Step-by-step design of proteins for small molecule interaction: a review on recent milestones

Protein design is the field of synthetic biology that aims at developing de-novo custom made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the art software is briefly explored.publishe

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

Machine Learning Guided Exploration of an Empirical Ribozyme Fitness Landscape

Okinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyFitness landscape of a biomolecule is a representation of its activity as a function of its sequence. Properties of a fitness landscape determine how evolution proceeds. Therefore, the distribution of functional variants and more importantly, the connectivity of these variants within the sequence space are important scientific questions. Exploration of these spaces, however, is impeded by the combinatorial explosion of the sequence space. High-throughput experimental methods have recently reduced this impediment but only modestly. Better computational methods are needed to fully utilize the rich information from these experimental data to better understand the properties of the fitness landscape. In this work, I seek to improve this exploration process by combining data from massively parallel experimental assay with smart library design using advanced computational techniques. I focus on an artificial RNA enzyme or ribozyme that can catalyze a ligation reaction between two RNA fragments. This chemistry is analogous to that of the modern RNA polymeraseenzymes, therefore, represents an important reaction in the origin of life. In the first chapter, I discuss the background to this work in the context of evolutionary theory of fitness landscape and its implications in biotechnology. In chapter 2, I explore the use of processes borrowed from the field of evolutionary computation to solve optimization problems using real experimental sequence-activity data. In chapter 3, I investigate the use of supervised machine learning models to extract information on epistatic interactions from the dataset collected during multiple rounds of directed evolution. I investigate and experimentally validate the extent to which a deep learning model can be used to guide a completely computational evolutionary algorithm towards distant regions of the fitness landscape. In the final chapter, I perform a comprehensive experimental assay of the combinatorial region explored by the deep learning-guided evolutionary algorithm. Using this dataset, I analyze higher-order epistasis and attempt to explain the increased predictability of the region sampled by the algorithm. Finally, I provide the first experimental evidence of a large RNA ‘neutral network’. Altogether, this work represents the most comprehensive experimental and computational study of the RNA ligase ribozyme fitness landscape to date, providing important insights into the evolutionary search space possibly explored during the earliest stages of life.doctoral thesi

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates

cii Student Papers - 2021

In this collection of papers, we, the Research Group Critical Information Infrastructures (cii) from the Karlsruhe Institute of Technology, present nine selected student research articles contributing to the design, development, and evaluation of critical information infrastructures. During our courses, students mostly work in groups and deal with problems and issues related to sociotechnical challenges in the realm of (critical) information systems. Student papers came from four different cii courses, namely Emerging Trends in Digital Health, Emerging Trends in Internet Technologies, Critical Information Infrastructures, and Digital Health in the winter term of 2020 and summer term of 2021

Technology, Science, and Culture: A Global Vision

The aim of the Workshop: Technology, Science, and Culture - A Global Vision is to create a discussion forum on research related to the fields of Water Science, Food Science, Intelligent Systems, Molecular Biomedicine, and Creation and Theories of Culture. The workshop is intended to discuss research on current problems, relevant methodologies, and future research streams and to create an environment for the exchange of ideas and collaboration among participants

Characterizing molecular-scale interactions between antimicrobial peptides and model cell membranes

Due to the escalating challenge of antibiotic resistance in bacteria over the past several decades, interest in the identification and development of antibiotic alternatives has intensified. Antimicrobial peptides (AMPs), which serve as part of the innate immune systems of most eukaryotic organisms, are being researched extensively as potential alternatives. However, the mechanism behind their bactericidal capabilities is not well understood. Previous studies have suggested that AMPs may first attach to the cell membranes, leading to pore formation caused by peptide insertion, lipid removal in the form of peptide-lipid aggregates, or a combination of both mechanisms. In addition to the lack of mechanistic knowledge, a significant hurdle in AMP-based drug development is their potential cytotoxicity to mammalian cells. Understanding AMP interactions with eukaryotic model membranes would allow therapeutics to be tailored for preferential action toward specific classes of bacterial membranes. In this study, we developed novel methods of quartz crystal microbalance with dissipation monitoring (QCM-D) data analysis to determine the fundamental mechanism of action between eukaryotic and bacterial membrane mimics and select membrane-active AMPs. A new technique for creating supported membranes composed entirely of anionic lipids was developed to model Gram-positive bacterial membranes. Atomic force microscopy (AFM) imaging was also used to capture the progression of AMP-induced changes in supported lipid membranes over time and to validate our method of QCM-D analysis. QCM-D and AFM were used to investigate the molecular-scale interactions of four peptides, alamethicin, chrysophsin-3, sheep myeloid antimicrobial peptide (SMAP-29) and indolicidin, with a supported zwitterionic membrane, which served as a model for eukaryotic cell membranes. Since established methods of QCM-D analysis were not sufficient to provide information about these interaction mechanisms, we developed a novel method of using QCM-D overtones to probe molecular events occurring within supported lipid membranes. Also, most previous studies that have used AFM imaging to investigate AMP-membrane interactions have been inconclusive due to AFM limitations and poor image quality. We were able to capture high-resolution AFM images that clearly show the progression of AMP-induced defects in the membrane. Each AMP produced a unique QCM-D signature that clearly distinguished their mechanism of action and provided information on peptide addition to and lipid removal from the membrane. Alamethicin, an alpha-helical peptide, predominantly demonstrated a pore formation mechanism. Chrysophsin-3 and SMAP-29, which are also alpha-helical peptides of varied lengths, inserted into the membrane and adsorbed to the membrane surface. Indolicidin, a shorter peptide that forms a folded, boat-shaped structure, was shown to adsorb and partially insert into the membrane. An investigation of rates at which the peptide actions were initiated revealed that the highest initial interaction rate was demonstrated by SMAP-29, the most cationic peptide in this study. The mechanistic variations in peptide action were related to their fundamental structural properties including length, net charge, hydrophobicity, hydrophobic moment, accessible surface area and the probability of alpha-helical secondary structures. Due to the charges associated with anionic lipids, previous studies have not been successful in forming consistent anionic supported lipid membranes, which were required to mimic Gram-positive bacterial membranes. We developed a new protocol for forming anionic supported lipid membranes and supported vesicle films using a vesicle fusion process. Chrysophsin-3 was shown to favor insertion into the anionic lipid bilayer and did not adsorb to the surface as it did with zwitterionic membranes. When introduced to supported anionic vesicle films, chrysophsin-3 caused some vesicles to rupture, likely through lipid membrane disruption. This study demonstrated that molecular-level interactions between antimicrobial peptides and model cell membranes are largely determined by peptide structure, peptide concentration, and membrane lipid composition. Novel techniques for analyzing QCM-D overtone data were also developed, which could enable the extraction of more molecular orientation and interaction dynamics information from other QCM-D studies. A new method of forming supported anionic membranes was also designed, which may be used to further investigate the behavior of bacterial membranes in future studies. Insight into AMP-membrane interactions and development of AMP structure-activity relationships will facilitate the selection and design of more efficient AMPs for use in therapeutics that could impact the lives of millions of people per year who are threatened by antibiotic-resistant organisms

Discovery of Self-Assembling π\pi-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

Electronically-active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from $\pi$-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically-active $\pi$-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylene vinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20$^3$ = 8,000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems