Inherent versus induced protein flexibility: Comparisons within and between apo and holo structures

Marks C., Shi J., Deane C.M.

Protein function is often facilitated by the existence of multiple stable conformations. Structure prediction algorithms need to be able to model these different conformations accurately and produce an ensemble of structures that represent a target's conformational diversity rather than just a single state. Here, we investigate whether current loop prediction algorithms are capable of this. We use the algorithms to predict the structures of loops with multiple experimentally determined conformations, and the structures of loops with only one conformation, and assess their ability to generate and select decoys that are close to any, or all, of the observed structures.We find that while loops with only one known conformation are predicted well, conformationally diverse loops are modelled poorly, and in most cases the predictions returned by the methods do not resemble any of the known conformers. Our results contradict the often-held assumption that multiple native conformations will be present in the decoy set, making the production of accurate conformational ensembles impossible, and hence indicating that current methodologies are not well suited to prediction of conformationally diverse, often functionally important protein regions.marks@stats.ox.ac.uk.Supplementary data are available at Bioinformatics online.

Finn R.D., Coggill P., Eberhardt R.Y., Eddy S.R., Mistry J., Mitchell A.L., Potter S.C., Punta M., Qureshi M., Sangrador-Vegas A., Salazar G.A., Tate J., Bateman A.

In the last two years the Pfam database (http://pfam.xfam.org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved. Some Pfam entries (1.6%) which have no matches to reference proteomes remain; we are working with UniProt to see if sequences from them can be incorporated into reference proteomes. Pfam-B, the automatically-generated supplement to Pfam, has been removed. The current release (Pfam 29.0) includes 16 295 entries and 559 clans. The facility to view the relationship between families within a clan has been improved by the introduction of a new tool.

Gaudreault F., Chartier M., Najmanovich R.

Protein movements form a continuum from large domain rearrangements (including folding and restructuring) to side-chain rotamer changes and small rearrangements. Understanding side-chain flexibility upon binding is important to understand molecular recognition events and predict ligand binding.In the present work, we developed a well-curated non-redundant dataset of 188 proteins in pairs of structures in the Apo (unbound) and Holo (bound) forms to study the extent and the factors that guide side-chain rotamer changes upon binding.Our analysis shows that side-chain rotamer changes are widespread with only 10% of binding sites displaying no conformational changes. Overall, at most five rotamer changes account for the observed movements in 90% of the cases. Furthermore, rotamer changes are essential in 32% of flexible binding sites. The different amino acids have a 11-fold difference in their probability to undergo changes. Side-chain flexibility represents an intrinsic property of amino acids as it correlates well with configurational entropy differences. Furthermore, on average b-factors and solvent accessible surface areas can discriminate flexible side-chains in the Apo form. Finally, there is a rearrangement of the hydrogen-bonding network upon binding primarily with a loss of H-bonds with water molecules and a gain of H-bonds with protein residues for flexible residues. Interestingly, only 25% of side chains capable of forming H-bonds do so with the ligand upon binding. In terms of drug design, this last result shows that there is a large number of potential interactions that may be exploited to modulate the specificity and sensitivity of inhibitors.rafael.najmanovich@usherbrooke.ca.

Chang D.T., Yao T.-., Fan C.-., Chiang C.-., Bai Y.-.

This work presents the Apo-Holo DataBase (AH-DB, http://ahdb.ee.ncku.edu.tw/ and http://ahdb.csbb.ntu.edu.tw/), which provides corresponding pairs of protein structures before and after binding. Conformational transitions are commonly observed in various protein interactions that are involved in important biological functions. For example, copper-zinc superoxide dismutase (SOD1), which destroys free superoxide radicals in the body, undergoes a large conformational transition from an 'open' state (apo structure) to a 'closed' state (holo structure). Many studies have utilized collections of apo-holo structure pairs to investigate the conformational transitions and critical residues. However, the collection process is usually complicated, varies from study to study and produces a small-scale data set. AH-DB is designed to provide an easy and unified way to prepare such data, which is generated by identifying/mapping molecules in different Protein Data Bank (PDB) entries. Conformational transitions are identified based on a refined alignment scheme to overcome the challenge that many structures in the PDB database are only protein fragments and not complete proteins. There are 746,314 apo-holo pairs in AH-DB, which is about 30 times those in the second largest collection of similar data. AH-DB provides sophisticated interfaces for searching apo-holo structure pairs and exploring conformational transitions from apo structures to the corresponding holo structures.

