Established in 2018, 5DGlobal is a firm specialising in independent Strategic Advisory, Design and Operational Services for infrastructure planning, delivery and operations. We support public and private sector clients who own, operate and maintain capital assets. Our clients include government departments, government owned corporations, statutory authorities and private companies...

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6D Global Technologies, Inc. | 2,962 followers on LinkedIn. 6D Global Technologies, Inc. is a premier digital experience solutions provider. Our services and products allow our customers to create remarkable digital experiences across marketing channels and devices, optimize and measure it, ...

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This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 5 Results 5.1 Implementation Details Our primary feature extractor is AlphaFold, implemented in OpenFold [3, 31]. The multiple sequence alignment (MSA) is computed using Colabfold [43]. Sequence features from the Evoformer and Structure Module are aggregated as input to the decoder. An adapter maps the backbone hidden dimension to D = 128. Our amino acid projections and single mutation decoders are implemented with a linear layer. The higher-order mutation decoder transforms the previous embedding with a 2-layer MLP. These representations are aggregated and fed into a 3-layer MLP to predict ∆∆G. We train on the cDNA display proteolysis dataset [72], which leverages a high throughput proteolysis screen enabled by next generation sequencing (NGS) to extract noisy ∆∆G values for 100 miniproteins totaling over 100,000 single and double mutations. To evaluate generalization on unseen proteins, we remove proteins from our training set that have high sequence similarity using MMSeqs2 [70] to any protein in our evaluation benchmarks. The short protein lengths (< 100 amino acids) keep the memory requirement small for the higher-order decoder. See Section A.1 for more information about cDNA and other datasets. We fine-tune a pre-trained backbone on single mutations for 20 epochs. Then, we finetune the model on both single and double mutations for 100 epochs using a cosine learning rate schedule with 10 warmup epochs. We use a batch size of 3 proteins due to the high memory requirements of AlphaFold. We use a learning rate of 3×10−4 and weight decay of 0.5. Training takes 6 hours on 3 A100 GPUs. 5.2 Finding Stabilizing Double Mutations We evaluate our model’s ability to find the stabilizing mutations on cDNA2. cDNA2 is our validation split of the cDNA double mutations dataset, consisting of 18 mini-proteins totaling 22,000 double mutations. The proteins in the validation set have at most 36% homology with those in the cDNA training set. Of these mutations, only 198 or 0.8% are stabilizing with ∆∆G < −0.5 kcal/mol [8]. Like protein engineering, this scenario closely resembles the challenge of identifying a small number of stabilizing mutations amidst a much larger pool of destabilizing or neutral mutations. We evaluate different methods on stabilizing mutations in Table 1. Stabilizing rs is the Spearman coefficient on the experimentally stabilizing subset introduced in [65]. Normalized discounted cumulative gain (nDCG) [30] measures the quality of the ranked mutation list by taking into account its experimentally validated ∆∆G and position in the list [59]. Detection Precision (DetPr) is the proportion of experimentally validated stabilizing mutations among the top K = 30 predictions. We adapt previous state-of-the-art single mutation predictors by naively adding both single mutation predictions. A detailed description of the metrics is found in Section A.2. Our model demonstrates exceptional performance in prioritizing stabilizing double mutations over destabilizing ones, achieving a significantly higher normalized discounted cumulative gain of 0.43 compared to 0.25, as well as a superior detection precision of 0.16 compared to 0.10. Our model additionally improves classification metrics Matthews Correlation Coefficient (MCC) and Area under Precision-Recall Curve (AUC) by 0.02 and 0.03, respectively. Table 2: Multiple Mutation Results on PTMul. Our additive baseline naively adds predicted single mutation ∆∆Gs. Our model presents a strong mutation assessor. The proteins in PTMul have at most 35% sequence similarity to those in the training set. Parenthesis is standard error across 7 runs. To the best of our knowledge, Mutate Everything is the first work that models all double mutations in a computationally tractable manner. Figure 4 shows the runtime of several methods on a protein of 317 amino acids on an A100 GPU. The dashed line indicates the transition from evaluating single mutants to double mutants. Mutate Everything predicts ∆∆G for all single and double mutations in one pass of the model. It runs in 0.6 seconds using an ESM2 backbone, and 12.1 seconds on an AlphaFold backbone. PROSTATA [74] also uses the ESM2 backbone but takes a protein sequence and its mutated sequence as input and outputs the change in stability. PROSTATA takes 306 hours to evaluate all double mutations with a batch size of 768. On an 8 GPU node, this will take 1.5 days. Figure 4: Runtime analysis. Our model’s runtime is constant. 