Rosetta FunFolDes - A general framework for the computational design of functional proteins

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are "designable", meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the "designability" of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins-Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo "functionless" fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis

Mat á styrkgildum umbrotsefna í efnaskiptalíkönum með Monte Carlo hermun

Skortur á gögnum hefur lengi hamlað þróun á kvikum líkönum í kerfislíffræði. Erfitt er að mæla gildi hraðafasta og styrkgildi allra um- brotsefna í lífverum. Tilraun var gerð til að meta styrkgildi umbrots- efna í grunnefnaskiptalíkani E.coli með slembisýnatöku á varmafræði- lega mögulegum ástöndum. Með þeim má reikna beint út sérstaka hraðafasta sem eiga að nálga kvika eiginleika frumunnar. Einnig er fjallað um C++ útfærslu af ACHR-sampling reikniritinu sem nýtt var við slembisýnatökuna. Styrkgildin sem fengust úr slembisýnatökunni pössuðu illa við mæld gildi úr E.coli. C++ útfærsla ACHR-sampling reikniritsins var ekki mikið hraðari en MATLAB útfærsla þess

Due to violations of article 194 of the General Penal Code nr. 19/1940

Þessi ritgerð fjallar um brot gegn nauðgunarákvæði 194. gr. almennra hegningarlaga nr. 19/1940 og hvernig refsingar koma fyrir. Dómar voru skoðaðir þar sem megináherslan var lögð á að sjá hvernig refsað væri fyrir brot gegn áðurnefndri lagagrein. Þróun lagaákvæðisins um nauðgun í almennu hegningarlögunum var umfjöllun í sér kafla, þá voru ýmis hugtök er varða kynferðisbrot útskýrð. Hugtakið refsing var skilgreind og sérstök áhersla lögð á Hæstaréttardóma eftir síðustu lagabreytingu. Fjallað var um Noreg í sjöunda kafla ritgerðarinnar þar sem lög og dómstólar eru hluti af umfjölluninni. Varpað er fram þeirri mynd sem er í norsku réttarkerfi þegar kemur að kynferðisbrotum. Niðurstöður sýndu að þrátt fyrir víðan refsiramma í 1. mgr. 194. gr. almennra hegningarlaga eru refsingar fyrir nauðgun heldur í lægri kantinum en það er í takt við réttarvitund almennings sem hefur lengi krafist þyngri refsinga. Þetta virðist vera sameiginlegt vandamál beggja þjóða sem teknar voru fyrir. Þá sést einnig á skýrslum frá norsku lögreglunni að þrátt fyrir að nauðgunarmálum virðist fækka liggur vandamálið í þeirra löggjöf og er ekki að finna samþykkisskilyrði þar líkt og er að finna hér. Það þarf að fara í mikla vinnu til að auka traust almennings á dómstólum með því meðal annars að fara í mikla forvarnar vinnu, minnka málsmeðferðartímann, og nota refsiramman betur. Það er alveg ljóst að of fá mál eru tilkynnt bæði á Íslandi og í Noregi og það þarf að vera markmið dómsvaldsins að laga það.Abstract This thesis is about the violation of the rape provision of article 194 of the General Penal Code no. 19/1940 and how punishments occur. Verdicts in court cases where examined, where the main focus was placed on how the court punishes offenders that commit a crime against the aformentioned article of law. The development of the statutory provision on rape in the General Penal Code was discussed in a separate chapter, where various concepts related to sexual offenses were explained. The concept of punishment was defined and special emphasis was placed on the Supreme Court verdicts after the last change of the law. Norway was discussed in the seventh chapter of the thesis, where laws and the courts are part of the discussion and the image of the Norwegian legal system when it comes to sexual offenses is presented. Results showed that despite the wide penalty framework in paragraph 1. article 194 in the General Penal Code, the punishments for rape are rather on the lower side, but this is in line with the legal awareness of the public, which has long demanded heavier punishments. This appears to be a common problem for both nations addressed. It can also be seen from the reports from the Norwegian police that even though the number of rape cases seems to be decreasing the problem lies in their legislation and there are no condition for consent in Norway as it is here in Iceland. A lot of work needs to be done to increase the public ́s trust in the court system as well do a lot of preventive work, reducing the procedural time, and use the penalty framework with harsher penalties. It is quite clear that too few cases are reported in both Iceland and Norway and it is has to be the goal of the judiciary to fix this.Óheimilt er að afrita verkefnið að hluta eða í heild án leyfis höfundar hverju sinni

Physics-informed deep neural network for rigid-body protein docking

International audienceProteins are biological macromolecules that perform many essential roles within all living organisms. Many protein functions arise from physical interactions between them and also with other biomolecules (e.g. DNA, metabolites). Being able to predict whether and how two proteins interact is an important problem in fundamental biological research and translational drug discovery. In this work, we present an energy-based model for generating ensembles of rigid-body transformations to predict the configuration of protein complexes. The method incorporates strong, interpretable physical priors. By construction, it is also SE(3) equivariant and fully-differentiable back to the atomic structure. We rely on the observation that bound protein-protein complexes show high geometric and chemical complementarity at the interface of the two proteins. Our method efficiently makes use of this prior by generating on-the-fly point cloud representations of the solvent-excluded surfaces of the proteins. Through learned point descriptors, we can infer regions of high complementarity between the two proteins and compute a proxy for the binding energy. By sampling transformations expected to adopt low energy states, we generate ensembles of bound poses where the two protein surfaces are brought into contact. We expect that the strong physical priors enforced by the network construction will aid in generalization to other related tasks and lead to a richer human understanding of the process behind the generation and scoring of the docked poses. The fact that the method is also fully differentiable allows for gradient-based modifications of the atomic structure which could be critical in tasks related to unbound docking or protein design which remain outstanding problems in protein modelling

DiffMaSIF: Surface-based Protein-Protein Docking with Diffusion Models

International audiencePredicting protein-protein complexes is a central challenge of computational structural biology. Existing state-of-the-art methods rely on co-evolution learned on large amino acid sequence datasets and thus often fall short on both transient and engineered interfaces (which are of particular interest in therapeutic applications) where co-evolutionary signals are absent or minimal. To address this, we introduce DIFFMASIF, a novel score-based diffusion model for rigid protein-protein docking. Instead of sequence-based features, DIFFMASIF uses a protein molecular surfacebased encoder-decoder architecture to effectively learn physical complementarity. The encoder uses learned geometric features extracted from protein surface point clouds. It directly learns binding site complementary through prediction of contact sites as an auxiliary loss, and also allows for specification of known binding sites during inference. It is followed by a decoder predicting rotation and translation via SO(3) diffusion. We show that DIFFMASIF achieves state-of-the-art among deep Llearning methods for rigid body docking, in particular on structurally novel interfaces and low sequence conservation. This provides a significant advance towards accurate modelling of low co-evolution protein interactions and their many practical applications