Recent availability of Large Language Models (LLMs) has led to the development of numerous LLM-based approaches aimed at providing natural language interfaces for various end-user tasks. These end-user tasks in turn can typically be accomplished by orchestrating a given set of APIs. In practice, natural language task requests (user queries) are often incomplete, i.e., they may not contain all the information required by the APIs. While LLMs excel at natural language processing (NLP) tasks, they frequently hallucinate on missing information or struggle with orchestrating the APIs. The key idea behind our proposed approach is to leverage logical reasoning and classical AI planning along with an LLM for accurately answering user queries including identification and gathering of any missing information in these queries. Our approach uses an LLM and ASP (Answer Set Programming) solver to translate a user query to a representation in Planning Domain Definition Language (PDDL) via an intermediate representation in ASP. We introduce a special API “get_info_api” for gathering missing information. We model all the APIs as PDDL actions in a way that supports dataflow between the APIs. Our approach then uses a classical AI planner to generate an orchestration of API calls (including calls to get_info_api) to answer the user query. Our evaluation results show that our approach significantly outperforms a pure LLM based approach by achieving over 95% success rate in most cases on a dataset containing complete and incomplete single goal and multi-goal queries where the multi-goal queries may or may not require dataflow among the APIs.

§ INTRODUCTION

Customers of large organizations have a variety of questions or requests (collectively known as queries in the following) pertaining to the organization’s domain of operation. Providing relevant and accurate responses to such user queries is critical and requires a thorough analysis of the user’s context, product features, domain knowledge, and organization policies. The user queries may encompass a variety of types - data lookup and aggregation queries, advice requests, how-to questions, record update requests or a combination of these types.

Overview of user query answering using LLMs and Classical Planning

Recently, transformer-based large language models (LLMs) have shown wide success on many natural language understanding and translation tasks, also demonstrating some general database querying <cit.> and reasoning and planning <cit.> capability on diverse tasks without having to be retrained. However, the data and knowledge required for accurately answering customers' queries are partly or completely organization internal and not available to LLMs trained on publicly available data. Even in case of organization internal LLM deployments, it is often not feasible to give LLMs direct access to databases for various security and privacy reasons. In lieu of that, organizations develop APIs to make these internal artifacts programmatically accessible to the organization’s applications.

Several frameworks and techniques have been proposed for answering user queries using a combination of LLM and tools/APIs, e.g. LangChain <cit.>, Gorilla <cit.>, ToolFormer <cit.>, and TravelPlanner <cit.>. However, such frameworks rely on LLMs for selecting and composing tools and as a result either do not scale well beyond a small set of APIs/tools or have limited API orchestration capability. These weaknesses limit the use of such frameworks for practical industrial applications.

To address these limitations, some recent works have investigated the use of an external classical planner along with an LLM. Given a description of the possible initial states of the world, a description of the desired goals, and a description of a set of possible actions, the classical planning problem involves synthesizing a plan that, when applied to any initial state, generates a state which contains the desired goals (goal state) <cit.>. The approaches presented in <cit.> have demonstrated that utilizing an LLM to create the task PDDL (a representation of a user query as a planning problem in Planning Domain Definition Language) from a natural language planning task description, and then utilizing an external classical planner to compute a plan, yields better performance than relying solely on an LLM for end-to-end planning. However, these approaches have been shown to support only classical planning tasks, which hinders their use for answering user queries in the presence of APIs.

Furthermore, all the above mentioned approaches assume complete user queries, i.e., queries that contain all the required information for computing an answer to the query. In practice however, user queries are often incomplete. In general, detecting and gathering missing information depends on the granularity of the underlying atomic actions or APIs as well as dataflow among them at runtime. For example, if a user wants to book a flight and provides the source and destination airports information but the flight booking API requires the travel date as well, the user query is an incomplete query with respect to the available APIs. The AutoConcierge framework <cit.> can detect missing information for a pre-defined goal assuming that the required information for accomplishing the goal is known a-priori. However, there is still a need for an approach that can handle different kinds of possibly incomplete queries.

Fig. <ref> presents the high level architecture of our approach for supporting several kinds of user queries using a given set of APIs. We translate a user query to a task PDDL (query's representation in PDDL) and use a classical AI planner for orchestrating APIs (plan) for the generated task PDDL. The plan execution component executes the plan by invoking the APIs in the specified order. For how-to questions, the plan is not executed but sent to the response generation component. Finally, the response generation component generates the overall response to be sent to the user from the individual outputs of the API calls.

In this paper, our focus is on the Task PDDL Generation and Planner components in particular for supporting incomplete user queries.

Our approach for translating a user query to task PDDL involves a novel combination of an LLM and logical reasoning using Answer Set Programming (ASP) <cit.>. We use an LLM to generate an intermediate representation of a user query in ASP. Our LLM prompting technique is generic and allows API specifications to be plugged in. This step is described in Section <ref>. Such intermediate representations allow us to use an ASP solver to deterministically infer information and bridge the syntactic and semantic heterogeneities between a user query and the target task PDDL. This step uses a semantic model of the domain as ASP rules. We refer to the collection of inferred information as materialized representation of the user query. This step is described in Section <ref>. We obtain the task PDDL by converting the materialized representation which is in the ASP syntax to PDDL syntax using deterministic procedural code. This step is describe in Section <ref>.

In the next step, we use a classical planner with the task PDDL and an offline created PDDL domain model representing the APIs as PDDL actions (Section <ref>).
In addition to the given set of functionality providing APIs, we introduce a special API  for gathering missing information from the user or an external system at runtime in order to support incomplete queries. The planner returns a plan (including calls to get_info_api in case of incomplete queries) such that the execution of the plan computes the answer to the user query. The plan generation step is described in Section <ref>.

Since there aren't any benchmark datasets of incomplete queries to be answered using APIs, we generated a dataset containing single goal and multi-goal complete and incomplete natural language queries based off a set of APIs described in Section <ref>. We refer to an intent in a user query as a goal. Our evaluation results on this dataset show that our approach significantly outperforms a pure LLM based approach by achieving over 95% success rate in most cases.

§ SPECIFICATION OF APIS AS PDDL ACTIONS

Throughout this paper, we use the following APIs which are derived from the set of publicly available Intuit Developer APIs[<https://developer.intuit.com/app/developer/homepage>] for experimental purposes.

* Profit and loss report API: Generates profit and loss report for a given time period.

* Expense and spend report API: Generates expense and spend report for a given time period.

* Invoices and sales report API: Generates invoices and sales report for a given time period.

* Charge lookup API: Generates detailed report for a given charge amount on a given date.

* Help API: Provides answer to a given how-to question in a product.

* Contact API: Connects customer to a human customer agent over a given communication channel for a conversation on a given topic

* Advice API: Provides advice for a given personal finance or a small business relation question.

* Create invoice API: Creates a new invoice for given amount and invoice detail.

* Update customer API: Updates a customer profile with new first name, last name, phone, and email.

