Hung-Pin Peng

Structural Repertoire of HIV-1-Neutralizing Antibodies Targeting the CD4 Supersite in 14 Donors.

The site on the HIV-1 gp120 glycoprotein that binds the CD4 receptor is recognized by broadly reactive antibodies, several of which neutralize over 90% of HIV-1 strains. To understand how antibodies achieve such neutralization, we isolated CD4-binding-site (CD4bs) antibodies and analyzed 16 co-crystal structures -8 determined here- of CD4bs antibodies from 14 donors. The 16 antibodies segregated by recognition mode and developmental ontogeny into two types: CDR H3-dominated and VH-gene-restricted. Both could achieve greater than 80% neutralization breadth, and both could develop in the same donor. Although paratope chemistries differed, all 16 gp120-CD4bs antibody complexes showed geometric similarity, with antibody-neutralization breadth correlating with antibody-angle of approach relative to the most effective antibody of each type. The repertoire for effective recognition of the CD4 supersite thus comprises antibodies with distinct paratopes arrayed about two optimal geometric orientations, one achieved by CDR H3 ontogenies and the other achieved by VH-gene-restricted ontogenies.

Determination of naringenin and its glucuronide conjugate in rat plasma and brain tissue by high-performance liquid chromatography.

An isocratic high-performance liquid chromatographic method with ultraviolet detection was utilized for the investigation of the pharmacokinetics of naringenin and its glucuronide conjugate in rat plasma and brain tissue. Plasma and brain tissue were deproteinized by acetonitrile, then centrifuged for sample clean-up. The drugs were separated by a reversed-phase C18 column with a mobile phase consisting of acetonitrile-orthophosphoric acid solution (pH 2.5-2.8) (36:64, v/v). The detection limits of naringenin in rat plasma and brain tissue were 50 ng/ml and 0.4 microg/g, respectively. The glucuronide conjugate of naringenin was evaluated by the deconjugated enzyme beta-glucuronidase. The naringenin conjugation ratios in rat plasma and brain tissue were 0.86 and 0.22, respectively, 10 min after naringenin (20 mg/kg, i.v.) administration. The mean naringenin conjugation ratio in plasma was approximately four fold that in brain tissue.

Origins of specificity and affinity in antibody–protein interactions

Significance Natural antibodies perform their biological function by recognizing all sorts of foreign proteins—seemly unlimited structural and sequence diversities in antigens can be recognized by a limited repertoire of antibodies, for which the sequence and structure are relatively homogeneous. We found that the energetically critical epitope portions are largely composed of backbone atoms, side-chain carbons, and hydrogen bond donors/acceptors. These key components are ubiquitous on protein surfaces and can be recognized by the enriched aromatic side chains and, to a lesser extent, short-chain hydrophilic residues on the antibody paratopes; antibodies, with relatively limited sequence and structural diversities in the antigen binding sites, can recognize unlimited protein antigens through recognizing the common physicochemical features on all protein surfaces. Natural antibodies are frequently elicited to recognize diverse protein surfaces, where the sequence features of the epitopes are frequently indistinguishable from those of nonepitope protein surfaces. It is not clearly understood how the paratopes are able to recognize sequence-wise featureless epitopes and how a natural antibody repertoire with limited variants can recognize seemingly unlimited protein antigens foreign to the host immune system. In this work, computational methods were used to predict the functional paratopes with the 3D antibody variable domain structure as input. The predicted functional paratopes were reasonably validated by the hot spot residues known from experimental alanine scanning measurements. The functional paratope (hot spot) predictions on a set of 111 antibody–antigen complex structures indicate that aromatic, mostly tyrosyl, side chains constitute the major part of the predicted functional paratopes, with short-chain hydrophilic residues forming the minor portion of the predicted functional paratopes. These aromatic side chains interact mostly with the epitope main chain atoms and side-chain carbons. The functional paratopes are surrounded by favorable polar atomistic contacts in the structural paratope–epitope interfaces; more that 80% these polar contacts are electrostatically favorable and about 40% of these polar contacts form direct hydrogen bonds across the interfaces. These results indicate that a limited repertoire of antibodies bearing paratopes with diverse structural contours enriched with aromatic side chains among short-chain hydrophilic residues can recognize all sorts of protein surfaces, because the determinants for antibody recognition are common physicochemical features ubiquitously distributed over all protein surfaces.

