Sire: An interoperability engine for prototyping algorithms and exchanging information between molecular simulation programs.

Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company.

Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.

Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular libraries. To design a robust AL protocol, it is important to understand the influence of AL parameters, as well as the features of the data sets on the outcomes. We use four affinity data sets for different targets (TYK2, USP7, D2R, Mpro) to systematically evaluate the performance of machine learning models [Gaussian process (GP) model and Chemprop model], sample selection protocols, and the batch size based on metrics describing the overall predictive power of the model (R2, Spearman rank, root-mean-square error) as well as the accurate identification of top 2%/5% binders (Recall, F1 score). Both models have a comparable Recall of top binders on large data sets, but the GP model surpasses the Chemprop model when training data are sparse. A larger initial batch size, especially on diverse data sets, increased the Recall of both models as well as overall correlation metrics. However, for subsequent cycles, smaller batch sizes of 20 or 30 compounds proved to be desirable. Furthermore, adding artificial Gaussian noise to the data up to a certain threshold still allowed the model to identify clusters with top-scoring compounds. However, excessive noise (<1σ) did impact the model's predictive and exploitative capabilities.

Phase I study of peposertib and avelumab with or without palliative radiotherapy in patients with advanced solid tumors.

INTRODUCTION: We report results from a phase I, three-part, dose-escalation study of peposertib, a DNA-dependent protein kinase inhibitor, in combination with avelumab, an immune checkpoint inhibitor, with or without radiotherapy in patients with advanced solid tumors. MATERIALS AND METHODS: Peposertib 100-400 mg twice daily (b.i.d.) or 100-250 mg once daily (q.d.) was administered in combination with avelumab 800 mg every 2 weeks in Part A or avelumab plus radiotherapy (3 Gy/fraction × 10 days) in Part B. Part FE assessed the effect of food on the pharmacokinetics of peposertib plus avelumab. The primary endpoint in Parts A and B was dose-limiting toxicity (DLT). Secondary endpoints were safety, best overall response per RECIST version 1.1, and pharmacokinetics. The recommended phase II dose (RP2D) and maximum tolerated dose (MTD) were determined in Parts A and B. RESULTS: In Part A, peposertib doses administered were 100 mg (n = 4), 200 mg (n = 11), 250 mg (n = 4), 300 mg (n = 6), and 400 mg (n = 4) b.i.d. Of DLT-evaluable patients, one each had DLT at the 250-mg and 300-mg dose levels and three had DLT at the 400-mg b.i.d. dose level. In Part B, peposertib doses administered were 100 mg (n = 3), 150 mg (n = 3), 200 mg (n = 4), and 250 mg (n = 9) q.d.; no DLT was reported in evaluable patients. Peposertib 200 mg b.i.d. plus avelumab and peposertib 250 mg q.d. plus avelumab and radiotherapy were declared as the RP2D/MTD. No objective responses were observed in Part A or B; one patient had a partial response in Part FE. Peposertib exposure was generally dose proportional. CONCLUSIONS: Peposertib doses up to 200 mg b.i.d. in combination with avelumab and up to 250 mg q.d. in combination with avelumab and radiotherapy were tolerable in patients with advanced solid tumors; however, antitumor activity was limited. GOV IDENTIFIER: NCT03724890.

Markov State Models: To Optimize or Not to Optimize.

Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.

Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins, we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings did not show a significant change in performance.

SILVR: Guided Diffusion for Molecule Generation.

Computationally generating new synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machine learning models beyond conventional pharmacophoric methods have shown promise in the generation of novel small-molecule compounds but require significant tuning for a specific protein target. Here, we introduce a method called selective iterative latent variable refinement (SILVR) for conditioning an existing diffusion-based equivariant generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We use the SARS-CoV-2 main protease fragments from Diamond XChem that form part of the COVID Moonshot project as a reference dataset for conditioning the molecule generation. The SILVR rate controls the extent of conditioning, and we show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments, meaning that the new molecules fit the binding site without knowledge of the protein. We can also merge up to 3 fragments into a new molecule without affecting the quality of molecules generated by the underlying generative model. Our method is generalizable to any protein target with known fragments and any diffusion-based model for molecule generation.