In order to be able to use a classical planner for computing an orchestration of available APIs, we model each available API as an action in PDDL. PDDL serves as a standardized encoding of classical planning problems <cit.>. A PDDL representation of an action consists of the action's pre-conditions and  effects defined using logical formulas with domain predicates, local variables (action's parameters) and constants. Note that unlike familiar procedural programming languages PDDL actions' outputs are also declared as part of action's parameters. The PDDL representation of a planning problem is typically separated into two files: a domain PDDL file and a task PDDL file, both of which become inputs to the planner. Broadly, the domain PDDL file includes declaration of  object types, predicates, and specification of actions. An action's specification includes declaration of its parameters using the object types as well as preconditions and effects defined in terms of the predicates. The task PDDL file provides a list of objects to ground the domain, and the problem’s initial state and goal conditions defined in terms of the predicates.

Below the PDDL representation of the profit&loss API as action . The action generates a profit and loss report for given time period. The pre-condition of the action means that variables  and  have type  as well as have a value (i.e., they are not NULL). The  var represents the generated report. The pre-condition also includes that the  must have the type  but must not have a value (indicating that  doesn't represent an already previously generated report). The effects of the action mean that after execution of the action the value of  is set. Furthermore, the effects mean that after the execution of the action, the generated report  has  and  as start date and end date of the generated report  respectively.

A classical planner will find the above action for a user goal requesting a profit and loss report for given start and end dates. However, if the start date or the end date or both are not provided, a planner will fail to find  as relevant action.

We address this problem by introducing a special action  to gather information from the user or an external system at runtime. We model  as a PDDL action as shown below. The  action requires a variable of a type that is not set and ensures that it is set after the execution of .

[Plan for query Show me my profit and loss report]

[Plan for query Can I see my profit and loss statement from March to May 2023? I would like to discuss my profits further over chat]

Plan for an incomplete and a complete query

This modeling of  enables a planner to include get_info_api calls in the plan for gathering missing information. For example, for the query in Fig. <ref> we aim at detecting the profit & loss report API, and asking the user for the missing report time period. Similarly, in case of a more complex user query in Fig. <ref>, we aim at detecting the profit & loss report API and the contact API as well as the the profit & loss report as the conversation topic with the customer agent.

For the purpose of this paper we have modeled the domain PDDL manually as efficient authoring of domain PDDL is out of scope of this work. However, we would like to point that approaches such as <cit.> may be leveraged for (semi-) automatically generating the domain PDDL for large domains. Refer to Appendix <ref> for the specification of all APIs in our dataset.

§ USER QUERY TO ASP REPRESENTATION

Steps for translating a user query to task PDDL

As illustrated in Fig. <ref>, in order to generate task PDDL for a user query, in the first step, we use an LLM for translating user query to an intermediate representation in ASP. The main reason behind this step is that LLMs perform well on such translation tasks while they hallucinate when they are also required to generate logically derivable information <cit.>. In the second step, we use a logical reasoner for inferring such information similar to approaches presented in <cit.>.

§.§ User Query to Intermediate Representation

We construct the LLM prompt with following steps for translating user query to an intermediate representation in ASP.

Step 1: Define a set of supported goals. The set of goals doesn't need to have 1:1 correspondence with the set of APIs. But, the set of goals corresponds to expected user requests. Such a modeling enables decoupling of user requests from APIs as the end users can not be expected to be familiar with the APIs (cf. OpenAI function calling approach [<https://platform.openai.com/docs/guides/function-calling>]).

Step 2: Describe argument types. For each argument of the supported goals, define the type by giving a few examples or the set of possible values as appropriate. Below example defines argument types for date period and communication channel. See Appendix <ref> for definition of all argument types for our dataset.

Step 3: Describe domain goals. Describe each goal using a name, description and required information for the goal. Refer to Appendix <ref> for complete list of supported domain goals.

Step 4: Define instructions. We instruct the LLM to extract goals and required information from the user query.

Step 5: Construct LLM prompt LLM prompt also includes a few in-context examples that are independent of the domain of our dataset. Refer to Appendix <ref> for complete list of in-context examples.

Below are a few example queries and their respective intermediate representations in ASP as returned by the LLM.

Profit and loss report.

Show me 2023 Q1 detailed expense report.

I want to chat with a representative.

Provide me with the profit and loss statement for the last quarter and put me on a phone call with a representative to discuss it.

The query in Example <ref> is a complete query. The query in Example <ref> is a complete query with with two goals and dataflow. The profit & loss report  is the topic of the conversation for the contact . The queries in Example <ref> and Example <ref> are incomplete queries as the query in in Example <ref> doesn't contain the conversation topic and the query in Example <ref> doesn't contain start and end dates of the report.

§.§ Intermediate Representation to Materialized Representation

An intermediate representation captures the content of the user query using formats and predicates that are closer to those of typical user utterances. In general, user queries cannot be expected to be formulated using the same vocabulary and format as the arguments of the APIs. In this step, we infer additional information as well as bridge the syntactic and semantic gaps. We accomplish this by using an ASP solver, with the intermediate representation and domain rules as inputs. For our current implementation we use Clingo <cit.> python package[<https://pypi.org/project/clingo/>] as the ASP solver.

Below a snippet of the domain rules for our dataset (see Appendix <ref> for all domain rules). Note that even though the domain rules needed for our current dataset are rather simple and few in number, our framework of first translating the query to an intermediate representation in ASP allows us to plug-in a large number of complex rules if needed.

The first rule translates the goal type to the type used in the vocabulary of the domain PDDL. The second and third rules infer  and  from the the user provided report_period. These rules also add the data types ,  for the values to facilitate the planning in the later step. For our example queries in Section <ref>, the ASP solver returns below materialized representations after applying the domain rules on the intermediate representations of the queries.

Example <ref> materialized representation:

Example <ref> materialized representation:

Example <ref> materialized representation:

Example <ref> materialized representation:

§ ORCHESTRATE APIS USING PLANNER

§.§ Task PDDL Generation

Materialized representation to task PDDL for the  query Profit and loss report.

A materialized representation contains all user provided information in the target terminology and format. The next and the last step is to generate a plan. In order to be able to do that, we need to convert the materialized representation to a PDDL representation (task PDDL).

Fig. <ref> illustrates this process using Example <ref>. Every goal $x$ becomes a $var$ and every goal type $t$ becomes a $var\_type$. For each goal $x$ of type $t$, (a) add $(has\_type \; x \; t)$ to the init section, (b) for each argument $a$ of $t$ and predicate $p$, a var $x\_a$ is added to the objects, $(has\_type \; x\_a \; t)$ is added to init, $(p \; x \; x\_a)$ is added to goal, and if $x\_a$ has a value $v$, then $(has\_value \; x\_a \; v)$ is added to init. Refer to Appendix <ref> for the complete algorithm for generating materialized representation to task PDDL. The output of the algorithm, the task PDDL for Example <ref> is shown on the right side Fig. <ref>. Refer to Appendix <ref> for the task PDDLs of other example queries.

