Multistate Design Research Articles

Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses

Influenza is a yearly threat to global public health. Rapid changes in influenza surface proteins resulting from antigenic drift and shift events make it difficult to readily identify antibodies with broadly neutralizing activity against different influenza subtypes with high frequency, specifically antibodies targeting the receptor binding domain (RBD) on influenza HA protein. We developed an optimized computational design method that is able to optimize an antibody for recognition of large panels of antigens. To demonstrate the utility of this multistate design method, we used it to redesign an antiinfluenza antibody against a large panel of more than 500 seasonal HA antigens of the H1 subtype. As a proof of concept, we tested this method on a variety of known antiinfluenza antibodies and identified those that could be improved computationally. We generated redesigned variants of antibody C05 to the HA RBD and experimentally characterized variants that exhibited improved breadth and affinity against our panel. C05 mutants exhibited improved affinity for three of the subtypes used in design by stabilizing the CDRH3 loop and creating favorable electrostatic interactions with the antigen. These mutants possess increased breadth and affinity of binding while maintaining high-affinity binding to existing targets, surpassing a major limitation up to this point.

Hydrolytic zinc metallopeptides using a computational multi-state design approach

Combination of multi-state design and long-timescale conformational dynamics as a powerful strategy to obtain metalloenzymes.

Peptide design by optimization on a data-parameterized protein interaction landscape

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein-peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-xL, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2-binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

ICFN: an efficient exact algorithm for multistate protein design.

MotivationMultistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design.ResultsWe have developed an efficient exact algorithm called interconnected cost function networks (iCFN) for multistate protein design. Its generic formulation allows for a wide array of applications such as stability, affinity and specificity designs while addressing concerns such as global flexibility of protein backbones. iCFN treats each substate design as a weighted constraint satisfaction problem (WCSP) modeled through a CFN; and it solves the coupled WCSPs using novel bounds and a depth-first branch-and-bound search over a tree structure of sequences, substates, and conformations. When iCFN is applied to specificity design of a T-cell receptor, a problem of unprecedented size to exact methods, it drastically reduces search space and running time to make the problem tractable. Moreover, iCFN generates experimentally-agreeing receptor designs with improved accuracy compared with state-of-the-art methods, highlights the importance of modeling backbone flexibility in protein design, and reveals molecular mechanisms underlying binding specificity.Availability and implementation https://shen-lab.github.io/software/iCFN Supplementary information Supplementary data are available at Bioinformatics online.

Characterizing residence patterns of North Atlantic right whales in the southeastern USA with a multistate open robust design model

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 36:279-295 (2018) - DOI: https://doi.org/10.3354/esr00902 Characterizing residence patterns of North Atlantic right whales in the southeastern USA with a multistate open robust design model A. M. Krzystan1,*, T. A. Gowan1, W. L. Kendall2, J. Martin3, J. G. Ortega-Ortiz4, K. Jackson1, A. R. Knowlton5, P. Naessig6, M. Zani5, D. W. Schulte6, C. R. Taylor6 1Florida Fish & Wildlife Research Institute, St. Petersburg, FL 33701, USA 2U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO 80523, USA 3U.S. Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL 32653, USA 4University of South Florida, College of Marine Science, St. Petersburg, FL 33701, USA 5New England Aquarium, Boston, MA 02110, USA 6Sea to Shore Alliance, Sarasota, FL 34233, USA *Corresponding author: andrea.krzystan@myfwc.com ABSTRACT: Effective conservation of endangered North Atlantic right whales Eubalaena glacialis requires information about their spatio-temporal distribution. Understanding temporal distribution is particularly important, because a portion of the population migrates between high-latitude summer feeding grounds off the northeastern USA and Canadian Maritimes coasts and lower-latitude calving and wintering grounds off the southeastern US coast (SEUS). Here, we modeled SEUS residence patterns using photo-identification data from coastal South Carolina, Georgia, and Florida from 7 winter seasons (2004/2005-2010/2011). We used multistate open robust design models to evaluate effects of reproductive status, demographic group, and environmental conditions on SEUS residence. Model estimates accounted for temporal variation and imperfect detection and provided probabilities of entering the SEUS, staying in the SEUS, and being sighted in the SEUS. We also derived estimates for residence time and seasonal abundance. We observed staggered arrival and departure patterns and demographic differences in residence patterns that are characteristic of a differential migration strategy. Calving females arrived earliest and, in most seasons, had mean residence periods more than twice as long as other demographic groups. Conversely, adult males arrived the latest and had the shortest residence times. Within-season detection was positively influenced by survey effort, and overall seasonal mean (±SE) detection rate estimates ranged from 0.83 ± 0.08 for non-calving adult females to 0.98 ± 0.02 for calving females. Results provide insights into right whale behavior, biology, and temporal distribution in the SEUS and can be used to evaluate spatially and temporally dynamic management measures. KEY WORDS: Mark-recapture · Open robust design · Phenology · Residence · Eubalaena glacialis · North Atlantic right whale Full text in pdf format Supplementary material PreviousNextCite this article as: Krzystan AM, Gowan TA, Kendall WL, Martin J and others (2018) Characterizing residence patterns of North Atlantic right whales in the southeastern USA with a multistate open robust design model. Endang Species Res 36:279-295. https://doi.org/10.3354/esr00902 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 36. Online publication date: August 29, 2018 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2018 Inter-Research.

