Cys127 (IFP507)), whereas some (Ile133, His125, Tyr134) were not found for any of the three major IFPs. Although the interactions with known residues are not necessarily of the same type as described in literature, the important residues and interactions have been identified, but never occur all together in the same IFP. In addition, we could identify Phe276, Val223 and Gln249 as important residues that were not identified in literature so far and might be relevant for further studies and evaluation. Phe276 and Val223 could also be identified with the aggregated\(_{\hbox {occ30}}\) frame proposed by Bouysset and Fiorucci [18] (see Additional file 1: Table S1), but Gln249 was not detected, highlighting the importance of analysing individual networks as they occur over a period of time. For MC-LF a similar trend is observed and the most frequent IFP patterns are shown in the appendix (see Additional file 1: Fig. S1b–d).
IFP comparison between MC congeners simulation
The IFP sets of MC-LR and MC-LF were merged to compare them and similarity evaluated based on the inverse Rogers-Tanimoto dissimilarity. A small proportion of identical IFPs (0.58%, 865 IFPs) could be identified between both MC congeners, 18.12% (or 26913 IFPs) were identified as similar, and 85.89% (127550 IFPs) as dissimilar. Please note that the numbers do not add up to 100% because one IFP can belong to several similarity classes depending on the reference IFP used for similarity calculation, therefore the values reflect the total number of comparisons in a data set.
Based on the number of IFPs in the different similarity classes, we conclude that while toxic MC congeners share some binding patterns, they also have distinct binding patterns which are specific to the respective MC congener. Figure 7 shows identical and similar IFPs of MC-LR and MC-LF. Similar and identical IFPs are connected by red or black vertical lines, respectively. IFPs of MC-LR map back to approximately four larger areas in MC-LF, which shows that both MC congeners share certain binding patterns, which confirms our initial hypothesis. The two MC congeners do not share too many binding patterns, as the majority of IFPs are not linked.
In Fig. 8a–c, a set of identical and similar IFPs of both MC congeners that map to each other were selected based on the majority of occurrences. The two identical IFPs occurs 755 times in MC-LR (IFP 185, see Fig. 8a) and 79 times in MC-LF (IFP 397, see Fig. 8b). Both IFPs differ in only one interaction (Ile133 in MC-LR) demonstrating that our similarity threshold for identical IFPs is suitable to detect common IFPs across MC congeners and therefore across MD simulation data sets. Ile130, Ile133, Tyr134 and Trp206 are known in literature and could be efficiently retrieved. In comparison to the aggregated\(_{\hbox {occ30}}\) IFP not all interactions were retrieved for both MC congeners. Interestingly, Ile133 is more frequently occurring for MC-LF, although it was identified here for MC-LR which was not even detected in the aggregated\(_{\hbox {occ30}}\) IFP.
The two similar IFPs is also IFP 185 of MC-LR (occurrence 755, see Fig. 8a) and IFP409 in MC-LF (occurrence 2733, see Fig. 8c). Both share an overall interaction pattern with a difference in two protein residues (Arg96, Tyr272) and four interactions, indicating a good threshold chosen for similar IFP. Again, important residues for binding were identified that are described in literature: Cys127, Ile130, Ile133, Tyr134, Trp206 (both MC congeners), and Arg96 and Tyr272 for MC-LF. Interestingly, also here the aggregated\(_{\hbox {occ30}}\) IFP misses some interactions (e.g., Ile133 for MC-LR) even though this interaction should be retrieved for MC-LF, but not for this frequently occurring IFP (see Fig. 8c) identified here.
