Mutational Scanning Research Articles

Breakthrough infection elicits hypermutated IGHV3-53/3-66 public antibodies with broad and potent neutralizing activity against SARS-CoV-2 variants including the emerging EG.5 lineages.

The rapid emergence of SARS-CoV-2 variants of concern (VOCs) calls for efforts to study broadly neutralizing antibodies elicited by infection or vaccination so as to inform the development of vaccines and antibody therapeutics with broad protection. Here, we identified two convalescents of breakthrough infection with relatively high neutralizing titers against all tested viruses. Among 50 spike-specific monoclonal antibodies (mAbs) cloned from their B cells, the top 6 neutralizing mAbs (KXD01-06) belong to previously defined IGHV3-53/3-66 public antibodies. Although most antibodies in this class are dramatically escaped by VOCs, KXD01-06 all exhibit broad neutralizing capacity, particularly KXD01-03, which neutralize SARS-CoV-2 from prototype to the emerging EG.5.1 and FL.1.5.1. Deep mutational scanning reveals that KXD01-06 can be escaped by current and prospective variants with mutations on D420, Y421, L455, F456, N460, A475 and N487. Genetic and functional analysis further indicates that the extent of somatic hypermutation is critical for the breadth of KXD01-06 and other IGHV3-53/3-66 public antibodies. Overall, the prevalence of broadly neutralizing IGHV3-53/3-66 public antibodies in these two convalescents provides rationale for novel vaccines based on this class of antibodies. Meanwhile, KXD01-06 can be developed as candidates of therapeutics against SARS-CoV-2 through further affinity maturation.

Abstract A037: Mechanisms of resistance to BAY 2927088, the first reversible inhibitor targeting EGFR exon 20 insertion mutations in non-small cell lung cancer

Abstract Molecular therapies targeting EGFR-mutant non-small cell lung cancer (NSCLC) have dramatically improved prognosis for patients with the classical activating mutations, L858R and exon 19 deletion. However, tumors harboring EGFR exon 20 insertions have been historically more difficult to target. Agents such as amivantamab and mobocertinib have recently been granted accelerated approval for treatment of lung cancer patients with exon 20 insertions, but agents with an improved selectivity profile versus wild-type EGFR are still needed. BAY 2927088, currently in phase I clinical trials, is a novel reversible inhibitor that targets EGFR exon 20 insertion mutations with decreased activity towards wild-type EGFR. Although most patients with advanced EGFR-mutant NSCLC eventually progress due to acquired resistance to EGFR inhibitors, the mechanisms of resistance to BAY 2927088 are currently unknown. Using two different methods of in vitro resistance generation by inhibitor treatment of sensitive cell lines, we identified mechanisms of intrinsic and acquired resistance to BAY 2927088 and other EGFR inhibitors. Incubation with a high concentration of inhibitor selected for drug-tolerant persister cells that underwent a transcriptional state transition resembling EMT (epithelial-mesenchymal transition). Conversely, gradual escalation of inhibitor concentrations resulted in acquisition of oncogenic NRAS mutations. In a parallel approach, we performed a deep mutational scanning assay of the EGFR D770_N771insSVD kinase domain, replacing each amino acid with every other possible amino acid, identifying both known and novel mutations that cause resistance to BAY 2927088 and other EGFR inhibitors. As expected, the C797S mutation was enriched as a mechanism of acquired resistance only to irreversible EGFR exon 20 insertion inhibitors. Surprisingly, the T790M gatekeeper mutation was found to cause resistance to all EGFR exon 20 insertion inhibitors tested, including irreversible inhibitors. Several novel resistance mutations clustering predominantly in exons 20 and 21 were also identified. Citation Format: Gizem Karsli Uzunbas, Quinn McVeigh, Akansha Gupta, Stephanie Hoyt, Kristen Doucette, Hasmik Keshishian, Steven Carr, Xiaoping Yang, David Root, Andrew D Cherniack, Franziska Siegel, Stephan Siegel, Matthew Meyerson, Heidi Greulich. Mechanisms of resistance to BAY 2927088, the first reversible inhibitor targeting EGFR exon 20 insertion mutations in non-small cell lung cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A037.

