As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and  Allosteric regulation is often a mechanism of choice for proteins and biomolecular assemblies to operate in complex signalling cascades and to modulate their activity levels, adapting to binding partners in the cellular environment during signal transduction, catalysis, and gene regulation ,2,3,4,5.Understanding molecular mechanisms of allosteric regulation in proteins has attracted considerable attention in both academia and industry owing to the importance of discovering allosteric modulators of therapeutically important targets . These eWhile orthostery-based therapies have enhanced the quality of life for patients, they have brought forth many daunting challenges for which allostery may provide new solutions. Drug discovery against more diverse protein targets can result in less toxic and more specific therapies. The incorporation of dynamic and network analysis tools has proven their effectiveness in drug discovery studies of several target proteins ,33,34,35Allostery is generally defined as a reversible functional and conformational modulation at one site resulting from a remote perturbation in a protein ,37,38. TAllosteric effects are not easily detectable by any single method as they can take many forms, and a diverse pool of conformational samples is often needed to expose these rare events ,45,46,47chanisms ,15. Struchanisms ,56. Preschanisms ,56,57.Allosteric interactions and communications can be conveniently described and characterized by dynamic networks of interactions between components of biological systems. The organization and evolution of dynamic residue interaction networks in proteins allows for formation of ensembles of pathways that transmit signals by propagating conformational fluctuations and functional motions between distant sites. Recent years have witnessed the development of various approaches that investigate NMR chemical-shift perturbations to identify allosteric networks in proteins ,61,62,63The ensembles of allosteric communications and protein residues involved in signal transmission via population-shift or dynamics-based allostery can be experimentally examined by NMR spectroscopy ,65,66. NNMR chemical exchange saturation transfer (CEST) experiments ,69,70 caThere is increasing evidence that dynamic allostery may be a common feature in many protein interactions, and as a result, allosteric mechanisms are no longer viewed as being mediated solely through structural transitions that select specific stable conformational states.Even though NMR technologies opened the doors to us to greatly understand the mechanisms of allostery, over 80% of the structures available from the Protein Data Bank (PDB) comprise macromolecules solved by X-ray crystallography . These aHuman diseases and traits have been associated with single nucleotide polymorphisms (SNPs) through the use of genome-wide association studies (GWAS) ,88. TherPrecision medicine combines pharmacology and genomics to exploit genetic variation in the human population in order to deliver innocuous but powerful drugs to certain groups of individuals. This is based on the idea that an individual’s genetic makeup determines their reaction to a drug, good or bad ,98. For Mutations have also been associated with drug resistance in numerous pathogenic diseases such as influenza , tubercuOrthosteric and allosteric drugs are distinguished from each other by their molecular mechanism of action. The former generally binds to the active site and is in competition with biological cofactors or substrates. Alternatively, allosteric drugs and modulators can modify or obstruct the active site when bound to an allosteric site. As allosteric binding sites are significantly less conserved compared to orthosteric sites, the principal advantage of allosteric drugs lie in their higher specificities and thus lower risks of toxic side effects ,118,119.Orthosteric drugs have been successfully used in therapy, for instance, to reduce viral loads in HIV patients to undetectable levels . UnfortuMycobacterium tuberculosis (Mtb) has played a key role in research towards alleviation of TB. The protein pyruvate kinase was observed to exhibit some allosteric mechanism and deemed an attractive target of Mtb therapy [Chlamydia pneumoniae; and Chlamydia trachomatis, which causes a number of sexually transmitted infections by associating with other proteins to facilitate transport processes in the cell [Trypanosoma cruzi [Targeting allosteric sites of functional proteins in  therapy . Similar therapy  and yet  therapy . Bacterithe cell ,132,133.the cell . Anotherma cruzi .Protein allostery research has been ongoing for years, and our understanding of protein allosteric modulation is strengthening . PubliclCentral to this work, we propose an integration of in silico approaches that can be used for allosteric drug discovery . The proSome examples of the use of the proposed workflow to investigate allosteric phenomena in proteins of medical significance are as follows: (1) Perturbation response scanning (PRS) was combined with all-atom MD and DRN to investigate the allosteric potential of remote residues to effect conformational changes  and has As a first step for in silico allosteric modulator identification, one requires a target protein. Three-dimensional structures for the investigated target may already be available fully or in part from the PDB . In the There is a long list of user-friendly web servers available for modelling protein 3D structures, some examples of which include I-TASSER , ModWeb de novo allosteric sites.Before attempting to identify an allosteric site of a protein, it is good practice to determine whether the literature already describes it from previous work. The AlloSteric Database (ASD), which hosts a collection of known allosteric targets and their modulators, is a major source of relevant literature and annotations pertaining to the concept of allostery . The datPockets/cavities present in proteins might be functionally important and have been recognized as conventional sites for ligand binding . For thoSmall-molecule docking to a target protein can give us an indication as to whether and how strongly a ligand might bind to a certain surface, but limiting the search space to a primary binding site  can be oblivious to allosteric pockets. In blind docking (BD), the whole protein surface is scanned for putative binding sites and is especially useful when one has not been determined a priori ,192,193.eceptors . Jin andeceptors . Based oeceptors .Due to the increased search space, stochastic strategies are typically used to make the exploration of the larger target surface computationally tractable . One wayIn their pioneering paper introducing BD using AutoDock, Hetényi and van der Spoel recommended over 100 independent docking runs with flexible ligands , which wKey regions important for the stability of subtilisin  were determined using PRS, in which the covariance matrix was obtained from equilibrated portions of MD trajectories . PRS wasBy factoring out the bound small molecule, to instead perturb single residues from a receptor and to record the linear response, is a rapid way of approximating biological perturbations in proteins. These perturbation events can be instantiated by various intrinsic and extrinsic factors, which may in turn have far-reaching effects ,36. In pAs reviewed recently by Liang and coworkers, the last decade has produced an array of various tools that bridge multiple interdisciplinary concepts for the study of protein dynamics and allostery regulatory mechanisms . More spBC) and averaged shortest path (L), calculated for each protein residue (node) using MD trajectory data. While BC measures the number of geodesics (shortest paths between two nodes) going through an intervening node [L averages the number of geodesics inbound to a node. Both metrics gain robustness in predicting medium-to-long-distance relational information from averaged information across conformational ensembles. By computing the average geodesics between every residue pair, L would effectively be maximized when a structure is compact and minimized otherwise. As BC is evaluated for intervening residues linking pairs of residues, it gives a good indication of the importance of that residue for information flow within the protein. Penkler et al. for the first time showed that there is correlation between BC and 1/RMSF (root mean square fluctuation) as well as between BC and 1/L [BC values. This correlation, later, was also identified in other studies [Trypanosoma brucei—the causative agent of Human African Trypanosomiasis—five of which inhibited the pathogen’s growth in vitro [Networks are typically represented by either an adjacency matrix or an edge list . In stating node , L avera and 1/L . Interes studies . More rein vitro .In a new approach developed by Sheik Amamuddy and coworkers, a statistically guided network was able to show two coupled conformational changes associated with antiretroviral (ARV) drug resistance, using one-tailed t-tests on collections of residue pairwise distances determined from ensembles of protease structures sampled by MD . DespiteA functional site that mediates communication pathways and determines organization of the residue interaction networks often coincides or tightly couples with coevolving residues. Statistical coupling analysis (SCA), mutual information (MI) model, and covariance-based approaches have employed sequence-based analysis of residue coevolution in homologous families to show that functional residues in residue networks are connected via strong evolutionary relationships ,254,255 After having scanned the structure of interest using the allosteric site prediction approaches to obtain various sources of support for putative ligand binding sites, a consensus can be drawn before proceeding to the identification of allosteric modulators.MD is an invaluable tool in the hands of protein allostery researchers . MD expeCoarse-grained models are computationally effective and enable simulations of long timescales for large allosteric systems and assemblies, thereby allowing for observation of allosteric structural and dynamic changes . Functioα atoms) ,284,284.