What geometrically constrained models can tell us about real-world protein contact maps.

The mechanisms by which a protein's 3D structure can be determined based on its amino acid sequence have long been one of the key mysteries of biophysics. Often simplistic models, such as those derived from geometric constraints, capture bulk real-world 3D protein-protein properties well. One approach is using protein contact maps (PCMs) to better understand proteins' properties. In this study, we explore the emergent behaviour of contact maps for different geometrically constrained models and compare them to real-world protein systems. Specifically, we derive an analytical approximation for the distribution of amino acid distances, denoted asP(s), using a mean-field approach based on a geometric constraint model. This approximation is then validated for amino acid distance distributions generated from a 2D and 3D version of the geometrically constrained random interaction model. For real protein data, we show how the analytical approximation can be used to fit amino acid distance distributions of protein chain lengths ofL ≈ 100,L ≈ 200, andL ≈ 300 generated from two different methods of evaluating a PCM, a simple cutoff based method and a shadow map based method. We present evidence that geometric constraints are sufficient to model the amino acid distance distributions of protein chains in bulk and amino acid sequences only play a secondary role, regardless of the definition of the PCM.

A single-arm, multicenter, phase II trial of osimertinib in patients with epidermal growth factor receptor exon 18 G719X, exon 20 S768I, or exon 21 L861Q mutations.

BACKGROUND: For patients with stage IV non-small-cell lung cancer with epidermal growth factor receptor (EGFR) exon 19 deletions and exon 21 L858R mutations, osimertinib is the standard of care. Investigating the activity and safety of osimertinib in patients with EGFR exon 18 G719X, exon 20 S768I, or exon 21 L861Q mutations is of clinical interest. PATIENTS AND METHODS: Patients with stage IV non-small-cell lung cancer with confirmed EGFR exon 18 G719X, exon 20 S768I, or exon 21 L861Q mutations were eligible. Patients were required to have measurable disease, an Eastern Cooperative Oncology Group performance status of 0 or 1, and adequate organ function. Patients were required to be EGFR tyrosine kinase inhibitor-naive. The primary objective was objective response rate, and secondary objectives were progression-free survival, safety, and overall survival. The study used a two-stage design with a plan to enroll 17 patients in the first stage, and the study was terminated after the first stage due to slow accrual. RESULTS: Between May 2018 and March 2020, 17 patients were enrolled and received study therapy. The median age of patients was 70 years (interquartile range 62-76), the majority were female (n = 11), had a performance status of 1 (n = 10), and five patients had brain metastases at baseline. The objective response rate was 47% [95% confidence interval (CI) 23% to 72%], and the radiographic responses observed were partial response (n = 8), stable disease (n = 8), and progressive disease (n = 1). The median progression-free survival was 10.5 months (95% CI 5.0-15.2 months), and the median OS was 13.8 months (95% CI 7.3-29.2 months). The median duration on treatment was 6.1 months (range 3.6-11.9 months), and the most common adverse events (regardless of attribution) were diarrhea, fatigue, anorexia, weight loss, and dyspnea. CONCLUSIONS: This trial suggests osimertinib has activity in patients with these uncommon EGFR mutations.

Efficient Purification of Cowpea Chlorotic Mottle Virus by a Novel Peptide Aptamer.

