High-resolution prediction of protein helix positions and orientations

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction

Accurately predicting drug-target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug-target affinity prediction have been treated as separate aspects of drug-target interaction, with few methods combining these tasks within a unified deep learning system to accelerate drug development. In this study, we propose EMPDTA, an end-to-end framework that integrates protein pocket prediction and drug-target affinity prediction to provide a comprehensive understanding of drug-target interactions. The EMPDTA framework consists of three main modules: pocket online detection, multimodal representation learning for affinity prediction, and multi-task joint training. The performance and potential of the proposed framework have been validated across diverse benchmark datasets, achieving robust results in both tasks. Furthermore, the visualization results of the predicted pockets demonstrate accurate pocket detection, confirming the effectiveness of our framework.

Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors

Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.

In Silico Discovery of Small Molecule Modulators Targeting the Achilles' Heel of SARS-CoV-2 Spike Protein

The spike protein of SARS-CoV-2 has been a promising target for developing vaccines and therapeutics due to its crucial role in the viral entry process. Previously reported cryogenic electron microscopy (cryo-EM) structures have revealed that free fatty acids (FFA) bind with SARS-CoV-2 spike protein, stabilizing its closed conformation and reducing its interaction with the host cell target in vitro. Inspired by these, we utilized a structure-based virtual screening approach against the conserved FFA-binding pocket to identify small molecule modulators of SARS-CoV-2 spike protein, which helped us identify six hits with micromolar binding affinities. Further evaluation of their commercially available and synthesized analogs enabled us to discover a series of compounds with better binding affinities and solubilities. Notably, our identified compounds exhibited similar binding affinities against the spike proteins of the prototypic SARS-CoV-2 and a currently circulating Omicron BA.4 variant. Furthermore, the cryo-EM structure of the compound SPC-14 bound spike revealed that SPC-14 could shift the conformational equilibrium of the spike protein toward the closed conformation, which is human ACE2 (hACE2) inaccessible. Our identified small molecule modulators targeting the conserved FFA-binding pocket could serve as the starting point for the future development of broad-spectrum COVID-19 intervention treatments.

Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers

In this chapter the scale-consistent approach to the derivation of coarse-grained force fields developed in our laboratory is presented, in which the effective energy function originates from the potential of mean force of the system under consideration and embeds atomistically detailed interactions in the resulting energy terms through use of Kubo's cluster-cumulant expansion, appropriate selection of the major degrees of freedom to be averaged out in the derivation of analytical approximations to the energy terms, and appropriate expression of the interaction energies at the all-atom level in these degrees of freedom. Our approach enables the developers to find correct functional forms of the effective coarse-grained energy terms, without having to import them from all-atom force fields or deriving them on a heuristic basis. In particular, the energy terms derived in such a way exhibit correct dependence on coarse-grained geometry, in particular on site orientation. Moreover, analytical formulas for the multibody (correlation) terms, which appear to be crucial for coarse-grained modeling of many of the regular structures such as, e.g., protein α-helices and β-sheets, can be derived in a systematic way. Implementation of the developed theory to the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules, which consists of the UNRES (for proteins), NARES-2P (for nucleic acids), and SUGRES-1P (for polysaccharides) components, and is being developed in our laboratory is described. Successful applications of UNICORN to the prediction of protein structure, simulating the folding and stability of proteins and nucleic acids, and solving biological problems are discussed.

A survey of the role of nitrile groups in protein–ligand interactions

In classical medicinal chemistry, nitrile groups were commonly considered as bioisosteres of carbonyl, hydroxyl and carboxyl groups, as well as halogen atoms. However, there is a lack of in-depth understanding about the structural and energetic characteristics of nitrile groups in protein–ligand interactions. Here, we have surveyed the Protein Data Bank and ChEMBL databases with the goal of characterizing such protein–ligand interactions for nitrile-containing compounds. We discuss the versatile roles of nitrile groups in improving binding affinities, and give special attention to examples of displacing and mimicking binding-site waters by nitrile groups. We expect that this review article will further inspire medicinal chemists to exploit nitrile groups rationally in structure-based drug design.

In Silico Identification of a Novel Hinge-Binding Scaffold for Kinase Inhibitor Discovery

To explore novel kinase hinge-binding scaffolds, we carried out structure-based virtual screening against p38α MAPK as a model system. With the assistance of developed kinase-specific structural filters, we identify a novel lead compound that selectively inhibits a panel of kinases with threonine as the gatekeeper residue, including BTK and LCK. These kinases play important roles in lymphocyte activation, which encouraged us to design novel kinase inhibitors as drug candidates for ameliorating inflammatory diseases and cancers. Therefore, we chemically modified our substituted triazole-class lead compound to improve the binding affinity and selectivity via a "minimal decoration" strategy, which resulted in potent and selective kinase inhibitors against LCK (18 nM) and BTK (8 nM). Subsequent crystallographic experiments validated our design. These rationally designed compounds exhibit potent on-target inhibition against BTK in B cells or LCK in T cells, respectively. Our work demonstrates that structure-based virtual screening can be applied to facilitate the development of novel chemical entities in crowded chemical space in the field of kinase inhibitor discovery.

Structural insights into the γ-lactamase activity and substrate enantioselectivity of an isochorismatase-like hydrolase from Microbacterium hydrocarbonoxydans

(+)-γ-lactamase catalyzes the specific hydrolysis of (+)-γ-lactam out of the racemic γ-lactam (2-Azabicyclo[2.2.1]hept-5-en-3-one) to leave optically pure (−)-γ-lactam, which is the key building block of antiviral drugs such as carbovir and abacavir. However, no structural data has been reported on how the enzymes bind the γ-lactams and achieve their enantioselectivities. We previously identified an isochorismatase-like hydrolase (IHL, Mh33H4-5540) with (+)-γ-lactamase activity, which constitutes a novel family of γ-lactamase. Here, we first discovered that this enzyme actually hydrolyzed both (+)- and (−)-γ-lactam, but with apparently different specificities. We determined the crystal structures of the apo-form, (+)-γ-lactam bound, and (−)-γ-lactam bound forms of the enzyme. The structures showed that the binding sites of both (+) and (−)-γ-lactam resemble those of IHLs, but the “cover” loop conserved in IHLs is lacking in the enzyme, probably resulting in its incomplete enantioselectivity. Structural, biochemical, and molecular dynamics simulation studies demonstrated that the steric clash caused by the binding-site residues, especially the side-chain of Cys111 would reduce the binding affinity of (−)-γ-lactam and possibly the catalytic efficiency, which might explain the different catalytic specificities of the enantiomers of γ-lactam. Our results would facilitate the directed evolution and application of Mh33H4-5540 in antiviral drug synthesis.