High-throughput screening of genetic and cellular drivers of syncytium formation induced by the spike protein of SARS-CoV-2

Mapping mutations and discovering cellular determinants that cause the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to induce infected cells to form syncytia would facilitate the development of strategies for blocking the formation of such cell–cell fusion. Here we describe high-throughput screening methods based on droplet microfluidics and the size-exclusion selection of syncytia, coupled with large-scale mutagenesis and genome-wide knockout screening via clustered regularly interspaced short palindromic repeats (CRISPR), for the large-scale identification of determinants of cell–cell fusion. We used the methods to perform deep mutational scans in spike-presenting cells to pinpoint mutable syncytium-enhancing substitutions in two regions of the spike protein (the fusion peptide proximal region and the furin-cleavage site). We also used a genome-wide CRISPR screen in cells expressing the receptor angiotensin-converting enzyme 2 to identify inhibitors of clathrin-mediated endocytosis that impede syncytium formation, which we validated in hamsters infected with SARS-CoV-2. Finding genetic and cellular determinants of the formation of syncytia may reveal insights into the physiological and pathological consequences of cell–cell fusion.

Repeated Omicron exposures override ancestral SARS-CoV-2 immune imprinting.

The continuing emergence of SARS-CoV-2 variants highlights the need to update COVID-19 vaccine compositions. However, immune imprinting induced by vaccination based on the ancestral (hereafter referred to as WT) strain would compromise the antibody response to Omicron-based boosters1-5. Vaccination strategies to counter immune imprinting are critically needed. Here we investigated the degree and dynamics of immune imprinting in mouse models and human cohorts, especially focusing on the role of repeated Omicron stimulation. In mice, the efficacy of single Omicron boosting is heavily limited when using variants that are antigenically distinct from WT-such as the XBB variant-and this concerning situation could be mitigated by a second Omicron booster. Similarly, in humans, repeated Omicron infections could alleviate WT vaccination-induced immune imprinting and generate broad neutralization responses in both plasma and nasal mucosa. Notably, deep mutational scanning-based epitope characterization of 781 receptor-binding domain (RBD)-targeting monoclonal antibodies isolated from repeated Omicron infection revealed that double Omicron exposure could induce a large proportion of matured Omicron-specific antibodies that have distinct RBD epitopes to WT-induced antibodies. Consequently, immune imprinting was largely mitigated, and the bias towards non-neutralizing epitopes observed in single Omicron exposures was restored. On the basis of the deep mutational scanning profiles, we identified evolution hotspots of XBB.1.5 RBD and demonstrated that these mutations could further boost the immune-evasion capability of XBB.1.5 while maintaining high ACE2-binding affinity. Our findings suggest that the WT component should be abandoned when updating COVID-19 vaccines, and individuals without prior Omicron exposure should receive two updated vaccine boosters.

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

High-throughput analysis system of interaction kinetics for data-driven antibody design

Surface plasmon resonance (SPR) is widely used for antigen–antibody interaction kinetics analysis. However, it has not been used in the screening phase because of the low throughput of measurement and analysis. Herein, we proposed a high-throughput SPR analysis system named “BreviA” using the Brevibacillus expression system. Brevibacillus was transformed using a plasmid library containing various antibody sequences, and single colonies were cultured in 96-well plates. Sequence analysis was performed using bacterial cells, and recombinant antibodies secreted in the supernatant were immobilized on a sensor chip to analyze their interactions with antigens using high-throughput SPR. Using this system, the process from the transformation to 384 interaction analyses can be performed within a week. This system utility was tested using an interspecies specificity design of an anti-human programmed cell death protein 1 (PD-1) antibody. A plasmid library containing alanine and tyrosine mutants of all complementarity-determining region residues was generated. A high-throughput SPR analysis was performed against human and mouse PD-1, showing that the mutation in the specific region enhanced the affinity for mouse PD-1. Furthermore, deep mutational scanning of the region revealed two mutants with > 100-fold increased affinity for mouse PD-1, demonstrating the potential efficacy of antibody design using data-driven approach.

