Deep neck space infections in end-stage renal disease patients: Prevalence and mortality.

Deep neck space infections (DNSI) are severe infections within the layers of neck fascia that are known to be associated with underlying immunocompromised states. Although uremia associated with kidney disease is known to cause immune system dysfunction, DNSI in patients with kidney disease has been poorly studied. This study investigated the prevalence of DNSI and the associated risk of mortality within the United States end-stage renal disease (ESRD) population, using a retrospective cohort study design and the United States Renal Data System database of patients (ages 18-100) who initiated dialysis therapy between 2005 and 2019. International Classification of Disease-9 and -10 codes were used to identify the diagnosis of DNSI and comorbid conditions. Of the 705,891 included patients, 2.2% had a diagnosis of DNSI. Variables associated with increased risk of DNSI were female sex, black compared to white race, catheter, or graft compared to arteriovenous fistula (AVF) access, autoimmune disease, chronic tonsillitis, diagnoses in the Charlson Comorbidity Index (CCI), tobacco use, and alcohol dependence. DNSI diagnosis was an independent risk factor for mortality, which was also associated with other comorbidity factors such as older age, catheter or graft compared to AVF access, comorbidities in the CCI, tobacco use, and alcohol dependence. Because of the increased mortality risk of DSNI in the ESRD population, health professionals should encourage good oral hygiene practices and smoking cessation, and they should closely monitor these patients to reduce poor outcomes.

Ethno-demographic disparities in humoral responses to the COVID-19 vaccine among healthcare workers.

The COVID-19 pandemic had a profound impact on global health, but rapid vaccine administration resulted in a significant decline in morbidity and mortality rates worldwide. In this study, we sought to explore the temporal changes in the humoral immune response against SARS-CoV-2 healthcare workers (HCWs) in Augusta, GA, USA, and investigate any potential associations with ethno-demographic features. Specifically, we aimed to compare the naturally infected individuals with naïve individuals to understand the immune response dynamics after SARS-CoV-2 vaccination. A total of 290 HCWs were included and assessed prospectively in this study. COVID status was determined using a saliva-based COVID assay. Neutralizing antibody (NAb) levels were quantified using a chemiluminescent immunoassay system, and IgG levels were measured using an enzyme-linked immunosorbent assay method. We examined the changes in antibody levels among participants using different statistical tests including logistic regression and multiple correspondence analysis. Our findings revealed a significant decline in NAb and IgG levels at 8-12 months postvaccination. Furthermore, a multivariable analysis indicated that this decline was more pronounced in White HCWs (odds ratio [OR] = 2.1, 95% confidence interval [CI] = 1.07-4.08, p = 0.02) and IgG (OR = 2.07, 95% CI = 1.04-4.11, p = 0.03) among the whole cohort. Booster doses significantly increased IgG and NAb levels, while a decline in antibody levels was observed in participants without booster doses at 12 months postvaccination. Our results highlight the importance of understanding the dynamics of immune response and the potential influence of demographic factors on waning immunity to SARS-CoV-2. In addition, our findings emphasize the value of booster doses to ensure durable immunity.

Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schrödinger Computational Platform.

d-Serine is a coagonist of the N-methyl d-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, d-serine is synthesized from its l-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO). Many studies have linked decreased d-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, it is feasible to employ DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with the best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound potency. A new class of DAO inhibitors with desirable properties has been discovered from this endeavor. Our modeling technology on this program has not only enhanced the efficiency of structure-activity relationship development but also helped to identify a previously unexplored subpocket for further optimization.

A Descriptor Set for Quantitative Structure-property Relationship Prediction in Biologics.

There has been a remarkable increase in the number of biologics, especially monoclonal antibodies, in the market over the last decade. In addition to attaining the desired binding to their targets, a crucial aspect is the 'developability' of these drugs, which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability, low immunogenicity and low poly-specificity. The lack of any of these desirable properties can lead to significant hurdles in advancing them to the clinic and are often discovered only during late stages of drug development. Hence, in silico methods for early detection of these properties, particularly the ones that affect aggregation and solubility in the earlier stages can be highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors that is ideal for making liability predictions using a QSPR (quantitative structure-property relationship) approach. This set offers a high degree of feature diversity that may coarsely be classified based on (1) sequence (2) structure and (3) surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process of a biologics drug candidate. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results show that the descriptors are well suited to assist in the improvement of protein properties of systems that exhibit poor solubility or aggregation.

Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.

Benzosiloles with Crystallization-Induced Emission Enhancement of Electrochemiluminescence: Synthesis, Electrochemistry, and Crystallography.

Crystallization-induced emission enhancement (CIEE) was demonstrated for the first time for electrochemilunimescence (ECL) with two new benzosiloles. Compared with their solution, the films of the two benzosiloles gave CIEE of 24 and 16 times. The mechanism of the CIEE-ECL was examined by spooling ECL spectroscopy, X-ray crystal structure analysis, photoluminescence, and DFT calculations. This CIEE-ECL system is a complement to the well-established aggregation-induced emission enhancement (AIEE) systems. Unique intermolecular interactions are noted in the crystalline chromophore. The first heterogeneous ECL system is established for organic compounds with highly hydrophobic properties.

qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X-Ray Electron Density Maps.

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.

AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches.

Protein aggregation is a phenomenon that has attracted considerable attention within the pharmaceutical industry from both a developability standpoint (to ensure stability of protein formulations) and from a research perspective for neurodegenerative diseases. Experimental identification of aggregation behavior in proteins can be expensive; and hence, the development of accurate computational approaches is crucial. The existing methods for predicting protein aggregation rely mostly on the primary sequence and are typically trained on amyloid-like proteins. However, the training bias toward beta amyloid peptides may worsen prediction accuracy of such models when applied to larger protein systems. Here, we present a novel algorithm to identify aggregation-prone regions in proteins termed "AggScore" that is based entirely on three-dimensional structure input. The method uses the distribution of hydrophobic and electrostatic patches on the surface of the protein, factoring in the intensity and relative orientation of the respective surface patches into an aggregation propensity function that has been trained on a benchmark set of 31 adnectin proteins. AggScore can accurately identify aggregation-prone regions in several well-studied proteins and also reliably predict changes in aggregation behavior upon residue mutation. The method is agnostic to an amyloid-specific aggregation context and thus may be applied to globular proteins, small peptides and antibodies.

Improving Accuracy, Diversity, and Speed with Prime Macrocycle Conformational Sampling.

A novel method for exploring macrocycle conformational space, Prime macrocycle conformational sampling (Prime-MCS), is introduced and evaluated in the context of other available algorithms (Molecular Dynamics, LowModeMD in MOE, and MacroModel Baseline Search). The algorithms were benchmarked on a data set of 208 macrocycles which was curated for diversity from the Cambridge Structural Database, the Protein Data Bank, and the Biologically Interesting Molecule Reference Dictionary. The algorithms were evaluated in terms of accuracy (ability to reproduce the crystal structure), diversity (coverage of conformational space), and computational speed. Prime-MCS most reliably reproduced crystallographic structures for RMSD thresholds >1.0 Å, most often produced the most diverse conformational ensemble, and was most often the fastest algorithm. Detailed analysis and examination of both typical and outlier cases were performed to reveal characteristics, shortcomings, expected performance, and complementarity of the methods.

Cerebrospinal fluid cell-free mitochondrial DNA is associated with HIV replication, iron transport, and mild HIV-associated neurocognitive impairment.

BACKGROUND: Mitochondria are abundant organelles critical for energy metabolism and brain function. Mitochondrial DNA (mtDNA), released during cellular injury and as part of the innate immune response to viral pathogens, contains CpG motifs that act as TLR-9 ligands. We investigated relationships between cerebrospinal fluid (CSF) cell-free mtDNA levels and HIV viral load (VL), biomarkers of inflammation and iron transport, and neurocognitive (NC) function in the CNS HIV Antiretroviral Therapy Effects Research (CHARTER) cohort. METHODS: We quantified cell-free mtDNA in CSF by droplet digital PCR in 332 CHARTER participants who underwent comprehensive neuropsychiatric evaluation. NC performance was assessed using the global deficit score (GDS) as either a continuous or a binary measure (GDS ≥ 0.5, impaired vs. GDS < 0.5, unimpaired). CSF, clinical, and biomarker data from the earliest available time point were analyzed. Cell-free mtDNA associations with CSF inflammation and iron-related biomarkers [CXCL10, IL-6, IL-8, TNF-a, transferrin (TF), ceruloplasmin (CP), and vascular endothelial growth factor (VEGF)], VL, and GDS were evaluated by multivariable regression. RESULTS: CSF cell-free mtDNA levels were significantly lower in participants with undetectable (vs. detectable) VL in either plasma (p < 0.001) or CSF (p < 0.001) and in those on antiretroviral therapy (ART; p < 0.001). Participants on ART with undetectable VL in both CSF and plasma had lower mtDNA levels than those with detectable VL in both compartments (p = 0.001). Higher mtDNA levels were observed in participants in the highest vs. lowest tertile (T3 vs. T1) of CSF CXCL10 (T3 vs. T1, p < 0.001) and TNF-a (T3 vs. T1, p < 0.05) in unadjusted analyses. MtDNA levels also correlated with CSF leukocyte count. After adjusting for CSF leukocyte count and VL, mtDNA levels were also associated with other inflammation- and iron-related biomarkers in CSF, including TF (T3 vs. T1, p < 0.05) and CP (T3 vs. T1, p < 0.05). With additional correction for ART use, mtDNA was also negatively associated with CSF VEGF (p < 0.05) and IL-6 (p = 0.05). We observed no associations of CSF mtDNA levels with age or GDS-defined NC impairment. CONCLUSIONS: CSF cell-free mtDNA levels were associated with HIV RNA and ART status, as well as with biomarkers of iron transport and VEGF, a growth factor with known effects on mitochondrial integrity and autophagy. CSF mtDNA may be a biomarker of iron dysregulation and/or neuroinflammation during HIV infection.

