A long-lasting goal of computational biochemists, medicinal chemists, and structural biologists has been the development of tools capable of deciphering the molecule–molecule interaction code that produces a rich variety of complex biomolecular assemblies comprised of the many different simple and biological molecules of life: water, small metabolites, cofactors, substrates, proteins, DNAs, and RNAs. Software applications that can mimic the interactions amongst all of these species, taking account of the laws of thermodynamics, would help gain information for understanding qualitatively and quantitatively key determinants contributing to the energetics of the bimolecular recognition process. This, in turn, would allow the design of novel compounds that might bind at the intermolecular interface by either preventing or reinforcing the recognition. HINT, hydropathic interaction, was a model and software code developed from a deceptively simple idea of Donald Abraham with the close collaboration with Glen Kellogg at Virginia Commonwealth University. HINT is based on a function that scores atom–atom interaction using LogP, the partition coefficient of any molecule between two phases; here, the solvents are water that mimics the cytoplasm milieu and octanol that mimics the protein internal hydropathic environment. This review summarizes the results of the extensive and successful collaboration between Abraham and Kellogg at VCU and the group at the University of Parma for testing HINT in a variety of different biomolecular interactions, from proteins with ligands to proteins with DNA. Proteins play many functions in living systems as carriers, enzymes, antibodies, receptors, hormones, mechanical support, and storage. In essentially all these functions, proteins interact with small molecules, metals, and other proteins or peptides. Whenever an interaction occurs between two or more molecules, the recognition is dictated by three key elements: i) complementarity in shape, ii) complementarity in interacting moieties, and iii) free energy of binding. There is also a fourth element that deals with time, i.e., how rapidly molecules see each other and how long they stay together. In some cases, this is a few milliseconds, and in other cases, they remain associated for their entire life before degradation.Recognition is based on structural and chemical complementarity . Structude novo, modeling system that extended the connection matrix-based CLOG-P system of Here, we will focus on a very simple approach that exploits both experimental and computational information to generate a score of the interaction that is directly related to the free energy of binding . This apInspired by the pioneering work of David Weininger at Daylight CIS and other researchers, the HINT program was rewritten in the late 1990s as a collection of object-oriented toolkit functions to enable the more facile creation of new application programs exploiting the HINT interaction model . The proA major event in the history of HINT was the development of a very long-term collaboration between VCU’s Abraham and Kellogg and Andrea Mozzarelli, Pietro Cozzini, and several exceptional students at the University of Parma. This collaboration and the numerous exciting results we found are reported in the following paragraphs. It should be noted that many of the innovations of the HINT model were inspired by the various interesting projects that the VCU and University of Parma teams carried out together.a that is derived from a LogP library coupled with algorithms to appropriately parse this information, where LogP is the partition function of an atom between water and 1-octanol. These two media were selected as representative of the polar and apolar environments within a protein. The atom-type LogP library was adapted from the CLOG-P approach  and solvent-accessible surface area (Si):f(rij) represents a function of the distance between the two atoms, i and j. The atomistic Si parameters are applied to represent “exposure” of that atom for interaction with atoms in other molecules. Atom–atom distances are obtained either from the three-dimensional structure of the complex or from a model generated by docking procedures or homology modeling. The higher the resolution (or reliability) of the structures, the more precise the prediction  sum over all atom–atom interactions:The core of the HINT code is the assignment to each atom of a factor approach  with extapproach . HINT cotion see . ConsequA key feature of HINT is the exploitation of the experimentally determined LogP values, thus avoiding complex equations and approximations present in most of the other methods developed for predicting protein–ligand interactions. The other conceptual advantage of using LogP is that this parameter provides an overall representation of the energetics of the encounter process between two molecules, without dividing the energetics into specific enthalpic contributions, such as electrostatic bonds, hydrogen bonds, and van der Waals bonds, a procedure that is not thermodynamically legitimate . In addifragment constants atomistic and to properly sign polar interactions (both Brønsted–Lowry acids and bases are polar)  commingling with another (1-octanol) that is a stand-in for lipids and membranes. Nevertheless, a few tricks had to be applied, e.g., to make the Hansch and Leo e polar) .hydrogen bonds, Lewis acids and bases, hydrophobic interactions, favorable, unfavorable, solvation, and desolvation. Thus, HINT’s numerical or graphical output is always very interpretable, and it includes all biomolecular interactions to some degree, as they are all holistically encoded in LogP. HINT is, however, not necessarily as accurate as specialized software that focuses on specific phenomena, such as electrostatics, with finely tuned algorithms and parameters. It is, thus, difficult, if not impossible, to directly quantitate HINT’s relative performance compared to other tools in a meaningful way: first, because there are no other tools in that space and, second, because “improved understanding” is not a definable metric.While quantitative aspects of programs like HINT are often used for comparative purposes, e.g., in dock scoring, virtual screening, and even LogP prediction, we have always tried to emphasize the qualitative value of HINT. Most importantly, HINT “thinks” like a medicinal chemist—perhaps Donald Abraham in particular—with respect to its language: However, to illustrate the quality of HINT code prediction for the strength of protein and nucleotide complexes, it was tested in many different types of interacting molecules and compared with experimentally determined dissociation constants. The overall results are remarkable, given the simplicity of the code and the speed of calculations. More importantly, virtually all of our studies produced results that suggested or proved new principles of biomolecular interaction. In the next sections, we summarize several cases where HINT has been applied.Many proteins function via interactions with small ligands that exhibit a wide range of features covering most of the chemical space. For example, hemoglobin binds oxygen, which is a neutral molecule, intracellular hormone/vitamin receptors bind mostly apolar ligands, such as estrogens and retinoids, and proteases bind polar–apolar polypeptide chains. This is made possible by sites where ligands bind that possess architectures dictated by the amino acids composing the pocket or site. Since members of the amino acid set are endowed with large differences in the polarity/apolarity of their sidechains, they can accommodate ionic and hydrophobic ligand interactions. In addition, the complementarity between the host (protein) and guest (ligand) may be obtained by optimized conformational changes of either one or the other of the interacting partners to obtain a “perfect” fit or, in a different view, by the selection of the protein and ligand conformations that perfectly match by steric and chemical complementarity. The net result is the formation of a binary complex characterized by an equilibrium between free and ligand-bound molecules regulated by a dissociation constant. The lower the value of the dissociation constant, the higher the match, i.e., the higher the number and overall strength of the interactions.vide infra)  polarity/apolarity of ligands and/or protein active sites; ii) roles of ligand- and protein-bound water before and upon complex formation; iii) ionization state of ligand moieties and amino acid lateral chains contributing to shape the active sites, i.e., computational titration; iv) orientation of hydrogen atoms bound to polar residues and involved in H-bonds, i.e., the rank toolbox, and v) their actual contribution to the binding free energy, i.e., the relevance toolbox (e infra) .As structural information derived by X-ray crystallography is of paramount relevance to HINT analysis, as well as most of the codes aimed at the prediction of protein–ligand energetics, the quality of the three-dimensional structures significantly impacts the confidence of the predicted scores.We first evaluated by HINT the free energy of binding for 53 protein–ligand complexes formed by 17 proteins of known three-dimensional structure and characterized by different active site polarities . This anHINT scores for the protein–ligand complexes were plotted against the experimental binding affinity, obtaining a remarkably good correlation with a standard error of 2.6 kcal/mol, which translates into a prediction of affinity within about two orders of magnitude . Even ber2 equal to 0.99 , thus affecting the scores. A telling story is represented