Deep mutational scanning highlights a role for cytosolic regions in Hrd1 function

Misfolded endoplasmic reticulum (ER) proteins are degraded through a process called ER-associated degradation (ERAD). Soluble, lumenal ERAD targets are recognized, retrotranslocated across the ER membrane, ubiquitinated, extracted from the membrane, and degraded by the proteasome using an ERAD pathway containing a ubiquitin ligase called Hrd1. To determine how Hrd1 mediates these processes, we developed a deep mutational scanning approach to identify residues involved in Hrd1 function, including those exclusively required for lumenal degradation. We identify several regions required for different Hrd1 functions. Most surprisingly, we find two cytosolic regions of Hrd1 required for lumenal ERAD substrate degradation. Using invivo and invitro approaches, we define roles for disordered regions between structural elements that are required for Hrd1 autoubiquitination and substrate interaction. Our results demonstrate that disordered cytosolic regions promote substrate retrotranslocation by controlling Hrd1 activation and establishing directionality of retrotranslocation for lumenal substrate across the ER membrane.

Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein.

The influenza virus polymerase is important for adaptation to new hosts and, as a determinant of mutation rate, for the process of adaptation itself. We performed a deep mutational scan of the polymerase basic 1 (PB1) protein to gain insights into the structural and functional constraints on the influenza RNA-dependent RNA polymerase. We find that PB1 is highly constrained at specific sites that are only moderately predicted by the global structure or larger domain. We identified a number of beneficial mutations, many of which have been shown to be functionally important or observed in influenza virus' natural evolution. Overall, our atlas of PB1 mutations and their fitness impacts serves as an important resource for future studies of influenza replication and evolution.

PiggyBac-mediated genomic integration of linear dsDNA-based library for deep mutational scanning in mammalian cells.

Deep mutational scanning (DMS) makes it possible to perform massively parallel quantification of the relationship between genetic variants and phenotypes of interest. However, the difficulties in introducing large variant libraries into mammalian cells greatly hinder DMS under physiological states. Here, we developed two novel strategies for DMS library construction in mammalian cells, namely 'piggyBac-in vitro ligation' and 'piggyBac-in vitro ligation-PCR'. For the first strategy, we took the 'in vitro ligation' approach to prepare high-diversity linear dsDNAs, and integrate them into the mammalian genome with a piggyBac transposon system. For the second strategy, we further added a PCR step using the in vitro ligation dsDNAs as templates, for the construction of high-content genome-integrated libraries via large-scale transfection. Both strategies could successfully establish genome-integrated EGFP-chromophore-randomized libraries in HEK293T cells and enrich the green fluorescence-chromophore amino-acid sequences. And we further identified a novel transcriptional activator peptide with the 'piggyBac-in vitro ligation-PCR' strategy. Our novel strategies greatly facilitate the construction of large variant DMS library in mammalian cells, and may have great application potential in the future.

Comparative Analysis of Conformational Dynamics and Systematic Characterization of Cryptic Pockets in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 Spike Complexes with the ACE2 Host Receptor: Confluence of Binding and Structural Plasticity in Mediating Networks of Conserved Allosteric Sites.

In the current study, we explore coarse-grained simulations and atomistic molecular dynamics together with binding energetics scanning and cryptic pocket detection in a comparative examination of conformational landscapes and systematic characterization of allosteric binding sites in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 spike full-length trimer complexes with the host receptor ACE2. Microsecond simulations, Markov state models and mutational scanning of binding energies of the SARS-CoV-2 BA.2 and BA.2.75 receptor binding domain complexes revealed the increased thermodynamic stabilization of the BA.2.75 variant and significant dynamic differences between these Omicron variants. Molecular simulations of the SARS-CoV-2 Omicron spike full-length trimer complexes with the ACE2 receptor complemented atomistic studies and enabled an in-depth analysis of mutational and binding effects on conformational dynamic and functional adaptability of the Omicron variants. Despite considerable structural similarities, Omicron variants BA.2, BA.2.75 and XBB.1 can induce unique conformational dynamic signatures and specific distributions of the conformational states. Using conformational ensembles of the SARS-CoV-2 Omicron spike trimer complexes with ACE2, we conducted a comprehensive cryptic pocket screening to examine the role of Omicron mutations and ACE2 binding on the distribution and functional mechanisms of the emerging allosteric binding sites. This analysis captured all experimentally known allosteric sites and discovered networks of inter-connected and functionally relevant allosteric sites that are governed by variant-sensitive conformational adaptability of the SARS-CoV-2 spike structures. The results detailed how ACE2 binding and Omicron mutations in the BA.2, BA.2.75 and XBB.1 spike complexes modulate the distribution of conserved and druggable allosteric pockets harboring functionally important regions. The results are significant for understanding the functional roles of druggable cryptic pockets that can be used for allostery-mediated therapeutic intervention targeting conformational states of the Omicron variants.