Conclusion
Here we presented IFPAggVis, a library for systematic aggregation and comparison of IFPs to reduce the number of IFPs derived from MD simulations. The visualisations provide an overview to analyse simulations to derive biological knowledge and temporal development of interactions during simulation. We were able to identify representative IFPs based on our example data. Moreover, our aggregation method has the advantage of representing more realistic networks and analyse specific differences, since non-covalent interactions can form and break. Our analysis showed that we could reproduce known interacting residues from literature, which do never occur together in our representative IFPs. In addition, we were able to show that the aggregated\(_{\hbox {occ30}}\) IFP is a valid approach for a quick overview of IFPs, but suffers from missing interactions that occur frequently in individual IFPs and are therefore likely to be important. Moreover, we provide an estimate and visualisation to compare IFPs derived from different MD simulations and help to assess similarity of IFPs. IFPAggVis is a first step towards aggregation and comparison of IFPs derived from MD simulations and can be easily applied to other systems. Therefore, there are many possibilities for future developments. Currently, interactions are analysed at the residue level and the ligand is treated as a single entity. Incorporating interaction-based analysis at the atomic level could facilitate comparison between IFPs from different MD simulations. In addition, this approach could help to include or exclude certain atomic groups of the ligand that are of particular interest to the user. Moreover, the inclusion of interaction analysis in the 3D view could help to facilitate the analysis of interactions. From MD simulations, we can derive the 3D coordinates, but it is currently difficult to map the individual selected IFPs back to the respective frame of the MD simulation trajectory. Therefore, we want to include an automatic mapping of the IFPs to the frame of the MD simulation trajectory to improve the understanding of the interaction by including a 3D representation of the interacting molecules. In addition, the next step is to incorporate atom-based analysis, which will allow the inclusion and exclusion of specific chemical groups and could lead to improved analysis of interactions and comparison of IFPs between different MD simulations. Although we were able to massively aggregate the number of IFPs derived from the MD simulation, we believe that it is still possible to further aggregate and reduce the number of IFPs to a few representatives. These representatives could be analysed in more detail to improve our understanding of interaction and binding, or used for machine learning approaches.
Availability of data and materials
All the scripts and data used in this paper are available open source at Zenodo. [67, 68]. IFPAggVis as library is published on Github and tutorials are available to explain a typical workflow and usage. Project name: IFPAggVis. Project home page: https://github.com/LSI-UniKonstanz/IFPAggVis. Operating system(s): Platform independent. Programming language: Python. Other requirements: Python 3.8 or higher, and several open-source Python packages: ProLIF, MDAnalysis, RDKit, NumPy, tqdm, pandas, scikit-learn, Matplotlib, imageio, Networkx, DyNetx. License: Apache License 2.0. Any restrictions to use by non-academics: None.
References
Adcock SA, McCammon JA (2006) Molecular dynamics: survey of methods for simulating the activity of proteins. Chem Rev 106(5):1589–1615. https://doi.org/10.1021/cr040426m
Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9(9):646–652. https://doi.org/10.1038/nsb0902-646