Free energy calculation provides insight into the action mechanism of selective PARP-1 inhibitor

Selective poly (ADP-ribose) polymerase (PARP)-1 inhibitor represents promising therapy against cancers with a good balance between efficacy and safety. Owing to the conserved structure between PARP-1 and PARP-2, most of the clinical and experimental drugs show equivalent inhibition against both targets. Most recently, it's disclosed a highly selective PARP-1 inhibitor (NMS-P118) with promising pharmacokinetic properties. Herein, we combined molecular simulation with free energy calculation to gain insights into the selective mechanism of NMS-P118. Our results suggest the reduction of binding affinity for PARP-2 is attributed to the unfavorable conformational change of protein, which is accompanied by a significant energy penalty. Alanine-scanning mutagenesis study further reveals the important role for a tyrosine residue of donor loop (Tyr889(PARP-1) and Tyr455(PARP-2)) in contributing to the ligand selectivity. Retrospective structural analysis indicates the ligand-induced movement of Tyr455(PARP-2) disrupts the intra-molecule hydrogen bonding network, which partially accounts for the "high-energy" protein conformation in the presence of NMS-P118. Interestingly, such effect isn't observed in other non-selective PARP inhibitors including BMN673 and A861695, which validates the computational prediction. Our work provides energetic insight into the subtle variations in the crystal structures and could facilitate rational design of new selective PARP inhibitor.

Structure-Based Discovery of Novel and Selective 5-Hydroxytryptamine 2B Receptor Antagonists for the Treatment of Irritable Bowel Syndrome

Here we employed structure-based ligand discovery techniques to explore a recently determined crystal structure of the 5-hydroxytryptamine 2B (5-HT2B) receptor. Ten compounds containing a novel chemical scaffold were identified; among them, seven molecules were active in cellular function assays with the most potent one exhibiting an IC50 value of 27.3 nM. We then systematically probed the binding characteristics of this scaffold by designing, synthesizing, and testing a series of structural modifications. The structure-activity relationship studies strongly support our predicted binding model. The binding profiling across a panel of 11 5-HT receptors indicated that these compounds are highly selective for the 5-HT2B receptor. Oral administration of compound 15 (30 mg/kg) produced significant attenuation of visceral hypersensitivity in a rat model of irritable bowel syndrome (IBS). We expect this novel scaffold will serve as the foundation for the development of 5-HT2B antagonists for the treatment of IBS.

Discovery of 2-Acylaminothiophene-3-Carboxamides as Multitarget Inhibitors for BCR-ABL Kinase and Microtubules

The emergence of drug resistance of the BCR-ABL kinase inhibitor imatinib, especially toward the T315I gatekeeper mutation, poses a great challenge to targeted therapy in treating chronic myeloid leukemia (CML) patients. To discover novel inhibitors against drug-resistant CML bearing T315I mutation, we applied a physics-based hierarchical virtual screening approach to dock a large chemical library against ATP binding pockets of both wild-type (WT) and T315I mutant ABL kinases in a combinatorial fashion. This strategy automatically resulted in 87 compounds satisfying structural and energetic criteria of both WT and T315I mutant kinases. Among them, nine compounds, which share a common thiophene-based scaffold and adopt similar binding poses, were chosen for experimental testing and one of them was shown to have low micromolar inhibition activities against both WT and mutant ABL kinases. Structure-activity relationship analysis with a series of structural modifications based on 2-acylaminothiophene-3-carboxamide scaffold supports our predicted binding mode. Interestingly, the same chemical scaffold was also enriched in our previous virtual screening campaign against colchicine site of microtubules using the same computational protocol, which suggests our virtual screening strategy is capable of discovering small-molecule ligands targeting distinct protein binding sites without sharing any sequential and structural similarity. Furthermore, the multitarget inhibition activity of this class of compounds was assessed in cellular experiments. We expect that the 2-acylaminothiophene-3-carboxamide scaffold may serve as a promising starting point for developing multitarget inhibitors in cancer treatment by targeting both kinases and microtubules.

Accelerating molecular simulations of proteins using Bayesian inference on weak information

Atomistic molecular dynamics (MD) simulations of protein molecules are too computationally expensive to predict most native structures from amino acid sequences. Here, we integrate "weak" external knowledge into folding simulations to predict protein structures, given their sequence. For example, we instruct the computer "to form a hydrophobic core," "to form good secondary structures," or "to seek a compact state." This kind of information has been too combinatoric, nonspecific, and vague to help guide MD simulations before. Within atomistic replica-exchange molecular dynamics (REMD), we develop a statistical mechanical framework, modeling using limited data with coarse physical insight(s) (MELD + CPI), for harnessing weak information. As a test, we apply MELD + CPI to predict the native structures of 20 small proteins. MELD + CPI samples to within less than 3.2 Å from native for all 20 and correctly chooses the native structures (<4 Å) for 15 of them, including ubiquitin, a millisecond folder. MELD + CPI is up to five orders of magnitude faster than brute-force MD, satisfies detailed balance, and should scale well to larger proteins. MELD + CPI may be useful where physics-based simulations are needed to study protein mechanisms and populations and where we have some heuristic or coarse physical knowledge about states of interest.

Predicting Molecular Targets for Small-Molecule Drugs with a Ligand-Based Interaction Fingerprint Approach

The computational prediction of molecular targets for small-molecule drugs remains a great challenge. Herein we describe a ligand-based interaction fingerprint (LIFt) approach for target prediction. Together with physics-based docking and sampling methods, we assessed the performance systematically by modeling the polypharmacology of 12 kinase inhibitors in three stages. First, we examined the capacity of this approach to differentiate true targets from false targets with the promiscuous binder staurosporine, based on native complex structures. Second, we performed large-scale profiling of kinase selectivity on the clinical drug sunitinib by means of computational simulation. Third, we extended the study beyond kinases by modeling the cross-inhibition of bromodomain-containing protein 4 (BRD4) for 10 well-established kinase inhibitors. On this basis, we made prospective predictions by exploring new kinase targets for the anticancer drug candidate TN-16, originally known as a colchicine site binder and microtubule disruptor. As a result, p38α was highlighted from a panel of 187 different kinases. Encouragingly, our prediction was validated by an in vitro kinase assay, which showed TN-16 as a low-micromolar p38α inhibitor. Collectively, our results suggest the promise of the LIFt approach in predicting potential targets for small-molecule drugs.