A multistate open robust design: population dynamics, reproductive effort, and phenology of sea turtles from tagging data

AbstractUnderstanding population dynamics, and how it is influenced by exogenous and endogenous factors, is important to the study and conservation of species. Moreover, for migratory species, the phenology and duration of use of a given location can also influence population structure and dynamics. For many species, breeding abundance, survival, and reproductive performance, as well as phenology of nesting, are often the most accessible, and therefore, practical elements of their life history to study. For a population of hawksbill sea turtles (Eretmochelys imbricata), we modeled population change for nesters and total adult females, survival, and breeding probability, from 25 yr of intensive tagging data. We modeled breeding probability as a function of the number of years since last breeding and tested for differences between neophyte and experienced nesters. For each year, we also estimated the number of clutches deposited per female, and phenology of use, for neophytes and experienced nesters. To implement the analysis, we developed a novel generalized multistate open robust design mark–recapture modeling framework, with parameters for survival and transition probabilities, and for each primary period, state structure and arrival, persistence, and detection probabilities. Derived parameters included abundance of observable and unobservable components of the population, residence time, expected arrival and departure periods, and per‐period intensity of study area use. Abundance of nesters increased over most of the time series. Survival probability was 0.935 ± 0.01 (estimate ± SE). Virtually all hawksbills skipped at least one year of nesting. Breeding probability increased by skipping a second year, but then decreased thereafter. Subsequent breeding probability was lower for neophyte nesters than for experienced nesters, but the effect was weaker than the effect of years since breeding. Clutch frequency varied by year, with no discernable pattern of differences between neophytes and experienced nesters. Mean arrival and departure dates also varied, with a slight shift of nesting activity to earlier in the season. The multistate open robust design model developed here provides a flexible framework for modeling the dynamics of structured migratory populations and the phenology and duration of their seasonal use of study areas.

Integrating linear optimization with structural modeling to increase HIV neutralization breadth.

Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.

New Explorations of Data on Prison and Postconfinement Supervision Experiences

ABSTRACTAs part of a recent redesign, postconviction community supervision (PCCS) data was expanded in the Bureau of Justice Statistics’ (BJS) National Corrections Reporting Program (NCRP). The individual-level, multistate design of the NCRP data allow researchers to study the behavior of released offenders supervised in the community in a manner that was not previously possible. This article demonstrates the NCRP’s utility by providing novel descriptions of supervision populations. The purpose of this article is to inspire researchers to consider how these data might be applied in their own research. Descriptive statistics and data descriptions are provided as illustrations to generate discussion about future data use as BJS continues to expand the PCCS coverage across states and over time.

Rosetta:MSF: a modular framework for multi-state computational protein design.

Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta’s protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)8-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design.