The cowpea chlorotic mottle virus (CCMV) is a plant virus explored as a nanotechnological platform. The robust self-assembly mechanism of its capsid protein allows for drug encapsulation and targeted delivery. Additionally, the capsid nanoparticle can be used as a programmable platform to display different molecular moieties. In view of future applications, efficient production and purification of plant viruses are key steps. In established protocols, the need for ultracentrifugation is a significant limitation due to cost, difficult scalability, and safety issues. In addition, the purity of the final virus isolate often remains unclear. Here, an advanced protocol for the purification of the CCMV from infected plant tissue was developed, focusing on efficiency, economy, and final purity. The protocol involves precipitation with PEG 8000, followed by affinity extraction using a novel peptide aptamer. The efficiency of the protocol was validated using size exclusion chromatography, MALDI-TOF mass spectrometry, reversed-phase HPLC, and sandwich immunoassay. Furthermore, it was demonstrated that the final eluate of the affinity column is of exceptional purity (98.4%) determined by HPLC and detection at 220 nm. The scale-up of our proposed method seems to be straightforward, which opens the way to the large-scale production of such nanomaterials. This highly improved protocol may facilitate the use and implementation of plant viruses as nanotechnological platforms for in vitro and in vivo applications.

mRNA interactions with disordered regions control protein activity.

The cytoplasm is compartmentalized into different translation environments. mRNAs use their 3'UTRs to localize to distinct cytoplasmic compartments, including TIS granules (TGs). Many transcription factors, including MYC, are translated in TGs. It was shown that translation of proteins in TGs enables the formation of protein complexes that cannot be established when these proteins are translated in the cytosol, but the mechanism is poorly understood. Here we show that MYC protein complexes that involve binding to the intrinsically disordered region (IDR) of MYC are only formed when MYC is translated in TGs. TG-dependent protein complexes require TG-enriched mRNAs for assembly. These mRNAs bind to a new and widespread RNA-binding domain in neutral or negatively charged IDRs in several transcription factors, including MYC. RNA-IDR interaction changes the conformational ensemble of the IDR, enabling the formation of MYC protein complexes that act in the nucleus and control functions that cannot be accomplished by cytosolically-translated MYC. We propose that certain mRNAs have IDR chaperone activity as they control IDR conformations. In addition to post-translational modifications, we found a novel mode of protein activity regulation. Since RNA-IDR interactions are prevalent, we suggest that mRNA-dependent control of protein functional states is widespread.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1].

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Outcomes with durvalumab after chemoradiotherapy in stage IIIA-N2 non-small-cell lung cancer: an exploratory analysis from the PACIFIC trial.

BACKGROUND: The phase III PACIFIC trial (NCT02125461) established consolidation durvalumab as standard of care for patients with unresectable, stage III non-small-cell lung cancer (NSCLC) and no disease progression following chemoradiotherapy (CRT). In some cases, patients with stage IIIA-N2 NSCLC are considered operable, but the relative benefit of surgery is unclear. We report a post hoc, exploratory analysis of clinical outcomes in the PACIFIC trial, in patients with or without stage IIIA-N2 NSCLC. MATERIALS AND METHODS: Patients with unresectable, stage III NSCLC and no disease progression after ≥2 cycles of platinum-based, concurrent CRT were randomized 2 : 1 to receive durvalumab (10 mg/kg intravenously; once every 2 weeks for up to 12 months) or placebo, 1-42 days after CRT. The primary endpoints were progression-free survival (PFS; assessed by blinded independent central review according to RECIST version 1.1) and overall survival (OS). Treatment effects within subgroups were estimated by hazard ratios (HRs) from unstratified Cox proportional hazards models. RESULTS: Of 713 randomized patients, 287 (40%) had stage IIIA-N2 disease. Baseline characteristics were similar between patients with and without stage IIIA-N2 NSCLC. With a median follow-up of 14.5 months (range: 0.2-29.9 months), PFS was improved with durvalumab versus placebo in both patients with [HR = 0.46; 95% confidence interval (CI), 0.33-0.65] and without (HR = 0.62; 95% CI 0.48-0.80) stage IIIA-N2 disease. Similarly, with a median follow-up of 25.2 months (range: 0.2-43.1 months), OS was improved with durvalumab versus placebo in patients with (HR = 0.56; 95% CI 0.39-0.79) or without (HR = 0.78; 95% CI 0.57-1.06) stage IIIA-N2 disease. Durvalumab had a manageable safety profile irrespective of stage IIIA-N2 status. CONCLUSIONS: Consistent with the intent-to-treat population, treatment benefits with durvalumab were confirmed in patients with stage IIIA-N2, unresectable NSCLC. Prospective studies are needed to determine the optimal treatment approach for patients who are deemed operable.

