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Understanding how ligand binding influences protein flexibility is important, especially in rational drug design. Protein flexibility upon ligand binding is analyzed herein using 305 proteins with 2369 crystal structures with ligands (holo) and 1679 without (apo). Each protein has at least two apo and two holo structures for analysis. The inherent variation in structures with and without ligands is first established as a baseline. This baseline is then compared to the change in conformation in going from the apo to holo states to probe induced flexibility. The inherent backbone flexibility across the apo structures is roughly the same as the variation across holo structures. The induced backbone flexibility across apo-holo pairs is larger than that of the apo or holo states, but the increase in RMSD is less than 0.5 Å. Analysis of χ1 angles revealed a distinctly different pattern with significant influences seen for ligand binding on side-chain conformations in the binding site. Within the apo and holo states themselves, the variation of the χ1 angles is the same. However, the data combining both apo and holo states show significant displacements. Upon ligand binding, χ1 angles are frequently pushed to new orientations outside the range seen in the apo states. Influences on binding-site variation could not be easily attributed to features such as ligand size or x-ray structure resolution. By combining these findings, we find that most binding site flexibility is compatible with the common practice in flexible docking, where backbones are kept rigid and side chains are allowed some degree of flexibility.

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Two families of binding affinity estimation methodologies are described which were utilized in the SAMPL3 trypsin/fragment binding affinity challenge. The first is a free energy decomposition scheme based on a thermodynamic cycle, which included separate contributions from enthalpy and entropy of binding as well as a solvent contribution. Enthalpic contributions were estimated with PM6-DH2 semiempirical quantum mechanical interaction energies, which were modified with a statistical error correction procedure. Entropic contributions were estimated with the rigid-rotor harmonic approximation, and solvent contributions to the free energy were estimated with several different methods. The second general methodology is the empirical score LISA, which contains several physics-based terms trained with the large PDBBind database of protein/ligand complexes. Here we also introduce LISA+, an updated version of LISA which, prior to scoring, classifies systems into one of four classes based on a ligand's hydrophobicity and molecular weight. Each version of the two methodologies (a total of 11 methods) was trained against a compiled set of known trypsin binders available in the Protein Data Bank to yield scaling parameters for linear regression models. Both raw and scaled scores were submitted to SAMPL3. Variants of LISA showed relatively low absolute errors but also low correlation with experiment, while the free energy decomposition methods had modest success when scaling factors were included. Nonetheless, re-scaled LISA yielded the best predictions in the challenge in terms of RMS error, and six of these models placed in the top ten best predictions by RMS error. This work highlights some of the difficulties of predicting binding affinities of small molecular fragments to protein receptors as well as the benefit of using training data.

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A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions, specifically for computed interaction energies between chemical fragments comprising a protein-ligand complex. An HIV-II protease crystal structure with a bound ligand (indinavir) was chosen as a model protein-ligand complex. The complex was decomposed into twenty-one (21) interacting fragment pairs, which were studied using a number of computational methods. The chemically accurate complete basis set coupled cluster theory (CCSD(T)/CBS) interaction energies were used as reference values to generate our error estimates. In our analysis we observed significant systematic and random errors in most methods, which was surprising especially for parameterized classical and semiempirical quantum mechanical calculations. After propagating these fragment-based error estimates over the entire protein-ligand complex, our total error estimates for many methods are large compared to the experimentally determined free energy of binding. Thus, we conclude that statistical error analysis is a necessary addition to any scoring function attempting to produce reliable binding affinity predictions.

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The routine prediction of three-dimensional protein structure from sequence remains a challenge in computational biochemistry. It has been intuited that calculated energies from physics-based scoring functions are able to distinguish native from nonnative folds based on previous performance with small proteins and that conformational sampling is the fundamental bottleneck to successful folding. We demonstrate that as protein size increases, errors in the computed energies become a significant problem. We show, by using error probability density functions, that physics-based scores contain significant systematic and random errors relative to accurate reference energies. These errors propagate throughout an entire protein and distort its energy landscape to such an extent that modern scoring functions should have little chance of success in finding the free energy minima of large proteins. Nonetheless, by understanding errors in physics-based score functions, they can be reduced in a post-hoc manner, improving accuracy in energy computation and fold discrimination.