α atoms) ,291,292.α atoms) ,294,295 α atoms) ,302,303 Given the complexity of thermodynamic and kinetic factors underlying allosteric regulatory events, stochastic MSMs ,309,310 In the following section, we describe some analysis strategies that have shown their efficacy in investigation of allosteric effects using data obtained from conformational sampling.BC [After performing preliminary quality checks on the simulated protein dynamics data, conventional metrics such as the root mean square deviation (RMSD), residue root mean square fluctuation (RMSF), radius of gyration (Rg), and geometry calculations (distances and angles) can be performed to infer the stabilities (or instabilities) within protein–ligand complexes for the previously docked compounds ,326,327.BC . DynamicBC . An implBC ,330. MorAs each of the aforementioned methods highlights different facets of simulated protein dynamics, we provide some details about their principles and the types of information that can be retrieved using such techniques. In each case, the proteins or complexes are assumed to be free from periodic boundaries and corrected from rotational and translational effects. In the following section, we describe several approaches that can be used for analyzing results obtained from simulated protein dynamics.N is the number of atoms, m is the atomic mass, x is the generalized coordinate, and The root mean square deviation evaluated between a target and a given reference structure gives an idea of the overall deviation of all atoms of the structure from those of the reference. The RMSD evaluated at time For an MD simulation, it is important that the structures are properly aligned such that correct distances are evaluated. As it is often the case that trajectory-specific topologies are used in independent simulations, care should be applied when interpreting and comparing pairs of such systems, for instance, in an allosteric modulator-bound and a modulator-free protein or in a wild-type and a variant, as each protein may have a slightly different starting conformation as reference. Work by Penkler and Tastan Bishop comparing allosteric effects from ligand-bound and ligand-free Hsp90MSD data .While RMSD calculates an average obtained from a collection of atom coordinates, an RMSF value is computed for each atom as the standard deviation of atomic fluctuations recorded over time. For each atom, the RMSF gives an estimate of its variability around its time-averaged value, once more from aligned conformations. Comparing RMSF values for homologous residue positions across two systems thus allows for the local inspection of differential residue flexibility, given an allosteric perturbation.As an example of the use of RMSF for the detection and partial characterization of allosteric effects, Brown and coworkers observed increased rigidities across several deleterious variants of the renin-angiotensinogen complex with respect to the wild-type complex at sites distal to the dimer interface where known nsSNVs had been introduced .i.In the calculation of Rg, radii are first computed between each atomic , Karamzadeh and coworkers computed several geometric features, such as the inter-domain angles and distances, torsion angles, and pairwise residue distances in order to build machine-learning classifiers able to discriminate between the redox states .The application of ED to analyze protein behaviour mainly focuses on the dominant modes of motion, which are obtained by decomposing the covariance matrix and by ranking the eigenvectors  in descending order of the corresponding eigenvalues ,332. By While we have gained important insights into the function of allosteric proteins, the quantitative characterization of these highly dynamic and often elusive processes continues to present formidable technical and conceptual challenges. Allosteric events in biological systems occur on different spatial and temporal scales and involve a complex interplay of thermodynamic and dynamic changes that are difficult to observe, simulate, and interpret.Experimental studies of protein systems indicate that allosteric regulation may involve a combination of the classical models of allostery, i.e., conformational selection, dynamic allostery, and induced fit. An understanding, at the structural level, of the relationships between protein robustness, allosteric drug binding, and disease may be of use in the development of theoretical and experimental approaches bridging structure-based network analysis of protein targets with modelling of protein interaction networks and pathways. The complexity and diversity of these processes require innovative theoretical and data-driven approaches that can bridge advances in structural and quantitative biology in transformative yet practical ways. The development of novel integrated research strategies should address these challenges by strengthening and advancing the interface between molecular biophysics, network biology, and data science. We argue that the next breakthrough in the discovery of allosteric drugs may require such integration of traditional biophysical approaches with systems biology and experiment-guided machine-learning tools to bridge a detailed microscopic analysis with macroscopic modelling of allosteric phenomena in cellular networks and signalling pathways.


Ser adjacent to the P-cluster in the MoFe protein, and the residues near the protein-protein interface. In particular, it is found that: (i) within each Fe protein, the switch regions I and II are coupled to the [4Fe-4S] cluster; (ii) within each half of the complex, the switch regions I and II are coupled to the loop containing β-188Ser; (iii) between the two halves of the complex, the regions near the nucleotide binding pockets of the two Fe proteins  are also mechanically coupled. Notably, we found that residues next to the P-cluster (in particular the loop containing β-188Ser) are important for communication between the two halves.The enzyme nitrogenase reduces dinitrogen to ammonia utilizing electrons, protons, and energy obtained from the hydrolysis of ATP. Mo-dependent nitrogenase is a symmetric dimer, with each half comprising an ATP-dependent reductase, termed the Fe Protein, and a catalytic protein, known as the MoFe protein, which hosts the electron transfer P-cluster and the active-site metal cofactor (FeMo-co). A series of synchronized events for the electron transfer have been characterized experimentally, in which electron delivery is coupled to nucleotide hydrolysis and regulated by an intricate allosteric network. We report a graph theory analysis of the mechanical coupling in the nitrogenase complex as a key step to understanding the dynamics of allosteric regulation of nitrogen reduction. This analysis shows that regions near the active sites undergo large-scale, large-amplitude correlated motions that enable communications within each half and between the two halves of the complex. Computational predictions of mechanically regions were validated against an analysis of the solution phase dynamics of the nitrogenase complex via hydrogen-deuterium exchange. These regions include the P-loops and the switch regions in the Fe proteins, the loop containing the residue β-188 Enzymatic function is often the result of a sequence of events, including precisely synchronized electron, proton and substrate delivery, which goes beyond the mere chemistry at the active site. To achieve this level of sophistication, enzymes must exploit a network of long-range communication through which chemical events, such as the binding of an effector to an allosteric site, hydrolysis of ATP, or electrochemical activity at a given location, affect on the function of a remote site. The nitrogenase complex vividly exemplifies the hierarchical complexity of enzymes. Nitrogenase catalyzes the reduction of atmospheric dinitrogen to ammonia, whose activity is regulated by long-range mechanical coupling between distant regions of the complex. After decades since the first structure of the nitrogenase complex was solved, this coupling network is still unclear. We