Kumar S, Kim M-H (2021) SMPLIP-score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors. J Cheminform 13(1):28. https://doi.org/10.1186/s13321-021-00507-1
Desaphy J, Raimbaud E, Ducrot P, Rognan D (2013) Encoding protein–ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 53(3):623–637. https://doi.org/10.1021/ci300566n
Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K (2014) The interplay between molecular modeling and chemoinformatics to characterize protein–ligand and protein–protein interactions landscapes for drug discovery. In: Advances in protein chemistry and structural biology, vol 96. Elsevier, London, pp 1–37. https://doi.org/10.1016/bs.apcsb.2014.06.001
Gelpi J, Hospital A, Goñi R, Orozco M (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem. https://doi.org/10.2147/AABC.S70333
Yu I, Feig M, Sugita Y (2018) High-performance data analysis on the big trajectory data of cellular scale all-atom molecular dynamics simulations. J Phys Conf Ser 1036:012009. https://doi.org/10.1088/1742-6596/1036/1/012009
Schlick T, Portillo-Ledesma S (2021) Biomolecular modeling thrives in the age of technology. Nat Comput Sci 1(5):321–331. https://doi.org/10.1038/s43588-021-00060-9
Bedart C, Renault N, Chavatte P, Porcherie A, Lachgar A, Capron M, Farce A (2022) SINAPs: a software tool for analysis and visualization of interaction networks of molecular dynamics simulations. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.1c00854
Shukla R, Tripathi T (2020) Molecular dynamics simulation of protein and protein–ligand complexes. In: Singh DB (ed) Computer-aided drug design. Springer, Singapore, pp 133–161. https://doi.org/10.1007/978-981-15-6815-2_7
Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38. https://doi.org/10.1016/0263-7855(96)00018-5
Badaczewska-Dawid AE, Nithin C, Wroblewski K, Kurcinski M, Kmiecik S (2022) MAPIYA contact map server for identification and visualization of molecular interactions in proteins and biological complexes. Nucl Acids Res. https://doi.org/10.1093/nar/gkac307
Motono C, Yanagida S, Sato M, Hirokawa T (2021) MDContactCom: a tool to identify differences of protein molecular dynamics from two MD simulation trajectories in terms of interresidue contacts. Bioinformatics. https://doi.org/10.1093/bioinformatics/btab538
Mercadante D, Gräter F, Daday C (2018) CONAN: a tool to decode dynamical information from molecular interaction maps. Biophys J 114(6):1267–1273. https://doi.org/10.1016/j.bpj.2018.01.033
Bekker H, Berendsen HJC, Dijkstra EJ, Achterop S, Vondrumen R, Vanderspoel D, Sijbers A, Keegstra H, Renardus MKR (1993) Gromacs—a parallel computer for molecular-dynamics simulations. In: DeGroot R, Nadrchal J (eds) Physics computing’92. 4th International Conference on Computational Physics (PC 92). World Scientific Publishing, Singapore, pp 252–256
Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O (2011) MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem 32:2319–2327. https://doi.org/10.1002/jcc.21787
Kokh DB, Doser B, Richter S, Ormersbach F, Cheng X, Wade RC (2020) A workflow for exploring ligand dissociation from a macromolecule: efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories. J Chem Phys 153(12):125102. https://doi.org/10.1063/5.0019088
Bouysset C, Fiorucci S (2021) ProLIF: a library to encode molecular interactions as fingerprints. J Cheminform 13(1):72. https://doi.org/10.1186/s13321-021-00548-6
Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein–ligand binding interactions. J Med Chem 47(2):337–344. https://doi.org/10.1021/jm030331x
Mpamhanga CP, Chen B, McLay IM, Willett P (2006) Knowledge-based interaction fingerprint scoring: a simple method for improving the effectiveness of fast scoring functions. J Chem Inf Model 46(2):686–698. https://doi.org/10.1021/ci050420d
Marcou G, Rognan D (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 47(1):195–207. https://doi.org/10.1021/ci600342e