by an analysis of the binding affinity of penicillopepsin–ligand complexes measured at pHs 3.5, 4.5, and 5.5 and by three-dimensional structures determined at the same pH values. HINT score prediction using these data produced a linear correlation with  to 0.99 , indicatTo address, in a more general way, the dependence of the HINT score on the protonation state of interacting groups of ligand and protein, a protocol called “computational titration” was designed . The steWe applied the computational titration algorithm to the analysis of the interaction between neuraminidase and nine inhibitors for which three-dimensional structures and inhibition constants were known from the literature , betweenAnother factor that shows a profound impact on the predictions based on HINT, but also to all other codes, is the quality of the three-dimensional structures. The higher the resolution, the lower the standard error in the HINT prediction. We have observed that by selecting within the 53 complexes only structures determined with a resolution better than 2.5 Å, the standard error of the prediction improved to 1.8 kcal/mol compared to 2.6 kcal/mol when the included complexes possess a resolution within 3.2 Å (unpublished data). A better crystallographic resolution leads to a more precise geometry and well-defined distances of interacting groups that impact HINT scores.Overall, the HINT score for a protein–ligand complex depends, in addition to other contributions, on hydrogen-bonding contribution. A particular property of HINT is that it is very sensitive to the positioning and orientation of hydrogen-bonding protons. It is well known that hydrogen atoms can rarely be detected crystallographically, except for structures with resolutions higher than ∼1 Å. Thus, automated procedures were put in place to insert hydrogen atoms bound to heavy atoms and to correctly orient them to optimize the geometry for hydrogen bonds and, therefore, their strength and scores, without altering the position of the crystallographically determined heavy atoms. The energetic contribution of hydrogen bonds also plays a significant role when water molecules bound to protein active sites and/or ligands are considered (see the following section).Nothing happens in biological processes without the direct or indirect contribution of water. Protein–ligand complexes, as well as most biological complexes, form in water and often involve the displacement of many water molecules from protein binding sites. However, a few water molecules might be retained that further stabilize the complex association. Water displacement or retention participates in the overall free energy of binding, but the detailed estimation of each water molecule’s contribution is often far from trivial. Therefore, hit identification and, most of all, lead optimization might suffer from the uncertainty of retaining or removing specific water molecules. This has been and continues to be an interesting problem to us, and we have attempted to rationalize water’s role and contribution by means of the HINT code and specifically developed tools , such as2)-L-Leu-NH2−based ligands  water molecules in active sites; 2) water molecules deeply inserted into cavities; 3) buried water molecules; 4) first shell external water molecules (within 4 Å of the nearest protein heavy atom); and 5) second shell external water molecules  conserved water molecules in binding sites bridging protein–ligand interaction; 2) conserved water molecules in binding sites not relevant for protein–ligand interaction; 3) conserved water molecules in binding site cavities; 4) conserved water molecules in peripheral binding site regions; 5) water molecules displaced by ligands replacing their function, i.e., functionally displaced; 6) water molecules displaced by ligands only occupying their room, i.e., sterically displaced; and 7) missing waters. Water molecules with Rank >1.5 and HINT score <150 can be considered as sterically displaceable and, thus, potentially removable by ligands at moderate cost. Water molecules having, instead, Rank values in the 1.5–4.0 range and HINT scores >150 can be more relevant and quite likely retained with respect to drug design, while water molecules with Rank >4 are too buried in the binding site and of less interest .ad hoc designed ligands  can significantly affect the energetics of protein–ligand interaction. By using a training set of thirteen proteins, crystallized in the native and complexed state, with a total number of 125 water molecules, we combined information coming from the HINT score and Rank in the following equation, describing the overall probability (PA) of specific water to be Relevant:PR and PH are calculated by nonlinear polynomial regressions and correspond to the percent probability for conservation based on the Rank and HINT score, respectively, while WR and WH are the weights of Rank and HINT score probabilities, respectively  hot Clearly, the emphasis on modern drug discovery has shifted over the past decade or so toward higher-throughput modeling approaches, such as virtual screening. Before using HINT for extensive docking experiments, we were curious whether docking scores from various scoring functions correlated better with RMSD (root-mean-squared distance) or free energy of binding. In other words, does reproducing a crystal structure by docking, which is how most scoring functions are optimized, necessarily produce accurate predictions of binding energy? We examined 19 protein–ligand complexes for which X-ray crystallographic structures and binding energy data were available, and we calculated experimental vs. computationally-derived free energy correlation by means of the HINT free energy scoring tool and other scoring functions . CorrelaThe HINT scoring function described earlier has indeed been a powerful tool for virtual screening  with a fHaemophilus influenzae O-acetylserine sulfhydrylase (OASS) with affinities ranging from μM to mM, were identified with good correlation between computational and experimental data, excluding peptides bearing a positive charge, which are likely overestimated by HINT. These results, combined with X-ray structures of the three best complexes, defined a pharmacophoric scaffold for the design of peptidomimetic inhibitors for this enzyme  14 pentapeptides, binding to s enzyme . 2) To ilgorithm , docked crobials . 3) The crobials  details Protein–protein interactions are emerging as one of the most important features of biological structures. Building an understanding of their contributions is challenging because of many of the aforementioned issues. However, an important advantage of the HINT model in evaluating protein–protein interactions is that the key hydrophobic–hydrophobic interactions are handled in a robust atom-to-atom manner , not lumWhile protein–ligand interactions are complex on their own, the addition of more degrees of freedom when two proteins interact is another level of complexity. For example, in a typical protein–ligand interaction, there will likely be only a few relevant water molecules, but at a protein–protein interaction surface, there can be one or two dozen. Using the aforementioned computational titration algorithm  would tyTo explore the water issue, we adapted the rank and relevance algorithms to protein–protein interactions in order to probe the roles of water molecules found specifically at protein–protein interfaces ; 2013. TTo further examine our insistence on the importance of water at protein–protein interfaces, we reported a docking study on a small number of protein–protein complexes both “dry”, as allowed by the native ZDOCK  program,As we were developing HINT, implementing an intramolecular score was a very simple addition. However, it was not immediately obvious how to apply it to interesting and important problems until we started thinking about the difficulties of dealing with low-resolution crystal structures and how their reflection data are collected and refined. When the resolution is high, the reflection data are more than adequate to model the structure within the electron density envelopes and features, such as hydrophobic interactions, are seen. However, at low resolution, somewhat crude molecular mechanics forcefield algorithms are applied, and hydrophobic interactions—that are not explicit in these force fields—are often lost. We used the intramolecular HINT score as an adjuvant to contemporary structure refinement protocols . We showin silico. The intramolecular HINT scores for the alpha, beta, gamma, delta, and omicron variants confirm that all mutated trimeric spike protein structures are similarly or more stable than the wild-type. In addition, these scores show that the receptor-binding domains of these mutants are more stable than the wild-type. The HINT intramolecular scoring is very rapid, and presuming the structures are well-modeled is suggestive that it may be useful as a pre-screening tool for as-yet undiscovered mutants for any protein.More recently, χ1 for asparagine, aspartic acid, cysteine, histidine, isoleucine, leucine, methionine, phenylalanine, proline, serine, threonine, tryptophan, and tyrosine, and χ1 and χ2 for arginine, glutamine, glutamic acid, and lysine. The former, thus, have three parses per chess square and the latter nine  interface of hemoglobin are largely unaffected by the deoxy to oxy hemoglobin transitions (and dominated by hydrophobic contacts between the two subunits), while the α1β2 interface, with just as many hydrogen bonds, has few hydrophobic interactions  and shifter nine . These mde novo atomistic minimization. More importantly, however, using HINT