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Physical differences in small molecule binding between enzymes and nonenzymes were found through mining the protein-ligand database, Binding MOAD (Mother of All Databases). The data suggest that divergent approaches may be more productive for improving the affinity of ligands for the two classes of proteins. High-affinity ligands of enzymes are much larger than those with low affinity, indicating that the addition of complementary functional groups is likely to improve the affinity of an enzyme inhibitor. However, this process may not be as fruitful for ligands of nonenzymes. High- and low-affinity ligands of nonenzymes are nearly the same size, so modest modifications and isosteric replacement might be most productive. The inherent differences between enzymes and nonenzymes have significant ramifications for scoring functions and structure-based drug design. In particular, nonenzymes were found to have greater ligand efficiencies than enzymes. Ligand efficiencies are often used to indicate druggability of a target, and this finding supports the feasibility of nonenzymes as drug targets. The differences in ligand efficiencies do not appear to come from the ligands; instead, the pockets yield different amino acid compositions despite very similar distributions of amino acids in the overall protein sequences.

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Binding MOAD (Mother of All Databases) is a database of 9836 protein-ligand crystal structures. All biologically relevant ligands are annotated, and experimental binding-affinity data is reported when available. Binding MOAD has almost doubled in size since it was originally introduced in 2004, demonstrating steady growth with each annual update. Several technologies, such as natural language processing, help drive this constant expansion. Along with increasing data, Binding MOAD has improved usability. The website now showcases a faster, more featured viewer to examine the protein-ligand structures. Ligands have additional chemical data, allowing for cheminformatics mining. Lastly, logins are no longer necessary, and Binding MOAD is freely available to all at http://www.BindingMOAD.org.

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We have recently announced the largest database of protein-ligand complexes, Binding MOAD (Mother of All Databases). After the August 2004 update, Binding MOAD contains 6816 complexes. There are 2220 protein families and 3316 unique ligands. After searching 6000+ crystallography papers, we have obtained binding data for 1793 (27%) of the complexes. We have also created a non-redundant set of complexes with only one complex from each protein family; in that set, 630 (28%) of the unique complexes have binding data. Here, we present information about the data provided at the Binding MOAD website. We also present the results of mining Binding MOAD to map the degree of solvent exposure for binding sites. We have determined that most cavities and ligands (70-85%) are well buried in the complexes. This fits with the common paradigm that a large degree of contact between the ligand and protein is significant in molecular recognition. GoCAV and the GoCAV viewer are the tools we created for this study. To share our data and make our online dataset more useful to other research groups, we have integrated the viewer into the Binding MOAD website (www.BindingMOAD.org).

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Binding MOAD (Mother of All Databases) is the largest collection of high-quality, protein-ligand complexes available from the Protein Data Bank. At this time, Binding MOAD contains 5331 protein-ligand complexes comprised of 1780 unique protein families and 2630 unique ligands. We have searched the crystallography papers for all 5000+ structures and compiled binding data for 1375 (26%) of the protein-ligand complexes. The binding-affinity data ranges 13 orders of magnitude. This is the largest collection of binding data reported to date in the literature. We have also addressed the issue of redundancy in the data. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were chosen by tightest binding, best resolution, etc. For the 1780 "best" complexes that comprise the nonredundant version of Binding MOAD, 475 (27%) have binding data. This significant collection of protein-ligand complexes will be very useful in elucidating the biophysical patterns of molecular recognition and enzymatic regulation. The complexes with binding-affinity data will help in the development of improved scoring functions and structure-based drug discovery techniques. The dataset can be accessed at http://www.BindingMOAD.org.