used a computationally inexpensive, yet accurate approach, which is based on graph theory, to elucidate how distant regions of the protein complex communicate with each other. In particular, our study provides valuable insights on how the two halves of nitrogenase communicate and for this communication, what the most important regions are, laying out a strategy for future mechanistic studies. The Fe protein is a γ2-homodimer, containing a [4Fe-4S] cluster and two nucleotide-binding sites. Here, we will refer to the two monomers of the Fe protein as FeA and FeB. Through a series of synchronized events, known as the “Fe protein cycle”, each Fe protein delivers one electron at a time to the MoFe protein. cluster of the Fe protein to the oxidized P-cluster of the MoFe protein; the combination of these two steps is referred to as the “deficit-spending” model. cysteinate ligands.The covariance matrix of the displacements of the residues M  and the corresponding helices in the β component  of the MoFe protein are strongly coupled to the Fe1 protein. The α1β1 region involved in the coupling extends from these α-helices located at the docking interface near the P-cluster to the [4Fe-4S] cluster in the Fe protein. In particular, in the ATP-bound complex the loop containing β-188Ser (residues 186–190) is highly coupled with the switch regions of the Fe protein  and the β-strand (strand Ο5 in Fe1A and strand P5 in Fe1B) that the P-loop is bridging, along with three other β-strands in the same β-sheet . All these structural elements envelope the nucleotide binding sites. This result suggests that a perturbation introduced in the nucleotide binding pockets  is propagated to the nucleotide binding sites in the opposite unit. The mechanical coupling in the ADP-bound complex  of the C1 and α1β1 in the ADP- and ATP-bound complexes is illustrated in Ser and the Fe protein is smaller in the ADP-bound complex. The decreased mechanical coupling and the increased flexibility of this loop in the ADP-bound complex are a consequence of less efficient coupling pathways  between the loop and the Fe protein.In addition to the differences in flexibility, the ATP- and ADP-bound complexes also present differences in the mechanical coupling. Some of these differences have already been highlighted above. Here, we further comment on additional differences at the Fe protein/MoFe protein interface. The change in the mechanical coupling between Fe7, α8 in the α component and the corresponding helices α33, α34 in the β component increase in the ADP-bound complex, except two residues at one end of the helix α7, α-119Gln and α-120Glu, which are located near the β-188Ser loop. The other two helices (α35 and α36) in the β component, which are also located near the protein-protein surface, have decreased mechanical coupling with the Fe1 in the ADP-bound complex, while half of the corresponding helices α9 and α10 in the α component have increased. This asymmetric change of the mechanical coupling in the α and β component may be due to the rolling motion of the Fe protein over the surface of the MoFe protein, from β to α component (see below). This motion not only affects the protein-protein interaction at the interface but may also have influence on the mechanical coupling between the residues near the P-cluster and the Fe protein. The results between α2β2 and Fe2 are shown in In addition, the mechanical coupling between the Fe protein and the helices α1 to Fe2), and between a specific region, the P-loop in the two halves (from P-loop in Fe1 to P-loop in Fe2) are show in The communication network between different regions of the nitrogenase complex was studied by analyzing the strongest mechanical coupling pathways between residues of one region and residues of another region as extracted from underlying mechanical coupling graph based on the most efficient paths described by the geodesic matrix. The residues with the largest influence on the mechanical coupling are identified by the cost-weighted betweenness centrality χ. As we discussed in the Methods, the higher the χ value of a given residue, the higher the number of pathways passing through it; residues with large χ value represent effective chokepoints for the communication between two regions. We analyzed the communication within each half of the nitrogenase complex, and between the two halves of the nitrogenase complex. Results for the most important residues in the communication within each half of the complex (Fe-αβ), between the two halves  have the largest influence (~90%). In addition, residues α-63Ala and β-90His, which are next to three residues that have been found important for the electron transfer , and the residue β-189Phe linked to the β-188Ser are found also important for the communication . Equally interesting, some of the residues in the FeMo-co binding pocket show χFe-αβ values within the 50% of the highest χFe-αβ value. Notably, these residues include α-71Val, which has been suggested to play a role in substrate selectivity. clusters and the P-clusters. The large majority of the communication pathways go through the side of the P-cluster opposite to the FeMo-co, and none of them passes near the FeMo-co itself  is also important for the communication, with a χ equal to the 58% the maximum value. In addition, residue Fe2B-68Glu, located at the protein-surface end of the switch II region, has an appreciable χ value (~51% of the maximum) as well. Again, the large majority of the communication pathways go through the side of the P-cluster opposite to the FeMo-co, and none of them passes near the FeMo-co itself.Among all of the structural elements composing the Fe proteins, the P-loops stand out as potential allosteric sites for the communication between the two Fe proteins. The most important residues of the communication network between the P-loops of the two Fe proteins are shown in The mechanical coupling between distant parts of the nitrogenase complex is regulated by the underlying conformational dynamics. Allosteric signaling is instantaneously distributed through the scaffold of the complex via protein normal modes. Here, we analyze the protein vibrational modes that influence the communication network the most.k on the mechanical coupling in the nitrogenase complex was quantified by the overlap k, i.e., they will have the highest Ik values for the coupling between Fe1 and α1β1 and between Fe1 and Fe2 calculated for the top 50 normal modes are reported in e.g., mode 1, 2, 3, or 4 have larger influence on the communication between the two halves (IkFe1-Fe2 > 0.40), while the influence is much smaller on the communication within each half of the complex (IkFe-αβ < 0.25). As expected, the communication between the two halves relies more on delocalized motions, while more localized motions between Fe and αβ can give rise to the coupling within each half of the complex.The influence of a given normal mode k, χkX−Y . Results−Y,IkX−Y . It is eIk values reported above refer to the global coupling between the Fe proteins and/or the MoFe protein, i.e., all residues-to-all residues. We have also explored how the conformational dynamics of the complex couple specific regions of the nitrogenase complex. Results are qualitatively similar to those obtained for the global coupling. As an example, the IkPloop1-Ploop2 for the coupling between the P-loops in the two Fe protein are given in The R, describing the conformational change from the ATP-bound to the ADP-bound Fe protein complex is calculated. As can be seen from R. Remarkably, these three modes are among the most important modes for the mechanical coupling, especially between the P-loops of two Fe proteins.The analysis discussed so far is based on harmonic vibrational modes, therefore on harmonic displacements about the equilibrium  configuration. Therefore, it cannot address the effect of transient structural changes, which can either enhance or weaken the relevance of the coupling pathways and, consequently, change the χ value associated with a given residue or region. More importantly, potential transient large-amplitude structural changes, postulated to be relevant for the electron transfer, are clearly not accessible through a Gaussian model. The only available information about the structural dynamics (change) during catalysis comes from the inspection of the crystal structure of the ATP- and ADP-bound nitrogenase complex, which reveals a structural, rigid body rearrangement of the Fe protein over the MoFe protein upon ATP hydrolysis. It is therefore of interest to see if this structural reorganization correlates with the mechanical coupling deduced from equilibrium structures. To this end, the overlap between the vibrational modes and the unit vector, ΔR would yield. In order to achieve this, the mechanical coupling analysis was repeated using a pseudo-“covariance matrix” constructed as R can introduce an efficient mechanical coupling between the switch region I and the [4Fe-4S] cluster, the β-Ser188 loop and the P-cluster within either half of the ATP-bound complex. Asymmetry between the two halves is observed since the mechanical couplings between the P-loop and β-Ser188 loop, the