Identification of novel JMJD2A inhibitor scaffold using shape and electrostatic similarity search combined with docking method and MM-GBSA approach

We present a hierarchical workflow combining shape- and electrostatic-based virtual screening for the identification of novel Jumonji domain-containing protein 2A (JMJD2A) inhibitors.

Structure and specificity of the bacterial cysteine methyltransferase effector NleE suggests a novel substrate in human DNA repair pathway

Enteropathogenic E. coli (EPEC) and related enterobacteria rely on a type III secretion system (T3SS) effector NleE to block host NF-κB signaling. NleE is a first in class, novel S-adenosyl-L-methionine (SAM)-dependent methyltransferase that methylates a zinc-coordinating cysteine in the Npl4-like Zinc Finger (NZF) domains in TAB2/3 adaptors in the NF-κB pathway, but its mechanism of action and other human substrates are unknown. Here we solve crystal structure of NleE-SAM complex, which reveals a methyltransferase fold different from those of known ones. The SAM, cradled snugly at the bottom of a deep and narrow cavity, adopts a unique conformation ready for nucleophilic attack by the methyl acceptor. The substrate NZF domain can be well docked into the cavity, and molecular dynamic simulation indicates that Cys673 in TAB2-NZF is spatially and energetically favorable for attacking the SAM. We further identify a new NleE substrate, ZRANB3, that functions in PCNA binding and remodeling of stalled replication forks at the DNA damage sites. Specific inactivation of the NZF domain in ZRANB3 by NleE offers a unique opportunity to suggest that ZRANB3-NZF domain functions in DNA repair processes other than ZRANB3 recruitment to DNA damage sites. Our analyses suggest a novel and unexpected link between EPEC infection, virulence proteins and genome integrity.

Pathogens often manipulate host functions by posttranslational modifications such as ubiquitination and methylation. The NF-κB pathway is most critical for immune defense against infection, thereby frequently targeted by bacterial virulence factors. NleE, a virulence effector from EPEC, is a SAM-dependent methyltransferase that modifies a zinc-finger cysteine in TAB2/3 in the NF-κB pathway. NleE is not homologous to any known methyltransferases. We present the crystal structure of SAM-bound NleE that shows a novel methyltransferase fold with a unique SAM-binding mode. Computational docking and molecular dynamics simulation illustrate a structural and chemical mechanism underlying NleE recognition of the NZF and catalyzing site-specific cysteine methylation. Subsequent substrate specificity analyses identify an N-terminal region in TAB3 required for efficient NleE recognition as well as another NZF protein ZRANB3 being a new substrate of NleE. NleE-catalyzed cysteine methylation also disrupts the ubiquitin chain-binding of ZRANB3-NZF domain, providing new insights into ZRANB3-NZF functioning in DNA damage repair. These results reinforce the idea of harnessing bacterial effectors as a tool for dissecting eukaryotic functions.

Broad-spectrum allosteric inhibition of herpesvirus proteases

Herpesviruses rely on a homodimeric protease for viral capsid maturation. A small molecule, DD2, previously shown to disrupt dimerization of Kaposi’s sarcoma-associated herpesvirus protease (KSHV Pr) by trapping an inactive monomeric conformation and two analogues generated through carboxylate bioisosteric replacement (compounds 2 and 3) were shown to inhibit the associated proteases of all three human herpesvirus (HHV) subfamilies (α, β, and γ). Inhibition data reveal that compound 2 has potency comparable to or better than that of DD2 against the tested proteases. Nuclear magnetic resonance spectroscopy and a new application of the kinetic analysis developed by Zhang and Poorman [Zhang, Z. Y., Poorman, R. A., et al. (1991) J. Biol. Chem. 266, 15591–15594] show DD2, compound 2, and compound 3 inhibit HHV proteases by dimer disruption. All three compounds bind the dimer interface of other HHV proteases in a manner analogous to binding of DD2 to KSHV protease. The determination and analysis of cocrystal structures of both analogues with the KSHV Pr monomer verify and elaborate on the mode of binding for this chemical scaffold, explaining a newly observed critical structure–activity relationship. These results reveal a prototypical chemical scaffold for broad-spectrum allosteric inhibition of human herpesvirus proteases and an approach for the identification of small molecules that allosterically regulate protein activity by targeting protein–protein interactions.

Evaluation and application of MD-PB/SA in structure-based hierarchical virtual screening

Molecular dynamics (MD) based molecular mechanics Poisson-Boltzmann and surface area (MM-PB/SA) calculation (MD-PB/SA) has been widely used to estimate binding free energies for receptor-ligand complexes. While numerous reports have focused on assessing accuracy and efficiency, fewer studies have paid attention to performance in lead discovery. In the present study, we report a critical evaluation of MD-PB/SA in hierarchical virtual screening (HVS) both theoretically and practically. It is shown that based on native poses, MD-PB/SA could be well applied to predict the relative binding energy for both congeneric and diverse ligands for different protein targets. However, there is a limitation for MD-PB/SA to distinguish the native pose of one ligand from the artificial pose of another when a huge difference exists between two molecules. By combining a physics-based scoring function with a knowledge-based structural filter, we improve the predictability and validate the practical use of MD-PB/SA in lead discovery by identifying novel inhibitors of p38 MAP kinase. We also expand our study to other protein targets such as HIV-1 RT and NA to assess the general validity of MD-PB/SA.

Protein engineering and the use of molecular modeling and simulation: the case of heterodimeric Fc engineering

Computational and structure guided methods can make significant contributions to the development of solutions for difficult protein engineering problems, including the optimization of next generation of engineered antibodies. In this paper, we describe a contemporary industrial antibody engineering program, based on hypothesis-driven in silico protein optimization method. The foundational concepts and methods of computational protein engineering are discussed, and an example of a computational modeling and structure-guided protein engineering workflow is provided for the design of best-in-class heterodimeric Fc with high purity and favorable biophysical properties. We present the engineering rationale as well as structural and functional characterization data on these engineered designs.