Amemiya T., Koike R., Kidera A., Ota M.

Proteins are flexible molecules that undergo structural changes to function. The Protein Data Bank contains multiple entries for identical proteins determined under different conditions, e.g. with and without a ligand molecule, which provides important information for understanding the structural changes related to protein functions. We gathered 839 protein structural pairs of ligand-free and ligand-bound states from monomeric or homo-dimeric proteins, and constructed the Protein Structural Change DataBase (PSCDB). In the database, we focused on whether the motions were coupled with ligand binding. As a result, the protein structural changes were classified into seven classes, i.e. coupled domain motion (59 structural changes), independent domain motion (70), coupled local motion (125), independent local motion (135), burying ligand motion (104), no significant motion (311) and other type motion (35). PSCDB provides lists of each class. On each entry page, users can view detailed information about the motion, accompanied by a morphing animation of the structural changes. PSCDB is available at http://idp1.force.cs.is.nagoya-u.ac.jp/pscdb/.

Marsh J., Teichmann S.

Protein interactions are often accompanied by significant changes in conformation. We have analyzed the relationships between protein structures and the conformational changes they undergo upon binding. Based upon this, we introduce a simple measure, the relative solvent accessible surface area, which can be used to predict the magnitude of binding-induced conformational changes from the structures of either monomeric proteins or bound subunits. Applying this to a large set of protein complexes suggests that large conformational changes upon binding are common. In addition, we observe considerable enrichment of intrinsically disordered sequences in proteins predicted to undergo large conformational changes. Finally, we demonstrate that the relative solvent accessible surface area of monomeric proteins can be used as a simple proxy for protein flexibility. This reveals a powerful connection between the flexibility of unbound proteins and their binding-induced conformational changes, consistent with the conformational selection model of molecular recognition.

Amemiya T., Koike R., Fuchigami S., Ikeguchi M., Kidera A.

The causal relationship between protein structural change and ligand binding was classified and annotated for 839 nonredundant pairs of crystal structures in the Protein Data Bank—one with and the other without a bound low-molecular-weight ligand molecule. Protein structural changes were first classified into either domain or local motions depending on the size of the moving protein segments. Whether the protein motion was coupled with ligand binding was then evaluated based on the location of the ligand binding site and by application of the linear response theory of protein structural change. Protein motions coupled with ligand binding were further classified into either closure or opening motions. This classification revealed the following: (i) domain motions coupled with ligand binding are dominated by closure motions, which can be described by the linear response theory; (ii) local motions frequently accompany order–disorder or α-helix–coil conformational transitions ; and (iii) transferase activity (Enzyme Commission number 2) is the predominant function among coupled domain closure motions. This could be explained by the closure motion acting to insulate the reaction site of these enzymes from environmental water.

Qi G., Hayward S.

Conformational change induced by the binding of a substrate or coenzyme is a poorly understood stage in the process of enzyme catalysed reactions. For enzymes that exhibit a domain movement, the conformational change can be clearly characterized and therefore the opportunity exists to gain an understanding of the mechanisms involved. The development of the non-redundant database of protein domain movements contains examples of ligand-induced domain movements in enzymes, but this valuable data has remained unexploited. The domain movements in the non-redundant database of protein domain movements are those found by applying the DynDom program to pairs of crystallographic structures contained in Protein Data Bank files. For each pair of structures cross-checking ligands in their Protein Data Bank files with the KEGG-LIGAND database and using methods that search for ligands that contact the enzyme in one conformation but not the other, the non-redundant database of protein domain movements was refined down to a set of 203 enzymes where a domain movement is apparently triggered by the binding of a functional ligand. For these cases, ligand binding information, including hydrogen bonds and salt-bridges between the ligand and specific residues on the enzyme is presented in the context of dynamical information such as the regions that form the dynamic domains, the hinge bending residues, and the hinge axes. The presentation at a single website of data on interactions between a ligand and specific residues on the enzyme alongside data on the movement that these interactions induce, should lead to new insights into the mechanisms of these enzymes in particular, and help in trying to understand the general process of ligand-induced domain closure in enzymes. The website can be found at: http://www.cmp.uea.ac.uk/dyndom/enzymeList.do

Koska J., Spassov V.Z., Maynard A.J., Yan L., Austin N., Flook P.K., Venkatachalam C.M.