scoring and its core interaction model has enabled our view of the structure to be built upon a free energy framework and to account for some more subtle features of interactions that are not necessarily detected by molecular mechanics-based approaches. For example, sidechain maps of aromatic residues, phenylalanine, tyrosine, and tryptophan, show evidence of pi–pi stacking and pi–cation interactions  while both have similarly consistent interaction roles , and anthracycline antibiotics, such as doxorubicin, daunorubicin, and others . In the first example, HINT analysis of a computational model of the adducts derived by administration of chlorambucil to the shuttle vector plasmid pZ189 demonstrated that the methylenes, as well as the phenyl ring of this drug, can form favorable hydrophobic interactions with nucleotides near the adduct site in the minor groove of DNA, promoting alkylation at the N-3 position of adenine .Next, HINT was used to study the selectivity of doxorubicin intercalation and binding in all 64 unique base pair quartet combinations. The results showed that the interactions between doxorubicin and the base pairs above and below the intercalation site are mainly polar and favorable, resulting from acid–base interactions between the heteroatoms of the antineoplastic drug and the nucleotide bases. In addition, favorable hydrophobic contributions are also present. Specificity was mainly associated with hydrogen bonds, in particular with a base pair three positions away from the intercalating site . These aAnalysis of the interactions of the two antibiotics gentamicin and paromomycin with 12 designed analogs with ribosomal RNA also showed that rings III and IV of these compounds are involved in important polar interactions with rRNA . TherefoHINT was tested for its ability to evaluate interactions of much more complex systems, such as protein–DNA complexes. An initial study was conducted on the interactions between estrogen receptors (ER) alpha and beta and the corresponding estrogen-responsive elements (EREs) near estrogen-regulated genes . By analFor this particular set of complexes, the specificity of protein–DNA sequence binding did not appear to be much affected by water molecules. However, given the importance of the contribution of water in the thermodynamics of DNA–protein recognition, this investigation was extended in parallel to 39 additional DNA–protein complexes for which a three-dimensional structure was available . Thus, iA more comprehensive study on energy-based prediction of the specific recognition between amino acid residues and nucleotide bases was subsequently performed on a dataset of 100 high-resolution protein–DNA complexes  Figure  and usedFor some amino acid–base pairs, it was also possible to calculate a “water enhancement factor,” that is, the ability of bridging waters to enhance the energetics of the amino acid–base interaction . FinallyThe prediction of events in antiquity was a very profitable but risky job, from the prophet Cassandra to those relying on Sibilla oracle cards. In addition to gambling, which, by definition, remains unpredictable, the forecast of weather is perhaps the most common modern-day testing ground for prediction. In use are algorithms that take into account the many variables that dictate sunny or rainy days, windy or calm weather conditions, and temperature. In this case, the robustness of the prediction can be and is verified every day, and the applied algorithms are constantly adjusted with incremental but beneficial improvements.Prediction of the binding affinity between a protein and a ligand, or for any two biological molecules, independently of their molecular weights, is as challenging to predict as a protein structure. Multiplicity and diversity are the rules as many small energetic contributions lead to either loose, medium, or tight complexes. Some of these contributions are difficult to pinpoint as they deal with entropy or other emergent phenomena. HINT is one of the few codes that have attempted energetic evaluations of the molecular events associated with the formation of a protein–ligand complex—considering both enthalpic and entropic contributions—in a very simple and “natural” way . This idThe results of applications of HINT to many diverse protein–ligand and protein–nucleotide complexes with and without water contributions, reported herein, demonstrate that it is possible to obtain very usable, if not accurate, predictions of protein–ligand strength in short times and even with very low computational power. This paves the way for the design of chemical entities that correctly fit within protein active sites, enabling either inhibition or enhancement of their function, and potentially act as drugs to treat diseases. This was the dream of Abraham, and we are still pursuing it. We might be a bit closer!


Three-dimensional structures of protein–ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods.We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein–ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available.The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein–ligand complexes, and we expect further improvements and wide applications.The online version contains supplementary material available at 10.1186/s12859-023-05354-5. Molecular interactions between proteins and small molecule ligands are fundamental to biological processes, and three-dimensional structures of molecular complexes provide direct insights into their interactions associated with functions . Since ePrevious approaches predominantly employ the molecular docking methodology –8 that pAdvances in bioinformatics and deep learning have enabled accurate protein structure prediction using multiple sequence alignments (MSAs) and structural templates –17. SubsDeep generative modeling , 22 is aRecently, several methods have been proposed to apply diffusion-based generative models to protein–ligand complexes –36. For In this work, we propose an end-to-end framework to generate ensembles of protein–ligand complex structures by modeling their probability distributions with equivariant diffusion-based generative models Fig. . By incoWe model the joint distribution of 3D coordinates of protein ve model . The modve model  model, aWe used protein–ligand complex structures from the PDBbind  databaseThough we designed our model to be protein structure-free, we also trained another version of the model that accepts protein backbone templates as input for reference. We denote the original protein structure-free model as DPL (Diffusion model for Protein–Ligand complexes) and this protein structure-dependent version as DPL+S.To assess the generative capability of our models in terms of the reproducibility of the experimentally observed protein conformations and ligand binding poses, we sampled 64 structures for each complex in the PDBbind test set and compared them with the PDB-registered structures. We used TM-align  for struFigure entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaIn this experiment, we validated the effectiveness of protein structure-free modeling in a more challenging situation where ligand-bound protein structure was not available. We performed conformational sampling on 23 complexes from the PocketMiner  dataset,Figure ble Fig. B, as theble Fig. C. In facble Fig. C.Fig. 4AAn example of a Casein kinase II subunit alpha with an inhibitor bound to the substrate binding site (PDB ID 5OSZ) is presented in Fig. Figure The present results highlight the critical problem with previous protein structure-dependent approaches. Namely, preparing a protein structure suitable for ligand binding prior to determining the complex structure is an ill-posed problem, even though the sampling of binding poses is sensitive to inputs in these methods. Our approach aims to overcome this problem by generating the structure of protein–ligand complexes end-to-end without being biased by the input of protein structures.Our protein structure-free model, DPL, demonstrated the ability to sample complex structures in many protein–ligand complexes with binding poses comparable to those obtained by molecular docking. Furthermore, it is encouraging that our model was able to generalize to some of the proteins for which no related structure was used for training, and successfully sampled binding poses that were inaccessible by docking methods. However, its performance was still limited for complexes with small amounts of related training data, raising concerns about overfitting to the protein structures used in the training data t ranges from t that control how much signal is retained and how much noise is added, respectively. We use the variance preserving process [t satisfying We first define an equivariant diffusion process for atom coordinates ollowing . The dis process , 24 wherT timesteps, we can define the generative model asThe generative model is defined by the inverse of the diffusion process, where a sequence of latent variables t as We optimize the parameters shown in , if we dAs illustrated in Fig. We construct a single representation and a pair representation from a protein amino acid sequence and a ligand molecular graph. We utilize the 650 M parameters ESM-2  model, ae UniRef  databasee UniRef . The repWe jointly update the single and pair representations with the 12 Folding blocks described in the ESMFold . The FolIn the process of equivariant denoising, the final pair representation is symmetrized and transformed by a multi-layer perceptron (MLP) into a weight matrix Finally, we note that the model described above is SE(3)-equivariant, that is,Additional file 1. Supplementary information.