Roles for ordered and bulk solvent in ligand recognition and docking in two related cavities

A key challenge in structure-based discovery is accounting for modulation of protein-ligand interactions by ordered and bulk solvent. To investigate this, we compared ligand binding to a buried cavity in Cytochrome c Peroxidase (CcP), where affinity is dominated by a single ionic interaction, versus a cavity variant partly opened to solvent by loop deletion. This opening had unexpected effects on ligand orientation, affinity, and ordered water structure. Some ligands lost over ten-fold in affinity and reoriented in the cavity, while others retained their geometries, formed new interactions with water networks, and improved affinity. To test our ability to discover new ligands against this opened site prospectively, a 534,000 fragment library was docked against the open cavity using two models of ligand solvation. Using an older solvation model that prioritized many neutral molecules, three such uncharged docking hits were tested, none of which was observed to bind; these molecules were not highly ranked by the new, context-dependent solvation score. Using this new method, another 15 highly-ranked molecules were tested for binding. In contrast to the previous result, 14 of these bound detectably, with affinities ranging from 8 µM to 2 mM. In crystal structures, four of these new ligands superposed well with the docking predictions but two did not, reflecting unanticipated interactions with newly ordered waters molecules. Comparing recognition between this open cavity and its buried analog begins to isolate the roles of ordered solvent in a system that lends itself readily to prospective testing and that may be broadly useful to the community.

Ab initio modeling and experimental assessment of Janus Kinase 2 (JAK2) kinase-pseudokinase complex structure

The Janus Kinase 2 (JAK2) plays essential roles in transmitting signals from multiple cytokine receptors, and constitutive activation of JAK2 results in hematopoietic disorders and oncogenesis. JAK2 kinase activity is negatively regulated by its pseudokinase domain (JH2), where the gain-of-function mutation V617F that causes myeloproliferative neoplasms resides. In the absence of a crystal structure of full-length JAK2, how JH2 inhibits the kinase domain (JH1), and how V617F hyperactivates JAK2 remain elusive. We modeled the JAK2 JH1-JH2 complex structure using a novel informatics-guided protein-protein docking strategy. A detailed JAK2 JH2-mediated auto-inhibition mechanism is proposed, where JH2 traps the activation loop of JH1 in an inactive conformation and blocks the movement of kinase αC helix through critical hydrophobic contacts and extensive electrostatic interactions. These stabilizing interactions are less favorable in JAK2-V617F. Notably, several predicted binding interfacial residues in JH2 were confirmed to hyperactivate JAK2 kinase activity in site-directed mutagenesis and BaF3/EpoR cell transformation studies. Although there may exist other JH2-mediated mechanisms to control JH1, our JH1-JH2 structural model represents a verifiable working hypothesis for further experimental studies to elucidate the role of JH2 in regulating JAK2 in both normal and pathological settings.

Prediction of Long Loops with Embedded Secondary Structure using the Protein Local Optimization Program

Robust homology modeling to atomic-level accuracy requires in the general case successful prediction of protein loops containing small segments of secondary structure. Further, as loop prediction advances to success with larger loops, the exclusion of loops containing secondary structure becomes awkward. Here, we extend the applicability of the Protein Local Optimization Program (PLOP) to loops up to 17 residues in length that contain either helical or hairpin segments. In general, PLOP hierarchically samples conformational space and ranks candidate loops with a high-quality molecular mechanics force field. For loops identified to possess α-helical segments, we employ an alternative dihedral library composed of (ϕ,ψ) angles commonly found in helices. The alternative library is searched over a user-specified range of residues that define the helical bounds. The source of these helical bounds can be from popular secondary structure prediction software or from analysis of past loop predictions where a propensity to form a helix is observed. Due to the maturity of our energy model, the lowest energy loop across all experiments can be selected with an accuracy of sub-Ångström RMSD in 80% of cases, 1.0 to 1.5 Å RMSD in 14% of cases, and poorer than 1.5 Å RMSD in 6% of cases. The effectiveness of our current methods in predicting hairpin-containing loops is explored with hairpins up to 13 residues in length and again reaching an accuracy of sub-Ångström RMSD in 83% of cases, 1.0 to 1.5 Å RMSD in 10% of cases, and poorer than 1.5 Å RMSD in 7% of cases. Finally, we explore the effect of an imprecise surrounding environment, in which side chains, but not the backbone, are initially in perturbed geometries. In these cases, loops perturbed to 3Å RMSD from the native environment were restored to their native conformation with sub-Ångström RMSD.

Discovery of novel tubulin inhibitors via structure-based hierarchical virtual screening

To discover novel tubulin inhibitors, we performed structure-based virtual screening against the colchicine binding pocket. In combination with a hierarchical docking and scoring procedure, the structural information of an additional subpocket in colchicine site was applied to filter out the undesired docking hits. This strategy automatically resulted in 63 candidates meeting the structural and energetic criteria from a screening library containing approximately 100,000 diverse druglike compounds. Among them, nine molecules were chosen for experimental validation, which all share the similar binding pose and contain an enriched scaffold bearing thiophene core. Encouragingly, five compounds are active in tubulin polymerization assay. The most potent inhibitor, 2-(2-fluorobenzamido)-3-carboxamide-4,5-dimethylthiophene, is structurally distinct to any known colchicine site binders and has higher ligand efficiency than colchicine. On the basis of its predicted binding pose, we systematically probed its binding characteristics by testing series of structural modifications. The obtained structure-activity relationship results are consistent with our binding model, and the inhibition activities of two analogues are improved by 2-fold. We expect that the novel structure discovered in the present study may serve as a starting point for developing tubulin inhibitors with improved efficacy and fewer side effects. We also expect that our hierarchical strategy may be generally applicable in structure-based virtual screening campaigns.

Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors

Of great interest in recent years has been computationally predicting the novel polypharmacology of drug molecules. Here, we applied an "induced-fit" protocol to improve the homology models of 5-HT(2A) receptor, and we assessed the quality of these models in retrospective virtual screening. Subsequently, we computationally screened the FDA approved drug molecules against the best induced-fit 5-HT(2A) models and chose six top scoring hits for experimental assays. Surprisingly, one well-known kinase inhibitor, sorafenib, has shown unexpected promiscuous 5-HTRs binding affinities, K(i) = 1959, 56, and 417 nM against 5-HT(2A), 5-HT(2B), and 5-HT(2C), respectively. Our preliminary SAR exploration supports the predicted binding mode and further suggests sorafenib to be a novel lead compound for 5HTR ligand discovery. Although it has been well-known that sorafenib produces anticancer effects through targeting multiple kinases, carefully designed experimental studies are desirable to fully understand whether its "off-target" 5-HTR binding activities contribute to its therapeutic efficacy or otherwise undesirable side effects.

Structure prediction of loops with fixed and flexible stems

The prediction of loop structures is considered one of the main challenges in the protein folding problem. Regardless of the dependence of the overall algorithm on the protein data bank, the flexibility of loop regions dictates the need for special attention to their structures. In this article, we present algorithms for loop structure prediction with fixed stem and flexible stem geometry. In the flexible stem geometry problem, only the secondary structure of three stem residues on either side of the loop is known. In the fixed stem geometry problem, the structure of the three stem residues on either side of the loop is also known. Initial loop structures are generated using a probability database for the flexible stem geometry problem, and using torsion angle dynamics for the fixed stem geometry problem. Three rotamer optimization algorithms are introduced to alleviate steric clashes between the generated backbone structures and the side chain rotamers. The structures are optimized by energy minimization using an all-atom force field. The optimized structures are clustered using a traveling salesman problem-based clustering algorithm. The structures in the densest clusters are then utilized to refine dihedral angle bounds on all amino acids in the loop. The entire procedure is carried out for a number of iterations, leading to improved structure prediction and refined dihedral angle bounds. The algorithms presented in this article have been tested on 3190 loops from the PDBSelect25 data set and on targets from the recently concluded CASP9 community-wide experiment.

Mutagenesis and functional studies with succinate dehydrogenase inhibitors in the wheat pathogen Mycosphaerella graminicola

A range of novel carboxamide fungicides, inhibitors of the succinate dehydrogenase enzyme (SDH, EC 1.3.5.1) is currently being introduced to the crop protection market. The aim of this study was to explore the impact of structurally distinct carboxamides on target site resistance development and to assess possible impact on fitness.

We used a UV mutagenesis approach in Mycosphaerella graminicola, a key pathogen of wheat to compare the nature, frequencies and impact of target mutations towards five subclasses of carboxamides. From this screen we identified 27 amino acid substitutions occurring at 18 different positions on the 3 subunits constituting the ubiquinone binding (Qp) site of the enzyme. The nature of substitutions and cross resistance profiles indicated significant differences in the binding interaction to the enzyme across the different inhibitors. Pharmacophore elucidation followed by docking studies in a tridimensional SDH model allowed us to propose rational hypotheses explaining some of the differential behaviors for the first time. Interestingly all the characterized substitutions had a negative impact on enzyme efficiency, however very low levels of enzyme activity appeared to be sufficient for cell survival. In order to explore the impact of mutations on pathogen fitness in vivo and in planta, homologous recombinants were generated for a selection of mutation types. In vivo, in contrast to previous studies performed in yeast and other organisms, SDH mutations did not result in a major increase of reactive oxygen species levels and did not display any significant fitness penalty. However, a number of Qp site mutations affecting enzyme efficiency were shown to have a biological impact in planta.

Using the combined approaches described here, we have significantly improved our understanding of possible resistance mechanisms to carboxamides and performed preliminary fitness penalty assessment in an economically important plant pathogen years ahead of possible resistance development in the field.

FOLD HELICAL PROTEINS BY ENERGY MINIMIZATION IN DIHEDRAL SPACE AND A DFIRE-BASED STATISTICAL ENERGY FUNCTION

Statistical energy functions are discrete (or stepwise) energy functions that lack van der Waals repulsion. As a result, they are often applied directly to a given structure (native or decoy) without further energy minimization being performed to the structure. However, the full benefit (or hidden defect) of an energy function cannot be revealed without energy minimization. This paper tests a recently developed, all-atom statistical energy function by energy minimization with a fixed secondary helical structure in dihedral space. This is accomplished by combining the statistical energy function based on a distance-scaled finite ideal-gas reference (DFIRE) state with a simple repulsive interaction and an improper torsion energy function. The energy function was used to minimize 2000 random initial structures of 41 small and medium-sized helical proteins in a dihedral space with a fixed helical region. Results indicate that near-native structures for most studied proteins can be obtained by minimization alone. The average minimum root-mean-squared distance (rmsd) from the native structure for all 41 proteins is 4.1 Å. The energy function (together with a simple clustering of similar structures) also makes a reasonable selection of near-native structures from minimized structures. The average rmsd value and the average rank for the best structure in the top five is 6.8 Å and 2.4, respectively. The accuracy of the structures sampled and the structure selections can be improved significantly with the removal of flexible terminal regions in rmsd calculations and in minimization and with the increase in the number of minimizations. The minimized structures form an excellent decoy set for testing other energy functions because most structures are well-packed with minimum hard-core overlaps with correct hydrophobic/hydrophilic partitioning. They are available online at .

Fluctuations of backbone torsion angles obtained from NMR-determined structures and their prediction

Protein molecules exhibit varying degrees of flexibility throughout their three-dimensional structures. Protein structural flexibility is often characterized by fluctuations in the Cartesian coordinate space. On the other hand, the protein backbone can be mostly defined by two torsion angles ϕ and ψ only. We introduce a new flexibility descriptor, backbone torsion-angle fluctuation derived from the variation of backbone torsion angles from different NMR models. The torsion-angle fluctuations correlate with mean-squared spatial fluctuations derived from the same collection of NMR models. We developed a neural-network based real-value predictor based on sequence information only. The predictor achieved ten-fold cross-validated correlation coefficients of 0.59 and 0.60, and mean absolute errors of 22.7° and 24.3° for the angle fluctuation of ϕ and ψ, respectively. This predictor is expected to be useful for function prediction and protein structure prediction when predicted torsion angles are used as restraints. Both sequence- and structure-based prediction of torsion-angle fluctuation will be available at http://sparks.informatics.iupui.edu within the SPINE-X package.