We describe a method for docking a ligand into a protein receptor while allowing flexibility of the protein binding site. The method employs a multistep procedure that begins with the generation of protein and ligand conformations. An initial placement of the ligand is then performed by computing binding site hotspots. This initial placement is followed by a protein side-chain refinement stage that models protein flexibility. The final step of the process is an energy minimization of the ligand pose in the presence of the rigid receptor. Thus the algorithm models flexibility of the protein at two stages, before and after ligand placement. We validated this method by performing docking and cross docking studies of eight protein systems for which crystal structures were available for at least two bound ligands. The resulting rmsd values of the 21 docked protein−ligand complexes showed values of 2 Å or less for all but one of the systems examined. The method has two critical benefits for high throughput virtual screening studies. First, no user intervention is required in the docking once the initial binding site selection has been made in the protein. Second, the initial protein conformation generation needs to be performed only once for a given binding region. Also, the method may be customized in various ways depending on the particular scenario in which dockings are being performed. Each of the individual steps of the method is fully independent making it straightforward to explore different variants of the high level workflow to further improve accuracy and performance.

May A., Zacharias M.

Efficient treatment of conformational changes during docking of drug-like ligands to receptor molecules is a major computational challenge. A new docking methodology has been developed that includes ligand flexibility and both global backbone flexibility and side chain flexibility of the protein receptor. Whereas side chain flexibility is based on a discrete rotamer approach, global backbone conformational changes are modeled by relaxation in a few precalculated soft collective degrees of freedom of the receptor. The method was applied to docking of several known cyclin dependent kinase 2 inhibitors to the unbound kinase structure and to cross-docking of inhibitors to several bound kinase structures. Significant improvement of ranking and deviation of predicted binding geometries from experiment was obtained compared to docking to a rigid receptor. The inclusion of only the soft collective degrees of freedom during docking resulted in improved docking performance at a very modest increase (doubling) of the computational demand.

Reynolds C.H., Tounge B.A., Bembenek S.D.

Ligand efficiency (i.e., potency/size) has emerged as an important metric in drug discovery. In general, smaller, more efficient ligands are believed to have improved prospects for good drug properties (e.g., bioavailability). Our analysis of thousands of ligands across a variety of targets shows that ligand efficiency is dependent on ligand size with smaller ligands having greater efficiencies, on average, than larger ligands. We propose two primary causes for this size dependence: the inevitable reduction in the quality of fit between ligand and receptor as the ligand becomes larger and more complex and the reduction in accessible ligand surface area on a per atom basis as size increases. These results have far-ranging implications for analysis of high-throughput screening hits, fragment-based approaches to drug discovery, and even computational models of potency.

Zhao Y., Sanner M.F.

In this work, we validate and analyze the results of previously published cross docking experiments and classify failed dockings based on the conformational changes observed in the receptors. We show that a majority of failed experiments (i.e. 25 out of 33, involving four different receptors: cAPK, CDK2, Ricin and HIVp) are due to conformational changes in side chains near the active site. For these cases, we identify the side chains to be made flexible during docking calculation by superimposing receptors and analyzing steric overlap between various ligands and receptor side chains. We demonstrate that allowing these side chains to assume rotameric conformations enables the successful cross docking of 19 complexes (ligand all atom RMSD < 2.0 Å) using our docking software FLIPDock. The number of side receptor side chains interacting with a ligand can vary according to the ligand’s size and shape. Hence, when starting from a complex with a particular ligand one might have to extend the region of potential interacting side chains beyond the ones interacting with the known ligand. We discuss distance-based methods for selecting additional side chains in the neighborhood of the known active site. We show that while using the molecular surface to grow the neighborhood is more efficient than Euclidian-distance selection, the number of side chains selected by these methods often remains too large and additional methods for reducing their count are needed. Despite these difficulties, using geometric constraints obtained from the network of bonded and non-bonded interactions to rank residues and allowing the top ranked side chains to be flexible during docking makes 22 out of 25 complexes successful.