Tan L, Lounkine E, Bajorath J (2008) Similarity searching using fingerprints of molecular fragments involved in protein–ligand interactions. J Chem Inf Model 48(12):2308–2312. https://doi.org/10.1021/ci800322y
Crisman TJ, Sisay MT, Bajorath J (2008) Ligand-target interaction-based weighting of substructures for virtual screening. J Chem Inf Model 48(10):1955–1964. https://doi.org/10.1021/ci800229q
Da C, Kireev D (2014) Structural protein–ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J Chem Inf Model 54(9):2555–2561. https://doi.org/10.1021/ci500319f
Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M (2015) PLIP: fully automated protein–ligand interaction profiler. Nucl Acids Res 43:443–447. https://doi.org/10.1093/nar/gkv315
Jubb HC, Higueruelo AP, Ochoa-Montaño B, Pitt WR, Ascher DB, Blundell TL (2017) Arpeggio: a web server for calculating and visualising interatomic interactions in protein structures. J Mol Biol 429(3):365–371. https://doi.org/10.1016/j.jmb.2016.12.004
Li G-B, Yu Z-J, Liu S, Huang L-Y, Yang L-L, Lohans CT, Yang S-Y (2017) IFPTarget: a customized virtual target identification method based on protein–ligand interaction fingerprinting analyses. J Chem Inf Model 57(7):1640–1651. https://doi.org/10.1021/acs.jcim.7b00225
Jasper JB, Humbeck L, Brinkjost T, Koch O (2018) A novel interaction fingerprint derived from per atom score contributions: exhaustive evaluation of interaction fingerprint performance in docking based virtual screening. J Cheminform 10(1):15. https://doi.org/10.1186/s13321-018-0264-0
Wójcikowski M, Kukiełka M, Stepniewska-Dziubinska MM, Siedlecki P (2019) Development of a protein–ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions. Bioinformatics 35(8):1334–1341. https://doi.org/10.1093/bioinformatics/bty757
Thangapandian S, Idakwo G, Luttrell J, Hong H, Zhang C, Gong P (2023) Quantitative target-specific toxicity prediction modeling (QTTPM): coupling machine learning with dynamic protein–ligand interaction descriptors (DyPLIDs) to predict androgen receptor-mediated toxicity. In: Hong H (ed) Machine learning and deep learning in computational toxicology. Springer, Singapore, pp 263–295. https://doi.org/10.1007/978-3-031-20730-3_11
Szulc NA, Mackiewicz Z, Bujnicki JM, Stefaniak F (2023) Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA. Brief Bioinform 24(4):187. https://doi.org/10.1093/bib/bbad187
Landrum G (2021) RDKit: open-source cheminformatics software. https://github.com/rdkit/rdkit/. Release 2021.03.5 Release. rdkit/rdkit
Jaeger-Honz S, Nitschke J, Altaner S, Klein K, Dietrich DR, Schreiber F (2022) Investigation of microcystin conformation and binding towards ppp1 by molecular dynamics simulation. Chem Biol Interact 351:109766. https://doi.org/10.1016/j.cbi.2021.109766
Jaeger-Honz S, Nitschke J, Altaner S, Klein K, Dietrich DR, Schreiber F (2021) Molecular dynamics simulation of MC-congeners in complex with PPP1-replicate 1. Zenodo. https://doi.org/10.5281/zenodo.5017745
Jaeger-Honz S, Nitschke J, Altaner S, Klein K, Dietrich DR, Schreiber F (2021) Molecular dynamics simulation of MC-congeners in complex with PPP1-Replicate 2. Zenodo. https://doi.org/10.5281/zenodo.5017839
Jaeger-Honz S, Nitschke J, Altaner S, Klein K, Dietrich DR, Schreiber F (2021) Molecular dynamics simulation of MC-congeners in complex with PPP1-Replicate 3. Zenodo. https://doi.org/10.5281/zenodo.5017851
Driggers EM, Hale SP, Lee J, Terrett NK (2008) The exploration of macrocycles for drug discovery—an underexploited structural class. Nat Rev Drug Discov 7(7):608–624. https://doi.org/10.1038/nrd2590
Bouaïcha N, Miles C, Beach D, Labidi Z, Djabri A, Benayache N, Nguyen-Quang T (2019) Structural diversity, characterization and toxicology of microcystins. Toxins 11(12):714. https://doi.org/10.3390/toxins11120714
