Antibodies are fundamental effectors of humoral immunity, and have become a highly successful class of therapeutics. There is increasing evidence that antibodies utilize transient homotypic interactions to enhance function, and elucidation of such interactions can provide insights into their biology and new opportunities for their optimization as drugs. Yet the transitory nature of weak interactions makes them difficult to investigate. Capitalizing on their rich structural data and high conservation, we have characterized all the ways that antibody fragment antigen-binding (Fab) regions interact crystallographically. This approach led to the discovery of previously unrealized interfaces between antibodies. While diverse interactions exist, β-sheet dimers and variable-constant elbow dimers are recurrent motifs. Disulfide engineering enabled interactions to be trapped and investigated structurally and functionally, providing experimental validation of the interfaces and illustrating their potential for optimization. This work provides first insight into previously undiscovered oligomeric interactions between antibodies, and enables new opportunities for their biotherapeutic optimization.

Significance Weak transient interactions are fundamental to immune responses, enabling avidity-driven triggers for pathogen neutralization and cellular regulation. In contrast to obligate binding interactions that can be directly investigated structurally, the low or transitory abundance of weak interactions make them difficult to identify and characterize. This study leverages receptor agonism systems that are sensitive to oligomerization to investigate transient homotypic interfaces between antibody Fab regions. Our results show that self-association determinants are encoded naturally by the antibody germline through light chain complementarity determining region 2 (CDRL2), and these determinants can be engineered into antibodies to enhance their therapeutic properties. Insights into avidity-driven interactions create opportunities for optimization, and accordingly this work expands the engineering toolbox for antibody-based drugs.

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.

Drug extrusion through molecular efflux pumps is an important mechanism for the survival of many pathogenic bacteria by removing drugs, providing multidrug resistance (MDR). Understanding molecular mechanisms for drug extrusion in multidrug efflux pumps is important for the development of new antiresistance drugs. The AbgT family of transporters involved in the folic acid biosynthesis pathway represents one such important efflux pump system. In addition to the transport of the folic acid precursor p-amino benzoic acid (PABA), members of this family are involved in the efflux of several sulfa drugs, conferring drug resistance to the bacteria. With the availability of structures for two members of this family (YdaH and MtrF), we investigate molecular pathways for transport of PABA and a sulfa drug (sulfamethazine) particularly for the YdaH transporter using steered molecular dynamics. Our analyses reveal the probable ligand migration pathways through the transporter, which also identifies key residues along the transport pathway. In addition, simulations using both PABA and sulfamethazine show how the protein is able to transport ligands of different shapes and sizes out of the pathogen. Our observations confirm previously reported functional residues for transport along the pathways by which YdaH transporters achieve antibiotic resistance to shuttle drugs out of the cells.

Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.

Microalgae and cyanobacteria contribute roughly half of the global photosynthetic carbon assimilation. Faced with limited access to CO2 in aquatic environments, which can vary daily or hourly, these microorganisms have evolved use of an efficient CO2 concentrating mechanism (CCM) to accumulate high internal concentrations of inorganic carbon (C-i) to maintain photosynthetic performance. For eukaryotic algae, a combination of molecular, genetic and physiological studies using the model organism Chlamydomonas reinhardtii, have revealed the function and molecular characteristics of many CCM components, including active C-i uptake systems. Fundamental to eukaryotic C-i uptake systems are C-i transporters/channels located in membranes of various cell compartments, which together facilitate the movement of C-i from the environment into the chloroplast, where primary CO2 assimilation occurs. Two putative plasma membrane C-i transporters, HLA3 and LCI1, are reportedly involved in active C-i uptake. Based on previous studies, HLA3 clearly plays a meaningful role in HCO3- transport, but the function of LCI1 has not yet been thoroughly investigated so remains somewhat obscure. Here we report a crystal structure of the full-length LCI1 membrane protein to reveal LCI1 structural characteristics, as well as in vivo physiological studies in an LCI1 loss-of-function mutant to reveal the C-i species preference for LCI1. Together, these new studies demonstrate LCI1 plays an important role in active CO2 uptake and that LCI1 likely functions as a plasma membrane CO2 channel, possibly a gated channel.