Brylinski M., Skolnick J.

It is well known that ligand binding and release may induce a wide range of structural changes in a receptor protein, varying from small movements of loops or side chains in the binding pocket to large-scale domain hinge-bending and shear motions or even partial unfolding that facilitates the capture and release of a ligand. An interesting question is what in general are the conformational changes triggered by ligand binding? The aim of this work is analyze the magnitude of structural changes in a protein resulting from ligand binding to assess if the state of ligand binding needs to be included in template-based protein structure prediction algorithms. To address this issue, a nonredundant dataset of 521 paired protein structures in the ligand-free and ligand-bound form was created and used to estimate the degree of both local and global structure similarity between the apo and holo forms. In most cases, the proteins undergo relatively small conformational rearrangements of their tertiary structure upon ligand binding/release (most root-mean-square-deviations from native, RMSD, are

Damm K.L., Carlson H.A.

Receptor flexibility must be incorporated into structure-based drug design in order to portray a more accurate representation of a protein in solution. Our approach is to generate pharmacophore models based on multiple conformations of a protein and is very similar to solvent mapping of hot spots. Previously, we had success using computer-generated conformations of apo human immunodeficiency virus-1 protease (HIV-1p). Here, we examine the use of an NMR ensemble versus a collection of crystal structures, and we compare back to our previous study based on computer-generated conformations. To our knowledge, this is the first direct comparison of an NMR ensemble and a collection of crystal structures to incorporate protein flexibility in structure-based drug design. To provide an accurate comparison between the experimental sources, we used bound structures for our multiple protein structure (MPS) pharmacophore models. The models from an NMR ensemble and a collection of crystal structures were both able to discriminate known HIV-1p inhibitors from decoy molecules and displayed superior performance over models created from single conformations of the protein. Although the active-site conformations were already predefined by bound ligands, the use of MPS allows us to overcome the cross-docking problem and generate a model that does not simply reproduce the chemical characteristics of a specific ligand class. We show that there is more structural variation between 28 structures in an NMR ensemble than 90 crystal structures bound to a variety of ligands. MPS models from both sources performed well, but the model determined using the NMR ensemble appeared to be the most general yet accurate representation of the active site. This work encourages the use of NMR models in structure-based design.

Gunasekaran K., Nussinov R.

Proteins are dynamic molecules and often undergo conformational change upon ligand binding. It is widely accepted that flexible loop regions have a critical functional role in enzymes. Lack of consideration of binding site flexibility has led to failures in predicting protein functions and in successfully docking ligands with protein receptors. Here we address the question: which sequence and structural features distinguish the structurally flexible and rigid binding sites? We analyze high-resolution crystal structures of ligand bound (holo) and free (apo) forms of 41 proteins where no conformational change takes place upon ligand binding, 35 examples with moderate conformational change, and 22 cases where a large conformational change has been observed. We find that the number of residue-residue contacts observed per-residue (contact density) does not distinguish flexible and rigid binding sites, suggesting a role for specific interactions and amino acids in modulating the conformational changes. Examination of hydrogen bonding and hydrophobic interactions reveals that cases that do not undergo conformational change have high polar interactions constituting the binding pockets. Intriguingly, the large, aromatic amino acid tryptophan has a high propensity to occur at the binding sites of examples where a large conformational change has been noted. Further, in large conformational change examples, hydrophobic-hydrophobic, aromatic-aromatic, and hydrophobic-polar residue pair interactions are dominant. Further analysis of the Ramachandran dihedral angles (phi, psi) reveals that the residues adopting disallowed conformations are found in both rigid and flexible cases. More importantly, the binding site residues adopting disallowed conformations clustered narrowly into two specific regions of the L-Ala Ramachandran map. Examination of the dihedral angles changes upon ligand binding shows that the magnitude of phi, psi changes are in general minimal, although some large changes particularly between right-handed alpha-helical and extended conformations are seen. Our work further provides an account of conformational changes in the dihedral angles space. The findings reported here are expected to assist in providing a framework for predicting protein-ligand complexes and for template-based prediction of protein function.

Ejiohuo O., Bajia D., Pawlak J., Szczepankiewicz A.