HIV viral kinetics and T cell dynamics in antiretroviral naïve persons starting an integrase strand transfer inhibitor and protease inhibitor regimen.

BACKGROUND: Nucleos(t)ide reverse transcriptase inhibitor (NRTI)-sparing regimens may potentially minimize antiretroviral (ART) toxicities, but demonstrate mixed efficacy and toxicity results. The impact of an integrase strand transfer inhibitor (INSTI) and protease inhibitor (PI) regimen on HIV viral dynamics and T cell kinetics remains underdescribed. OBJECTIVE: To compare the effect of raltegravir + ritonavir boosted lopinavir (RAL + LPV/r) to efavirenz/tenofovir disoproxil fumarate/emtricitabine (EFV/TDF/FTC) on HIV kinetics and T cell dynamics. METHODS: Fifty participants naïve to ART underwent HIV viral kinetic sampling evaluated using biexponential mixed effects modeling. A subset of 28 subjects (with complete viral suppression) underwent flow cytometry and evaluation of soluble markers of inflammation at weeks 0, 4, and 48 of ART. RESULTS: RAL + LPV/r compared to EFV/TDF/FTC resulted in a prolonged first phase viral decay rate (18 vs. 13 days p < 0.01). From weeks 0 to 4, RAL + LPV/r was associated with a trend toward greater decreases in activated CD4+ T cells (-3.81 vs. -1.18 p = 0.09) and less decreases in activated effector memory CD4+ T cells (-0.63 vs. -2.69 p-0.07). These trends did not persist to week 48. No differences were noted at any time point for soluble markers of immune activation. CONCLUSIONS: The prolonged first phase viral decay observed with RAL + LPV/r in persons starting ART did not result in differences in viral suppression at week 48. We also observed trends in declines in certain cellular markers of immune activation but it remains unclear if this could translate to long-term immunologic benefits in persons on an INSTI + PI.

Increased cell-free mitochondrial DNA is a marker of ongoing inflammation and better neurocognitive function in virologically suppressed HIV-infected individuals.

Cell-free mitochondrial DNA (mtDNA) is a highly immunogenic molecule that is associated with several inflammatory conditions and with neurocognitive impairment during untreated HIV infection. Here, we investigate how cell-free mtDNA in cerebrospinal fluid (CSF) is associated with inflammation, neuronal damage, and neurocognitive functioning in the context of long-term suppressive antiretroviral therapy (ART). We quantified the levels of cell-free mtDNA in the CSF from 41 HIV-infected individuals with completely suppressed HIV RNA levels in blood plasma (<50 copies/mL) by droplet digital PCR. We measured soluble CD14, soluble CD163, interferon γ-induced protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), interleukin 6 (IL-6), interleukin 8 (IL-8), tumor necrosis factor-α (TNF-α), neopterin, and neurofilament light chain (NFL) by immunoassays in CSF supernatant or blood plasma. Higher levels of mtDNA in CSF were associated with higher levels of MCP-1 (r = 0.56, p < 0.01) in CSF and TNF-α (r = 0.43, p < 0.01) and IL-8 (r = 0.44, p < 0.01) in blood plasma. Subjects with a previous diagnosis of AIDS showed significantly higher levels of mtDNA (p < 0.01) than subjects without AIDS. The associations between mtDNA and MCP-1 in CSF and TNF-α in blood remained significant after adjusting for previous diagnosis of AIDS (p < 0.01). Additionally, higher levels of mtDNA were associated with a lower CD4 nadir (r = -0.41, p < 0.01) and lower current CD4% (r = -0.34, p = 0.03). Paradoxically, higher levels of mtDNA in CSF were significantly associated with better neurocognitive performance (r = 0.43, p = 0.02) and with less neuronal damage (i.e. lower NFL). Higher cell-free mtDNA is associated with inflammation during treated HIV infection, but the impact on neurocognitive functioning and neuronal damage remains unclear and may differ in the setting of suppressive ART.