Monitoring Variation in Small Coastal Dolphin Populations: An Example from Darwin, Northern Territory, Australia

Monitoring variation in populations of coastal dolphins presents a range of challenges. Many species occur at low local population levels, are cryptic and appear to range over larger areas than presumed. Here we present the results of a three and half year intensive monitoring study undertaken in Darwin Harbour and two neighboring sites (1086 km2). The study comprised multi-site robust design capture-recapture models that yielded estimates of abundance, apparent survival and temporary emigration on three species of coastal dolphins: Australian humpback (Sousa sahulensis), bottlenose (Tursiops sp.) and Australian snubfin (Orcaella heinsohni). Combining all three sites, abundance estimates varied between species. The Australian humpback was the most abundant species with a mean of 90, the bottlenose was stable at mean of 27 and the Australian snubfin varied widely from 19 to 70 with a mean of 41. Over time, Australian humpback abundance estimates showed a steady decline in Darwin Harbour but a population increase in the two neighboring sites was recorded, suggesting there were movements out of Darwin Harbour. However, the estimates of movement rates were not sufficiently sensitive to demonstrate this, due to the relatively small size of the local population and consequent low rates of observed movement. The multi-state robust design model offers the potential for more robust assessing of abundance estimates and population trends than other simpler designs. It is able to separate movements to and from a site from demographic changes on the site that otherwise might be confounded. The study highlights the substantial effort and time required to detect population trends for coastal dolphins is challenging, as is the need account for movement among sites when to populations are small and recapture rates are low.

Multistate Computational Protein Design with Backbone Ensembles.

The ability of computational protein design (CPD) to identify protein sequences possessing desired characteristics in vast sequence spaces makes it a highly valuable tool in the protein engineering toolbox. CPD calculations are typically performed using a single-state design (SSD) approach in which amino-acid sequences are optimized on a single protein structure. Although SSD has been successfully applied to the design of numerous protein functions and folds, the approach can lead to the incorrect rejection of desirable sequences because of the combined use of a fixed protein backbone template and a set of rigid rotamers. This fixed backbone approximation can be addressed by using multistate design (MSD) with backbone ensembles. MSD improves the quality of predicted sequences by using ensembles approximating conformational flexibility as input templates instead of a single fixed protein structure. In this chapter, we present a step-by-step guide to the implementation and analysis of MSD calculations with backbone ensembles. Specifically, we describe ensemble generation with the PertMin protocol, execution of MSD calculations for recapitulation of Streptococcal protein G domain β1 mutant stability, and analysis of computational predictions by sequence binning. Furthermore, we provide a comparison between MSD and SSD calculation results and discuss the benefits of multistate approaches to CPD.

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.

Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (Reeve et al. Proc Natl Acad Sci U S A 112(3):749-754, 2015), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned continuous rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme's catalytic function but selectively ablate binding of an inhibitor.

Turning a Negative into a Positive: Conversion of a Homodimer into a Heterodimer Using Negative State Repertoires

In this issue of Structure, Leaver-Fay et al. (2016) engineer bispecific antibodies using multistate computational protein design with negative state repertoires. In combination with additional mutations selected rationally, they produced antibodies that assembled as heterodimers with up to 93% purity.

Comets(Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Protein Design Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence

Practical protein design problems require designing sequences with a combination of affinity, stability, and specificity requirements. Multistate protein design algorithms model multiple structural or binding "states" of a protein to address these requirements. comets provides a new level of versatile, efficient, and provable multistate design. It provably returns the minimum with respect to sequence of any desired linear combination of the energies of multiple protein states, subject to constraints on other linear combinations. Thus, it can target nearly any combination of affinity (to one or multiple ligands), specificity, and stability (for multiple states if needed). Empirical calculations on 52 protein design problems showed comets is far more efficient than the previous state of the art for provable multistate design (exhaustive search over sequences). comets can handle a very wide range of protein flexibility and can enumerate a gap-free list of the best constraint-satisfying sequences in order of objective function value.