Clinical definition of acquired resistance to immunotherapy in patients with metastatic non-small-cell lung cancer.

Acquired resistance (AR) to programmed cell death protein 1/programmed death-ligand 1 [PD-(L)1] blockade is frequent in non-small-cell lung cancer (NSCLC), occurring in a majority of initial responders. Patients with AR may have unique properties of persistent antitumor immunity that could be re-harnessed by investigational immunotherapies. The absence of a consistent clinical definition of AR to PD-(L)1 blockade and lack of uniform criteria for ensuing enrollment in clinical trials remains a major barrier to progress; such clinical definitions have advanced biologic and therapeutic discovery. We examine the considerations and potential controversies in developing a patient-level definition of AR in NSCLC treated with PD-(L)1 blockade. Taking into account the specifics of NSCLC biology and corresponding treatment strategies, we propose a practical, clinical definition of AR to PD-(L)1 blockade for use in clinical reports and prospective clinical trials. Patients should meet the following criteria: received treatment that includes PD-(L)1 blockade; experienced objective response on PD-(L)1 blockade (inclusion of a subset of stable disease will require future investigation); have progressive disease occurring within 6 months of last anti-PD-(L)1 antibody treatment or rechallenge with anti-PD-(L)1 antibody in patients not exposed to anti-PD-(L)1 in 6 months.

Dynamic Profiling of ß-Coronavirus 3CL Mpro Protease Ligand-Binding Sites.

ß-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across ß-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.

Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations.

The quantum mechanical bespoke (QUBE) force-field approach has been developed to facilitate the automated derivation of potential energy function parameters for modeling protein-ligand binding. To date, the approach has been validated in the context of Monte Carlo simulations of protein-ligand complexes. We describe here the implementation of the QUBE force field in the alchemical free-energy calculation molecular dynamics simulation package SOMD. The implementation is validated by demonstrating the reproducibility of absolute hydration free energies computed with the QUBE force field across the SOMD and GROMACS software packages. We further demonstrate, by way of a case study involving two series of non-nucleoside inhibitors of HIV-1 reverse transcriptase, that the availability of QUBE in a modern simulation package that makes efficient use of graphics processing unit acceleration will facilitate high-throughput alchemical free-energy calculations.

Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies.

A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations and to flag molecules which will benefit the most from bespoke force field parametrization efforts.

Assessment of Binding Affinity via Alchemical Free-Energy Calculations.

Free-energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free-energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace, and OpenMM and uses the AMBER 14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset, the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall's τ was 0.53 ± 0.05, which are broadly comparable to or better than previously reported results using other methods.

Outcomes with durvalumab by tumour PD-L1 expression in unresectable, stage III non-small-cell lung cancer in the PACIFIC trial.