Protein–protein binding enables orderly biological self‐organization and is therefore considered a miracle of nature. Protein‒protein binding is driven by electrostatic forces, hydrogen bonding, van der Waals force, and hydrophobic interactions. Among these physical forces, only hydrophobic interactions can be considered long‐range intermolecular attractions between proteins due to the electrostatic shielding of surrounding water molecules. Low‐entropy hydration shells around proteins drive hydrophobic attraction among them that essentially coordinate protein‒protein binding. Here, an innovative method is developed for identifying low‐entropy regions of hydration shells of proteins by screening off pseudohydrophilic groups on protein surfaces and revealing that large low‐entropy regions of the hydration shells typically cover the binding sites of individual proteins. According to an analysis of determined protein complex structures, shape matching between a large low‐entropy hydration shell region of a protein and that of its partner at the binding sites is revealed as a universal law. Protein‒protein binding is thus found to be mainly guided by hydrophobic collapse between the shape‐matched low‐entropy hydration shells that is verified by bioinformatics analyses of hundreds of structures of protein complexes, which cover four test systems. A simple algorithm is proposed to accurately predict protein binding sites. An innovative method is developed for identifying low‐entropy regions of hydration shells of proteins by screening off pseudohydrophilic groups. Large low‐entropy regions of the hydration shells typically cover the binding sites of proteins. Shape matching between a large low‐entropy hydration shell region of a protein and that of its partner at the binding sites is revealed as a universal law. The physical mechanism responsible for protein‒protein binding can be considered the most important mechanism of bioogical self‐organization, functionalization, and diversity.Proteins serve a variety of important functions in organisms. A protein's intrinsic biological functions are normally expressed via precise binding with another protein , involving in nearly all physiological processes, including immune response, DNA replication, enzyme inhibition, and the signal transduction. Protein‒protein binding is a spontaneous physical contact of high specificity, established between two specific protein molecules, and protein‒protein binding is highly rare in extracellular fluid.1 At p Even though significant progress has been made toward the structural determination of protein complexes, many complexes that are crucial drug targets for cancer and diseases remain difficult to determine experimentally. Computational prediction of the peptide‐protein and protein‐protein binding using machine learning methods is advancing, speeding up immunogenic peptide screening and facilitate protein‐based drug design. Experimental approaches like double‐mutant cycles and paramagnetic relaxation enhancement have been used to investigate protein–protein binding mechanisms, providing information about potential transition states and intermediates, but these data's are often indirect or limited to the specific protein. Moreover, it is difficult to obtain the desired protein binding results by traditional molecular dynamics simulation. It remains a challenge to determine a common and direct biophysical mechanism of protein–protein interactions. Protein‒protein binding is one of the miracles of nature that human technology finds quite difficult to follow due to numberless possibilities in theory of the rotational conformation potentially sampled by two or more protein molecules as they interact. Protein‒protein docking focuses on predicting the natural complex structure of protein‒protein binding in computational informatics. Research on protein‒protein docking has become more popular due to its potential to predict important protein‒protein interactions (PPIs), such as the targeting action of recombinant protein drugs and antibody drugs, a variety of conformational search strategies have been applied to predict protein docking. Although searching algorithms normally aim to achieve the optimized conformations for protein complexes with the minimized free energy of the whole system, sampling of the conformational space in protein‒protein docking is still challenging due to the lack of understanding of the entropy state of the hydration shells of individual proteins.The July 1, 2005 issue of Science published a list of 125 important questions in science, one of the questions is “How do proteins find their partners?”.2 Even ‐bonds play an extremely important role in stabilizing protein structures. To form these intramolecular hydrogen bonds, nascent unfolded polypeptide chains need to escape from hydrogen bonding with surrounding polar water molecules under solution conditions that require entropy‐enthalpy compensation, according to the Gibbs free energy equation and the change in enthalpy.46 The2.2Figure  . Therefore, we can consider that these intramolecular hydrogen‐bonded surface hydrophilic groups cannot destroy the surrounding hydrophobic group‐induced low‐entropy water molecule network because their hydrophilicity is expressed by intramolecular hydrogen bonds. On protein surfaces, the hydration shells covering these intramolecular hydrogen‐bonded hydrophilic groups can be considered low‐entropy hydration shells, namely, these hydrophilic groups are pseudohydrophilic.There are at least three reasons that can cause protein surface hydrophilic groups to not express hydrophilicity. First, it is important to note that proteins normally have an abundance of intramolecular hydrogen bonds. For example, protein secondary structures arise from the hydrogen bonds formed between the amide proton and the carbonyl oxygen of the polypeptide backbone. Intramolecular hydrogen bonds play an extremely important role in stabilizing protein structures. To form these intramolecular hydrogen bonds, nascent unfolded polypeptide chains need to escape from hydrogen bonding with surrounding polar water molecules that require entropy‐enthalpy compensation during protein folding, according to the Gibbs free energy equation. It is worth noting that some residues are distant in sequence but form intramolecular hydrogen bonds with each other in the protein native structure. These hydrogen bonds are also stabilized by entropy‐enthalpy compensation during folding of the tertiary structures. This means that protein surface intramolecular hydrogen bonds saturate many of the hydrogen bonds formed by surface hydrophilic groups of proteins. The receptor‐binding domain (RBD) of the spike protein of the Omicron BA.4/5 variant of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is used to illustrate the intramolecular hydrogen bonds formed by surface hydrophilic groups of proteins  to form the low‐entropy hydration shells . Van der Waals radius is much larger than the hydrogen bonding distance (0.3 nm). In this way, the ordered water molecules in the low‐entropy hydration shells around hydrophobic side chains are inhibited from fluctuating and rearrangement and are therefore prevented from frequently hydrogen bonding with the backbone carbonyl oxygen atoms and amide hydrogen atoms. This means that the highly hydrophobic side chains can shield the hydrophilicity of the backbone atoms to a certain extent. Thus, the hydration shells covering the backbone carbonyl oxygen atoms and amide hydrogen atoms of these hydrophobic residues can be regarded as low‐entropy hydration shells because the hydrogen‐bond rearrangements between the backbone hydrophilic atoms and surrounding water molecules are inhibited. The RBD protein is also used to illustrate the shielding effect of the protruding hydrophobic side chains  or NH group in their long side‐chains, whereas the other portions of the side‐chains are highly hydrophobic alkyl and benzene ring structures . Therefore, ordered water molecules are fixed in the low‐entropy hydration shells around the highly hydrophobic alkyl and benzene ring structures and are expelled to van der Waals interaction operating distances. The single CO or NH group is most likely to hydrogen bond with the hydrophobic‐group‐induced low entropy water cages rather than destroy the ordered water molecule network  of the protein surfaces inside the binding site regions and outside the binding site regions separately for all the proteins in the test system  by using the above two rules  around the nonpolar surface is approximately equal to the standard molar entropy of solid water, which is ≈41 J mol K−1. The standard molar entropy of liquid water is ≈70 J mol K−1. Therefore, moving an ordered water molecule from a low‐entropy hydration shell to a free liquid result in a molar entropy difference ∆S of ≈29 J mol K−1. At a human body temperature of T = 309 K, the free energy increment is T∆S = 8961 J mol−1 for one water molecule with a removed hydration shell. Therefore, at T = 309 K, the binding force between ACE2 and the RBD at a distance of 10 nm is ≈4.37 nN. Obviously, hydrophobic interactions between the two proteins can provide enough binding force to drew the coronavirus approaching the recipient cell because each coronavirus has a mass of 1 femtogram. Experimental results have revealed that at a larger separation distance of two monomers (≈5 nm), the dynamic properties of confined water molecules exhibit considerable deviation from bulk‐like characteristics. This indicates that strong hydrophobic interactions between two proteins most likely have a starting distance greater than 10 nm.Hydrophobic interactions between two proteins in the binding process are physically powered by gradually removing ordered water molecules from the low‐entropy hydration shells at the binding site of the individual protein, which increases the entropy. The hydration shell  around a protein has been found to have dynamics distinct from those of bulk water to a distance of ≈5 nm.37 Thu3It is worth noting that some protein subunites are dynamic molecules with extensive conformational flexibility at the binding sites. If prediction of the binding sites is based on a flexible view of protein structure, searching protein docking results is limited and high computational cost. Nevertheless, the distribution of hydrophbic groups at binding sites of proteins is fixed by the surface topography. Thus, the low‐entropy hydration shell at the binding site of a protein subunit most likely spatial approximate match with that of the receptor protein at a certain time, make the two binding sites approaching to each other due to the hydrophobic interaction. Due to convenient interface fitting between the two binding sites and hydrophobic interaction between strong hydrophobic groups from both proteins, conformational flexibility of protein at the binding sites most likely is beneficial to the binding process. Moreover, it is possible for intrinsically disordered proteins to contain hydrophobic groups at some fragments, resulting in the possibility of forming low‐entropy hydration shells at the fragments. As a result, some disordered proteins can bind with rigid proteins to form stable complex structures. While protein binding sites can be approximately predicted by mapping the low‐entropy regions, protein configurational flexibility should also be considered to accurately predict PPIs.As a typical spontaneous reaction, the approaching stage of protein‒protein binding must release Gibbs free energy as it proceeds, but not be dominated by electrostatic interaction or hydrogen bonding between the two proteins due to the shielding effect of the polar water molecules of the hydration shells of individual proteins. When two paired proteins bind, their hydration shells must gradually be squeezed out at the binding sites by a physical mechanism. The reasons that the hydrophilic groups on protein surfaces do not express their hydrophilicity are summarized. The large low‐entropy regions of hydration shells of given proteins can be mapped by screening off the pseudohydrophilic groups on protein surfaces. By the analysis, we show that the binding sites of protein pairs were typically covered by the larger shape‐matched low‐entropy regions of the hydration shells, enabling the large low‐entropy regions of hydration shells to fully hydrophobic collapse in the binding