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Computational Biology Nurit Haspel Filip Jagodzinski Kevin Molloy Editors Algorithms and Methods in Structural Bioinformatics Computational Biology AdvisoryEditors GordonCrippen,UniversityofMichigan,AnnArbor,MI,USA JosephFelsenstein,UniversityofWashington,Seattle,WA,USA DanGusfield,UniversityofCalifornia,Davis,CA,USA SorinIstrail,BrownUniversity,Providence,RI,USA Thomas Lengauer, Max Planck Institute for Computer Science, Saarbrücken, Germany MarcellaMcClure,MontanaStateUniversity,Bozeman,MT,USA MartinNowak,HarvardUniversity,Cambridge,MA,USA DavidSankoff,UniversityofOttawa,Ottawa,ON,Canada RonShamir,TelAvivUniversity,TelAviv,Israel MikeSteel,UniversityofCanterbury,Christchurch,NewZealand GaryStormo,WashingtonUniversityinSt.Louis,St.Louis,MO,USA SimonTavaré,UniversityofCambridge,Cambridge,UK TandyWarnow,UniversityofIllinoisatUrbana-Champaign,Urbana,IL,USA LonnieWelch,OhioUniversity,Athens,OH,USA Editor-in-Chief AndreasDress,CAS-MPGPartnerInstituteforComputationalBiology,Shanghai, China MichalLinial,HebrewUniversityofJerusalem,Jerusalem,Israel OlgaTroyanskaya,PrincetonUniversity,Princeton,NJ,USA MartinVingron,MaxPlanckInstituteforMolecularGenetics,Berlin,Germany EditorialBoardMembers RobertGiegerich,UniversityofBielefeld,Bielefeld,Germany JanetKelso,MaxPlanckInstituteforEvolutionaryAnthropology,Leipzig, Germany Gene Myers, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden,Germany PavelPevzner,UniversityofCalifornia,SanDiego,CA,USA Endorsed by the International Society for Computational Biology, the Computa- tional Biology series publishes the very latest, high-quality research devoted to specificissuesincomputer-assistedanalysisofbiologicaldata.Themainemphasis is on current scientific developments and innovative techniques in computational biology (bioinformatics), bringing to light methods from mathematics, statistics and computer science that directly address biological problems currently under investigation. The series offers publications that present the state-of-the-art regarding the problemsinquestion;showcomputationalbiology/bioinformaticsmethodsatwork; andfinallydiscussanticipateddemandsregardingdevelopmentsinfuturemethod- ology. Titles can range from focused monographs, to undergraduate and graduate textbooks,andprofessionaltext/referenceworks. Nurit Haspel • Filip Jagodzinski • Kevin Molloy Editors Algorithms and Methods in Structural Bioinformatics Editors NuritHaspel FilipJagodzinski DepartmentofComputerScience ComputerScience UniversityofMassachusettsBoston WesternWashingtonUniversity Boston,MA,USA Bellingham,WA,USA KevinMolloy ISAT/CSBuildingRoom216 JamesMadisonUniversity Harrisonburg,VA,USA ISSN1568-2684 ISSN2662-2432 (electronic) ComputationalBiology ISBN978-3-031-05913-1 ISBN978-3-031-05914-8 (eBook) https://doi.org/10.1007/978-3-031-05914-8 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerland AG2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Thethree-dimensionalstructureandfunctionofmoleculespresentmanychallenges andopportunitiesfordevelopinganunderstandingofbiologicalsystems.Withthe increasingavailabilityofmolecularstructuresandtheadvancingaccuracyofstruc- turepredictionsandmolecularsimulations,thespaceforalgorithmicadvancement onmanyanalyticalandpredictiveproblemsisbothbroadanddeep.Tosupportthis field, a rich set of methods and algorithms are available, addressing a variety of importantproblemssuchasprotein-proteininteractions,theeffectofmutationson proteinstructureandfunction,andproteinstructuredetermination. Recently, a deep learning-based algorithm, AlphaFold, made tremendous progress in predicting the three-dimensional structures of proteins, known as the protein folding problem. However, many problems still remain unsolved. In particular, the experimental resolution of protein structures, especially large macromolecules, still lags behind the availability of protein sequences. Modeling protein-protein interactions and protein binding still remain a challenge. Even whenthethree-dimensionalstructureoftwointeractingproteinsisknown,itisstill difficulttodeterminethecomplexformedbythetwoproteins.Itisalsochallenging to model and analyze conformational transitions in proteins, due to the transient natureofintermediatestructures. Chapter “Protein-Ligand Binding with Applications in Molecular Docking” presents a multi-dimensional analysis approach to protein-ligand binding. Under- standing protein-ligand binding from different perspectives (energetics, structure, homology,etc.)canprovideinsightstofurtherdrugdesignandproteinconformation studies. This chapter reviews basic principles and recent advances in protein- ligand binding, including the underlying thermodynamics basis, computational methodologies, and freely