Nested Multistate Design for Maximizing Probabilistic Performance in Persistent Observation Campaigns

The paper presents a nested multistate methodology for the design of mechanical systems (e.g., a fleet of vehicles) involved in extended campaigns of persistent surveillance. It uses multidisciplinary systems analysis and behavioral-Markov modeling to account for stochastic metrics such as reliability and availability across multiple levels of system performance. The effects of probabilistic failure states at the vehicle level are propagated to mission operations at the campaign level by nesting various layers of Markov and estimated-Markov models. A key attribute is that the designer can then quantify the impact of physical changes in the vehicle, even those physical changes not related to component failure rates, on the predicted chance of maintaining campaign operations above a particular success threshold. The methodology is demonstrated on the design of an unmanned aircraft for an ice surveillance mission requiring omnipresence over Antarctica. Probabilistic results are verified with Monte Carlo analysis and show that even aircraft design parameters not directly related to component failure rates have a significant impact on the number of aircraft lost and missions aborted over the course of the campaign.

Prediction of Stable Globular Proteins Using Negative Design with Non-native Backbone Ensembles

Accurate predictions of protein stability have great potential to accelerate progress in computational protein design, yet the correlation of predicted and experimentally determined stabilities remains a significant challenge. To address this problem, we have developed a computational framework based on negative multistate design in which sequence energy is evaluated in the context of both native and non-native backbone ensembles. This framework was validated experimentally with the design of ten variants of streptococcal protein G domain β1 that retained the wild-type fold, and showed a very strong correlation between predicted and experimental stabilities (R(2) = 0.86). When applied to four different proteins spanning a range of fold types, similarly strong correlations were also obtained. Overall, the enhanced prediction accuracies afforded by this method pave the way for new strategies to facilitate the generation of proteins with novel functions by computational protein design.

Low-Power Heterogeneous Graphene Nanoribbon-CMOS Multistate Volatile Memory Circuit

Graphene is an emerging nanomaterial believed to be a potential candidate for post-Si nanoelectronics due to its exotic properties. Recently, a new graphene nanoribbon crossbar (xGNR) device was proposed which exhibits negative differential resistance (NDR). In this article, a multistate memory design is presented that can store multiple bits in a single cell enabled by this xGNR device, called graphene nanoribbon tunneling random access memory (GNTRAM). An approach to increase the number of bits per cell is explored alternative to physical scaling to overcome CMOS SRAM limitations. A comprehensive design for quaternary GNTRAM is presented as a baseline, implemented with a heterogeneous integration between graphene and CMOS. Sources of leakage and approaches to mitigate them are investigated. This design is extensively benchmarked against 16nm CMOS SRAMs and 3T DRAM. The proposed quaternary cell shows up to 2.27× density benefit versus 16nm CMOS SRAMs and 1.8× versus 3T DRAM. It has comparable read performance and is power efficient up to 1.32× during active period and 818× during standby against high-performance SRAMs. Multistate GNTRAM has the potential to realize high-density low-power nanoscale embedded memories. Further improvements may be possible by using graphene more extensively, as graphene transistors become available in the future.