Deep mutational scanning reveals the molecular determinants of RNA polymerase-mediated adaptation and tradeoffs

RNA polymerase (RNAP) is emblematic of complex biological systems that control multiple traits involving trade-offs such as growth versus maintenance. Laboratory evolution has revealed that mutations in RNAP subunits, including RpoB, are frequently selected. However, we lack a systems view of how mutations alter the RNAP molecular functions to promote adaptation. We, therefore, measured the fitness of thousands of mutations within a region of rpoB under multiple conditions and genetic backgrounds, to find that adaptive mutations cluster in two modules. Mutations in one module favor growth over maintenance through a partial loss of an interaction associated with faster elongation. Mutations in the other favor maintenance over growth through a destabilized RNAP-DNA complex. The two molecular handles capture the versatile RNAP-mediated adaptations. Combining both interaction losses simultaneously improved maintenance and growth, challenging the idea that growth-maintenance tradeoff resorts only from limited resources, and revealing how compensatory evolution operates within RNAP.

Deep mutational scanning of influenza A virus neuraminidase facilitates the identification of drug resistance mutations in vivo.

NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA's mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.

An AsCas12f-based compact genome-editing tool derived by deep mutational scanning and structural analysis

SpCas9 and AsCas12a are widely utilized as genome-editing tools in human cells. However, their relatively large size poses a limitation for delivery by cargo-size-limited adeno-associated virus (AAV) vectors. The type V-F Cas12f from Acidibacillus sulfuroxidans is exceptionally compact (422 amino acids) and has been harnessed as a compact genome-editing tool. Here, we developed an approach, combining deep mutational scanning and structure-informed design, to successfully generate two AsCas12f activity-enhanced (enAsCas12f) variants. Remarkably, the enAsCas12f variants exhibited genome-editing activities in human cells comparable with those of SpCas9 and AsCas12a. The cryoelectron microscopy (cryo-EM) structures revealed that the mutations stabilize the dimer formation and reinforce interactions with nucleic acids to enhance their DNA cleavage activities. Moreover, enAsCas12f packaged with partner genes in an all-in-one AAV vector exhibited efficient knock-in/knock-out activities and transcriptional activation in mice. Taken together, enAsCas12f variants could offer a minimal genome-editing platform for invivo gene therapy.

Antibodies targeting a quaternary site on SARS-CoV-2 spike glycoprotein prevent viral receptor engagement by conformational locking

SARS-CoV-2 continues to evolve, with many variants evading clinically authorized antibodies. To isolate monoclonal antibodies (mAbs) with broadly neutralizing capacities against the virus, we screened serum samples from convalescing COVID-19 patients. We isolated two mAbs, 12-16 and 12-19, which neutralized all SARS-CoV-2 variants tested, including the XBB subvariants, and prevented infection in hamsters challenged with Omicron BA.1 intranasally. Structurally, both antibodies targeted a conserved quaternary epitope located at the interface between the N-terminal domain and subdomain 1, uncovering a site of vulnerability on SARS-CoV-2 spike. These antibodies prevented viral receptor engagement by locking the receptor-binding domain (RBD) of spike in the down conformation, revealing a mechanism of virus neutralization for non-RBD antibodies. Deep mutational scanning showed that SARS-CoV-2 could mutate to escape 12-19, but such mutations are rarely found in circulating viruses. Antibodies 12-16 and 12-19 hold promise as prophylactic agents for immunocompromised persons who do not respond robustly to COVID-19 vaccines.

Application of deep mutational scanning technology in protein research

As central players in cellular structure and function, proteins have long been central themes in life science research. Analyzing the impact of protein sequence variation on its structure and function is one of the important means to study proteins. In recent years, a technology called deep mutational scanning (DMS) has been widely used in the field of protein research. It introduces thousands of mutations in parallel in specific regions of proteins through high-abundance DNA libraries. After screening, high-throughput sequencing is employed to score each mutation, revealing sequence-function correlations. Due to its high-throughput, fast and easy, and labor-saving features, DMS has become an important method for protein function research and protein engineering. This review briefly summarizes the principle of DMS technology, highlighting its applications in mammalian cells. Moreover, this review analyzes the current technical bottlenecks, aiming to facilitate relevant research.

