Loop modeling: Sampling, filtering, and scoring

We describe a fast and accurate protocol, LoopBuilder, for the prediction of loop conformations in proteins. The procedure includes extensive sampling of backbone conformations, side chain addition, the use of a statistical potential to select a subset of these conformations, and, finally, an energy minimization and ranking with an all-atom force field. We find that the Direct Tweak algorithm used in the previously developed LOOPY program is successful in generating an ensemble of conformations that on average are closer to the native conformation than those generated by other methods. An important feature of Direct Tweak is that it checks for interactions between the loop and the rest of the protein during the loop closure process. DFIRE is found to be a particularly effective statistical potential that can bias conformation space toward conformations that are close to the native structure. Its application as a filter prior to a full molecular mechanics energy minimization both improves prediction accuracy and offers a significant savings in computer time. Final scoring is based on the OPLS/SBG-NP force field implemented in the PLOP program. The approach is also shown to be quite successful in predicting loop conformations for cases where the native side chain conformations are assumed to be unknown, suggesting that it will prove effective in real homology modeling applications. Proteins 2008. © 2007 Wiley-Liss, Inc

AIRR Community Guide to Planning and Performing AIRR-Seq Experiments

The development of high-throughput sequencing of adaptive immune receptor repertoires (AIRR-seq of IG and TR rearrangements) has provided a new frontier for in-depth analysis of the immune system. The last decade has witnessed an explosion in protocols, experimental methodologies, and computational tools. In this chapter, we discuss the major considerations in planning a successful AIRR-seq experiment together with basic strategies for controlling and evaluating the outcome of the experiment. Members of the AIRR Community have authored several chapters in this edition, which cover step-by-step instructions to successfully conduct, analyze, and share an AIRR-seq project

Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community's Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work. Keywords: B-cell Receptor (BCR); IG; T-cell Receptor (TCR); TR; antibody; immunoglobulin; immunology; inflammation; next generation sequencing (NGS)

Developmental Pathway of the MPER-Directed HIV-1-Neutralizing Antibody 10E8

Antibody 10E8 targets the membrane-proximal external region (MPER) of HIV-1 gp41, neutralizes >97% of HIV-1 isolates, and lacks the auto-reactivity often associated with MPER-directed antibodies. The developmental pathway of 10E8 might therefore serve as a promising template for vaccine design, but samples from time-of-infection—often used to infer the B cell record—are unavailable. In this study, we used crystallography, next-generation sequencing (NGS), and functional assessments to infer the 10E8 developmental pathway from a single time point. Mutational analysis indicated somatic hypermutation of the 2nd-heavy chain-complementarity determining region (CDR H2) to be critical for neutralization, and structures of 10E8 variants with V-gene regions reverted to genomic origin for heavy-and-light chains or heavy chain-only showed structural differences >2 Å relative to mature 10E8 in the CDR H2 and H3. To understand these developmental changes, we used bioinformatic sieving, maximum likelihood, and parsimony analyses of immunoglobulin transcripts to identify 10E8-lineage members, to infer the 10E8-unmutated common ancestor (UCA), and to calculate 10E8-developmental intermediates. We were assisted in this analysis by the preservation of a critical D-gene segment, which was unmutated in most 10E8-lineage sequences. UCA and early intermediates weakly bound a 26-residue-MPER peptide, whereas HIV-1 neutralization and epitope recognition in liposomes were only observed with late intermediates. Antibody 10E8 thus develops from a UCA with weak MPER affinity and substantial differences in CDR H2 and H3 from the mature 10E8; only after extensive somatic hypermutation do 10E8-lineage members gain recognition in the context of membrane and HIV-1 neutralization

Structure and immune recognition of trimeric pre-fusion HIV-1 Env.