P-cluster and the FeMo-co within one half  are similar to the ATP-bound complex itself, while the one between the [4Fe-4S] cluster and the P-cluster is similar within the other half . Notably, the conformational motion taking place upon ATP hydrolysis can also introduce an efficient mechanical coupling between the loop containing β-Ser188 of one half and the P-cluster of the other half of the ATP-bound complex.Taking a step further, we investigated what type of mechanical coupling the displacement described by only the vector Δ′=ΔRiΔRj . VariousSer and β-69Ala. In the presence of the Fe protein, the peptide with β-188Ser had a dramatic decrease in deuterium uptake, whereas the peptide with residue β-69Ala showed only a slight change. The changes detected in regions distal from the Fe protein docking site are consistent with our model of mechanical coupling.The computational model for the mechanical coupling discussed in the previous sections was validated against an analysis of the solution phase dynamics of the protein complex. We conducted a set of hydrogen-deuterium exchange experiments on the MoFe protein under two conditions, alone in solution and in the presence of ATP-bound Fe protein. Specifically, we monitored deuterium exchange after 1 hour, at which point all relevant long-time scale motions in the nitrogenase complex would have been sampled. In general, greater exchange was observed for peptides from free MoFe protein, than in the presence of ATP-bound Fe protein . DecreasTo investigate the effects of the presence of Fe protein on long-distance communication of MoFe protein, we searched for regions with correlated patterns of H/D exchange. Based on the difference in percent deuterium exchange between regions, we built a correlation matrix for free MoFe protein and MoFe protein in the presence of ATP-bound Fe protein . After oLys (peptide 39–54 near Switch I region of the Fe protein), α-63Ala (peptide 63–85 near the P-cluster) and α-119Gln (peptide 119–134 near Switch II region of the Fe protein). The second group contains the following residues: α-71Val (peptide 67–85 near the FeMo-co), α-164Glu (peptide 151–165 near the [4Fe-4S] cluster in the Fe protein), β-479Leu and β-497Leu (peptide 473–489 and 495–523 at the α1β1-α2β2 interface). Both groups show positive correlation with H/D exchange in the free MoFe protein. When the full nitrogenase complex is formed, trends in protein dynamics for group one and two become negatively correlated. The third and fourth groups contain residues in the β subunit that are near the P-cluster, β-69Ala (peptide 58–77), β-90His (peptide 78–98) and β188Ser (peptide 183–192) and correlation patterns are dependent on complex stoichiometry. In the free MoFe protein, the residues β-69Ala and β-188Ser are strongly correlated with each other and negatively correlated with β-90His as well as with the first and second group. In the ATP-bound Fe protein, these three residues are part of a more nuanced interaction network. For example, the region around residue β-90His remains similarly correlated with all peptides except the one containing residue β-188Ser (which changes from negative to positive). Whereas the peptide containing β-188Ser has positive correlation with all residues from the second group and negative correlation with β-69Ala. The peptide containing β-69Ala changed from positive to negative correlation with β-188Ser and from negative to positive correlation with group one upon Fe protein binding.To evaluate our mechanical coupling model, we investigated the correlation patterns of the “chokepoint" residues. On the experimental time scale, H/D exchange revealed four types of correlation patterns in free MoFe protein and in the presence of the ATP-bound Fe protein . The firAla and β-188Ser, two resides proximal to the P-cluster ligation site. In free MoFe protein, these residues are positively correlated. Upon binding of ATP-bound Fe protein, β-69Ala and β-188Ser  become negatively correlated. At the same time, H/D exchange of β-69Ala is positively correlated with the P-cluster environment in the α subunit of the MoFe protein. Equally interesting is the relationship of β-188Ser/β-69Ala protein dynamics with α1β1-α2β2 interface (residues β-479Leu/β-497Leu). Despite spatial separation, the P-cluster ligation site and the α1β1/α2β2 interface show a strong correlation. However, only β-188Ser is sensitive to the presence of ATP-bound Fe protein. In summary, our H/D exchange experiments reveal correlated patterns of dynamics precisely where computationally derived mechanical coupling networks predict.Detailed analysis of the theoretical and experimental data made it immediately evident that regions identified as important for the Fe protein/MoFe protein coupling in the computational analysis correspond (or are adjacent) to regions with high H/D exchange correlation. In addition, the analysis revealed that the region surrounding the P-cluster is sensitive to the presence of ATP-bound Fe protein. This agrees with observed structural changes when nitrogenase is poised at different steps in the catalytic cycle. Of partiC of the displacements of amino acid residues as obtained from an anisotropic network model. cluster of the Fe protein and the αAlthough the present analysis does not yet provide sufficient information to directly derive a step-by-step regulatory mechanism from the observed mechanical coupling in the nitrogenase complex, it is able to identify structural motifs that have been proposed experimentally to be important for the enzymatic regulation as the most strongly coupled. Importantly, it allows quantifying the magnitude of the coupling between any two residues or two regions, and furthermore identifying potential allosteric pathways between them. In particular, our analysis informs us how the two halves of nitrogenase might communicate and what the most important regions for this communication are, laying down the basis for future mechanistic studies.S1 Table(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigNormalized correlation matrix C of the residues displacements for (A) the ATP-bound nitrogenase complex and (B) the ADP-bound nitrogenase complex. The correlation between Fe1 and α1β1, Fe2 and α2β2, Fe1 and Fe2 are highlighted in dashed blocks. The locations of the cysteine ligands to the [4Fe-4S] cluster in Fe1 and P-cluster in α1β1 are indicated by arrows in correlation between Fe1 and α1β1.(TIF)Click here for additional data file.S3 Fig1β1 to any residue in Fe1 and (B) residues in Fe2 to any residue in Fe1 in ADP-bound complex. The ATP analogues, [4Fe-4S] clusters, P-clusters, and FeMo-co are shown in mesh surface. Residue 186–190 in β1 are shown in spheres in (A); one α-helix and three β-sheets in each monomer of Fe2 appear red in (B) (following the numbering in the 2AFI [I] PDB file).(A) residues in α(TIF)Click here for additional data file.S4 FigM in the ADP-bound complex relative to the ATP-bound complex, either positive or negative. The Fe1, Fe2, α2β2 proteins are shown in white. The ATP analogues, [4Fe-4S] clusters, P-clusters, and FeMo-co are shown as mesh surfaces. Residues 186–190 in β1 are highlighted in spheres. Residues at Fe protein/MoFe protein interface with large deviation upon ATP hydrolysis appear in dark red or dark blue.The change is shown as largest deviation of (TIF)Click here for additional data file.S5 Fig1 and Y = α1β1; (B) X = Fe1 and Y = Fe2. Each circle represents a residue. χ is shown as the percentage relative to the corresponding maximum χX-Y.(A) X = Fe(TIF)Click here for additional data file.S6 Fig1 and Y = α1β1; (B) X = Fe1 and Y = Fe2, (C) X = P-loops in Fe1 and Y = P-loops in Fe2.(A) X = Fe(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Figi.e., OX-Y, between the motions of the ATP-bound complex as obtained from ANM and the ATP- to ADP-bound complex atomic displacement as inferred from the corresponding crystal structures are shown. The subscript of the X or Y elements indicates which half of the complex the element belongs to.Overlaps of coupling pathways between structural elements X and Y, (TIF)Click here for additional data file.S9 FigRed and blue circles correspond to negative and positive correlations, respectively. White indicates no correlation. Diameter of the circle represents significance; larger is more significant correlation. Peptides are group based on hierarchical clustering. Green bar indicates peptides which are negatively correlated in free MoFe protein.