Massively-multiplexed epitope mapping techniques for viral antigen discovery.

Following viral infection, viral antigens bind specifically to receptors on the surface of lymphocytes thereby activating adaptive immunity in the host. An epitope, the smallest structural and functional unit of an antigen, binds specifically to an antibody or antigen receptor, to serve as key sites for the activation of adaptive immunity. The complexity and diverse range of epitopes are essential to study and map for the diagnosis of disease, the design of vaccines and for immunotherapy. Mapping the location of these specific epitopes has become a hot topic in immunology and immune therapy. Recently, epitope mapping techniques have evolved to become multiplexed, with the advent of high-throughput sequencing and techniques such as bacteriophage-display libraries and deep mutational scanning. Here, we briefly introduce the principles, advantages, and disadvantages of the latest epitope mapping techniques with examples for viral antigen discovery.

Combined prediction and design reveals the target recognition mechanism of an intrinsically disordered protein interaction domain

An increasing number of protein interaction domains and their targets are being found to be intrinsically disordered proteins (IDPs). The corresponding target recognition mechanisms are mostly elusive because of challenges in performing detailed structural analysis of highly dynamic IDP-IDP complexes. Here, we show that by combining recently developed computational approaches with experiments, the structure of the complex between the intrinsically disordered C-terminal domain (CTD) of protein 4.1G and its target IDP region in NuMA can be dissected at high resolution. First, we carry out systematic mutational scanning using dihydrofolate reductase-based protein complementarity analysis to identify essential interaction regions and key residues. The results are found to be highly consistent with an α/β-type complex structure predicted by AlphaFold2 (AF2). We then design mutants based on the predicted structure using a deep learning protein sequence design method. The solved crystal structure of one mutant presents the same core structure as predicted by AF2. Further computational prediction and experimental assessment indicate that the well-defined core structure is conserved across complexes of 4.1G CTD with other potential targets. Thus, we reveal that an intrinsically disordered protein interaction domain uses an α/β-type structure module formed through synergistic folding to recognize broad IDP targets. Moreover, we show that computational prediction and experiment can be jointly applied to segregate true IDP regions from the core structural domains of IDP-IDP complexes and to uncover the structure-dependent mechanisms of some otherwise elusive IDP-IDP interactions.

Fitness effects ofmutations to SARS-CoV-2 proteins.

Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here, we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.

Multiple mechanisms of self-association of chemokine receptors CXCR4 and CCR5 demonstrated by deep mutagenesis

Chemokine receptors are members of the rhodopsin-like class A GPCRs whose signaling through G proteins drives the directional movement of cells in response to a chemokine gradient. Chemokine receptors CXCR4 and CCR5 have been extensively studied due to their roles in leukocyte development and inflammation and their status as coreceptors for HIV-1 infection, among other roles. Both receptors form dimers or oligomers of unclear function. While CXCR4 has been crystallized in a dimeric arrangement, available atomic resolution structures of CCR5 are monomeric. To investigate their dimerization interfaces, we used a bimolecular fluorescence complementation (BiFC)-based screen and deep mutational scanning to find mutations that change how the receptors self-associate, either via specific oligomer assembly or alternative mechanisms of clustering in close proximity. Many disruptive mutations promoted self-associations nonspecifically, suggesting they aggregated in the membrane. A mutationally intolerant region was found on CXCR4 that matched the crystallographic dimer interface, supporting this dimeric arrangement in living cells. A mutationally intolerant region was also observed on the surface of CCR5 by transmembrane helices 3 and 4. Mutations predicted from the scan to reduce BiFC were validated and were localized in the transmembrane domains as well as the C-terminal cytoplasmic tails where they reduced lipid microdomain localization. A mutation in the dimer interface of CXCR4 had increased binding to the ligand CXCL12 and yet diminished calcium signaling. There was no change in syncytia formation with cells expressing HIV-1 Env. The data highlight that multiple mechanisms are involved in self-association of chemokine receptor chains.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates.

AbstractThe joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the "nominal" distribution of possible changes, prior to mutation or selection; (2) the "de novo" distribution of realized mutations; and (3) the "fixed" distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong-selection/weak-mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.