CAPRISA, 2014.The human immunodeficiency virus type 1 (HIV-1) envelope (Env) spike, comprising three gp120 and three gp41 subunits, is a conformational machine that facilitates HIV-1 entry by rearranging from a mature unliganded state, through receptor-bound intermediates, to a post-fusion state. As the sole viral antigen on the HIV-1 virion surface, Env is both the target of neutralizing antibodies and a focus of vaccine efforts. Here we report the structure at 3.5 Å resolution for an HIV-1 Env trimer captured in a mature closed state by antibodies PGT122 and 35O22. This structure reveals the pre-fusion conformation of gp41, indicates rearrangements needed for fusion activation, and defines parameters of immune evasion and immune recognition. Pre-fusion gp41 encircles amino- and carboxy-terminal strands of gp120 with four helices that form a membrane-proximal collar, fastened by insertion of a fusion peptide-proximal methionine into a gp41-tryptophan clasp. Spike rearrangements required for entry involve opening the clasp and expelling the termini. N-linked glycosylation and sequence-variable regions cover the pre-fusion closed spike; we used chronic cohorts to map the prevalence and location of effective HIV-1-neutralizing responses, which were distinguished by their recognition of N-linked glycan and tolerance for epitope-sequence variation

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

Developmental pathway for potent V1V2-directed HIV-neutralizing antibodies.

CAPRISA, 2014.Antibodies capable of neutralizing HIV-1 often target variable regions 1 and 2 (V1V2) of the HIV-1 envelope, but the mechanism of their elicitation has been unclear. Here we define the developmental pathway by which such antibodies are generated and acquire the requisite molecular characteristics for neutralization. Twelve somatically related neutralizing antibodies (CAP256-VRC26.01-12) were isolated from donor CAP256 (from the Centre for the AIDS Programme of Research in South Africa (CAPRISA)); each antibody contained the protruding tyrosine-sulphated, anionic antigen-binding loop (complementarity-determining region (CDR) H3) characteristic of this category of antibodies. Their unmutated ancestor emerged between weeks 30-38 post-infection with a 35-residue CDR H3, and neutralized the virus that superinfected this individual 15 weeks after initial infection. Improved neutralization breadth and potency occurred by week 59 with modest affinity maturation, and was preceded by extensive diversification of the virus population. HIV-1 V1V2-directed neutralizing antibodies can thus develop relatively rapidly through initial selection of B cells with a long CDR H3, and limited subsequent somatic hypermutation. These data provide important insights relevant to HIV-1 vaccine development

Glycosylator: a Python framework for the rapid modeling of glycans

BACKGROUND Carbohydrates are a class of large and diverse biomolecules, ranging from a simple monosaccharide to large multi-branching glycan structures. The covalent linkage of a carbohydrate to the nitrogen atom of an asparagine, a process referred to as N-linked glycosylation, plays an important role in the physiology of many living organisms. Most software for glycan modeling on a personal desktop computer requires knowledge of molecular dynamics to interface with specialized programs such as CHARMM or AMBER. There are a number of popular web-based tools that are available for modeling glycans (e.g., GLYCAM-WEB (http:// https://dev.glycam.org/gp/ ) or Glycosciences.db ( http://www.glycosciences.de/ )). However, these web-based tools are generally limited to a few canonical glycan conformations and do not allow the user to incorporate glycan modeling into their protein structure modeling workflow. RESULTS Here, we present Glycosylator, a Python framework for the identification, modeling and modification of glycans in protein structure that can be used directly in a Python script through its application programming interface (API) or through its graphical user interface (GUI). The GUI provides a straightforward two-dimensional (2D) rendering of a glycoprotein that allows for a quick visual inspection of the glycosylation state of all the sequons on a protein structure. Modeled glycans can be further refined by a genetic algorithm for removing clashes and sampling alternative conformations. Glycosylator can also identify specific three-dimensional (3D) glycans on a protein structure using a library of predefined templates. CONCLUSIONS Glycosylator was used to generate models of glycosylated protein without steric clashes. Since the molecular topology is based on the CHARMM force field, new complex sugar moieties can be generated without modifying the internals of the code. Glycosylator provides more functionality for analyzing and modeling glycans than any other available software or webserver at present. Glycosylator will be a valuable tool for the glycoinformatics and biomolecular modeling communities