Dietrich D, Hoeger S (2005) Guidance values for microcystins in water and cyanobacterial supplement products (blue–green algal supplements): A reasonable or misguided approach? Toxicol Appl Pharmacol 203(3):273–289. https://doi.org/10.1016/j.taap.2004.09.005
Pouria S, Andrade A, Barbosa J, Cavalcanti R, Barreto V, Ward C, Preiser W, Poon GK, Neild G, Codd G (1998) Fatal microcystin intoxication in haemodialysis unit in Caruaru, Brazil. Lancet 352(9121):21–26. https://doi.org/10.1016/S0140-6736(97)12285-1
Azevedo SMFO, Carmichael WW, Jochimsen EM, Rinehart KL, Lau S, Shaw GR, Eaglesham GK (2002) Human intoxication by microcystins during renal dialysis treatment in Caruaru-brazil. Toxicology 181–182:441–446. https://doi.org/10.1016/S0300-483X(02)00491-2
Yuan M, Carmichael WW, Hilborn ED (2006) Microcystin analysis in human sera and liver from human fatalities in Caruaru, Brazil 1996. Toxicon 48(6):627–640. https://doi.org/10.1016/j.toxicon.2006.07.031
MacKintosh C, Beattie KA, Klumpp S, Cohen P, Codd GA (1990) Cyanobacterial microcystin-LR is a potent and specific inhibitor of protein phosphatases 1 and 2A from both mammals and higher plants. FEBS Lett 264(2):187–192. https://doi.org/10.1016/0014-5793(90)80245-E
Hastie CJ, Borthwick EB, Morrison LF, Codd GA, Cohen PTW (2005) Inhibition of several protein phosphatases by a non-covalently interacting microcystin and a novel cyanobacterial peptide, nostocyclin. Biochim Biophys Acta Gen Subj 1726(2):187–193. https://doi.org/10.1016/j.bbagen.2005.06.005
Hoeger SJ, Schmid D, Blom JF, Ernst B, Dietrich DR (2007) Analytical and functional characterization of microcystins [asp3]MC-RR and [asp3, dhb7]MC-RR: consequences for risk assessment? Environ Sci Technol 41(7):2609–2616. https://doi.org/10.1021/es062681p
Fontanillo M, Köhn M (2018) Microcystins: synthesis and structure–activity relationship studies toward PP1 and PP2a. Bioorg Med Chem 26(6):1118–1126. https://doi.org/10.1016/j.bmc.2017.08.040
Gowers RJ, Linke M, Barnoud J, Reddy TJE, Melo MN, Seyler SL, Dotson DL, Domański J, Buchoux S, Kenney IM, Beckstein O (2016) MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations. In: Benthall S, Rostrup S (eds) Proceedings of the 15th Python in science conference, Austin, TX, pp 98–105. https://doi.org/10.25080/Majora-629e541a-00e. SciPy
Bock CW, Katz AK, Markham GD, Glusker JP (1999) Manganese as a replacement for magnesium and zinc: functional comparison of the divalent ions. J Am Chem Soc 121(32):7360–7372. https://doi.org/10.1021/ja9906960
...Harris CR, Millman KJ, Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, Kerkwijk MH, Brett M, Haldane A, R’ıo JF, Wiebe M, Peterson P, G’erard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE (2020) Array programming with NumPy. Nature 585(7825):357–362. https://doi.org/10.1038/s41586-020-2649-2
Costa-Luis C, Larroque SK, Altendorf K, Mary H, Richardsheridan, Korobov M, Yorav-Raphael N, Ivanov I, Bargull M, Rodrigues N, CHEN G, Lee A, Newey C, James Coales J, Zugnoni M, Pagel MD, mjstevens777 Dektyarev M, Rothberg A, Alexander, Panteleit D, Dill, F, FichteFoll, Sturm G, HeoHeo, Kemenade H, McCracken J, MapleCCC, Nordlund M (2021) tqdm: a fast, extensible progress bar for Python and CLI. Zenodo. https://doi.org/10.5281/zenodo.5517697
The Pandas Development Team: Pandas-Dev/pandas: Pandas. https://github.com/pandas-dev/pandas. Version 1.3.3, BSD-3-Clause (2021)
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Chem Eng 9(3):90–95. https://doi.org/10.1109/MCSE.2007.55
Klein A, Wallkötter S, Silvester S, Tanbakuchi A, Actions-User, Müller P, Nunez-Iglesias J, Harfouche M, Dennis Lee A, McCormick M, Organic Irradiation, Rai A, Ladegaard A, Smith TD, lschr, Kemenade H, Vaillant G, Jackwalker64, Nises J, Komarčević M, Rreilink, Schambach M, Andrew, Dusold C, Gohlke C, DavidKorczynski Kohlgrüber F, Yang G, Inggs G (2023) imageio/imageio: v2.28.0. Zenodo. https://doi.org/10.5281/zenodo.7857504