Antibody variable domain sequence diversity is generated by recombination of germline segments. The third complementarity-determining region of the heavy chain (CDR H3) is the region of highest sequence diversity and is formed by the joining of heavy chain V H , D H and J H germline segments combined with random nucleotide trimming and additions between these segments. We show that CDR H3 and junctional segment length distributions are biased in human antibody repertoires as a function of V H , V L and J H germline segment utilization. Most length biases are apparent in the naive and antigen experienced B cell compartments but not in nonproductive recombination products, indicating B cell selection as a major driver of these biases. Our findings reveal biases in the antibody CDR H3 diversity landscape shaped by V H , V L , and J H germline segment use during naive and antigen-experienced repertoire selection.

Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first "Highland Games" competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development-related properties of six antibodies from their amino acid sequences alone. Predictions included purification-influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all-prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in-house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools.

Antibody variable domain sequence diversity is generated by recombination of germline segments. The third complementarity-determining region of the heavy chain (CDR H3) is the region of highest sequence diversity and is formed by the joining of heavy chain VH, DH and JH germline segments combined with random nucleotide trimming and additions between these segments. We show that CDR H3 length distribution is biased in human antibody repertoires as a function of VH, VL and JH germline segment utilization. Most length biases are apparent in the naïve B cell compartment, with a significant bias towards shorter CDR H3 sequences observed in association with a subset of VH and VL germlines in the antigen experienced compartment. Similar biases were not observed in nonproductive heavy chain recombination products, indicating selection of the repertoire during B cell maturation as a major driver of the length biases. Some VH-associated CDR H3 length biases are dependent on utilization of specific JH germline segments in a manner not directly linked to JH segment length in the germline, but are rather associated with selection of differentially trimmed JH segments in the naïve compartment. In addition, DH segment and N-region random nucleotide insertion lengths within CDR H3 in the naïve compartment were also biased by specific VH/JH germline combinations, indicating a complex set of constraints between germline segments selected during repertoire maturation. Our findings reveal biases in the antibody diversity landscape shaped by VH, VL, and JH germline features with implications for mechanisms of naïve and immune repertoire selection.

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug's potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs' sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.

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.

Predicting protein motions is important for bridging the gap between protein structure and function. With growing numbers of structures of the same or closely related proteins becoming available, it is now possible to understand more about the intrinsic dynamics of a protein with principal component analysis (PCA) of the motions apparent within ensembles of experimental structures. In this paper, we compare the motions extracted from experimental ensembles of 50 different proteins with the modes of motion predicted by several types of coarse-grained elastic network models (ENMs) which additionally take into account more details of either the protein geometry or the amino acid specificity. We further compare the structural variations in the experimental ensembles with the motions sampled in molecular dynamics (MD) simulations for a smaller subset of 17 proteins with available trajectories. We find that the correlations between the motions extracted from MD trajectories and experimental structure ensembles are slightly different than those for the ENMs, possibly reflecting potential sampling biases. We find that there are small gains in the predictive power of the ENMs in reproducing motions present in either experimental or MD ensembles by accounting for the protein geometry rather than the amino acid specificity of the interactions.

Protein functional mechanisms usually require conformational changes, and often there are known structures for the different conformational states. However, usually neither the origin of the driving force nor the underlying pathways for these conformational transitions is known. Exothermic chemical reactions may be an important source of forces that drive conformational changes. Here we investigate this type of force originating from ATP hydrolysis in the chaperonin GroEL, by applying forces originating from the chemical reaction. Specifically, we apply directed forces to drive the GroEL conformational changes and learn that there is a highly specific direction for applied forces to drive the closed form to the open form. For this purpose, we utilize coarse-grained elastic network models. Principal component analysis on 38 GroEL experimental structures yields the most important motions, and these are used in structural interpolation for the construction of a coarse-grained free energy landscape. In addition, we investigate a more random application of forces with a Monte Carlo method and demonstrate pathways for the closed-open conformational transition in both directions by computing trajectories that are shown upon the free energy landscape. Initial root mean square deviation (RMSD) between the open and closed forms of the subunit is 14.7 Å and final forms from our simulations reach an average RMSD of 3.6 Å from the target forms, closely matching the level of resolution of the coarse-grained model.