5.3 Higher-order Mutation Results We evaluate our model for predicting changes in thermodynamic stability for higher-order mutations in ProTherm. ProTherm is a database of thermodynamic (∆∆G) and thermostability (∆Tm) experimental characterization of protein mutations curated from the literature. We consider a subset of this database that contains ∆∆G values for higher-order mutations, named ProTherm Multiple (PTMul). PTMul contains 858 mutations [44]. We keep 846 experimentally validated ∆∆G for higher-order mutants, including 536 doubles and 183 triples, after removing 4 duplicate and 8 ambiguous mutations. PTMul proteins have at most 35% homology to the proteins used in training. Table 2 shows our results on PTMul. A description of baselines is provided in Section A.3. Our additive baselines naively add the sum of single mutation ∆∆Gs. This baseline rigorously evaluates our model’s ability to learn epistasis, or the non-additive interactions when combining two mutations. Mean always predicts the global test dataset mean statistic. ESM and MSA perform poorly when comparing mutations across proteins. On ProTherm, Mutate Everything achieves the state-of-the-art performance of 0.53 Spearman correlation rs, compared to 0.50 of our additive baseline. A breakdown of PTMul performance on double mutations and more (> 2) mutations is found in Table 6. While other methods also handle multiple mutations, they usually adopt a mutation- or protein-level train and test split. This results in data leakage since the same protein or a homolog will have mutations in both the training and testing sets, leading to inflated reported metrics [16, 18, 35, 64]. In our study, we use sequence similarity-based splitting where our training proteins have at most 35% homology to those in PTMul. To fairly evaluate generalization to new proteins, we exclude these inflated comparisons from our study. Table 3: Comparisons on Single Mutation Thermodynamic Stability prediction in S669. We report regression and classification metrics on the forward and reverse datasets for structure-based approaches (top block) and sequence-based approaches (bottom block). r refers to Spearman correlation coefficient. Our training proteins have at most 30% sequence similarity with those in S669. Mutate Everything is state-of-the-art on S669 forward. 5.4 Single Mutation Results We additionally validate our model’s ability to predict changes in thermodynamic stability under single mutations. We compare Mutate Everything to prior works on the newly introduced S669 dataset, which was established to address the data leakage issue and enable fair comparisons between existing methods [54]. We also evaluate the commonly studied reverse dataset setting, in which the model takes the mutant sequence as input and predicts ∆∆G for mutating back to the wild-type sequence. The experimental value is obtained by negating the original ∆∆G value going from the wild-type sequence to the mutant sequence. S669 is well curated and extensive, containing 94 proteins totaling 669 mutations [54]. S669 proteins have at most 30% sequence similarity with those in our training set to ensure separation between the training and validation sets. Table 3 shows our comparisons with prior works. Mutate Everything is state-of-the-art on S669, achieving a Spearman correlation of 0.56, where the prior art obtained 0.53. Mutate Everything outperforms the existing sequence model by a large margin, with a 6-point gain on Spearman correlation and a 3-point improvement on AUC. Our method is even as powerful as the latest structure-based methods which have access to atom coordinates. Mutate Everything is competitive on the reverse dataset evaluation, in which the original and mutated sequences are flipped and ∆∆G negated. Our performance drops on reverse mutations because reverse mutations are out of distribution for our model. We did not perform data augmentation to train on reversed mutations as commonly done in the literature [18, 36, 74]. We found that it was beneficial to bias our reverse predictions using ∆∆G to the amino acid found in the original sequence. We found the performance to be similar across 5 runs (< 0.01 standard error). Table 4: Comparisons on mutation fitness prediction in ProteinGym. We report ranking and classification metrics on both a subset of ProteinGym with stability-like phenotypes and the entire ProteinGym dataset. Our stability model can generalize to all phenotypes, outperforming existing methods on stability-like phenotypes and performing similarly to existing methods otherwise. The maximum homology between our training set and ProteinGym proteins is 43%. 