available databases. End-point free energy methods, whichhaveshowntobesignificantlymoreefficientthanthepopularalchemicaland transitionalpathsamplingmethods,arediscussedinmoredetail.Thechapterends with a brief review of molecular docking and its applications to high throughput screeninginearly-stagedrugdiscovery. Chapter“ExplainingSmallMoleculeBindingSpecificitywithVolumetricRep- resentationsofProteinBindingSites”presentsadvancesinalgorithmicapproaches v vi Preface for studying the volumetric properties of molecular surfaces and electrostatic isopotentials.Elucidatingthesurfacepropertiesofmoleculesisneededforadvances indrugdesignandproteinligandbinding. Chapter “Machine Learning-Based Approaches for Protein Conformational Exploration” surveys computational methods for conformational exploration of proteins. The chapter provides a detailed discussion of the challenges of using thesemethods,theirstrengths,andtheirshortcomings.Thesurveyedtopicsinclude physics-based methods such as molecular dynamics as well as geometry-based methods and a focus on new machine learning-based strategies that have been a researchhotspotforcomputationalbiologistsinrecentyears. Chapter“LowRankApproximationMethodsforIdentifyingImpactfulPairwise ProteinMutations”describesrecentadvancesintheuseofmachinelearning-based approaches, including low rank sampling, for efficiently identifying similarities amongproteinsandstudyingtheeffectsofpairwisemutations.Identifyingprotein classesandsimilaritiesamongsetsofproteinshasrelevancetohomologymodeling andcomputationalexperimentsthataimtobetterunderstandnewproteinstructures basedontheirsimilaritytootherbiomolecules. Chapter “Detection and Analysis of Amino Acid Insertions and Deletions” showcaseson-goingworkthatreliesonroboticsandcoarse-grainedcombinatorial approachesforpredictingtheeffectsoninsertionanddeletions(indels)onprotein structural stability. Even a single amino acid substitution can cause significant changes to a protein’s shape and function. A variety of approaches have been developedinthepastdecadeforinferringtheeffectsofsubstitutionmutations,but veryfewaddresstheeffectsofinsertionordeletion(indel)mutations. Chapter “DeepTracer Web Service for Fast and Accurate De Novo Protein Complex Structure Prediction from Cryo-EM” introduces DeepTracer—a Web servicefordeeplearning-baseddenovoproteincomplexstructurepredictionfrom Cryo-EM. Cryo-EM is increasingly being used to resolve protein structures. The resolution of most Cryo-EM resolved entries in the protein data bank (PDB) is medium or low, in which fine-level details are obscured, but new technology and improvedcomputationalmethodsallowformoreaccuratestructureprediction. Boston,MA,USA NuritHaspel Bellingham,WA,USA FilipJagodzinski Harrisonburg,VA,USA KevinMolloy January2022 Contents Protein-LigandBindingwithApplicationsinMolecularDocking ......... 1 NikitaMishraandNeginForouzesh 1 Introduction.................................................................... 1 1.1 ThermodynamicBasisofProtein-LigandInteraction................. 2 2 ComputationalMethodsforEstimatingBindingFreeEnergy.............. 4 2.1 AlchemicalMethods.................................................... 4 2.2 TransitionPathSamplingMethods..................................... 5 2.3 End-PointMethods ..................................................... 6 3 Protein-LigandBindingDatabases........................................... 8 4 MolecularDocking............................................................ 9 5 Conclusion..................................................................... 11 References ......................................................................... 11 ExplainingSmallMoleculeBindingSpecificitywithVolumetric RepresentationsofProteinBindingSites...................................... 17 ZiyiGuoandBrianY.Chen 1 Introduction.................................................................... 17 1.1 ComparisonAlgorithmsforExaminingSpecificity................... 18 2 SpecificityAssignment........................................................ 19 2.1 BindingSiteRepresentations........................................... 20 2.2 MetricsforBindingSiteComparison.................................. 21 2.3 ComparisonAlgorithms ................................................ 22 2.4 StatisticalModelsforBindingSiteComparison ...................... 25 3 ComponentLocalization...................................................... 26 3.1 FoundationsofStructure-BasedComponentLocalization............ 27 3.2 UsingCSGforComponentLocalization .............................. 30 3.3 StatisticalModelsforComponentLocalization....................... 32 3.4 VolumetricAlignment .................................................. 34 3.5 FlexibleRepresentationsforComponentLocalization................ 