Modeling protein loops with knowledge-based prediction of sequence-structure alignment

MOTIVATION As protein structure database expands, protein loop modeling remains an important and yet challenging problem. Knowledge-based protein loop prediction methods have met with two challenges in methodology development: (1) loop boundaries in protein structures are frequently problematic in constructing length-dependent loop databases for protein loop predictions; (2) knowledge-based modeling of loops of unknown structure requires both aligning a query loop sequence to loop templates and ranking the loop sequence-template matches. RESULTS We developed a knowledge-based loop prediction method that circumvents the need of constructing hierarchically clustered length-dependent loop libraries. The method first predicts local structural fragments of a query loop sequence and then structurally aligns the predicted structural fragments to a set of non-redundant loop structural templates regardless of the loop length. The sequence-template alignments are then quantitatively evaluated with an artificial neural network model trained on a set of predictions with known outcomes. Prediction accuracy benchmarks indicated that the novel procedure provided an alternative approach overcoming the challenges of knowledge-based loop prediction. AVAILABILITY http://cmb.genomics.sinica.edu.tw

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes.

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

Structure-Based Design and Synthesis of Highly Potent SARS-CoV 3CL Protease Inhibitors

In a successful example of lead optimization by computer modeling prediction, computational technology was used to optimize a lead inhibitor (TL‐3) of the SARS‐CoV 3CL protease. A novel C 2‐symmetric diol (1) was then designed and synthesized, and displayed higher affinity than the original lead compound by one order of magnitude in its inhibition constant (0.6→0.073 μm). We believe that this approach has provided a platform for further lead optimization.WILEY-VCHThis article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

Prediction of Carbohydrate Binding Sites on Protein Surfaces with 3-Dimensional Probability Density Distributions of Interacting Atoms

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.

Loop-sequence features and stability determinants in antibody variable domains by high-throughput experiments.

Protein loops are frequently considered as critical determinants in protein structure and function. Recent advances in high-throughput methods for DNA sequencing and thermal stability measurement have enabled effective exploration of sequence-structure-function relationships in local protein regions. Using these data-intensive technologies, we investigated the sequence-structure-function relationships of six complementarity-determining regions (CDRs) and ten non-CDR loops in the variable domains of a model vascular endothelial growth factor (VEGF)-binding single-chain antibody variable fragment (scFv) whose sequence had been optimized via a consensus-sequence approach. The results show that only a handful of residues involving long-range tertiary interactions distant from the antigen-binding site are strongly coupled with antigen binding. This implies that the loops are passive regions in protein folding; the essential sequences of these regions are dictated by conserved tertiary interactions and the consensus local loop-sequence features contribute little to protein stability and function.

Molecular Evolution of Cystine-Stabilized Miniproteins as Stable Proteinaceous Binders

Small cystine-stabilized proteins are desirable scaffolds for therapeutics and diagnostics. Specific folding and binding properties of the proteinaceous binders can be engineered with combinatorial protein libraries in connection with artificial molecular evolution. The combinatorial protein libraries are composed of scaffold variants with random sequence variation, which inevitably produces a portion of the library sequences incompatible with the parent structure. Here, we used artificial molecular evolution to elucidate structure-determining residues in a smallest cystine-stabilized scaffold. The structural determinant information was then applied to designing cystine-stabilized miniproteins binding to human vascular endothelial growth factor. This work demonstrated a general methodology on engineering artificial cystine-stabilized proteins as antibody mimetics with simultaneously enhanced folding and binding properties.