§.§ Plan generation

Once the task PDDL is generated, all we need to do is to call a PDDL planner with the task PDDL and the domain PDDL. In our implementation we use the Fast Downward Planner [<https://www.fast-downward.org/HomePage>] <cit.> with configuration parameters alias = lama and search-time-limit = 1. In other implementations, where compatibility to PDDL may not be important, one may also choose an appropriate ASP based planner <cit.>.

Using an external classical AI planner has several benefits such as:

* Scalability: AI planners scale well wrt number of APIs as long as the functionality of APIs can be defined in terms of (Inputs, Outputs, Preconditions, Effects) with logical formulas.

* Support for interaction: In case of incomplete queries the generated plan includes calls to get_info_api API for gathering information from user

* Optimality: APIs can be assigned a cost; Planner computes an optimal plan wrt the cost function.

* Graceful failure: For out of domain queries planner won’t generate a plan rather than hallucinating.

For the example query Show me 2023 Q1 detailed expense report, the planner generates the plan:

For the example query Provide me with the profit and loss statement for the last quarter and then put me on a phone call with a representative to discuss it, the planner generates:

Note that the the contact topic is bound to the generated profit and loss report .

For the example query I want to chat with a representative, the planner generates:

For the example query Profit and loss report, the planner generates:

§ EXPERIMENTS

In this section we present the evaluation results of our approach on a generated dataset containing natural language user queries related to various topics such as generation of profit & loss reports, invoice creation, and how-to help requests.

§.§ Dataset Generation

The initial step in the dataset generation process involves using GPT-4 to generate user queries that represent single goal tasks executable via a subset of the APIs described in Section <ref>. GPT-4 is prompted with instructions and in-context examples to guide the generation process and ensure that the resulting queries align with the requirements of the API. Refer to Appendix <ref> for an example LLM prompt for dataset generation.

We use the same process to create more complex multi-goal queries simulating a real-world scenario where a user might seek to perform a series of actions in a single request. For example, “Can I see my profit and loss statement from March to May 2023? I would like to discuss my profits further over chat.”. GPT-4 is prompted to generate coherent sequences where the output of one goal execution would become the input of another (multi-goal with dataflow), and complex queries which required multiple APIs to be executed independently (multi-goal without dataflow). Once a sufficient number of single and multi-goal queries are generated, we manually review and annotate the generated queries with the ground truth values for the APIs and entities as their arguments. Refer to Appendix <ref> for some sample data in the dataset.

§.§ Results and Analysis

We consider a query as successfully processed iff the generated plan for answering the query contains all the ground truth APIs with correct entities as their arguments. In particular, the  calls correspond to missing entity values in incomplete queries.
This allows us to also measure the success rate of incomplete queries where the planner should generate  actions for missing entities instead of the LLM hallucinating on entity values not present in the query. In our evaluation, a processed query is either correct or wrong, and never fractionally correct.

Success rate % of our approach compared with a baseline of end-to-end LLM based approach on single goal queries

5cComplete Queries     5cIncomplete Queries

#     2cGPT-4     2cGPT3.5     #     2cGPT4     2cGPT3.5

Base-line     Our Approach     Base-line     Our Approach         Base-line     Our Approach     Base-line     Our Approach

profit & loss report     70     22.86     98.57     81.43     100     2     0     100     0     100

expense report     42     23.81     100     90.48     100     0     -     -     -     -

invoice sales report     33     54.55     90.91     84.85     93.94     12     0     100     0     91.67

charge lookup     33     81.82     100     96.97     93.94     5     40.00     100     0      100

how-to help     60     68.33     98.33    68.33     90.00     0     -     -     -     -

contact us request     10     40.00      100     70.00     100     47     0     91.49     0     85.11

financial advice     100     81.00     94.00     94.00      97.00     0     -     -     -     -

create invoice     40     57.50     100     100     100     20     0     100     0     100

update customer     3     0     100     100     100     30     6.67     100     6.67     100

Success rate % of our approach compared with a baseline of end-to-end LLM based approach on multi-goal queries.

5cComplete Queries     5cIncomplete Queries

#     2cGPT-4     2cGPT-3.5     #     2cGPT-4     2cGPT-3.5

Base-line     Our Approach     Base-line     Our Approach         Base-line     Our Approach     Base-line     Our Approach

2 APIs w/o dataflow     15     0      100     0     100     10     0      100     0     100

2 APIs with dataflow     20     0     90     0      70     10     0     80     0     60

3 APIs with dataflow     4     0     100     0     75     16     0     75     0     62.50

Table <ref> and Table <ref> present the success rate of our system on single goal and multi-goal queries respectively. The rows denote the different types of queries in our dataset. Columns 2 and 7 denote the number of complete and incomplete queries respectively. In case of single goal queries, we report success rate for each goal type. In case of multi-goal queries, we distinguish between queries with 2 goals and 3 goals with or without dataflow. A query contains at least one goal and zero or more entities as arguments of the goals. The success rates reported in Table <ref> and Table <ref> are at most equal to the smaller of API orchestration success rate and entity values extraction success rate of the respective classes. See Appendix  <ref> for API orchestration success rates and entity values extraction success rates.

We compare our method to a baseline where an LLM alone extracts the goals and entities in a query and performs orchestration of APIs. The baseline utilizes function calling method from  <cit.> where APIs represented as function descriptions are used by the LLM to translate natural language query into function calls. Refer to Appendix <ref> for the LLM prompt used for the baseline approach. In our experiments, we observe that our approach significantly outperforms the baseline in most cases for single goal queries. The baseline approach either fails to detect either the correct API or extract the entities in a query correctly. In addition, it performs poorly on incomplete queries. In particular, the baseline approach with GPT-4 asks unnecessary clarification questions in case of complete queries and both GPT-4 and GPT-3.5 hallucinate on missing entity values in case of incomplete queries. We also observe that our approach can handle multi-goal complete and incomplete queries with high success rate while the baseline completely fails to orchestrate these queries correctly.

Overall, the increase in success rate in our approach can be attributed to the use of an LLM only for translating a user query coupled with the use of deterministic tools such as a logical reasoner and a planner for inferring additional information and generating a plan respectively. In particular, using an LLM to translate to an intermediate representation that is closer to the user query increases the translation accuracy as well as minimizes the hallucination. Furthermore, using a logical reasoner facilitates accurate mapping to target schema with the help of ASP rules even in complex domains where an LLM would often generate incorrect inferences. Similarly, using an external planner computes only feasible plans. In case of single goal complete queries, the increase in success rate is due to the use of intermediate representation and reasoning, and the planner doesn't add any additional value as the materialized representation itself can be seen as an equivalent to a plan. In case of single goal or multi-goal incomplete queries as well multi-goal complete queries with dataflow, the increase in the success rate  is due to use of intermediate representation, logical reasoning, and the planner.