5.5 Generalization to other Phenotypes We show that stability predictors generalize to other phenotypes as well. ProteinGym substitutions is a dataset of 87 proteins with 1.6 million mutation sets labeled with a generic fitness score, such as activity, growth, or expression. The mutation sets have up to 20 substitutions and 14 proteins contain higher-order mutations. The ProteinGym-Stability split contains 7 proteins and 26,000 mutations with fitness labels that are known to correlate with thermodynamic stability (thermostability, abundance, expression). ProteinGym proteins have at most 45% sequence similarity with those in our training set. Due to memory constraints, we truncate proteins to at most 2000 amino acids. Table 4 compares our work with previous works. Our MSA baseline uses the empirical amino acid distribution in the MSA at a given position to compute a likelihood ratio. The MSA retrieval prior weights the scores using the per-position first-order statistics in a multiple sequence alignment. On ProteinGym-Stability, Mutate Everything obtains a Spearman correlation of 0.52, compared to 0.50 of the next best method, TranceptEVE. On the one protein with labeled stability, Mutate Everything obtains 0.51 vs 0.33 of Tranception with MSA retrieval [51]. On the full ProteinGym benchmark, we outperform the strongest methods by ensembling our predictions with Tranception [49]. Ensembling our method with evolutionary models by averaging scores gains 0.11 points on ProteinGym and 0.01 points on ProteinGym-Stability, suggesting that our model learns complementary information to the evolutionary models. For comparison, TransEVE ensembles an evolutionary model (Tranception) and a family-based model (EVE) for a 0.03 point gain [50]. We found the performance to be similar across 5 runs (< 0.01 standard error). An in-depth study on how protein stability related to function can be found in [26]. 5.6 Ablation Studies We ablate several architectural design choices in Table 5. Core architectural designs are evaluated on S669. Architectural designs for only multiple mutations are evaluated on cDNA2. We analyze the effect of different feature extractors on single mutation stability assessment in Table 5a. ESM2 [38] takes only sequence as input whereas MSA-Transformer [60] and AlphaFold [31] take the sequence and its MSA as input. Both ESM2 and MSA-Transformer are trained on masking residue modeling while AlphaFold is also trained on structure prediction. MSA input improves the stability prediction performance and AlphaFold’s architecture and training further improve stability prediction. Backbone. (c) Backbone Optimization on S669. Finetuning the AlphaFold backbone improves performance. (d) Higher-order Modeling on cDNA2. We model the ∆∆G of the double mutation as a residual combined with the ∆∆G of its single mutation. Table 5: Default Settings are marked in grey. Mutate Everything ablation experiments. We try fine-tuning and freezing the AlphaFold backbone during stability finetuning in Table 5c. We find that fine-tuning the backbone improves stability prediction performance. Backbone Optimization The technique to aggregate mutation representations for higher-order mutations is ablated in Table 5b. To control for feature dimension size, we reduce the head dimension when aggregating with outer product and flattening. Aggregating with product and summation performs similarly on double mutations. We add the features for a natural extension to more mutations. Aggregation In the direct prediction model, the multi-mutant head directly predicts ∆∆G. In the multiply and add models, the multi-mutant head learns how interactions among single mutations affect the higher-order mutation’s ∆∆G. In these models, the multi-mutant head output learns an additive bias or multiplicative scaling to the sum of single mutation ∆∆Gs. In Table 5d, we show that learning a bias performs the strongest among these three options. Higher Order Modeling