Automated site preparation in physics-based rescoring of receptor ligand complexes

Hydrogen atoms are not typically observable in X-ray crystal structures, but inferring their locations is often important in structure-based drug design. In addition, protonation states of the protein can change in response to ligand binding, as can the orientations of OH groups, a subtle form of "induced fit." We implement and evaluate an automated procedure for optimizing polar hydrogens in protein-binding sites in complex with ligands. Specifically, we apply the previously described Independent Cluster Decomposition Algorithm (ICDA) algorithm (Li et al., Proteins 2007;66:824-837), which assigns the ionization states of titratable residues, the amide orientations of Asn/Gln side chains, the imidazole ring orientation in His, and the orientations of OH/SH groups, in a unified algorithm. We test the utility of this method for identifying nativelike ligand poses using 247 protein-ligand complexes from an established database of docked decoys. Pose selection is performed with a physics-based scoring function based on a molecular mechanics energy function and a Generalized Born implicit solvent model. The use of the ICDA receptor preparation protocol, implemented with no knowledge of the native ligand pose, increases the accuracy of pose selection significantly, with the average RMSD over all complexes decreasing from 2.7 to 1.5 A when applying ICDA. Large improvements are seen for specific classes of binding sites with titratable groups, such as aspartyl proteases.

Energy-based analysis and prediction of the orientation between light- and heavy-chain antibody variable domains

Diversity in antibody structure is crucial to the ability of the adaptive immune system to recognize the tremendously diverse set of potential antigens. The diversity in structure is most apparent in the six hypervariable loops of the complementarity-determining regions. However, given that these loops occur at the interface of the heavy- and light-chain variable domains and form the antigen-binding site, the relative orientation of the heavy- and light-chain variable domains can create another source of structural diversity leading to changes in antigen binding. Here, we first reexamine the diversity of V(L):V(H) orientations in existing antibody crystal structures using 153 nonredundant sequences, demonstrating that the variation in V(L):V(H) orientation is greater than that expected from effects of crystal packing, antigen binding, or the presence of antibody constant regions and increases, on average, as sequence similarity decreases for residues in the interface between the domains. We developed a tool for predicting the relative orientations of the heavy- and light-chain variable domains using side-chain rotamer sampling in the interface and molecular-mechanics-based energy calculations. When using variable domain backbones from the crystal structures, the predicted orientation is very close (<1 A RMSD) to the crystallographically observed orientation in most cases, confirming that the V(L):V(H) orientation is determined by the antibody sequence and suggesting an approach to predicting the relative orientation of the variable domains when building homology models of antibodies. When applied to antibody homology models generated from templates with 55-75% sequence identity, we predict the V(L):V(H) orientation of 20 antibodies with an average/median RMSD of 2.1/1.6 A to the crystal structures.

Refining homology models by combining replica-exchange molecular dynamics and statistical potentials

A protocol is presented for the global refinement of homology models of proteins. It combines the advantages of temperature-based replica-exchange molecular dynamics (REMD) for conformational sampling and the use of statistical potentials for model selection. The protocol was tested using 21 models. Of these 14 were models of 10 small proteins for which high-resolution crystal structures were available, the remainder were targets of the recent CASPR exercise. It was found that REMD in combination with currently available force fields could sample near-native conformational states starting from high-quality homology models. Conformations in which the backbone RMSD of secondary structure elements (SSE-RMSD) was lower than the starting value by 0.5-1.0 A were found for 15 out of the 21 cases (average 0.82 A). Furthermore, when a simple scoring function consisting of two statistical potentials was used to rank the structures, one or more structures with SSE-RMSD of at least 0.2 A lower than the starting value was found among the five best ranked structures in 11 out of the 21 cases. The average improvement in SSE-RMSD for the best models was 0.42 A. However, none of the scoring functions tested identified the structures with the lowest SSE-RMSD as the best models although all identified the native conformation as the one with lowest energy. This suggests that while the proposed protocol proved effective for the refinement of high-quality models of small proteins scoring functions remain one of the major limiting factors in structure refinement. This and other aspects by which the methodology could be further improved are discussed.

Probing the structures of leishmanial farnesyl pyrophosphate synthases: homology modeling and docking studies

Leishmania donovani and Leishmania major farnesyl pyrophosphate synthase ( LdFPPS and LmFPPS) are potential targets for the development of antileishmanial therapy. The protein sequence for LdFPPS was recently elucidated in our laboratory. Highly refined homology models were generated using the protein sequences of LdFPPS and the closely related LmFPPS enzyme. A ligand-refined model of LmFPPS with a bound bisphosphonate ligand was generated using restraint-guided molecular mechanics followed by quantum mechanics/molecular mechanics refinement. The ligand-refined model of LmFPPS was further validated through extensive pose validation, enrichment, and other docking studies involving known bisphosphonate inhibitors. The model was able to explain the critical binding site interactions and site-directed mutagenesis data obtained from experimental studies on related FPPS enzymes. The ligand-refined model in conjunction with the validated docking protocol could be utilized in the future for structure-based virtual screening and rational drug design studies against these targets.

Computational study of the dynamics of mannose disaccharides free in solution and bound to the potent anti-HIV virucidal protein cyanovirin

In this paper, we present a computational study of the dynamics of the potent anti-HIV virucidal protein cyanovirin in complex with mannose disaccharides. Recently, it has been experimentally demonstrated that cyanovirin binds mannose oligomers on the surface of glycoprotein gp120. gp120, a protein on the surface of the HIV virus, is key in the process of viral docking and transfer of genetic material into human cells. Cyanovirin prevents the transfer of viral RNA into human cells. In this study, we found that, among all residues that show nuclear Overhauser effects in the solution NMR experiments, residues Glu41 and Arg76 appear to interact with the sugar at the high-affinity binding site through stronger Coulombic interactions. In particular, Arg76 participates in a dynamical mechanism that caps and locks the sugar once it is bound to the protein. We also studied the distribution of glycosidic torsional angles of mannose disaccharides in solution and compared it with those when bound at the high- and low-affinity sites of the protein. Throughout our 20 ns simulations, we find that the sugar bound to the high-affinity site preserves the most favorable conformation in solution while the sugar bound at the low-affinity site does not. The sugar at the low-affinity site can adopt both conformations, but we find it most predominantly on the one that is least probable for the free sugar in solution. We also carried out a detailed study of the interactions between the disaccharides and different amino acids as well as between the disaccharide and the solvent at both binding locations.