(TIF)Click here for additional data file. 22 Sep 2020Dear Dr Raugei,Thank you very much for submitting your manuscript "Mechanical Coupling in the Nitrogenase Complex" for consideration at PLOS Computational Biology.https://doi.org/10.1016/j.str.2020.03.014; on a very different system) may help you address Reviewer 1's comment about causality.As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments. Towards this end, a stronger connection and discussion of the results of calculations with the HDX experiment is absolutely needed. By the way, our recent publication  and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Turkan HalilogluAssociate EditorPLOS Computational BiologyNir Ben-TalDeputy EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Allosteric regulation in the nitrogenase complex is studied in this work by means of graph theory analysis. The motivation for this work is the lack of complete understanding of the mechanism of allosteric communication at different stages of the protein functional cycle. There are partial data on local conformational changes and indirect evidences on the effect of mutations on functional sites of the complex. All the above prompted authors to explore the mechanical coupling between the protein sites, using graph theoretical approach developed in the group.The authors demonstrate “mechanical coupling” between functionally involved regions, as well as analyze significance of individual normal modes contributions to the coupling. They also support their calculations with the data from HDX experiments. The major caveat of the approach is that it is based on the analysis of correlated motions, while correlation does not necessarily causality. Further, they claim that regions with “highly correlated or anti-correlated motions are mechanically coupled through efficient energy transfer path”. This conclusion seem to be unjustified as in the condensed matter of the protein there are no selected channels of the energy transformation, and all work produced in the structure is a result of multibody interactions between amino acid residues. If authors should resort to network consideration of protein, they should not be mixing it with the claims on the analysis of the protein energetics. If they decide to consider the causality and energetics, they may use, for example, AlloSIgMA web-server , allowing to calculate the allosteric free energy as a result of the perturbationand to estimate effects of mutations .The HDX data is of interest, but should be better linked to results of calcualtions and further discussed.The discussion is too lengthy, sometimes too speculative when results on mechanical coupling are interpreted in terms of the energy/electron transfer.“Important allosteric centers” is a bad phrasing.Figures 2 and 3 should be merged in one, or Figure 2 should be moved to supplementary. Figure 9 - to supplementary as well.Reviewer #2: The authors describe an engaging and comprehensive study of synchronized and allosteric motion of the nitrogenase proteins. Importantly, the theoretical studies are connected to isotope effect experiments, and the entire body of work is place into an appealing modern perspective. While this form of modeling is simple and fast, it can be extremely useful and I believe that the paper will prove to be of great value and interest to researchers in the field. There was an early study of Liao and Beratan that reported coarse grained normal mode analysis of the nitrogenase proteins as well ). It would be interesting to compare the results of the two studies and to place the early albeit less comprehensive study into the context of this modern analysis, if possible.Reviewer #3: Huang et al. identified communication pathways in the nitrogenase complex comprised of two Fe and two FeMo-co proteins. Nucleotide hydrolysis in the Fe- protein initiates a long-range electron transfer from the Fe-proteins to the FeMo-co. Electron transfer to the active site of FeMo occurs by intermediate electron transfer steps through the 4Fe-4S clusters and P-cluster. It is known  that the nucleotide hydrolysis and the consequent electron transfer is coupled with some structural rearrangements, especially at the interface of the Fe and two FeMo-co proteins. Structures of the complex before and after the hydrolysis are available. These two endpoints indicate that the structural changes may involve a rigid body movement of the Fe-proteins. Additionally, electron transfer in one subunit slows down the electron transfer in the other subunit. The mechanism of communication is unknown. The authors performed a sophisticated network analysis using just these two structures as inputs to unravel the mechanistic pathway.The paper presents a very sophisticated analysis for an input of just two structures. However, in my experience, it is often the case that pathways connecting two endpoint structures require further optimization as local dynamics may enhance or diminish parts of the path. Can the authors comment on this possibility?Additionally, the 2AFK structure (used by the author) has been made obsolete and replaced in the PDB by 4WZB. Can the authors compare the structures to ensure something wasn't incorrect in the older, obsolete one?The betweenness centrality used here relies on the shortest-path connecting distant domains. However, an allosteric path does not always take the shortest path through the protein systems. Sometimes, depending on specific residues, a slightly longer path may be more effective for communication, especially when electron transfer is involved. The effect of the distance can be offset by the exponential energetic dependence of the electron transfer rates. Do the authors agree with this possibility? If so, can they extend their analysis to consider pathways other than the shortest path?I'm unclear on how the correlations were determined for the H/D experiments. Can the authors add a more detailed explanation along with an equation?Other minor points:Page 2: "800 Ang.^2,20" It's hard to read this because the formatting of the reference is identical to the exponent; please revise.Page 17: "two show opposite trend in..." - trendsFigure 9: "Swtich" in the figure should be "Switch".Reviewer #4: The manuscript proposed by Simone Raugei and co-workers deals with the investigation of the mechanical coupling in the Nitrogenase complex by means of a recent developed technique that the authors already presented in a previous publication (ref. 38 in the manuscript).The paper is well written, and the result section is clear, maybe conclusion could be more concise. After minor revisions I recommend this manuscript for publication in PLOS computational biology.Basically I have only some doubts from the technical part that I believe the authors will be able to dispel without problems.If I have understood correctly, in the technique adopted by the authors one compute the covariance matrix for two crystal structures (those reported in Table S1 of the SI) reported in ref. 29. It is important here to underline for clarity some more features of these two pdb.• Why in table S1 are reported different number of residues for 2AFK and 2AFI, if the enzyme is the same and come from the same organism (Azotobacter vinelandii)?• These differences maybe are not significant for the analysis of the matrix C, but the authors should say something about…• How many residues in the two pdb?Regarding matrix C:• Only Calpha are considered? Or more?• I guess that all cofactors are considered, right?Then, why the authors decided to consider this approach instead of a molecular dynamic (MD) approach? We all know that MD for such metal-protein systems is quite demanding, but a word on this issue should be beneficial for the readers.Finally, the authors validated their results based on the H / D exchange experiments, a fact that gives value to the manuscript. However C matrix is computed on just two structures, and I would like that the authors could comment this in light of the standard MD PCA analysis made on a complete trajectory.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: NoneReviewer #3: NoneReviewer #4: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see  12 Nov 2020AttachmentResponse_20201103.pdfSubmitted filename: Click here for additional data file. 1 Dec 2020Dear Dr Raugei,Thank you very much for submitting your manuscript "Mechanical Coupling in the Nitrogenase Complex" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript reflecting the review recommendations of Reviewer #1 as best as you can.   Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Turkan HalilogluAssociate EditorPLOS Computational BiologyNir Ben-TalDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The current revision is not satisfactory for reasons below and should be amended.Authors:Here we would like to reiterate that the method we have developed was designed to filter outpairs of residues whose covariant motion is purely coincidental and evaluate potentialcommunication pathways between distant region of a protein complex. The major differencebetween our M-matrix analysis and a simple covariance analysis of motion, is that if two residues have correlated motions but do not have an efficient path for mechanical coupling between them (large geodesic distance in the covariance space), a small (poorly coupled) mechanical coupling (as described by the M matrix) will be produced. In this way, geodesic weighting filters out motions that are merely coincidental. Clearly, the existence of a pathway is a necessary but not sufficient condition for causality. This is actually an important point, which was added in the revised manuscript (page 23).Reviewer:Explain directly in the work what “mechanical coupling between them (large geodesic distance in the covariance space)” means in terms of protein dynamics that underlies allosteric communication.Authors:“We like to emphasize that correlation or communication here does not imply causality. In fact, strong mechanical coupling indicates an efficient coupling pathway connecting two residues or regions which show correlated or anti-correlated motions. Clearly, the existence of a pathway is a necessary but not sufficient condition for causality. A study of effects of suppression or delay of the motion of one region on the motion of the other will be necessary to consider causality.”Reviewer:It should be directly stated in teh above paragraph that causality can only be quantified via analysing the enegetics of allosteric signalling with corresponding references .Authors:We also realize that the language adopted in the manuscript was not clear. In particular, asoriginally discussed, the concept of energy transfer could have led to misinterpretations. We used the term energy transfer to indicate instantaneous flow of kinetic  energy mediated by protein vibrations . We have revised and consolidated the language to address this point. For example, where appropriate, “energy” is changed to “information” and “energy flow” changed to “communication”.Reviewer:“information”, “structural or dynamic information”, “information transfer” and other similar usage of the word “information” is not correct and should be changed throughput the text.Authors:There was an early study of Liao and Beratan that reported coarse grained normal mode analysis of the nitrogenase proteins as well ). It would be interesting to compare the results of the two studies and to place the early albeit less comprehensive study into the context of this modern analysis, if possible.We thank the Reviewer for pointing out the work by Liao and Beratan. We compare their results with ours in the Results and Discussion of the revised manuscript (page 12).“Within each Fe protein, the P-loops are coupled to the protein-protein interface and the [4Fe-4S] cluster through the switch region I and II, respectively. Liao and Beratan discussed this coupling in their computational analysis of the isolated Fe protein. Using a coarse-grained model, they identified correlated regions in the Fe protein and reported that residues in the P-loops and switch regions are relatively rigid in the slowest normal mode, which can be important for function-relevant conformational changes. As we will discuss below, we show that there is indeed an efficient communication pathway between these regions, which, in the nitrogenase complex, extends up to the interior of the MoFe protein.”Reviewer:Since authors compared their results with those od Liao and Beratan at the request of second reviewer, I do not understand why they did not obtain the picture of the energetics of allosteric signalling using AlloSigMA cited in my previous review. In this case authors would be able to talk about causality and energetics avoiding to use wrong terminology as noted above.Reviewer #2: The authors have addressed all referee concerns and the paper is suitable for publication.Reviewer #3: The authors have sufficiently addressed my concerns.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: NoneReviewer #3: None**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. 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Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package. Diverse biological processes are dictated by ligand-induced conformational changes in target proteins. Modern medicine has harnessed the ability to control protein structure and function through the introduction of small molecules as therapeutic interventions to diseases. Despite the importance of Protein-Ligand Interactions (PLI) in medicine and biology and keen insight into the multitude of factors governing ligand recognition, including hydrogen bonding ,2, π-intractions , and hydractions , the devReliance on experimental methods to identify and confirm PLIs is time-consuming and expensive. In contrast, computational methods can save time and resources by filtering large compound libraries to identify smaller subsets of ligands that are likely to bind to the protein of interest. In this manner, reliable PLI predictive algorithms can significantly accelerate the discovery of new treatments, eliminate toxic drug candidates, and efficiently guide medicinal chemistry . CurrentThe advent of Machine Learning (ML) and Deep Learning (DL) approaches have created a path towards solving previously challenging unsolved problems in biology and chemistry ,15,16,17End-to-end learning, a powerful ML/DL technique, has gained interest in recent years since once the model is trained the users are only required to provide standard protein and ligand descriptors as input . The endStrategies to overcome these limitations have largely focused on developing new methodologies to reduce target and compound structure space to 1D representations, thereby providing a dense framework for ML/DL to operate on a small number of input features. Reduction of 3D protein structure information into 1D allows the machine to efficiently learn 3D space features of the protein secondary structure, which are required for ligand interaction. This information, when combined with the way a convolution network considers the input space, makes the model unbiased towards the protein conformation, thereby not being limited by the existence of high-resolution protein-ligand structures adequate for PLI prediction. This feature can solve a major drawback for most virtual screening methods since only a limited portion of the proteins’ conformational space can be crystalized.Herein, we outline a new ML-based algorithm termed SSnet for the prediction of PLIs. SSnet uses a 1D representation based on the curvature and torsion of the protein backbone. Mathematically, curvature and torsion are sufficient to reasonably represent the 3D structure of a protein  and, theIn the methods and rationale section, we demonstrate how the secondary structure of proteins is used in the ML/DL framework. We discuss the representation of ligands following the introduction of SSnet model, possible evaluation criteria, its merits and demerits. We discuss the datasets used in this work to validate and train SSnet. In the results section, we first select the ligand descriptor to be used in SSnet. We validate SSnet trained on two models named: SSnet:DUD-E, a model trained on DUD-E,  and SSneProtein structures exhibit a large conformational variety. Many automated and manual sorting databases, like SCOP , CATH 4, DALI 4, and proA protein can be represented by the rization . In facts, scalar curvature sThe secondary structure information can be retrieved by a smooth curve generated by a cubic spline fit of the CA atoms. no acids ,47. We lA molecule can be represented by the SMILES string, which represents its various bonds and orientations. However, the SMILES string encodes dense information making it difficult for an algorithm to decipher and learn chemical properties. A number of alternative representations for ligands have been proposed that model varying aspects of the ligand in a more machine readable format. The hope is that ML algorithms can more effectively use these representations for prediction. Since ligand representation is an ongoing research topic, we consider four different methods: CLP , GNN 3434, Avaloi to K. We define the convolution operation as:f is a function known as the rectified linear unit (ReLU), The convolutional network in this research uses filter functions over the protein vector N can be referred to as R times for each row in R column vector regardless of the number of columns in the latent space We, in our SSnet model, use four different branches with filter sizes of The concatenation of vectors The method described above is similar to a technique recently used in speech verification systems, where the window sizes need to be dynamic because the length of audio snippet is unknown ,52. In sy, which is either 0 or 1, and denote all the parameters inside the network as M is the number of samples in the dataset. By optimizing this loss function the neural network can learn to extract meaningful features from the protein and ligand features that relate to binding. At first, all weights are initialized randomly and we use back propagation to update the parameters and minimize loss. All operations defined are differentiable, including the collapse of the latent space with Global Max Pooling such that errors in the loss function can back propagate through the network to update all parameters, including the convolutional operations.After embedding the protein and the ligand, we concatenate the vectors together and feed them into the final neural network branch, resulting in a prediction of binding, The hyperparameters optimized for the model and speed of execution are provided in A neural network generally exhibits a large number of weights to be optimized so that complex information can be learned; however, some of this information could be irrelevant to a prediction task. For example, consider the task of identifying if a certain image contains a horse or not. If all horse images also contain a date information on the image and images without horse do not contain date information, the machine will quickly learn to detect the date rather than the goal object (a horse in this case). Therefore, it is essential to verify what a neural network considers “influential” for classification after training. Selvaraju et al.  proposedk is the row in the final convolutional layer, Z is a normalization term, Grad-CAM is computed by taking the gradient weight This heatmap The evaluation criteria for PLI are generally presented by the area under the curve of the receiver operating characteristics (AUCROC) , BoltzmaComplementary to AUCROC, EF and BEDROC allow the model to be examined considering its ability to enrich the top ranked ligands. A large number of studies have employed EF to test their models ,58, and,Most state-of-the-art models for PLI predictions use human and C. elegans created by Liu et al.  