35 3.6 SolidRepresentationsofElectrostaticIsopotentials ................. 37 4 Discussion ..................................................................... 39 vii viii Contents 4.1 FutureDirections........................................................ 40 References ......................................................................... 41 Machine Learning-Based Approaches for Protein ConformationalExploration.................................................... 47 FatemehAfrasiabi,RaminDehghanpoor,andNuritHaspel 1 Introduction.................................................................... 47 2 BiophysicalandEmpiricalMethods ......................................... 48 3 Physics-BasedComputationalMethods...................................... 49 3.1 MolecularDynamics.................................................... 49 3.2 MonteCarloBasedSearchMethod.................................... 51 4 GeometricandRobotics-InspiredMethods.................................. 52 4.1 MotionPlanningMethods.............................................. 52 5 MachineLearning-BasedMethods........................................... 53 5.1 DimensionalityReductionTechniques................................. 54 5.2 Autoencoders............................................................ 55 6 ToolkitsforApplyingMachineLearning .................................... 57 6.1 TopologyandClustering................................................ 57 6.2 UsingaprioriKnowledge.............................................. 58 7 Conclusions.................................................................... 58 References ......................................................................... 59 LowRankApproximationMethodsforIdentifyingImpactful PairwiseProteinMutations..................................................... 63 ChrisDaw,BrianBarragan-Cruz,NicholasMajeske,FilipJagodzinski, TanzimaIslam,andBrianHutchinson 1 Introduction.................................................................... 63 2 RelatedWork.................................................................. 65 3 Methods........................................................................ 66 3.1 Phase1:GenerateExhaustivePairwiseData.......................... 67 3.2 Phase2:SamplingMethods............................................ 70 3.3 Phase3:SmoothApproximationMethods ............................ 71 3.4 EvaluationMetrics...................................................... 74 4 Results:SVDSmoothing ..................................................... 75 4.1 SVDApproximationandSamplingError ............................. 80 5 Results:CaseStudyon2LZM................................................ 82 6 ConclusionsandFutureWork ................................................ 85 References ......................................................................... 85 DetectionandAnalysisofAminoAcidInsertionsandDeletions ........... 89 MuneebaJilani,NuritHaspel,andFilipJagodzinski 1 Introduction.................................................................... 89 2 ComputationalMethodsofInDelDetection................................. 91 3 ComputationalMethodsofInDelAnalysis.................................. 92 3.1 MachineLearningBasedMethods..................................... 93 Contents ix 3.2 Detecting Functional and Fitness Effects of InDels on ProteinStructure ........................................................ 94 3.3 PlasticityofProteinstoInDels......................................... 95 4 Conclusion..................................................................... 96 References ......................................................................... 97 DeepTracerWebServiceforFastandAccurateDeNovoProtein ComplexStructurePredictionfromCryo-EM ............................... 101 DongSi,HanzeMeng,JonasPfab,YinruiDeng,YutongXie,Jackson Tan,SheungHimMartinChow,JasonChen,andAditiJain 1 Introduction.................................................................... 101 2 Procedures..................................................................... 104 3 Results ......................................................................... 107 3.1 Architecture ............................................................. 107 3.2 PredictionEvaluation................................................... 108 3.3 UI/UXFeatures ......................................................... 109 4 Discussion ..................................................................... 112 4.1 DesignPhilosophy...................................................... 112 4.2 FutureFeatures.......................................................... 112 References ......................................................................... 113