FK506-binding protein 51 (FKBP51 or FKBP5) serves as a crucial stress modulator implicated in mental disorders, presenting a potential target for intervention. Inhibitors like SAFit2, rapamycin, and tacrolimus exhibit promising interactions with this protein. Despite these advances, challenges persist in diversifying FKBP5 ligands, prompting further exploration of interaction partners. Hence, this study aims to identify other potential ligands. Employing molecular docking, we generated complexes with various ligands (rapamycin, tacrolimus, SAFit2-Selective antagonist of FKBP51 by induced fit, ascomycin, pimecrolimus, rosavin, salidroside, curcumin, apigenin, uvaricin, ruscogenin, neoruscogenin, pumicalagin, castalagin, and grandinin). We identified the top 3 best ligands, of which ruscogenin and neoruscogenin had notable abilities to cross the blood-brain barrier and have high gastrointestinal absorption, like curcumin. Toxicity predictions show ruscogenin and neoruscogenin to be the least toxic based on oral toxicity classification (Class VI). Tyrosine (Tyr113) formed consistent interactions with all ligands in the complex, reinforcing their potential and involvement in stress modulation. Molecular dynamic (MD) simulation validated strong interactions between our three key ligands and FKBP5 protein and provided an understanding of the stability of the complex. The binding free energy (ΔG) of the best ligands (based on pharmacological properties) from MD simulation analysis is -31.78 kcal/mol for neoruscogenin, -30.41 kcal/mol for ruscogenin, and -27.6 kcal/mol for curcumin. These molecules, therefore, can serve as therapeutic molecules or biomarkers for research in stress-impacted mental disorders. While offering therapeutic implications for mental disorders by attenuating stress impact, it is crucial to emphasize that these ligands’ transition to clinical applications necessitates extensive experimental research, including clinical trials, to unravel the intricate molecular and neural pathways involved in these interactions.

Giordano D., D’Arminio N., Marabotti A., Facchiano A.

Raisinghani N., Parikh V., Foley B., Verkhivker G.

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2, which combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with the view that the intrinsic dynamics of allosteric proteins are defined by the structural topology of the fold and favor conserved conformational motions driven by soft modes. Our findings provide evidence that AlphaFold2 combined with randomized alanine sequence masking can yield accurate and consistent results in predicting moderate conformational adjustments between apo and holo states, especially for proteins with localized changes upon ligand binding. For large hinge-like domain movements, the proposed approach can predict functional conformations characteristic of both apo and ligand-bound holo ensembles in the absence of ligand information. These results are relevant for using this AlphaFold adaptation for probing conformational selection mechanisms according to which proteins can adopt multiple conformations, including those that are competent for ligand binding. The results of this study indicate that robust modeling of functional protein states may require more accurate characterization of flexible regions in functional conformations and the detection of high-energy conformations. By incorporating a wider variety of protein structures in training datasets, including both apo and holo forms, the model can learn to recognize and predict the structural changes that occur upon ligand binding.

Matinja A.I., Kamarudin N.H., Leow A.T., Oslan S.N., Ali M.S.

Cold-active enzymes have recently gained popularity because of their high activity at lower temperatures than their mesophilic and thermophilic counterparts, enabling them to withstand harsh reaction conditions and enhance industrial processes. Cold-active lipases are enzymes produced by psychrophiles that live and thrive in extremely cold conditions. Cold-active lipase applications are now growing in the detergency, synthesis of fine chemicals, food processing, bioremediation, and pharmaceutical industries. The cold adaptation mechanisms exhibited by these enzymes are yet to be fully understood. Using phylogenetic analysis, and advanced deep learning-based protein structure prediction tool Alphafold2, we identified an evolutionary processes in which a conserved cold-active-like motif is presence in a distinct subclade of the tree and further predicted and simulated the three-dimensional structure of a putative cold-active lipase with the cold active motif, Glalip03, from Glaciozyma antarctica PI12. Molecular dynamics at low temperatures have revealed global stability over a wide range of temperatures, flexibility, and the ability to cope with changes in water and solvent entropy. Therefore, the knowledge we uncover here will be crucial for future research into how these low-temperature-adapted enzymes maintain their overall flexibility and function at lower temperatures.