Mitochondrial injury and cognitive function in HIV infection and methamphetamine use.

OBJECTIVE: In this work, we evaluated the association of human immunodeficiency virus (HIV) infection and methamphetamine (METH) use with mitochondrial injury in the brain and its implication on neurocognitive impairment. DESIGN: Mitochondria carry their genome (mtDNA) and play a critical role in cellular processes in the central nervous system. METH is commonly used in HIV-infected populations. HIV infection and METH use can cause damage to mtDNA and lead to neurocognitive morbidity. We evaluated HIV infection and METH use with mitochondrial injury in the brain. METHODS: We obtained white and gray matter from Brodmann areas 7, 8, 9, 46 of the following: HIV-infected individuals with history of past METH use (HIV+METH+, n = 16), HIV-infected individuals with no history of past METH use (HIV+METH-, n = 11), and HIV-negative controls (HIV-METH-, n = 30). We used the 'common deletion', a 4977 bp mutation, as a measurement of mitochondrial injury, and quantified levels of mtDNA and 'common deletion' by droplet digital PCR, and evaluated in relation to neurocognitive functioning [Global Deficit Score (GDS)]. RESULTS: Levels of mtDNA and mitochondrial injury were highest in white matter of Brodmann area 46. A higher relative proportion of mtDNA carrying the 'common deletion' was associated with lower GDS (P < 0.01) in HIV+METH+ but higher GDS (P < 0.01) in HIV+METH-. CONCLUSIONS: Increased mitochondrial injury was associated with worse neurocognitive function in HIV+METH- individuals. Among HIV+METH+ individuals, an opposite effect was seen.

Subtype associations with HIV-associated neurocognitive disorder in China.

Factors associated with HIV-associated neurocognitive disorders (HAND) include CD4(+) nadir and count, HIV RNA level, and HIV-1 subtype. Here, we investigated demographical and clinical markers with respect to HAND in a homogenous Chinese population. Individuals with HAND (global deficit score ≥0.5) had lower nadir (p < 0.01) and CD4(+) counts (p = 0.03). HAND was also associated with AIDS (p < 0.01), but subtype was not (p = 0.198). Furthermore, worse impairment correlated with higher viral diversity (r = 0.16, p < 0.01), lower nadir (r = -0.17, p < 0.01), and CD4(+) counts (r = -0.11, p = 0.01). These remained significant even when correcting for subtype. Our findings suggest that subtype does not have a major impact on HAND.

Cell-free mitochondrial DNA in CSF is associated with early viral rebound, inflammation, and severity of neurocognitive deficits in HIV infection.

Cell-free mitochondiral DNA (mtDNA) is an immunogenic molecule associated with many inflammatory conditions. We evaluated the relationship between cell-free mtDNA in cerebrospinal fluid (CSF) and neurocognitive performance and inflammation during HIV infection. In a cross-sectional analysis, we evaluated the association of mtDNA levels with clinical assessments, inflammatory markers, and neurocognitive performance in 28 HIV-infected individuals. In CSF, we measured mtDNA levels by droplet digital PCR, and soluble CD14 and CD163, neurofilament light, and neopterin by ELISA. In blood and CSF, we measured soluble IP-10, MCP-1, TNF-α, and IL-6 by ELISA, and intracellular expression of IL-2, IFN-γ, and TNF-α in CD4(+) and CD8(+) T cells by flow cytometry. We also evaluated the relationship between CSF pleocytosis and mtDNA longitudinally in another set of five individuals participating in an antiretroviral treatment (ART) interruption study. Cell-free CSF mtDNA levels strongly correlated with neurocognitive performance among individuals with neurocognitive impairment (NCI) (r = 0.77, p = 0.001). CSF mtDNA also correlated with levels of IP-10 in CSF (r = 0.70, p = 0.007) and MCP-1 in blood plasma (r = 0.66, p = 0.01) in individuals with NCI. There were no significant associations between inflammatory markers and mtDNA in subjects without NCI, and levels of mtDNA did not differ between subjects with and without NCI. MtDNA levels preceded pleocytosis and HIV RNA following ART interruption. Cell-free mtDNA in CSF was strongly associated with the severity of neurocognitive dysfunction and inflammation only in individuals with NCI. Our findings suggest that within a subset of subjects cell-free CSF mtDNA is associated with inflammation and degree of NCI.