Desmosomes are robust adhesive junctions found in tissues that resist mechanical stress. Their intercellular region is composed of densely packed desmocollin (Dsc) and desmoglein (Dsg) adhesive proteins. Previous studies demonstrated that E-cadherin (Ecad), an essential cell-cell adhesion protein, is required for the initial assembly of desmosomes. However, the mechanistic role of Ecad in desmosome formation is unknown. Here, we use single molecule force measurements with an atomic force microscope (AFM), super resolution imaging, and protein docking computations, to resolve the roles of Ecad and isoform 2 of Dsc and Dsg in the desmosome assembly process. AFM force measurements reveal that Ecad forms short-lived interactions with Dsg2 but does not bind to Dsc2. Force measurements with Ecad mutants and protein docking demonstrate that Ecad and Dsg2 bind in a novel trans-conformation mediated by the conserved Leu 175 on the Ecad ectodomain. Single molecule AFM measurements also show that Dsc2 forms long-lived bonds with Dsg2, but interacts only weakly with Dsc2. Taken together, this data suggests that while Ecad and Dsg2 from opposing cells interact and facilitate desmosome assembly, Dsg2 subsequently dissociates from Ecad and binds to Dsc2 to mediate robust adhesion in mature desmosomes. We confirm this using super resolution imaging of desmosomes in human keratinocytes and show that while Ecad is localized to nascent desmosomes, it is excluded as the desmosomes mature.

Evaluating protein structures requires reliable free energies with good estimates of both potential energies and entropies. Although there are many demonstrated successes from using knowledge-based potential energies, computing entropies of proteins has lagged far behind. Here we take an entirely different approach and evaluate knowledge-based conformational entropies of proteins based on the observed frequencies of contact changes between amino acids in a set of 167 diverse proteins, each of which has two alternative structures. The results show that charged and polar interactions break more often than hydrophobic pairs. This pattern correlates strongly with the average solvent exposure of amino acids in globular proteins, as well as with polarity indices and the sizes of the amino acids. Knowledge-based entropies are derived by using the inverse Boltzmann relationship, in a manner analogous to the way that knowledge-based potentials have been extracted. Including these new knowledge-based entropies almost doubles the performance of knowledge-based potentials in selecting the native protein structures from decoy sets. Beyond the overall energy-entropy compensation, a similar compensation is seen for individual pairs of interacting amino acids. The entropies in this report have immediate applications for 3D structure prediction, protein model assessment, and protein engineering and design.

ABSTRACTIt is known that over half of the proteins encoded by most organisms function as oligomeric complexes. Oligomerization confers structural stability and dynamics changes in proteins. We investigate the effects of oligomerization on protein dynamics and its functional significance for a set of 145 multimeric proteins. Using coarse‐grained elastic network models, we inspect the changes in residue fluctuations upon oligomerization and then compare with residue conservation scores to identify the functional significance of these changes. Our study reveals conservation of about ½ of the fluctuations, with ¼ of the residues increasing in their mobilities and ¼ having reduced fluctuations. The residues with dampened fluctuations are evolutionarily more conserved and can serve as orthosteric binding sites, indicating their importance. We also use triosephosphate isomerase as a test case to understand why certain enzymes function only in their oligomeric forms despite the monomer including all required catalytic residues. To this end, we compare the residue communities (groups of residues which are highly correlated in their fluctuations) in the monomeric and dimeric forms of the enzyme. We observe significant changes to the dynamical community architecture of the catalytic core of this enzyme. This relates to its functional mechanism and is seen only in the oligomeric form of the protein, answering why proteins are oligomeric structures. Proteins 2017; 85:1422–1434. © 2017 Wiley Periodicals, Inc.