process. The entropy increase caused by hydrophobic attraction guides the docking process and provides binding affinity. Despite the difficulty in identifying low‐entropy hydration shells around a protein using experimental methods, theoretical approaches allow us to map large low‐entropy regions of hydration shells around proteins that can be used to accurately predict protein binding sites. The spatial layout of the low‐entropy region of the protein's hydration shell acts like a “lock and key” for guiding protein‒protein binding in a precise manner.4 IDs of these proteins according to the PDB database were labeled in all the figures. To show the distribution of low‐entropy hydration shells on the surface of proteins at the binding sites in these figures, the structural biology visualization software PyMOL to display the low‐entropy hydration shell areas was used. The list of PDB files of the test systems was illustrated in protein–protein docking benchmark version 4.0.In this study, it was used numerous experimentally determined protein structures to study protein‐protein docking mechanism. All the 3D structure data of protein molecules were obtained from the Protein Data Bank (PDB).72 IDs For example, the charge amounts of carbon atoms on residue sidechains mainly range from −0.27 to −0.09e and make it difficult to form hydrogen bonds with water molecules, a phenomenon known as hydrophobicity. Conversely, the larger electronegativity of nitrogen atoms (mainly from −0.47 to −0.8e) and oxygen atoms (from −0.51 to −0.76) allows these atoms to form hydrogen bonds with surrounding water molecules, a phenomenon known as hydrophilicity. When hydrophilic groups on protein surface in aqueous solution make favorable interior hydrogen bonds with each other but not with water molecules (the charge amount of oxygen atom in TIP3P model was −0.834e), it would argue that the hydration shells of hydrophilic groups were also low‐entropy.The topological structures in charmm36 force field describe in detail of all atomic charge amount and spatial conformations of amino acids, which can be used to identify hydrophobicity and hydrophilicity of residues  on protein surface form stable intramolecular hydrogen bonds with each other, it was consider that the hydration shell of the hydrophilic groups was also low‐entropy. The intramolecular hydrogen bonds can be identified by using PyMOL. Second, When the side chains of isoleucine, valine, leucine, tyrosine, tryptophan, cysteine, methionine, phenylalanine, lysine, arginine, histidine, proline, and alanine residues, containing highly hydrophobic structures, were located on the protein surface and protrude outward to surrounding water molecules, which can shield the hydrophilic backbone carbonyl oxygen atoms and amide hydrogen atoms to forming hydrogen bonds with water molecules. Thus, the hydration shells of backbone carbonyl oxygen atoms and amide hydrogen atoms of these above residues were considered as low‐entropy. Third, the hydration shells of the CO and NH groups of tryptophan, tyrosine, and lysine should also be low‐entropy because of long hydrophobic chain. On the protein surface, only the rest of the hydrophilic groups can normally express their hydrophilicity. On a protein surface, after screening off pseudohydrophilic groups, genuine hydrophilic groups were no longer distributed throughout the surface, several regions on the surface were only composed of hydrophobic groups and pseudohydrophilic groups  and 22 CAPRI 46,50,54 target protein complexes.Based on the above results, the low‐entropy regions of the hydration shells of the two proteins are plotted in Figure 65 ranThe authors declare no conflict of interest.L.Yang, L.Ye, and X.H. formulated the study. L.Yang, S.G., C.H., L.S., J.S., X.M. and C.L. collected and analyzed the PDB data. L.Yang, C.L., C.H. wrote programs. L.Yang, and X.H. wrote the paper, and all authors contributed to revising it. All authors discussed the results and theoretical interpretations.The authors declare the methods used in this paper have been protected by our patents, such as, the Chinese Patent No: 202 210 138 581.X. and America patent and European patent “Method and device for protein‐protein docking based on identification of low‐entropy hydration layer on protein surface”.Supporting InformationClick here for additional data file.


One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the automata according to a properly chosen rule, and to build a pairwise similarity matrix and determine common ancestors among different species in a simpler and less computationally demanding computer codes when compared to other methods. Bioinformatics has been growing recently and consolidating itself as a research area, bringing together researchers from different areas such as molecular biology, physics, mathematics, computer science, deep learning technology, and science, among others, to analyze, interpret, and process biological data. Its origins date back to the first protein sequencing studies in the 1950s when insulin was sequenced well before the first microcomputers . DatabasSimilarities among sequences, i. e. homologous sequences, hold important information, such as similar functions or as an indication of a recent common ancestor . Indeed,Considering that the p-distance measures the differences between two sequences and that information loss may occur when transforming a protein sequence into a cellular automaton, our results confirm that this loss is minimal, and that our methodology can be used in the analysis of similar proteins. Another advantage is that we use a simple comparison metric not requiring more elaborate processing methods or image textures.L, S, N, f and B, with L a n-dimensional spatial lattice of cells with values i = 1, 2, 3, …, M at time t. Each cell assumes values in the set of possible states S. The neighborhood N of a given cell i is the set of cells considered in the transition rule f. Finally, the boundary conditions of the automaton is represented by B.A Cellular automaton (CA) is a discrete dynamical system in both space and time evolving under a given spatially local rule. Despite their simplicity they often model complex systems . It is di it given by the cells i − 1, i and i + 1:S = {0, 1}. Therefore, we have a total of 23 = 8 different possible neighborhoods. The transition function f expresses the state assumed by each cell in the next time step according to its neighborhood as a query list for each possible neighborhood state. We thus have a total of 28 = 256 possible evolution rules for the cellular automaton, each rule enumerated 0 to 255 given by the decimal form of it binary representation, as exemplified in B determining the neighborhood of cells at the extremities of the cellular automaton can be of four types: fixed contour, random, periodic and reflecting [cM+1 = c1 and c0 = cM. t according to the evolution rule.Here we consider in the presen work one-dimensional cellular automata with a neighborhood of cell flecting . We consSince we use a two state cellular automaton, each amino acid will be encoded in a binary code, with different possibilities studied in the literature , 18, 30.P, the initial condition will have a size of M = 8 × P. As a first illustration of our approach we show in t = 500 steps . The temporal evolution of the cellular automaton transforms the sequence of characters that hides information from this protein into an image that carries many important characteristics of this protein. For this reason, the image of the cellular automaton is used in several works [The first step is to align the different protein sequences considered such that each associated automata are of the same size. Deletions and losses are identified and properly represented in each automaton by the respective codes in al works , 7 to moCAA and CAB as:ith cells of automata CAA and CAB at step t, respectively. The size of the automaton is M = 8 × P, where P is the size of the aligned protein sequence. As shown in the next section the Hamming distance saturates after a relatively small number of steps. We denominate this saturated value the Stationary Hamming Distance (SHD), which is then used to build the similarity matrix.The images of the associated cellular automata provide signatures for different proteins and are used to determine similarities/differences between species. A comparison between those images can be performed with a low computational cost using the Hamming distance from information theory  given byWe apply our approach to the following four protein sequences: beta-globin, NADH Dehydrogenase 5 (ND 5), NADH Dehydrogenase 6 (ND 6) and transferrin. This choice was motivated by the requirement to be able to perform comparisons with previous results in the literature , 32. AllProtein ND 5 is a sub-unit of the mitochondrial respiratory enzyme complex I (NADH: ubiquinone oxidoreductase) , and is We then build distance matrices from the SHD and the p-distance, and obtain corresponding dendrograms using the average method from R studio hierarchical grouping , 36, andThe NADH Dehydrogenase 6 (ND 6) protein is a sub-unit of the NADH dehydrogenase (ubiquinone) enzyme, located in the mitochondrial inner membrane. Mutations or errors in their sequences can cause Leigh’s disease and spinal muscular atrophy . ProteinTransferrin is an iron-binding protein keeping iron at a low concentration in biological fluids. Serum transferrin (TF) is present in mammals, amphibians, and fish , and plaα and β, which have distinct chains of amino acids, two dimers of α − β form hemoglobin. Its principal function is to carry oxygen from blood to tissues. Mutations in the beta-globin chain can cause sickle cell anemia [Hemoglobin comprises two chain pairs l anemia . We consl anemia , and theOur approach yields a consistent classification of the identified clusters. At variance the results in  and the We discussed and showed that Cellular automata are a tool for visual comparing of protein sequences and for determining their similarity. We expanded the use of this tool by introducing the use of the Hamming distance from information theory, in order to compare the cellular automata images obtained. Our approach allows to determine phylogenetic relations among species with a good accuracy if one considers that one protein was used in each of the dendrogram presented, but nevertheless has some limitations. We applied it to lysozyme protein sequences (not shown here), with inconclusive results, with the possible explanation that the sequences for this cases are not homologous but are the result of convergent evolution. In this case, the resulting dendrograms from both our method and by using p-distance cannot approximate species with recent common ancestors.The method’s main advantage is that the cellular automaton is a graphical method; thus, many complex sequence behaviors can be analyzed using the image of the cellular automaton associated with the protein. Furthermore, the Hamming distance is an intuitive metric for measuring the distance between pairs of protein cellular automata, so our method can be the first approach professionals use to analyze fast information from sets of protein sequences. The main disadvantage of the method is that it is not free of alignment. The sequences must be aligned so that the automata have the same size to calculate the Hamming distance. But compared to methods that use alignment, such as p-distance, which also builds the distance matrix, the main advantage of our method is that it codes each amino acid according to its structure so that similar amino acids have closer codes. Thus, our distance measure will give different weights to different mutations. As also observed in , who useO(N2), the step of calculating the distances between the automata has complexity O(N2), and the last step, which is the construction of the dendrograms in their groupings we use the UPGMA method to group the species and this method has complexity O(N2). Considering the computational costs of the three steps, the proposed method has complexity O(N2). Thus, the proposed method has the lowest possible cost.The complexity of this method can be calculated from the complexity of the three steps of the method. The first step is the generation of cellular automata, which has complexity Other approaches use textures from images  to compaS1 File(PDF)Click here for additional data file.