Guest Editorial

This special issue on advances in design of ultra-low power circuits and systems il- lustrates a broad spectrum of challenges facing designers of high-performance and energy-efficient circuits in emerging technologies. Seeking to improve both the energy efficiency and throughput density, designers have turned to optimization methods tar- geting devices with interconnects to system-level power savings. The articles in this issue represent the cross-layer approach and span various emerging technologies, novel devices, and architectures. This issue contains eight scholarly articles, the experimental results of which show potential/promise to be ultra energy-efficient through innovation in device, logic, and architecture without compromising performance. Each manuscript was assigned to at least three reviewers and has undergone two or more rounds of revisions in the peer- review process. Overall 26 manuscripts were submitted and the following eight articles were selected based on the reviews and scope/fit into the special issue. Gaillardon et al., present a transversal survey on energy-efficient techniques ranging from devices to architectures. They look into various novel device architectures such as fully depleted planar devices, trigate geometries and gate-all-around structures that can allow reaching a high level of performance while reducing power consumption. Exploiting such devices can also lead to innovative circuits and architectural-level power management solutions like adaptive voltage and frequency scaling. Sitik et al., present a 20nm FinFET-based low-swing clocking methodology to pre- serve the dynamic power savings of low-swing clocking while minimizing the effect of leakage power consumption, clock skew, and timing violations. The technique reveals the premise for low-power/high-performance applications while achieving substantial power savings at high switching frequencies. Zhang et al., explore resource pooling where multiple architectural components are shared among the cores for improving system energy efficiency and reducing total chip area for 3D multicore processors. They present a 3D multicore architecture with poolable cache resources and introduce a runtime management policy to improve en- ergy efficiency of 3D systems. Khasanvis et al., introduce a low-power multi-state memory concept based on graphene crossbar devices. They present a multi-state memory design that can store multiple bits in a single cell. This work exploits a bilayer graphene nano-ribbon cross- bar tunneling device that exhibits negative differential resistance and hence can be used as multi-valued logic, which can be implemented along with CMOS lithography. Kang et al., present an overview of the spin-based devices and circuits by adding the spin freedom of electrons for usages such as memories and logic for achieving ultra-low power systems. This tutorial addresses various spin-based memory and logic systems; toggle MRAM, various from of STT-MRAM, Domain-wall systems, Spin-wave logic, partial/dynamic reconfigurable logic systems, and a neuromorphic framework to name a few. Azghadi et al., present a novel neuromorphic system based on a programmable neu- romorphic VLSI device that is comprise of silicon neurons and event-driven synapses, with programmable synaptic weight circuits. The device and neuromorphic system present a real-time low-power computing platform for exploring embedded neuromor- phic event-driven computational systems. Graziano et al., investigate molecular quantum-dot cellular automata (mQCA) as an emerging paradigm for high-performance low-power nanoscale computations. They provide a thorough analysis of mQCA-based circuits and a first assessment on the energy of a mQCA which is the most promising feature of this technology. At the system level, Le et al., investigate energy harvesting techniques for designing autonomous wireless sensor networks. The authors have presented an efficient energy harvesting system, which is compatible with various environmental sources such as light, heat, or wind.

Understanding Enzyme Molecular Evolution toward Thermal Adaptation using Multistate Computational Protein Design

A careful balance between structural-stability and flexibility (allostery) are hallmarks of enzymatic function and temperature can affect both properties. Canonical (fixed-backbone) enzyme design strategies currently do not consider the role of these properties. Here we describe the rational design of 100 temperature-adapted adenylate kinase (AdK) enzymes via a novel multistate design strategy that takes into consideration the impact of conformational changes to the backbone structure and stability, based on boundaries set by molecular evolution. In turn, each AdK protein variant was expressed and experimentally characterized for thermostability and temperature-adapted function. Comparison of the experimental temperature of maximum activity to the melting-temperature across all 100 variants reveals a strong correlation between these two parameters. In turn, experimental stability data was used to produce accurate predictions of thermostability via multistage nodes, providing the requisite complement for de novo temperature-adapted enzyme design. To the best of our knowledge the rational design of temperature adaptive enzyme function has not been realized, prior to this work. In principle, this level of design-based analysis can be applied to any protein, paving the way toward identifying and understanding the hallmarks of enzymatic allostery as it relates to the thermodynamic and structural limits of function.

Discovery of Substrates for a SET Domain Lysine Methyltransferase Predicted by Multistate Computational Protein Design

Characterization of lysine methylation has proven challenging despite its importance in biological processes such as gene transcription, protein turnover, and cytoskeletal organization. In contrast to other key posttranslational modifications, current proteomics techniques have thus far shown limited success at characterizing methyl-lysine residues across the cellular landscape. To complement current biochemical characterization methods, we developed a multistate computational protein design procedure to probe the substrate specificity of the protein lysine methyltransferase SMYD2. Modeling of substrate-bound SMYD2 identified residues important for substrate recognition and predicted amino acids necessary for methylation. Peptide- and protein- based substrate libraries confirmed that SMYD2 activity is dictated by the motif [LFM]-1-K(∗)-[AFYMSHRK]+1-[LYK]+2 around the target lysine K(∗). Comprehensive motif-based searches and mutational analysis further established four additional substrates of SMYD2. Our methodology paves the way to systematically predict and validate posttranslational modification sites while simultaneously pairing them with their associated enzymes.