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PURPOSE: Recent studies have implicated the proximal penis as a potential site-specific structure for radiation-related erectile dysfunction (ED). In this study, we evaluated by means of a validated patient-administered questionnaire whether radiation doses to the bulb of the penis and/or the proximal corporeal bodies were predictive for the development of brachytherapy-induced ED. METHODS AND MATERIALS: Thirty patients who underwent permanent prostate brachytherapy between April 1995 and October 1999 and developed brachytherapy-induced ED were paired with 30 similar men who maintained potency after implantation. None of the 60 patients received supplemental external beam radiation therapy, either before or after implantation. Potency was assessed by patient self-administration of the specific erectile questions of the International Index of Erectile Function. The questionnaire consisted of 5 questions with a maximum score of 25. Postimplant potency was defined as an International Index of Erectile Function score > or =11. Mean and median follow-up was 48.3 +/- 14.4 months and 48.0 months, respectively (range: 26.6-79.3 months). The bulb of the penis and the proximal crura were outlined at 0.5-cm intervals on the Day 0 postimplant CT scan. The radiation dose distribution to the bulb of the penis and adjacent crura was defined in terms of the minimum dose delivered to 25%, 50%, 70%, 75%, 90%, and 95% of the bulb (D(25), D(50), D(70), D(75), D(90), and D(95)). RESULTS: The radiation dose delivered to the bulb of the penis and the proximal crura in men with brachytherapy-induced ED was statistically greater for all evaluated dosimetric parameters (D(25), D(50), D(70), D(75), D(90), and D(95)). Stepwise linear regression analysis indicated that penile bulb dose parameter D(50), the postimplant prostate CT volume, and patient age at implant were predictive of postimplant ED, whereas the crura dose D(25) approached statistical significance. Seventy-five percent of the impotent men had a bulb D(25) >60% of prescribed minimum peripheral dose (mPD), whereas 80% of potent men had a bulb D(25) < or =60% mPD. Using the D(50) bulb parameter, 70% of ED men had a dose >40% mPD, whereas 90% of potent men had a dose < or =40% mPD. Similar cut points for D(25) and D(50) crura doses were 40% and 28% mPD. The crura D(25) cut point was exceeded by 50% of the ED patients and only 7% of the potent patients. CONCLUSION: This is the first study to evaluate potency preservation and radiation doses to the proximal penis by means of a validated patient-administered quality-of-life instrument. Our data confirm prior reports that radiation doses to the proximal penis are predictive of brachytherapy-induced ED. In a stepwise linear regression analysis, the strongest predictors of potency preservation were bulb D(50), postimplant prostate CT volume, and patient age. With Day 0 dosimetric evaluation, the penile bulb D(50) and D(25) should be maintained below 40% and 60% mPD, respectively, whereas the crura D(50) and D(25) should be maintained below 40% and 28% mPD, respectively, to maximize posttreatment potency.

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PURPOSE: Because of the apparent relationship between potency loss and radiation doses to the erectile bodies, there is increasing rationale for incorporating penile bulb dosimetry into treatment planning and posttreatment evaluation. Because the location and shape of the penile bulb have not been described in detail on various imaging modalities, we herein describe the anatomic boundaries of the penile bulb on computed tomography (CT), magnetic resonance imaging (MR), and transrectal ultrasound (TRUS), before and after brachytherapy. METHODS AND MATERIALS: Nonenhanced axial CT images were taken on a CTi CT Scanner (General Electric Medical Systems, Milwaukee, WI) with the patient in the supine position. Settings were at 300 ma, 140 kvp, 4-s scan time per slice, and collimation of 3 mm with data obtained at 3-mm intervals. Nonenhanced MR images were obtained with a 1.5 Tesla Signa Horizon LX Scanner using fast spin-echo T1-weighted (TR/TE, 466/20) and T2-weighted (TR/TE, 8000/90) images, with a slice thickness of 2 mm and an interslice gap of 0.5 mm. TRUS images were obtained with a Siemens SONOLINE Prima ultrasound machine at 6.0 MHz and a Winston-Barzell stepper unit. RESULTS: The penile bulb is best visualized on T2-weighted MR images in the axial, sagittal, and coronal planes, appearing as an oval-shaped, hyperintense midline structure. On axial CT imaging, the bulb of the penis is typically readily identifiable, bounded by the paired crura laterally, the corpora spongiosum anteriorly, and the levator ani posteriorly. The penile bulb is typically well visualized on transverse TRUS, but usually only faintly seen on sagittal TRUS. The bulb is partially obscured on postimplant CT and MR images, presumably because of implant-related edema. Bulb volumes vary markedly from patient to patient, ranging from 5.6 to 12.4 cc (median: 8.1 cc). CONCLUSION: Closer attention to penile erectile tissue doses should lead to improved external beam radiation and brachytherapy delivery. It will benefit the radiation oncology community to become familiar with these imaging findings, so that penile bulb dosimetry can be incorporated into our daily practice.

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