Predicting a Protein’s Stability
under a Million Mutations: Results

Aspects of Thermal QCD Phenomenology at Intermediate Gauge/'t Hooft Coupling: Bibliography

240 Stories To Learn About Startups Of The Year

90 Stories To Learn About Startup Strategy

Let's learn about via these 35 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Startup Ideas /Learn Repo 1. Optimize Your Startup Ideas for Low Competition "Our main competitor is Microsoft, but we are very smart, so I’m sure our product will win." Famous last words... 2. Startup Interview with Andrey Ustyugov, CEO, Planner 5D Planner 5D CEO talks about what is driving the team to build the best interior design and home improvement platform. 3. paypal's UX for digital identity Startup Idea: An online identity platform that offers individuals and businesses low-cost services. 4. Unfairness Helped Me Build a $1M Side Business How to find the right business idea that maximises your probability for success. 5. Just Launched ConnectDome to Solve Networking for Developers Interacting to exchange info and develop professional or social contacts can be tricky. Check out the newly launched ConnectDome for a potential solution. 6. [ANN] Pitch.Tech - A Dottech Domains & Startup Grind Competition For Ideapreneurs Win $10,000 in equity-free funding and over $100,000 in startup benefits. 7. Looking for Micro SaaS Ideas? Here Are 7 You Could Build In 2023 Here's a list of Micro SaaS ideas that you could use as inspiration when coming up with your own Micro SaaS app project to work on. 8. The Art and Science of Crafting a Job Position in the Startup Stage When hiring, especially for start-ups, you can and should collaboratively design a position that honors your colleague's aspirations. 9. Growing a Startup is all About Decision Making I love reading the insights of successful entrepreneurs contemplating on their decision-making evolution and try to draw insights from my own experience as a startup owner. The more I’m into it, the more I realize that growing a startup is all about decision making. 10. How Copyright Strikes Helped Boost my Freelance Music Career By researching an analysis on why I got a lot of orders, I figured out an issue with the music industry. It’s called “Copyright”. 11. 7 Tips for Successful Startup Launch If you've never started a business, you might be a little scared the first time around. Mainly because it requires a lot of work and planning. Furthermore, only about half of all activities last longer than five years. 12. Social-as-a-Service Concept “There are only two ways to make money in business: one is to bundle; the other is unbundle.” — Jim Barksdale, former CEO and President of Netscape. 13. The Killer Framework to Generate SaaS Ideas for Martech When it comes to building SaaS products, every tool out there is a solution to a problem that an audience is facing. 14. How to Avoid Burning $80K+ on Tech Events: 10 Tips for Exhibiting Your Startup Efficiently Learn how to exhibit at startup events and tech conferences – and get the most out of your time and money! 15. 3 Questions That Can Help You To Generate Great Startup Ideas Is there a repeatable process for coming up with new and good startup ideas? In this post, we distill insights from great founders and technologists, like Eric Yuan of Zoom, Marc Andreesen, Ben Horowitz, etc and share three simple questions you can ask yourself to generate startup ideas worth pursuing. 16. 2022's 10 Most Promising Startup Ideas Are you considering launching a startup in 2022? We’ve put together ten innovative startup ideas that will inspire you to get started on your new business! 17. Reasons That Will Prompt You To Start Your Startup No Reasons That Will Prompt You To Start Your Startup No. Working in an office as a team member and being the leader of the team are like two sides of the coin. 18. The Future of Email Working in Sales for the past 5 years means the browser tab I most consistently have open is my email. It's also one of the very few active app notifications I have on my phone. 19. A Gread Idea Validation Method Used By Stripe Leading tech companies like Stripe are using this simple research method to validate their ideas and build products customers really need. 20. 9 Solid Online Marketplace Businesses to Start in 2022 Starting an online marketplace company requires a lot of brainstorming. Here is a list of the best marketplace ideas, together with reasons to invest. 21. Life After Coronavirus: The Emergence of Fundamentally New Business Models Time to set sail for your new ideas 22. How To Easily Validate Startup Ideas The first step in building a startup is validating the idea. It is a huge risk building a product without know if there are demand for it. The easiest and cheapest way to validate a start up idea is always cold email people at first. 23. Ways To Make Money As A Developer There are two types of people. Ones are already working as programmers and there are others who just code in their free time. It doesn’t matter which group you fit in, but the thing that most people want is to make some extra money as programmers. So, in this article, I’ll show you a couple of ways to make money as a developer. 24. Let’s Talk About Startup Structure Your startup organizational structure is the foundation of the empire you’re about to build. Here are the different options to consider before choosing one. 25. How and Why Makers Should Set a Deadline They Can't Extend Hi, makers:) 26. The Benefits of Idea Validation and How to Conduct It [Part 1] Idea validation is the process of gathering evidence around ideas through experimentation to make fast, informed and de-risked decisions. 27. Why You Shouldn't Keep Your Startup a Secret Have you ever seen the owner of a kebab joint slap a massive, buzzing wasp with his bare hand directly into the grill whilst you pitch your new startup to him? If this also happens to you, your business idea is not good. 28. How to Get Startup Ideas Using 3 Frameworks Note: This article is part of my resource newsletters↗️ where I share insights on building things in tech. Join me :)‍ 29. 10 Startup Ideas Spun Off Product Hunt's Business Model This time I have prepared a bunch of ideas (more ideas - less data) based on the same or similar business model as Product Hunt. Next week you will receive one idea that is bouncing in my head for you but I have to make more research on it, so stay tuned…. 30. Revue Definitely Isn't the Only Dutch Startup Worth an Undisclosed Sum Twitter's acquisition of Revue—basically Dutch Substack; founded two years earlier—got me Googling what else is being built in the Netherlands right now. 31. How To Use The Ikigai Concept to Find Purpose Amidst Chaos A guide to find meaning and purpose in your life in the most unlikely circumstances, including a global pandemic, through Ikigai. 32. Do You Know How to Fail Fast With Your Next Idea? I am sharing all the knowledge I gathered during my research in four months of testing different business ideas. Before starting investing lots of time and building an MVP (minimum viable product) you can test ideas with adtotyping and get some metrics without writing a single line of code. This process shows a powerful way to validate ideas with the help of Facebook. 33. The Business Model of Uber | Guide for Budding Entrepreneurs Uber, the taxi titan was founded by Travis Kalanick and Garrett Camp. With its headquarters located in California, United States, it has spread to over 65 countries and 600 cities worldwide. Its rise to fame was steady and calculated, bringing in success from every corner of the globe. The company is valued at over a whopping $70 Billion and it continues to grow exponentially. 34. Top SaaS Product Ideas for Startups of the Future No business wants to jump on every platform, and SaaS is no exception. You always need to brainstorm many ideas and get some guidance in order to succeed. 35. Should You Ride the Hype-Train to Launch your Startup Faster? Do you want to create a startup but are lacking an idea? Have you noticed a trend but don’t know how to capitalize on it? Maybe you wonder why are so many new billion-dollar tech companies popping up every year? Thank you for checking out the 35 most read stories about Startup Ideas on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