Assignment of polar states for protein amino acid residues using an interaction cluster decomposition algorithm and its application to high resolution protein structure modeling

We have developed a new method (Independent Cluster Decomposition Algorithm, ICDA) for creating all-atom models of proteins given the heavy-atom coordinates, provided by X-ray crystallography, and the pH. In our method the ionization states of titratable residues, the crystallographic mis-assignment of amide orientations in Asn/Gln, and the orientations of OH/SH groups are addressed under the unified framework of polar states assignment. To address the large number of combinatorial possibilities for the polar hydrogen states of the protein, we have devised a novel algorithm to decompose the system into independent interacting clusters, based on the observation of the crucial interdependence between the short range hydrogen bonding network and polar residue states, thus significantly reducing the computational complexity of the problem and making our algorithm tractable using relatively modest computational resources. We utilize an all atom protein force field (OPLS) and a Generalized Born continuum solvation model, in contrast to the various empirical force fields adopted in most previous studies. We have compared our prediction results with a few well-documented methods in the literature (WHATIF, REDUCE). In addition, as a preliminary attempt to couple our polar state assignment method with real structure predictions, we further validate our method using single side chain prediction, which has been demonstrated to be an effective way of validating structure prediction methods without incurring sampling problems. Comparisons of single side chain prediction results after the application of our polar state prediction method with previous results with default polar state assignments indicate a significant improvement in the single side chain predictions for polar residues.

Evaluation of coarse grained models for hydrogen bonds in proteins

Backbone hydrogen bonds contribute very importantly to the stability of proteins and therefore they must be appropriately represented in protein folding simulations. Simple models are frequently used in theoretical approaches to this process, but their simplifications are often confronted with the need to be true to the physics of the interactions. Here we study the effects of different levels of coarse graining in the modeling of backbone hydrogen bonds. We study three different models taken from the bibliography in a twofold fashion. First, we calculate the hydrogen bonds in 2gb1, an (alpha + beta)-protein, and see how different backbone representations and potentials can mimic the effects of real hydrogen bonds both in helices and sheets. Second, we use an evolutionary method for protein fragment assembly to locate the global energy minimum for a set of small beta-proteins with these models. This way, we assess the effects of coarse graining in hydrogen bonding models and show what can be expected from them when used in simulation experiments.

Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge-based potentials, and clustering

In this article, we present an iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing secondary structure elements (SSEs). The protocol is based on three modules: a torsion-space local sampling algorithm, a knowledge-based potential, and a conformational clustering algorithm. Alternative methods are tested for each module in the protocol. For each segment, random initial conformations were constructed by perturbing the native dihedral angles of loops (and SSEs) of the segment to be refined while keeping the protein body fixed. Two refinement procedures based on molecular mechanics force fields - using either energy minimization or molecular dynamics - were also tested but were found to be less successful than the IMO protocol. We found that DFIRE is a particularly effective knowledge-based potential and that clustering algorithms that are biased by the DFIRE energies improve the overall results. Results were further improved by adding an energy minimization step to the conformations generated with the IMO procedure, suggesting that hybrid strategies that combine both knowledge-based and physical effective energy functions may prove to be particularly effective in future applications.

Assessment of protein folding potentials with an evolutionary method

Many different protein folding potentials have been developed in the last decades, based upon knowledge of experimentally determined protein structures. Decoy-based techniques are frequently used to assess these force fields, but other methods can explore different features in the performance of the interaction schemes, thus helping in their evaluation. Here, we propose an evolutionary strategy to efficiently assess folding potentials. We apply it to three potentials with different characteristics, taken from the bibliography. A search for minimum energy protein topologies, treated as arrangements of rigid protein fragments, is performed. The method, applied to a set of helix bundle proteins, shows the different behavior of the studied potentials, providing a reasonably fast tool to evaluate their advantages and limitations.

Advances in homology protein structure modeling

Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function.

New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies

We created a homology model of the homo-tetrameric pore domain of HERG using the crystal structure of the bacterial potassium channel, KvAP, as a template. We docked a set of known blockers with well-characterized effects on channel function into the lumen of the pore between the selectivity filter and extracellular entrance using a novel docking and refinement procedure incorporating Glide and Prime. Key aromatic groups of the blockers are predicted to form multiple simultaneous ring stacking and hydrophobic interactions among the eight aromatic residues lining the pore. Furthermore, each blocker can achieve these interactions via multiple docking configurations. To further interpret the docking results, we mapped hydrophobic and hydrophilic potentials within the lumen of each refined docked complex. Hydrophilic iso-potential contours define a 'propeller-shaped' volume at the selectivity filter entrance. Hydrophobic contours define a hollow 'crown-shaped' volume located above the 'propeller', whose hydrophobic 'rim' extends along the pore axis between Tyr652 and Phe656. Blockers adopt conformations/binding orientations that closely mimic the shapes and properties of these contours. Blocker basic groups are localized in the hydrophilic 'propeller', forming electrostatic interactions with Ser624 rather than a generally accepted pi-cation interaction with Tyr652. Terfenadine, cisapride, sertindole, ibutilide, and clofilium adopt similar docked poses, in which their N-substituents bridge radially across the hollow interior of the 'crown' (analogous to the hub and spokes of a wheel), and project aromatic/hydrophobic portions into the hydrophobic 'rim'. MK-499 docks with its longitudinal axis parallel to the axis of the pore and 'crown', and its hydrophobic groups buried within the hydrophobic 'rim'.

Conformational changes in protein loops and helices induced by post-translational phosphorylation

Post-translational phosphorylation is a ubiquitous mechanism for modulating protein activity and protein-protein interactions. In this work, we examine how phosphorylation can modulate the conformation of a protein by changing the energy landscape. We present a molecular mechanics method in which we phosphorylate proteins in silico and then predict how the conformation of the protein will change in response to phosphorylation. We apply this method to a test set comprised of proteins with both phosphorylated and non-phosphorylated crystal structures, and demonstrate that it is possible to predict localized phosphorylation-induced conformational changes, or the absence of conformational changes, with near-atomic accuracy in most cases. Examples of proteins used for testing our methods include kinases and prokaryotic response regulators. Through a detailed case study of cyclin-dependent kinase 2, we also illustrate how the computational methods can be used to provide new understanding of how phosphorylation drives conformational change, why substituting Glu or Asp for a phosphorylated amino acid does not always mimic the effects of phosphorylation, and how a phosphatase can “capture” a phosphorylated amino acid. This work illustrates how computational methods can be used to elucidate principles and mechanisms of post-translational phosphorylation, which can ultimately help to bridge the gap between the number of known sites of phosphorylation and the number of structures of phosphorylated proteins.