Aflatoxin is a potent mycotoxin of Aspergillus flavus that has been classified as a Group I carcinogen. O-methyltransferase A (Omt-A) is a critical enzymein the formation of aflatoxin. It catalyzes the methylation of norsalic acid to form the highly toxic intermediate averantin. The ligand-protein interactionof Omt-A was performed with piperlonguminin and blasticidins. The maximum affinity of -10.6 was found for the 5ICC_A piperlonguminine at site1 . Compounds such as Blasticidin S, Neoeriocitrin, Blasticidin S - hydrochloric acid, 6,6''-Bigenkwanin, Pipernomaline, and Eriodictyol were foundto have binding features to protein residues, as shown by computational interaction at the molecular level. Aspergillus flavus andare severely damaged by this pathogen. Disease-causing fungi are widely considered the greatest threat to the global grain industry. A. flavus isa pathogenic fungus, meaning that it causes disease in living things such as humans and animals [Fungal pathogens are considered the greatest threat to grain production worldwide. They are the main host of the fungus  animals ,2. It i animals . Aflatox animals . Human cHepatocellular carcinoma and immune system suppression are just two of the serious health risks associated with the consumption of plants and foodscontaminated with aflatoxins, making the inhibition of Omt-A an attractive target for reducing aflatoxin contamination .The goaIn this research, a retrospective observational study was conducted. This research strategy was chosen because there is a wealth of information on thecomputational behaviour of antifungal agents against the O-methyltransferase-A (Omt-A) proteins of aflatoxin-producing fungi. The information came from awide range of sources, including scientific articles, online databases, and interviews. The proliferation of Omt-A in aflatoxin-forming fungi has been studiedin the context of the effects of natural antifungal agents. The data were then statistically analysed to draw conclusions. The results of the study served tobetter understand how these compounds affect Omt-A in aflatoxigenic fungi and, consequently, fungal proliferation.A. flavus as query sequence were aligned against the Protein Data Bank (PDB) using NCBI-BLASTP to find homologoussequences. The autologous templates were also later detected using the Swiss Model, HHpred, Phyre, and RaptorX web servers. The best structure was modelled wasretrieved as the structure of Thalictrum flavum (PDBID: 5ICC chain _A) was selected as the best template. The three-dimensional model proteinwas refined as a homology model based on several sequences alignment using Clustal Omega. The validity of the structure has been demonstrated using genetic andenergetic factors. The powerful mechanics of CHARMM, which adheres to the conventional dynamic cascade technique and was used to study the energy and motion ofthe structured molecule, greatly improved the 5ICC. PROCHECK was used to investigate the quality and stereo-chemical properties of the model[The primary amino acid sequence of OmtA was retrieved from the NCBI Protein Database with accession number AAS90104.1. The primary sequence of Omt-A consistedof 418 amino acid residues with a predicted molecular weight of 46.82 kDa using the ProtParam server [Molecular docking software predicts the three-dimensional structure of protein-molecule interactions. Docking systems are search algorithms with scoring.Before that we predicted the active site of the target protein (5ICC_ChainA) was analyzed using the online server SCFBio-Active Site prediction(http://www.scfbio-iitd.res.in/dock/ActiveSite.jsp) . The seaPolyketide-derived aflatoxins are highly carcinogenic in selective aflatoxigenic fungi. Numerous organic intermediates are involved in their biosynthesis,which is mediated by several enzymes. O-methyltransferase (Omt-A) is a crucial enzyme among them, responsible for the formation of O-methylsterigmatocystin. We predWe performed a computational assessment of the protein-ligand interaction using SeamDock after certified the binding module (cluster) obtained fromSCFBio-Active Site prediction. The interaction the highest affinity of -10.6  were obtained for the 5ICC_A piperlonguminine at the site1 interaction .in vitro and in vivo, will be performed to evaluate the potential drug-like properties of the selected smallmolecule.Gly195 is primary amino acid residue involved as hydrogen bond with the piperlonguminine (CID: 5320621). Trp149, Met166, Leu219 and Trp258 are some crucialamino acid residues behave as hydrophobic interaction with the ligand. Piperlonguminine is a compound that holds significant importance in various applications. It is commonly used as a selective agent in molecular biologyexperiments and cell culture systems. Above all the blasticidin s inhibits protein synthesis by targeting the peptidyl transferase center of the ribosome,making it effective in the selection and maintenance of cells that have been genetically modified to express the blasticidin resistance gene. This compound'ssignificance lies in its ability to enable researchers to selectively grow and study cells that have incorporated the resistance gene, aiding in the understandingof gene function, protein expression, and other related biological processes.The docking result of Swiss Dock is shown in Piperlonguminine and blasticidin were identified as two of the natural chemicals that actively interact with Omt-A protein. Based on the results of thisstudy of the interaction between the Omt-A protein and synthetic chemicals, it is hypothesized that piperlonguminine may be the most effective compound forcontrolling aflatoxin.Authors extend their appreciation to the Researchers Supporting Project Number (RSP2023R306), King Saud University, Riyadh, Saudi Arabia.