Our approach requires per query one LLM call, one ASP solver call, and one planner call. The total execution time for processing one query in case of GPT-4 is 3–5 seconds and 0.5–1 seconds in case of GPT-3.5. In both cases over 99% of total time is consumed by the LLM call(s) in the translation step. Note that our LLM response times are measured in a setup with shared resources across all LLM projects within our organization. We believe that the latency will be significantly lower with dedicated LLM access.

§ CONCLUSION

In this paper, we studied the problem of answering incomplete user queries in presence of APIs. To the best of our knowledge, ours is the first approach to address this problem. Our approach introduces a novel combination of LLMs, logical reasoning, and classical AI planning to support queries that can be complete or incomplete requiring only one API or an orchestration of multiple APIs. Furthermore, our approach supports queries of different kinds such as information seeking queries, how-to queries, and state changing queries. Our evaluation results show that our approach achieves high success rate (over 95% in most cases including 100% in some cases). Our approach is generic in the sense that it doesn't depend on a particular set of APIs but allows API specifications to be plugged in. The significant success rate improvement as compared to a pure LLM based baseline can be attributed to the use of interpretable intermediate representation, logical reasoning, and classical AI planning.

Our approach has a few limitations which we plan to address in our future work. Currently, we send the metadata for all supported goals of the domain to an LLM as part of the prompt. This technique can overshoot the LLM token limit in cases where there are a large number of possible goals in the domain. Currently, our approach only supports queries but not user's soft preferences. One way to address this gap, at least for some types of user preferences, could be to translate them to a cost function which AI planners can directly support. Lastly, the use of AI planner requires the APIs be specified with accurate IOPE specifications in PDDL which may not be applicable for all APIs or difficult to create for APIs with complex functionality.

§ TRANSLATION PROMPT

§.§ Argument Types

§.§ Domain Goals

§.§ In-context Examples

§ DOMAIN MODELING

§.§ Domain PDDL

§.§ Domain Rules

§ QUERY ASP TO TASK PDDL

§.§ Query ASP to Task PDDL Algorithm

str_objects $\gets$ ""
str_init $\gets$ ""
str_goal $\gets$ ""
Initialize objects $\gets \{var\_type : \emptyset, var : \emptyset \}$. Initialize all_vars $\gets \emptyset$.
each goal in goals
Add goal to objects[var_type].
each var in goals[goal]
add goals[goal][var][type] to objects[var_type]
add var to all_vars
each atom in the ASP model
goal_var, goal_name $\gets$ atom.arguments[0], atom.arguments[1]
atom.name == "goal"
Add goal_var to objects[var]
str_init += "(has_type " + goal_var + " " + goal_name + ")"
each arg of goals[goal_name]
arg_var $\gets$ goal_var+"_"+arg
arg_type $\gets$ goals[goal_name][arg]["type"]
pred_name $\gets$ goals[goal_name][arg]["predicate"]
Add arg_var to $objects[var]$
str_init += "(has_type " + arg_var + " "  + arg_type + ")"
str_goal += "(" + pred_name + " " + goal_var + " " + arg_var + ")"
atom.name in all_vars
goal_var $\gets$ atom.arguments[0].name
var $\gets$ goal_var + "_" + atom.name
str_goal += "(" + atom.name + " " + goal_var + " " + var + ")"
str_objects += " ".join(objects[var]) + " - " + var
str_objects += " ".join(objects[var_type]) + " - " + var_type

§.§ Example Task PDDLs

§ EVALUATION

§.§ LLM prompt for dataset generation

Example prompt for generating user query and entities related to expense report

§.§ Samples from the dataset

Query     gt_API     gt_entity1     gt_value1     gt_entity2     gt_value2

Q1 2023 P&L review?     profit_loss     startperiod     1/1/23     endperiod     3/31/23

Why was I charged $75?     charge_lookup     dateofcharge     []     amountofcharge     75

§.§ Baseline Prompt

Here, functions are the APIs modelled as OpenAI function specifications and query refers to the user query of interest.

§.§ Evaluation Results

5cComplete Queries     5cIncomplete Queries

#     2cGPT-4     2cGPT-3.5     #     2cGPT-4     2cGPT-3.5

Baseline     Our Approach     Baseline     Our Approach         Baseline     Our Approach     Baseline     Our Approach

profit & loss report    70    22.86    98.57     97.14   100    2    0     100     100   100

expense spend report    42    30.95    100    97.62    100    0    -     -   -   -

invoice sales report    33    63.64    90.91    87.88    93.94    12    16.67     100   66.67   100

charge lookup    33    81.82    100    100    96.97    5    40.00     100   100   100

how-to help    60    70.00    98.33    68.33    90.00     0   -     -   -   -

contact us request    10    40.00    100    80.00    100    47    14.89    93.62   44.68   95.74

financial advice    100    81.00    94.90    94.00    97.00    0    -    -   -   -

create invoice    40    60.00    100    100    100    20    0    100   100   100

update customer    3    0    100    100    100    30    6.67    100   100   100

API orchestration success rate % of our approach compared with a baseline of end-to-end LLM based approach on single goal queries

5cComplete Queries     5cIncomplete Queries

#     2cGPT-4     2cGPT-3.5     #     2cGPT-4     2cGPT-3.5

Baseline     Our Approach     Baseline     Our Approach         Baseline     Our Approach     Baseline     Our Approach

profit & loss report    70    22.86    98.57    81.43    100    2    0    100    0    100

expense spend report    42    23.81    100     90.48   100    0    -    -   -   -

invoice sales report    33    54.55    96.97    84.85    96.97    12    0    100   0   91.67

charge lookup    33    81.82    100    96.97    93.94    5    40.00    100   0   100

how-to help    60    68.33    98.33    68.33    95.00    0    -    -   -   -

contact us request    10    40.00    100    70.00    100    47    0    97.87   0   87.23

financial advice    100     81.00   99.00    94.00    100    0     -   -   -   -

create invoice    40    57.50    100    100    100    20    0    100   0   100

update customer    3    0    100    100    100    30    6.67    100   6.67   100

Entity extraction success rate % of our approach compared with a baseline of end-to-end LLM based approach on single goal queries

5cComplete Queries     5cIncomplete Queries

#     2cGPT-4     2cGPT-3.5     #     2cGPT-4     2cGPT-3.5

Base-line     Our Approach     Base-line     Our Approach         Base-line     Our Approach     Base-line     Our Approach

2 APIs w/o dataflow     15     0     100    0     100     10     0    100     0     100

2 APIs with dataflow     20     0    100     0     80     10     0     80     0     80

3 APIs with dataflow     4     0    100     0     75     16     0     100     0     81.25

API orchestration success rate % of our approach compared with a baseline of end-to-end LLM based approach on multi goal complete queries