The posiAlthough humans and C. elegans dataset provides good benchmarking against other ML approach for PLI prediction, it does not contain enough PLI instances for use in real world application. Database of Useful Decoys:Enhanced (DUD-E) dataset provides a large number of PLI instances along with computationally generated decoys as the negative PLI instances. More specifically, DUD-E contains 22,886 positive PLIs and 1.4 million decoy over 102 target proteins. The 102 target proteins in DUD-E were divided into 72 and 30 for training and testing, respectively. Each target proteins in DUD-E contains 224 active ligands for each of which 50 decoys that have similar 1D physico-chemical properties, employed to remove bias against dissimilar 2D topology. These decoys are unlikely to bind and, therefore, were considered as negative interactions. The net total interactions considered for training were approximately 16 thousand positive PLI and 1 million decoys (negative PLI). In lieu of balancing data, the negative PLIs were dynamically constructed by randomly selecting from 1 million decoys to match the number of positive in each iteration. This trained model is termed SSnet:DUD-E. A schematic representation of the model is shown in The decoys generated computationally faces the problem of false negatives; therefore, an experimental dataset could be more reliable for SSnet. We considered the BindingDB (BDB) dataset,  which is(1)The target has PDB ID cross-referenced as 3D structure. The first annotated structure is taken as reference PDB file.(2)The ligand has SMILES representation in the entry.(3)Record has IC50 value (a measure of strength of binding) and is either less than x (active) or greater than y (inactive).The values for x assessed were 10 nM, 25 nM, and 100 nM, while the values for y assessed were x nM or 10,000 nM. The preliminary analysis showed that x = 100 nM and y = 10,000 nM provides the best balance between AUCROC and EFSnet:BDB . The datTo access an independent dataset, we utilized maximum unbiased validation (MUV) dataset created by Rogers and Hahn . The MUVComputational methods to predict PLI are often limited by a lack of accurate 3D structures of the regulatory conformation of a target of interest, or by time-consuming calculations of diverse protein and ligand conformations. Thus, there is a need for a predictive PLI platform that is both rapid and able to function independent of the target protein conformation. Towards this aim, we have employed an ML/DL-based approach (SSnet) based on the curvature and torsion of a protein backbone to develop a predictive PLI algorithm capable of screening 1-billion+ compounds in a manner of days. Specifically, SSnet requires only 18 min for computation of one million PLIs to a target using GPU  accelerated node with Intel Xeon E5-2695v4 2.1 GHz 18-core Broadwell processors and 30 min for ten thousand compounds without GPU acceleration. Herein, we first compare SSnet on various computational datasets, such as humans, C.elegans, and DUD-E. Then, we benchmark SSnet by training on completely experimental dataset BDB and compared against state-of-the-art ML/DL PLI algorithms. Lastly, to demonstrate the utility and accuracy of SSnet, we employ the Grad-CAM visualization approach to extract structural features most important to ligand recognition and binding. The Grad-CAM approach both validates the SSnet approach but can also function as a guide to couple ML-based PLI prediction to downstream analysis using traditional docking-based approaches. Grad-CAM analysis reveals that SSnet can accurately identify regulatory binding sites within protein targets of interest. Importantly, the ability of SSnet to identify these sites is independent of protein conformation and is able to identify cryptic and allosteric sites without prior information of their regulatory roles. In this manner, we demonstrate that SSnet mitigates many of the limitations of alternative predictive PLI approaches, while retaining high accuracy and speed.A key bottleneck in development of PLI prediction is the selection of the optimal representation of ligand structure. Several methods of reducing ligand representation to ML/DL methods have been developed. Gómez-Bombarelli et al.  created Convolutional neural networks (CNN) have to update a large number of weights and, therefore, require a large amount of data instances (number of unique PLIs). However, in the human and C. elegans datasets, the number of instances are insufficient, causing SSnet to overfit . To overC. elegans datasets; and, poor comparison metric for end-user, specifically AUCROC which measures overall accuracy without any information pertaining to usability. To provide a clear demonstration of usability, both AUCROC and EF, have been employed to evaluate algorithm accuracy, as well as usability. To alleviate any potential bias in SSnet optimization, we trained SSnet on the same dataset as found in the existing literature for direct comparison against models compared. We also retrained the state-of-the-art existing model: GNN-CNN on a larger dataset DUD-E to obtain direct comparisons.Evaluation of the accuracy of PLI prediction platforms can be complicated by numerous factors, including but not limited to: training set bias as mentioned in the sec:three section; lack of true negative instances , as is the case for DUD-E, humans and We compared SSnet with PLI specific methods: BLM , RLS-avgSSnet was trained with the DUD-E dataset, referred as SSnet:DUD-E. The AUCROC is shown in We compared SSnet:DUD-E with vina  and sminAs GNN-CNN is currently the best performing ML/DL for PLI prediction, we compared AUCROC of SSnet by training GNN-CNN (GNN-CNN:DUD-E) following the protocol outlined by Tsubaki et al.  on the sRogers and Hahn  generateBDB is a database of PLIs with reported experimental values for PLI in terms of IC50, EC50, kp value was 0.0. The test signifies that SSnet outputs are significantly different than that of GNN-CNN. SSnet:BDB tested on DUD-E has an average AUCROC of 0.81, while GNN-CNN:BDB has 0.79 . SSnet:BAs described in the evaluation criteria section, AUCROC is not necessarily the best metric for PLI prediction evaluation. While high AUCROC demonstrates the ability of a model in discerning true positives from false positives, this is only useful when selecting drugs with scores greater than 0.5 for SSnet. This might not be feasible when selecting drugs from extremely huge datasets, like ZINC, which has ≈1.5 billion ligands. For screening such large databases, enrichment factor (EF) should be employed. In fact, as EF shows the likelihood of finding true actives from the top scoring subset of the database, it provides a more reliable metric for picking high scoring ligands. Furthermore, many studies have shown that ranking based on either metric alone is not a sufficient indication of performance ,76,77.eference . SSnet:BCheck for ligand similarity by comparing Tanimoto Coefficient (TC) score  for eachCheck for fold similarity by comparing TM Score ,80 for pSSnet, being an ML/DL model, is not immune to the pitfalls of overfitting. This includes not only removing replicated examples but also reducing observations with significant similarities. Prior to comparison with advance methods, we made sure to remove possible overlap between BDB and DUD-E dataset using the following protocol:The TC score is a measure of molecular similarity  that comTo compare the fold similarity between the two datasets, we used the TM score ,80. The Ericksen et al.  compiledBoosting consensus score (BCS) is a gradient-based decision tree framework which is trained on binary labels (actives and decoys) on an individual decision tree for each target where the input is composed of docking scores obtained from each docking method. For each target, the docking scores were provided to other off-targets for boosting the model performance.Mean-variance consensus (MVC) is a parameterized function based on gaussian distribution of the scores.Mean, median (Med), maximum (Max), and minimum (Min) are the statistics obtained from normalized scores across the docking methods.Ericksen et al.  further The consensus scoring does increase the performance for each target. However, SSnet outperforms or is equivalent to the best consensus-based scoring methods for 3 targets in