Cyanobacteria, also known as blue–green algae, are ubiquitous organisms on the planet. They contain tremendous protein machineries that are of interest to the biotechnology industry and beyond. Recently, the number of annotated cyanobacterial genomes has expanded, enabling structural studies on known gene-coded proteins to accelerate. This review focuses on the advances in mass spectrometry (MS) that have enabled structural proteomics studies to be performed on the proteins and protein complexes within cyanobacteria. The review also showcases examples whereby MS has revealed critical mechanistic information behind how these remarkable machines within cyanobacteria function. Cyanobacteria, also known as blue–green algae, are among the oldest and most populous organisms on the planet. They have great potential in applications in biotechnology  includinUntil the early 2000s, obtaining cyanobacterial genome sequences was challenging due to the strong symbiotic relationship of cyanobacteria with other organisms and the strict requirement for axenic cultures for effective genome sequencing . In addiSimultaneous to these developments, the field of cyanobacterial proteomics has also been expanding rapidly . With deSynechococcus sp. PCC 7002 genome [Arthrospira platensis was confirmed using mass spectrometry (MS) to be alanine [Phormidium rubidium was found to have 49 differences compared with its gene-derived sequence, which ultimately led to a more precise structure of its resulting complex [Despite the focus of this review being proteomics, it is important that the integration of genomics and proteomics and the parallel advancement of both techniques continues. Indeed, due to the complexity of cyanobacterial genetic analysis, mistakes can occur within genome sequences that can subsequently be ‘corrected’ through proteomic analysis. For example, a proteogenomics study by Zhao and co-workers was able to correct 38 predicted gene-coding regions of the 2 genome . Single  alanine ,30. Inde complex . MoreoveStructural proteomics is the analysis of 3D protein structures with the aim to understand how proteins function on a genome-wide scale ,33. TradMS alone, whereby a protein is infused directly into a mass spectrometer, has provided a wealth of information on protein structures within cyanobacteria . ThroughMS excels in its ability to resolve minor mass differences on intact proteins and thus can distinguish many protein proteoforms. Through MS/MS experiments, the sites of PTMs can be localised and their abundance quantified . These aSynechococcus sp. PCC 7002 [Nostoc sp. PCC 7120 [II signal transduction protein is an important factor in signalling nitrogen deficiency [Overall, MS analysis of cyanobacterial proteins has identified several types of PTMs. For instance, proteomic studies determined 23 and 27 different types of PTM in PCC 7002  and NostPCC 7120 , respectPCC 7120 ,101–107,PCC 7120 ,108,109,PCC 7120 , lipidatPCC 7120 ,112, proPCC 7120 ,114, malPCC 7120 , methylaPCC 7120 ,83, succPCC 7120 ,117 and PCC 7120  as illusficiency ,91,93, wficiency .Another prominent example of PTMs in cyanobacteria is in controlling circadian rhythm. Within this, KaiC undergoes autophosphorylation, KaiA enhances autophosphorylation of KaiC, while KaiB antagonises it . Moreovein vivo through biological characterisation.Overall, despite ancestral origin, the different types of PTMs found across cyanobacteria species are vast, with new modifications continuing to be discovered. As the diversity of MS methods to study PTMs evolves , our abi. used a combination of size-exclusion chromatography, ion exchange chromatography and sucrose density gradient centrifugation followed by MS, collectively termed Co-Frac-MS, to map the protein interactome within Synechocystis sp. PCC 6803 revealing new insights into photosynthesis, cell mobility and lipid metabolism [MS studies on cyanobacteria have led to the identification of novel protein complexes that are dependent upon the cyanobacteria’s cellular context. These findings have often been a result of looking at intact protein complexes rather than individual proteins. Indeed, if a novel protein is detected within a macromolecular complex of known function, this on its own provides functional insight into the role of the newly identified protein, which may alter in response to cellular stimuli. Moreover, a study by Guerreiro et al. noted that when looking at global protein levels, the fluctuation in proteins in response to light was not very pronounced . Howevertabolism . With adtabolism .On the structural level, the ability of native MS to monitor oligomeric states of proteins and determine protein complex topology has been utilised to reveal new insight within several protein complexes within cyanobacteria. Hackenberg et al. used native MS to show that the cystathionine β-synthase (CBS)-chloroplast protein (CP12) fusion protein, as a single entity, can form a hexameric structure that has suggested roles in redox regulation . EisenbeOther work of note where determining the oligomeric statuses’ of proteins has been advantageous is centred around the carboxysome; the compartments within cyanobacteria that are responsible for fixing carbon from inorganic substances . The assWith the increasing developments in structural MS techniques, not only are new proteins and their interaction partners being discovered, but detailed mechanistic information of how key complexes function within cyanobacteria are becoming apparent. Two prominent examples of protein machineries that are noteworthy of further discussion are the circadian clock and photosynthesis.6B6 complex [6B6 and KaiC6B6A12) that enabled their structural characterisation by single particle cryo-electron microscopy together with hydrogen-deuterium exchange MS and cross-linking MS [As mentioned previously, phosphorylation is key in controlling circadian rhythm. In addition to mapping sites of phosphorylation on KaiC ,121, nat complex . Followinking MS . Since tnking MS . These d6f, NAD(P)H quinone oxidoreductase complex, and ATP synthase , that is responsible for water splitting, oxygen evolution and plastoquinone reduction. Two proteins, Psb27 and Psb28, are important in the successful repair of photosystem II [559, acting to protect the photosystem II subcomplexes until the photosystem II is ready to function [When light levels are too high during photosynthesis, protective mechanisms within cyanobacteria must be in place to prevent photodamage. Orange carotenoid protein (OCP) has known photoprotective capabilities and binds to the PBS when light levels are too high . Native ystem II ,175. A cystem II . In contystem II . Using afunction .Together, these MS studies have revealed insight into the PBS, photosystem I and photosystem II within the photosynthetic machinery of cyanobacteria. However, these structural MS studies are only the beginning with the developments in Alphafold 2 now providing more insight into even the intrinsically disordered regions within the PBS that can be further refined using structural MS . We foreStructural proteomics can provide a wealth of information on how proteins function. Within cyanobacteria, structural MS has played a pivotal role in deciphering protein post-translational states, determining protein interaction partners, and revealing mechanistic details behind how proteins function. In this review, we have showcased examples of how structural MS has provided information on circadian rhythm, carbon fixation and photosynthesis. However, we envisage that many macromolecular complexes within cyanobacteria are yet to benefit from structural MS studies, the knowledge of which will significantly advance our understanding of how cyanobacteria function and produce their remarkably efficient protein machines.Cyanobacteria are bursting with biotechnological potential.Advances in structural mass spectrometry are providing great insight into protein proteoforms and their interaction partners that together provide insight into how these function within cyanobacteria.This review highlights examples of where structural mass spectrometry has advanced our knowledge of important molecular mechanisms within cyanobacteria.