Classical cadherin cell-cell adhesion proteins are essential for the formation and maintenance of tissue structures; their primary function is to physically couple neighboring cells and withstand mechanical force. Cadherins from opposing cells bind in two distinct trans conformations: strand-swap dimers and X-dimers. As cadherins convert between these conformations, they form ideal bonds (i.e., adhesive interactions that are insensitive to force). However, the biophysical mechanism for ideal bond formation is unknown. Here, we integrate single-molecule force measurements with coarse-grained and atomistic simulations to resolve the mechanistic basis for cadherin ideal bond formation. Using simulations, we predict the energy landscape for cadherin adhesion, the transition pathways for interconversion between X-dimers and strand-swap dimers, and the cadherin structures that form ideal bonds. Based on these predictions, we engineer cadherin mutants that promote or inhibit ideal bond formation and measure their force-dependent kinetics using single-molecule force-clamp measurements with an atomic force microscope. Our data establish that cadherins adopt an intermediate conformation as they shuttle between X-dimers and strand-swap dimers; pulling on this conformation induces a torsional motion perpendicular to the pulling direction that unbinds the proteins and forms force-independent ideal bonds. Torsional motion is blocked when cadherins associate laterally in a cis orientation, suggesting that ideal bonds may play a role in mechanically regulating cadherin clustering on cell surfaces.

Background: Opsins are the only class of proteins used for light perception in image-forming eyes. Gene duplication and subsequent functional divergence of opsins have played an important role in expanding photoreceptive capabilities of organisms by altering what wavelengths of light are absorbed by photoreceptors (spectral tuning). However, new opsin copies may also acquire novel function or subdivide ancestral functions through changes to temporal, spatial or the level of gene expression. Here, we test how opsin gene copies diversify in function and evolutionary fate by characterizing four rhabdomeric (G(q)-protein coupled) opsins in the scallop, Argopecten irradians, identified from tissue-specific transcriptomes.Results: Under a phylogenetic analysis, we recovered a pattern consistent with two rounds of duplication that generated the genetic diversity of scallop G(q)-opsins. We found strong support for differential expression of paralogous G(q)-opsins across ocular and extra-ocular photosensitive tissues, suggesting that scallop G(q)-opsins are used in different biological contexts due to molecular alternations outside and within the protein-coding regions. Finally, we used available protein models to predict which amino acid residues interact with the light-absorbing chromophore. Variation in these residues suggests that the four G(q)-opsin paralogs absorb different wavelengths of light.Conclusions: Our results uncover novel genetic and functional diversity in the light-sensing structures of the scallop, demonstrating the complicated nature of G(q)-opsin diversification after gene duplication. Our results highlight a change in the nearly ubiquitous shadow response in molluscs to a narrowed functional specificity for visual processes in the eyed scallop. Our findings provide a starting point to study how gene duplication may coincide with eye evolution, and more specifically, different ways neofunctionalization of G(q)-opsins may occur.

Predicting conformational changes of proteins is needed in order to fully comprehend functional mechanisms. With the large number of available structures in sets of related proteins, it is now possible to directly visualize the clusters of conformations and their conformational transitions through the use of principal component analysis. The most striking observation about the distributions of the structures along the principal components is their highly non-uniform distributions. In this work, we use principal component analysis of experimental structures of 50 diverse proteins to extract the most important directions of their motions, sample structures along these directions, and estimate their free energy landscapes by combining knowledge-based potentials and entropy computed from elastic network models. When these resulting motions are visualized upon their coarse-grained free energy landscapes, the basis for conformational pathways becomes readily apparent. Using three well-studied proteins, T4 lysozyme, serum albumin, and sarco-endoplasmic reticular Ca2+ adenosine triphosphatase (SERCA), as examples, we show that such free energy landscapes of conformational changes provide meaningful insights into the functional dynamics and suggest transition pathways between different conformational states. As a further example, we also show that Monte Carlo simulations on the coarse-grained landscape of HIV-1 protease can directly yield pathways for force-driven conformational changes.

The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high-for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods-Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them.