5cComplete Queries     5cIncomplete Queries

#     2cGPT-4     2cGPT-3.5     #     2cGPT-4     2cGPT-3.5

Base-line     Our Approach     Base-line     Our Approach         Base-line     Our Approach     Base-line     Our Approach

2 APIs w/o dataflow     15     0     100     0     100     10     0     100     0     100

2 APIs with dataflow     20     0     90.00     0     70     10     0     90     0     60

3 APIs with dataflow     4     0     100     0     75.00     16     0     75     0     62.50

Entity extraction success rate % of our approach compared with a baseline of end-to-end LLM based approach on multi goal queries


# Friends in need: how chaperonins recognize and remodel proteins that require
folding assistance

George Stan  [<EMAIL_ADDRESS>George H. Lorimer<EMAIL_ADDRESS>Center
for Biomolecular Structure and Organization, Department of Chemistry and
Biochemistry, University of Maryland, College Park, Maryland 20742, USA  D.
Thirumalai<EMAIL_ADDRESS>[

###### Abstract

Chaperonins are biological nanomachines that help newly translated proteins to
fold by rescuing them from kinetically trapped misfolded states. Protein
folding assistance by the chaperonin machinery is obligatory in vivo for a
subset of proteins in the bacterial proteome. Chaperonins are large oligomeric
complexes, with unusual seven fold symmetry (group I) or eight/nine fold
symmetry (group II), that form double-ring constructs, enclosing a central
cavity that serves as the folding chamber. Dramatic large-scale conformational
changes, that take place during ATP-driven cycles, allow chaperonins to bind
misfolded proteins, encapsulate them into the expanded cavity and release them
back into the cellular environment, regardless of whether they are folded or
not. The theory associated with the iterative annealing mechanism, which
incorporated the conformational free energy landscape description of protein
folding, quantitatively explains most, if not all, the available data.
Misfolded conformations are associated with low energy minima in a rugged
energy landscape. Random disruptions of these low energy conformations result
in higher free energy, less folded, conformations that can stochastically
partition into the native state. Two distinct mechanisms of annealing action
have been described. Group I chaperonins (GroEL homologues in eubacteria and
endosymbiotic organelles), recognize a large number of misfolded proteins non-
specifically and operate through highly coordinated cooperative motions. By
contrast, the less well understood group II chaperonins (CCT in Eukarya and
thermosome/TF55 in Archaea), assist a selected set of substrate proteins.
Sequential conformational changes within a CCT ring are observed, perhaps
promoting domain-by-domain substrate folding. Chaperonins are implicated in
bacterial infection, autoimmune disease, as well as protein aggregation and
degradation diseases. Understanding the chaperonin mechanism and the specific
proteins they rescue during the cell cycle is important not only for the
fundamental aspect of protein folding in the cellular environment, but also
for effective therapeutic strategies.

Keywords: GroEL, GroES, chaperonins, substrate recognition, folding
assistance, misfolding, aggregation

g>Grayc P[1]>p#1 University of Cincinnati] Department of Chemistry, University
of Cincinnati, Cincinnati, Ohio 45221, USA UT Austin]Department of Chemistry
and Department of Physics, University of Texas, Austin, Texas 78712, USA

##  1 Introduction

Protein folding in the cell is not always a spontaneous process due to
unproductive pathways of misfolding and aggregation. Chaperonin molecules in
bacterium prevent such off-pathway reactions and promote protein folding
through spectacular ATP-driven cycles of binding and releasing substrate
proteins (SPs). Chaperonins are distinguished among the molecular chaperone
family by the presence of a cavity that offers a productive environment for
protein folding, thus preventing unwarranted inter protein interactions, which
could occur in the crowded cellular environment. Many chaperones are known as
heat-shock proteins (Hsp), alluding to their overexpression under stress
conditions, although their action is also required under normal cell growth.
Availability of chaperone assistance at critical junctures, for example
through thermotolerance, ensures cell viability even when cellular functions
would otherwise be overwhelmed. More broadly, comprehensive protein quality
control relies on a range of chaperone subfamilies, classified according to
their molecular weight, to deliver assistance with essential processes in the
protein lifecycle: folding/refolding, with an important role for Hsp60/Hsp10
(GroEL/S), Hsp90 and Hsp70/Hsp40 classes (DnaK/DnaJ); protection against
oxidative stress, Hsp33; disaggregation, Hsp100 (Hsp104/ClpB), Hsp70/Hsp40,
and small Hsps (sHsp); and degradation, Hsp100 (Clp family, p97, the
proteasome Rpt1-6 ring) 1, 2, 3, 4.

Two distinct chaperonin classes have been identified. GroEL and its co-
chaperonin GroES in Escherichia coli (Figure 1) are the prototype for
chaperonin systems found in eubacteria and endosymbiotic organelles, or Group
I chaperonins. The thermosome (Figure 1) and TCP-1 ring complex (TRiC, or CCT
for chaperonin-containing TCP1) are representative for archaeal and eukaryotic
cells, respectively, and are known as Group II chaperonins. Structural
characterization 5, 6, 7, 8 reveals that chaperonins have an oligomeric,
double-ring structure, composed of two (thermosome) or more (CCT) distinct
subunits within the same ring or identical subunits (GroEL). Within each
subunit there are three distinct domains: the ATP-binding equatorial domain,
the flexible apical domain and the intermediate hinge region. The co-
chaperonin GroES is a single-ring oligomer with identical subunits, capping
one of the GroEL rings (Figure 1). This elaborate annealing machinery is
present in nearly all organisms and it is essential for cell survival 9.

Why does folding of some proteins in the crowded cellular milieu require
chaperonin assistance? This requirement does not exist in vitro, as favorable
conditions for folding can be identified for known chaperonin substrates.
Cellular conditions, however, are unfavorable (non-permissive) for a subset of
these proteins, leading to formation of misfolded conformations. To reach the
native state from the misfolded conformations, proteins must overcome large
free energy barriers, a feat which could prove difficult to accomplish within
the biological time scale. Moreover, misfolded proteins expose patches of
hydrophobic amino acids, making them potential targets for aggregation or
leaving them vulnerable to degradation. Chaperonins rescue proteins trapped in
misfolded conformations and allow them to reach the native state within a
protected folding chamber.

It should be noted that the chaperone annealing action does not alter the
three-dimensional conformation of the native protein, in accord with
Anfinsen’s hypothesis 10 that the information needed for the native state is
encoded solely in the amino acid sequence. Instead, chaperones induce pathways
that ensure the correct folding of newly translated or newly translocated
proteins 11.

Figure 1: Prototypes of chaperonin classes. (a) Group I chaperonin GroEL and
its co-chaperonin GroES (purple) (b) Group II chaperonin thermosome. Three
domains, equatorial (red), intermediate (green), and apical (yellow), are
distinguished within each subunit. The domains belonging to one subunit of
each chaperonin are higlighted. Adapted from Ref. 7.