One of the most important aspects of successful cancer therapy is the identification of a target protein for inhibition interaction. Conventionally, this consists of screening a panel of genes to assess which is mutated and then developing a small molecule to inhibit the interaction of two proteins or to simply inhibit a specific protein from all interactions. In previous work, we have proposed computational methods that analyze protein–protein networks using both topological approaches and thermodynamic quantification provided by Gibbs free energy. In order to make these approaches both easier to implement and free of arbitrary topological filtration criteria, in the present paper, we propose a modification of the topological–thermodynamic analysis, which focuses on the selection of the most thermodynamically stable proteins and their subnetwork interaction partners with the highest expression levels. We illustrate the implementation of the new approach with two specific cases, glioblastoma (glioma brain tumors) and chronic lymphatic leukoma (CLL), based on the publicly available patient-derived datasets. We also discuss how this can be used in clinical practice in connection with the availability of approved and investigational drugs. Cancer is one of the most challenging diseases worldwide because its complexity is intricately bound to the physiological deregulation of ourselves. Although a significant improvement in diagnosis and treatment has occurred in the past few years, progress has been slow, and the rate of death should reach a staggering 13.2 million deaths worldwide by 2030 [The generally accepted strategy for cancer treatment is to remove the solid tumor by surgery, if possible, and then to target malignant cells with radiation therapy, focusing on the tumor site. Additionally, chemotherapeutic approaches with systemic and targeted modes of delivery are commonly used with the highest possible level of specificity and selectivity of the pharmacological agent or a combination of drugs. A drug may be designed to bind to general targets, such as DNA replication mechanisms or nucleotide synthesis machinery of the cell since malignant cells typically divide faster than normal cells and abolish pro-apoptotic signaling. Chemotherapy agents also commonly inhibit targets that belong to metabolic or protein–protein interaction (PPI) networks. The proper identification of molecular mechanisms that drive tumorigenesis and cancer progression is crucially important for providing efficacious therapeutics since tumorigenesis can be viewed as the dysregulation of protein–protein interaction networks that control the cellular function, especially growth and division. However, what complicates this approach is the presence of redundancies and complex feedback loops that provide robustness in this vast network of interacting proteins. In the language of network theory, this complex architecture confers the integrity of the entire system in the case of vertex (protein) or edge (interaction) failure . As a coFortunately, at present, the knowledge of the interactions between cellular proteins is now sufficiently well developed at the molecular level so that modeling complex molecular processes can be performed at a high level of confidence. Protein interactomes have been well characterized by the use of refined experimental techniques, such as two-hybrid yeast, affinity pull-down mass spectrometry, biochemical techniques, and next-generation sequencing. The massive amount of resultant data have been made available through online databases. Furthermore, recent progress in data mining, high throughput data generation relative to gene, protein, and metabolic networks, as well as computer simulations of protein–protein interactions ,6, offerComplex networks are very common in the life sciences, at the level of system descriptions ranging from genes to proteins to cells and also to organisms and even societies. Mathematically, a network is typically described by a directed or undirected graph G =  with vertex and edge sets V and E, respectively. An edge is allocated to the graph where there is a known interaction between two elements, each of which corresponds to a distinct vertex. The corresponding interaction represents either the direct binding of the two interacting elements, the functional activation of one element by another, or the initiation of enzymatic catalysis. Real-world networks demonstrate a modular structure whereby subsets of vertices are organized in clusters when they are tightly connected internally and loosely connected to other vertices outside the given cluster . This orWithin the scope of applications of PPI modeling in the context of cancer chemotherapy, early model development provided a link between the protein level of description and cancer epidemiology ,6. Speci t) pair .Typically, cancer patients are treated with the maximum tolerated dose (MTD) of some chemotherapeutic agent that is designed to target so-called oncogenes and/or microtubule dynamics. Often, the chemical agent is designed to target genes that are overexpressed. Usually, there is little regard for the protein coded for by the gene and its biochemical and/or cellular function. In our previous work, we have been exploring the use of Gibbs energy and protein–protein interaction (PPI) network topology for selecting protein targets for inhibition . Specifically, PPI networks involved in the signaling pathways that are over-expressed in a given type of cancer were analyzed. Briefly, the approach we have used, as described in the literature ,9, is toFinding good PPI targets to interrupt is a challenge. For example, ,17 suggeIn the following sections, we describe a new approach to select specific PPIs based on Gibbs free energy and subnetwork interaction partners. If our new technique is used with the human foldome , it is now possible to contemplate truly personalized medicine ,13,14. INow, let us look at a real-world example of 514 brain cancer mRNA samples. The data are from glioma patients from the TCGA database . After mUsing the same methodology, we constructed Above, we have described the target selection for the inhibition of protein–protein interactions for patient-specific types of cancer. We have provided two concrete examples: glioma and CLL. Each of these examples results in a diagram showing the most important targets based on the number of patients in the database with similar profiles. In general, the next step in the application of this process is to seek approved or investigational drugs that are able to inhibit these interactions specifically and selectively. This can be accomplished by using either DrugBank , ChEMBL As briefly discussed in the Introduction, our proposed approach is to again use mRNA from blood or biopsied tissue, normalize the values, and instead of overlaying it on the entire human PPI network, we overlay it on the appropriate KEGG pathway(s) . For exaIt should be emphasized here that in spite of some similarities, the current paper uses a distinct methodology compared to the previously published work. The former approach involves essentially removing the protein (vertex) from the network, thus removing all the possible interactions for that protein. This protein is selected by first computing the Gibbs energy for each protein in the entire human PPI network and then performing a topological filtration on the energy landscape. In this smaller network, we then computed the Betti number for the resulting network. We subsequently recomputed the Betti number after removing each protein and selecting the protein that changed the Betti number the most, which reduces the complexity of the network the most.The new approach starts with the merging of the appropriate KEGG pathways and then computing the Gibbs energy of each protein. The next step in the new algorithm is to find the protein with the largest Gibbs energy. That protein is not to be removed, but rather the interaction with that protein and the neighboring protein with the largest expression value. Consequently, the overall aim is to discover the protein–protein interaction. Accordingly, no Betti number computation is needed in contrast to the previous approach. Below, we describe in detail each step of the new and improved approach.After the adjacency list of protein–protein interactions from the merged network is obtained , the nexi under consideration. Since we usually do not have knowledge of the exact concentration of the protein, we use the normalized mRNA as a surrogate, which is a good approximation. The denominator is the sum of all the normalized mRNA data for the proteins connected to node i and includes the concentration value for i. The argument of the ln function will be less than 1, so the result is a negative number, representing Gibbs free energy. The more negative the Gibbs free energy value, the more important the contribution of the selected protein i to the stability of the PPI network. Technically, Equation (3) is not an equality because the units do not match on each side. We rescale the concentration to protein I; thus, the relation is a mapping.When the data are rescaled between 0 and 1, it indicates that the most up-regulated protein is set to 1 and the most down-regulated protein is set to 0. The rationale for this is discussed in . In EquaAfter we have computed the Gibbs energy of each protein, we then select the one that has the largest absolute value, meaning the most negative. This protein contributes the most to the overall energy of the tumor cells. Once that protein is identified, we select the nearest neighbor that has the largest expression or the node that has the individual highest concentration and is also connected to the identified node with the highest absolute Gibbs energy. These two nodes, which are certainly connected to each other, comprise the link to inhibit. In other words, these two proteins are the ones to be targeted for inhibition (to limit their reactivity). In this paper, we have proposed a new method for the optimized, patient-specific selection of drug targets for cancer and possibly other diseases. The method involves the analysis of protein–protein interaction networks and their subsets related to the over-expressed proteins in cancer cells of interest. It then quantifies the importance of the targets for inhibition based on their Gibbs free energy. Subsequent proteins interacting with the target are ranked according to their expression levels. Two specific examples have been evaluated this way: glioma and CLL. We have also discussed how this approach can be further applied in the clinical setting by searching for approved and investigational drugs that inhibit the protein interactions identified in our methodology.Our results have three major implications for cancer therapy, namely (i) the methodology introduced here defines a quantitatively-based strategy to identify potential oncotargets for any cancer chemotherapy treatment for which PPI networks have been generated; (ii) it identifies and ranks potential targets of inhibition in combination chemotherapy which offer the most promising clinical outcomes; and (iii) it provides rapid identification of molecular targets with existing approved or experimental pharmacological agents in the context of personalized medicine based on individual tumors types and histological subtypes. However, we are well aware of the need for experimental validation of this at both an in vitro and an in vivo level.With the ever-increasing pace of modern technological development, it is highly probable that in the foreseeable future, a patient diagnosed with cancer will be biopsied, from which a DNA sequence of both malignant and stromal cells will be performed that will subsequently be used to inform the treatment plan. This treatment plan will be based on the generation of a PPI network for both somatic and germline cells, allowing for the computational identification of ranked oncotargets. Furthermore, combining these results with the existence and pharmacological profiles of corresponding oncotarget inhibitors will result in a rationally designed patient-specific combination chemotherapy. This strategy can be further refined by aiming for minimal side effects for normal cells in cases where off-target interactions of the selected inhibitors are known or can be computationally predicted through docking or artificial intelligence approaches. This strategy, when properly validated by in vitro and in vivo assays, is capable of overcoming drug resistance when applied in an iterative, time-dependent manner.Finally, we hope that this strategy may assist in the development of integrated methods based on a number of fields of science, such as bioinformatics, computational drug discovery, artificial intelligence, and pharmacokinetics, with applications in medicine and healthcare. This trend in the introduction of modern and sophisticated medical-research-related advances will eventually bring tangible clinical benefits to patients, as well as cost-savings and greater efficiencies to healthcare and medical insurance providers. However, it must also be kept in mind that associated clinical, societal, and ethical risks are present when innovation is initially implemented. Therefore, a balanced assessment of the risks and benefits of such strategies must be carefully investigated before clinical introduction .