Hagberg A, Swart P, Chult SD (2008) Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States). www.osti.gov/biblio/960616/
Rossetti G, Bot, Norman U, Dormán H, Dorner M (2021) GiulioRossetti/dynetx. Zenodo. https://doi.org/10.5281/zenodo.5599265
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P (2020) Scipy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17(3):261–272. https://doi.org/10.1038/s41592-019-0686-2
Rácz A, Bajusz D, Héberger K (2018) Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints. J Cheminform 10(1):48. https://doi.org/10.1186/s13321-018-0302-y
Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G (2015) Molecular fingerprint similarity search in virtual screening. Methods 71:58–63. https://doi.org/10.1016/j.ymeth.2014.08.005
Bero SA, Muda AK, Choo YH, Muda NA, Pratama SF (2017) Similarity measure for molecular structure: a brief review. J Phys Conf Ser 892:012015. https://doi.org/10.1088/1742-6596/892/1/012015
Kumar A, Zhang KYJ (2018) Advances in the development of shape similarity methods and their application in drug discovery. Front Chem 6:315. https://doi.org/10.3389/fchem.2018.00315
Stumpfe D, Bajorath J (2011) Similarity searching. WIREs Comput Mol Sci 1(2):260–282. https://doi.org/10.1002/wcms.23
O’Boyle NM, Sayle RA (2016) Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminform 8(1):36. https://doi.org/10.1186/s13321-016-0148-0
Martin YC, Kofron JL, Traphagen LM (2002) Do structurally similar molecules have similar biological activity? J Med Chem 45(19):4350–4358. https://doi.org/10.1021/jm020155c
Fassio AV, Shub L, Ponzoni L, McKinley J, O’Meara MJ, Ferreira RS, Keiser MJ, Melo Minardi RC (2022) Prioritizing virtual screening with interpretable interaction fingerprints. J Chem Inf Model 62(18):4300–4318. https://doi.org/10.1021/acs.jcim.2c00695
Fisette O, Lagüe P, Gagné S, Morin S (2012) Synergistic applications of MD and NMR for the study of biological systems. J Biotechnol Biomed 2012:1–12. https://doi.org/10.1155/2012/254208
Jaeger-Honz S, Klein K, Schreiber F (2023) Interaction fingerprints for molecular dynamics simulation of MC-LR and MC-LF with PPP1-data, scripts and libraries. Zenodo. https://doi.org/10.5281/zenodo.10423389
Jaeger-Honz S, Klein K, Schreiber F (2023) Interaction fingerprints for molecular dynamics simulation of MC-LR and MC-LF with PPP1-libraries and scripts. Zenodo. https://doi.org/10.5281/zenodo.10424417
Acknowledgements
We thank the anonymous reviewers for their helpful and constructive feedback and comments, which contributed to the improvement of the manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 251654672-TRR 161.
Author information
Authors and Affiliations
Contributions
SJ-H: Investigation, Software, Writing-Original Draft, Visualisation, Formal analysis. KK: Conceptualisation, Supervision, Writing-Review and Editing. FS: Conceptualisation, Supervision, Funding acquisition, Resources, Writing-Review and Editing.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Additional file 1. Table S1
: Aggregated\(_{\hbox{occ30}}\) IFP for MC-LR and MC-LF derived with ProLIF [18]. Fig. S1: Comparison of IFP similarity within MC-LF and most frequent IFP visualised as networks. Fig. S2: Zoomed in figure of number of IFPs (a, b) and interactions (c, d) after x1 and x2 filters are applied on MC-LR and MC-LF dataset.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Jaeger-Honz, S., Klein, K. & Schreiber, F. Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data. J Cheminform 16, 28 (2024). https://doi.org/10.1186/s13321-024-00822-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s13321-024-00822-3