Here, we provide our perspective on the substrate recognition mechanisms for
the two chaperonin types. A number of reviews describe in detail other
fundamental features of the chaperonin machinery, including structure,
allosteric motion and ATPase activity 12, 13, 14, 15, 16, 17, 18, 19, 20, 21.
We refer the interested reader to these accounts for a broader picture of the
chaperonin mechanisms.

We also briefly examine the role of chaperonin in disease and point to
extensive research in the area 22. Prevention of aggregation through
chaperonin assisted folding of non-native polypeptides naturally suggests that
defects in the chaperonin machinery may result in disease. The extreme
situation, the absence of chaperonin, is fatal, as a consequence of the
essential nature of this machinery for the cell. Besides these immediate
implications, chaperonins are also found to be major immunogens that play an
important role in infection, autoimmune disease, and idiopathic diseases such
as arthritis and atherosclerosis. Considering the potential therapeutic use,
the study of chaperonin assisted protein folding is likely to suggest valuable
practical approaches.

##  2 Chaperonin hemicycle

Chaperonins operate as continuous annealing machines by alternating
encapsulation of substrate proteins within the cavity of each ring. These
encapsulation events are enabled by large scale, coordinated, conformational
transitions that take place in conjunction with ATP and GroES binding in the
active ring of GroEL. In this section, we focus on the series of events that
occur during the GroEL hemicycle.

Figure 2: Reaction hemicycle of GroEL illustrating the substrate protein (SP)
folding assistance. EL and ES stand for GroEL and GroES respectively. The
GroEL T state has a high affinity for SP binding. Upon ATP and GroES binding,
the SP is displaced into the expanded GroEL cavity, where productive folding
can take place. Dissociation of the complex occurs upon the initiation of a
folding reaction in the opposite GroEL ring. The structures of the T, R, and
R′′ states are known. Reproduced from Ref. 34 ©(2007) National Academy of
Sciences.

At the initiation of the chaperonin cycle, termed the T state (Figure 2),
GroEL presents a nearly continuous hydrophobic ring formed at the mouth of the
cavity by the seven apical domain binding sites 5. This state has high
affinity for non-native polypeptides, which also present exposed hydrophobic
surfaces. Binding of misfolded proteins to GroEL prevents the formation of
irreversible protein aggregates. Upon ATP and GroES binding to the same ring,
large-scale, entirely concerted, domain motions in that ring result in
doubling the size of the cavity. During these transformations, GroES, which
occupies the same apical binding sites as the SP 23, 6, 24, 25, displaces the
SP in the largely expanded cavity. As a result of these spectacular allosteric
transitions, the SP is presented with a completely different, mostly
hydrophilic, environment that promotes SP folding. The chaperonin cycle is
completed by ATP hydrolysis and the binding of ATP in the opposite ring, which
initiates the cycle in that ring. These events trigger the release of GroES,
ADP and SP from the folding chamber. Stringent GroEL substrates require
several cycles of binding and release in order to reach their native state. In
each cycle, productive folding, if it were to occur at all takes place within
the cavity 21.

##  3 Iterative Annealing Mechanism

The function of the GroEL machinery can be quantitatively understood within
the Iterative Annealing Mechanism (IAM) framework 26. This mechanism is
described in the framework of the energy landscape, which associates a free
energy to each conformational state of the protein (Figure 3). During each
cycle, the SP is rescued from one of the low energy minima, that corresponds
to a misfolded state. From the ensuing higher free energy state, the protein
chain undergoes kinetic partitioning 27 to either the native state or to the
same or a different low energy minimum.

Figure 3: Energy landscape perspective of the chaperonin annealing action.

Protein folding in a model cavity has been investigated using implicit solvent
and coarse-grained models for the SP 28, 29, 30, 31, 32, 33, 34. These studies
have provided several important clues about how protein folding occurs in
confinement. It turns out that an optimum range of interactions between the
cavity wall and the SP results in enhanced stability and folding rates.

##  4 GroEL substrate protein binding mechanism

GroEL manifests a promiscuous behavior towards binding non-native
polypeptides. Misfolded proteins, that expose hydrophobic residues, are
recognized by GroEL without preference for a specific secondary or tertiary
structure 35, 36. Despite the large number of proteins that can form complexes
with GroEL 35, in vivo only about 5-10% of E. coli proteins can afford to use
the chaperonin machinery under normal conditions 37, 38. Even upon heat
stress, only about 30% of E. coli proteins require folding assistance 39. The
relatively reduced participation of GroEL to protein folding in the cell
prompts us to wonder why only a subset of proteins of the entire organism uses
chaperonin assistance. Given the GroEL promiscuity, how does GroEL
discriminate between substrates and non-substrates within a proteome?

Addressing these questions is challenging, from an experimental point of view,
because of the inherent difficulty in arresting structures of complexes formed
between GroEL and non-native polypeptides. Somewhat surprisingly, even after
25 years, the only available crystal structures of GroEL-bound ligands
correspond to the GroEL-GroES complex 6 and to peptides bound to GroEL 40 or
to the GroEL apical domain fragment 24, 25, while a number of lower resolution
cryo-EM structures 41, 42, 43, 44 are available. Nevertheless, these
structures, as well as a number of biochemical studies, identified the GroEL
binding sites and the multivalent binding of stringent substrate proteins.
Bioinformatic analyses complementing these studies pinpointed chaperonin
signaling pathways and chemical character conservation at functionally
relevant sites.

Characterization of the GroEL binding sites using mutational 23 and
crystallographic 6 studies pointed towards a mostly hydrophobic groove between
two amphiphatic helices (Figure 4), as well as a nearby loop, located in the
apical domain of each GroEL subunit. Specific residues implicated in GroES and
SP binding are Tyr 199, Tyr 203, Phe 204, Leu 234, Leu 237, Leu 259, Val 263,
Val 264 23. In addition, these studies led to the key observation that the
same GroEL region responsible for recognizing misfolded substrates is
ultimately destined to form the interface with GroES in the course of the
chaperonin cycle. Strikingly, the structures of peptides bound to GroEL
overlap significantly 25, suggesting that strong restrictions are imposed on
the bound conformation.

Bioinformatic analysis of a large number of chaperonin sequences further
revealed that the various chaperonin functions (peptide binding, nucleotide
binding, GroES and SP release) require that the chemical character and not the
identities of specific amino acids be preserved 45. Moreover, this study lent
support to the sequence analysis by Kass and Horovitz 46, which suggested that
correlated mutations couple residue doublets or triplets along signaling
pathways within GroEL or between GroEL and GroES.

Multivalent binding of stringent SP substrates was suggested to be implicated
in the GroEL unfoldase action. This action is brought about by the large scale
conformational transitions that take place in coordinated fashion in all GroEL
subunits during the chaperonin cycle, resulting in an increased separation of
the apical binding sites. At the initiation of the chaperonin cycle, the seven
GroEL binding sites form a nearly continuous ring at the cavity opening.
Stringent GroEL substrates, such as malate dehydrogenase and Rubisco (not
natural substrates for GroEL, however Rubisco is a substrate of the Rubisco
binding protein, GroEL homolog in chloroplast), appear to interact with at
least three consecutive binding sites 47, 48. By contrast, Rhodanese, which is
a less-stringent substrate, requires two non-contiguous binding sites 47. The
effect of this displacement, corroborated with multivalent SP binding, is to
impart a stretching force to the SP 12.