terms of AUCROC. We note that the resources and time required for consensus scoring is significant. SSnet, therefore, serves as a balance between accuracy and resources/time required.SSnet:BDB has the mean EFormances . Table 6The validation of SSnet demonstrates its reliability. Furthermore, we see that SSnet learns beyond the biases that plague many ML approaches to PLI. Thus, understanding the underlying features learned by SSnet is also of vital importance. To decipher the inner workings of SSnet, we unraveled the global max pooling layer (GMP), shown in Using SSnet:DUD-E, we tested the proteins in the test set of DUD-E dataset (# of unique proteins = 30) and considered all of their ligand interactions. The results demonstrate that t-SNE clearly distinguishes all proteins (# of clusters = 30), as seen in ML/DL models have large number of weights that are optimized to learn complex information; therefore, it is important to investigate which input features are critical for the learning process of the model. In most of the previous studies , a neuraPlasmodium falciparum is in complex with glyburide. Hewitt et al. [Grad-CAM highlights the important residues for ligand recognition. In all cases, the ligand forms several types of PLIs, some of which were analyzed as shown in t et al.  showed tThe interaction between proteins and ligands can be mediated through both covalent and non-covalent interactions. Furthermore, various non-covalent interactions that participate in PLIs, including but not limited to hydrogen bonding, Van der Waals interaction, and electrostatic interaction, have been identified. To decipher if SSnet is learning general information critical for PLI, we conducted a test on The Catalytic Site Atlas (CSA) database . The CSAFrom the Grad-CAM highlighted structures, we identified the residues that maximally influenced PLI prediction for SSnet. These residues were then cross-referenced to CSA. Can SSnet predict the same results using an unbound protein structure or a different conformation of the same protein?Can SSnet detect cryptic sites based on unbound protein structures?The binding of a ligand perturbs the secondary structure and can cause significant differences from the original unbound protein structure. We investigated the ability of SSnet in predicting ligand binding based on an unbound protein structure. We divulged our focus on answering two key questions:To address the first question, Some proteins have binding sites that are not easily detectable. These proteins, termed cryptic proteins, have binding sites that are present in a protein-ligand complex crystal structures but not necessarily in the apo protein crystal structures . The chaHomo sapiens .As the ligand binds at flexible regions in the protein, we observe large conformation changes that result in the formation of a pocket for ligand binding. This example shows that the predicted binding is a consequence of each individual chain, considered independently. Thus, the applicability of SSnet can be expanded for predicting PLIs that involve multiple protein chains.Grad-CAM analysis, as well as conformation independence of PLI prediction, shows that SSnet learns crucial details of the input features required for predicting PLI. The torsion and curvature of the protein structure effectively describe the features required for PLI while remaining compact, as it is a vectorized format of the complex protein structure. The ability of SSnet to predict PLIs regardless of the crystal conformation showcases the versatile nature of the model. Grad-CAM analysis of the PLI prediction enhances the result of the screening as it can be quickly paired with pre-existing docking tools that require rigid bounding box for accurate posing and subsequent docking.With our analysis, it seems that protein folds play one of the key factor in PLI. This is highlighted by the high accuracy of SSnet and the grad-CAM analysis, where we observed that regions near the binding pocket were most influential for the prediction task (89% accurate withing 8 Å of a residue), signifying fold dependency of a ligand. It is important to note that SSnet had no prior information about the binding pocket. The claim that a molecule should have lower than 500 db as molecular weight to be drug-like [The study of PLI is an important field for progress in pharmaceutical industry and potentially extended to any biological applications. The limitations of the existing tools for predicting PLI, however, has stalled the progress in these fields. PLI computations suffer from large compute times as accurate PLI prediction require accounting of large number of physicochemical properties. Furthermore, biases arise for the ML-based PLI prediction tools due to imbalances in the representation of these physicochemical properties in the training dataset. On the other hand, classical methods rely on stochastic optimizations, such as Monte-Carlo or genetic algorithm type approaches, to generate the poses for the ligands and subsequently minimize the bound structures. These approaches require precise 3D conformation and have much higher computing times. SSnet does not show biases in physicochemical properties and necessity of accurate 3D conformation while requiring significantly less computing time. This is achieved by utilization of secondary structure information in the form of curvature and torsion of the protein backbone. The ML model employed enables fast computation once the model is trained as once the weights are fixed, prediction is result of multiple subsequent matrix transformation. The CNN framework employed enables SSnet to learn PLI patterns across wide range of residue interactions, encrypted into the torsion and curvature of the protein backbone. The overall architecture of SSnet, therefore, works in tandem to eliminate biases that plague many other ML approaches, while retaining the speed.SSnet outperforms several notable ML algorithms in terms of AUCROC when trained and tested on humans and C. elegans dataset. Furthermore, SSnet outperforms the state-of-the-art ML approach GNN-CNN in terms of AUCROC and EF1% when using both models trained on DUD-E and BDB datasets. This comparison holds true even when both the models were tested on independent test sets. Moreover, SSnet:BDB performs better if not equivalent to the classical methods in terms of both AUCROC and EF, while being orders of magnitude faster than any of the traditional VS approaches.The SSnet model utilizes secondary structure information of the protein and, since it only processes CA atoms, it does not necessarily require high resolution structural information. The analysis done on bound-unbound proteins show that SSnet can predict similar results even with different conformations of the protein, including cryptic sites. SSnet requires a single conformation to predict whether a ligand is active or not, even if the protein-ligand complex has different tertiary structure. Grad-CAM analysis not only addressed the validity of SSnet learning appropriate details from the input features but also provides the user an intuitive visualization of potential binding site for PLI.SSnet can be coupled with traditional VS/docking algorithm as pre-screen to filter ligands. Moreover, Grad-CAM analysis showed that SSnet is able to provide accurate prediction of ligand binding sites: active, allosteric, and cryptic sites. As most of VS/docking algorithms necessitate prior knowledge of the binding site, this information can be used to trim ligand search space and determine the box placement. Such information is not retrievable by most of the other VS methods for PLIs prediction. Furthermore, a standalone package has been provided for Linux, Windows, and OS-X, making it readily available to users at all levels of computational expertise and not just users familiar with programming. These features of SSnet allow it to be seamlessly integrated into existing VS workflow, where SSnet can be used to cull large databases to a small size and determine the bounding box for subsequent docking algorithms.The top scoring docked poses can then be used directly in experimental setup or further analysis using techniques, like Molecular Dynamics, to study the PLI.Our study suggests that end-to-end learning models based on the secondary structure of proteins have great potential in bioinformatics, which is not just confined to protein ligand prediction and can be extended to various biological studies, such as protein-protein interaction, protein-DNA interaction, protein-RNA interactions, etc. Inspired by the t-SNE results for the last layer in protein embedding, we propose a possible latent space for proteins that encodes important information about the protein bioactivity, and further exploration could result in a metric to compare proteins based on their bioactivity. We leave these explorations of both the SSnet model and the underlying latent space for future work.https://github.com/ekraka/SSnet) under MIT License without any restrictions or liability.To ensure replicability of both model generation, as well as model validation, all scripts developed and implemented in this work are provided through GitHub (