Morcos C.A., Haiba N.S., Bassily R.W., Abu-Serie M.M., El-Yazbi A.F., Soliman O.A., Khattab S.N., Teleb M.

Dong C., Huang Y., Lin X., Zhang H., Gao Y.Q.

Raisinghani N., Parikh V., Foley B., Verkhivker G.

AbstractProteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2 that combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with the view that the intrinsic dynamics of allosteric proteins is defined by the structural topology of the fold and favors conserved conformational motions driven by soft modes. Our findings support the notion that AlphaFold2 approaches can yield reasonable accuracy in predicting minor conformational adjustments between apo and holo states, especially for proteins with moderate localized changes upon ligand binding. However, for large, hinge-like domain movements, AlphaFold2 tends to predict the most stable domain orientation which is typically the apo form rather than the full range of functional conformations characteristic of the holo ensemble. These results indicate that robust modeling of functional protein states may require more accurate characterization of flexible regions in functional conformations and detection of high energy conformations. By incorporating a wider variety of protein structures in training datasets including both apo and holo forms, the model can learn to recognize and predict the structural changes that occur upon ligand binding.

Feidakis C.P., Krivak R., Hoksza D., Novotny M.

A single protein structure is rarely sufficient to capture the conformational variability of a protein. Both bound and unbound (holo and apo) forms of a protein are essential for understanding its geometry and making meaningful comparisons. Nevertheless, docking or drug design studies often still consider only single protein structures in their holo form, which are for the most part rigid. With the recent explosion in the field of structural biology, large, curated datasets are urgently needed. Here, we use a previously developed application (AHoJ) to perform a comprehensive search for apo-holo pairs for 468,293 biologically relevant protein-ligand interactions across 27,983 proteins. In each search, the binding pocket is captured and mapped across existing structures within the same UniProt, and the mapped pockets are annotated as apo or holo, based on the presence or absence of ligands. We assemble the results into a database, AHoJ-DB (www.apoholo.cz/db), that captures the variability of proteins with identical sequences, thereby exposing the agents responsible for the observed differences in geometry. We report several metrics for each annotated pocket, and we also include binding pockets that form at the interface of multiple chains. Analysis of the database shows that about 24% of the binding sites occur at the interface of two or more chains and that less than 50% of the total binding sites processed have an apo form in the PDB. These results can be used to train and evaluate predictors, discover potentially druggable proteins, and reveal protein- and ligand-specific relationships that were previously obscured by intermittent or partial data. Availability: http://apoholo.cz/db

Paggi J.M., Pandit A., Dror R.O.

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target—for example, a protein—docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

Khachatryan H., Matevosyan M., Harutyunyan V., Gevorgyan S., Shavina A., Tirosyan I., Gabrielyan Y., Ayvazyan M., Bozdaganyan M., Fakhar Z., Gharaghani S., Zakaryan H.

AbstractThe coronavirus disease 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis with millions of confirmed cases and related deaths. The main protease (Mpro) of SARS-CoV-2 is crucial for viral replication and presents an attractive target for drug development. Despite the approval of some drugs, the search for effective treatments continues. In this study, we systematically evaluated 342 holo-crystal structures of Mpro to identify optimal conformations for structure-based virtual screening (SBVS). Our analysis revealed limited structural flexibility among the structures. Three docking programs, AutoDock Vina, rDock, and Glide were employed to assess the efficiency of virtual screening, revealing diverse performances across selected Mpro structures. We found that the structures 5RHE, 7DDC, and 7DPU (PDB Ids) consistently displayed the lowest EF, AUC, and BEDROCK scores. Furthermore, these structures demonstrated the worst pose prediction results in all docking programs. Two structural differences contribute to variations in docking performance: the absence of the S1 subsite in 7DDC and 7DPU, and the presence of a subpocket in the S2 subsite of 7DDC, 7DPU, and 5RHE. These findings underscore the importance of selecting appropriate Mpro conformations for SBVS, providing valuable insights for advancing drug discovery efforts.

Pulsford S.B., Outram M.A., Förster B., Rhodes T., Williams S.J., Badger M.R., Price G.D., Jackson C.J., Long B.M.