BACKGROUND: In the PACIFIC trial, durvalumab significantly improved progression-free and overall survival (PFS/OS) versus placebo, with manageable safety, in unresectable, stage III non-small-cell lung cancer (NSCLC) patients without progression after chemoradiotherapy (CRT). We report exploratory analyses of outcomes by tumour cell (TC) programmed death-ligand 1 (PD-L1) expression. PATIENTS AND METHODS: Patients were randomly assigned (2:1) to intravenous durvalumab 10 mg/kg every 2 weeks or placebo ≤12 months, stratified by age, sex, and smoking history, but not PD-L1 status. Where available, pre-CRT samples were tested for PD-L1 expression (immunohistochemistry) and scored at pre-specified (25%) and post hoc (1%) TC cut-offs. Treatment-effect hazard ratios (HRs) were estimated from unstratified Cox proportional hazards models (Kaplan-Meier-estimated medians). RESULTS: In total, 713 patients were randomly assigned, 709 of whom received at least 1 dose of study treatment durvalumab (n = 473) or placebo (n = 236). Some 451 (63%) were PD-L1-assessable: 35%, 65%, 67%, 33%, and 32% had TC ≥25%, <25%, ≥1%, <1%, and 1%-24%, respectively. As of 31 January 2019, median follow-up was 33.3 months. Durvalumab improved PFS versus placebo (primary-analysis data cut-off, 13 February 2017) across all subgroups [HR, 95% confidence interval (CI); medians]: TC ≥25% (0.41, 0.26-0.65; 17.8 versus 3.7 months), <25% (0.59, 0.43-0.82; 16.9 versus 6.9 months), ≥1% (0.46, 0.33-0.64; 17.8 versus 5.6 months), <1% (0.73, 0.48-1.11; 10.7 versus 5.6 months), 1%-24% [0.49, 0.30-0.80; not reached (NR) versus 9.0 months], and unknown (0.59, 0.42-0.83; 14.0 versus 6.4 months). Durvalumab improved OS across most subgroups (31 January 2019 data cut-off; HR, 95% CI; medians): TC ≥ 25% (0.50, 0.30-0.83; NR versus 21.1 months), <25% (0.89, 0.63-1.25; 39.7 versus 37.4 months), ≥1% (0.59, 0.41-0.83; NR versus 29.6 months), 1%-24% (0.67, 0.41-1.10; 43.3 versus 30.5 months), and unknown (0.60, 0.43-0.84; 44.2 versus 23.5 months), but not <1% (1.14, 0.71-1.84; 33.1 versus 45.6 months). Safety was similar across subgroups. CONCLUSIONS: PFS benefit with durvalumab was observed across all subgroups, and OS benefit across all but TC <1%, for which limitations and wide HR CI preclude robust conclusions.

Self-organized emergence of folded protein-like network structures from geometric constraints.

The intricate three-dimensional geometries of protein tertiary structures underlie protein function and emerge through a folding process from one-dimensional chains of amino acids. The exact spatial sequence and configuration of amino acids, the biochemical environment and the temporal sequence of distinct interactions yield a complex folding process that cannot yet be easily tracked for all proteins. To gain qualitative insights into the fundamental mechanisms behind the folding dynamics and generic features of the folded structure, we propose a simple model of structure formation that takes into account only fundamental geometric constraints and otherwise assumes randomly paired connections. We find that despite its simplicity, the model results in a network ensemble consistent with key overall features of the ensemble of Protein Residue Networks we obtained from more than 1000 biological protein geometries as available through the Protein Data Base. Specifically, the distribution of the number of interaction neighbors a unit (amino acid) has, the scaling of the structure's spatial extent with chain length, the eigenvalue spectrum and the scaling of the smallest relaxation time with chain length are all consistent between model and real proteins. These results indicate that geometric constraints alone may already account for a number of generic features of protein tertiary structures.

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of cyclophilin A.

Proteins need to interconvert between many conformations in order to function, many of which are formed transiently, and sparsely populated. Particularly when the lifetimes of these states approach the millisecond timescale, identifying the relevant structures and the mechanism by which they interconvert remains a tremendous challenge. Here we introduce a novel combination of accelerated MD (aMD) simulations and Markov state modelling (MSM) to explore these 'excited' conformational states. Applying this to the highly dynamic protein CypA, a protein involved in immune response and associated with HIV infection, we identify five principally populated conformational states and the atomistic mechanism by which they interconvert. A rational design strategy predicted that the mutant D66A should stabilise the minor conformations and substantially alter the dynamics, whereas the similar mutant H70A should leave the landscape broadly unchanged. These predictions are confirmed using CPMG and R1ρ solution state NMR measurements. By efficiently exploring functionally relevant, but sparsely populated conformations with millisecond lifetimes in silico, our aMD/MSM method has tremendous promise for the design of dynamic protein free energy landscapes for both protein engineering and drug discovery.