Taken together, these important results suggest that substrate recognition
involves peptides that occupy the GroEL binding site in a similar conformation
as the GroES mobile loop. For stringent GroEL SPs, multiple interfaces are
formed involving these peptides and several contiguous GroEL subunits. The
peptide complementarity to the GroEL binding sites is defined, as in the GroES
case, by amino acids whose chemical character is strongly conserved.

##  5 Identification of GroEL substrates at the proteome level

The promiscuous behavior of GroEL towards binding non-native polypeptides
appears to be at variance with the relatively small fraction of protein chains
in an organism that actually use the GroEL machinery. However, common features
of GroEL substrates and similar conformations of bound peptides, as discussed
above, suggest a set of requirements for GroEL recognition. Several
computational approaches 49, 50, 51, 52, 53 and proteomic studies 54, 55 were
successful in identifying and characterizing GroEL substrates within whole
proteome.

Proteomic and biochemical studies 54, 55 provided the first experimental
identification of GroEL SPs, on a proteome-wide scale, in E. coli. These
studies found that 252 of the $\sim$2400 cytosolic proteins in E. coli
interact with GroEL. Among this set of proteins, 85 are stringent substrates
under normal growth conditions and they occupy 75-80% of the GroEL capacity.
Additional in vivo studies 56 involving a temperature-sensitive lethal E. coli
mutant suggested a wider set of $\sim$300 GroEL interacting proteins,
including some that had not been revealed by previous in vitro experiments.
The latter study raises the possibility that even transient GroEL interaction,
in the cellular environment, suffices to prevent aggregation of misfolded
proteins. The set of obligate in vivo substrates was subsequently narrowed to
$\simeq$ 60 proteins identified in experiments using GroE-depleted conditions
57, and completed by the addition of 20 novel substrates identified using
cell-free proteomics 58. GroEL substrates were also identified in other
bacteria, such as Thermus thermophilus 59 and Bacillus subtilis 60.

One line of computational research focuses on identifying polypeptide regions
within proteins that render them natural substrates for GroEL. The underlying
hypothesis is that natural SPs have the same sequence complementarity to the
GroEL binding site as GroES 49, 50. Therefore, SPs possess sequence patterns
similar to the GroES mobile loop segment 23-31, GGIVLTGAA, which binds to
GroEL. In one approach 49, SP binding motifs are defined as strong hydrophobic
patches (i.e. containing amino acids L, V, I, F, M) having 40-50% sequence
similarity to the GroES segment GGIVLTG. The sequence similarity is evaluated
using a pairwise alignment between the protein sequence and the peptide
GGIVLTG and allowing amino acid substitutions that preserve the chemical
character (hydrophobic-hydrophobic or same charge). In a different approach
50, the binding motif is required to match the pattern G_IVL_G_A that includes
NC=6 GroES amino acids in contact with GroEL (Figure 4) and three arbitrary
amino acids (“_”). Pattern matching takes into account possible amino acid
substitutions that preserve the chemical character, as well as less strongly
bound peptides, having four (G_IVL) and five (G_IVL_G) contacts. Natural SPs
must possess multiple copies of the binding motif, NB, to satisfy the required
multivalent binding to GroEL. About a third of the sequences in the E. coli
proteome are expected to be natural SPs 39. This method retrieves the expected
fraction of natural SPs in E. coli for sequences that satisfy $4<$ N${}_{C}<6$
and 2 $<$ N${}_{B}<4$. No preferred secondary structure emerges in this set of
proteins. This method is able to identify 80% of experimentally determined
natural substrate proteins for GroEL from E. coli 54, 55 and predicted SPs in
several other proteomes.

GroEL must not only recognize proteins that require folding assistance, but
also the protein conformations that must be remodeled. How does GroEL
discriminate between native conformations, which it should not recruit, from
the misfolded conformations of proteins it must selectively assist? A
structural and bioinformatic analysis 61 found that GroES-type binding motifs
are not significantly exposed to solvent in the native conformation of GroEL
SPs. This result suggests that GroEL recognition of misfolded conformations of
SPs requires that multiple GroES-type binding motifs be solvent-exposed. In
accord with this hypothesis, molecular dynamics simulations that probe
extensively the conformational space of an obligate GroEL substrate, DapA 62,
reveal that its GroES-type binding motifs are solvent-exposed in unfolding
intermediates, but are inaccessible in the native conformation. These studies
find that, for the seven motifs identified within the DapA sequence, the
average solvent-exposed area per residue increases from $\simeq$ 74Å in the
native conformation to $\simeq$ 182Å in the intermediate structures.
Experimental studies using hydrogen-exchange coupled with mass spectrometry 63
support the increased exposure of the hydrophobic segments and loss of
hydrogen bonds that accompany the destabilization of the TIM-barrel core of
this substrate.

Figure 4:  (a) Contacts between GroEL helices H and I (cyan) and the GroES
mobile loop (pink). Sidechains of the residues that form the closest contacts
are shown in red (GroEL) and green (GroES). (b) Schematic representation of
contacts between GroEL and GroES. Reproduced from Ref. 50.

A different line of computational research 51, 64, 53, 52 uses machine
learning approaches to examine physicochemical characteristics of E. coli
proteins that indicate a requirement for GroE-dependent folding. Among two
sets of in vivo substrates 55, 56, stringent dependence on GroEL correlates
with low folding propensity and high translation efficiency 51. Secondary
structure content, as well as contact order, which quantifies the average
distance along the polypeptide chain between amino acids that form native
contacts, were not found to distinguish GroEL SPs from other proteins.
Consistently, this study found that homologues of these SPs in Ureaplasma
urealyticum, an organism that lacks a chaperonin system, do not possess
sequence characteristics that would require them to recruit the GroE system.
Additional features were found by two other studies to separate GroEL SPs from
GroE-independent proteins. One found that lower rate of evolution,
hydrophobicity, and aggregation propensity are characteristics of GroEL SPs
53, however it was later argued that the estimation of aggregation propensity
may reflect the algorithm bias towards amyloid structure 64. Solubilities of
E. coli proteins are found to display a bimodal distribution within a cell-
free system in the absence of chaperones, with stringent GroEL SPs belonging
to the more aggregation-prone set 65. In agreement with these results, the
second computational approach was successful in distinguishing the GroEL
requirement for the previously identified substrate classes 55 on the basis of
decreasing folding propensity and increasing likelihood of aggregation 52. To
probe the substrate requirements in controlled fashion, recent experiments
used computationally designed substrates based on the enhanced green
fluorescence protein (eGFP) 66, 67. These in vitro and in vivo studies showed
that GroEL dependence of eGFP variants increases with increasing frustration
68, effected through point mutations66, or contact order 69, engineered
through circular permutations67. Intriguingly, as noted above, in vivo GroEL
SPs are not distinguishable from non-substrates through the contact order
parameter. This suggests that other features play a larger role in determining
GroE-dependence.

