A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates

Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: Meeting new challenges

© 2014 Elsevier Ltd All rights reserved. Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems

An evolutionary basis for protein design and structure prediction

The sequence diversity of protein families is a result of the biophysical selection pressures that shaped their evolutionary history. Among the dominant pressures is selection for protein thermostability, which in itself is an attractive target in protein engineering because of its importance for various biopharmaceutical properties, the performance of industrial enzymes, and the ability to design new protein functions. In the first part of this thesis, we use models of evolutionary dynamics and biophysical fitness functions to derive the relationship between amino acid frequencies in sites of proteins and the stability effects of mutations. This analysis suggests that a commonly applied assumption (that amino acids frequencies are Boltzmann distributed) is inaccurate, and we provide a new relation consistent with the current understanding of evolutionary dynamics and protein fitness. Next, we study the extent to which the evolutionary pattern of amino acid substitutions can be explained by protein stability, as predicted using all-atom models of protein energetics. We show that at least 65\% of the substitution pattern can be explained by thermostability. With the same model, we show that functional sites (e.g. active sites or binding sites) can be predicted when the apparent evolutionary site-rate deviates significantly from that of a stability-only null-model of evolution. Finally, we study how the strength of selective pressure affects the evolutionary behavior of proteins, again using the same models, but this time generating evolutionary trajectories. We find that energetic coupling between amino acids (coevolution) and the detriment of mutation increases as the strength of selection increases. Antibodies are a key molecular component of the adaptive immune system of vertebrates and an important biopharmaceutical molecule. In the second part of the thesis, we predict and design the structure of antibodies by using energetics derived from sequence alignments and following the evolutionary encoded modular segmentation of the molecule. Through multiple design and test iterations, we were able to design antibodies, which express stably and, in some cases, bind target antigens. The developed structure prediction algorithm performs as well as other methods, is in some cases more accurate, and produces models with lower chemical strain. We use the structure prediction method to study a tumor-associated carbohydrate binding antibody.Finally, we also review the literature on design of symmetrical protein self-assembly, and study the dynamical properties of a partially disordered chaperone protein, calreticulin

Atomistic simulation of protein evolution reveals sequence covariation and time-dependent fluctuations of site-specific substitution rates.

Thermodynamic stability is a crucial fitness constraint in protein evolution and is a central factor in shaping the sequence landscapes of proteins. The correlation between stability and molecular fitness depends on the mechanism that relates the biophysical property with biological function. In the simplest case, stability and fitness are related by the amount of folded protein. However, when proteins are toxic in the unfolded state, the fitness function shifts, resulting in higher stability under mutation-selection balance. Likewise, a higher population size results in a similar change in protein stability, as it magnifies the effect of the selection pressure in evolutionary dynamics. This study investigates how such factors affect the evolution of protein stability, site-specific mutation rates, and residue-residue covariation. To simulate evolutionary trajectories with realistic modeling of protein energetics, we develop an all-atom simulator of protein evolution, RosettaEvolve. By evolving proteins under different fitness functions, we can study how the fitness function affects the distribution of proposed and accepted mutations, site-specific rates, and the prevalence of correlated amino acid substitutions. We demonstrate that fitness pressure affects the proposal distribution of mutational effects, that changes in stability can largely explain variations in site-specific substitution rates in evolutionary trajectories, and that increased fitness pressure results in a stronger covariation signal. Our results give mechanistic insight into the evolutionary consequences of variation in protein stability and provide a basis to rationalize the strong covariation signal observed in natural sequence alignments

Computational design of protein self-assembly

Protein self-assembly is extensively used in nature to build functional biomolecules and provides a general approach to design molecular complexes with many intriguing applications. Although computational design of protein-protein interfaces remains difficult, much progress has recently been made in de novo design of protein assemblies with cyclic, helical, cubic, internal and lattice symmetries. Here, we discuss some of the underlying biophysical principles of self-assembly that influence the design problem and highlight methodological advances that have made self-assembly design a fruitful area of protein design

A thermodynamic model of protein structure evolution explains empirical amino acid substitution matrices

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi-nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life

Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors

<div><p>The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10³ fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes.</p></div

Structural representations of ligand binding modes compared to experimental structures.

<p>Incorrect loop placement and incorrect ligand orientation often prevent Rosetta from converging on the experimental ligand binding mode. Ligand binding modes from the experimental structures are shown in gray and the top ranked model via clustering by ligand RMSD is shown in yellow for <b>A)</b> A2Ar, <b>B)</b> KOR and <b>C)</b> H1R. Cases where top ranked binding modes captured the experimental binding mode within 2.0 Å were <b>D)</b> DOR and <b>E)</b> M2R<b>.</b></p

Sampling efficiency for ligand docking results.

<p>Reported is the negative log of the sampling efficiency of ligand binding modes within 2.0 Å RMSD of the bioactive ligand conformation within the experimental structure as measured over the ligand heavy-atoms.</p>*<p>denotes where sampling efficiency of Rosetta is worse than the worst-case uniform sampling scenario.</p><p># ND denotes not defined. No binding modes within 2.0 Å were sampled for this case.</p>1<p>fold improvement over USE2.0 of bioactive ligand is given in parentheses.</p>2<p>fold improvement over USE2.0 of ligand conformers is given in parentheses.</p

Structural representations of transmembrane helical regions from GPCR comparative models.

<p><b>A)</b> TM7 in the top ranked comparative model of CXCR4 (blue) deviates from experimental structure (gray), specifically at W283 (highlighted in yellow). Cases where helical kinks exist in the template but are resolved in the comparative model include <b>B)</b> S1P1R, where the top ranked model (blue) resolves the kink in TM2 cause by P84 (highlighted in red) in the D3R template (green) and <b>C)</b> KOR, where the top ranked model (blue) resolves the kink in TM4 caused by G178 (highlighted in red) from the DOR template (green). The top ranked model is the best scoring model of the largest cluster, where clustering is performed on pairwise full receptor C-alpha RMSD over the top ten percent of comparative models by energy.</p