35 Stories To Learn About Startup Ideas

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31 DeSci Projects to Turn Your Next Scientific Idea into Reality

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A closer look at smart places trends with CEO Andrey Ustyugov. Planner 5D Which of the following topics are you most excited by? Smart places. Why does it excite you? We are witnessing smart spaces become an essential part of our lives. IoT has contributed significantly to this trend, as connectivity between devices we use and spaces we go to has increased dramatically over the last five years. Not too long ago, the idea of a “smart home” seemed too costly for many. Today, many of us are using devices that make our homes smarter, such as voice assistants and various home automation tools for electricity, to name a few. When we look at our lives on a larger scale in cities and countries, “smart” technologies are penetrating our lives in the form of face recognition technologies, contactless payments, and motion sensors. Those are all pieces of a global ecosystem of smart places. As we adopt smart technologies, they make us question whether they're safe and ethical and offer the privacy we want. Primarily those questions come from the fear of the unknown. What are the trends in this area or products in this area that you are the fondest of? Why? I am the fondest of smart homes. The connectivity within the smart home as a part of a larger ecosystem is an exciting feature. This ecosystem (of Google, Apple, or Amazon devices) is constantly evolving, becoming more intelligent and self-improving. I prefer to see it as an extensive network of interconnected units. Smart devices are changing our lives in the same way as wearable devices. Smartwatches have changed how we think about health data and manage it. It is now possible to share this data with a physician, track it in real-time and generate insights from it. I am sure we will see the same with smart homes in the near future. The home renovation and home improvement markets are evolving at a slower pace. It is happening because once the house is built, it usually takes about 10 years before any renovations are needed. We can't do anything to make it faster at this point, but this will definitely change in the future. What are the positive impacts they can have on society? Large volumes of data are being processed and organized. This process helps humans better understand our behavior and make significant changes in our habits and everyday lives. We can reduce electricity and water consumption and make more efficient use of our homes. The data produced by those systems can also help with the timely maintenance of a home, such as diagnosing possible incidents and predicting certain infrastructure damage or breakage. If a light bulb is to burn out tomorrow, it would be great to know about it a day before ordering a new one. Knowing that water pipes will start leaking before they do would allow me to call a plumber to fix the problem in advance. What are the negative impacts they can have on society? The biggest challenge here is privacy. With our homes becoming smarter, digital systems collect more information about us and our behavior. It is crucial to predict possible risks and do our best to mitigate them. Let’s imagine that I have a 24/7 surveillance system in my home. I am anxious about someone spying on me. I can turn the system off, but when I feel unwell and am home alone, the system wouldn’t be able to alert the emergency services. We are now at the point where smart technologies feel scary and unfriendly. There will come a time when this will change, and we will better understand how they work. What are your predictions on how these technologies will evolve? The ecosystems forming around smart places will become more and more standardized. IoT started as a “garage movement,” where every region or every country would have technologies and devices not compatible with each other. Then large tech companies came into play and started making “home kits” to integrate various products and services. This standardization process will take a lot of time, and I don’t see the smart places trend evolving without it. What are your predictions on the ethics of future societies in relation to these technologies? When technologies are readily available to everyone, this improves the ethics of tech products. The more diverse the audience, the more varied the data the system learns from. And while privacy questions are very important to everyone, I believe unique protocols and standardized solutions for all types of smart devices and technologies can regulate their use. Would you like to take a stab at answering some of these questions? The link for the template is , just start writing! Interested in what others had to say in their answers? Click . Interested in reading the content from all of our writing prompts? Click . HERE HERE HERE

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