##  6 Specific recognition of substrate proteins by group II chaperonins

In contrast to the extensive knowledge of the set of proteins that require
assistance from the GroEL-GroES system, relatively little is currently known
about the substrates of group II chaperonins. The presence of distinct subunit
types within group II chaperonins suggests that specialized binding mechanisms
were developed to target different substrates. However, the extent of subunit
heterogeneity varies among members of this class. In archaeal chaperonins, one
70, two 71, or three 72 distinct subunit types are identified, whereas in
eukaryotic chaperonins eight different subunits are described 73.
Correspondingly, it is plausible that different substrate recognition
mechanisms are used by archaeal and eukaryotic chaperonins. Group II
chaperonins have been suggested to employ a sequential, rather than
cooperative, mechanism for conformational transitions, consistent with their
suggested domain-by-domain folding of SPs and specific SP interaction.

Archaeal chaperonins are abundant in the cell (approximately 1-2% of cellular
proteins) and have low subunit heterogeneity as a result of gene
interconversion 72. These facts prompted the suggestion that, like GroEL, they
assist folding of a large set of proteins perhaps through a promiscuous
mechanism. In support of this hypothesis, it is noted that thermosome, which
has two subunit types, assists folding of GroEL substrates green fluorescence
protein 74 and cythrate synthase 75. The coexistence of group I and group II
chaperonins within the archaebacterium Methanosarcina mazei 76 provides an
unique opportunity to compare and contrast the annealing action of the two
chaperonin classes. Both chaperonins contribute to the folding of 13% of the
proteins in the archaeal cytosol, albeit the two sets of substrates are non-
overlapping 77.

The less abundant eukaryotic chaperonin CCT (0.1% of cellular proteins) uses a
significantly different mechanism of substrate recognition than GroEL. CCT was
initially suggested to interact only with actins and tubulins 78. Recently,
numerous other substrates have been identified, including some that contain
tryptophan-aspartic acid repeats 16. Substrates include the myosin heavy
chain, the Von Hippel-Lindau (VHL) tumor suppressor, cyclin E, and the cell
division control protein. Charged residues on the surface of CCT SPs appear to
be required for recognition by the eukaryotic machinery. Intriguingly, CCT
substrates cannot be folded by other prokaryotic or eukaryotic chaperones 79.

A challenging aspect of the CCT substrate recognition mechanism is the lack of
knowledge of the CCT binding site. Several proposals exist regarding the
localization of CCT binding sites. One assumes structural homology to the
GroEL binding sites, formed by two apical domain helices 6. In contrast to the
GroEL binding site, the two CCT helices have a mostly hydrophilic character,
which would be consistent with the notion that CCT recognizes surface charged
residues 80. A second proposed CCT binding site involves a flexible helical
protrusion 81 that acts as a built-in lid for the chaperonin cavity. Finally,
the inner side of the closed cavity was also suggested as CCT binding site 82.
This region has a mostly charged and polar character, a feature similar to the
lining of the GroEL cavity wall. At this time, few experimental data are
available to unambiguously define the CCT binding site. A study that used
photocrosslinking and fluorescence spectroscopy to probe VHL binding 83,
provides strong indication that the CCT binding sites are located within helix
11, which is structurally homologous to the GroEL binding site.

It is possible that more than one of these proposed locations correspond to in
vivo CCT binding sites. This would not be completely surprising given the
diversity among CCT substrates and the CCT inhomogeneous oligomeric structure
16. Distinct CCT subunits may serve the purpose of providing the versatility
to recognize different substrates.

##  7 Implication of chaperonins in disease

An intriguing connection was made between the Hsp60 chaperonin class and prion
disease 84. Prion proteins are suggested to form fibrillar aggregates upon
conversion of the normal cellular form PrPC, having primarily $\alpha$-helical
structure, into a $\beta$-sheet rich misfolded conformation, PrPSc.
Experiments performed in vitro found that GroEL promotes conversion to the
disease-related PrPSc 84. These authors proposed that in vivo validation of
the chaperonin-assisted conversion would provide a natural target for clinical
approaches.

Mutations in human chaperonins result in diseases, such as the hereditary
spastic paraplegia 85, or the McKusick-Kaufman Syndrome 86. Chaperonins have
been implicated, through autoimmune response, as putative causes of diseases
such as rheumatoid arthritis, atherosclerosis, and inflammation 87.
Immunosuppresive action of chaperonins has been described in animal models of
juvenile arthritis 88 and diabetes 89, as well as in human pregnancy 90.
Immunization with a mycobacterial chaperonin was suggested to protect against
arthritis 91. To date, there is no clear understanding of the subset of
chaperonin SPs affected by these mutations and the precise effect of these
mutations on chaperonin annealing action 92. Mastering the intricacies of the
chaperonin action will provide answers to these questions and suggest
effective therapies.

##  8 Conclusions

Protein folding assistance mediated by chaperonins is a critical quality
control mechanism to maintain protein homeostasis. Selective recruitment of
substrate proteins by chaperonins represents a fundamental regulatory step in
the remodeling action, given the limited availability of chaperonins within
the cytosol and the stringent dependence of a subset of proteins on this
assistance. Remarkably, GroEL substrate selectivity is achieved even as the
chaperonin promiscuously binds misfolded proteins. As highlighted here,
research efforts to elucidate the substrate recognition mechanism have
primarily focused on two complementary questions. One question is focused on
how GroEL binds substrate proteins that require its assistance. As the GroEL
binding site is well established and the GroES co-chaperone competes with
substrates during the chaperonin cycle, this suggests that natural SPs include
polypeptide regions similar to the GroES loops that participate in the
interface with GroEL. The additional observation that substrates interact with
multiple GroEL subunits (2-3) further defines the requirement that several
GroES-type motifs be present within the polypeptide chain, at least for
stringent substrates. The other question refers to which proteins are likely
to require folding assistance in vivo. Here, low partition factor (fraction of
molecules that fold spontaneously) and high aggregation propensity emerge as
important factors that underlie the GroEL requirement. In addition, such
factors can help to explain the extent of GroEL dependence among known
substrates.

##  9 Acknowledgments

The authors gratefully acknowledge stimulating comments from Amnon Horovitz.

## Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.

## Author Contributions

All authors conceived and executed the project, and wrote the report.

## Funding

DT is grateful to the National Science Foundation (CHE 19-00033) for support.
Additional support from the Welch Foundation (F-0019) through the Collie-Welch
Regents Chair is greatly acknowledged. GS would like to acknowledge the
National Science Foundation (MCB-2136816) for support.

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