Cyanobacterial CO 2 concentrating mechanisms (CCMs) sequester a globally consequential proportion of carbon into the biosphere. Proteinaceous microcompartments, called carboxysomes, play a critical role in CCM function, housing two enzymes to enhance CO 2 fixation: carbonic anhydrase (CA) and Rubisco. Despite its importance, our current understanding of the carboxysomal CAs found in α-cyanobacteria, CsoSCA, remains limited, particularly regarding the regulation of its activity. Here, we present a structural and biochemical study of CsoSCA from the cyanobacterium Cyanobium sp. PCC7001. Our results show that the Cyanobium CsoSCA is allosterically activated by the Rubisco substrate ribulose-1,5-bisphosphate and forms a hexameric trimer of dimers. Comprehensive phylogenetic and mutational analyses are consistent with this regulation appearing exclusively in cyanobacterial α-carboxysome CAs. These findings clarify the biologically relevant oligomeric state of α-carboxysomal CAs and advance our understanding of the regulation of photosynthesis in this globally dominant lineage.

Zhu J., Gu Z., Pei J., Lai L.

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of proteinligand binding mode is essential....

Liu R., Clayton J., Shen M., Bhatnagar S., Shen J.

Ivanenkov Yan A., Evteev Sergei A., Malyshev Alexander S., Terentiev Victor A., Bezrukov Dmitry S., Ereshchenko Alexey V., Korzhenevskaya Anastasia A., Zagribelnyy Bogdan A., Shegay Petr V., Kaprin Andrey D.

The development of novel small drug molecules is a complex and important cross-disciplinary task. In the early stages of development, chemoinformatics and bioinformatics methods are routinely used to reduce the cost of finding a lead compound. Among the tools of medicinal chemistry, docking and molecular dynamics occupy a special place. These methods are used to predict the possible mechanism of binding of a potential ligand to a protein target. However, in order to perform a docking study, it is necessary to know the spatial structure of the protein under investigation. Although databases of crystallographic structures are available, the three-dimensional representations of many protein molecules have not been reported. There is therefore a need to model such three-dimensional conformations. Several computer algorithms have been published to solve this problem. AlphaFold is considered by the scientific community to be the most effective approach to predicting the three-dimensional structure of proteins. However, the scope of its application in medicinal chemistry, especially for virtual screening, remains unclear. This review describes methods for predicting the three-dimensional structure of a protein and provides representative examples of the use of AlphaFold for the design and rational selection of potential ligands. Special attention is given to publications presenting the results of experimental validation of the approach. On the basis of performed analysis, the main problems in the field and possible ways to solve them are formulated.The bibliography includes 154 references.

Tse L.H., Cheung S.T., Lee S., Wong Y.H.

Melatonin is a neuroendocrine hormone that regulates the circadian rhythm and many other physiological processes. Its functions are primarily exerted through two subtypes of human melatonin receptors, termed melatonin type-1 (MT1) and type-2 (MT2) receptors. Both MT1 and MT2 receptors are generally classified as Gi-coupled receptors owing to their well-recognized ability to inhibit cAMP accumulation in cells. However, it remains an enigma as to why melatonin stimulates cAMP production in a number of cell types that express the MT1 receptor. To address if MT1 can dually couple to Gs and Gi proteins, we employed a highly sensitive luminescent biosensor (GloSensorTM) to monitor the real-time changes in the intracellular cAMP level in intact live HEK293 cells that express MT1 and/or MT2. Our results demonstrate that the activation of MT1, but not MT2, leads to a robust enhancement on the forskolin-stimulated cAMP formation. In contrast, the activation of either MT1 or MT2 inhibited cAMP synthesis driven by the activation of the Gs-coupled β2-adrenergic receptor, which is consistent with a typical Gi-mediated response. The co-expression of MT1 with Gs enabled melatonin itself to stimulate cAMP production, indicating a productive coupling between MT1 and Gs. The possible existence of a MT1-Gs complex was supported through molecular modeling as the predicted complex exhibited structural and thermodynamic characteristics that are comparable to that of MT1-Gi. Taken together, our data reveal that MT1, but not MT2, can dually couple to Gs and Gi proteins, thereby enabling the bi-directional regulation of adenylyl cyclase to differentially modulate cAMP levels in cells that express different complements of MT1, MT2, and G proteins.