Many proteins are chemically modified after they are synthesized in the cell. These post-translational modifications can modulate the ability of a protein to perform chemical reactions and to interact with other proteins. At the cellular level, for example, these chemical modifications are critical for allowing the cell to respond to its environment and control its division. One of the most common mechanisms by which proteins can be modified is by phosphorylation—the addition of a phosphate group to an amino acid side chain of the protein. Thousands of proteins are known to be modified by phosphorylation, but only for a small minority of these do we have any detailed understanding of how the chemical modification regulates the function of the protein. The authors describe a computational method that can make testable predictions about the structural changes that occur in a protein induced by post-translational phosphorylation. Their results show that the method can produce structural models of the phosphorylated proteins with near-atomic accuracy, and provide insight into the energetics of conformational switches driven by phosphorylation. As such, the computational method complements experiments aimed at understanding the mechanisms of protein regulation by phosphorylation.

Conformer generation under restraints

Conformational sampling by direct optimization of an all-atom energy function is ineffective and inefficient because of the ruggedness of the energy landscape. Discrete sampling schemes represent an attractive alternative for generating ensembles of conformers consistent with spatial restraints derived from empirical data. Conformational sampling is becoming increasingly important for structure prediction as the bottleneck in accurate prediction shifts from energy functions to the methods used to find low-energy conformers. Experimental structure determination remains a perennial challenge as investigators tackle larger macromolecular systems, and begin to incorporate more complete descriptions of uncertainty, heterogeneity and dynamics into their models. Computational approaches that combine dense, discrete sampling with all-atom energy evaluation and refinement may help to overcome the remaining barriers to solving these problems.

Protein loop structure prediction with flexible stem geometries

The structure prediction of loops with flexible stem residues is addressed in this article. While the secondary structure of the stem residues is assumed to be known, the geometry of the protein into which the loop must fit is considered to be unknown in our methodology. As a consequence, the compatibility of the loop with the remainder of the protein is not used as a criterion to reject loop decoys. The loop structure prediction with flexible stems is more difficult than fitting loops into a known protein structure in that a larger conformational space has to be covered. The main focus of the study is to assess the precision of loop structure prediction if no information on the protein geometry is available. The proposed approach is based on (1) dihedral angle sampling, (2) structure optimization by energy minimization with a physically based energy function, (3) clustering, and (4) a comparison of strategies for the selection of loops identified in (3). Steps (1) and (2) have similarities to previous approaches to loop structure prediction with fixed stems. Step (3) is based on a new iterative approach to clustering that is tailored for the loop structure prediction problem with flexible stems. In this new approach, clustering is not only used to identify conformers that are likely to be close to the native structure, but clustering is also employed to identify far-from-native decoys. By discarding these decoys iteratively, the overall quality of the ensemble and the loop structure prediction is improved. Step (4) provides a comparative study of criteria for loop selection based on energy, colony energy, cluster density, and a hybrid criterion introduced here. The proposed method is tested on a large set of 3215 loops from proteins in the Pdb-Select25 set and to 179 loops from proteins from the Casp6 experiment.

What role do surfaces play in GB models? A new-generation of surface-generalized born model based on a novel gaussian surface for biomolecules

We have developed a version of our surface generalized Born (SGB) model that employs a Gaussian surface, as opposed to the van der Waals surface used previously. The Gaussian surface is smooth and its properties are analytically differentiable with respect to the positions of atoms. A significant advantage of a solvent model based on this analytically differentiable surface is the availability of analytical gradients of the surface and solvation forces. An efficient and robust algorithm is designed to construct and triangulate the Gaussian surface for large biomolecules with arbitrary shapes, and to compute the various terms required for energy gradients. The Gaussian surface is shown to better mimic the boundary between the solute and solvent by properly addressing solvent accessibility, as is demonstrated by comparisons with standard Poisson-Boltzmann calculations for proteins of different sizes. These results also demonstrate that surface definition is a dominant contribution to differences between GB and PB calculations, especially if the system is large. Application of the new surface to prediction of long loop regions is presented, and significant improvement in the energetics is seen compared with results obtained using the van der Waals surface, even in the absence of optimized empirical correction terms that were used in the latter calculations.

Molecular mechanics methods for predicting protein–ligand binding

Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed.

A fast algorithm for low-resolution protein structure prediction

We propose a new approach for the protein tertiary structure prediction based on the concept of mini-threading. The method identifies useful fragments in Protein Data Bank (PDB) with variable lengths and retrieves spatial restraints. The multidimensional scaling method and least-squares minimization are used to build coarse-grain structural models. Our method uses the information in the PDB efficiently and the prediction time is in minutes when compared to hours and days required by existing methods.

Homology modeling of opioid receptor-ligand complexes using experimental constraints

Opioid receptors interact with a variety of ligands, including endogenous peptides, opiates, and thousands of synthetic compounds with different structural scaffolds. In the absence of experimental structures of opioid receptors, theoretical modeling remains an important tool for structure-function analysis. The combination of experimental studies and modeling approaches allows development of realistic models of ligand-receptor complexes helpful for elucidation of the molecular determinants of ligand affinity and selectivity and for understanding mechanisms of functional agonism or antagonism. In this review we provide a brief critical assessment of the status of such theoretical modeling and describe some common problems and their possible solutions. Currently, there are no reliable theoretical methods to generate the models in a completely automatic fashion. Models of higher accuracy can be produced if homology modeling, based on the rhodopsin X-ray template, is supplemented by experimental structural constraints appropriate for the active or inactive receptor conformations, together with receptor-specific and ligand-specific interactions. The experimental constraints can be derived from mutagenesis and cross-linking studies, correlative replacements of ligand and receptor groups, and incorporation of metal binding sites between residues of receptors or receptors and ligands. This review focuses on the analysis of similarity and differences of the refined homology models of mu, delta, and kappa-opioid receptors in active and inactive states, emphasizing the molecular details of interaction of the receptors with some representative peptide and nonpeptide ligands, underlying the multiple modes of binding of small opiates, and the differences in binding modes of agonists and antagonists, and of peptides and alkaloids.