Docking covalent inhibitors: a parameter free approach to pose prediction and scoring.

Although many popular docking programs include a facility to account for covalent ligands, large-scale systematic docking validation studies of covalent inhibitors have been sparse. In this paper, we present the development and validation of a novel approach for docking and scoring covalent inhibitors, which consists of conventional noncovalent docking, heuristic formation of the covalent attachment point, and structural refinement of the protein-ligand complex. This approach combines the strengths of the docking program Glide and the protein structure modeling program Prime and does not require any parameter fitting for the study of additional covalent reaction types. We first test this method by predicting the native binding geometry of 38 covalently bound complexes. The average RMSD of the predicted poses is 1.52 Å, and 76% of test set inhibitors have an RMSD of less than 2.0 Å. In addition, the apparent affinity score constructed herein is tested on a virtual screening study and the characterization of the SAR properties of two different series of congeneric compounds with satisfactory success.

Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction.

We present the blinded prediction results in the Second Antibody Modeling Assessment (AMA-II) using a fully automatic antibody structure prediction method implemented in the programs BioLuminate and Prime. We have developed a novel knowledge based approach to model the CDR loops, using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physics-based energy function (VSGB2.0), which improves the model quality significantly. H3 loop modeling remains the most challenging task. Our ab initio loop prediction performs well for the H3 loop in the crystal structure context, and allows improved results when refining the H3 loops in the context of homology models. For the 10 human and mouse derived antibodies in this assessment, the average RMSDs for the homology model Fv and framework regions are 1.19 Å and 0.74 Å, respectively. The average RMSDs for five non-H3 CDR loops range from 0.61 Å to 1.05 Å, and the H3 loop average RMSD is 2.91 Å using our knowledge-based loop prediction approach. The ab initio H3 loop predictions yield an average RMSD of 1.28 Å when performed in the context of the crystal structure and 2.67 Å in the context of the homology modeled structure. Notably, our method for predicting the H3 loop in the crystal structure environment ranked first among the seven participating groups in AMA-II, and our method made the best prediction among all participants for seven of the ten targets.

Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments.

Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.

Ab initio structure prediction of the antibody hypervariable H3 loop.

Antibodies have the capability of binding a wide range of antigens due to the diversity of the six loops constituting the complementarity determining region (CDR). Among the six loops, the H3 loop is the most diverse in structure, length, and sequence identity. Prediction of the three-dimensional structures of antibodies, especially the CDR loops, is an important step in the computational design and engineering of novel antibodies for improved affinity and specificity. Although it has been demonstrated that the conformation of the five non-H3 loops can be accurately predicted by comparing their sequences against databases of canonical loop conformations, no such connection has been established for H3 loops. In this work, we present the results for ab initio structure prediction of the H3 loop using conformational sampling and energy calculations with the program Prime on a dataset of 53 loops ranging in length from 4 to 22 residues. When the prediction is performed in the crystal environment and including symmetry mates, the median backbone root mean square deviation (RMSD) is 0.5 Å to the crystal structure, with 91% of cases having an RMSD of less than 2.0 Å. When the prediction is performed in a noncrystallographic environment, where the scaffold is constructed by swapping the H3 loops between homologous antibodies, 70% of cases have an RMSD below 2.0 Å. These results show promise for ab initio loop predictions applied to modeling of antibodies.

Novel procedure for modeling ligand/receptor induced fit effects.

We present a novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques. While traditional rigid-receptor docking methods are useful when the receptor structure does not change substantially upon ligand binding, success is limited when the protein must be "induced" into the correct binding conformation for a given ligand. We provide an in-depth description of our novel methodology and present results for 21 pharmaceutically relevant examples. Traditional rigid-receptor docking for these 21 cases yields an average RMSD of 5.5 A. The average ligand RMSD for docking to a flexible receptor for the 21 pairs is 1.4 A; the RMSD is < or =1.8 A for 18 of the cases. For the three cases with RMSDs greater than 1.8 A, the core of the ligand is properly docked and all key protein/ligand interactions are captured.